Multi scales based sparse matrix spectral clustering image segmentation

NASA Astrophysics Data System (ADS)

Liu, Zhongmin; Chen, Zhicai; Li, Zhanming; Hu, Wenjin

2018-04-01

In image segmentation, spectral clustering algorithms have to adopt the appropriate scaling parameter to calculate the similarity matrix between the pixels, which may have a great impact on the clustering result. Moreover, when the number of data instance is large, computational complexity and memory use of the algorithm will greatly increase. To solve these two problems, we proposed a new spectral clustering image segmentation algorithm based on multi scales and sparse matrix. We devised a new feature extraction method at first, then extracted the features of image on different scales, at last, using the feature information to construct sparse similarity matrix which can improve the operation efficiency. Compared with traditional spectral clustering algorithm, image segmentation experimental results show our algorithm have better degree of accuracy and robustness.

Handwritten text line segmentation by spectral clustering

NASA Astrophysics Data System (ADS)

Han, Xuecheng; Yao, Hui; Zhong, Guoqiang

2017-02-01

Since handwritten text lines are generally skewed and not obviously separated, text line segmentation of handwritten document images is still a challenging problem. In this paper, we propose a novel text line segmentation algorithm based on the spectral clustering. Given a handwritten document image, we convert it to a binary image first, and then compute the adjacent matrix of the pixel points. We apply spectral clustering on this similarity metric and use the orthogonal kmeans clustering algorithm to group the text lines. Experiments on Chinese handwritten documents database (HIT-MW) demonstrate the effectiveness of the proposed method.

Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

NASA Astrophysics Data System (ADS)

Ma, Xiaoke; Wang, Bingbo; Yu, Liang

2018-01-01

Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

Diverse Power Iteration Embeddings and Its Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Huang H.; Yoo S.; Yu, D.

2014-12-14

Abstract—Spectral Embedding is one of the most effective dimension reduction algorithms in data mining. However, its computation complexity has to be mitigated in order to apply it for real-world large scale data analysis. Many researches have been focusing on developing approximate spectral embeddings which are more efficient, but meanwhile far less effective. This paper proposes Diverse Power Iteration Embeddings (DPIE), which not only retains the similar efficiency of power iteration methods but also produces a series of diverse and more effective embedding vectors. We test this novel method by applying it to various data mining applications (e.g. clustering, anomaly detectionmore » and feature selection) and evaluating their performance improvements. The experimental results show our proposed DPIE is more effective than popular spectral approximation methods, and obtains the similar quality of classic spectral embedding derived from eigen-decompositions. Moreover it is extremely fast on big data applications. For example in terms of clustering result, DPIE achieves as good as 95% of classic spectral clustering on the complex datasets but 4000+ times faster in limited memory environment.« less

Remote sensing imagery classification using multi-objective gravitational search algorithm

NASA Astrophysics Data System (ADS)

Zhang, Aizhu; Sun, Genyun; Wang, Zhenjie

2016-10-01

Simultaneous optimization of different validity measures can capture different data characteristics of remote sensing imagery (RSI) and thereby achieving high quality classification results. In this paper, two conflicting cluster validity indices, the Xie-Beni (XB) index and the fuzzy C-means (FCM) (Jm) measure, are integrated with a diversity-enhanced and memory-based multi-objective gravitational search algorithm (DMMOGSA) to present a novel multi-objective optimization based RSI classification method. In this method, the Gabor filter method is firstly implemented to extract texture features of RSI. Then, the texture features are syncretized with the spectral features to construct the spatial-spectral feature space/set of the RSI. Afterwards, cluster of the spectral-spatial feature set is carried out on the basis of the proposed method. To be specific, cluster centers are randomly generated initially. After that, the cluster centers are updated and optimized adaptively by employing the DMMOGSA. Accordingly, a set of non-dominated cluster centers are obtained. Therefore, numbers of image classification results of RSI are produced and users can pick up the most promising one according to their problem requirements. To quantitatively and qualitatively validate the effectiveness of the proposed method, the proposed classification method was applied to classifier two aerial high-resolution remote sensing imageries. The obtained classification results are compared with that produced by two single cluster validity index based and two state-of-the-art multi-objective optimization algorithms based classification results. Comparison results show that the proposed method can achieve more accurate RSI classification.

A study of the tolerance block approach to special stratification. [winter wheat in Kansas

NASA Technical Reports Server (NTRS)

Richardson, W. (Principal Investigator)

1979-01-01

The author has identified the following significant results. Twelve winter wheat LACIE segments in Kansas were used to compare the performance of three clustering methods: (1) BCLUST, which uses a spectral distance function to accumulate clusters; (2) blocks-alone, which divides spectral space into equally populated blocks; and (3) block-seeds, which uses spectral means of blocks-alone as seeds for accumulating distance-type clusters. Both BCLUST and block-seeds performed equally well and outperformed blocks-alone significantly. Their average variance ratio of about 0.5 showed imperfect separation of wheat from non-wheat. This result points to the need to explore the achievable crop separability in the spectral/temporal domain, and suggest evaluating derived features rather than data channels as a means to achieve purer spectral strata.

Spectral gene set enrichment (SGSE).

PubMed

Frost, H Robert; Li, Zhigang; Moore, Jason H

2015-03-03

Gene set testing is typically performed in a supervised context to quantify the association between groups of genes and a clinical phenotype. In many cases, however, a gene set-based interpretation of genomic data is desired in the absence of a phenotype variable. Although methods exist for unsupervised gene set testing, they predominantly compute enrichment relative to clusters of the genomic variables with performance strongly dependent on the clustering algorithm and number of clusters. We propose a novel method, spectral gene set enrichment (SGSE), for unsupervised competitive testing of the association between gene sets and empirical data sources. SGSE first computes the statistical association between gene sets and principal components (PCs) using our principal component gene set enrichment (PCGSE) method. The overall statistical association between each gene set and the spectral structure of the data is then computed by combining the PC-level p-values using the weighted Z-method with weights set to the PC variance scaled by Tracy-Widom test p-values. Using simulated data, we show that the SGSE algorithm can accurately recover spectral features from noisy data. To illustrate the utility of our method on real data, we demonstrate the superior performance of the SGSE method relative to standard cluster-based techniques for testing the association between MSigDB gene sets and the variance structure of microarray gene expression data. Unsupervised gene set testing can provide important information about the biological signal held in high-dimensional genomic data sets. Because it uses the association between gene sets and samples PCs to generate a measure of unsupervised enrichment, the SGSE method is independent of cluster or network creation algorithms and, most importantly, is able to utilize the statistical significance of PC eigenvalues to ignore elements of the data most likely to represent noise.

[A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

PubMed

Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

2011-10-01

Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

Understanding 3D human torso shape via manifold clustering

NASA Astrophysics Data System (ADS)

Li, Sheng; Li, Peng; Fu, Yun

2013-05-01

Discovering the variations in human torso shape plays a key role in many design-oriented applications, such as suit designing. With recent advances in 3D surface imaging technologies, people can obtain 3D human torso data that provide more information than traditional measurements. However, how to find different human shapes from 3D torso data is still an open problem. In this paper, we propose to use spectral clustering approach on torso manifold to address this problem. We first represent high-dimensional torso data in a low-dimensional space using manifold learning algorithm. Then the spectral clustering method is performed to get several disjoint clusters. Experimental results show that the clusters discovered by our approach can describe the discrepancies in both genders and human shapes, and our approach achieves better performance than the compared clustering method.

Fast multipole methods on a cluster of GPUs for the meshless simulation of turbulence

NASA Astrophysics Data System (ADS)

Yokota, R.; Narumi, T.; Sakamaki, R.; Kameoka, S.; Obi, S.; Yasuoka, K.

2009-11-01

Recent advances in the parallelizability of fast N-body algorithms, and the programmability of graphics processing units (GPUs) have opened a new path for particle based simulations. For the simulation of turbulence, vortex methods can now be considered as an interesting alternative to finite difference and spectral methods. The present study focuses on the efficient implementation of the fast multipole method and pseudo-particle method on a cluster of NVIDIA GeForce 8800 GT GPUs, and applies this to a vortex method calculation of homogeneous isotropic turbulence. The results of the present vortex method agree quantitatively with that of the reference calculation using a spectral method. We achieved a maximum speed of 7.48 TFlops using 64 GPUs, and the cost performance was near 9.4/GFlops. The calculation of the present vortex method on 64 GPUs took 4120 s, while the spectral method on 32 CPUs took 4910 s.

Numerical Modeling of 3D Seismic Wave Propagation around Yogyakarta, the Southern Part of Central Java, Indonesia, Using Spectral-Element Method on MPI-GPU Cluster

NASA Astrophysics Data System (ADS)

Sudarmaji; Rudianto, Indra; Eka Nurcahya, Budi

2018-04-01

A strong tectonic earthquake with a magnitude of 5.9 Richter scale has been occurred in Yogyakarta and Central Java on May 26, 2006. The earthquake has caused severe damage in Yogyakarta and the southern part of Central Java, Indonesia. The understanding of seismic response of earthquake among ground shaking and the level of building damage is important. We present numerical modeling of 3D seismic wave propagation around Yogyakarta and the southern part of Central Java using spectral-element method on MPI-GPU (Graphics Processing Unit) computer cluster to observe its seismic response due to the earthquake. The homogeneous 3D realistic model is generated with detailed topography surface. The influences of free surface topography and layer discontinuity of the 3D model among the seismic response are observed. The seismic wave field is discretized using spectral-element method. The spectral-element method is solved on a mesh of hexahedral elements that is adapted to the free surface topography and the internal discontinuity of the model. To increase the data processing capabilities, the simulation is performed on a GPU cluster with implementation of MPI (Message Passing Interface).

Extending a Tandem Mass Spectral Library to Include MS2 Spectra of Fragment Ions Produced In-Source and MSn Spectra.

PubMed

Yang, Xiaoyu; Neta, Pedatsur; Stein, Stephen E

2017-11-01

Tandem mass spectral library searching is finding increased use as an effective means of determining chemical identity in mass spectrometry-based omics studies. We previously reported on constructing a tandem mass spectral library that includes spectra for multiple precursor ions for each analyte. Here we report our method for expanding this library to include MS 2 spectra of fragment ions generated during the ionization process (in-source fragment ions) as well as MS 3 and MS 4 spectra. These can assist the chemical identification process. A simple density-based clustering algorithm was used to cluster all significant precursor ions from MS 1 scans for an analyte acquired during an infusion experiment. The MS 2 spectra associated with these precursor ions were grouped into the same precursor clusters. Subsequently, a new top-down hierarchical divisive clustering algorithm was developed for clustering the spectra from fragmentation of ions in each precursor cluster, including the MS 2 spectra of the original precursors and of the in-source fragments as well as the MS n spectra. This algorithm starts with all the spectra of one precursor in one cluster and then separates them into sub-clusters of similar spectra based on the fragment patterns. Herein, we describe the algorithms and spectral evaluation methods for extending the library. The new library features were demonstrated by searching the high resolution spectra of E. coli extracts against the extended library, allowing identification of compounds and their in-source fragment ions in a manner that was not possible before. Graphical Abstract ᅟ.

Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition

PubMed Central

Cui, Zhiming; Zhao, Pengpeng

2014-01-01

A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity. PMID:24605045

Color analysis and image rendering of woodblock prints with oil-based ink

NASA Astrophysics Data System (ADS)

Horiuchi, Takahiko; Tanimoto, Tetsushi; Tominaga, Shoji

2012-01-01

This paper proposes a method for analyzing the color characteristics of woodblock prints having oil-based ink and rendering realistic images based on camera data. The analysis results of woodblock prints show some characteristic features in comparison with oil paintings: 1) A woodblock print can be divided into several cluster areas, each with similar surface spectral reflectance; and 2) strong specular reflection from the influence of overlapping paints arises only in specific cluster areas. By considering these properties, we develop an effective rendering algorithm by modifying our previous algorithm for oil paintings. A set of surface spectral reflectances of a woodblock print is represented by using only a small number of average surface spectral reflectances and the registered scaling coefficients, whereas the previous algorithm for oil paintings required surface spectral reflectances of high dimension at all pixels. In the rendering process, in order to reproduce the strong specular reflection in specific cluster areas, we use two sets of parameters in the Torrance-Sparrow model for cluster areas with or without strong specular reflection. An experiment on a woodblock printing with oil-based ink was performed to demonstrate the feasibility of the proposed method.

[A cloud detection algorithm for MODIS images combining Kmeans clustering and multi-spectral threshold method].

PubMed

Wang, Wei; Song, Wei-Guo; Liu, Shi-Xing; Zhang, Yong-Ming; Zheng, Hong-Yang; Tian, Wei

2011-04-01

An improved method for detecting cloud combining Kmeans clustering and the multi-spectral threshold approach is described. On the basis of landmark spectrum analysis, MODIS data is categorized into two major types initially by Kmeans method. The first class includes clouds, smoke and snow, and the second class includes vegetation, water and land. Then a multi-spectral threshold detection is applied to eliminate interference such as smoke and snow for the first class. The method is tested with MODIS data at different time under different underlying surface conditions. By visual method to test the performance of the algorithm, it was found that the algorithm can effectively detect smaller area of cloud pixels and exclude the interference of underlying surface, which provides a good foundation for the next fire detection approach.

Determination of Cluster Distances from Chandra Imaging Spectroscopy and Sunyaev-Zeldovich Effect Measurements. I; Analysis Methods and Initial Results

NASA Technical Reports Server (NTRS)

Bonamente, Massimiliano; Joy, Marshall K.; Carlstrom, John E.; LaRoque, Samuel J.

2004-01-01

X-ray and Sunyaev-Zeldovich Effect data ca,n be combined to determine the distance to galaxy clusters. High-resolution X-ray data are now available from the Chandra Observatory, which provides both spatial and spectral information, and interferometric radio measurements of the Sunyam-Zeldovich Effect are available from the BIMA and 0VR.O arrays. We introduce a Monte Carlo Markov chain procedure for the joint analysis of X-ray and Sunyaev-Zeldovich Effect data. The advantages of this method are the high computational efficiency and the ability to measure the full probability distribution of all parameters of interest, such as the spatial and spectral properties of the cluster gas and the cluster distance. We apply this technique to the Chandra X-ray data and the OVRO radio data for the galaxy cluster Abell 611. Comparisons with traditional likelihood-ratio methods reveal the robustness of the method. This method will be used in a follow-up paper to determine the distance of a large sample of galaxy clusters for which high-resolution Chandra X-ray and BIMA/OVRO radio data are available.

A Poisson nonnegative matrix factorization method with parameter subspace clustering constraint for endmember extraction in hyperspectral imagery

NASA Astrophysics Data System (ADS)

Sun, Weiwei; Ma, Jun; Yang, Gang; Du, Bo; Zhang, Liangpei

2017-06-01

A new Bayesian method named Poisson Nonnegative Matrix Factorization with Parameter Subspace Clustering Constraint (PNMF-PSCC) has been presented to extract endmembers from Hyperspectral Imagery (HSI). First, the method integrates the liner spectral mixture model with the Bayesian framework and it formulates endmember extraction into a Bayesian inference problem. Second, the Parameter Subspace Clustering Constraint (PSCC) is incorporated into the statistical program to consider the clustering of all pixels in the parameter subspace. The PSCC could enlarge differences among ground objects and helps finding endmembers with smaller spectrum divergences. Meanwhile, the PNMF-PSCC method utilizes the Poisson distribution as the prior knowledge of spectral signals to better explain the quantum nature of light in imaging spectrometer. Third, the optimization problem of PNMF-PSCC is formulated into maximizing the joint density via the Maximum A Posterior (MAP) estimator. The program is finally solved by iteratively optimizing two sub-problems via the Alternating Direction Method of Multipliers (ADMM) framework and the FURTHESTSUM initialization scheme. Five state-of-the art methods are implemented to make comparisons with the performance of PNMF-PSCC on both the synthetic and real HSI datasets. Experimental results show that the PNMF-PSCC outperforms all the five methods in Spectral Angle Distance (SAD) and Root-Mean-Square-Error (RMSE), and especially it could identify good endmembers for ground objects with smaller spectrum divergences.

Weighted graph cuts without eigenvectors a multilevel approach.

PubMed

Dhillon, Inderjit S; Guan, Yuqiang; Kulis, Brian

2007-11-01

A variety of clustering algorithms have recently been proposed to handle data that is not linearly separable; spectral clustering and kernel k-means are two of the main methods. In this paper, we discuss an equivalence between the objective functions used in these seemingly different methods--in particular, a general weighted kernel k-means objective is mathematically equivalent to a weighted graph clustering objective. We exploit this equivalence to develop a fast, high-quality multilevel algorithm that directly optimizes various weighted graph clustering objectives, such as the popular ratio cut, normalized cut, and ratio association criteria. This eliminates the need for any eigenvector computation for graph clustering problems, which can be prohibitive for very large graphs. Previous multilevel graph partitioning methods, such as Metis, have suffered from the restriction of equal-sized clusters; our multilevel algorithm removes this restriction by using kernel k-means to optimize weighted graph cuts. Experimental results show that our multilevel algorithm outperforms a state-of-the-art spectral clustering algorithm in terms of speed, memory usage, and quality. We demonstrate that our algorithm is applicable to large-scale clustering tasks such as image segmentation, social network analysis and gene network analysis.

Markov Chain Monte Carlo Joint Analysis of Chandra X-Ray Imaging Spectroscopy and Sunyaev-Zel'dovich Effect Data

NASA Technical Reports Server (NTRS)

Bonamente, Massimillano; Joy, Marshall K.; Carlstrom, John E.; Reese, Erik D.; LaRoque, Samuel J.

2004-01-01

X-ray and Sunyaev-Zel'dovich effect data can be combined to determine the distance to galaxy clusters. High-resolution X-ray data are now available from Chandra, which provides both spatial and spectral information, and Sunyaev-Zel'dovich effect data were obtained from the BIMA and Owens Valley Radio Observatory (OVRO) arrays. We introduce a Markov Chain Monte Carlo procedure for the joint analysis of X-ray and Sunyaev- Zel'dovich effect data. The advantages of this method are the high computational efficiency and the ability to measure simultaneously the probability distribution of all parameters of interest, such as the spatial and spectral properties of the cluster gas and also for derivative quantities such as the distance to the cluster. We demonstrate this technique by applying it to the Chandra X-ray data and the OVRO radio data for the galaxy cluster A611. Comparisons with traditional likelihood ratio methods reveal the robustness of the method. This method will be used in follow-up paper to determine the distances to a large sample of galaxy cluster.

Modified vegetation indices for Ganoderma disease detection in oil palm from field spectroradiometer data

NASA Astrophysics Data System (ADS)

Shafri, Helmi Z. M.; Anuar, M. Izzuddin; Saripan, M. Iqbal

2009-10-01

High resolution field spectroradiometers are important for spectral analysis and mobile inspection of vegetation disease. The biggest challenges in using this technology for automated vegetation disease detection are in spectral signatures pre-processing, band selection and generating reflectance indices to improve the ability of hyperspectral data for early detection of disease. In this paper, new indices for oil palm Ganoderma disease detection were generated using band ratio and different band combination techniques. Unsupervised clustering method was used to cluster the values of each class resultant from each index. The wellness of band combinations was assessed by using Optimum Index Factor (OIF) while cluster validation was executed using Average Silhouette Width (ASW). 11 modified reflectance indices were generated in this study and the indices were ranked according to the values of their ASW. These modified indices were also compared to several existing and new indices. The results showed that the combination of spectral values at 610.5nm and 738nm was the best for clustering the three classes of infection levels in the determination of the best spectral index for early detection of Ganoderma disease.

Saliency detection algorithm based on LSC-RC

NASA Astrophysics Data System (ADS)

Wu, Wei; Tian, Weiye; Wang, Ding; Luo, Xin; Wu, Yingfei; Zhang, Yu

2018-02-01

Image prominence is the most important region in an image, which can cause the visual attention and response of human beings. Preferentially allocating the computer resources for the image analysis and synthesis by the significant region is of great significance to improve the image area detecting. As a preprocessing of other disciplines in image processing field, the image prominence has widely applications in image retrieval and image segmentation. Among these applications, the super-pixel segmentation significance detection algorithm based on linear spectral clustering (LSC) has achieved good results. The significance detection algorithm proposed in this paper is better than the regional contrast ratio by replacing the method of regional formation in the latter with the linear spectral clustering image is super-pixel block. After combining with the latest depth learning method, the accuracy of the significant region detecting has a great promotion. At last, the superiority and feasibility of the super-pixel segmentation detection algorithm based on linear spectral clustering are proved by the comparative test.

WINGS-SPE. III. Equivalent width measurements, spectral properties, and evolution of local cluster galaxies

NASA Astrophysics Data System (ADS)

Fritz, J.; Poggianti, B. M.; Cava, A.; Moretti, A.; Varela, J.; Bettoni, D.; Couch, W. J.; D'Onofrio D'Onofrio, M.; Dressler, A.; Fasano, G.; Kjærgaard, P.; Marziani, P.; Moles, M.; Omizzolo, A.

2014-06-01

Context. Cluster galaxies are the ideal sites to look at when studying the influence of the environment on the various aspects of the evolution of galaxies, such as the changes in their stellar content and morphological transformations. In the framework of wings, the WIde-field Nearby Galaxy-cluster Survey, we have obtained optical spectra for ~6000 galaxies selected in fields centred on 48 local (0.04 < z < 0.07) X-ray selected clusters to tackle these issues. Aims: By classifying the spectra based on given spectral lines, we investigate the frequency of the various spectral types as a function of both the clusters' properties and the galaxies' characteristics. In this way, using the same classification criteria adopted for studies at higher redshift, we can consistently compare the properties of the local cluster population to those of their more distant counterparts. Methods: We describe a method that we have developed to automatically measure the equivalent width of spectral lines in a robust way, even in spectra with a non optimal signal-to-noise ratio. This way, we can derive a spectral classification reflecting the stellar content, based on the presence and strength of the [Oii] and Hδ lines. Results: After a quality check, we are able to measure 4381 of the ~6000 originally observed spectra in the fields of 48 clusters, of which 2744 are spectroscopically confirmed cluster members. The spectral classification is then analysed as a function of galaxies' luminosity, stellar mass, morphology, local density, and host cluster's global properties and compared to higher redshift samples (MORPHS and EDisCS). The vast majority of galaxies in the local clusters population are passive objects, being also the most luminous and massive. At a magnitude limit of MV < -18, galaxies in a post-starburst phase represent only ~11% of the cluster population, and this fraction is reduced to ~5% at MV < -19.5, which compares to the 18% at the same magnitude limit for high-z clusters. "Normal" star-forming galaxies (e(c)) are proportionally more common in local clusters. Conclusions: The relative occurrence of post-starbursts suggests a very similar quenching efficiency in clusters at redshifts in the 0 to ~1 range. Furthermore, more important than the global environment, the local density seems to be the main driver of galaxy evolution in local clusters at least with respect to their stellar populations content. Based on observations taken at the Anglo Australian Telescope (3.9 m- AAT) and at the William Herschel Telescope (4.2 m-WHT).Full Table A.1 is available in electronic form at both the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/566/A32 and by querying the wings database at http://web.oapd.inaf.it/wings/new/index.htmlAppendices are available in electronic form at http://www.aanda.org

Carbon X-ray absorption spectra of fluoroethenes and acetone: A study at the coupled cluster, density functional, and static-exchange levels of theory

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fransson, Thomas; Norman, Patrick; Coriani, Sonia

2013-03-28

Near carbon K-edge X-ray absorption fine structure spectra of a series of fluorine-substituted ethenes and acetone have been studied using coupled cluster and density functional theory (DFT) polarization propagator methods, as well as the static-exchange (STEX) approach. With the complex polarization propagator (CPP) implemented in coupled cluster theory, relaxation effects following the excitation of core electrons are accounted for in terms of electron correlation, enabling a systematic convergence of these effects with respect to electron excitations in the cluster operator. Coupled cluster results have been used as benchmarks for the assessment of propagator methods in DFT as well as themore » state-specific static-exchange approach. Calculations on ethene and 1,1-difluoroethene illustrate the possibility of using nonrelativistic coupled cluster singles and doubles (CCSD) with additional effects of electron correlation and relativity added as scalar shifts in energetics. It has been demonstrated that CPP spectra obtained with coupled cluster singles and approximate doubles (CC2), CCSD, and DFT (with a Coulomb attenuated exchange-correlation functional) yield excellent predictions of chemical shifts for vinylfluoride, 1,1-difluoroethene, trifluoroethene, as well as good spectral features for acetone in the case of CCSD and DFT. Following this, CPP-DFT is considered to be a viable option for the calculation of X-ray absorption spectra of larger {pi}-conjugated systems, and CC2 is deemed applicable for chemical shifts but not for studies of fine structure features. The CCSD method as well as the more approximate CC2 method are shown to yield spectral features relating to {pi}*-resonances in good agreement with experiment, not only for the aforementioned molecules but also for ethene, cis-1,2-difluoroethene, and tetrafluoroethene. The STEX approach is shown to underestimate {pi}*-peak separations due to spectral compressions, a characteristic which is inherent to this method.« less

Carbon X-ray absorption spectra of fluoroethenes and acetone: a study at the coupled cluster, density functional, and static-exchange levels of theory.

PubMed

Fransson, Thomas; Coriani, Sonia; Christiansen, Ove; Norman, Patrick

2013-03-28

Near carbon K-edge X-ray absorption fine structure spectra of a series of fluorine-substituted ethenes and acetone have been studied using coupled cluster and density functional theory (DFT) polarization propagator methods, as well as the static-exchange (STEX) approach. With the complex polarization propagator (CPP) implemented in coupled cluster theory, relaxation effects following the excitation of core electrons are accounted for in terms of electron correlation, enabling a systematic convergence of these effects with respect to electron excitations in the cluster operator. Coupled cluster results have been used as benchmarks for the assessment of propagator methods in DFT as well as the state-specific static-exchange approach. Calculations on ethene and 1,1-difluoroethene illustrate the possibility of using nonrelativistic coupled cluster singles and doubles (CCSD) with additional effects of electron correlation and relativity added as scalar shifts in energetics. It has been demonstrated that CPP spectra obtained with coupled cluster singles and approximate doubles (CC2), CCSD, and DFT (with a Coulomb attenuated exchange-correlation functional) yield excellent predictions of chemical shifts for vinylfluoride, 1,1-difluoroethene, trifluoroethene, as well as good spectral features for acetone in the case of CCSD and DFT. Following this, CPP-DFT is considered to be a viable option for the calculation of X-ray absorption spectra of larger π-conjugated systems, and CC2 is deemed applicable for chemical shifts but not for studies of fine structure features. The CCSD method as well as the more approximate CC2 method are shown to yield spectral features relating to π∗-resonances in good agreement with experiment, not only for the aforementioned molecules but also for ethene, cis-1,2-difluoroethene, and tetrafluoroethene. The STEX approach is shown to underestimate π∗-peak separations due to spectral compressions, a characteristic which is inherent to this method.

Star clusters: age, metallicity and extinction from integrated spectra

NASA Astrophysics Data System (ADS)

González Delgado, Rosa M.; Cid Fernandes, Roberto

2010-01-01

Integrated optical spectra of star clusters in the Magellanic Clouds and a few Galactic globular clusters are fitted using high-resolution spectral models for single stellar populations. The goal is to estimate the age, metallicity and extinction of the clusters, and evaluate the degeneracies among these parameters. Several sets of evolutionary models that were computed with recent high-spectral-resolution stellar libraries (MILES, GRANADA, STELIB), are used as inputs to the starlight code to perform the fits. The comparison of the results derived from this method and previous estimates available in the literature allow us to evaluate the pros and cons of each set of models to determine star cluster properties. In addition, we quantify the uncertainties associated with the age, metallicity and extinction determinations resulting from variance in the ingredients for the analysis.

Semi-Supervised Data Summarization: Using Spectral Libraries to Improve Hyperspectral Clustering

NASA Technical Reports Server (NTRS)

Wagstaff, K. L.; Shu, H. P.; Mazzoni, D.; Castano, R.

2005-01-01

Hyperspectral imagers produce very large images, with each pixel recorded at hundreds or thousands of different wavelengths. The ability to automatically generate summaries of these data sets enables several important applications, such as quickly browsing through a large image repository or determining the best use of a limited bandwidth link (e.g., determining which images are most critical for full transmission). Clustering algorithms can be used to generate these summaries, but traditional clustering methods make decisions based only on the information contained in the data set. In contrast, we present a new method that additionally leverages existing spectral libraries to identify materials that are likely to be present in the image target area. We find that this approach simultaneously reduces runtime and produces summaries that are more relevant to science goals.

Spike sorting using locality preserving projection with gap statistics and landmark-based spectral clustering.

PubMed

Nguyen, Thanh; Khosravi, Abbas; Creighton, Douglas; Nahavandi, Saeid

2014-12-30

Understanding neural functions requires knowledge from analysing electrophysiological data. The process of assigning spikes of a multichannel signal into clusters, called spike sorting, is one of the important problems in such analysis. There have been various automated spike sorting techniques with both advantages and disadvantages regarding accuracy and computational costs. Therefore, developing spike sorting methods that are highly accurate and computationally inexpensive is always a challenge in the biomedical engineering practice. An automatic unsupervised spike sorting method is proposed in this paper. The method uses features extracted by the locality preserving projection (LPP) algorithm. These features afterwards serve as inputs for the landmark-based spectral clustering (LSC) method. Gap statistics (GS) is employed to evaluate the number of clusters before the LSC can be performed. The proposed LPP-LSC is highly accurate and computationally inexpensive spike sorting approach. LPP spike features are very discriminative; thereby boost the performance of clustering methods. Furthermore, the LSC method exhibits its efficiency when integrated with the cluster evaluator GS. The proposed method's accuracy is approximately 13% superior to that of the benchmark combination between wavelet transformation and superparamagnetic clustering (WT-SPC). Additionally, LPP-LSC computing time is six times less than that of the WT-SPC. LPP-LSC obviously demonstrates a win-win spike sorting solution meeting both accuracy and computational cost criteria. LPP and LSC are linear algorithms that help reduce computational burden and thus their combination can be applied into real-time spike analysis. Copyright © 2014 Elsevier B.V. All rights reserved.

Application of a XMM-Newton EPIC Monte Carlo to Analysis And Interpretation of Data for Abell 1689, RXJ0658-55 And the Centaurus Clusters of Galaxies

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andersson, Karl E.; /Stockholm U. /SLAC; Peterson, J.R.

2007-04-17

We propose a new Monte Carlo method to study extended X-ray sources with the European Photon Imaging Camera (EPIC) aboard XMM Newton. The Smoothed Particle Inference (SPI) technique, described in a companion paper, is applied here to the EPIC data for the clusters of galaxies Abell 1689, Centaurus and RXJ 0658-55 (the ''bullet cluster''). We aim to show the advantages of this method of simultaneous spectral-spatial modeling over traditional X-ray spectral analysis. In Abell 1689 we confirm our earlier findings about structure in temperature distribution and produce a high resolution temperature map. We also confirm our findings about velocity structuremore » within the gas. In the bullet cluster, RXJ 0658-55, we produce the highest resolution temperature map ever to be published of this cluster allowing us to trace what looks like the motion of the bullet in the cluster. We even detect a south to north temperature gradient within the bullet itself. In the Centaurus cluster we detect, by dividing up the luminosity of the cluster in bands of gas temperatures, a striking feature to the north-east of the cluster core. We hypothesize that this feature is caused by a subcluster left over from a substantial merger that slightly displaced the core. We conclude that our method is very powerful in determining the spatial distributions of plasma temperatures and very useful for systematic studies in cluster structure.« less

Spectral-clustering approach to Lagrangian vortex detection.

PubMed

Hadjighasem, Alireza; Karrasch, Daniel; Teramoto, Hiroshi; Haller, George

2016-06-01

One of the ubiquitous features of real-life turbulent flows is the existence and persistence of coherent vortices. Here we show that such coherent vortices can be extracted as clusters of Lagrangian trajectories. We carry out the clustering on a weighted graph, with the weights measuring pairwise distances of fluid trajectories in the extended phase space of positions and time. We then extract coherent vortices from the graph using tools from spectral graph theory. Our method locates all coherent vortices in the flow simultaneously, thereby showing high potential for automated vortex tracking. We illustrate the performance of this technique by identifying coherent Lagrangian vortices in several two- and three-dimensional flows.

Discrete Wavelet Transform-Based Whole-Spectral and Subspectral Analysis for Improved Brain Tumor Clustering Using Single Voxel MR Spectroscopy.

PubMed

Yang, Guang; Nawaz, Tahir; Barrick, Thomas R; Howe, Franklyn A; Slabaugh, Greg

2015-12-01

Many approaches have been considered for automatic grading of brain tumors by means of pattern recognition with magnetic resonance spectroscopy (MRS). Providing an improved technique which can assist clinicians in accurately identifying brain tumor grades is our main objective. The proposed technique, which is based on the discrete wavelet transform (DWT) of whole-spectral or subspectral information of key metabolites, combined with unsupervised learning, inspects the separability of the extracted wavelet features from the MRS signal to aid the clustering. In total, we included 134 short echo time single voxel MRS spectra (SV MRS) in our study that cover normal controls, low grade and high grade tumors. The combination of DWT-based whole-spectral or subspectral analysis and unsupervised clustering achieved an overall clustering accuracy of 94.8% and a balanced error rate of 7.8%. To the best of our knowledge, it is the first study using DWT combined with unsupervised learning to cluster brain SV MRS. Instead of dimensionality reduction on SV MRS or feature selection using model fitting, our study provides an alternative method of extracting features to obtain promising clustering results.

Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction

ERIC Educational Resources Information Center

Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T.

2012-01-01

Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…

Active constrained clustering by examining spectral Eigenvectors

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; desJardins, Marie; Xu, Qianjun

2005-01-01

This work focuses on the active selection of pairwise constraints for spectral clustering. We develop and analyze a technique for Active Constrained Clustering by Examining Spectral eigenvectorS (ACCESS) derived from a similarity matrix.

An algorithm for spatial heirarchy clustering

NASA Technical Reports Server (NTRS)

Dejesusparada, N. (Principal Investigator); Velasco, F. R. D.

1981-01-01

A method for utilizing both spectral and spatial redundancy in compacting and preclassifying images is presented. In multispectral satellite images, a high correlation exists between neighboring image points which tend to occupy dense and restricted regions of the feature space. The image is divided into windows of the same size where the clustering is made. The classes obtained in several neighboring windows are clustered, and then again successively clustered until only one region corresponding to the whole image is obtained. By employing this algorithm only a few points are considered in each clustering, thus reducing computational effort. The method is illustrated as applied to LANDSAT images.

Energy spectra of X-ray clusters of galaxies

NASA Technical Reports Server (NTRS)

Avni, Y.

1976-01-01

A procedure for estimating the ranges of parameters that describe the spectra of X-rays from clusters of galaxies is presented. The applicability of the method is proved by statistical simulations of cluster spectra; such a proof is necessary because of the nonlinearity of the spectral functions. Implications for the spectra of the Perseus, Coma, and Virgo clusters are discussed. The procedure can be applied in more general problems of parameter estimation.

A population of gamma-ray emitting globular clusters seen with the Fermi Large Area Telescope

DOE PAGES

Abdo, A. A.

2010-11-24

Context. Globular clusters with their large populations of millisecond pulsars (MSPs) are believed to be potential emitters of high-energy gamma-ray emission. The observation of this emission provides a powerful tool to assess the millisecond pulsar population of a cluster, is essential for understanding the importance of binary systems for the evolution of globular clusters, and provides complementary insights into magnetospheric emission processes. Aims. Our goal is to constrain the millisecond pulsar populations in globular clusters from analysis of gamma-ray observations. Methods. We use 546 days of continuous sky-survey observations obtained with the Large Area Telescope aboard the Fermi Gamma-ray Spacemore » Telescope to study the gamma-ray emission towards 13 globular clusters. Results. Steady point-like high-energy gamma-ray emission has been significantly detected towards 8 globular clusters. Five of them (47 Tucanae, Omega Cen, NGC 6388, Terzan 5, and M 28) show hard spectral power indices (0.7 < Γ < 1.4) and clear evidence for an exponential cut-off in the range 1.0 - 2.6 GeV, which is the characteristic signature of magnetospheric emission from MSPs. Three of them (M 62, NGC 6440 and NGC 6652) also show hard spectral indices (1.0 < Γ < 1.7), however the presence of an exponential cut-off can not be unambiguously established. Three of them (Omega Cen, NGC 6388, NGC 6652) have no known radio or X-ray MSPs yet still exhibit MSP spectral properties. From the observed gamma-ray luminosities, we estimate the total number of MSPs that is expected to be present in these globular clusters. We show that our estimates of the MSP population correlate with the stellar encounter rate and we estimate 2600 - 4700 MSPs in Galactic globular clusters, commensurate with previous estimates. Conclusions. The observation of high-energy gamma-ray emission from globular clusters thus provides a reliable independent method to assess their millisecond pulsar populations.« less

MapReduce implementation of a hybrid spectral library-database search method for large-scale peptide identification.

PubMed

Kalyanaraman, Ananth; Cannon, William R; Latt, Benjamin; Baxter, Douglas J

2011-11-01

A MapReduce-based implementation called MR-MSPolygraph for parallelizing peptide identification from mass spectrometry data is presented. The underlying serial method, MSPolygraph, uses a novel hybrid approach to match an experimental spectrum against a combination of a protein sequence database and a spectral library. Our MapReduce implementation can run on any Hadoop cluster environment. Experimental results demonstrate that, relative to the serial version, MR-MSPolygraph reduces the time to solution from weeks to hours, for processing tens of thousands of experimental spectra. Speedup and other related performance studies are also reported on a 400-core Hadoop cluster using spectral datasets from environmental microbial communities as inputs. The source code along with user documentation are available on http://compbio.eecs.wsu.edu/MR-MSPolygraph. ananth@eecs.wsu.edu; william.cannon@pnnl.gov. Supplementary data are available at Bioinformatics online.

Community detection using Kernel Spectral Clustering with memory

NASA Astrophysics Data System (ADS)

Langone, Rocco; Suykens, Johan A. K.

2013-02-01

This work is related to the problem of community detection in dynamic scenarios, which for instance arises in the segmentation of moving objects, clustering of telephone traffic data, time-series micro-array data etc. A desirable feature of a clustering model which has to capture the evolution of communities over time is the temporal smoothness between clusters in successive time-steps. In this way the model is able to track the long-term trend and in the same time it smooths out short-term variation due to noise. We use the Kernel Spectral Clustering with Memory effect (MKSC) which allows to predict cluster memberships of new nodes via out-of-sample extension and has a proper model selection scheme. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness as a valid prior knowledge. The latter, in fact, allows the model to cluster the current data well and to be consistent with the recent history. Here we propose a generalization of the MKSC model with an arbitrary memory, not only one time-step in the past. The experiments conducted on toy problems confirm our expectations: the more memory we add to the model, the smoother over time are the clustering results. We also compare with the Evolutionary Spectral Clustering (ESC) algorithm which is a state-of-the art method, and we obtain comparable or better results.

Automated cloud screening of AVHRR imagery using split-and-merge clustering

NASA Technical Reports Server (NTRS)

Gallaudet, Timothy C.; Simpson, James J.

1991-01-01

Previous methods to segment clouds from ocean in AVHRR imagery have shown varying degrees of success, with nighttime approaches being the most limited. An improved method of automatic image segmentation, the principal component transformation split-and-merge clustering (PCTSMC) algorithm, is presented and applied to cloud screening of both nighttime and daytime AVHRR data. The method combines spectral differencing, the principal component transformation, and split-and-merge clustering to sample objectively the natural classes in the data. This segmentation method is then augmented by supervised classification techniques to screen clouds from the imagery. Comparisons with other nighttime methods demonstrate its improved capability in this application. The sensitivity of the method to clustering parameters is presented; the results show that the method is insensitive to the split-and-merge thresholds.

Clustering PPI data by combining FA and SHC method.

PubMed

Lei, Xiujuan; Ying, Chao; Wu, Fang-Xiang; Xu, Jin

2015-01-01

Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value.

Clustering PPI data by combining FA and SHC method

PubMed Central

2015-01-01

Clustering is one of main methods to identify functional modules from protein-protein interaction (PPI) data. Nevertheless traditional clustering methods may not be effective for clustering PPI data. In this paper, we proposed a novel method for clustering PPI data by combining firefly algorithm (FA) and synchronization-based hierarchical clustering (SHC) algorithm. Firstly, the PPI data are preprocessed via spectral clustering (SC) which transforms the high-dimensional similarity matrix into a low dimension matrix. Then the SHC algorithm is used to perform clustering. In SHC algorithm, hierarchical clustering is achieved by enlarging the neighborhood radius of synchronized objects continuously, while the hierarchical search is very difficult to find the optimal neighborhood radius of synchronization and the efficiency is not high. So we adopt the firefly algorithm to determine the optimal threshold of the neighborhood radius of synchronization automatically. The proposed algorithm is tested on the MIPS PPI dataset. The results show that our proposed algorithm is better than the traditional algorithms in precision, recall and f-measure value. PMID:25707632

Shapiro effect as a possible cause of the low-frequency pulsar timing noise in globular clusters

NASA Astrophysics Data System (ADS)

Larchenkova, T. I.; Kopeikin, S. M.

2006-01-01

A prolonged timing of millisecond pulsars has revealed low-frequency uncorrelated (infrared) noise, presumably of astrophysical origin, in the pulse arrival time (PAT) residuals for some of them. Currently available pulsar timing methods allow the statistical parameters of this noise to be reliably measured by decomposing the PAT residual function into orthogonal Fourier harmonics. In most cases, pulsars in globular clusters show a low-frequency modulation of their rotational phase and spin rate. The relativistic time delay of the pulsar signal in the curved spacetime of randomly distributed and moving globular cluster stars (the Shapiro effect) is suggested as a possible cause of this modulation. Extremely important (from an astrophysical point of view) information about the structure of the globular cluster core, which is inaccessible to study by other observational methods, could be obtained by analyzing the spectral parameters of the low-frequency noise caused by the Shapiro effect and attributable to the random passages of stars near the line of sight to the pulsar. Given the smallness of the aberration corrections that arise from the nonstationarity of the gravitational field of the randomly distributed ensemble of stars under consideration, a formula is derived for the Shapiro effect for a pulsar in a globular cluster. The derived formula is used to calculate the autocorrelation function of the low-frequency pulsar noise, the slope of its power spectrum, and the behavior of the σz statistic that characterizes the spectral properties of this noise in the form of a time function. The Shapiro effect under discussion is shown to manifest itself for large impact parameters as a low-frequency noise of the pulsar spin rate with a spectral index of n = -1.8 that depends weakly on the specific model distribution of stars in the globular cluster. For small impact parameters, the spectral index of the noise is n = -1.5.

Alignment and integration of complex networks by hypergraph-based spectral clustering

NASA Astrophysics Data System (ADS)

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Alignment and integration of complex networks by hypergraph-based spectral clustering.

PubMed

Michoel, Tom; Nachtergaele, Bruno

2012-11-01

Complex networks possess a rich, multiscale structure reflecting the dynamical and functional organization of the systems they model. Often there is a need to analyze multiple networks simultaneously, to model a system by more than one type of interaction, or to go beyond simple pairwise interactions, but currently there is a lack of theoretical and computational methods to address these problems. Here we introduce a framework for clustering and community detection in such systems using hypergraph representations. Our main result is a generalization of the Perron-Frobenius theorem from which we derive spectral clustering algorithms for directed and undirected hypergraphs. We illustrate our approach with applications for local and global alignment of protein-protein interaction networks between multiple species, for tripartite community detection in folksonomies, and for detecting clusters of overlapping regulatory pathways in directed networks.

Nonlocal Coulomb correlations in pure and electron-doped Sr2IrO4 : Spectral functions, Fermi surface, and pseudo-gap-like spectral weight distributions from oriented cluster dynamical mean-field theory

NASA Astrophysics Data System (ADS)

Martins, Cyril; Lenz, Benjamin; Perfetti, Luca; Brouet, Veronique; Bertran, François; Biermann, Silke

2018-03-01

We address the role of nonlocal Coulomb correlations and short-range magnetic fluctuations in the high-temperature phase of Sr2IrO4 within state-of-the-art spectroscopic and first-principles theoretical methods. Introducing an "oriented-cluster dynamical mean-field scheme", we compute momentum-resolved spectral functions, which we find to be in excellent agreement with angle-resolved photoemission spectra. We show that while short-range antiferromagnetic fluctuations are crucial to accounting for the electronic properties of Sr2IrO4 even in the high-temperature paramagnetic phase, long-range magnetic order is not a necessary ingredient of the insulating state. Upon doping, an exotic metallic state is generated, exhibiting cuprate-like pseudo-gap spectral properties, for which we propose a surprisingly simple theoretical mechanism.

Dimension Reduction of Hyperspectral Data on Beowulf Clusters

NASA Technical Reports Server (NTRS)

El-Ghazawi, Tarek

2000-01-01

Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operation. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold a great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, which is used widely in remote sensing, is the Principal Components Analysis (PCA). In light of the growing number of spectral channels of modern instruments, the paper reports on the development of a parallel PCA and its implementation on two Beowulf cluster configurations, on with fast Ethernet switch and the other is with a Myrinet interconnection.

Bayesian statistics as a new tool for spectral analysis - I. Application for the determination of basic parameters of massive stars

NASA Astrophysics Data System (ADS)

Mugnes, J.-M.; Robert, C.

2015-11-01

Spectral analysis is a powerful tool to investigate stellar properties and it has been widely used for decades now. However, the methods considered to perform this kind of analysis are mostly based on iteration among a few diagnostic lines to determine the stellar parameters. While these methods are often simple and fast, they can lead to errors and large uncertainties due to the required assumptions. Here, we present a method based on Bayesian statistics to find simultaneously the best combination of effective temperature, surface gravity, projected rotational velocity, and microturbulence velocity, using all the available spectral lines. Different tests are discussed to demonstrate the strength of our method, which we apply to 54 mid-resolution spectra of field and cluster B stars obtained at the Observatoire du Mont-Mégantic. We compare our results with those found in the literature. Differences are seen which are well explained by the different methods used. We conclude that the B-star microturbulence velocities are often underestimated. We also confirm the trend that B stars in clusters are on average faster rotators than field B stars.

Fast algorithm for spectral mixture analysis of imaging spectrometer data

NASA Astrophysics Data System (ADS)

Schouten, Theo E.; Klein Gebbinck, Maurice S.; Liu, Z. K.; Chen, Shaowei

1996-12-01

Imaging spectrometers acquire images in many narrow spectral bands but have limited spatial resolution. Spectral mixture analysis (SMA) is used to determine the fractions of the ground cover categories (the end-members) present in each pixel. In this paper a new iterative SMA method is presented and tested using a 30 band MAIS image. The time needed for each iteration is independent of the number of bands, thus the method can be used for spectrometers with a large number of bands. Further a new method, based on K-means clustering, for obtaining endmembers from image data is described and compared with existing methods. Using the developed methods the available MAIS image was analyzed using 2 to 6 endmembers.

Automatic categorization of anatomical landmark-local appearances based on diffeomorphic demons and spectral clustering for constructing detector ensembles.

PubMed

Hanaoka, Shouhei; Masutani, Yoshitaka; Nemoto, Mitsutaka; Nomura, Yukihiro; Yoshikawa, Takeharu; Hayashi, Naoto; Ohtomo, Kuni

2012-01-01

A method for categorizing landmark-local appearances extracted from computed tomography (CT) datasets is presented. Anatomical landmarks in the human body inevitably have inter-individual variations that cause difficulty in automatic landmark detection processes. The goal of this study is to categorize subjects (i.e., training datasets) according to local shape variations of such a landmark so that each subgroup has less shape variation and thus the machine learning of each landmark detector is much easier. The similarity between each subject pair is measured based on the non-rigid registration result between them. These similarities are used by the spectral clustering process. After the clustering, all training datasets in each cluster, as well as synthesized intermediate images calculated from all subject-pairs in the cluster, are used to train the corresponding subgroup detector. All of these trained detectors compose a detector ensemble to detect the target landmark. Evaluation with clinical CT datasets showed great improvement in the detection performance.

[An improved low spectral distortion PCA fusion method].

PubMed

Peng, Shi; Zhang, Ai-Wu; Li, Han-Lun; Hu, Shao-Xing; Meng, Xian-Gang; Sun, Wei-Dong

2013-10-01

Aiming at the spectral distortion produced in PCA fusion process, the present paper proposes an improved low spectral distortion PCA fusion method. This method uses NCUT (normalized cut) image segmentation algorithm to make a complex hyperspectral remote sensing image into multiple sub-images for increasing the separability of samples, which can weaken the spectral distortions of traditional PCA fusion; Pixels similarity weighting matrix and masks were produced by using graph theory and clustering theory. These masks are used to cut the hyperspectral image and high-resolution image into some sub-region objects. All corresponding sub-region objects between the hyperspectral image and high-resolution image are fused by using PCA method, and all sub-regional integration results are spliced together to produce a new image. In the experiment, Hyperion hyperspectral data and Rapid Eye data were used. And the experiment result shows that the proposed method has the same ability to enhance spatial resolution and greater ability to improve spectral fidelity performance.

Optimal wavelength band clustering for multispectral iris recognition.

PubMed

Gong, Yazhuo; Zhang, David; Shi, Pengfei; Yan, Jingqi

2012-07-01

This work explores the possibility of clustering spectral wavelengths based on the maximum dissimilarity of iris textures. The eventual goal is to determine how many bands of spectral wavelengths will be enough for iris multispectral fusion and to find these bands that will provide higher performance of iris multispectral recognition. A multispectral acquisition system was first designed for imaging the iris at narrow spectral bands in the range of 420 to 940 nm. Next, a set of 60 human iris images that correspond to the right and left eyes of 30 different subjects were acquired for an analysis. Finally, we determined that 3 clusters were enough to represent the 10 feature bands of spectral wavelengths using the agglomerative clustering based on two-dimensional principal component analysis. The experimental results suggest (1) the number, center, and composition of clusters of spectral wavelengths and (2) the higher performance of iris multispectral recognition based on a three wavelengths-bands fusion.

Investigation of correlation classification techniques

NASA Technical Reports Server (NTRS)

Haskell, R. E.

1975-01-01

A two-step classification algorithm for processing multispectral scanner data was developed and tested. The first step is a single pass clustering algorithm that assigns each pixel, based on its spectral signature, to a particular cluster. The output of that step is a cluster tape in which a single integer is associated with each pixel. The cluster tape is used as the input to the second step, where ground truth information is used to classify each cluster using an iterative method of potentials. Once the clusters have been assigned to classes the cluster tape is read pixel-by-pixel and an output tape is produced in which each pixel is assigned to its proper class. In addition to the digital classification programs, a method of using correlation clustering to process multispectral scanner data in real time by means of an interactive color video display is also described.

Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field.

PubMed

Holtzman, Benjamin K; Paté, Arthur; Paisley, John; Waldhauser, Felix; Repetto, Douglas

2018-05-01

The earthquake rupture process comprises complex interactions of stress, fracture, and frictional properties. New machine learning methods demonstrate great potential to reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Clustering of 46,000 earthquakes of 0.3 < M L < 1.5 from the Geysers geothermal field (CA) yields groupings that have no reservoir-scale spatial patterns but clear temporal patterns. Events with similar spectral properties repeat on annual cycles within each cluster and track changes in the water injection rates into the Geysers reservoir, indicating that changes in acoustic properties and faulting processes accompany changes in thermomechanical state. The methods open new means to identify and characterize subtle changes in seismic source properties, with applications to tectonic and geothermal seismicity.

Testing spectral models for stellar populations with star clusters - II. Results

NASA Astrophysics Data System (ADS)

González Delgado, Rosa M.; Cid Fernandes, Roberto

2010-04-01

High spectral resolution evolutionary synthesis models have become a routinely used ingredient in extragalactic work, and as such deserve thorough testing. Star clusters are ideal laboratories for such tests. This paper applies the spectral fitting methodology outlined in Paper I to a sample of clusters, mainly from the Magellanic Clouds and spanning a wide range in age and metallicity, fitting their integrated light spectra with a suite of modern evolutionary synthesis models for single stellar populations. The combinations of model plus spectral library employed in this investigation are Galaxev/STELIB, Vazdekis/MILES, SED@/GRANADA and Galaxev/MILES+GRANADA, which provide a representative sample of models currently available for spectral fitting work. A series of empirical tests are performed with these models, comparing the quality of the spectral fits and the values of age, metallicity and extinction obtained with each of them. A comparison is also made between the properties derived from these spectral fits and literature data on these nearby, well studied clusters. These comparisons are done with the general goal of providing useful feedback for model makers, as well as guidance to the users of such models. We find the following. (i) All models are able to derive ages that are in good agreement both with each other and with literature data, although ages derived from spectral fits are on average slightly older than those based on the S-colour-magnitude diagram (S-CMD) method as calibrated by Girardi et al. (ii) There is less agreement between the models for the metallicity and extinction. In particular, Galaxev/STELIB models underestimate the metallicity by ~0.6 dex, and the extinction is overestimated by 0.1 mag. (iii) New generations of models using the GRANADA and MILES libraries are superior to STELIB-based models both in terms of spectral fit quality and regarding the accuracy with which age and metallicity are retrieved. Accuracies of about 0.1 dex in age and 0.3 dex in metallicity can be achieved as long as the models are not extrapolated beyond their expected range of validity.

Recognizing different tissues in human fetal femur cartilage by label-free Raman microspectroscopy

NASA Astrophysics Data System (ADS)

Kunstar, Aliz; Leijten, Jeroen; van Leuveren, Stefan; Hilderink, Janneke; Otto, Cees; van Blitterswijk, Clemens A.; Karperien, Marcel; van Apeldoorn, Aart A.

2012-11-01

Traditionally, the composition of bone and cartilage is determined by standard histological methods. We used Raman microscopy, which provides a molecular "fingerprint" of the investigated sample, to detect differences between the zones in human fetal femur cartilage without the need for additional staining or labeling. Raman area scans were made from the (pre)articular cartilage, resting, proliferative, and hypertrophic zones of growth plate and endochondral bone within human fetal femora. Multivariate data analysis was performed on Raman spectral datasets to construct cluster images with corresponding cluster averages. Cluster analysis resulted in detection of individual chondrocyte spectra that could be separated from cartilage extracellular matrix (ECM) spectra and was verified by comparing cluster images with intensity-based Raman images for the deoxyribonucleic acid/ribonucleic acid (DNA/RNA) band. Specific dendrograms were created using Ward's clustering method, and principal component analysis (PCA) was performed with the separated and averaged Raman spectra of cells and ECM of all measured zones. Overall (dis)similarities between measured zones were effectively visualized on the dendrograms and main spectral differences were revealed by PCA allowing for label-free detection of individual cartilaginous zones and for label-free evaluation of proper cartilaginous matrix formation for future tissue engineering and clinical purposes.

Efficient red luminescence from organic-soluble Au25 clusters by ligand structure modification

NASA Astrophysics Data System (ADS)

Mathew, Ammu; Varghese, Elizabeth; Choudhury, Susobhan; Pal, Samir Kumar; Pradeep, T.

2015-08-01

An efficient method to enhance visible luminescence in a visibly non-luminescent organic-soluble 4-(tert butyl)benzyl mercaptan (SBB)-stabilized Au25 cluster has been developed. This method relies mainly on enhancing the surface charge density on the cluster by creating an additional shell of thiolate on the cluster surface, which enhances visible luminescence. The viability of this method has been demonstrated by imparting red luminescence to various ligand-protected quantum clusters (QCs), observable to the naked eye. The bright red luminescent material derived from Au25SBB18 clusters was characterized using UV-vis and luminescence spectroscopy, TEM, SEM/EDS, XPS, TG, ESI and MALDI mass spectrometry, which collectively proposed an uncommon molecular formula of Au29SBB24S, suggested to be due to different stapler motifs protecting the Au25 core. The critical role of temperature on the emergence of luminescence in QCs has been studied. The restoration of the surface ligand shell on the Au25 cluster and subsequent physicochemical modification to the cluster were probed by various mass spectral and spectroscopic techniques. Our results provide fundamental insights into the ligand characteristics determining luminescence in QCs.An efficient method to enhance visible luminescence in a visibly non-luminescent organic-soluble 4-(tert butyl)benzyl mercaptan (SBB)-stabilized Au25 cluster has been developed. This method relies mainly on enhancing the surface charge density on the cluster by creating an additional shell of thiolate on the cluster surface, which enhances visible luminescence. The viability of this method has been demonstrated by imparting red luminescence to various ligand-protected quantum clusters (QCs), observable to the naked eye. The bright red luminescent material derived from Au25SBB18 clusters was characterized using UV-vis and luminescence spectroscopy, TEM, SEM/EDS, XPS, TG, ESI and MALDI mass spectrometry, which collectively proposed an uncommon molecular formula of Au29SBB24S, suggested to be due to different stapler motifs protecting the Au25 core. The critical role of temperature on the emergence of luminescence in QCs has been studied. The restoration of the surface ligand shell on the Au25 cluster and subsequent physicochemical modification to the cluster were probed by various mass spectral and spectroscopic techniques. Our results provide fundamental insights into the ligand characteristics determining luminescence in QCs. Electronic supplementary information (ESI) available: Additional data on characterization of red luminescent Au29 QC and comparison with parent Au25SBB18 are given. See DOI: 10.1039/c5nr03457d

Open clusters. III. Fundamental parameters of B stars in NGC 6087, NGC 6250, NGC 6383, and NGC 6530 B-type stars with circumstellar envelopes

NASA Astrophysics Data System (ADS)

Aidelman, Y.; Cidale, L. S.; Zorec, J.; Panei, J. A.

2018-02-01

Context. Stellar physical properties of star clusters are poorly known and the cluster parameters are often very uncertain. Methods: Our goals are to perform a spectrophotometric study of the B star population in open clusters to derive accurate stellar parameters, search for the presence of circumstellar envelopes, and discuss the characteristics of these stars. The BCD spectrophotometric system is a powerful method to obtain stellar fundamental parameters from direct measurements of the Balmer discontinuity. To this end, we wrote the interactive code MIDE3700. The BCD parameters can also be used to infer the main properties of open clusters: distance modulus, color excess, and age. Furthermore, we inspected the Balmer discontinuity to provide evidence for the presence of circumstellar disks and identify Be star candidates. We used an additional set of high-resolution spectra in the Hα region to confirm the Be nature of these stars. Results: We provide Teff, log g, Mv, Mbol, and spectral types for a sample of 68 stars in the field of the open clusters NGC 6087, NGC 6250, NGC 6383, and NGC 6530, as well as the cluster distances, ages, and reddening. Then, based on a sample of 230 B stars in the direction of the 11 open clusters studied along this series of three papers, we report 6 new Be stars, 4 blue straggler candidates, and 15 B-type stars (called Bdd) with a double Balmer discontinuity, which indicates the presence of circumstellar envelopes. We discuss the distribution of the fraction of B, Be, and Bdd star cluster members per spectral subtype. The majority of the Be stars are dwarfs and present a maximum at the spectral type B2-B4 in young and intermediate-age open clusters (<40 Myr). Another maximum of Be stars is observed at the spectral type B6-B8 in open clusters older than 40 Myr, where the population of Bdd stars also becomes relevant. The Bdd stars seem to be in a passive emission phase. Conclusions: Our results support previous statements that the Be phenomenon is present along the whole main sequence band and occurs in very different evolutionary states. We find clear evidence of an increase of stars with circumstellar envelopes with cluster age. The Be phenomenon reaches its maximum in clusters of intermediate age (10-40 Myr) and the number of B stars with circumstellar envelopes (Be plus Bdd stars) is also high for the older clusters (40-100 Myr). Observations taken at CASLEO, operating under agreement of CONICET and the Universities of La Plata, Córdoba, and San Juan, Argentina.Tables 1, 2, 9-16 are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/610/A30

Radio Sources Toward Galaxy Clusters at 30 GHz

NASA Technical Reports Server (NTRS)

Coble, K.; Bonamente, M.; Carlstrom, J. E.; Dawson, K.; Hasler, N.; Holzapfel, W.; Joy, M.; LaRoque, S.; Marrone, D. P.; Reese, E. D.

2007-01-01

Extra-galactic radio sources are a significant contaminant in cosmic microwave background and Sunyaev-Zeldovich effect experiments. Deep interferometric observations with the BIMA and OVRO arrays are used to characterize the spatial, spectral, and flux distributions of radio sources toward massive galaxy clusters at 28.5 GHz. We compute counts of mJy source fluxes from 89 fields centered on known massive galaxy clusters and 8 non-cluster fields. We find that source counts in the inner regions of the cluster fields (within 0.5 arcmin of the cluster center) are a factor of 8.9 (+4.2 to -3.8) times higher than counts in the outer regions of the cluster fields (radius greater than 0.5 arcmin). Counts in the outer regions of the cluster fields are in turn a factor of 3.3 (+4.1 -1.8) greater than those in the noncluster fields. Counts in the non-cluster fields are consistent with extrapolations from the results of other surveys. We compute spectral indices of mJy sources in cluster fields between 1.4 and 28.5 GHz and find a mean spectral index of al[ja = 0.66 with an rms dispersion of 0.36, where flux S varies as upsilon(sup -alpha). The distribution is skewed, with a median spectral index of 0.72 and 25th and 75th percentiles of 0.51 and 0.92, respectively. This is steeper than the spectral indices of stronger field sources measured by other surveys.

Spectral functions of strongly correlated extended systems via an exact quantum embedding

NASA Astrophysics Data System (ADS)

Booth, George H.; Chan, Garnet Kin-Lic

2015-04-01

Density matrix embedding theory (DMET) [Phys. Rev. Lett. 109, 186404 (2012), 10.1103/PhysRevLett.109.186404], introduced an approach to quantum cluster embedding methods whereby the mapping of strongly correlated bulk problems to an impurity with finite set of bath states was rigorously formulated to exactly reproduce the entanglement of the ground state. The formalism provided similar physics to dynamical mean-field theory at a tiny fraction of the cost but was inherently limited by the construction of a bath designed to reproduce ground-state, static properties. Here, we generalize the concept of quantum embedding to dynamic properties and demonstrate accurate bulk spectral functions at similarly small computational cost. The proposed spectral DMET utilizes the Schmidt decomposition of a response vector, mapping the bulk dynamic correlation functions to that of a quantum impurity cluster coupled to a set of frequency-dependent bath states. The resultant spectral functions are obtained on the real-frequency axis, without bath discretization error, and allows for the construction of arbitrary dynamic correlation functions. We demonstrate the method on the one- (1D) and two-dimensional (2D) Hubbard model, where we obtain zero temperature and thermodynamic limit spectral functions, and show the trivial extension to two-particle Green's functions. This advance therefore extends the scope and applicability of DMET in condensed-matter problems as a computationally tractable route to correlated spectral functions of extended systems and provides a competitive alternative to dynamical mean-field theory for dynamic quantities.

Machine learning reveals cyclic changes in seismic source spectra in Geysers geothermal field

PubMed Central

Paisley, John

2018-01-01

The earthquake rupture process comprises complex interactions of stress, fracture, and frictional properties. New machine learning methods demonstrate great potential to reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Clustering of 46,000 earthquakes of 0.3 < ML < 1.5 from the Geysers geothermal field (CA) yields groupings that have no reservoir-scale spatial patterns but clear temporal patterns. Events with similar spectral properties repeat on annual cycles within each cluster and track changes in the water injection rates into the Geysers reservoir, indicating that changes in acoustic properties and faulting processes accompany changes in thermomechanical state. The methods open new means to identify and characterize subtle changes in seismic source properties, with applications to tectonic and geothermal seismicity. PMID:29806015

Spatial clustering of pixels of a multispectral image

DOEpatents

Conger, James Lynn

2014-08-19

A method and system for clustering the pixels of a multispectral image is provided. A clustering system computes a maximum spectral similarity score for each pixel that indicates the similarity between that pixel and the most similar neighboring. To determine the maximum similarity score for a pixel, the clustering system generates a similarity score between that pixel and each of its neighboring pixels and then selects the similarity score that represents the highest similarity as the maximum similarity score. The clustering system may apply a filtering criterion based on the maximum similarity score so that pixels with similarity scores below a minimum threshold are not clustered. The clustering system changes the current pixel values of the pixels in a cluster based on an averaging of the original pixel values of the pixels in the cluster.

Community structure from spectral properties in complex networks

NASA Astrophysics Data System (ADS)

Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

2005-06-01

We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

Spectral-element simulation of two-dimensional elastic wave propagation in fully heterogeneous media on a GPU cluster

NASA Astrophysics Data System (ADS)

Rudianto, Indra; Sudarmaji

2018-04-01

We present an implementation of the spectral-element method for simulation of two-dimensional elastic wave propagation in fully heterogeneous media. We have incorporated most of realistic geological features in the model, including surface topography, curved layer interfaces, and 2-D wave-speed heterogeneity. To accommodate such complexity, we use an unstructured quadrilateral meshing technique. Simulation was performed on a GPU cluster, which consists of 24 core processors Intel Xeon CPU and 4 NVIDIA Quadro graphics cards using CUDA and MPI implementation. We speed up the computation by a factor of about 5 compared to MPI only, and by a factor of about 40 compared to Serial implementation.

Spectroscopically Enhanced Method and System for Multi-Factor Biometric Authentication

NASA Astrophysics Data System (ADS)

Pishva, Davar

This paper proposes a spectroscopic method and system for preventing spoofing of biometric authentication. One of its focus is to enhance biometrics authentication with a spectroscopic method in a multifactor manner such that a person's unique ‘spectral signatures’ or ‘spectral factors’ are recorded and compared in addition to a non-spectroscopic biometric signature to reduce the likelihood of imposter getting authenticated. By using the ‘spectral factors’ extracted from reflectance spectra of real fingers and employing cluster analysis, it shows how the authentic fingerprint image presented by a real finger can be distinguished from an authentic fingerprint image embossed on an artificial finger, or molded on a fingertip cover worn by an imposter. This paper also shows how to augment two widely used biometrics systems (fingerprint and iris recognition devices) with spectral biometrics capabilities in a practical manner and without creating much overhead or inconveniencing their users.

Clustering analysis of line indices for LAMOST spectra with AstroStat

NASA Astrophysics Data System (ADS)

Chen, Shu-Xin; Sun, Wei-Min; Yan, Qi

2018-06-01

The application of data mining in astronomical surveys, such as the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) survey, provides an effective approach to automatically analyze a large amount of complex survey data. Unsupervised clustering could help astronomers find the associations and outliers in a big data set. In this paper, we employ the k-means method to perform clustering for the line index of LAMOST spectra with the powerful software AstroStat. Implementing the line index approach for analyzing astronomical spectra is an effective way to extract spectral features for low resolution spectra, which can represent the main spectral characteristics of stars. A total of 144 340 line indices for A type stars is analyzed through calculating their intra and inter distances between pairs of stars. For intra distance, we use the definition of Mahalanobis distance to explore the degree of clustering for each class, while for outlier detection, we define a local outlier factor for each spectrum. AstroStat furnishes a set of visualization tools for illustrating the analysis results. Checking the spectra detected as outliers, we find that most of them are problematic data and only a few correspond to rare astronomical objects. We show two examples of these outliers, a spectrum with abnormal continuumand a spectrum with emission lines. Our work demonstrates that line index clustering is a good method for examining data quality and identifying rare objects.

MOCASSIN-prot: a multi-objective clustering approach for protein similarity networks.

PubMed

Keel, Brittney N; Deng, Bo; Moriyama, Etsuko N

2018-04-15

Proteins often include multiple conserved domains. Various evolutionary events including duplication and loss of domains, domain shuffling, as well as sequence divergence contribute to generating complexities in protein structures, and consequently, in their functions. The evolutionary history of proteins is hence best modeled through networks that incorporate information both from the sequence divergence and the domain content. Here, a game-theoretic approach proposed for protein network construction is adapted into the framework of multi-objective optimization, and extended to incorporate clustering refinement procedure. The new method, MOCASSIN-prot, was applied to cluster multi-domain proteins from ten genomes. The performance of MOCASSIN-prot was compared against two protein clustering methods, Markov clustering (TRIBE-MCL) and spectral clustering (SCPS). We showed that compared to these two methods, MOCASSIN-prot, which uses both domain composition and quantitative sequence similarity information, generates fewer false positives. It achieves more functionally coherent protein clusters and better differentiates protein families. MOCASSIN-prot, implemented in Perl and Matlab, is freely available at http://bioinfolab.unl.edu/emlab/MOCASSINprot. emoriyama2@unl.edu. Supplementary data are available at Bioinformatics online.

Coexpression of CdSe and CdSe/CdS quantum dots in live cells using molecular hyperspectral imaging technology

NASA Astrophysics Data System (ADS)

Li, Qingli; Peng, Hui; Wang, Jing; Wang, Yiting; Guo, Fangmin

2015-11-01

A direct spatial and spectral observation of CdSe and CdSe/CdS quantum dots (QDs) as probes in live cells is performed by using a custom molecular hyperspectral imaging (MHI) system. Water-soluble CdSe and CdSe/CdS QDs are synthesized in aqueous solution under the assistance of high-intensity ultrasonic irradiation and incubated with colon cancer cells for bioimaging. Unlike the traditional fluorescence microscopy methods, MHI technology can identify QD probes according to their spectral signatures and generate coexpression and stain titer maps by a clustering method. The experimental results show that the MHI method has potential to unmix biomarkers by their spectral information, which opens up a pathway of optical multiplexing with many different QD probes.

Spectral Archives: Extending Spectral Libraries to Analyze both Identified and Unidentified Spectra

PubMed Central

Frank, Ari M.; Monroe, Matthew E.; Shah, Anuj R.; Carver, Jeremy J.; Bandeira, Nuno F.; Moore, Ronald J.; Anderson, Gordon A.; Smith, Richard D.; Pevzner, Pavel A.

2011-01-01

MS/MS experiments generate multiple, nearly identical spectra of the same peptide in various laboratories, but proteomics researchers typically do not leverage the unidentified spectra produced in other labs to decode spectra generated in their own labs. We propose a spectral archives approach that clusters MS/MS datasets, representing similar spectra by a single consensus spectrum. Spectral archives extend spectral libraries by analyzing both identified and unidentified spectra in the same way and maintaining information about spectra of peptides shared across species and conditions. Thus archives offer both traditional library spectrum similarity-based search capabilities along with novel ways to analyze the data. By developing a clustering tool, MS-Cluster, we generated a spectral archive from ~1.18 billion spectra that greatly exceeds the size of existing spectral repositories. We advocate that publicly available data should be organized into spectral archives, rather than be analyzed as disparate datasets, as is mostly the case today. PMID:21572408

Reformulation of Traditional Chamomile Oil: Quality Controls and Fingerprint Presentation Based on Cluster Analysis of Attenuated Total Reflectance–Infrared Spectral Data

PubMed Central

Sakhteman, Amirhossein; Faridi, Pouya; Daneshamouz, Saeid; Akbarizadeh, Amin Reza; Borhani-Haghighi, Afshin; Mohagheghzadeh, Abdolali

2017-01-01

Herbal oils have been widely used in Iran as medicinal compounds dating back to thousands of years in Iran. Chamomile oil is widely used as an example of traditional oil. We remade chamomile oils and tried to modify it with current knowledge and facilities. Six types of oil (traditional and modified) were prepared. Microbial limit tests and physicochemical tests were performed on them. Also, principal component analysis, hierarchical cluster analysis, and partial least squares discriminant analysis were done on the spectral data of attenuated total reflectance–infrared in order to obtain insight based on classification pattern of the samples. The results show that we can use modified versions of the chamomile oils (modified Clevenger-type apparatus method and microwave method) with the same content of traditional ones and with less microbial contaminations and better physicochemical properties. PMID:28585466

Reformulation of Traditional Chamomile Oil: Quality Controls and Fingerprint Presentation Based on Cluster Analysis of Attenuated Total Reflectance-Infrared Spectral Data.

PubMed

Zargaran, Arman; Sakhteman, Amirhossein; Faridi, Pouya; Daneshamouz, Saeid; Akbarizadeh, Amin Reza; Borhani-Haghighi, Afshin; Mohagheghzadeh, Abdolali

2017-10-01

Herbal oils have been widely used in Iran as medicinal compounds dating back to thousands of years in Iran. Chamomile oil is widely used as an example of traditional oil. We remade chamomile oils and tried to modify it with current knowledge and facilities. Six types of oil (traditional and modified) were prepared. Microbial limit tests and physicochemical tests were performed on them. Also, principal component analysis, hierarchical cluster analysis, and partial least squares discriminant analysis were done on the spectral data of attenuated total reflectance-infrared in order to obtain insight based on classification pattern of the samples. The results show that we can use modified versions of the chamomile oils (modified Clevenger-type apparatus method and microwave method) with the same content of traditional ones and with less microbial contaminations and better physicochemical properties.

SHIPS: Spectral Hierarchical Clustering for the Inference of Population Structure in Genetic Studies

PubMed Central

Bouaziz, Matthieu; Paccard, Caroline; Guedj, Mickael; Ambroise, Christophe

2012-01-01

Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising solution to infer fine-scale genetic patterns. PMID:23077494

Asymptotic stability of spectral-based PDF modeling for homogeneous turbulent flows

NASA Astrophysics Data System (ADS)

Campos, Alejandro; Duraisamy, Karthik; Iaccarino, Gianluca

2015-11-01

Engineering models of turbulence, based on one-point statistics, neglect spectral information inherent in a turbulence field. It is well known, however, that the evolution of turbulence is dictated by a complex interplay between the spectral modes of velocity. For example, for homogeneous turbulence, the pressure-rate-of-strain depends on the integrated energy spectrum weighted by components of the wave vectors. The Interacting Particle Representation Model (IPRM) (Kassinos & Reynolds, 1996) and the Velocity/Wave-Vector PDF model (Van Slooten & Pope, 1997) emulate spectral information in an attempt to improve the modeling of turbulence. We investigate the evolution and asymptotic stability of the IPRM using three different approaches. The first approach considers the Lagrangian evolution of individual realizations (idealized as particles) of the stochastic process defined by the IPRM. The second solves Lagrangian evolution equations for clusters of realizations conditional on a given wave vector. The third evolves the solution of the Eulerian conditional PDF corresponding to the aforementioned clusters. This last method avoids issues related to discrete particle noise and slow convergence associated with Lagrangian particle-based simulations.

Separation of arteries and veins in the cerebral cortex using physiological oscillations by optical imaging of intrinsic signal

NASA Astrophysics Data System (ADS)

Hu, Dewen; Wang, Yucheng; Liu, Yadong; Li, Ming; Liu, Fayi

2010-05-01

An automated method is presented for artery-vein separation in cerebral cortical images recorded with optical imaging of the intrinsic signal. The vessel-type separation method is based on the fact that the spectral distribution of intrinsic physiological oscillations varies from arterial regions to venous regions. In arterial regions, the spectral power is higher in the heartbeat frequency (HF), whereas in venous regions, the spectral power is higher in the respiration frequency (RF). The separation method was begun by extracting the vascular network and its centerline. Then the spectra of the optical intrinsic signals were estimated by the multitaper method. A standard F-test was performed on each discrete frequency point to test the statistical significance at the given level. Four periodic physiological oscillations were examined: HF, RF, and two other eigenfrequencies termed F1 and F2. The separation of arteries and veins was implemented with the fuzzy c-means clustering method and the region-growing approach by utilizing the spectral amplitudes and power-ratio values of the four eigenfrequencies on the vasculature. Subsequently, independent spectral distributions in the arteries, veins, and capillary bed were estimated for comparison, which showed that the spectral distributions of the intrinsic signals were very distinct between the arterial and venous regions.

Separation of arteries and veins in the cerebral cortex using physiological oscillations by optical imaging of intrinsic signal.

PubMed

Hu, Dewen; Wang, Yucheng; Liu, Yadong; Li, Ming; Liu, Fayi

2010-01-01

An automated method is presented for artery-vein separation in cerebral cortical images recorded with optical imaging of the intrinsic signal. The vessel-type separation method is based on the fact that the spectral distribution of intrinsic physiological oscillations varies from arterial regions to venous regions. In arterial regions, the spectral power is higher in the heartbeat frequency (HF), whereas in venous regions, the spectral power is higher in the respiration frequency (RF). The separation method was begun by extracting the vascular network and its centerline. Then the spectra of the optical intrinsic signals were estimated by the multitaper method. A standard F-test was performed on each discrete frequency point to test the statistical significance at the given level. Four periodic physiological oscillations were examined: HF, RF, and two other eigenfrequencies termed F1 and F2. The separation of arteries and veins was implemented with the fuzzy c-means clustering method and the region-growing approach by utilizing the spectral amplitudes and power-ratio values of the four eigenfrequencies on the vasculature. Subsequently, independent spectral distributions in the arteries, veins, and capillary bed were estimated for comparison, which showed that the spectral distributions of the intrinsic signals were very distinct between the arterial and venous regions.

Detecting communities in large networks

NASA Astrophysics Data System (ADS)

Capocci, A.; Servedio, V. D. P.; Caldarelli, G.; Colaiori, F.

2005-07-01

We develop an algorithm to detect community structure in complex networks. The algorithm is based on spectral methods and takes into account weights and link orientation. Since the method detects efficiently clustered nodes in large networks even when these are not sharply partitioned, it turns to be specially suitable for the analysis of social and information networks. We test the algorithm on a large-scale data-set from a psychological experiment of word association. In this case, it proves to be successful both in clustering words, and in uncovering mental association patterns.

A genetic graph-based approach for partitional clustering.

PubMed

Menéndez, Héctor D; Barrero, David F; Camacho, David

2014-05-01

Clustering is one of the most versatile tools for data analysis. In the recent years, clustering that seeks the continuity of data (in opposition to classical centroid-based approaches) has attracted an increasing research interest. It is a challenging problem with a remarkable practical interest. The most popular continuity clustering method is the spectral clustering (SC) algorithm, which is based on graph cut: It initially generates a similarity graph using a distance measure and then studies its graph spectrum to find the best cut. This approach is sensitive to the parameters of the metric, and a correct parameter choice is critical to the quality of the cluster. This work proposes a new algorithm, inspired by SC, that reduces the parameter dependency while maintaining the quality of the solution. The new algorithm, named genetic graph-based clustering (GGC), takes an evolutionary approach introducing a genetic algorithm (GA) to cluster the similarity graph. The experimental validation shows that GGC increases robustness of SC and has competitive performance in comparison with classical clustering methods, at least, in the synthetic and real dataset used in the experiments.

Toward the optimization of normalized graph Laplacian.

PubMed

Xie, Bo; Wang, Meng; Tao, Dacheng

2011-04-01

Normalized graph Laplacian has been widely used in many practical machine learning algorithms, e.g., spectral clustering and semisupervised learning. However, all of them use the Euclidean distance to construct the graph Laplacian, which does not necessarily reflect the inherent distribution of the data. In this brief, we propose a method to directly optimize the normalized graph Laplacian by using pairwise constraints. The learned graph is consistent with equivalence and nonequivalence pairwise relationships, and thus it can better represent similarity between samples. Meanwhile, our approach, unlike metric learning, automatically determines the scale factor during the optimization. The learned normalized Laplacian matrix can be directly applied in spectral clustering and semisupervised learning algorithms. Comprehensive experiments demonstrate the effectiveness of the proposed approach.

From Stars to Super-Planets: The Low-Mass IMF in the Young Cluster IC348

NASA Technical Reports Server (NTRS)

Najita, Joan R.; Tiede, Glenn P.; Carr, John S.

2000-01-01

We investigate the low-mass population of the young cluster IC348 down to the deuterium-burning limit, a fiducial boundary between brown dwarf and planetary mass objects, using a new and innovative method for the spectral classification of late-type objects. Using photometric indices, constructed from HST/NICMOS narrow-band imaging, that measure the strength of the 1.9 micron water band, we determine the spectral type and reddening for every M-type star in the field, thereby separating cluster members from the interloper population. Due to the efficiency of our spectral classification technique, our study is complete from approximately 0.7 solar mass to 0.015 solar mass. The mass function derived for the cluster in this interval, dN/d log M alpha M(sup 0.5), is similar to that obtained for the Pleiades, but appears significantly more abundant in brown dwarfs than the mass function for companions to nearby sun-like stars. This provides compelling observational evidence for different formation and evolutionary histories for substellar objects formed in isolation vs. as companions. Because our determination of the IMF is complete to very low masses, we can place interesting constraints on the role of physical processes such as fragmentation in the star and planet formation process and the fraction of dark matter in the Galactic halo that resides in substellar objects.

Extending Stability Through Hierarchical Clusters in Echo State Networks

PubMed Central

Jarvis, Sarah; Rotter, Stefan; Egert, Ulrich

2009-01-01

Echo State Networks (ESN) are reservoir networks that satisfy well-established criteria for stability when constructed as feedforward networks. Recent evidence suggests that stability criteria are altered in the presence of reservoir substructures, such as clusters. Understanding how the reservoir architecture affects stability is thus important for the appropriate design of any ESN. To quantitatively determine the influence of the most relevant network parameters, we analyzed the impact of reservoir substructures on stability in hierarchically clustered ESNs, as they allow a smooth transition from highly structured to increasingly homogeneous reservoirs. Previous studies used the largest eigenvalue of the reservoir connectivity matrix (spectral radius) as a predictor for stable network dynamics. Here, we evaluate the impact of clusters, hierarchy and intercluster connectivity on the predictive power of the spectral radius for stability. Both hierarchy and low relative cluster sizes extend the range of spectral radius values, leading to stable networks, while increasing intercluster connectivity decreased maximal spectral radius. PMID:20725523

ERTS-1 imagery and native plant distributions

NASA Technical Reports Server (NTRS)

Musick, H. B.; Mcginnies, W.; Haase, E.; Lepley, L. K.

1974-01-01

A method is developed for using ERTS spectral signature data to determine plant community distribution and phenology without resolving individual plants. An Exotech ERTS radiometer was used near ground level to obtain spectral signatures for a desert plant community, including two shrub species, ground covered with live annuals in April and dead ones in June, and bare ground. It is shown that comparisons of scene types can be made when spectral signatures are expressed as a ratio of red reflectivity to IR reflectivity or when they are plotted as red reflectivity vs. IR reflectivity, in which case the signature clusters of each component are more distinct. A method for correcting and converting the ERTS radiance values to reflectivity values for comparison with ground truth data is appended.

Partial Discharge Spectral Characterization in HF, VHF and UHF Bands Using Particle Swarm Optimization.

PubMed

Robles, Guillermo; Fresno, José Manuel; Martínez-Tarifa, Juan Manuel; Ardila-Rey, Jorge Alfredo; Parrado-Hernández, Emilio

2018-03-01

The measurement of partial discharge (PD) signals in the radio frequency (RF) range has gained popularity among utilities and specialized monitoring companies in recent years. Unfortunately, in most of the occasions the data are hidden by noise and coupled interferences that hinder their interpretation and renders them useless especially in acquisition systems in the ultra high frequency (UHF) band where the signals of interest are weak. This paper is focused on a method that uses a selective spectral signal characterization to feature each signal, type of partial discharge or interferences/noise, with the power contained in the most representative frequency bands. The technique can be considered as a dimensionality reduction problem where all the energy information contained in the frequency components is condensed in a reduced number of UHF or high frequency (HF) and very high frequency (VHF) bands. In general, dimensionality reduction methods make the interpretation of results a difficult task because the inherent physical nature of the signal is lost in the process. The proposed selective spectral characterization is a preprocessing tool that facilitates further main processing. The starting point is a clustering of signals that could form the core of a PD monitoring system. Therefore, the dimensionality reduction technique should discover the best frequency bands to enhance the affinity between signals in the same cluster and the differences between signals in different clusters. This is done maximizing the minimum Mahalanobis distance between clusters using particle swarm optimization (PSO). The tool is tested with three sets of experimental signals to demonstrate its capabilities in separating noise and PDs with low signal-to-noise ratio and separating different types of partial discharges measured in the UHF and HF/VHF bands.

Modified fuzzy c-means applied to a Bragg grating-based spectral imager for material clustering

NASA Astrophysics Data System (ADS)

Rodríguez, Aida; Nieves, Juan Luis; Valero, Eva; Garrote, Estíbaliz; Hernández-Andrés, Javier; Romero, Javier

2012-01-01

We have modified the Fuzzy C-Means algorithm for an application related to segmentation of hyperspectral images. Classical fuzzy c-means algorithm uses Euclidean distance for computing sample membership to each cluster. We have introduced a different distance metric, Spectral Similarity Value (SSV), in order to have a more convenient similarity measure for reflectance information. SSV distance metric considers both magnitude difference (by the use of Euclidean distance) and spectral shape (by the use of Pearson correlation). Experiments confirmed that the introduction of this metric improves the quality of hyperspectral image segmentation, creating spectrally more dense clusters and increasing the number of correctly classified pixels.

Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra

NASA Astrophysics Data System (ADS)

Unglert, K.; Radić, V.; Jellinek, A. M.

2016-06-01

Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as self-organizing maps (SOM) and principal component analysis (PCA) can help to quickly and automatically identify important patterns related to impending eruptions. For the first time, we evaluate the performance of SOM and PCA on synthetic volcano seismic spectra constructed from observations during two well-studied eruptions at Klauea Volcano, Hawai'i, that include features observed in many volcanic settings. In particular, our objective is to test which of the techniques can best retrieve a set of three spectral patterns that we used to compose a synthetic spectrogram. We find that, without a priori knowledge of the given set of patterns, neither SOM nor PCA can directly recover the spectra. We thus test hierarchical clustering, a commonly used method, to investigate whether clustering in the space of the principal components and on the SOM, respectively, can retrieve the known patterns. Our clustering method applied to the SOM fails to detect the correct number and shape of the known input spectra. In contrast, clustering of the data reconstructed by the first three PCA modes reproduces these patterns and their occurrence in time more consistently. This result suggests that PCA in combination with hierarchical clustering is a powerful practical tool for automated identification of characteristic patterns in volcano seismic spectra. Our results indicate that, in contrast to PCA, common clustering algorithms may not be ideal to group patterns on the SOM and that it is crucial to evaluate the performance of these tools on a control dataset prior to their application to real data.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra

NASA Astrophysics Data System (ADS)

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-04-01

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models’ performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Variable selection based on clustering analysis for improvement of polyphenols prediction in green tea using synchronous fluorescence spectra.

PubMed

Shan, Jiajia; Wang, Xue; Zhou, Hao; Han, Shuqing; Riza, Dimas Firmanda Al; Kondo, Naoshi

2018-03-13

Synchronous fluorescence spectra, combined with multivariate analysis were used to predict flavonoids content in green tea rapidly and nondestructively. This paper presented a new and efficient spectral intervals selection method called clustering based partial least square (CL-PLS), which selected informative wavelengths by combining clustering concept and partial least square (PLS) methods to improve models' performance by synchronous fluorescence spectra. The fluorescence spectra of tea samples were obtained and k-means and kohonen-self organizing map clustering algorithms were carried out to cluster full spectra into several clusters, and sub-PLS regression model was developed on each cluster. Finally, CL-PLS models consisting of gradually selected clusters were built. Correlation coefficient (R) was used to evaluate the effect on prediction performance of PLS models. In addition, variable influence on projection partial least square (VIP-PLS), selectivity ratio partial least square (SR-PLS), interval partial least square (iPLS) models and full spectra PLS model were investigated and the results were compared. The results showed that CL-PLS presented the best result for flavonoids prediction using synchronous fluorescence spectra.

Measuring the Redshift Dependence of The Cosmic Microwave Background Monopole Temperature With Planck Data

NASA Technical Reports Server (NTRS)

De Martino, I.; Atrio-Barandela, F.; Da Silva, A.; Ebling, H.; Kashlinsky, A.; Kocevski, D.; Martins, C. J. A. P.

2012-01-01

We study the capability of Planck data to constrain deviations of the cosmic microwave background (CMB) blackbody temperature from adiabatic evolution using the thermal Sunyaev-Zeldovich anisotropy induced by clusters of galaxies. We consider two types of data sets depending on how the cosmological signal is removed: using a CMB template or using the 217 GHz map. We apply two different statistical estimators, based on the ratio of temperature anisotropies at two different frequencies and on a fit to the spectral variation of the cluster signal with frequency. The ratio method is biased if CMB residuals with amplitude approximately 1 microK or larger are present in the data, while residuals are not so critical for the fit method. To test for systematics, we construct a template from clusters drawn from a hydro-simulation included in the pre-launch Planck Sky Model. We demonstrate that, using a proprietary catalog of X-ray-selected clusters with measured redshifts, electron densities, and X-ray temperatures, we can constrain deviations of adiabatic evolution, measured by the parameter a in the redshift scaling T (z) = T0(1 + z)(sup 1-alpha), with an accuracy of sigma(sub alpha) = 0.011 in the most optimal case and with sigma alpha = 0.018 for a less optimal case. These results represent a factor of 2-3 improvement over similar measurements carried out using quasar spectral lines and a factor 6-20 with respect to earlier results using smaller cluster samples.

Construction of multi-scale consistent brain networks: methods and applications.

PubMed

Ge, Bao; Tian, Yin; Hu, Xintao; Chen, Hanbo; Zhu, Dajiang; Zhang, Tuo; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

Mapping human brain networks provides a basis for studying brain function and dysfunction, and thus has gained significant interest in recent years. However, modeling human brain networks still faces several challenges including constructing networks at multiple spatial scales and finding common corresponding networks across individuals. As a consequence, many previous methods were designed for a single resolution or scale of brain network, though the brain networks are multi-scale in nature. To address this problem, this paper presents a novel approach to constructing multi-scale common structural brain networks from DTI data via an improved multi-scale spectral clustering applied on our recently developed and validated DICCCOLs (Dense Individualized and Common Connectivity-based Cortical Landmarks). Since the DICCCOL landmarks possess intrinsic structural correspondences across individuals and populations, we employed the multi-scale spectral clustering algorithm to group the DICCCOL landmarks and their connections into sub-networks, meanwhile preserving the intrinsically-established correspondences across multiple scales. Experimental results demonstrated that the proposed method can generate multi-scale consistent and common structural brain networks across subjects, and its reproducibility has been verified by multiple independent datasets. As an application, these multi-scale networks were used to guide the clustering of multi-scale fiber bundles and to compare the fiber integrity in schizophrenia and healthy controls. In general, our methods offer a novel and effective framework for brain network modeling and tract-based analysis of DTI data.

[A New Distance Metric between Different Stellar Spectra: the Residual Distribution Distance].

PubMed

Liu, Jie; Pan, Jing-chang; Luo, A-li; Wei, Peng; Liu, Meng

2015-12-01

Distance metric is an important issue for the spectroscopic survey data processing, which defines a calculation method of the distance between two different spectra. Based on this, the classification, clustering, parameter measurement and outlier data mining of spectral data can be carried out. Therefore, the distance measurement method has some effect on the performance of the classification, clustering, parameter measurement and outlier data mining. With the development of large-scale stellar spectral sky surveys, how to define more efficient distance metric on stellar spectra has become a very important issue in the spectral data processing. Based on this problem and fully considering of the characteristics and data features of the stellar spectra, a new distance measurement method of stellar spectra named Residual Distribution Distance is proposed. While using this method to measure the distance, the two spectra are firstly scaled and then the standard deviation of the residual is used the distance. Different from the traditional distance metric calculation methods of stellar spectra, when used to calculate the distance between stellar spectra, this method normalize the two spectra to the same scale, and then calculate the residual corresponding to the same wavelength, and the standard error of the residual spectrum is used as the distance measure. The distance measurement method can be used for stellar classification, clustering and stellar atmospheric physical parameters measurement and so on. This paper takes stellar subcategory classification as an example to test the distance measure method. The results show that the distance defined by the proposed method is more effective to describe the gap between different types of spectra in the classification than other methods, which can be well applied in other related applications. At the same time, this paper also studies the effect of the signal to noise ratio (SNR) on the performance of the proposed method. The result show that the distance is affected by the SNR. The smaller the signal-to-noise ratio is, the greater impact is on the distance; While SNR is larger than 10, the signal-to-noise ratio has little effect on the performance for the classification.

A clustering algorithm for determining community structure in complex networks

NASA Astrophysics Data System (ADS)

Jin, Hong; Yu, Wei; Li, ShiJun

2018-02-01

Clustering algorithms are attractive for the task of community detection in complex networks. DENCLUE is a representative density based clustering algorithm which has a firm mathematical basis and good clustering properties allowing for arbitrarily shaped clusters in high dimensional datasets. However, this method cannot be directly applied to community discovering due to its inability to deal with network data. Moreover, it requires a careful selection of the density parameter and the noise threshold. To solve these issues, a new community detection method is proposed in this paper. First, we use a spectral analysis technique to map the network data into a low dimensional Euclidean Space which can preserve node structural characteristics. Then, DENCLUE is applied to detect the communities in the network. A mathematical method named Sheather-Jones plug-in is chosen to select the density parameter which can describe the intrinsic clustering structure accurately. Moreover, every node on the network is meaningful so there were no noise nodes as a result the noise threshold can be ignored. We test our algorithm on both benchmark and real-life networks, and the results demonstrate the effectiveness of our algorithm over other popularity density based clustering algorithms adopted to community detection.

Infrared spectral imaging as a novel approach for histopathological recognition in colon cancer diagnosis

NASA Astrophysics Data System (ADS)

Nallala, Jayakrupakar; Gobinet, Cyril; Diebold, Marie-Danièle; Untereiner, Valérie; Bouché, Olivier; Manfait, Michel; Sockalingum, Ganesh Dhruvananda; Piot, Olivier

2012-11-01

Innovative diagnostic methods are the need of the hour that could complement conventional histopathology for cancer diagnosis. In this perspective, we propose a new concept based on spectral histopathology, using IR spectral micro-imaging, directly applied to paraffinized colon tissue array stabilized in an agarose matrix without any chemical pre-treatment. In order to correct spectral interferences from paraffin and agarose, a mathematical procedure is implemented. The corrected spectral images are then processed by a multivariate clustering method to automatically recover, on the basis of their intrinsic molecular composition, the main histological classes of the normal and the tumoral colon tissue. The spectral signatures from different histological classes of the colonic tissues are analyzed using statistical methods (Kruskal-Wallis test and principal component analysis) to identify the most discriminant IR features. These features allow characterizing some of the biomolecular alterations associated with malignancy. Thus, via a single analysis, in a label-free and nondestructive manner, main changes associated with nucleotide, carbohydrates, and collagen features can be identified simultaneously between the compared normal and the cancerous tissues. The present study demonstrates the potential of IR spectral imaging as a complementary modern tool, to conventional histopathology, for an objective cancer diagnosis directly from paraffin-embedded tissue arrays.

A k-space method for acoustic propagation using coupled first-order equations in three dimensions.

PubMed

Tillett, Jason C; Daoud, Mohammad I; Lacefield, James C; Waag, Robert C

2009-09-01

A previously described two-dimensional k-space method for large-scale calculation of acoustic wave propagation in tissues is extended to three dimensions. The three-dimensional method contains all of the two-dimensional method features that allow accurate and stable calculation of propagation. These features are spectral calculation of spatial derivatives, temporal correction that produces exact propagation in a homogeneous medium, staggered spatial and temporal grids, and a perfectly matched boundary layer. Spectral evaluation of spatial derivatives is accomplished using a fast Fourier transform in three dimensions. This computational bottleneck requires all-to-all communication; execution time in a parallel implementation is therefore sensitive to node interconnect latency and bandwidth. Accuracy of the three-dimensional method is evaluated through comparisons with exact solutions for media having spherical inhomogeneities. Large-scale calculations in three dimensions were performed by distributing the nearly 50 variables per voxel that are used to implement the method over a cluster of computers. Two computer clusters used to evaluate method accuracy are compared. Comparisons of k-space calculations with exact methods including absorption highlight the need to model accurately the medium dispersion relationships, especially in large-scale media. Accurately modeled media allow the k-space method to calculate acoustic propagation in tissues over hundreds of wavelengths.

Memory color assisted illuminant estimation through pixel clustering

NASA Astrophysics Data System (ADS)

Zhang, Heng; Quan, Shuxue

2010-01-01

The under constrained nature of illuminant estimation determines that in order to resolve the problem, certain assumptions are needed, such as the gray world theory. Including more constraints in this process may help explore the useful information in an image and improve the accuracy of the estimated illuminant, providing that the constraints hold. Based on the observation that most personal images have contents of one or more of the following categories: neutral objects, human beings, sky, and plants, we propose a method for illuminant estimation through the clustering of pixels of gray and three dominant memory colors: skin tone, sky blue, and foliage green. Analysis shows that samples of the above colors cluster around small areas under different illuminants and their characteristics can be used to effectively detect pixels falling into each of the categories. The algorithm requires the knowledge of the spectral sensitivity response of the camera, and a spectral database consisted of the CIE standard illuminants and reflectance or radiance database of samples of the above colors.

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

DOE PAGES

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra; ...

2017-05-23

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splittingmore » of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.« less

Novel Histogram Based Unsupervised Classification Technique to Determine Natural Classes From Biophysically Relevant Fit Parameters to Hyperspectral Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

McCann, Cooper; Repasky, Kevin S.; Morin, Mikindra

Hyperspectral image analysis has benefited from an array of methods that take advantage of the increased spectral depth compared to multispectral sensors; however, the focus of these developments has been on supervised classification methods. Lack of a priori knowledge regarding land cover characteristics can make unsupervised classification methods preferable under certain circumstances. An unsupervised classification technique is presented in this paper that utilizes physically relevant basis functions to model the reflectance spectra. These fit parameters used to generate the basis functions allow clustering based on spectral characteristics rather than spectral channels and provide both noise and data reduction. Histogram splittingmore » of the fit parameters is then used as a means of producing an unsupervised classification. Unlike current unsupervised classification techniques that rely primarily on Euclidian distance measures to determine similarity, the unsupervised classification technique uses the natural splitting of the fit parameters associated with the basis functions creating clusters that are similar in terms of physical parameters. The data set used in this work utilizes the publicly available data collected at Indian Pines, Indiana. This data set provides reference data allowing for comparisons of the efficacy of different unsupervised data analysis. The unsupervised histogram splitting technique presented in this paper is shown to be better than the standard unsupervised ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. Finally, this improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA.« less

Influence of spectral resolution, spectral range and signal-to-noise ratio of Fourier transform infra-red spectra on identification of high explosive substances

NASA Astrophysics Data System (ADS)

Banas, Krzysztof; Banas, Agnieszka M.; Heussler, Sascha P.; Breese, Mark B. H.

2018-01-01

In the contemporary spectroscopy there is a trend to record spectra with the highest possible spectral resolution. This is clearly justified if the spectral features in the spectrum are very narrow (for example infra-red spectra of gas samples). However there is a plethora of samples (in the liquid and especially in the solid form) where there is a natural spectral peak broadening due to collisions and proximity predominately. Additionally there is a number of portable devices (spectrometers) with inherently restricted spectral resolution, spectral range or both, which are extremely useful in some field applications (archaeology, agriculture, food industry, cultural heritage, forensic science). In this paper the investigation of the influence of spectral resolution, spectral range and signal-to-noise ratio on the identification of high explosive substances by applying multivariate statistical methods on the Fourier transform infra-red spectral data sets is studied. All mathematical procedures on spectral data for dimension reduction, clustering and validation were implemented within R open source environment.

SpectralNET – an application for spectral graph analysis and visualization

PubMed Central

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-01-01

Background Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Results Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). Conclusion SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from . Source code is available upon request. PMID:16236170

SpectralNET--an application for spectral graph analysis and visualization.

PubMed

Forman, Joshua J; Clemons, Paul A; Schreiber, Stuart L; Haggarty, Stephen J

2005-10-19

Graph theory provides a computational framework for modeling a variety of datasets including those emerging from genomics, proteomics, and chemical genetics. Networks of genes, proteins, small molecules, or other objects of study can be represented as graphs of nodes (vertices) and interactions (edges) that can carry different weights. SpectralNET is a flexible application for analyzing and visualizing these biological and chemical networks. Available both as a standalone .NET executable and as an ASP.NET web application, SpectralNET was designed specifically with the analysis of graph-theoretic metrics in mind, a computational task not easily accessible using currently available applications. Users can choose either to upload a network for analysis using a variety of input formats, or to have SpectralNET generate an idealized random network for comparison to a real-world dataset. Whichever graph-generation method is used, SpectralNET displays detailed information about each connected component of the graph, including graphs of degree distribution, clustering coefficient by degree, and average distance by degree. In addition, extensive information about the selected vertex is shown, including degree, clustering coefficient, various distance metrics, and the corresponding components of the adjacency, Laplacian, and normalized Laplacian eigenvectors. SpectralNET also displays several graph visualizations, including a linear dimensionality reduction for uploaded datasets (Principal Components Analysis) and a non-linear dimensionality reduction that provides an elegant view of global graph structure (Laplacian eigenvectors). SpectralNET provides an easily accessible means of analyzing graph-theoretic metrics for data modeling and dimensionality reduction. SpectralNET is publicly available as both a .NET application and an ASP.NET web application from http://chembank.broad.harvard.edu/resources/. Source code is available upon request.

Mixture-Tuned, Clutter Matched Filter for Remote Detection of Subpixel Spectral Signals

NASA Technical Reports Server (NTRS)

Thompson, David R.; Mandrake, Lukas; Green, Robert O.

2013-01-01

Mapping localized spectral features in large images demands sensitive and robust detection algorithms. Two aspects of large images that can harm matched-filter detection performance are addressed simultaneously. First, multimodal backgrounds may thwart the typical Gaussian model. Second, outlier features can trigger false detections from large projections onto the target vector. Two state-of-the-art approaches are combined that independently address outlier false positives and multimodal backgrounds. The background clustering models multimodal backgrounds, and the mixture tuned matched filter (MT-MF) addresses outliers. Combining the two methods captures significant additional performance benefits. The resulting mixture tuned clutter matched filter (MT-CMF) shows effective performance on simulated and airborne datasets. The classical MNF transform was applied, followed by k-means clustering. Then, each cluster s mean, covariance, and the corresponding eigenvalues were estimated. This yields a cluster-specific matched filter estimate as well as a cluster- specific feasibility score to flag outlier false positives. The technology described is a proof of concept that may be employed in future target detection and mapping applications for remote imaging spectrometers. It is of most direct relevance to JPL proposals for airborne and orbital hyperspectral instruments. Applications include subpixel target detection in hyperspectral scenes for military surveillance. Earth science applications include mineralogical mapping, species discrimination for ecosystem health monitoring, and land use classification.

X-ray emission from clusters of galaxies

NASA Technical Reports Server (NTRS)

Mushotzky, R. F.

1983-01-01

Some X-ray spectral observations of approximately 30 clusters of galaxies from HEAO-1 are summarized. There exists strong correlations between X-ray luminosity, L(x), and temperature kT in the form L(x)alphaT to the 2.3 power. This result combined with the L(x) central galaxy density relation and the virial theorem indicates that the core dadius of the gas should be roughly independent of L(x) or KT and that more luminous clusters have a greater fraction of their virial mass in gas. The poor correlation of KT and optical velocity dispersion seems to indicate that clusters have a variety of equations of state. There is poor agreement between X-ray imaging observations and optical and X-ray spectral measures of the polytropic index. Most clusters show Fe emission lines with a strong indication that they all have roughly 1/2 solar abundance. The evidence for cooling in the cores of several clusters is discussed based on spectral observations with the Einstein solid state spectrometer.

On the Unusually High Temperature of the Cluster of Galaxies 1E 0657-56

NASA Technical Reports Server (NTRS)

Yaqoob, Tahir

1999-01-01

A recent X-ray observation of the cluster 1E 0657-56 (z = 0.296) with ASC,4 implied an unusually high temperature of approx. 17 keV. Such a high temperature would make it the hottest known cluster and severely constrain cosmological models since, in a Universe with critical density (Omega = 1) the probability of observing such a cluster is only approx. 4 x 10(exp -5). Here we test the robustness of this observational result since it has such important implications. We analysed the data using a variety of different data analysis methods and spectral analysis assumptions and find a temperature of approx. 11 - 12 keV in all cases, except for one class of spectral fits. These are fits in which the absorbing column density is fixed at the Galactic value. Using simulated data for a 12 keV cluster, we show that a high temperature of approx. 17 keV is artificially obtained if the true spectrum has a stronger low-energy cut-off than that for Galactic absorption only. The apparent extra absorption may be astrophysical in origin, (either intrinsic or line-of-sight), or it may be a problem with the low-energy CCD efficiency. Although significantly lower than previous measurements, this temperature of kT approx. 11 - 12 keV is still relatively high since only a few clusters have been found to have temperatures higher than 10 keV and the data therefore still present some difficulty for an Omega = 1 Universe. Our results will also be useful to anyone who wants to estimate the systematic errors involved in different methods of background subtraction of ASCA data for sources with similar signal-to-noise to that of the IE 0657-56 data reported here.

Modulated Modularity Clustering as an Exploratory Tool for Functional Genomic Inference

PubMed Central

Stone, Eric A.; Ayroles, Julien F.

2009-01-01

In recent years, the advent of high-throughput assays, coupled with their diminishing cost, has facilitated a systems approach to biology. As a consequence, massive amounts of data are currently being generated, requiring efficient methodology aimed at the reduction of scale. Whole-genome transcriptional profiling is a standard component of systems-level analyses, and to reduce scale and improve inference clustering genes is common. Since clustering is often the first step toward generating hypotheses, cluster quality is critical. Conversely, because the validation of cluster-driven hypotheses is indirect, it is critical that quality clusters not be obtained by subjective means. In this paper, we present a new objective-based clustering method and demonstrate that it yields high-quality results. Our method, modulated modularity clustering (MMC), seeks community structure in graphical data. MMC modulates the connection strengths of edges in a weighted graph to maximize an objective function (called modularity) that quantifies community structure. The result of this maximization is a clustering through which tightly-connected groups of vertices emerge. Our application is to systems genetics, and we quantitatively compare MMC both to the hierarchical clustering method most commonly employed and to three popular spectral clustering approaches. We further validate MMC through analyses of human and Drosophila melanogaster expression data, demonstrating that the clusters we obtain are biologically meaningful. We show MMC to be effective and suitable to applications of large scale. In light of these features, we advocate MMC as a standard tool for exploration and hypothesis generation. PMID:19424432

Towards Effective Clustering Techniques for the Analysis of Electric Power Grids

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hogan, Emilie A.; Cotilla Sanchez, Jose E.; Halappanavar, Mahantesh

2013-11-30

Clustering is an important data analysis technique with numerous applications in the analysis of electric power grids. Standard clustering techniques are oblivious to the rich structural and dynamic information available for power grids. Therefore, by exploiting the inherent topological and electrical structure in the power grid data, we propose new methods for clustering with applications to model reduction, locational marginal pricing, phasor measurement unit (PMU or synchrophasor) placement, and power system protection. We focus our attention on model reduction for analysis based on time-series information from synchrophasor measurement devices, and spectral techniques for clustering. By comparing different clustering techniques onmore » two instances of realistic power grids we show that the solutions are related and therefore one could leverage that relationship for a computational advantage. Thus, by contrasting different clustering techniques we make a case for exploiting structure inherent in the data with implications for several domains including power systems.« less

Clustering analysis strategies for electron energy loss spectroscopy (EELS).

PubMed

Torruella, Pau; Estrader, Marta; López-Ortega, Alberto; Baró, Maria Dolors; Varela, Maria; Peiró, Francesca; Estradé, Sònia

2018-02-01

In this work, the use of cluster analysis algorithms, widely applied in the field of big data, is proposed to explore and analyze electron energy loss spectroscopy (EELS) data sets. Three different data clustering approaches have been tested both with simulated and experimental data from Fe 3 O 4 /Mn 3 O 4 core/shell nanoparticles. The first method consists on applying data clustering directly to the acquired spectra. A second approach is to analyze spectral variance with principal component analysis (PCA) within a given data cluster. Lastly, data clustering on PCA score maps is discussed. The advantages and requirements of each approach are studied. Results demonstrate how clustering is able to recover compositional and oxidation state information from EELS data with minimal user input, giving great prospects for its usage in EEL spectroscopy. Copyright © 2017 Elsevier B.V. All rights reserved.

The IMACS Cluster Building Survey. I. Description of the Survey and Analysis Methods

NASA Technical Reports Server (NTRS)

Oemler Jr., Augustus; Dressler, Alan; Gladders, Michael G.; Rigby, Jane R.; Bai, Lei; Kelson, Daniel; Villanueva, Edward; Fritz, Jacopo; Rieke, George; Poggianti, Bianca M.;

Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-12-09

We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less

MaRaCluster: A Fragment Rarity Metric for Clustering Fragment Spectra in Shotgun Proteomics.

PubMed

The, Matthew; Käll, Lukas

2016-03-04

Shotgun proteomics experiments generate large amounts of fragment spectra as primary data, normally with high redundancy between and within experiments. Here, we have devised a clustering technique to identify fragment spectra stemming from the same species of peptide. This is a powerful alternative method to traditional search engines for analyzing spectra, specifically useful for larger scale mass spectrometry studies. As an aid in this process, we propose a distance calculation relying on the rarity of experimental fragment peaks, following the intuition that peaks shared by only a few spectra offer more evidence than peaks shared by a large number of spectra. We used this distance calculation and a complete-linkage scheme to cluster data from a recent large-scale mass spectrometry-based study. The clusterings produced by our method have up to 40% more identified peptides for their consensus spectra compared to those produced by the previous state-of-the-art method. We see that our method would advance the construction of spectral libraries as well as serve as a tool for mining large sets of fragment spectra. The source code and Ubuntu binary packages are available at https://github.com/statisticalbiotechnology/maracluster (under an Apache 2.0 license).

Spherical Harmonic Analysis of Particle Velocity Distribution Function: Comparison of Moments and Anisotropies using Cluster Data

NASA Technical Reports Server (NTRS)

Gurgiolo, Chris; Vinas, Adolfo F.

2009-01-01

This paper presents a spherical harmonic analysis of the plasma velocity distribution function using high-angular, energy, and time resolution Cluster data obtained from the PEACE spectrometer instrument to demonstrate how this analysis models the particle distribution function and its moments and anisotropies. The results show that spherical harmonic analysis produced a robust physical representation model of the velocity distribution function, resolving the main features of the measured distributions. From the spherical harmonic analysis, a minimum set of nine spectral coefficients was obtained from which the moment (up to the heat flux), anisotropy, and asymmetry calculations of the velocity distribution function were obtained. The spherical harmonic method provides a potentially effective "compression" technique that can be easily carried out onboard a spacecraft to determine the moments and anisotropies of the particle velocity distribution function for any species. These calculations were implemented using three different approaches, namely, the standard traditional integration, the spherical harmonic (SPH) spectral coefficients integration, and the singular value decomposition (SVD) on the spherical harmonic methods. A comparison among the various methods shows that both SPH and SVD approaches provide remarkable agreement with the standard moment integration method.

Employing broadband spectra and cluster analysis to assess thermal defoliation of cotton

USDA-ARS?s Scientific Manuscript database

Growers and field scouts need assistance in surveying cotton (Gossypium hirsutum L.) fields subjected to thermal defoliation to reap the benefits provided by this nonchemical defoliation method. A study was conducted to evaluate broadband spectral data and unsupervised classification as tools for s...

Closed-cage tungsten oxide clusters in the gas phase.

PubMed

Singh, D M David Jeba; Pradeep, T; Thirumoorthy, Krishnan; Balasubramanian, Krishnan

2010-05-06

During the course of a study on the clustering of W-Se and W-S mixtures in the gas phase using laser desorption ionization (LDI) mass spectrometry, we observed several anionic W-O clusters. Three distinct species, W(6)O(19)(-), W(13)O(29)(-), and W(14)O(32)(-), stand out as intense peaks in the regular mass spectral pattern of tungsten oxide clusters suggesting unusual stabilities for them. Moreover, these clusters do not fragment in the postsource decay analysis. While trying to understand the precursor material, which produced these clusters, we found the presence of nanoscale forms of tungsten oxide. The structure and thermodynamic parameters of tungsten clusters have been explored using relativistic quantum chemical methods. Our computed results of atomization energy are consistent with the observed LDI mass spectra. The computational results suggest that the clusters observed have closed-cage structure. These distinct W(13) and W(14) clusters were observed for the first time in the gas phase.

Weighted community detection and data clustering using message passing

NASA Astrophysics Data System (ADS)

Shi, Cheng; Liu, Yanchen; Zhang, Pan

2018-03-01

Grouping objects into clusters based on the similarities or weights between them is one of the most important problems in science and engineering. In this work, by extending message-passing algorithms and spectral algorithms proposed for an unweighted community detection problem, we develop a non-parametric method based on statistical physics, by mapping the problem to the Potts model at the critical temperature of spin-glass transition and applying belief propagation to solve the marginals corresponding to the Boltzmann distribution. Our algorithm is robust to over-fitting and gives a principled way to determine whether there are significant clusters in the data and how many clusters there are. We apply our method to different clustering tasks. In the community detection problem in weighted and directed networks, we show that our algorithm significantly outperforms existing algorithms. In the clustering problem, where the data were generated by mixture models in the sparse regime, we show that our method works all the way down to the theoretical limit of detectability and gives accuracy very close to that of the optimal Bayesian inference. In the semi-supervised clustering problem, our method only needs several labels to work perfectly in classic datasets. Finally, we further develop Thouless-Anderson-Palmer equations which heavily reduce the computation complexity in dense networks but give almost the same performance as belief propagation.

Spectral Clustering of Hermean craters hollows

NASA Astrophysics Data System (ADS)

Lucchetti, Alice; Pajola, Maurizio; Cremonese, Gabriele; Carli, Cristian; Marzo, Giuseppe; Roush, Ted

2017-04-01

The Mercury Dual Imaging System (MDIS, Hawkins et al., 2007) onboard NASA MESSENGER (MErcury Surface, Space ENvironment, GEochemistry, and Ranging) spacecraft, provided high-resolution images of "hollows", i.e. shallow, irregular, rimless, flat-floored depressions with bright interiors and halos, often found on crater walls, rims, floors and central peaks (Blewett et al., 2011, 2013). The formation mechanism of these features was suggested to be related to the depletion of subsurface volatiles (Blewett et al., 2011, Vaughan et al., 2012). To understand the hollows' mineralogical composition, which can provide new insights on Mercury's surface characterization, we applied a spectral clustering method to different craters where hollows are present. We chose, as first test case, the 20 km wide Dominici crater due to previous multiple spectral detection (Vilas et al., 2016). We used the MDIS WAC dataset covering Dominici crater with a scale of 935 m/pixel through eight filters, ranging from 0.433 to 0.996 μm. First, the images have been photometrically corrected using the Hapke parameters (Hapke et al., 2002) derived in Domingue et al. (2015). We then applied a statistical clustering over the entire dataset based on a K-means partitioning algorithm (Marzo et al., 2006). This approach was developed and evaluated by Marzo et al. (2006, 2008, 2009) and makes use of the Calinski and Harabasz criterion (Calinski, T., Harabasz, J., 1974) to identify the intrinsically natural number of clusters, making the process unsupervised. The natural number of ten clusters was identified and spectrally separates the Dominici surrounding terrains from its interior, as well as the two hollows from their edges. The units located on the brightest part of the south wall/rim of Dominici crater clearly present a wide absorption band between 0.558 and 0.828 μm. Hollows surrounding terrains typically present a red slope in the VNIR with a possible weak absorption band centered at 0.748 μm, while the interior of Dominici crater shows almost no absorption between 0.558 and 0.828 μm, but a possible absorption towards the IR is still evident. This detection is similar to what was described in Vilas et al. (2016), even if it is not located in the crater center as previously reported. The application of the clustering technique provides results similar to those reported in Vilas et al. (2016) and permits a deeper detailed study of the terrain spectral differences such as the discrimination of areas with a possible diagnostic absorption indicative of sulfides (e.g. MgS as suggested by Vilas et al., 2016). In addition, we were able to separate possible intermediate terrains that can be defined as "spectral transition" terrains, likely a mixture between the previously mentioned terrains (MgS, Vilas et al., 2016), or new compositional units. The next step is to choose other targets to apply the same clustering technique in order to characterize the different crater hollows presented on Hermean surface.

Dynamic Information Networks: Geometry, Topology and Statistical Learning for the Articulation of Structure

DTIC Science & Technology

2015-06-23

T. Bates, S. Brocklebank, S. Pauls, and D.Rockmore, A spectral clustering approach to the structure of personality: contrasting the FFM and...A spectral clustering approach to the structure of personality: contrasting the FFM and HEXACO models, Journal of Research in Personality, Volume 57

Lightweight biometric detection system for human classification using pyroelectric infrared detectors.

PubMed

Burchett, John; Shankar, Mohan; Hamza, A Ben; Guenther, Bob D; Pitsianis, Nikos; Brady, David J

2006-05-01

We use pyroelectric detectors that are differential in nature to detect motion in humans by their heat emissions. Coded Fresnel lens arrays create boundaries that help to localize humans in space as well as to classify the nature of their motion. We design and implement a low-cost biometric tracking system by using off-the-shelf components. We demonstrate two classification methods by using data gathered from sensor clusters of dual-element pyroelectric detectors with coded Fresnel lens arrays. We propose two algorithms for person identification, a more generalized spectral clustering method and a more rigorous example that uses principal component regression to perform a blind classification.

Embedded Star Formation in the Eagle Nebula with Spitzer GLIMPSE

NASA Astrophysics Data System (ADS)

Indebetouw, R.; Robitaille, T. P.; Whitney, B. A.; Churchwell, E.; Babler, B.; Meade, M.; Watson, C.; Wolfire, M.

2007-09-01

We present new Spitzer photometry of the Eagle Nebula (M16, containing the optical cluster NGC 6611) combined with near-infrared photometry from 2MASS. We use dust radiative transfer models, mid-infrared and near-infrared color-color analysis, and mid-infrared spectral indices to analyze point-source spectral energy distributions, select candidate YSOs, and constrain their mass and evolutionary state. Comparison of the different protostellar selection methods shows that mid-infrared methods are consistent, but as has been known for some time, near-infrared-only analysis misses some young objects. We reveal more than 400 protostellar candidates, including one massive YSO that has not been previously highlighted. The YSO distribution supports a picture of distributed low-level star formation, with no strong evidence of triggered star formation in the ``pillars.'' We confirm the youth of NGC 6611 by a large fraction of infrared excess sources and reveal a younger cluster of YSOs in the nearby molecular cloud. Analysis of the YSO clustering properties shows a possible imprint of the molecular cloud's Jeans length. Multiwavelength mid-IR imaging thus allows us to analyze the protostellar population, to measure the dust temperature and column density, and to relate these in a consistent picture of star formation in M16.

A SPECTRAL GRAPH APPROACH TO DISCOVERING GENETIC ANCESTRY1

PubMed Central

Lee, Ann B.; Luca, Diana; Roeder, Kathryn

2010-01-01

Mapping human genetic variation is fundamentally interesting in fields such as anthropology and forensic inference. At the same time, patterns of genetic diversity confound efforts to determine the genetic basis of complex disease. Due to technological advances, it is now possible to measure hundreds of thousands of genetic variants per individual across the genome. Principal component analysis (PCA) is routinely used to summarize the genetic similarity between subjects. The eigenvectors are interpreted as dimensions of ancestry. We build on this idea using a spectral graph approach. In the process we draw on connections between multidimensional scaling and spectral kernel methods. Our approach, based on a spectral embedding derived from the normalized Laplacian of a graph, can produce more meaningful delineation of ancestry than by using PCA. The method is stable to outliers and can more easily incorporate different similarity measures of genetic data than PCA. We illustrate a new algorithm for genetic clustering and association analysis on a large, genetically heterogeneous sample. PMID:20689656

A meta-classifier for detecting prostate cancer by quantitative integration of in vivo magnetic resonance spectroscopy and magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Viswanath, Satish; Tiwari, Pallavi; Rosen, Mark; Madabhushi, Anant

2008-03-01

Recently, in vivo Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) have emerged as promising new modalities to aid in prostate cancer (CaP) detection. MRI provides anatomic and structural information of the prostate while MRS provides functional data pertaining to biochemical concentrations of metabolites such as creatine, choline and citrate. We have previously presented a hierarchical clustering scheme for CaP detection on in vivo prostate MRS and have recently developed a computer-aided method for CaP detection on in vivo prostate MRI. In this paper we present a novel scheme to develop a meta-classifier to detect CaP in vivo via quantitative integration of multimodal prostate MRS and MRI by use of non-linear dimensionality reduction (NLDR) methods including spectral clustering and locally linear embedding (LLE). Quantitative integration of multimodal image data (MRI and PET) involves the concatenation of image intensities following image registration. However multimodal data integration is non-trivial when the individual modalities include spectral and image intensity data. We propose a data combination solution wherein we project the feature spaces (image intensities and spectral data) associated with each of the modalities into a lower dimensional embedding space via NLDR. NLDR methods preserve the relationships between the objects in the original high dimensional space when projecting them into the reduced low dimensional space. Since the original spectral and image intensity data are divorced from their original physical meaning in the reduced dimensional space, data at the same spatial location can be integrated by concatenating the respective embedding vectors. Unsupervised consensus clustering is then used to partition objects into different classes in the combined MRS and MRI embedding space. Quantitative results of our multimodal computer-aided diagnosis scheme on 16 sets of patient data obtained from the ACRIN trial, for which corresponding histological ground truth for spatial extent of CaP is known, show a marginally higher sensitivity, specificity, and positive predictive value compared to corresponding CAD results with the individual modalities.

Analysis of Spectral-type A/B Stars in Five Open Clusters

NASA Astrophysics Data System (ADS)

Wilhelm, Ronald J.; Rafuil Islam, M.

2014-01-01

We have obtained low resolution (R = 1000) spectroscopy of N=68, spectral-type A/B stars in five nearby open star clusters using the McDonald Observatory, 2.1m telescope. The sample of blue stars in various clusters were selected to test our new technique for determining interstellar reddening and distances in areas where interstellar reddening is high. We use a Bayesian approach to find the posterior distribution for Teff, Logg and [Fe/H] from a combination of reddened, photometric colors and spectroscopic line strengths. We will present calibration results for this technique using open cluster star data with known reddening and distances. Preliminary results suggest our technique can produce both reddening and distance determinations to within 10% of cluster values. Our technique opens the possibility of determining distances for blue stars at low Galactic latitudes where extinction can be large and differential. We will also compare our stellar parameter determinations to previously reported MK spectral classifications and discuss the probability that some of our stars are not members of their reported clusters.

Kernel spectral clustering with memory effect

NASA Astrophysics Data System (ADS)

Langone, Rocco; Alzate, Carlos; Suykens, Johan A. K.

2013-05-01

Evolving graphs describe many natural phenomena changing over time, such as social relationships, trade markets, metabolic networks etc. In this framework, performing community detection and analyzing the cluster evolution represents a critical task. Here we propose a new model for this purpose, where the smoothness of the clustering results over time can be considered as a valid prior knowledge. It is based on a constrained optimization formulation typical of Least Squares Support Vector Machines (LS-SVM), where the objective function is designed to explicitly incorporate temporal smoothness. The latter allows the model to cluster the current data well and to be consistent with the recent history. We also propose new model selection criteria in order to carefully choose the hyper-parameters of our model, which is a crucial issue to achieve good performances. We successfully test the model on four toy problems and on a real world network. We also compare our model with Evolutionary Spectral Clustering, which is a state-of-the-art algorithm for community detection of evolving networks, illustrating that the kernel spectral clustering with memory effect can achieve better or equal performances.

Absorption spectroscopy and multi-angle scattering measurements in the visible spectral range for the geographic classification of Italian exravirgin olive oils

NASA Astrophysics Data System (ADS)

Mignani, Anna G.; Ciaccheri, Leonardo; Cimato, Antonio; Sani, Graziano; Smith, Peter R.

2004-03-01

Absorption spectroscopy and multi-angle scattering measurements in the visible spectral range are innovately used to analyze samples of extra virgin olive oils coming from selected areas of Tuscany, a famous Italian region for the production of extra virgin olive oil. The measured spectra are processed by means of the Principal Component Analysis method, so as to create a 3D map capable of clustering the Tuscan oils within the wider area of Italian extra virgin olive oils.

A K-means multivariate approach for clustering independent components from magnetoencephalographic data.

PubMed

Spadone, Sara; de Pasquale, Francesco; Mantini, Dante; Della Penna, Stefania

2012-09-01

Independent component analysis (ICA) is typically applied on functional magnetic resonance imaging, electroencephalographic and magnetoencephalographic (MEG) data due to its data-driven nature. In these applications, ICA needs to be extended from single to multi-session and multi-subject studies for interpreting and assigning a statistical significance at the group level. Here a novel strategy for analyzing MEG independent components (ICs) is presented, Multivariate Algorithm for Grouping MEG Independent Components K-means based (MAGMICK). The proposed approach is able to capture spatio-temporal dynamics of brain activity in MEG studies by running ICA at subject level and then clustering the ICs across sessions and subjects. Distinctive features of MAGMICK are: i) the implementation of an efficient set of "MEG fingerprints" designed to summarize properties of MEG ICs as they are built on spatial, temporal and spectral parameters; ii) the implementation of a modified version of the standard K-means procedure to improve its data-driven character. This algorithm groups the obtained ICs automatically estimating the number of clusters through an adaptive weighting of the parameters and a constraint on the ICs independence, i.e. components coming from the same session (at subject level) or subject (at group level) cannot be grouped together. The performances of MAGMICK are illustrated by analyzing two sets of MEG data obtained during a finger tapping task and median nerve stimulation. The results demonstrate that the method can extract consistent patterns of spatial topography and spectral properties across sessions and subjects that are in good agreement with the literature. In addition, these results are compared to those from a modified version of affinity propagation clustering method. The comparison, evaluated in terms of different clustering validity indices, shows that our methodology often outperforms the clustering algorithm. Eventually, these results are confirmed by a comparison with a MEG tailored version of the self-organizing group ICA, which is largely used for fMRI IC clustering. Copyright © 2012 Elsevier Inc. All rights reserved.

Personalized microbial network inference via co-regularized spectral clustering.

PubMed

Imangaliyev, Sultan; Keijser, Bart; Crielaard, Wim; Tsivtsivadze, Evgeni

2015-07-15

We use Human Microbiome Project (HMP) cohort (Peterson et al., 2009) to infer personalized oral microbial networks of healthy individuals. To determine clustering of individuals with similar microbial profiles, co-regularized spectral clustering algorithm is applied to the dataset. For each cluster we discovered, we compute co-occurrence relationships among the microbial species that determine microbial network per cluster of individuals. The results of our study suggest that there are several differences in microbial interactions on personalized network level in healthy oral samples acquired from various niches. Based on the results of co-regularized spectral clustering we discover two groups of individuals with different topology of their microbial interaction network. The results of microbial network inference suggest that niche-wise interactions are different in these two groups. Our study shows that healthy individuals have different microbial clusters according to their oral microbiota. Such personalized microbial networks open a better understanding of the microbial ecology of healthy oral cavities and new possibilities for future targeted medication. The scripts written in scientific Python and in Matlab, which were used for network visualization, are provided for download on the website http://learning-machines.com/. Copyright © 2015 Elsevier Inc. All rights reserved.

Information Extraction from Large-Multi-Layer Social Networks

DTIC Science & Technology

2015-08-06

mization [4]. Methods that fall into this category include spec- tral algorithms, modularity methods, and methods that rely on statistical inference...Snijders and Chris Baerveldt, “A multilevel network study of the effects of delinquent behavior on friendship evolution,” Journal of mathematical sociol- ogy...1970. [10] Ulrike Luxburg, “A tutorial on spectral clustering,” Statistics and Computing, vol. 17, no. 4, pp. 395–416, Dec. 2007. [11] R. A. Fisher, “On

Multilevel Hierarchical Kernel Spectral Clustering for Real-Life Large Scale Complex Networks

PubMed Central

Mall, Raghvendra; Langone, Rocco; Suykens, Johan A. K.

2014-01-01

Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks. PMID:24949877

A Dimensionality Reduction-Based Multi-Step Clustering Method for Robust Vessel Trajectory Analysis

PubMed Central

Liu, Jingxian; Wu, Kefeng

2017-01-01

The Shipboard Automatic Identification System (AIS) is crucial for navigation safety and maritime surveillance, data mining and pattern analysis of AIS information have attracted considerable attention in terms of both basic research and practical applications. Clustering of spatio-temporal AIS trajectories can be used to identify abnormal patterns and mine customary route data for transportation safety. Thus, the capacities of navigation safety and maritime traffic monitoring could be enhanced correspondingly. However, trajectory clustering is often sensitive to undesirable outliers and is essentially more complex compared with traditional point clustering. To overcome this limitation, a multi-step trajectory clustering method is proposed in this paper for robust AIS trajectory clustering. In particular, the Dynamic Time Warping (DTW), a similarity measurement method, is introduced in the first step to measure the distances between different trajectories. The calculated distances, inversely proportional to the similarities, constitute a distance matrix in the second step. Furthermore, as a widely-used dimensional reduction method, Principal Component Analysis (PCA) is exploited to decompose the obtained distance matrix. In particular, the top k principal components with above 95% accumulative contribution rate are extracted by PCA, and the number of the centers k is chosen. The k centers are found by the improved center automatically selection algorithm. In the last step, the improved center clustering algorithm with k clusters is implemented on the distance matrix to achieve the final AIS trajectory clustering results. In order to improve the accuracy of the proposed multi-step clustering algorithm, an automatic algorithm for choosing the k clusters is developed according to the similarity distance. Numerous experiments on realistic AIS trajectory datasets in the bridge area waterway and Mississippi River have been implemented to compare our proposed method with traditional spectral clustering and fast affinity propagation clustering. Experimental results have illustrated its superior performance in terms of quantitative and qualitative evaluations. PMID:28777353

A Random Walk Approach to Query Informative Constraints for Clustering.

PubMed

Abin, Ahmad Ali

2017-08-09

This paper presents a random walk approach to the problem of querying informative constraints for clustering. The proposed method is based on the properties of the commute time, that is the expected time taken for a random walk to travel between two nodes and return, on the adjacency graph of data. Commute time has the nice property of that, the more short paths connect two given nodes in a graph, the more similar those nodes are. Since computing the commute time takes the Laplacian eigenspectrum into account, we use this property in a recursive fashion to query informative constraints for clustering. At each recursion, the proposed method constructs the adjacency graph of data and utilizes the spectral properties of the commute time matrix to bipartition the adjacency graph. Thereafter, the proposed method benefits from the commute times distance on graph to query informative constraints between partitions. This process iterates for each partition until the stop condition becomes true. Experiments on real-world data show the efficiency of the proposed method for constraints selection.

Beyond Low-Rank Representations: Orthogonal clustering basis reconstruction with optimized graph structure for multi-view spectral clustering.

PubMed

Wang, Yang; Wu, Lin

2018-07-01

Low-Rank Representation (LRR) is arguably one of the most powerful paradigms for Multi-view spectral clustering, which elegantly encodes the multi-view local graph/manifold structures into an intrinsic low-rank self-expressive data similarity embedded in high-dimensional space, to yield a better graph partition than their single-view counterparts. In this paper we revisit it with a fundamentally different perspective by discovering LRR as essentially a latent clustered orthogonal projection based representation winged with an optimized local graph structure for spectral clustering; each column of the representation is fundamentally a cluster basis orthogonal to others to indicate its members, which intuitively projects the view-specific feature representation to be the one spanned by all orthogonal basis to characterize the cluster structures. Upon this finding, we propose our technique with the following: (1) We decompose LRR into latent clustered orthogonal representation via low-rank matrix factorization, to encode the more flexible cluster structures than LRR over primal data objects; (2) We convert the problem of LRR into that of simultaneously learning orthogonal clustered representation and optimized local graph structure for each view; (3) The learned orthogonal clustered representations and local graph structures enjoy the same magnitude for multi-view, so that the ideal multi-view consensus can be readily achieved. The experiments over multi-view datasets validate its superiority, especially over recent state-of-the-art LRR models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Taxonomy and clustering in collaborative systems: The case of the on-line encyclopedia Wikipedia

NASA Astrophysics Data System (ADS)

Capocci, A.; Rao, F.; Caldarelli, G.

2008-01-01

In this paper we investigate the nature and structure of the relation between imposed classifications and real clustering in a particular case of a scale-free network given by the on-line encyclopedia Wikipedia. We find a statistical similarity in the distributions of community sizes both by using the top-down approach of the categories division present in the archive and in the bottom-up procedure of community detection given by an algorithm based on the spectral properties of the graph. Regardless of the statistically similar behaviour, the two methods provide a rather different division of the articles, thereby signaling that the nature and presence of power laws is a general feature for these systems and cannot be used as a benchmark to evaluate the suitability of a clustering method.

Anomaly clustering in hyperspectral images

NASA Astrophysics Data System (ADS)

Doster, Timothy J.; Ross, David S.; Messinger, David W.; Basener, William F.

2009-05-01

The topological anomaly detection algorithm (TAD) differs from other anomaly detection algorithms in that it uses a topological/graph-theoretic model for the image background instead of modeling the image with a Gaussian normal distribution. In the construction of the model, TAD produces a hard threshold separating anomalous pixels from background in the image. We build on this feature of TAD by extending the algorithm so that it gives a measure of the number of anomalous objects, rather than the number of anomalous pixels, in a hyperspectral image. This is done by identifying, and integrating, clusters of anomalous pixels via a graph theoretical method combining spatial and spectral information. The method is applied to a cluttered HyMap image and combines small groups of pixels containing like materials, such as those corresponding to rooftops and cars, into individual clusters. This improves visualization and interpretation of objects.

Integrated J- and H-band spectra of globular clusters in the LMC: implications for stellar population models and galaxy age dating

NASA Astrophysics Data System (ADS)

Lyubenova, M.; Kuntschner, H.; Rejkuba, M.; Silva, D. R.; Kissler-Patig, M.; Tacconi-Garman, L. E.

2012-07-01

Context. The rest-frame near-IR spectra of intermediate age (1-2 Gyr) stellar populations are dominated by carbon based absorption features offering a wealth of information. Yet, spectral libraries that include the near-IR wavelength range do not sample a sufficiently broad range of ages and metallicities to allow for accurate calibration of stellar population models and thus the interpretation of the observations. Aims: In this paper we investigate the integrated J- and H-band spectra of six intermediate age and old globular clusters in the Large Magellanic Cloud (LMC). Methods: The observations for six clusters were obtained with the SINFONI integral field spectrograph at the ESO VLT Yepun telescope, covering the J (1.09-1.41 μm) and H-band (1.43-1.86 μm) spectral range. The spectral resolution is 6.7 Å in J and 6.6 Å in H-band (FWHM). The observations were made in natural seeing, covering the central 24″ × 24″ of each cluster and in addition sampling the brightest eight red giant branch and asymptotic giant branch (AGB) star candidates within the clusters' tidal radii. Targeted clusters cover the ages of ~1.3 Gyr (NGC 1806, NGC 2162), 2 Gyr (NGC 2173) and ~13 Gyr (NGC 1754, NGC 2005, NGC 2019). Results.H-band C2 and K-band 12CO (2-0) feature strengths for the LMC globular clusters are compared to the models of Maraston (2005). C2 is reasonably well reproduced by the models at all ages, while 12CO (2-0) shows good agreement for older (age ≥ 2 Gyr) populations, but the younger (1.3 Gyr) globular clusters do not follow the models. We argue that this is due to the fact that the empirical calibration of the models relies on only a few Milky Way carbon star spectra, which show different 12CO (2-0) index strengths than the LMC stars. The C2 absorption feature strength correlates strongly with age. It is present essentially only in populations that have 1-2 Gyr old stars, while its value is consistent with zero for older populations. The distinct spectral energy distribution observed for the intermediate age globular clusters in the J- and H-bands agrees well with the model predictions of Maraston for the contribution from the thermally pulsing AGB phase. Conclusions: In this pilot project we present an empirical library of six LMC globular cluster integrated near-IR spectra that are useful for testing stellar population models in this wavelength regime. We show that the H-band C2 absorption feature and the J-, H-band spectral shape can be used as an age indicator for intermediate age stellar populations in integrated spectra of star clusters and galaxies. Based on observation collected at the ESO Paranal La Silla Observatory, Chile, Prog. ID 078.B-0205.Table 2 is available in electronic form at http://www.aanda.orgJ- and H-spectra are only available at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/543/A75

Detection and tracking of gas plumes in LWIR hyperspectral video sequence data

NASA Astrophysics Data System (ADS)

Gerhart, Torin; Sunu, Justin; Lieu, Lauren; Merkurjev, Ekaterina; Chang, Jen-Mei; Gilles, Jérôme; Bertozzi, Andrea L.

2013-05-01

Automated detection of chemical plumes presents a segmentation challenge. The segmentation problem for gas plumes is difficult due to the diffusive nature of the cloud. The advantage of considering hyperspectral images in the gas plume detection problem over the conventional RGB imagery is the presence of non-visual data, allowing for a richer representation of information. In this paper we present an effective method of visualizing hyperspectral video sequences containing chemical plumes and investigate the effectiveness of segmentation techniques on these post-processed videos. Our approach uses a combination of dimension reduction and histogram equalization to prepare the hyperspectral videos for segmentation. First, Principal Components Analysis (PCA) is used to reduce the dimension of the entire video sequence. This is done by projecting each pixel onto the first few Principal Components resulting in a type of spectral filter. Next, a Midway method for histogram equalization is used. These methods redistribute the intensity values in order to reduce icker between frames. This properly prepares these high-dimensional video sequences for more traditional segmentation techniques. We compare the ability of various clustering techniques to properly segment the chemical plume. These include K-means, spectral clustering, and the Ginzburg-Landau functional.

Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints.

PubMed

Keitel, Anne; Gross, Joachim

2016-06-01

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles ("fingerprints"), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease.

The panchromatic Hubble Andromeda Treasury. V. Ages and masses of the year 1 stellar clusters

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fouesneau, Morgan; Johnson, L. Clifton; Weisz, Daniel R.

We present ages and masses for 601 star clusters in M31 from the analysis of the six filter integrated light measurements from near-ultraviolet to near-infrared wavelengths, made as part of the Panchromatic Hubble Andromeda Treasury (PHAT). We derive the ages and masses using a probabilistic technique, which accounts for the effects of stochastic sampling of the stellar initial mass function. Tests on synthetic data show that this method, in conjunction with the exquisite sensitivity of the PHAT observations and their broad wavelength baseline, provides robust age and mass recovery for clusters ranging from ∼10{sup 2} to 2 × 10{sup 6}more » M {sub ☉}. We find that the cluster age distribution is consistent with being uniform over the past 100 Myr, which suggests a weak effect of cluster disruption within M31. The age distribution of older (>100 Myr) clusters falls toward old ages, consistent with a power-law decline of index –1, likely from a combination of fading and disruption of the clusters. We find that the mass distribution of the whole sample can be well described by a single power law with a spectral index of –1.9 ± 0.1 over the range of 10{sup 3}-3 × 10{sup 5} M {sub ☉}. However, if we subdivide the sample by galactocentric radius, we find that the age distributions remain unchanged. However, the mass spectral index varies significantly, showing best-fit values between –2.2 and –1.8, with the shallower slope in the highest star formation intensity regions. We explore the robustness of our study to potential systematics and conclude that the cluster mass function may vary with respect to environment.« less

VizieR Online Data Catalog: WIYN open cluster study. LIX. RVs of NGC 6791 (Tofflemire+, 2014)

NASA Astrophysics Data System (ADS)

Tofflemire, B. M.; Gosnell, N. M.; Mathieu, R. D.; Platais, I.

2014-11-01

Our observations utilize the Hydra Multi-Object Spectrograph (MOS) on the WIYN 3.5m telescope. We use 3.1'' diameter fibers along with the bench spectrograph echelle grating, resulting in a spectral resolution of ~20000 (15km/s). See Geller et al. 2008 (cat. J/AJ/135/2264; Paper XXXII) for full details about our observing and data reduction procedures. Variations in our methods from previous WIYN Open Cluster Study (WOCS) radial velocity papers are given in Section 3. (3 data files).

Network clustering and community detection using modulus of families of loops.

PubMed

Shakeri, Heman; Poggi-Corradini, Pietro; Albin, Nathan; Scoglio, Caterina

2017-01-01

We study the structure of loops in networks using the notion of modulus of loop families. We introduce an alternate measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link usage optimally. We propose weighting networks using these expected link usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks.

Classification of high-resolution multi-swath hyperspectral data using Landsat 8 surface reflectance data as a calibration target and a novel histogram based unsupervised classification technique to determine natural classes from biophysically relevant fit parameters

NASA Astrophysics Data System (ADS)

McCann, C.; Repasky, K. S.; Morin, M.; Lawrence, R. L.; Powell, S. L.

2016-12-01

Compact, cost-effective, flight-based hyperspectral imaging systems can provide scientifically relevant data over large areas for a variety of applications such as ecosystem studies, precision agriculture, and land management. To fully realize this capability, unsupervised classification techniques based on radiometrically-calibrated data that cluster based on biophysical similarity rather than simply spectral similarity are needed. An automated technique to produce high-resolution, large-area, radiometrically-calibrated hyperspectral data sets based on the Landsat surface reflectance data product as a calibration target was developed and applied to three subsequent years of data covering approximately 1850 hectares. The radiometrically-calibrated data allows inter-comparison of the temporal series. Advantages of the radiometric calibration technique include the need for minimal site access, no ancillary instrumentation, and automated processing. Fitting the reflectance spectra of each pixel using a set of biophysically relevant basis functions reduces the data from 80 spectral bands to 9 parameters providing noise reduction and data compression. Examination of histograms of these parameters allows for determination of natural splitting into biophysical similar clusters. This method creates clusters that are similar in terms of biophysical parameters, not simply spectral proximity. Furthermore, this method can be applied to other data sets, such as urban scenes, by developing other physically meaningful basis functions. The ability to use hyperspectral imaging for a variety of important applications requires the development of data processing techniques that can be automated. The radiometric-calibration combined with the histogram based unsupervised classification technique presented here provide one potential avenue for managing big-data associated with hyperspectral imaging.

A scoring metric for multivariate data for reproducibility analysis using chemometric methods

PubMed Central

Sheen, David A.; de Carvalho Rocha, Werickson Fortunato; Lippa, Katrice A.; Bearden, Daniel W.

2017-01-01

Process quality control and reproducibility in emerging measurement fields such as metabolomics is normally assured by interlaboratory comparison testing. As a part of this testing process, spectral features from a spectroscopic method such as nuclear magnetic resonance (NMR) spectroscopy are attributed to particular analytes within a mixture, and it is the metabolite concentrations that are returned for comparison between laboratories. However, data quality may also be assessed directly by using binned spectral data before the time-consuming identification and quantification. Use of the binned spectra has some advantages, including preserving information about trace constituents and enabling identification of process difficulties. In this paper, we demonstrate the use of binned NMR spectra to conduct a detailed interlaboratory comparison and composition analysis. Spectra of synthetic and biologically-obtained metabolite mixtures, taken from a previous interlaboratory study, are compared with cluster analysis using a variety of distance and entropy metrics. The individual measurements are then evaluated based on where they fall within their clusters, and a laboratory-level scoring metric is developed, which provides an assessment of each laboratory’s individual performance. PMID:28694553

Cloud classification from satellite data using a fuzzy sets algorithm: A polar example

NASA Technical Reports Server (NTRS)

Key, J. R.; Maslanik, J. A.; Barry, R. G.

1988-01-01

Where spatial boundaries between phenomena are diffuse, classification methods which construct mutually exclusive clusters seem inappropriate. The Fuzzy c-means (FCM) algorithm assigns each observation to all clusters, with membership values as a function of distance to the cluster center. The FCM algorithm is applied to AVHRR data for the purpose of classifying polar clouds and surfaces. Careful analysis of the fuzzy sets can provide information on which spectral channels are best suited to the classification of particular features, and can help determine likely areas of misclassification. General agreement in the resulting classes and cloud fraction was found between the FCM algorithm, a manual classification, and an unsupervised maximum likelihood classifier.

Cluster Physics with Merging Galaxy Clusters

NASA Astrophysics Data System (ADS)

Molnar, Sandor

Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard ΛCDM model, where the total density is dominated by the cosmological constant (Λ) and the matter density by cold dark matter (CDM), structure formation is hierarchical, and clusters grow mostly by merging. Mergers of two massive clusters are the most energetic events in the universe after the Big Bang, hence they provide a unique laboratory to study cluster physics. The two main mass components in clusters behave differently during collisions: the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulence are developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thus our review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clusters is to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses. New high spatial and spectral resolution ground and space based telescopes will come online in the near future. Motivated by these new opportunities, we briefly discuss methods which will be feasible in the near future in studying merging clusters.

Globular Cluster Abundances from High-resolution, Integrated-light Spectroscopy. IV. The Large Magellanic Cloud: α, Fe-peak, Light, and Heavy Elements

NASA Astrophysics Data System (ADS)

Colucci, Janet E.; Bernstein, Rebecca A.; Cameron, Scott A.; McWilliam, Andrew

2012-02-01

We present detailed chemical abundances in eight clusters in the Large Magellanic Cloud (LMC). We measure abundances of 22 elements for clusters spanning a range in age of 0.05-12 Gyr, providing a comprehensive picture of the chemical enrichment and star formation history of the LMC. The abundances were obtained from individual absorption lines using a new method for analysis of high-resolution (R ~ 25,000), integrated-light (IL) spectra of star clusters. This method was developed and presented in Papers I, II, and III of this series. In this paper, we develop an additional IL χ2-minimization spectral synthesis technique to facilitate measurement of weak (~15 mÅ) spectral lines and abundances in low signal-to-noise ratio data (S/N ~ 30). Additionally, we supplement the IL abundance measurements with detailed abundances that we measure for individual stars in the youngest clusters (age < 2 Gyr) in our sample. In both the IL and stellar abundances we find evolution of [α/Fe] with [Fe/H] and age. Fe-peak abundance ratios are similar to those in the Milky Way (MW), with the exception of [Cu/Fe] and [Mn/Fe], which are sub-solar at high metallicities. The heavy elements Ba, La, Nd, Sm, and Eu are significantly enhanced in the youngest clusters. Also, the heavy to light s-process ratio is elevated relative to the MW ([Ba/Y] >+0.5) and increases with decreasing age, indicating a strong contribution of low-metallicity asymptotic giant branch star ejecta to the interstellar medium throughout the later history of the LMC. We also find a correlation of IL Na and Al abundances with cluster mass in the sense that more massive, older clusters are enriched in the light elements Na and Al with respect to Fe, which implies that these clusters harbor star-to-star abundance variations as is common in the MW. Lower mass, intermediate-age, and young clusters have Na and Al abundances that are lower and more consistent with LMC field stars. Our results can be used to constrain both future chemical evolution models for the LMC and theories of globular cluster formation. This paper includes data gathered with the 6.5 m Magellan Telescopes located at Las Campanas Observatory, Chile.

Spectroscopic determination of fundamental parameters of small angular diameter galactic open clusters

NASA Astrophysics Data System (ADS)

Ahumada, A. V.; Claria, J. J.; Bica, E.; Parisi, M. C.; Torres, M. C.; Pavani, D. B.

We present integrated spectra obtained at CASLEO (Argentina) for 9 galactic open clusters of small angular diameter. Two of them (BH 55 and Rup 159) have not been the target of previous research. The flux-calibrated spectra cover the spectral range approx. 3600-6900 A. Using the equivalent widths (EWs) of the Balmer lines and comparing the cluster spectra with template spectra, we determined E(B-V) colour excesses and ages for the present cluster sample. The parameters obtained for 6 of the clusters show good agreement with previous determinations based mainly on photometric methods. This is not the case, however, for BH 90, a scarcely reddened cluster, for which Moffat and Vogt (1975, Astron. and Astroph. SS, 20, 125) derived E(B-V) = 0.51. We explain and justify the strong discrepancy found for this object. According to the present analysis, 3 clusters are very young (Bo 14, Tr 15 and Tr 27), 2 are moderately young (NGC 6268 and BH 205), 3 are Hyades-like clusters (Rup 164, BH 90 and BH 55) and only one is an intermediate-age cluster (Rup 159).

A Remote Sensing Image Fusion Method based on adaptive dictionary learning

NASA Astrophysics Data System (ADS)

He, Tongdi; Che, Zongxi

2018-01-01

This paper discusses using a remote sensing fusion method, based on' adaptive sparse representation (ASP)', to provide improved spectral information, reduce data redundancy and decrease system complexity. First, the training sample set is formed by taking random blocks from the images to be fused, the dictionary is then constructed using the training samples, and the remaining terms are clustered to obtain the complete dictionary by iterated processing at each step. Second, the self-adaptive weighted coefficient rule of regional energy is used to select the feature fusion coefficients and complete the reconstruction of the image blocks. Finally, the reconstructed image blocks are rearranged and an average is taken to obtain the final fused images. Experimental results show that the proposed method is superior to other traditional remote sensing image fusion methods in both spectral information preservation and spatial resolution.

The enigma of the open cluster M29 (NGC 6913) solved

DOE Office of Scientific and Technical Information (OSTI.GOV)

Straižys, V.; Milašius, K.; Černis, K.

2014-11-01

Determining the distance to the open cluster M29 (NGC 6913) has proven difficult, with distances determined by various authors differing by a factor of two or more. To solve this problem, we have initiated a new photometric investigation of the cluster in the Vilnius seven-color photometric system, supplementing it with available data in the BV and JHK {sub s} photometric systems and spectra of the nine brightest stars of spectral classes O and B. Photometric spectral classes and luminosities of 260 stars in a 15' × 15' area down to V = 19 mag are used to investigate the interstellarmore » extinction run with distance and to estimate the distance of the Great Cygnus Rift, ∼ 800 pc. The interstellar reddening law in the optical and near-infrared regions is found to be close to normal, with the ratio of extinction to color excess R{sub BV} = 2.87. The extinction A{sub V} of cluster members is between 2.5 and 3.8 mag, with a mean value of 2.97 mag, or E {sub B–V} = 1.03. The average distance of eight stars of spectral types O9-B2 is 1.54 ± 0.15 kpc. Two stars from the seven brightest stars are field stars: HDE 229238 is a background B0.5 supergiant and HD 194378 is a foreground F star. In the intrinsic color-magnitude diagram, seven fainter stars of spectral classes B3-B8 are identified as possible members of the cluster. The 15 selected members of the cluster of spectral classes O9-B8 plotted on the log L/L {sub ☉} versus log T {sub eff} diagram, together with the isochrones from the Padova database, give the age of the cluster as 5 ± 1 Myr.« less

Influence of silver and copper doping on luminescent properties of zinc-phosphate glasses after x-ray irradiation

NASA Astrophysics Data System (ADS)

Murashov, Alexander A.; Sidorov, Alexander I.; Shakhverdov, Teimur A.; Stolyarchuk, Maxim V.

2017-11-01

It is shown, experimentally, that in silver- and copper-containing zinc-phosphate glasses, metal molecular clusters are formed during the glass synthesis. X-ray irradiation of these glasses led to the considerable increase of its luminescence in visible spectral range. This effect is caused by the transformation of the charged metal molecular clusters into the neutral state. Luminescence and excitation spectra of the glass, doped with silver and copper simultaneously, change significantly in comparison with the spectra of glasses doped with one metal. The reason for this can be the formation of hybrid AgnCum molecular clusters. The computer simulation of the structure and optical properties of such clusters by the time-dependent density functional theory method is presented. It is shown that the optimal luminescent material for photonics application, in comparison with other studied materials, is glass, containing hybrid molecular clusters.

Hyperspectral imaging coupled with chemometric analysis for non-invasive differentiation of black pens

NASA Astrophysics Data System (ADS)

Chlebda, Damian K.; Majda, Alicja; Łojewski, Tomasz; Łojewska, Joanna

2016-11-01

Differentiation of the written text can be performed with a non-invasive and non-contact tool that connects conventional imaging methods with spectroscopy. Hyperspectral imaging (HSI) is a relatively new and rapid analytical technique that can be applied in forensic science disciplines. It allows an image of the sample to be acquired, with full spectral information within every pixel. For this paper, HSI and three statistical methods (hierarchical cluster analysis, principal component analysis, and spectral angle mapper) were used to distinguish between traces of modern black gel pen inks. Non-invasiveness and high efficiency are among the unquestionable advantages of ink differentiation using HSI. It is also less time-consuming than traditional methods such as chromatography. In this study, a set of 45 modern gel pen ink marks deposited on a paper sheet were registered. The spectral characteristics embodied in every pixel were extracted from an image and analysed using statistical methods, externally and directly on the hypercube. As a result, different black gel inks deposited on paper can be distinguished and classified into several groups, in a non-invasive manner.

Bayesian Decision Theoretical Framework for Clustering

ERIC Educational Resources Information Center

Chen, Mo

2011-01-01

In this thesis, we establish a novel probabilistic framework for the data clustering problem from the perspective of Bayesian decision theory. The Bayesian decision theory view justifies the important questions: what is a cluster and what a clustering algorithm should optimize. We prove that the spectral clustering (to be specific, the…

Effect of silver ions and clusters on the luminescence properties of Eu-doped borate glasses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiao, Qing, E-mail: jiaoqing@nbu.edu.cn; Wang, Xi; Qiu, Jianbei

2015-12-15

Highlights: • Ag{sup +} and Ag clusters are investigated in the borate glasses via ion exchange method. • The aggregation of silver ions to the clusters was controlled by the ion exchange concentration. • Eu{sup 3+}/Eu{sup 2+} ions emission was enhanced with the sensitization of the silver species. • Energy transfer process from Ag ions and Ag clusters to Eu ions is identified by the lifetime measurements. - Abstract: Silver ions and clusters were applied to Eu{sup 3+}-doped borate glasses via the Ag{sup +}–Na{sup +} ion exchange method. Eu{sup 3+}/Eu{sup 2+} ion luminescence enhancement was achieved after silver ion exchange.more » Absorption spectra showed no band at 420 nm, which indicates that silver nanoparticles can be excluded as a silver state in the glass. Silver ion aggregation into clusters during the ion exchange process may be inferred. The effect of silver ions and clusters on rare earth emissions was investigated using spectral information and lifetime measurements. Significant luminescence enhancements were observed from the energy transfer of Ag{sup +} ions and clusters to Eu{sup 3+}/Eu{sup 2+} ions, companied with the silver ions aggregated into the clusters state. The results of this research may extend the current understanding of interactions between rare-earth ions and Ag species.« less

An unsupervised classification approach for analysis of Landsat data to monitor land reclamation in Belmont county, Ohio

NASA Technical Reports Server (NTRS)

Brumfield, J. O.; Bloemer, H. H. L.; Campbell, W. J.

1981-01-01

Two unsupervised classification procedures for analyzing Landsat data used to monitor land reclamation in a surface mining area in east central Ohio are compared for agreement with data collected from the corresponding locations on the ground. One procedure is based on a traditional unsupervised-clustering/maximum-likelihood algorithm sequence that assumes spectral groupings in the Landsat data in n-dimensional space; the other is based on a nontraditional unsupervised-clustering/canonical-transformation/clustering algorithm sequence that not only assumes spectral groupings in n-dimensional space but also includes an additional feature-extraction technique. It is found that the nontraditional procedure provides an appreciable improvement in spectral groupings and apparently increases the level of accuracy in the classification of land cover categories.

Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm.

PubMed

Kamali, Tahereh; Stashuk, Daniel

2016-10-01

Robust and accurate segmentation of brain white matter (WM) fiber bundles assists in diagnosing and assessing progression or remission of neuropsychiatric diseases such as schizophrenia, autism and depression. Supervised segmentation methods are infeasible in most applications since generating gold standards is too costly. Hence, there is a growing interest in designing unsupervised methods. However, most conventional unsupervised methods require the number of clusters be known in advance which is not possible in most applications. The purpose of this study is to design an unsupervised segmentation algorithm for brain white matter fiber bundles which can automatically segment fiber bundles using intrinsic diffusion tensor imaging data information without considering any prior information or assumption about data distributions. Here, a new density based clustering algorithm called neighborhood distance entropy consistency (NDEC), is proposed which discovers natural clusters within data by simultaneously utilizing both local and global density information. The performance of NDEC is compared with other state of the art clustering algorithms including chameleon, spectral clustering, DBSCAN and k-means using Johns Hopkins University publicly available diffusion tensor imaging data. The performance of NDEC and other employed clustering algorithms were evaluated using dice ratio as an external evaluation criteria and density based clustering validation (DBCV) index as an internal evaluation metric. Across all employed clustering algorithms, NDEC obtained the highest average dice ratio (0.94) and DBCV value (0.71). NDEC can find clusters with arbitrary shapes and densities and consequently can be used for WM fiber bundle segmentation where there is no distinct boundary between various bundles. NDEC may also be used as an effective tool in other pattern recognition and medical diagnostic systems in which discovering natural clusters within data is a necessity. Copyright © 2016 Elsevier B.V. All rights reserved.

A clustering-based graph Laplacian framework for value function approximation in reinforcement learning.

PubMed

Xu, Xin; Huang, Zhenhua; Graves, Daniel; Pedrycz, Witold

2014-12-01

In order to deal with the sequential decision problems with large or continuous state spaces, feature representation and function approximation have been a major research topic in reinforcement learning (RL). In this paper, a clustering-based graph Laplacian framework is presented for feature representation and value function approximation (VFA) in RL. By making use of clustering-based techniques, that is, K-means clustering or fuzzy C-means clustering, a graph Laplacian is constructed by subsampling in Markov decision processes (MDPs) with continuous state spaces. The basis functions for VFA can be automatically generated from spectral analysis of the graph Laplacian. The clustering-based graph Laplacian is integrated with a class of approximation policy iteration algorithms called representation policy iteration (RPI) for RL in MDPs with continuous state spaces. Simulation and experimental results show that, compared with previous RPI methods, the proposed approach needs fewer sample points to compute an efficient set of basis functions and the learning control performance can be improved for a variety of parameter settings.

Clustering Genes of Common Evolutionary History

PubMed Central

Gori, Kevin; Suchan, Tomasz; Alvarez, Nadir; Goldman, Nick; Dessimoz, Christophe

2016-01-01

Phylogenetic inference can potentially result in a more accurate tree using data from multiple loci. However, if the loci are incongruent—due to events such as incomplete lineage sorting or horizontal gene transfer—it can be misleading to infer a single tree. To address this, many previous contributions have taken a mechanistic approach, by modeling specific processes. Alternatively, one can cluster loci without assuming how these incongruencies might arise. Such “process-agnostic” approaches typically infer a tree for each locus and cluster these. There are, however, many possible combinations of tree distance and clustering methods; their comparative performance in the context of tree incongruence is largely unknown. Furthermore, because standard model selection criteria such as AIC cannot be applied to problems with a variable number of topologies, the issue of inferring the optimal number of clusters is poorly understood. Here, we perform a large-scale simulation study of phylogenetic distances and clustering methods to infer loci of common evolutionary history. We observe that the best-performing combinations are distances accounting for branch lengths followed by spectral clustering or Ward’s method. We also introduce two statistical tests to infer the optimal number of clusters and show that they strongly outperform the silhouette criterion, a general-purpose heuristic. We illustrate the usefulness of the approach by 1) identifying errors in a previous phylogenetic analysis of yeast species and 2) identifying topological incongruence among newly sequenced loci of the globeflower fly genus Chiastocheta. We release treeCl, a new program to cluster genes of common evolutionary history (http://git.io/treeCl). PMID:26893301

A combined experimental and theoretical spectroscopic protocol for determination of the structure of heterogeneous catalysts: developing the information content of the resonance Raman spectra of M1 MoVO x .

PubMed

Kubas, Adam; Noak, Johannes; Trunschke, Annette; Schlögl, Robert; Neese, Frank; Maganas, Dimitrios

2017-09-01

Absorption and multiwavelength resonance Raman spectroscopy are widely used to investigate the electronic structure of transition metal centers in coordination compounds and extended solid systems. In combination with computational methodologies that have predictive accuracy, they define powerful protocols to study the spectroscopic response of catalytic materials. In this work, we study the absorption and resonance Raman spectra of the M1 MoVO x catalyst. The spectra were calculated by time-dependent density functional theory (TD-DFT) in conjunction with the independent mode displaced harmonic oscillator model (IMDHO), which allows for detailed bandshape predictions. For this purpose cluster models with up to 9 Mo and V metallic centers are considered to represent the bulk structure of MoVO x . Capping hydrogens were used to achieve valence saturation at the edges of the cluster models. The construction of model structures was based on a thorough bonding analysis which involved conventional DFT and local coupled cluster (DLPNO-CCSD(T)) methods. Furthermore the relationship of cluster topology to the computed spectral features is discussed in detail. It is shown that due to the local nature of the involved electronic transitions, band assignment protocols developed for molecular systems can be applied to describe the calculated spectral features of the cluster models as well. The present study serves as a reference for future applications of combined experimental and computational protocols in the field of solid-state heterogeneous catalysis.

TiO(2) doping by hydroxyurea at the nucleation stage: towards a new photocatalyst in the visible spectral range.

PubMed

Azouani, R; Tieng, S; Chhor, K; Bocquet, J-F; Eloy, P; Gaigneaux, E M; Klementiev, K; Kanaev, A V

2010-10-07

We report an original method of preparation of OCN-doped TiO(2) for photocatalysis in the visible spectral range. The preparation is achieved by a sol-gel route using titanium tetraisopropoxide precursor. Special attention was paid to fluid micromixing, which enables homogeneous reaction conditions in the reactor bulk and monodispersity of the produced clusters/nanoparticles. The dopant hydroxyurea (HyU, CH(4)N(2)O(2)) is injected into the reactive fluid at the nucleation stage, which lasts tens of milliseconds. The doping results in a strong yellow coloration of the nanocolloids due to the absorption band in the spectral range 380-550 nm and accelerates the aggregation kinetics of both nuclei at the induction stage and sub-nuclei units (clusters) at the nucleation stage. FTIR, Raman and UV-visible absorption analyses show the formation of a stable HyU-TiO(2) complex. EXAFS spectra indicate no appreciable changes of the first-shell Ti atom environment. The doping agent takes available surface sites of TiO(2) clusters/nanoparticles attaining ∼10% molar loading. The reaction kinetics then accelerates due to a longer collisional lifetime between nanoparticles induced by the formation of a weak [double bond, length as m-dash]OTi bond. The OCN-group bonding to titanium atoms produces a weakening of the C[double bond, length as m-dash]O double bond and a strengthening of the C-N and N-O bonds.

Identification of Reliable Components in Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS): a Data-Driven Approach across Metabolic Processes.

PubMed

Motegi, Hiromi; Tsuboi, Yuuri; Saga, Ayako; Kagami, Tomoko; Inoue, Maki; Toki, Hideaki; Minowa, Osamu; Noda, Tetsuo; Kikuchi, Jun

2015-11-04

There is an increasing need to use multivariate statistical methods for understanding biological functions, identifying the mechanisms of diseases, and exploring biomarkers. In addition to classical analyses such as hierarchical cluster analysis, principal component analysis, and partial least squares discriminant analysis, various multivariate strategies, including independent component analysis, non-negative matrix factorization, and multivariate curve resolution, have recently been proposed. However, determining the number of components is problematic. Despite the proposal of several different methods, no satisfactory approach has yet been reported. To resolve this problem, we implemented a new idea: classifying a component as "reliable" or "unreliable" based on the reproducibility of its appearance, regardless of the number of components in the calculation. Using the clustering method for classification, we applied this idea to multivariate curve resolution-alternating least squares (MCR-ALS). Comparisons between conventional and modified methods applied to proton nuclear magnetic resonance ((1)H-NMR) spectral datasets derived from known standard mixtures and biological mixtures (urine and feces of mice) revealed that more plausible results are obtained by the modified method. In particular, clusters containing little information were detected with reliability. This strategy, named "cluster-aided MCR-ALS," will facilitate the attainment of more reliable results in the metabolomics datasets.

Individual Human Brain Areas Can Be Identified from Their Characteristic Spectral Activation Fingerprints

PubMed Central

Keitel, Anne; Gross, Joachim

2016-01-01

The human brain can be parcellated into diverse anatomical areas. We investigated whether rhythmic brain activity in these areas is characteristic and can be used for automatic classification. To this end, resting-state MEG data of 22 healthy adults was analysed. Power spectra of 1-s long data segments for atlas-defined brain areas were clustered into spectral profiles (“fingerprints”), using k-means and Gaussian mixture (GM) modelling. We demonstrate that individual areas can be identified from these spectral profiles with high accuracy. Our results suggest that each brain area engages in different spectral modes that are characteristic for individual areas. Clustering of brain areas according to similarity of spectral profiles reveals well-known brain networks. Furthermore, we demonstrate task-specific modulations of auditory spectral profiles during auditory processing. These findings have important implications for the classification of regional spectral activity and allow for novel approaches in neuroimaging and neurostimulation in health and disease. PMID:27355236

The WAGGS project - I. The WiFeS Atlas of Galactic Globular cluster Spectra

NASA Astrophysics Data System (ADS)

Usher, Christopher; Pastorello, Nicola; Bellstedt, Sabine; Alabi, Adebusola; Cerulo, Pierluigi; Chevalier, Leonie; Fraser-McKelvie, Amelia; Penny, Samantha; Foster, Caroline; McDermid, Richard M.; Schiavon, Ricardo P.; Villaume, Alexa

2017-07-01

We present the WiFeS Atlas of Galactic Globular cluster Spectra, a library of integrated spectra of Milky Way and Local Group globular clusters. We used the WiFeS integral field spectrograph on the Australian National University 2.3 m telescope to observe the central regions of 64 Milky Way globular clusters and 22 globular clusters hosted by the Milky Way's low-mass satellite galaxies. The spectra have wider wavelength coverage (3300-9050 Å) and higher spectral resolution (R = 6800) than existing spectral libraries of Milky Way globular clusters. By including Large and Small Magellanic Cloud star clusters, we extend the coverage of parameter space of existing libraries towards young and intermediate ages. While testing stellar population synthesis models and analysis techniques is the main aim of this library, the observations may also further our understanding of the stellar populations of Local Group globular clusters and make possible the direct comparison of extragalactic globular cluster integrated light observations with well-understood globular clusters in the Milky Way. The integrated spectra are publicly available via the project website.

Empirical Green's function analysis: Taking the next step

USGS Publications Warehouse

Hough, S.E.

1997-01-01

An extension of the empirical Green's function (EGF) method is presented that involves determination of source parameters using standard EGF deconvolution, followed by inversion for a common attenuation parameter for a set of colocated events. Recordings of three or more colocated events can thus be used to constrain a single path attenuation estimate. I apply this method to recordings from the 1995-1996 Ridgecrest, California, earthquake sequence; I analyze four clusters consisting of 13 total events with magnitudes between 2.6 and 4.9. I first obtain corner frequencies, which are used to infer Brune stress drop estimates. I obtain stress drop values of 0.3-53 MPa (with all but one between 0.3 and 11 MPa), with no resolved increase of stress drop with moment. With the corner frequencies constrained, the inferred attenuation parameters are very consistent; they imply an average shear wave quality factor of approximately 20-25 for alluvial sediments within the Indian Wells Valley. Although the resultant spectral fitting (using corner frequency and ??) is good, the residuals are consistent among the clusters analyzed. Their spectral shape is similar to the the theoretical one-dimensional response of a layered low-velocity structure in the valley (an absolute site response cannot be determined by this method, because of an ambiguity between absolute response and source spectral amplitudes). I show that even this subtle site response can significantly bias estimates of corner frequency and ??, if it is ignored in an inversion for only source and path effects. The multiple-EGF method presented in this paper is analogous to a joint inversion for source, path, and site effects; the use of colocated sets of earthquakes appears to offer significant advantages in improving resolution of all three estimates, especially if data are from a single site or sites with similar site response.

Grain size mapping in shallow rivers using spectral information: a lab spectroradiometry perspective

NASA Astrophysics Data System (ADS)

Niroumand-Jadidi, Milad; Vitti, Alfonso

2017-10-01

Every individual attribute of a riverine environment defines the overall spectral signature to be observed by an optical sensor. The spectral characteristic of riverbed is influenced not only by the type but also the roughness of substrates. Motivated by this assumption, potential of optical imagery for mapping grain size of shallow rivers (< 1 m deep) is examined in this research. The previous studies concerned with grain size mapping are all built upon the texture analysis of exposed bed material using very high resolution (i.e. cm resolution) imagery. However, the application of texturebased techniques is limited to very low altitude sensors (e.g. UAVs) to ensure the sufficient spatial resolution. Moreover, these techniques are applicable only in the presence of exposed substrates along the river channel. To address these drawbacks, this study examines the effectiveness of spectral information to make distinction among grain sizes for submerged substrates. Spectroscopic experiments are performed in controlled condition of a hydraulic lab. The spectra are collected over a water flume in a range of water depths and bottoms with several grain sizes. A spectral convolution is performed to match the spectra to WorldView-2 spectral bands. The material type of substrates is considered the same for all the experiments with only variable roughness/size of grains. The spectra observed over dry beds revealed that the brightness/reflectance increases with the grain size across all the spectral bands. Based on this finding, the above-water spectra over a river channel are simulated considering different grain sizes in the bottom. A water column correction method is then used to retrieve the bottom reflectances. Then the inferred bottom reflectances are clustered to segregate among grain sizes. The results indicate high potential of the spectral approach for clustering grain sizes (overall accuracy of 92%) which opens up some horizons for mapping this valuable attribute of rivers using remotely sensed data.

A semi-supervised classification algorithm using the TAD-derived background as training data

NASA Astrophysics Data System (ADS)

Fan, Lei; Ambeau, Brittany; Messinger, David W.

2013-05-01

In general, spectral image classification algorithms fall into one of two categories: supervised and unsupervised. In unsupervised approaches, the algorithm automatically identifies clusters in the data without a priori information about those clusters (except perhaps the expected number of them). Supervised approaches require an analyst to identify training data to learn the characteristics of the clusters such that they can then classify all other pixels into one of the pre-defined groups. The classification algorithm presented here is a semi-supervised approach based on the Topological Anomaly Detection (TAD) algorithm. The TAD algorithm defines background components based on a mutual k-Nearest Neighbor graph model of the data, along with a spectral connected components analysis. Here, the largest components produced by TAD are used as regions of interest (ROI's),or training data for a supervised classification scheme. By combining those ROI's with a Gaussian Maximum Likelihood (GML) or a Minimum Distance to the Mean (MDM) algorithm, we are able to achieve a semi supervised classification method. We test this classification algorithm against data collected by the HyMAP sensor over the Cooke City, MT area and University of Pavia scene.

Far-infrared image restoration analysis of the protostellar cluster in S140

NASA Technical Reports Server (NTRS)

Lester, D. F.; Harvey, P. M.; Joy, M.; Ellis, H. B., Jr.

1986-01-01

Image restoration techniques are applied to one-dimensional scans at 50 and 100 microns of the protostellar cluster in S140. These measurements resolve the surrounding nebula clearly, and Fourier methods are used to match the effective beam profiles at these wavelengths. This allows the radial distribution of temperature and dust column density to be derived at a diffraction limited spatial resolution of 23 arcsec (0.1 pc). Evidence for heating of the S140 molecular cloud by a nearby ionization front is established, and the dissociation of molecules inside the ionization front is spatially well correlated with the heating of the dust. The far-infrared spectral distribution of the three near-infrared sources within 10 arcsesc of the cluster center is presented.

Comparative analysis on the selection of number of clusters in community detection

NASA Astrophysics Data System (ADS)

Kawamoto, Tatsuro; Kabashima, Yoshiyuki

2018-02-01

We conduct a comparative analysis on various estimates of the number of clusters in community detection. An exhaustive comparison requires testing of all possible combinations of frameworks, algorithms, and assessment criteria. In this paper we focus on the framework based on a stochastic block model, and investigate the performance of greedy algorithms, statistical inference, and spectral methods. For the assessment criteria, we consider modularity, map equation, Bethe free energy, prediction errors, and isolated eigenvalues. From the analysis, the tendency of overfit and underfit that the assessment criteria and algorithms have becomes apparent. In addition, we propose that the alluvial diagram is a suitable tool to visualize statistical inference results and can be useful to determine the number of clusters.

Searches for 3.5 keV Absorption Features in Cluster AGN Spectra

NASA Astrophysics Data System (ADS)

Conlon, Joseph P.

2018-06-01

We investigate possible evidence for a spectral dip around 3.5 keV in central cluster AGNs, motivated by previous results for archival Chandra observations of the Perseus cluster and the general interest in novel spectral features around 3.5 keV that may arise from dark matter physics. We use two deep Chandra observations of the Perseus and Virgo clusters that have recently been made public. In both cases, mild improvements in the fit (Δχ2 = 4.2 and Δχ2 = 2.5) are found by including such a dip at 3.5 keV into the spectrum. A comparable result (Δχ2 = 6.5) is found re-analysing archival on-axis Chandra ACIS-S observations of the centre of the Perseus cluster.

Toda Systems, Cluster Characters, and Spectral Networks

NASA Astrophysics Data System (ADS)

Williams, Harold

2016-11-01

We show that the Hamiltonians of the open relativistic Toda system are elements of the generic basis of a cluster algebra, and in particular are cluster characters of nonrigid representations of a quiver with potential. Using cluster coordinates defined via spectral networks, we identify the phase space of this system with the wild character variety related to the periodic nonrelativistic Toda system by the wild nonabelian Hodge correspondence. We show that this identification takes the relativistic Toda Hamiltonians to traces of holonomies around a simple closed curve. In particular, this provides nontrivial examples of cluster coordinates on SL n -character varieties for n > 2 where canonical functions associated to simple closed curves can be computed in terms of quivers with potential, extending known results in the SL 2 case.

SPT-GMOS: A GEMINI/GMOS-SOUTH SPECTROSCOPIC SURVEY OF GALAXY CLUSTERS IN THE SPT-SZ SURVEY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bayliss, M. B.; Ruel, J.; Stubbs, C. W.

We present the results of SPT-GMOS, a spectroscopic survey with the Gemini Multi-Object Spectrograph (GMOS) on Gemini South. The targets of SPT-GMOS are galaxy clusters identified in the SPT-SZ survey, a millimeter-wave survey of 2500 deg{sup 2} of the southern sky using the South Pole Telescope (SPT). Multi-object spectroscopic observations of 62 SPT-selected galaxy clusters were performed between 2011 January and 2015 December, yielding spectra with radial velocity measurements for 2595 sources. We identify 2243 of these sources as galaxies, and 352 as stars. Of the galaxies, we identify 1579 as members of SPT-SZ galaxy clusters. The primary goal ofmore » these observations was to obtain spectra of cluster member galaxies to estimate cluster redshifts and velocity dispersions. We describe the full spectroscopic data set and resulting data products, including galaxy redshifts, cluster redshifts, and velocity dispersions, and measurements of several well-known spectral indices for each galaxy: the equivalent width, W , of [O ii] λλ 3727, 3729 and H- δ , and the 4000 Å break strength, D4000. We use the spectral indices to classify galaxies by spectral type (i.e., passive, post-starburst, star-forming), and we match the spectra against photometric catalogs to characterize spectroscopically observed cluster members as a function of brightness (relative to m {sup ⋆}). Finally, we report several new measurements of redshifts for ten bright, strongly lensed background galaxies in the cores of eight galaxy clusters. Combining the SPT-GMOS data set with previous spectroscopic follow-up of SPT-SZ galaxy clusters results in spectroscopic measurements for >100 clusters, or ∼20% of the full SPT-SZ sample.« less

SPT-GMOS: A Gemini/GMOS-South Spectroscopic survey of galaxy clusters in the SPT-SZ survey

DOE PAGES

Bayliss, M. B.; Ruel, J.; Stubbs, C. W.; ...

2016-11-01

Here, we present the results of SPT-GMOS, a spectroscopic survey with the Gemini Multi-Object Spectrograph (GMOS) on Gemini South. The targets of SPT-GMOS are galaxy clusters identified in the SPT-SZ survey, a millimeter-wave survey of 2500 deg 2 of the southern sky using the South Pole Telescope (SPT). Multi-object spectroscopic observations of 62 SPT-selected galaxy clusters were performed between 2011 January and 2015 December, yielding spectra with radial velocity measurements for 2595 sources. We identify 2243 of these sources as galaxies, and 352 as stars. Of the galaxies, we identify 1579 as members of SPT-SZ galaxy clusters. The primary goalmore » of these observations was to obtain spectra of cluster member galaxies to estimate cluster redshifts and velocity dispersions. We describe the full spectroscopic data set and resulting data products, including galaxy redshifts, cluster redshifts, and velocity dispersions, and measurements of several well-known spectral indices for each galaxy: the equivalent width, W, of [O II] λλ3727, 3729 and H-δ, and the 4000 Å break strength, D4000. We use the spectral indices to classify galaxies by spectral type (i.e., passive, post-starburst, star-forming), and we match the spectra against photometric catalogs to characterize spectroscopically observed cluster members as a function of brightness (relative to m*). Lastly, we report several new measurements of redshifts for ten bright, strongly lensed background galaxies in the cores of eight galaxy clusters. Combining the SPT-GMOS data set with previous spectroscopic follow-up of SPT-SZ galaxy clusters results in spectroscopic measurements for >100 clusters, or ~20% of the full SPT-SZ sample.« less

SPT-GMOS: A Gemini/GMOS-South Spectroscopic survey of galaxy clusters in the SPT-SZ survey

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bayliss, M. B.; Ruel, J.; Stubbs, C. W.

Here, we present the results of SPT-GMOS, a spectroscopic survey with the Gemini Multi-Object Spectrograph (GMOS) on Gemini South. The targets of SPT-GMOS are galaxy clusters identified in the SPT-SZ survey, a millimeter-wave survey of 2500 deg 2 of the southern sky using the South Pole Telescope (SPT). Multi-object spectroscopic observations of 62 SPT-selected galaxy clusters were performed between 2011 January and 2015 December, yielding spectra with radial velocity measurements for 2595 sources. We identify 2243 of these sources as galaxies, and 352 as stars. Of the galaxies, we identify 1579 as members of SPT-SZ galaxy clusters. The primary goalmore » of these observations was to obtain spectra of cluster member galaxies to estimate cluster redshifts and velocity dispersions. We describe the full spectroscopic data set and resulting data products, including galaxy redshifts, cluster redshifts, and velocity dispersions, and measurements of several well-known spectral indices for each galaxy: the equivalent width, W, of [O II] λλ3727, 3729 and H-δ, and the 4000 Å break strength, D4000. We use the spectral indices to classify galaxies by spectral type (i.e., passive, post-starburst, star-forming), and we match the spectra against photometric catalogs to characterize spectroscopically observed cluster members as a function of brightness (relative to m*). Lastly, we report several new measurements of redshifts for ten bright, strongly lensed background galaxies in the cores of eight galaxy clusters. Combining the SPT-GMOS data set with previous spectroscopic follow-up of SPT-SZ galaxy clusters results in spectroscopic measurements for >100 clusters, or ~20% of the full SPT-SZ sample.« less

SPT-GMOS: A Gemini/GMOS-South Spectroscopic Survey of Galaxy Clusters in the SPT-SZ Survey

NASA Astrophysics Data System (ADS)

Bayliss, M. B.; Ruel, J.; Stubbs, C. W.; Allen, S. W.; Applegate, D. E.; Ashby, M. L. N.; Bautz, M.; Benson, B. A.; Bleem, L. E.; Bocquet, S.; Brodwin, M.; Capasso, R.; Carlstrom, J. E.; Chang, C. L.; Chiu, I.; Cho, H.-M.; Clocchiatti, A.; Crawford, T. M.; Crites, A. T.; de Haan, T.; Desai, S.; Dietrich, J. P.; Dobbs, M. A.; Doucouliagos, A. N.; Foley, R. J.; Forman, W. R.; Garmire, G. P.; George, E. M.; Gladders, M. D.; Gonzalez, A. H.; Gupta, N.; Halverson, N. W.; Hlavacek-Larrondo, J.; Hoekstra, H.; Holder, G. P.; Holzapfel, W. L.; Hou, Z.; Hrubes, J. D.; Huang, N.; Jones, C.; Keisler, R.; Knox, L.; Lee, A. T.; Leitch, E. M.; von der Linden, A.; Luong-Van, D.; Mantz, A.; Marrone, D. P.; McDonald, M.; McMahon, J. J.; Meyer, S. S.; Mocanu, L. M.; Mohr, J. J.; Murray, S. S.; Padin, S.; Pryke, C.; Rapetti, D.; Reichardt, C. L.; Rest, A.; Ruhl, J. E.; Saliwanchik, B. R.; Saro, A.; Sayre, J. T.; Schaffer, K. K.; Schrabback, T.; Shirokoff, E.; Song, J.; Spieler, H. G.; Stalder, B.; Stanford, S. A.; Staniszewski, Z.; Stark, A. A.; Story, K. T.; Vanderlinde, K.; Vieira, J. D.; Vikhlinin, A.; Williamson, R.; Zenteno, A.

2016-11-01

We present the results of SPT-GMOS, a spectroscopic survey with the Gemini Multi-Object Spectrograph (GMOS) on Gemini South. The targets of SPT-GMOS are galaxy clusters identified in the SPT-SZ survey, a millimeter-wave survey of 2500 deg2 of the southern sky using the South Pole Telescope (SPT). Multi-object spectroscopic observations of 62 SPT-selected galaxy clusters were performed between 2011 January and 2015 December, yielding spectra with radial velocity measurements for 2595 sources. We identify 2243 of these sources as galaxies, and 352 as stars. Of the galaxies, we identify 1579 as members of SPT-SZ galaxy clusters. The primary goal of these observations was to obtain spectra of cluster member galaxies to estimate cluster redshifts and velocity dispersions. We describe the full spectroscopic data set and resulting data products, including galaxy redshifts, cluster redshifts, and velocity dispersions, and measurements of several well-known spectral indices for each galaxy: the equivalent width, W, of [O II] λλ3727, 3729 and H-δ, and the 4000 Å break strength, D4000. We use the spectral indices to classify galaxies by spectral type (i.e., passive, post-starburst, star-forming), and we match the spectra against photometric catalogs to characterize spectroscopically observed cluster members as a function of brightness (relative to m⋆). Finally, we report several new measurements of redshifts for ten bright, strongly lensed background galaxies in the cores of eight galaxy clusters. Combining the SPT-GMOS data set with previous spectroscopic follow-up of SPT-SZ galaxy clusters results in spectroscopic measurements for >100 clusters, or ∼20% of the full SPT-SZ sample.

CHEERS: The chemical evolution RGS sample

NASA Astrophysics Data System (ADS)

de Plaa, J.; Kaastra, J. S.; Werner, N.; Pinto, C.; Kosec, P.; Zhang, Y.-Y.; Mernier, F.; Lovisari, L.; Akamatsu, H.; Schellenberger, G.; Hofmann, F.; Reiprich, T. H.; Finoguenov, A.; Ahoranta, J.; Sanders, J. S.; Fabian, A. C.; Pols, O.; Simionescu, A.; Vink, J.; Böhringer, H.

2017-11-01

Context. The chemical yields of supernovae and the metal enrichment of the intra-cluster medium (ICM) are not well understood. The hot gas in clusters of galaxies has been enriched with metals originating from billions of supernovae and provides a fair sample of large-scale metal enrichment in the Universe. High-resolution X-ray spectra of clusters of galaxies provide a unique way of measuring abundances in the hot intracluster medium (ICM). The abundance measurements can provide constraints on the supernova explosion mechanism and the initial-mass function of the stellar population. This paper introduces the CHEmical Enrichment RGS Sample (CHEERS), which is a sample of 44 bright local giant ellipticals, groups, and clusters of galaxies observed with XMM-Newton. Aims: The CHEERS project aims to provide the most accurate set of cluster abundances measured in X-rays using this sample. This paper focuses specifically on the abundance measurements of O and Fe using the reflection grating spectrometer (RGS) on board XMM-Newton. We aim to thoroughly discuss the cluster to cluster abundance variations and the robustness of the measurements. Methods: We have selected the CHEERS sample such that the oxygen abundance in each cluster is detected at a level of at least 5σ in the RGS. The dispersive nature of the RGS limits the sample to clusters with sharp surface brightness peaks. The deep exposures and the size of the sample allow us to quantify the intrinsic scatter and the systematic uncertainties in the abundances using spectral modeling techniques. Results: We report the oxygen and iron abundances as measured with RGS in the core regions of all 44 clusters in the sample. We do not find a significant trend of O/Fe as a function of cluster temperature, but we do find an intrinsic scatter in the O and Fe abundances from cluster to cluster. The level of systematic uncertainties in the O/Fe ratio is estimated to be around 20-30%, while the systematic uncertainties in the absolute O and Fe abundances can be as high as 50% in extreme cases. Thanks to the high statistics of the observations, we were able to identify and correct a systematic bias in the oxygen abundance determination that was due to an inaccuracy in the spectral model. Conclusions: The lack of dependence of O/Fe on temperature suggests that the enrichment of the ICM does not depend on cluster mass and that most of the enrichment likely took place before the ICM was formed. We find that the observed scatter in the O/Fe ratio is due to a combination of intrinsic scatter in the source and systematic uncertainties in the spectral fitting, which we are unable to separate. The astrophysical source of intrinsic scatter could be due to differences in active galactic nucleus activity and ongoing star formation in the brightest cluster galaxy. The systematic scatter is due to uncertainties in the spatial line broadening, absorption column, multi-temperature structure, and the thermal plasma models.

INFRARED OBSERVATIONAL MANIFESTATIONS OF YOUNG DUSTY SUPER STAR CLUSTERS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martínez-González, Sergio; Tenorio-Tagle, Guillermo; Silich, Sergiy, E-mail: sergiomtz@inaoep.mx

The growing evidence pointing at core-collapse supernovae as large dust producers makes young massive stellar clusters ideal laboratories to study the evolution of dust immersed in a hot plasma. Here we address the stochastic injection of dust by supernovae, and follow its evolution due to thermal sputtering within the hot and dense plasma generated by young stellar clusters. Under these considerations, dust grains are heated by means of random collisions with gas particles which result in the appearance of  infrared spectral signatures. We present time-dependent infrared spectral energy distributions that are to be expected from young stellar clusters. Our results aremore » based on hydrodynamic calculations that account for the stochastic injection of dust by supernovae. These also consider gas and dust radiative cooling, stochastic dust temperature fluctuations, the exit of dust grains out of the cluster volume due to the cluster wind, and a time-dependent grain size distribution.« less

2D evaluation of spectral LIBS data derived from heterogeneous materials using cluster algorithm

NASA Astrophysics Data System (ADS)

Gottlieb, C.; Millar, S.; Grothe, S.; Wilsch, G.

2017-08-01

Laser-induced Breakdown Spectroscopy (LIBS) is capable of providing spatially resolved element maps in regard to the chemical composition of the sample. The evaluation of heterogeneous materials is often a challenging task, especially in the case of phase boundaries. In order to determine information about a certain phase of a material, the need for a method that offers an objective evaluation is necessary. This paper will introduce a cluster algorithm in the case of heterogeneous building materials (concrete) to separate the spectral information of non-relevant aggregates and cement matrix. In civil engineering, the information about the quantitative ingress of harmful species like Cl-, Na+ and SO42- is of great interest in the evaluation of the remaining lifetime of structures (Millar et al., 2015; Wilsch et al., 2005). These species trigger different damage processes such as the alkali-silica reaction (ASR) or the chloride-induced corrosion of the reinforcement. Therefore, a discrimination between the different phases, mainly cement matrix and aggregates, is highly important (Weritz et al., 2006). For the 2D evaluation, the expectation-maximization-algorithm (EM algorithm; Ester and Sander, 2000) has been tested for the application presented in this work. The method has been introduced and different figures of merit have been presented according to recommendations given in Haddad et al. (2014). Advantages of this method will be highlighted. After phase separation, non-relevant information can be excluded and only the wanted phase displayed. Using a set of samples with known and unknown composition, the EM-clustering method has been validated regarding to Gustavo González and Ángeles Herrador (2007).

VLA Radio Observations of the HST Frontier Fields Cluster Abell 2744: The Discovery of New Radio Relics

NASA Astrophysics Data System (ADS)

Pearce, C. J. J.; van Weeren, R. J.; Andrade-Santos, F.; Jones, C.; Forman, W. R.; Brüggen, M.; Bulbul, E.; Clarke, T. E.; Kraft, R. P.; Medezinski, E.; Mroczkowski, T.; Nonino, M.; Nulsen, P. E. J.; Randall, S. W.; Umetsu, K.

2017-08-01

Cluster mergers leave distinct signatures in the intracluster medium (ICM) in the form of shocks and diffuse cluster radio sources that provide evidence for the acceleration of relativistic particles. However, the physics of particle acceleration in the ICM is still not fully understood. Here we present new 1-4 GHz Jansky Very Large Array (VLA) and archival Chandra observations of the HST Frontier Fields Cluster Abell 2744. In our new VLA images, we detect the previously known ˜2.1 Mpc radio halo and ˜1.5 Mpc radio relic. We carry out a radio spectral analysis from which we determine the relic’s injection spectral index to be {α }{inj}=-1.12+/- 0.19. This corresponds to a shock Mach number of { M }={2.05}-0.19+0.31 under the assumption of diffusive shock acceleration. We also find evidence for spectral steepening in the post-shock region. We do not find evidence for a significant correlation between the radio halo’s spectral index and ICM temperature. In addition, we observe three new polarized diffuse sources and determine two of these to be newly discovered giant radio relics. These two relics are located in the southeastern and northwestern outskirts of the cluster. The corresponding integrated spectral indices measure -1.81 ± 0.26 and -0.63 ± 0.21 for the SE and NW relics, respectively. From an X-ray surface brightness profile we also detect a possible density jump of R={1.39}-0.22+0.34 co-located with the newly discovered SE relic. This density jump would correspond to a shock front Mach number of { M }={1.26}-0.15+0.25.

Resolving anthropogenic aerosol pollution types - deconvolution and exploratory classification of pollution events

NASA Astrophysics Data System (ADS)

Äijälä, Mikko; Heikkinen, Liine; Fröhlich, Roman; Canonaco, Francesco; Prévôt, André S. H.; Junninen, Heikki; Petäjä, Tuukka; Kulmala, Markku; Worsnop, Douglas; Ehn, Mikael

2017-03-01

Mass spectrometric measurements commonly yield data on hundreds of variables over thousands of points in time. Refining and synthesizing this raw data into chemical information necessitates the use of advanced, statistics-based data analytical techniques. In the field of analytical aerosol chemistry, statistical, dimensionality reductive methods have become widespread in the last decade, yet comparable advanced chemometric techniques for data classification and identification remain marginal. Here we present an example of combining data dimensionality reduction (factorization) with exploratory classification (clustering), and show that the results cannot only reproduce and corroborate earlier findings, but also complement and broaden our current perspectives on aerosol chemical classification. We find that applying positive matrix factorization to extract spectral characteristics of the organic component of air pollution plumes, together with an unsupervised clustering algorithm, k-means+ + , for classification, reproduces classical organic aerosol speciation schemes. Applying appropriately chosen metrics for spectral dissimilarity along with optimized data weighting, the source-specific pollution characteristics can be statistically resolved even for spectrally very similar aerosol types, such as different combustion-related anthropogenic aerosol species and atmospheric aerosols with similar degree of oxidation. In addition to the typical oxidation level and source-driven aerosol classification, we were also able to classify and characterize outlier groups that would likely be disregarded in a more conventional analysis. Evaluating solution quality for the classification also provides means to assess the performance of mass spectral similarity metrics and optimize weighting for mass spectral variables. This facilitates algorithm-based evaluation of aerosol spectra, which may prove invaluable for future development of automatic methods for spectra identification and classification. Robust, statistics-based results and data visualizations also provide important clues to a human analyst on the existence and chemical interpretation of data structures. Applying these methods to a test set of data, aerosol mass spectrometric data of organic aerosol from a boreal forest site, yielded five to seven different recurring pollution types from various sources, including traffic, cooking, biomass burning and nearby sawmills. Additionally, three distinct, minor pollution types were discovered and identified as amine-dominated aerosols.

Comparison and Evaluation of Clustering Algorithms for Tandem Mass Spectra.

PubMed

Rieder, Vera; Schork, Karin U; Kerschke, Laura; Blank-Landeshammer, Bernhard; Sickmann, Albert; Rahnenführer, Jörg

2017-11-03

In proteomics, liquid chromatography-tandem mass spectrometry (LC-MS/MS) is established for identifying peptides and proteins. Duplicated spectra, that is, multiple spectra of the same peptide, occur both in single MS/MS runs and in large spectral libraries. Clustering tandem mass spectra is used to find consensus spectra, with manifold applications. First, it speeds up database searches, as performed for instance by Mascot. Second, it helps to identify novel peptides across species. Third, it is used for quality control to detect wrongly annotated spectra. We compare different clustering algorithms based on the cosine distance between spectra. CAST, MS-Cluster, and PRIDE Cluster are popular algorithms to cluster tandem mass spectra. We add well-known algorithms for large data sets, hierarchical clustering, DBSCAN, and connected components of a graph, as well as the new method N-Cluster. All algorithms are evaluated on real data with varied parameter settings. Cluster results are compared with each other and with peptide annotations based on validation measures such as purity. Quality control, regarding the detection of wrongly (un)annotated spectra, is discussed for exemplary resulting clusters. N-Cluster proves to be highly competitive. All clustering results benefit from the so-called DISMS2 filter that integrates additional information, for example, on precursor mass.

Detailed studies om three open clusters from Gaia ESO Survey (GES)

NASA Astrophysics Data System (ADS)

Balaguer-Núnez, L.; Casamiquela, L.; Jordana, N.; Massana, P.; Jordi, C.; Masana, E.

2017-03-01

We present results for the intermediate-age and old open clusters NGC 6633, NGC 6705 (M 11) and NGC 2682 (M 67). We have used new Str ̈omgren-Crawford photometry, proper motions from ROA observations and spectral information from Gaia-ESO Survey (GES), to study the physical parameters of the stars in the three cluster's areas. The astrometric studies cover an area of about 1°x2° and down to r' ˜ 17 while our INT-WFC CCD intermediate-band photometry covers an area of about 40'x40' down to V ˜ 19. The stars of those areas selected as cluster members from their proper motions, are classified into photometric regions and their physical parameters determined, using uvbyHβ photometry and standard relations among colour indices for each of the photometric regions of the HR diagram. That allows us to determine reddening, distances, absolute magnitudes, spectral types, effective temperatures, gravities and metallicities, thus providing an astrophysical characterization of the clusters. These results are compared with the physical parameters obtained from GES spectral data as well as radial velocities to confirm membership. All these data lead us to a comparison of photometric and spectroscopic physical parameters.

Adaptive coding of MSS imagery. [Multi Spectral band Scanners

NASA Technical Reports Server (NTRS)

Habibi, A.; Samulon, A. S.; Fultz, G. L.; Lumb, D.

1977-01-01

A number of adaptive data compression techniques are considered for reducing the bandwidth of multispectral data. They include adaptive transform coding, adaptive DPCM, adaptive cluster coding, and a hybrid method. The techniques are simulated and their performance in compressing the bandwidth of Landsat multispectral images is evaluated and compared using signal-to-noise ratio and classification consistency as fidelity criteria.

Geological applications of machine learning on hyperspectral remote sensing data

NASA Astrophysics Data System (ADS)

Tse, C. H.; Li, Yi-liang; Lam, Edmund Y.

2015-02-01

The CRISM imaging spectrometer orbiting Mars has been producing a vast amount of data in the visible to infrared wavelengths in the form of hyperspectral data cubes. These data, compared with those obtained from previous remote sensing techniques, yield an unprecedented level of detailed spectral resolution in additional to an ever increasing level of spatial information. A major challenge brought about by the data is the burden of processing and interpreting these datasets and extract the relevant information from it. This research aims at approaching the challenge by exploring machine learning methods especially unsupervised learning to achieve cluster density estimation and classification, and ultimately devising an efficient means leading to identification of minerals. A set of software tools have been constructed by Python to access and experiment with CRISM hyperspectral cubes selected from two specific Mars locations. A machine learning pipeline is proposed and unsupervised learning methods were implemented onto pre-processed datasets. The resulting data clusters are compared with the published ASTER spectral library and browse data products from the Planetary Data System (PDS). The result demonstrated that this approach is capable of processing the huge amount of hyperspectral data and potentially providing guidance to scientists for more detailed studies.

Recursive Hierarchical Image Segmentation by Region Growing and Constrained Spectral Clustering

NASA Technical Reports Server (NTRS)

Tilton, James C.

2002-01-01

This paper describes an algorithm for hierarchical image segmentation (referred to as HSEG) and its recursive formulation (referred to as RHSEG). The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HS WO) approach to region growing, which seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing. In addition, HSEG optionally interjects between HSWO region growing iterations merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the segmentation results, especially for larger images, it also significantly increases HSEG's computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) has been devised and is described herein. Included in this description is special code that is required to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. Implementations for single processor and for multiple processor computer systems are described. Results with Landsat TM data are included comparing HSEG with classic region growing. Finally, an application to image information mining and knowledge discovery is discussed.

Data Clustering

NASA Astrophysics Data System (ADS)

Wagstaff, Kiri L.

2012-03-01

On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained clustering, in which some partial information about item assignments or other components of the resulting output are already known and must be accommodated by the solution. Some algorithms seek a partition of the data set into distinct clusters, while others build a hierarchy of nested clusters that can capture taxonomic relationships. Some produce a single optimal solution, while others construct a probabilistic model of cluster membership. More formally, clustering algorithms operate on a data set X composed of items represented by one or more features (dimensions). These could include physical location, such as right ascension and declination, as well as other properties such as brightness, color, temporal change, size, texture, and so on. Let D be the number of dimensions used to represent each item, xi ∈ RD. The clustering goal is to produce an organization P of the items in X that optimizes an objective function f : P -> R, which quantifies the quality of solution P. Often f is defined so as to maximize similarity within a cluster and minimize similarity between clusters. To that end, many algorithms make use of a measure d : X x X -> R of the distance between two items. A partitioning algorithm produces a set of clusters P = {c1, . . . , ck} such that the clusters are nonoverlapping (c_i intersected with c_j = empty set, i != j) subsets of the data set (Union_i c_i=X). Hierarchical algorithms produce a series of partitions P = {p1, . . . , pn }. For a complete hierarchy, the number of partitions n’= n, the number of items in the data set; the top partition is a single cluster containing all items, and the bottom partition contains n clusters, each containing a single item. For model-based clustering, each cluster c_j is represented by a model m_j , such as the cluster center or a Gaussian distribution. The wide array of available clustering algorithms may seem bewildering, and covering all of them is beyond the scope of this chapter. Choosing among them for a particular application involves considerations of the kind of data being analyzed, algorithm runtime efficiency, and how much prior knowledge is available about the problem domain, which can dictate the nature of clusters sought. Fundamentally, the clustering method and its representations of clusters carries with it a definition of what a cluster is, and it is important that this be aligned with the analysis goals for the problem at hand. In this chapter, I emphasize this point by identifying for each algorithm the cluster representation as a model, m_j , even for algorithms that are not typically thought of as creating a “model.” This chapter surveys a basic collection of clustering methods useful to any practitioner who is interested in applying clustering to a new data set. The algorithms include k-means (Section 25.2), EM (Section 25.3), agglomerative (Section 25.4), and spectral (Section 25.5) clustering, with side mentions of variants such as kernel k-means and divisive clustering. The chapter also discusses each algorithm’s strengths and limitations and provides pointers to additional in-depth reading for each subject. Section 25.6 discusses methods for incorporating domain knowledge into the clustering process. This chapter concludes with a brief survey of interesting applications of clustering methods to astronomy data (Section 25.7). The chapter begins with k-means because it is both generally accessible and so widely used that understanding it can be considered a necessary prerequisite for further work in the field. EM can be viewed as a more sophisticated version of k-means that uses a generative model for each cluster and probabilistic item assignments. Agglomerative clustering is the most basic form of hierarchical clustering and provides a basis for further exploration of algorithms in that vein. Spectral clustering permits a departure from feature-vector-based clustering and can operate on data sets instead represented as affinity, or similarity matrices—cases in which only pairwise information is known. The list of algorithms covered in this chapter is representative of those most commonly in use, but it is by no means comprehensive. There is an extensive collection of existing books on clustering that provide additional background and depth. Three early books that remain useful today are Anderberg’s Cluster Analysis for Applications [3], Hartigan’s Clustering Algorithms [25], and Gordon’s Classification [22]. The latter covers basics on similarity measures, partitioning and hierarchical algorithms, fuzzy clustering, overlapping clustering, conceptual clustering, validations methods, and visualization or data reduction techniques such as principal components analysis (PCA),multidimensional scaling, and self-organizing maps. More recently, Jain et al. provided a useful and informative survey [27] of a variety of different clustering algorithms, including those mentioned here as well as fuzzy, graph-theoretic, and evolutionary clustering. Everitt’s Cluster Analysis [19] provides a modern overview of algorithms, similarity measures, and evaluation methods.

Methods of editing cloud and atmospheric layer affected pixels from satellite data

NASA Technical Reports Server (NTRS)

Nixon, P. R. (Principal Investigator); Wiegand, C. L.; Richardson, A. J.; Johnson, M. P.

1982-01-01

Practical methods of computer screening cloud-contaminated pixels from data of various satellite systems are proposed. Examples are given of the location of clouds and representative landscape features in HCMM spectral space of reflectance (VIS) vs emission (IR). Methods of screening out cloud affected HCMM are discussed. The character of subvisible absorbing-emitting atmospheric layers (subvisible cirrus or SCi) in HCMM data is considered and radiosonde soundings are examined in relation to the presence of SCi. The statistical characteristics of multispectral meteorological satellite data in clear and SCi affected areas are discussed. Examples in TIROS-N and NOAA-7 data from several states and Mexico are presented. The VIS-IR cluster screening method for removing clouds is applied to a 262, 144 pixel HCMM scene from south Texas and northeast Mexico. The SCi that remain after cluster screening are sited out by applying a statistically determined IR limit.

Different mechanisms of cluster explosion within a unified smooth particle hydrodynamics Thomas-Fermi approach: Optical and short-wavelength regimes compared

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rusek, Marian; Orlowski, Arkadiusz

2005-04-01

The dynamics of small ({<=}55 atoms) argon clusters ionized by an intense femtosecond laser pulse is studied using a time-dependent Thomas-Fermi model. The resulting Bloch-like hydrodynamic equations are solved numerically using the smooth particle hydrodynamics method without the necessity of grid simulations. As follows from recent experiments, absorption of radiation and subsequent ionization of clusters observed in the short-wavelength laser frequency regime (98 nm) differs considerably from that in the optical spectral range (800 nm). Our theoretical approach provides a unified framework for treating these very different frequency regimes and allows for a deeper understanding of the underlying cluster explosionmore » mechanisms. The results of our analysis following from extensive numerical simulations presented in this paper are compared both with experimental findings and with predictions of other theoretical models.« less

Efficient similarity-based data clustering by optimal object to cluster reallocation.

PubMed

Rossignol, Mathias; Lagrange, Mathieu; Cont, Arshia

2018-01-01

We present an iterative flat hard clustering algorithm designed to operate on arbitrary similarity matrices, with the only constraint that these matrices be symmetrical. Although functionally very close to kernel k-means, our proposal performs a maximization of average intra-class similarity, instead of a squared distance minimization, in order to remain closer to the semantics of similarities. We show that this approach permits the relaxing of some conditions on usable affinity matrices like semi-positiveness, as well as opening possibilities for computational optimization required for large datasets. Systematic evaluation on a variety of data sets shows that compared with kernel k-means and the spectral clustering methods, the proposed approach gives equivalent or better performance, while running much faster. Most notably, it significantly reduces memory access, which makes it a good choice for large data collections. Material enabling the reproducibility of the results is made available online.

Structure of fluorescent metal clusters on a DNA template.

NASA Astrophysics Data System (ADS)

Vdovichev, A. A.; Sych, T. S.; Reveguk, Z. V.; Smirnova, A. A.; Maksimov, D. A.; Ramazanov, R. R.; Kononov, A. I.

2016-08-01

Luminescent metal clusters are a subject of growing interest in recent years due to their bright emission from visible to near infrared range. Detailed structure of the fluorescent complexes of Ag and other metal clusters with ligands still remains a challenging task. In this joint experimental and theoretical study we synthesized Ag-DNA complexes on a DNA oligonucleotide emitting in violet- green spectral range. The structure of DNA template was determined by means of various spectral measurements (CD, MS, XPS). Comparison of the experimental fluorescent excitation spectra and calculated absorption spectra for different QM/MM optimized structures allowed us to determine the detailed structure of the green cluster containing three silver atoms in the stem of the DNA hairpin structure stabilized by cytosine-Ag+-cytosine bonds.

Time-Frequency Analysis And Pattern Recognition Using Singular Value Decomposition Of The Wigner-Ville Distribution

NASA Astrophysics Data System (ADS)

Boashash, Boualem; Lovell, Brian; White, Langford

1988-01-01

Time-Frequency analysis based on the Wigner-Ville Distribution (WVD) is shown to be optimal for a class of signals where the variation of instantaneous frequency is the dominant characteristic. Spectral resolution and instantaneous frequency tracking is substantially improved by using a Modified WVD (MWVD) based on an Autoregressive spectral estimator. Enhanced signal-to-noise ratio may be achieved by using 2D windowing in the Time-Frequency domain. The WVD provides a tool for deriving descriptors of signals which highlight their FM characteristics. These descriptors may be used for pattern recognition and data clustering using the methods presented in this paper.

MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ghose, Soumya, E-mail: soumya.ghose@case.edu; Mitra, Jhimli; Rivest-Hénault, David

Purpose: The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Methods: Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contoursmore » were selected in a spectral clustering manifold learning framework. This aids in clustering “similar” gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. Results: A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). Conclusions: An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention.« less

A high-significance measurement of correlation between unresolved IRAS sources and optically-selected galaxy clusters

NASA Astrophysics Data System (ADS)

Hincks, Adam D.; Hajian, Amir; Addison, Graeme E.

2013-05-01

We cross-correlate the 100 μm Improved Reprocessing of the IRAS Survey (IRIS) map and galaxy clusters at 0.1 < z < 0.3 in the maxBCG catalogue taken from the Sloan Digital Sky Survey, measuring an angular cross-power spectrum over multipole moments 150 < l < 3000 at a total significance of over 40σ. The cross-spectrum, which arises from the spatial correlation between unresolved dusty galaxies that make up the cosmic infrared background (CIB) in the IRIS map and the galaxy clusters, is well-fit by a single power law with an index of -1.28±0.12, similar to the clustering of unresolved galaxies from cross-correlating far-infrared and submillimetre maps at longer wavelengths. Using a recent, phenomenological model for the spectral and clustering properties of the IRIS galaxies, we constrain the large-scale bias of the maxBCG clusters to be 2.6±1.4, consistent with existing analyses of the real-space cluster correlation function. The success of our method suggests that future CIB-optical cross-correlations using Planck and Herschel data will significantly improve our understanding of the clustering and redshift distribution of the faint CIB sources.

Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery

NASA Technical Reports Server (NTRS)

Tarabalka, Yuliya; Tilton, James C.; Benediktsson, Jon Atli; Chanussot, Jocelyn

2011-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.

Observations of rich clusters of galaxies at metre wavelengths

NASA Technical Reports Server (NTRS)

Cane, H. V.; Erickson, W. C.; Hanisch, R. J.; Turner, P. J.

1981-01-01

Observations have been made at 10 frequencies between 50 and 120 MHz of 17 rich, X-ray emitting clusters of galaxies with the 78 x 156 m dipole array al Llanherne. The observed flux densities were compared to the flux densities expected on the basis of the known discrete sources in the fields. In no case was a significant flux excess found that might have indicated the presence of a diffuse halo component of radio emission in the cluster. For those clusters in which spectral indices could be determined, the spectra all tend to be much steeper than is normal for extragalactic radio sources, although a strict correlation between the X-ray luminosity and the low-frequency radio luminosity or spectral index is not found. The occurrence of large halo sources such as that which is present in the Coma cluster seems to be quite unusual.

Wavelet-based clustering of resting state MRI data in the rat.

PubMed

Medda, Alessio; Hoffmann, Lukas; Magnuson, Matthew; Thompson, Garth; Pan, Wen-Ju; Keilholz, Shella

2016-01-01

While functional connectivity has typically been calculated over the entire length of the scan (5-10min), interest has been growing in dynamic analysis methods that can detect changes in connectivity on the order of cognitive processes (seconds). Previous work with sliding window correlation has shown that changes in functional connectivity can be observed on these time scales in the awake human and in anesthetized animals. This exciting advance creates a need for improved approaches to characterize dynamic functional networks in the brain. Previous studies were performed using sliding window analysis on regions of interest defined based on anatomy or obtained from traditional steady-state analysis methods. The parcellation of the brain may therefore be suboptimal, and the characteristics of the time-varying connectivity between regions are dependent upon the length of the sliding window chosen. This manuscript describes an algorithm based on wavelet decomposition that allows data-driven clustering of voxels into functional regions based on temporal and spectral properties. Previous work has shown that different networks have characteristic frequency fingerprints, and the use of wavelets ensures that both the frequency and the timing of the BOLD fluctuations are considered during the clustering process. The method was applied to resting state data acquired from anesthetized rats, and the resulting clusters agreed well with known anatomical areas. Clusters were highly reproducible across subjects. Wavelet cross-correlation values between clusters from a single scan were significantly higher than the values from randomly matched clusters that shared no temporal information, indicating that wavelet-based analysis is sensitive to the relationship between areas. Copyright © 2015 Elsevier Inc. All rights reserved.

Finding the patterns in complex specimens by improving the acquisition and analysis of x-ray spectromicroscopy data

NASA Astrophysics Data System (ADS)

Lerotic, Mirna

Soft x-ray spectromicroscopy provides spectral data on the chemical speciation of light elements at sub-100 nanometer spatial resolution. The high resolution imaging places a strong demand on the microscope stability and on the reproducibility of the scanned image field, and the volume of data necessitates the need for improved data analysis methods. This dissertation concerns two developments in extending the capability of soft x-ray transmission microscopes to carry out studies of chemical speciation at high spatial resolution. One development involves an improvement in x-ray microscope instrumentation: a new Stony Brook scanning transmission x-ray microscope which incorporates laser interferometer feedback in scanning stage positions. The interferometer is used to control the position between the sample and focusing optics, and thus improve the stability of the system. A second development concerns new analysis methods for the study of chemical speciation of complex specimens, such as those in biological and environmental science studies. When all chemical species in a specimen are known and separately characterized, existing approaches can be used to measure the concentration of each component at each pixel. In other cases (such as often occur in biology or environmental science), where the specimen may be too complicated or provide at least some unknown spectral signatures, other approaches must be used. We describe here an approach that uses principal component analysis (similar to factor analysis) to orthogonalize and noise-filter spectromicroscopy data. We then use cluster analysis (a form of unsupervised pattern matching) to classify pixels according to spectral similarity, to extract representative, cluster-averaged spectra with good signal-to-noise ratio, and to obtain gradations of concentration of these representative spectra at each pixel. The method is illustrated with a simulated data set of organic compounds, and a mixture of lutetium in hematite used to understand colloidal transport properties of radionuclides. Also, we describe here an extension of that work employing an angle distance measure; this measure provides better classification based on spectral signatures alone in specimens with significant thickness variations. The method is illustrated using simulated data, and also to examine sporulation in the bacterium Clostridium sp.

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE Office of Scientific and Technical Information (OSTI.GOV)

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

Change detection and classification of land cover in multispectral satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries

DOE PAGES

Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...

2014-10-01

Neuromimetic machine vision and pattern recognition algorithms are of great interest for landscape characterization and change detection in satellite imagery in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methods to the environmental sciences, using adaptive sparse signal processing combined with machine learning. A Hebbian learning rule is used to build multispectral, multiresolution dictionaries from regional satellite normalized band difference index data. Land cover labels are automatically generated via our CoSA algorithm: Clustering of Sparse Approximations, using a clustering distance metric that combines spectral and spatial textural characteristics tomore » help separate geologic, vegetative, and hydrologie features. We demonstrate our method on example Worldview-2 satellite images of an Arctic region, and use CoSA labels to detect seasonal surface changes. In conclusion, our results suggest that neuroscience-based models are a promising approach to practical pattern recognition and change detection problems in remote sensing.« less

The DAFT/FADA survey. I. Photometric redshifts along lines of sight to clusters in the z = [0.4, 0.9] interval

NASA Astrophysics Data System (ADS)

Guennou, L.; Adami, C.; Ulmer, M. P.; Lebrun, V.; Durret, F.; Johnston, D.; Ilbert, O.; Clowe, D.; Gavazzi, R.; Murphy, K.; Schrabback, T.; Allam, S.; Annis, J.; Basa, S.; Benoist, C.; Biviano, A.; Cappi, A.; Kubo, J. M.; Marshall, P.; Mazure, A.; Rostagni, F.; Russeil, D.; Slezak, E.

2010-11-01

Context. As a contribution to the understanding of the dark energy concept, the Dark energy American French Team (DAFT, in French FADA) has started a large project to characterize statistically high redshift galaxy clusters, infer cosmological constraints from weak lensing tomography, and understand biases relevant for constraining dark energy and cluster physics in future cluster and cosmological experiments. Aims: The purpose of this paper is to establish the basis of reference for the photo-z determination used in all our subsequent papers, including weak lensing tomography studies. Methods: This project is based on a sample of 91 high redshift (z ≥ 0.4), massive (⪆3 × 1014 M_⊙) clusters with existing HST imaging, for which we are presently performing complementary multi-wavelength imaging. This allows us in particular to estimate spectral types and determine accurate photometric redshifts for galaxies along the lines of sight to the first ten clusters for which all the required data are available down to a limit of IAB = 24./24.5 with the LePhare software. The accuracy in redshift is of the order of 0.05 for the range 0.2 ≤ z ≤ 1.5. Results: We verified that the technique applied to obtain photometric redshifts works well by comparing our results to with previous works. In clusters, photo-z accuracy is degraded for bright absolute magnitudes and for the latest and earliest type galaxies. The photo-z accuracy also only slightly varies as a function of the spectral type for field galaxies. As a consequence, we find evidence for an environmental dependence of the photo-z accuracy, interpreted as the standard used spectral energy distributions being not very well suited to cluster galaxies. Finally, we modeled the LCDCS 0504 mass with the strong arcs detected along this line of sight. Based on observations made with the NASA/ESA Hubble Space Telescope, obtained from the data archive at the Space Telescope Institute and the Space Telescope European Coordinating Facility. STScI is operated by the association of Universities for Research in Astronomy, Inc. under the NASA contract NAS 5-26555. Also based on observations made with ESO Telescopes at Paranal and La Silla Observatories under programme ESO LP 166.A-0162. Also based on visiting astronomer observations, at Cerro Tololo Inter-American Observatory, National Optical Astronomy Observatory, which is operated by the Association of Universities for Research in Astronomy, under contract with the National Science Foundation.

Application of linear discriminant analysis and Attenuated Total Reflectance Fourier Transform Infrared microspectroscopy for diagnosis of colon cancer.

PubMed

Khanmohammadi, Mohammadreza; Bagheri Garmarudi, Amir; Samani, Simin; Ghasemi, Keyvan; Ashuri, Ahmad

2011-06-01

Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR) microspectroscopy was applied for detection of colon cancer according to the spectral features of colon tissues. Supervised classification models can be trained to identify the tissue type based on the spectroscopic fingerprint. A total of 78 colon tissues were used in spectroscopy studies. Major spectral differences were observed in 1,740-900 cm(-1) spectral region. Several chemometric methods such as analysis of variance (ANOVA), cluster analysis (CA) and linear discriminate analysis (LDA) were applied for classification of IR spectra. Utilizing the chemometric techniques, clear and reproducible differences were observed between the spectra of normal and cancer cases, suggesting that infrared microspectroscopy in conjunction with spectral data processing would be useful for diagnostic classification. Using LDA technique, the spectra were classified into cancer and normal tissue classes with an accuracy of 95.8%. The sensitivity and specificity was 100 and 93.1%, respectively.

3D tensor-based blind multispectral image decomposition for tumor demarcation

NASA Astrophysics Data System (ADS)

Kopriva, Ivica; Peršin, Antun

2010-03-01

Blind decomposition of multi-spectral fluorescent image for tumor demarcation is formulated exploiting tensorial structure of the image. First contribution of the paper is identification of the matrix of spectral responses and 3D tensor of spatial distributions of the materials present in the image from Tucker3 or PARAFAC models of 3D image tensor. Second contribution of the paper is clustering based estimation of the number of the materials present in the image as well as matrix of their spectral profiles. 3D tensor of the spatial distributions of the materials is recovered through 3-mode multiplication of the multi-spectral image tensor and inverse of the matrix of spectral profiles. Tensor representation of the multi-spectral image preserves its local spatial structure that is lost, due to vectorization process, when matrix factorization-based decomposition methods (such as non-negative matrix factorization and independent component analysis) are used. Superior performance of the tensor-based image decomposition over matrix factorization-based decompositions is demonstrated on experimental red-green-blue (RGB) image with known ground truth as well as on RGB fluorescent images of the skin tumor (basal cell carcinoma).

Integrated spectral study of small angular diameter galactic open clusters

NASA Astrophysics Data System (ADS)

Clariá, J. J.; Ahumada, A. V.; Bica, E.; Pavani, D. B.; Parisi, M. C.

2017-10-01

This paper presents flux-calibrated integrated spectra obtained at Complejo Astronómico El Leoncito (CASLEO, Argentina) for a sample of 9 Galactic open clusters of small angular diameter. The spectra cover the optical range (3800-6800 Å), with a resolution of ˜14 Å. With one exception (Ruprecht 158), the selected clusters are projected into the fourth Galactic quadrant (282o < l < 345o) near the Galactic plane (∣b∣ ≤ 9o). We performed simultaneous estimates of foreground interstellar reddening and age by comparing the continuum distribution and line strenghts of the cluster spectra with those of template cluster spectra with known parameters. We thus provide spectroscopic information independent from that derived through color-magnitude diagram studies. We found three clusters (Collinder 249, NGC 4463 and Ruprecht 122) younger than ˜40 Myr, four moderately young ones (BH 92, Harvard 5, Hogg 14 and Pismis 23) with ages within 200-400 Myr, and two intermediate-age ones (Ruprecht 158 and ESO 065-SC07) with ages within 1.0-2.2 Gyr. The derived foreground E(B - V) color excesses vary from around 0.0 in Ruprecht 158 to ˜1.1 in Pismis 23. In general terms, the results obtained show good agreement with previous photometric results. In Ruprecht 158 and BH 92, however, some differences are found between the parameters here obtained and previous values in the literature. Individual spectra of some comparatively bright stars located in the fields of 5 out of the 9 clusters here studied, allowed us to evaluate their membership status. The current cluster sample complements that of 46 open clusters previously studied by our group in an effort to gather a spectral library with several clusters per age bin. The cluster spectral library that we have been building is an important tool to tie studies of resolved and unresolved stellar content.

New tools for the tracing of ancient starbursts: Analysing globular cluster systems using Lick indices

NASA Astrophysics Data System (ADS)

Lilly, T.; Fritze-v. Alvensleben, U.; de Grijs, R.

2005-05-01

We present mathematically advanced tools for the determination of age, metallicity, and mass of old Globular Clusters (CGs) using both broad-band colors and spectral indices, and we present their application to the Globular Cluster Systems (GCSs) of elliptical galaxies. Since one of the most intriguing questions of today's astronomy aims at the evolutionary connection between (young) violently interacting galaxies at high-redshift and the (old) elliptical galaxies we observe nearby, it is necessary to reveal the possibly violent star-formation history of these old galaxies. By means of evolutionary synthesis models, we can show that, using the integrated light of a galaxy's (composite) stellar content alone, it is impossible to date (and, actually, to identify) even very strong starbursts if these events took place more than two or three Gyr ago. However, since large and violent starbursts are associated with the formation of GCs, GCSs are very good tracers of the most violent starburst events in the history of their host galaxies. Using our well-established Göttingen SED (Spectral Energy Distribution) analysis tool, we can reveal the age, metallicity, mass (and possibly extinction) of GCs by comparing the observations with an extensive grid of SSP model colors. This is done in a statistically advanced and reasonable way, including their 1 σ uncertainties. However, since for all colors the evolution slows down considerably at ages older than about 8 Gyr, even with several passbands and a long wavelength base line, the results are severely uncertain for old clusters. Therefore, we incorporated empirical calibrations for Lick indices in our models and developed a Lick indices analysis tool that works in the same way as the SED analysis tool described above. We compare the theoretical possibilities and limitations of both methods as well as their results for the example of the cD galaxy NGC 1399, for which both multi-color observations and, for a subsample of clusters, spectral indices are available, and address implications for the nature and origin of the observed bimodal color distribution.

Model for spectral and chromatographic data

DOEpatents

Jarman, Kristin [Richland, WA; Willse, Alan [Richland, WA; Wahl, Karen [Richland, WA; Wahl, Jon [Richland, WA

2002-11-26

A method and apparatus using a spectral analysis technique are disclosed. In one form of the invention, probabilities are selected to characterize the presence (and in another form, also a quantification of a characteristic) of peaks in an indexed data set for samples that match a reference species, and other probabilities are selected for samples that do not match the reference species. An indexed data set is acquired for a sample, and a determination is made according to techniques exemplified herein as to whether the sample matches or does not match the reference species. When quantification of peak characteristics is undertaken, the model is appropriately expanded, and the analysis accounts for the characteristic model and data. Further techniques are provided to apply the methods and apparatuses to process control, cluster analysis, hypothesis testing, analysis of variance, and other procedures involving multiple comparisons of indexed data.

Functional Connectivity Changes in Resting-State EEG as Potential Biomarker for Amyotrophic Lateral Sclerosis

PubMed Central

Iyer, Parameswaran Mahadeva; Egan, Catriona; Pinto-Grau, Marta; Burke, Tom; Elamin, Marwa; Nasseroleslami, Bahman; Pender, Niall; Lalor, Edmund C.; Hardiman, Orla

2015-01-01

Background Amyotrophic Lateral Sclerosis (ALS) is heterogeneous and overlaps with frontotemporal dementia. Spectral EEG can predict damage in structural and functional networks in frontotemporal dementia but has never been applied to ALS. Methods 18 incident ALS patients with normal cognition and 17 age matched controls underwent 128 channel EEG and neuropsychology assessment. The EEG data was analyzed using FieldTrip software in MATLAB to calculate simple connectivity measures and scalp network measures. sLORETA was used in nodal analysis for source localization and same methods were applied as above to calculate nodal network measures. Graph theory measures were used to assess network integrity. Results Cross spectral density in alpha band was higher in patients. In ALS patients, increased degree values of the network nodes was noted in the central and frontal regions in the theta band across seven of the different connectivity maps (p<0.0005). Among patients, clustering coefficient in alpha and gamma bands was increased in all regions of the scalp and connectivity were significantly increased (p=0.02). Nodal network showed increased assortativity in alpha band in the patients group. The Clustering Coefficient in Partial Directed Connectivity (PDC) showed significantly higher values for patients in alpha, beta, gamma, theta and delta frequencies (p=0.05). Discussion There is increased connectivity in the fronto-central regions of the scalp and areas corresponding to Salience and Default Mode network in ALS, suggesting a pathologic disruption of neuronal networking in early disease states. Spectral EEG has potential utility as a biomarker in ALS. PMID:26091258

Clustering the Orion B giant molecular cloud based on its molecular emission

PubMed Central

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H.; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R.; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2017-01-01

Context Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). Aims We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. Methods We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. Results A clustering analysis based only on the J = 1 – 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes (nH ~ 100 cm−3, ~ 500 cm−3, and > 1000 cm−3), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 − 0 line of HCO+ and the N = 1 − 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO+ and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO+ intensity ratio in UV-illuminated regions. Finer distinctions in density classes (nH ~ 7 × 103 cm−3 ~ 4 × 104 cm−3) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO+ (1 – 0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Conclusions Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers. PMID:29456256

Planck intermediate results. XLIII. Spectral energy distribution of dust in clusters of galaxies

NASA Astrophysics Data System (ADS)

Planck Collaboration; Adam, R.; Ade, P. A. R.; Aghanim, N.; Ashdown, M.; Aumont, J.; Baccigalupi, C.; Banday, A. J.; Barreiro, R. B.; Bartolo, N.; Battaner, E.; Benabed, K.; Benoit-Lévy, A.; Bersanelli, M.; Bielewicz, P.; Bikmaev, I.; Bonaldi, A.; Bond, J. R.; Borrill, J.; Bouchet, F. R.; Burenin, R.; Burigana, C.; Calabrese, E.; Cardoso, J.-F.; Catalano, A.; Chiang, H. C.; Christensen, P. R.; Churazov, E.; Colombo, L. P. L.; Combet, C.; Comis, B.; Couchot, F.; Crill, B. P.; Curto, A.; Cuttaia, F.; Danese, L.; Davis, R. J.; de Bernardis, P.; de Rosa, A.; de Zotti, G.; Delabrouille, J.; Désert, F.-X.; Diego, J. M.; Dole, H.; Doré, O.; Douspis, M.; Ducout, A.; Dupac, X.; Elsner, F.; Enßlin, T. A.; Finelli, F.; Forni, O.; Frailis, M.; Fraisse, A. A.; Franceschi, E.; Galeotta, S.; Ganga, K.; Génova-Santos, R. T.; Giard, M.; Giraud-Héraud, Y.; Gjerløw, E.; González-Nuevo, J.; Górski, K. M.; Gregorio, A.; Gruppuso, A.; Gudmundsson, J. E.; Hansen, F. K.; Harrison, D. L.; Hernández-Monteagudo, C.; Herranz, D.; Hildebrandt, S. R.; Hivon, E.; Hobson, M.; Hornstrup, A.; Hovest, W.; Hurier, G.; Jaffe, A. H.; Jaffe, T. R.; Jones, W. C.; Keihänen, E.; Keskitalo, R.; Khamitov, I.; Kisner, T. S.; Kneissl, R.; Knoche, J.; Kunz, M.; Kurki-Suonio, H.; Lagache, G.; Lähteenmäki, A.; Lamarre, J.-M.; Lasenby, A.; Lattanzi, M.; Lawrence, C. R.; Leonardi, R.; Levrier, F.; Liguori, M.; Lilje, P. B.; Linden-Vørnle, M.; López-Caniego, M.; Macías-Pérez, J. F.; Maffei, B.; Maggio, G.; Mandolesi, N.; Mangilli, A.; Maris, M.; Martin, P. G.; Martínez-González, E.; Masi, S.; Matarrese, S.; Melchiorri, A.; Mennella, A.; Migliaccio, M.; Miville-Deschênes, M.-A.; Moneti, A.; Montier, L.; Morgante, G.; Mortlock, D.; Munshi, D.; Murphy, J. A.; Naselsky, P.; Nati, F.; Natoli, P.; Nørgaard-Nielsen, H. U.; Novikov, D.; Novikov, I.; Oxborrow, C. A.; Pagano, L.; Pajot, F.; Paoletti, D.; Pasian, F.; Perdereau, O.; Perotto, L.; Pettorino, V.; Piacentini, F.; Piat, M.; Plaszczynski, S.; Pointecouteau, E.; Polenta, G.; Ponthieu, N.; Pratt, G. W.; Prunet, S.; Puget, J.-L.; Rachen, J. P.; Rebolo, R.; Reinecke, M.; Remazeilles, M.; Renault, C.; Renzi, A.; Ristorcelli, I.; Rocha, G.; Rosset, C.; Rossetti, M.; Roudier, G.; Rubiño-Martín, J. A.; Rusholme, B.; Santos, D.; Savelainen, M.; Savini, G.; Scott, D.; Stolyarov, V.; Stompor, R.; Sudiwala, R.; Sunyaev, R.; Sutton, D.; Suur-Uski, A.-S.; Sygnet, J.-F.; Tauber, J. A.; Terenzi, L.; Toffolatti, L.; Tomasi, M.; Tristram, M.; Tucci, M.; Valenziano, L.; Valiviita, J.; Van Tent, F.; Vielva, P.; Villa, F.; Wade, L. A.; Wehus, I. K.; Yvon, D.; Zacchei, A.; Zonca, A.

2016-12-01

Although infrared (IR) overall dust emission from clusters of galaxies has been statistically detected using data from the Infrared Astronomical Satellite (IRAS), it has not been possible to sample the spectral energy distribution (SED) of this emission over its peak, and thus to break the degeneracy between dust temperature and mass. By complementing the IRAS spectral coverage with Planck satellite data from 100 to 857 GHz, we provide new constraints on the IR spectrum of thermal dust emission in clusters of galaxies. We achieve this by using a stacking approach for a sample of several hundred objects from the Planck cluster sample. This procedure averages out fluctuations from the IR sky, allowing us to reach a significant detection of the faint cluster contribution. We also use the large frequency range probed by Planck, together with component-separation techniques, to remove the contamination from both cosmic microwave background anisotropies and the thermal Sunyaev-Zeldovich effect (tSZ) signal, which dominate at ν ≤ 353 GHz. By excluding dominant spurious signals or systematic effects, averaged detections are reported at frequencies 353 GHz ≤ ν ≤ 5000 GHz. We confirm the presence of dust in clusters of galaxies at low and intermediate redshifts, yielding an SED with a shape similar to that of the Milky Way. Planck's resolution does not allow us to investigate the detailed spatial distribution of this emission (e.g. whether it comes from intergalactic dust or simply the dust content of the cluster galaxies), but the radial distribution of the emission appears to follow that of the stacked SZ signal, and thus the extent of the clusters. The recovered SED allows us to constrain the dust mass responsible for the signal and its temperature.

Planck intermediate results: XLIII. Spectral energy distribution of dust in clusters of galaxies

DOE PAGES

Adam, R.; Ade, P. A. R.; Aghanim, N.; ...

2016-12-12

Although infrared (IR) overall dust emission from clusters of galaxies has been statistically detected using data from the Infrared Astronomical Satellite (IRAS), it has not been possible to sample the spectral energy distribution (SED) of this emission over its peak, and thus to break the degeneracy between dust temperature and mass. By complementing the IRAS spectral coverage with Planck satellite data from 100 to 857 GHz, we provide in this paper new constraints on the IR spectrum of thermal dust emission in clusters of galaxies. We achieve this by using a stacking approach for a sample of several hundred objectsmore » from the Planck cluster sample. This procedure averages out fluctuations from the IR sky, allowing us to reach a significant detection of the faint cluster contribution. We also use the large frequency range probed by Planck, together with component-separation techniques, to remove the contamination from both cosmic microwave background anisotropies and the thermal Sunyaev-Zeldovich effect (tSZ) signal, which dominate at ν ≤ 353 GHz. By excluding dominant spurious signals or systematic effects, averaged detections are reported at frequencies 353 GHz ≤ ν ≤ 5000 GHz. We confirm the presence of dust in clusters of galaxies at low and intermediate redshifts, yielding an SED with a shape similar to that of the Milky Way. Planck’s resolution does not allow us to investigate the detailed spatial distribution of this emission (e.g. whether it comes from intergalactic dust or simply the dust content of the cluster galaxies), but the radial distribution of the emission appears to follow that of the stacked SZ signal, and thus the extent of the clusters. Finally, the recovered SED allows us to constrain the dust mass responsible for the signal and its temperature.« less

Generation of spectral clusters in a mixture of noble and Raman-active gases.

PubMed

Hosseini, Pooria; Abdolvand, Amir; St J Russell, Philip

2016-12-01

We report a novel scheme for the generation of dense clusters of Raman sidebands. The scheme uses a broadband-guiding hollow-core photonic crystal fiber (HC-PCF) filled with a mixture of H₂, D₂, and Xe for efficient interaction between the gas mixture and a green laser pump pulse (532 nm, 1 ns) of only 5 μJ of energy. This results in the generation from noise of more than 135 rovibrational Raman sidebands covering the visible spectral region with an average spacing of only 2.2 THz. Such a spectrally dense and compact fiber-based source is ideal for applications where closely spaced narrow-band laser lines with high spectral power density are required, such as in spectroscopy and sensing. When the HC-PCF is filled with a H₂-D₂ mixture, the Raman comb spans the spectral region from the deep UV (280 nm) to the near infrared (1000 nm).

Spectral Clustering and Geomorphological Analysis on Mercury Hollows

NASA Astrophysics Data System (ADS)

Lucchetti, A.; Pajola, M.; Galluzzi, V.; Giacomini, L.; Carli, C.; Cremonese, G.; Marzo, G. A.; Massironi, M.; Roush, T.

2018-05-01

Characterization of hollows located in different craters to understand whether there is a similar trend from a compositional point of view, and whether a possible correlation exists between spectral behavior of hollows and geomorphological units.

Individualized Functional Parcellation of the Human Amygdala Using a Semi-supervised Clustering Method: A 7T Resting State fMRI Study.

PubMed

Zhang, Xianchang; Cheng, Hewei; Zuo, Zhentao; Zhou, Ke; Cong, Fei; Wang, Bo; Zhuo, Yan; Chen, Lin; Xue, Rong; Fan, Yong

2018-01-01

The amygdala plays an important role in emotional functions and its dysfunction is considered to be associated with multiple psychiatric disorders in humans. Cytoarchitectonic mapping has demonstrated that the human amygdala complex comprises several subregions. However, it's difficult to delineate boundaries of these subregions in vivo even if using state of the art high resolution structural MRI. Previous attempts to parcellate this small structure using unsupervised clustering methods based on resting state fMRI data suffered from the low spatial resolution of typical fMRI data, and it remains challenging for the unsupervised methods to define subregions of the amygdala in vivo . In this study, we developed a novel brain parcellation method to segment the human amygdala into spatially contiguous subregions based on 7T high resolution fMRI data. The parcellation was implemented using a semi-supervised spectral clustering (SSC) algorithm at an individual subject level. Under guidance of prior information derived from the Julich cytoarchitectonic atlas, our method clustered voxels of the amygdala into subregions according to similarity measures of their functional signals. As a result, three distinct amygdala subregions can be obtained in each hemisphere for every individual subject. Compared with the cytoarchitectonic atlas, our method achieved better performance in terms of subregional functional homogeneity. Validation experiments have also demonstrated that the amygdala subregions obtained by our method have distinctive, lateralized functional connectivity (FC) patterns. Our study has demonstrated that the semi-supervised brain parcellation method is a powerful tool for exploring amygdala subregional functions.

IRAS observations of the Rho Ophiuchi infrared cluster - Spectral energy distributions and luminosity function

NASA Technical Reports Server (NTRS)

Wilking, Bruce A.; Lada, Charles J.; Young, Eric T.

1989-01-01

High-sensitivity IRAS coadded survey data, coupled with new high-sensitivity near-IR observations, are used to investigate the nature of embedded objects over an 4.3-sq-pc area comprising the central star-forming cloud of the Ophiuchi molecular complex; the area encompasses the central cloud of the Rho Ophiuchi complex and includes the core region. Seventy-eight members of the embedded cluster were identified; spectral energy distributions were constructed for 53 objects and were compared with theoretical models to gain insight into their evolutionary status. Bolometric luminosities could be estimated for nearly all of the association members, leading to a revised luminosity function for this dust-embedded cluster.

AN EMPIRICAL METHOD FOR IMPROVING THE QUALITY OF RXTE HEXTE SPECTRA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garcia, Javier A.; Steiner, James F.; McClintock, Jeffrey E.

2016-03-01

We have developed a correction tool to improve the quality of Rossi X-ray Timing Explorer (RXTE) High Energy X-ray Timing Experiment (HEXTE) spectra by employing the same method we used earlier to improve the quality of RXTE Proportional Counter Array (PCA) spectra. We fit all of the hundreds of HEXTE spectra of the Crab individually to a simple power-law model, some 37 million counts in total for Cluster A and 39 million counts for Cluster B, and we create for each cluster a combined spectrum of residuals. We find that the residual spectrum of Cluster A is free of instrumental artifacts while that of Clustermore » B contains significant features with amplitudes ∼1%; the most prominent is in the energy range 30–50 keV, which coincides with the iodine K edge. Starting with the residual spectrum for Cluster B, via an iterative procedure we created the calibration tool hexBcorr for correcting any Cluster B spectrum of interest. We demonstrate the efficacy of the tool by applying it to Cluster B spectra of two bright black holes, which contain several million counts apiece. For these spectra, application of the tool significantly improves the goodness of fit, while affecting only slightly the broadband fit parameters. The tool may be important for the study of spectral features, such as cyclotron lines, a topic that is beyond the scope of this paper.« less

Compensated Crystal Assemblies for Type-II Entangled Photon Generation in Quantum Cluster States

DTIC Science & Technology

2010-03-01

in quantum computational architectures that operate by principles entirely distinct from any based on classical physics. In contrast with other...of the SPDC spectral function, to enable applications in regions that have not been accessible with other methods. Quantum Information and Computation ...Eliminating frequency and space-time correlations in multi-photon states, PRA 64, 063815, 2001 [2]A. Zeilinger et.al. Experimental One-way computing

Network Data: Statistical Theory and New Models

DTIC Science & Technology

2016-02-17

SECURITY CLASSIFICATION OF: During this period of review, Bin Yu worked on many thrusts of high-dimensional statistical theory and methodologies. Her...research covered a wide range of topics in statistics including analysis and methods for spectral clustering for sparse and structured networks...2,7,8,21], sparse modeling (e.g. Lasso) [4,10,11,17,18,19], statistical guarantees for the EM algorithm [3], statistical analysis of algorithm leveraging

Wavelet investigation of preferential concentration in particle-laden turbulence

NASA Astrophysics Data System (ADS)

Bassenne, Maxime; Urzay, Javier; Schneider, Kai; Moin, Parviz

2017-11-01

Direct numerical simulations of particle-laden homogeneous-isotropic turbulence are employed in conjunction with wavelet multi-resolution analyses to study preferential concentration in both physical and spectral spaces. Spatially-localized energy spectra for velocity, vorticity and particle-number density are computed, along with their spatial fluctuations that enable the quantification of scale-dependent probability density functions, intermittency and inter-phase conditional statistics. The main result is that particles are found in regions of lower turbulence spectral energy than the corresponding mean. This suggests that modeling the subgrid-scale turbulence intermittency is required for capturing the small-scale statistics of preferential concentration in large-eddy simulations. Additionally, a method is defined that decomposes a particle number-density field into the sum of a coherent and an incoherent components. The coherent component representing the clusters can be sparsely described by at most 1.6% of the total number of wavelet coefficients. An application of the method, motivated by radiative-heat-transfer simulations, is illustrated in the form of a grid-adaptation algorithm that results in non-uniform meshes refined around particle clusters. It leads to a reduction of the number of control volumes by one to two orders of magnitude. PSAAP-II Center at Stanford (Grant DE-NA0002373).

Clustering the Orion B giant molecular cloud based on its molecular emission.

PubMed

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. A clustering analysis based only on the J = 1 - 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes ( n H ~ 100 cm -3 , ~ 500 cm -3 , and > 1000 cm -3 ), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 - 0 line of HCO + and the N = 1 - 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO + and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO + intensity ratio in UV-illuminated regions. Finer distinctions in density classes ( n H ~ 7 × 10 3 cm -3 ~ 4 × 10 4 cm -3 ) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO + (1 - 0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers.

Neuroelectrical Decomposition of Spontaneous Brain Activity Measured with Functional Magnetic Resonance Imaging

PubMed Central

Liu, Zhongming; de Zwart, Jacco A.; Chang, Catie; Duan, Qi; van Gelderen, Peter; Duyn, Jeff H.

2014-01-01

Spontaneous activity in the human brain occurs in complex spatiotemporal patterns that may reflect functionally specialized neural networks. Here, we propose a subspace analysis method to elucidate large-scale networks by the joint analysis of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data. The new approach is based on the notion that the neuroelectrical activity underlying the fMRI signal may have EEG spectral features that report on regional neuronal dynamics and interregional interactions. Applying this approach to resting healthy adults, we indeed found characteristic spectral signatures in the EEG correlates of spontaneous fMRI signals at individual brain regions as well as the temporal synchronization among widely distributed regions. These spectral signatures not only allowed us to parcel the brain into clusters that resembled the brain's established functional subdivision, but also offered important clues for disentangling the involvement of individual regions in fMRI network activity. PMID:23796947

A combined experimental and theoretical spectroscopic protocol for determination of the structure of heterogeneous catalysts: developing the information content of the resonance Raman spectra of M1 MoVOx † †Electronic supplementary information (ESI) available. See DOI: 10.1039/c7sc01771e Click here for additional data file.

PubMed Central

Kubas, Adam; Noak, Johannes

2017-01-01

Absorption and multiwavelength resonance Raman spectroscopy are widely used to investigate the electronic structure of transition metal centers in coordination compounds and extended solid systems. In combination with computational methodologies that have predictive accuracy, they define powerful protocols to study the spectroscopic response of catalytic materials. In this work, we study the absorption and resonance Raman spectra of the M1 MoVOx catalyst. The spectra were calculated by time-dependent density functional theory (TD-DFT) in conjunction with the independent mode displaced harmonic oscillator model (IMDHO), which allows for detailed bandshape predictions. For this purpose cluster models with up to 9 Mo and V metallic centers are considered to represent the bulk structure of MoVOx. Capping hydrogens were used to achieve valence saturation at the edges of the cluster models. The construction of model structures was based on a thorough bonding analysis which involved conventional DFT and local coupled cluster (DLPNO-CCSD(T)) methods. Furthermore the relationship of cluster topology to the computed spectral features is discussed in detail. It is shown that due to the local nature of the involved electronic transitions, band assignment protocols developed for molecular systems can be applied to describe the calculated spectral features of the cluster models as well. The present study serves as a reference for future applications of combined experimental and computational protocols in the field of solid-state heterogeneous catalysis. PMID:28989667

A Comparison of the Near-Infrared Spectral Features of Early-Type Galaxies in the Virgo and Coma Clusters

NASA Astrophysics Data System (ADS)

Houdashelt, M. L.

1992-05-01

Initial results are presented from an examination of near-infrared spectra (6800 - 9200 Angstroms) of 34 early-type galaxies - 17 in the Virgo cluster, 10 in the Coma cluster and seven field members. It has previously been speculated that E/S0 galaxies of similar luminosity in the Virgo and Coma clusters have different red stellar populations. To explore this possibility, pseudo-equivalent widths of a number of near-IR spectral features have been measured. The important features studied include the TiO bands near 7100, 7890, 8197, 8500 and 8950 Angstroms, which are mainly produced by the late-type stars whose flux contributes only about 10-20\\ the near-IR. The strengths of the Ca triplet (8498, 8542, 8662 Angstroms) and Na I doublet (8183, 8195 Angstroms) are also measured, since these features are affected by the relative contribution of dwarf stars to the red light. Although the main focus of this work is the search for spectral differences among the Coma, Virgo and field E/S0 populations, each subgroup of galaxies (and the sample as a whole) are also examined for correlations among the feature strengths, galaxy color and luminosity.

Robust demarcation of basal cell carcinoma by dependent component analysis-based segmentation of multi-spectral fluorescence images.

PubMed

Kopriva, Ivica; Persin, Antun; Puizina-Ivić, Neira; Mirić, Lina

2010-07-02

This study was designed to demonstrate robust performance of the novel dependent component analysis (DCA)-based approach to demarcation of the basal cell carcinoma (BCC) through unsupervised decomposition of the red-green-blue (RGB) fluorescent image of the BCC. Robustness to intensity fluctuation is due to the scale invariance property of DCA algorithms, which exploit spectral and spatial diversities between the BCC and the surrounding tissue. Used filtering-based DCA approach represents an extension of the independent component analysis (ICA) and is necessary in order to account for statistical dependence that is induced by spectral similarity between the BCC and surrounding tissue. This generates weak edges what represents a challenge for other segmentation methods as well. By comparative performance analysis with state-of-the-art image segmentation methods such as active contours (level set), K-means clustering, non-negative matrix factorization, ICA and ratio imaging we experimentally demonstrate good performance of DCA-based BCC demarcation in two demanding scenarios where intensity of the fluorescent image has been varied almost two orders of magnitude. Copyright 2010 Elsevier B.V. All rights reserved.

Inductive sensor performance in partial discharges and noise separation by means of spectral power ratios.

PubMed

Ardila-Rey, Jorge Alfredo; Rojas-Moreno, Mónica Victoria; Martínez-Tarifa, Juan Manuel; Robles, Guillermo

2014-02-19

Partial discharge (PD) detection is a standardized technique to qualify electrical insulation in machines and power cables. Several techniques that analyze the waveform of the pulses have been proposed to discriminate noise from PD activity. Among them, spectral power ratio representation shows great flexibility in the separation of the sources of PD. Mapping spectral power ratios in two-dimensional plots leads to clusters of points which group pulses with similar characteristics. The position in the map depends on the nature of the partial discharge, the setup and the frequency response of the sensors. If these clusters are clearly separated, the subsequent task of identifying the source of the discharge is straightforward so the distance between clusters can be a figure of merit to suggest the best option for PD recognition. In this paper, two inductive sensors with different frequency responses to pulsed signals, a high frequency current transformer and an inductive loop sensor, are analyzed to test their performance in detecting and separating the sources of partial discharges.

An {alpha}-cluster model for {sub {Lambda}}{sup 9}Be spectroscopy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Filikhin, I. N., E-mail: ifilikhin@nccu.edu; Suslov, V. M.; Vlahovic, B.

An {alpha}-cluster model is applied to study low-lying spectrum of the {sub {Lambda}}{sup 9}Be hypernucleus. The three-body {alpha}{alpha}{Lambda} problem is numerically solved by the Faddeev equations in configuration space using phenomenological pair potentials. We found a set of the potentials that reproduces experimental data for the ground state (1/2{sup +}) binding energy and excitation energy of the 5/2{sup +} and 3/2{sup +} states, simultaneously. This set includes the Ali-Bodmer potential of the version 'e' for {alpha}{alpha} and modified Tang-Herndon potential for {alpha}{Lambda} interactions. The spin-orbit {alpha}{Lambda} interaction is given by modified Scheerbaum potential. Low-lying energy levels are evaluated applying amore » variant of the analytical continuation method in the coupling constant. It is shown that the spectral properties of {sub {Lambda}}{sup 9}Be can be classified as an analog of {sup 9}Be spectrum with the exception of several 'genuine hypernuclear states'. This agrees qualitatively with previous studies. The results are compared with experimental data and new interpretation of the spectral structure is discussed.« less

Pulmonary parenchyma segmentation in thin CT image sequences with spectral clustering and geodesic active contour model based on similarity

NASA Astrophysics Data System (ADS)

He, Nana; Zhang, Xiaolong; Zhao, Juanjuan; Zhao, Huilan; Qiang, Yan

2017-07-01

While the popular thin layer scanning technology of spiral CT has helped to improve diagnoses of lung diseases, the large volumes of scanning images produced by the technology also dramatically increase the load of physicians in lesion detection. Computer-aided diagnosis techniques like lesions segmentation in thin CT sequences have been developed to address this issue, but it remains a challenge to achieve high segmentation efficiency and accuracy without much involvement of human manual intervention. In this paper, we present our research on automated segmentation of lung parenchyma with an improved geodesic active contour model that is geodesic active contour model based on similarity (GACBS). Combining spectral clustering algorithm based on Nystrom (SCN) with GACBS, this algorithm first extracts key image slices, then uses these slices to generate an initial contour of pulmonary parenchyma of un-segmented slices with an interpolation algorithm, and finally segments lung parenchyma of un-segmented slices. Experimental results show that the segmentation results generated by our method are close to what manual segmentation can produce, with an average volume overlap ratio of 91.48%.

Phobos MRO/CRISM visible and near-infrared (0.5-2.5 μm) spectral modeling

NASA Astrophysics Data System (ADS)

Pajola, Maurizio; Roush, Ted; Dalle Ore, Cristina; Marzo, Giuseppe A.; Simioni, Emanuele

2018-05-01

This paper focuses on the spectral modeling of the surface of Phobos in the wavelength range between 0.5 and 2.5 μm. We exploit the Phobos Mars Reconnaissance Orbiter/Compact Reconnaissance Imaging Spectrometer for Mars (MRO/CRISM) dataset and extend the study area presented by Fraeman et al. (2012) including spectra from nearly the entire surface observed. Without a priori selection of surface locations we use the unsupervised K-means partitioning algorithm developed by Marzo et al. (2006) to investigate the spectral variability across Phobos surface. The statistical partitioning identifies seven clusters. We investigate the compositional information contained within the average spectra of four clusters using the radiative transfer model of Shkuratov et al. (1999). We use optical constants of Tagish Lake meteorite (TL), from Roush (2003), and pyroxene glass (PM80), from Jaeger et al. (1994) and Dorschner et al. (1995), as previously suggested by Pajola et al. (2013) as inputs for the calculations. The model results show good agreement in slope when compared to the averages of the CRISM spectral clusters. In particular, the best fitting model of the cluster with the steepest spectral slope yields relative abundances that are equal to those of Pajola et al. (2013), i.e. 20% PM80 and 80% TL, but grain sizes that are 12 μm smaller for PM80 and 4 μm smaller for TL (the grain sizes are 11 μm for PM80 and 20 μm for TL in Pajola et al. (2013), respectively). This modest discrepancy may arise from the fact that the areas observed by CRISM and those analyzed in Pajola et al. (2013) are on opposite locations on Phobos and are characterized by different morphological and weathering settings. Instead, as the clusters spectral slopes decrease, the best fits obtained show trends related to both relative abundance and grain size that is not observed for the cluster with the steepest spectral slope. With a decrease in slope there is general increase of relative percentage of PM80 from 12% to 18% and the associated decrease of TL from 88% to 82%. Simultaneously the PM80 grain sizes decrease from 9 to 5 μm and TL grain sizes increase from 13 to 16 μm. The best fitting models show relative abundances and grain sizes that partially overlap. This supports the hypothesis that from a compositional perspective the transition between the highest and lowest slopes on Phobos is subtle, and it is characterized by a smooth change of relative abundances and grain sizes, instead of a distinct dichotomy between the areas.

Identification of individual coherent sets associated with flow trajectories using coherent structure coloring

NASA Astrophysics Data System (ADS)

Schlueter-Kuck, Kristy L.; Dabiri, John O.

2017-09-01

We present a method for identifying the coherent structures associated with individual Lagrangian flow trajectories even where only sparse particle trajectory data are available. The method, based on techniques in spectral graph theory, uses the Coherent Structure Coloring vector and associated eigenvectors to analyze the distance in higher-dimensional eigenspace between a selected reference trajectory and other tracer trajectories in the flow. By analyzing this distance metric in a hierarchical clustering, the coherent structure of which the reference particle is a member can be identified. This algorithm is proven successful in identifying coherent structures of varying complexities in canonical unsteady flows. Additionally, the method is able to assess the relative coherence of the associated structure in comparison to the surrounding flow. Although the method is demonstrated here in the context of fluid flow kinematics, the generality of the approach allows for its potential application to other unsupervised clustering problems in dynamical systems such as neuronal activity, gene expression, or social networks.

Unexpected electronic perturbation effects of simple PEG environments on the optical properties of small cadmium chalcogenide clusters

NASA Astrophysics Data System (ADS)

Fukunaga, Naoto; Konishi, Katsuaki

2015-12-01

Poly(ethylene glycol) (PEG) has been widely used for the surface protection of inorganic nanoobjects because of its virtually `inert' nature, but little attention has been paid to its inherent electronic impacts on inorganic cores. Herein, we definitively show, through studies on optical properties of a series of PEG-modified Cd10Se4(SR)10 clusters, that the surrounding PEG environments can electronically affect the properties of the inorganic core. For the clusters with PEG units directly attached to an inorganic core (R = (CH2CH2O)nOCH3, 1-PEGn, n = 3, ~7, ~17, ~46), the absorption bands, associated with the low-energy transitions, continuously blue-shifted with the increasing PEG chain length. The chain length dependencies were also observed in the photoluminescence properties, particularly in the excitation spectral profiles. By combining the spectral features of several PEG17-modified clusters (2-Cm-PEG17 and 3) whose PEG and core units are separated by various alkyl chain-based spacers, it was demonstrated that sufficiently long PEG units, including PEG17 and PEG46, cause electronic perturbations in the cluster properties when they are arranged near the inorganic core. These unique effects of the long-PEG environments could be correlated with their large dipole moments, suggesting that the polarity of the proximal chemical environment is critical when affecting the electronic properties of the inorganic cluster core.Poly(ethylene glycol) (PEG) has been widely used for the surface protection of inorganic nanoobjects because of its virtually `inert' nature, but little attention has been paid to its inherent electronic impacts on inorganic cores. Herein, we definitively show, through studies on optical properties of a series of PEG-modified Cd10Se4(SR)10 clusters, that the surrounding PEG environments can electronically affect the properties of the inorganic core. For the clusters with PEG units directly attached to an inorganic core (R = (CH2CH2O)nOCH3, 1-PEGn, n = 3, ~7, ~17, ~46), the absorption bands, associated with the low-energy transitions, continuously blue-shifted with the increasing PEG chain length. The chain length dependencies were also observed in the photoluminescence properties, particularly in the excitation spectral profiles. By combining the spectral features of several PEG17-modified clusters (2-Cm-PEG17 and 3) whose PEG and core units are separated by various alkyl chain-based spacers, it was demonstrated that sufficiently long PEG units, including PEG17 and PEG46, cause electronic perturbations in the cluster properties when they are arranged near the inorganic core. These unique effects of the long-PEG environments could be correlated with their large dipole moments, suggesting that the polarity of the proximal chemical environment is critical when affecting the electronic properties of the inorganic cluster core. Electronic supplementary information (ESI) available: Details of synthetic procedures and characterisation data of the PEGylated thiols and clusters and additional absorption, photoluminescence emission and excitation spectral data. See DOI: 10.1039/c5nr06307h

Multispectral and Panchromatic used Enhancement Resolution and Study Effective Enhancement on Supervised and Unsupervised Classification Land – Cover

NASA Astrophysics Data System (ADS)

Salman, S. S.; Abbas, W. A.

2018-05-01

The goal of the study is to support analysis Enhancement of Resolution and study effect on classification methods on bands spectral information of specific and quantitative approaches. In this study introduce a method to enhancement resolution Landsat 8 of combining the bands spectral of 30 meters resolution with panchromatic band 8 of 15 meters resolution, because of importance multispectral imagery to extracting land - cover. Classification methods used in this study to classify several lands -covers recorded from OLI- 8 imagery. Two methods of Data mining can be classified as either supervised or unsupervised. In supervised methods, there is a particular predefined target, that means the algorithm learn which values of the target are associated with which values of the predictor sample. K-nearest neighbors and maximum likelihood algorithms examine in this work as supervised methods. In other hand, no sample identified as target in unsupervised methods, the algorithm of data extraction searches for structure and patterns between all the variables, represented by Fuzzy C-mean clustering method as one of the unsupervised methods, NDVI vegetation index used to compare the results of classification method, the percent of dense vegetation in maximum likelihood method give a best results.

A region-based segmentation method for ultrasound images in HIFU therapy

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhang, Dong, E-mail: dongz@whu.edu.cn; Liu, Yu; Yang, Yan

Purpose: Precisely and efficiently locating a tumor with less manual intervention in ultrasound-guided high-intensity focused ultrasound (HIFU) therapy is one of the keys to guaranteeing the therapeutic result and improving the efficiency of the treatment. The segmentation of ultrasound images has always been difficult due to the influences of speckle, acoustic shadows, and signal attenuation as well as the variety of tumor appearance. The quality of HIFU guidance images is even poorer than that of conventional diagnostic ultrasound images because the ultrasonic probe used for HIFU guidance usually obtains images without making contact with the patient’s body. Therefore, the segmentationmore » becomes more difficult. To solve the segmentation problem of ultrasound guidance image in the treatment planning procedure for HIFU therapy, a novel region-based segmentation method for uterine fibroids in HIFU guidance images is proposed. Methods: Tumor partitioning in HIFU guidance image without manual intervention is achieved by a region-based split-and-merge framework. A new iterative multiple region growing algorithm is proposed to first split the image into homogenous regions (superpixels). The features extracted within these homogenous regions will be more stable than those extracted within the conventional neighborhood of a pixel. The split regions are then merged by a superpixel-based adaptive spectral clustering algorithm. To ensure the superpixels that belong to the same tumor can be clustered together in the merging process, a particular construction strategy for the similarity matrix is adopted for the spectral clustering, and the similarity matrix is constructed by taking advantage of a combination of specifically selected first-order and second-order texture features computed from the gray levels and the gray level co-occurrence matrixes, respectively. The tumor region is picked out automatically from the background regions by an algorithm according to a priori information about the tumor position, shape, and size. Additionally, an appropriate cluster number for spectral clustering can be determined by the same algorithm, thus the automatic segmentation of the tumor region is achieved. Results: To evaluate the performance of the proposed method, 50 uterine fibroid ultrasound images from different patients receiving HIFU therapy were segmented, and the obtained tumor contours were compared with those delineated by an experienced radiologist. For area-based evaluation results, the mean values of the true positive ratio, the false positive ratio, and the similarity were 94.42%, 4.71%, and 90.21%, respectively, and the corresponding standard deviations were 2.54%, 3.12%, and 3.50%, respectively. For distance-based evaluation results, the mean values of the normalized Hausdorff distance and the normalized mean absolute distance were 4.93% and 0.90%, respectively, and the corresponding standard deviations were 2.22% and 0.34%, respectively. The running time of the segmentation process was 12.9 s for a 318 × 333 (pixels) image. Conclusions: Experiments show that the proposed method can segment the tumor region accurately and efficiently with less manual intervention, which provides for the possibility of automatic segmentation and real-time guidance in HIFU therapy.« less

A model for the infrared emission from an OB star cluster environment

NASA Technical Reports Server (NTRS)

Leisawitz, D.

1991-01-01

A model for the infrared emission from the neighborhood of an OB star cluster is described. The distribution of gas and dust around the stars, properties of the dust, and the cluster and interstellar radiation fields are variable. The model can be applied to regions around clusters embedded to various degrees in their parental molecular clouds (i.e., compact H II regions, blister-type H II regions, and the tenuous H II regions ionized by naked O stars). The model is used to simulate IRAS observations of a typical blister H II region. Infrared surface brightness and spectral energy distributions are predicted and the impact of limited spatial resolution is illustrated. The model results are shown to be consistent with observations of the exemplary outer Galaxy OB cluster NGC 7380. It is planned to use the model as a diagnostic tool to probe the physical conditions and dust properties in star-formation regions and, ultimately, in an interpretation of the spectral energy distributions of spiral galaxies.

Influence of the electron density on the characteristics of terahertz waves generated under laser–cluster interaction

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frolov, A. A., E-mail: frolov@ihed.ras.ru

2016-12-15

A theory of generation of terahertz radiation under laser–cluster interaction, developed earlier for an overdense cluster plasma [A. A. Frolov, Plasma Phys. Rep. 42. 637 (2016)], is generalized for the case of arbitrary electron density. The spectral composition of radiation is shown to substantially depend on the density of free electrons in the cluster. For an underdense cluster plasma, there is a sharp peak in the terahertz spectrum at the frequency of the quadrupole mode of a plasma sphere. As the electron density increases to supercritical values, this spectral line vanishes and a broad maximum at the frequency comparable withmore » the reciprocal of the laser pulse duration appears in the spectrum. The dependence of the total energy of terahertz radiation on the density of free electrons is analyzed. The radiation yield is shown to increase significantly under resonance conditions, when the laser frequency is close to the eigenfrequency of the dipole or quadrupole mode of a plasma sphere.« less

Optimal colour quality of LED clusters based on memory colours.

PubMed

Smet, Kevin; Ryckaert, Wouter R; Pointer, Michael R; Deconinck, Geert; Hanselaer, Peter

2011-03-28

The spectral power distributions of tri- and tetrachromatic clusters of Light-Emitting-Diodes, composed of simulated and commercially available LEDs, were optimized with a genetic algorithm to maximize the luminous efficacy of radiation and the colour quality as assessed by the memory colour quality metric developed by the authors. The trade-off of the colour quality as assessed by the memory colour metric and the luminous efficacy of radiation was investigated by calculating the Pareto optimal front using the NSGA-II genetic algorithm. Optimal peak wavelengths and spectral widths of the LEDs were derived, and over half of them were found to be close to Thornton's prime colours. The Pareto optimal fronts of real LED clusters were always found to be smaller than those of the simulated clusters. The effect of binning on designing a real LED cluster was investigated and was found to be quite large. Finally, a real LED cluster of commercially available AlGaInP, InGaN and phosphor white LEDs was optimized to obtain a higher score on memory colour quality scale than its corresponding CIE reference illuminant.

Cluster compression algorithm: A joint clustering/data compression concept

NASA Technical Reports Server (NTRS)

Hilbert, E. E.

1977-01-01

The Cluster Compression Algorithm (CCA), which was developed to reduce costs associated with transmitting, storing, distributing, and interpreting LANDSAT multispectral image data is described. The CCA is a preprocessing algorithm that uses feature extraction and data compression to more efficiently represent the information in the image data. The format of the preprocessed data enables simply a look-up table decoding and direct use of the extracted features to reduce user computation for either image reconstruction, or computer interpretation of the image data. Basically, the CCA uses spatially local clustering to extract features from the image data to describe spectral characteristics of the data set. In addition, the features may be used to form a sequence of scalar numbers that define each picture element in terms of the cluster features. This sequence, called the feature map, is then efficiently represented by using source encoding concepts. Various forms of the CCA are defined and experimental results are presented to show trade-offs and characteristics of the various implementations. Examples are provided that demonstrate the application of the cluster compression concept to multi-spectral images from LANDSAT and other sources.

Audio-based, unsupervised machine learning reveals cyclic changes in earthquake mechanisms in the Geysers geothermal field, California

NASA Astrophysics Data System (ADS)

Holtzman, B. K.; Paté, A.; Paisley, J.; Waldhauser, F.; Repetto, D.; Boschi, L.

2017-12-01

The earthquake process reflects complex interactions of stress, fracture and frictional properties. New machine learning methods reveal patterns in time-dependent spectral properties of seismic signals and enable identification of changes in faulting processes. Our methods are based closely on those developed for music information retrieval and voice recognition, using the spectrogram instead of the waveform directly. Unsupervised learning involves identification of patterns based on differences among signals without any additional information provided to the algorithm. Clustering of 46,000 earthquakes of $0.3

Millisecond Microwave Spikes: Statistical Study and Application for Plasma Diagnostics

NASA Astrophysics Data System (ADS)

Rozhansky, I. V.; Fleishman, G. D.; Huang, G.-L.

2008-07-01

We analyze a dense cluster of solar radio spikes registered at 4.5-6 GHz by the Purple Mountain Observatory spectrometer (Nanjing, China), operating in the 4.5-7.5 GHz range with 5 ms temporal resolution. To handle the data from the spectrometer, we developed a new technique that uses a nonlinear multi-Gaussian spectral fit based on χ2 criteria to extract individual spikes from the originally recorded spectra. Applying this method to the experimental raw data, we eventually identified about 3000 spikes for this event, which allows us to make a detailed statistical analysis. Various statistical characteristics of the spikes have been evaluated, including the intensity distributions, the spectral bandwidth distributions, and the distribution of the spike mean frequencies. The most striking finding of this analysis is the distributions of the spike bandwidth, which are remarkably asymmetric. To reveal the underlaying microphysics, we explore the local-trap model with the renormalized theory of spectral profiles of the electron cyclotron maser (ECM) emission peak in a source with random magnetic irregularities. The distribution of the solar spike relative bandwidths calculated within the local-trap model represents an excellent fit to the experimental data. Accordingly, the developed technique may offer a new tool with which to study very low levels of magnetic turbulence in the spike sources, when the ECM mechanism of the spike cluster is confirmed.

High Performance Parallel Architectures

NASA Technical Reports Server (NTRS)

El-Ghazawi, Tarek; Kaewpijit, Sinthop

1998-01-01

Traditional remote sensing instruments are multispectral, where observations are collected at a few different spectral bands. Recently, many hyperspectral instruments, that can collect observations at hundreds of bands, have been operational. Furthermore, there have been ongoing research efforts on ultraspectral instruments that can produce observations at thousands of spectral bands. While these remote sensing technology developments hold great promise for new findings in the area of Earth and space science, they present many challenges. These include the need for faster processing of such increased data volumes, and methods for data reduction. Dimension Reduction is a spectral transformation, aimed at concentrating the vital information and discarding redundant data. One such transformation, which is widely used in remote sensing, is the Principal Components Analysis (PCA). This report summarizes our progress on the development of a parallel PCA and its implementation on two Beowulf cluster configuration; one with fast Ethernet switch and the other with a Myrinet interconnection. Details of the implementation and performance results, for typical sets of multispectral and hyperspectral NASA remote sensing data, are presented and analyzed based on the algorithm requirements and the underlying machine configuration. It will be shown that the PCA application is quite challenging and hard to scale on Ethernet-based clusters. However, the measurements also show that a high- performance interconnection network, such as Myrinet, better matches the high communication demand of PCA and can lead to a more efficient PCA execution.

UV TO FAR-IR CATALOG OF A GALAXY SAMPLE IN NEARBY CLUSTERS: SPECTRAL ENERGY DISTRIBUTIONS AND ENVIRONMENTAL TRENDS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hernandez-Fernandez, Jonathan D.; Iglesias-Paramo, J.; Vilchez, J. M., E-mail: jonatan@iaa.es

2012-03-01

In this paper, we present a sample of cluster galaxies devoted to study the environmental influence on the star formation activity. This sample of galaxies inhabits in clusters showing a rich variety in their characteristics and have been observed by the SDSS-DR6 down to M{sub B} {approx} -18, and by the Galaxy Evolution Explorer AIS throughout sky regions corresponding to several megaparsecs. We assign the broadband and emission-line fluxes from ultraviolet to far-infrared to each galaxy performing an accurate spectral energy distribution for spectral fitting analysis. The clusters follow the general X-ray luminosity versus velocity dispersion trend of L{sub X}more » {proportional_to} {sigma}{sup 4.4}{sub c}. The analysis of the distributions of galaxy density counting up to the 5th nearest neighbor {Sigma}{sub 5} shows: (1) the virial regions and the cluster outskirts share a common range in the high density part of the distribution. This can be attributed to the presence of massive galaxy structures in the surroundings of virial regions. (2) The virial regions of massive clusters ({sigma}{sub c} > 550 km s{sup -1}) present a {Sigma}{sub 5} distribution statistically distinguishable ({approx}96%) from the corresponding distribution of low-mass clusters ({sigma}{sub c} < 550 km s{sup -1}). Both massive and low-mass clusters follow a similar density-radius trend, but the low-mass clusters avoid the high density extreme. We illustrate, with ABELL 1185, the environmental trends of galaxy populations. Maps of sky projected galaxy density show how low-luminosity star-forming galaxies appear distributed along more spread structures than their giant counterparts, whereas low-luminosity passive galaxies avoid the low-density environment. Giant passive and star-forming galaxies share rather similar sky regions with passive galaxies exhibiting more concentrated distributions.« less

The CCD photometry of the globular cluster Palomar 1.

NASA Astrophysics Data System (ADS)

Borissova, J.; Spassova, N.

1995-04-01

A CCD photometry of the halo cluster Palomar 1 is presented in the Thuan-Gunn photometric system. The principal sequences of the color-magnitude diagrams are delineated in different spectral bands. The color-magnitude diagrams of the cluster show a well defined red horizontal branch, a subgiant branch and a main-sequence down to about two magnitudes below the main sequence turnoff. The giant branch is absent and the brightest stars are the horizontal branch stars. The age of the cluster determined by comparison with the isochrones of Bell & Vanden Berg (1987) is consistent with an age in the interval 12-14Gyr. A distance modulus of (m-M)_g0_=15.38+/-0.15 magnitude and E(g-r)=0.16 has been derived. An estimate of the cluster structural parameters such as core radius and concentration parameter gives r_c_=1.5pc and c=1.46. A mass estimate of 1.1 10^3^Msun_ and a mass-to-light ratio of 1.79 have been obtained using King's (1966) method. The morphology of color-magnitude diagrams allows Pal 1 to be interpreted as probably a globular cluster rather than an old open one.

A hierarchical cluster analysis of normal-tension glaucoma using spectral-domain optical coherence tomography parameters.

PubMed

Bae, Hyoung Won; Ji, Yongwoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

2015-01-01

Normal-tension glaucoma (NTG) is a heterogenous disease, and there is still controversy about subclassifications of this disorder. On the basis of spectral-domain optical coherence tomography (SD-OCT), we subdivided NTG with hierarchical cluster analysis using optic nerve head (ONH) parameters and retinal nerve fiber layer (RNFL) thicknesses. A total of 200 eyes of 200 NTG patients between March 2011 and June 2012 underwent SD-OCT scans to measure ONH parameters and RNFL thicknesses. We classified NTG into homogenous subgroups based on these variables using a hierarchical cluster analysis, and compared clusters to evaluate diverse NTG characteristics. Three clusters were found after hierarchical cluster analysis. Cluster 1 (62 eyes) had the thickest RNFL and widest rim area, and showed early glaucoma features. Cluster 2 (60 eyes) was characterized by the largest cup/disc ratio and cup volume, and showed advanced glaucomatous damage. Cluster 3 (78 eyes) had small disc areas in SD-OCT and were comprised of patients with significantly younger age, longer axial length, and greater myopia than the other 2 groups. A hierarchical cluster analysis of SD-OCT scans divided NTG patients into 3 groups based upon ONH parameters and RNFL thicknesses. It is anticipated that the small disc area group comprised of younger and more myopic patients may show unique features unlike the other 2 groups.

Integrated spectral properties of 7 galactic open clusters

NASA Astrophysics Data System (ADS)

Ahumada, A. V.; Clariá, J. J.; Bica, E.; Piatti, A. E.

2000-01-01

This paper presents flux-calibrated integrated spectra in the range 3600-9000 Ä for 7 concentrated, relatively populous Galactic open clusters. We perform simultaneous estimates of age and foreground interstellar reddening by comparing the continuum distribution and line strengths of the cluster spectra with those of template cluster spectra with known parameters. For five clusters these two parameters have been determined for the first time (Ruprecht 144, BH 132, Pismis 21, Lyng\\aa 11 and BH 217), while the results here derived for the remaining two clusters (Hogg 15 and Melotte 105) show very good agreement with previous studies based mainly on colour-magnitude diagrams. We also provide metallicity estimates for six clusters from the equivalent widths of CaII triplet and TiO features. The present cluster sample improves the age resolution around solar metal content in the cluster spectral library for population synthesis. We compare the properties of the present sample with those of clusters in similar directions. Hogg 15 and Pismis 21 are among the most reddened clusters in sectors centered at l = 270o and l = 0o, respectively. Besides, the present results would favour an important dissolution rate of star clusters in these zones. Based on observations made at Complejo Astronómico El Leoncito, which is operated under agreement between the Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina and the National Universities of La Plata, Córdoba and San Juan, Argentina.

Clustering by reordering of similarity and Laplacian matrices: Application to galaxy clusters

NASA Astrophysics Data System (ADS)

Mahmoud, E.; Shoukry, A.; Takey, A.

2018-04-01

Similarity metrics, kernels and similarity-based algorithms have gained much attention due to their increasing applications in information retrieval, data mining, pattern recognition and machine learning. Similarity Graphs are often adopted as the underlying representation of similarity matrices and are at the origin of known clustering algorithms such as spectral clustering. Similarity matrices offer the advantage of working in object-object (two-dimensional) space where visualization of clusters similarities is available instead of object-features (multi-dimensional) space. In this paper, sparse ɛ-similarity graphs are constructed and decomposed into strong components using appropriate methods such as Dulmage-Mendelsohn permutation (DMperm) and/or Reverse Cuthill-McKee (RCM) algorithms. The obtained strong components correspond to groups (clusters) in the input (feature) space. Parameter ɛi is estimated locally, at each data point i from a corresponding narrow range of the number of nearest neighbors. Although more advanced clustering techniques are available, our method has the advantages of simplicity, better complexity and direct visualization of the clusters similarities in a two-dimensional space. Also, no prior information about the number of clusters is needed. We conducted our experiments on two and three dimensional, low and high-sized synthetic datasets as well as on an astronomical real-dataset. The results are verified graphically and analyzed using gap statistics over a range of neighbors to verify the robustness of the algorithm and the stability of the results. Combining the proposed algorithm with gap statistics provides a promising tool for solving clustering problems. An astronomical application is conducted for confirming the existence of 45 galaxy clusters around the X-ray positions of galaxy clusters in the redshift range [0.1..0.8]. We re-estimate the photometric redshifts of the identified galaxy clusters and obtain acceptable values compared to published spectroscopic redshifts with a 0.029 standard deviation of their differences.

Mg II Spectral Atlas and Flux Catalog for Late-Type Stars in the Hyades Cluster

NASA Technical Reports Server (NTRS)

Simon, Theodore

2001-01-01

In the course of a long-running IUE Guest Observer program, UV spectral images were obtained for more than 60 late-type members of the Hyades Cluster in order to investigate their chromospheric emissions. The emission line fluxes extracted from those observations were used to study the dependence of stellar dynamo activity upon age and rotation (IUE Observations of Rapidly Rotating Low-Mass Stars in Young Clusters: The Relation between Chromospheric Activity and Rotation). However, the details of those measurements, including a tabulation of the line fluxes, were never published. The purpose of the investigation summarized here was to extract all of the existing Hyades long-wavelength Mg II spectra in the IUE public archives in order to survey UV chromospheric emission in the cluster, thereby providing a consistent dataset for statistical and correlative studies of the relationship between stellar dynamo activity, rotation, and age over a broad range in mass.

Automated thematic mapping and change detection of ERTS-A images

NASA Technical Reports Server (NTRS)

Gramenopoulos, N. (Principal Investigator)

1975-01-01

The author has identified the following significant results. In the first part of the investigation, spatial and spectral features were developed which were employed to automatically recognize terrain features through a clustering algorithm. In this part of the investigation, the size of the cell which is the number of digital picture elements used for computing the spatial and spectral features was varied. It was determined that the accuracy of terrain recognition decreases slowly as the cell size is reduced and coincides with increased cluster diffuseness. It was also proven that a cell size of 17 x 17 pixels when used with the clustering algorithm results in high recognition rates for major terrain classes. ERTS-1 data from five diverse geographic regions of the United States were processed through the clustering algorithm with 17 x 17 pixel cells. Simple land use maps were produced and the average terrain recognition accuracy was 82 percent.

A spectral k-means approach to bright-field cell image segmentation.

PubMed

Bradbury, Laura; Wan, Justin W L

2010-01-01

Automatic segmentation of bright-field cell images is important to cell biologists, but difficult to complete due to the complex nature of the cells in bright-field images (poor contrast, broken halo, missing boundaries). Standard approaches such as level set segmentation and active contours work well for fluorescent images where cells appear as round shape, but become less effective when optical artifacts such as halo exist in bright-field images. In this paper, we present a robust segmentation method which combines the spectral and k-means clustering techniques to locate cells in bright-field images. This approach models an image as a matrix graph and segment different regions of the image by computing the appropriate eigenvectors of the matrix graph and using the k-means algorithm. We illustrate the effectiveness of the method by segmentation results of C2C12 (muscle) cells in bright-field images.

Segmentation of High Angular Resolution Diffusion MRI using Sparse Riemannian Manifold Clustering

PubMed Central

Wright, Margaret J.; Thompson, Paul M.; Vidal, René

2015-01-01

We address the problem of segmenting high angular resolution diffusion imaging (HARDI) data into multiple regions (or fiber tracts) with distinct diffusion properties. We use the orientation distribution function (ODF) to represent HARDI data and cast the problem as a clustering problem in the space of ODFs. Our approach integrates tools from sparse representation theory and Riemannian geometry into a graph theoretic segmentation framework. By exploiting the Riemannian properties of the space of ODFs, we learn a sparse representation for each ODF and infer the segmentation by applying spectral clustering to a similarity matrix built from these representations. In cases where regions with similar (resp. distinct) diffusion properties belong to different (resp. same) fiber tracts, we obtain the segmentation by incorporating spatial and user-specified pairwise relationships into the formulation. Experiments on synthetic data evaluate the sensitivity of our method to image noise and the presence of complex fiber configurations, and show its superior performance compared to alternative segmentation methods. Experiments on phantom and real data demonstrate the accuracy of the proposed method in segmenting simulated fibers, as well as white matter fiber tracts of clinical importance in the human brain. PMID:24108748

Geometry and topology of the space of sonar target echos.

PubMed

Robinson, Michael; Fennell, Sean; DiZio, Brian; Dumiak, Jennifer

2018-03-01

Successful synthetic aperture sonar target classification depends on the "shape" of the scatterers within a target signature. This article presents a workflow that computes a target-to-target distance from persistence diagrams, since the "shape" of a signature informs its persistence diagram in a structure-preserving way. The target-to-target distances derived from persistence diagrams compare favorably against those derived from spectral features and have the advantage of being substantially more compact. While spectral features produce clusters associated to each target type that are reasonably dense and well formed, the clusters are not well-separated from one another. In rather dramatic contrast, a distance derived from persistence diagrams results in highly separated clusters at the expense of some misclassification of outliers.

Development of advanced acreage estimation methods

NASA Technical Reports Server (NTRS)

Guseman, L. F., Jr. (Principal Investigator)

1980-01-01

The use of the AMOEBA clustering/classification algorithm was investigated as a basis for both a color display generation technique and maximum likelihood proportion estimation procedure. An approach to analyzing large data reduction systems was formulated and an exploratory empirical study of spatial correlation in LANDSAT data was also carried out. Topics addressed include: (1) development of multiimage color images; (2) spectral spatial classification algorithm development; (3) spatial correlation studies; and (4) evaluation of data systems.

Pattern recognition in volcano seismology - Reducing spectral dimensionality

NASA Astrophysics Data System (ADS)

Unglert, K.; Radic, V.; Jellinek, M.

2015-12-01

Variations in the spectral content of volcano seismicity can relate to changes in volcanic activity. Low-frequency seismic signals often precede or accompany volcanic eruptions. However, they are commonly manually identified in spectra or spectrograms, and their definition in spectral space differs from one volcanic setting to the next. Increasingly long time series of monitoring data at volcano observatories require automated tools to facilitate rapid processing and aid with pattern identification related to impending eruptions. Furthermore, knowledge transfer between volcanic settings is difficult if the methods to identify and analyze the characteristics of seismic signals differ. To address these challenges we evaluate whether a machine learning technique called Self-Organizing Maps (SOMs) can be used to characterize the dominant spectral components of volcano seismicity without the need for any a priori knowledge of different signal classes. This could reduce the dimensions of the spectral space typically analyzed by orders of magnitude, and enable rapid processing and visualization. Preliminary results suggest that the temporal evolution of volcano seismicity at Kilauea Volcano, Hawai`i, can be reduced to as few as 2 spectral components by using a combination of SOMs and cluster analysis. We will further refine our methodology with several datasets from Hawai`i and Alaska, among others, and compare it to other techniques.

Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering.

PubMed

Boari de Lima, Elisa; Meira, Wagner; Melo-Minardi, Raquel Cardoso de

2016-06-01

As increasingly more genomes are sequenced, the vast majority of proteins may only be annotated computationally, given experimental investigation is extremely costly. This highlights the need for computational methods to determine protein functions quickly and reliably. We believe dividing a protein family into subtypes which share specific functions uncommon to the whole family reduces the function annotation problem's complexity. Hence, this work's purpose is to detect isofunctional subfamilies inside a family of unknown function, while identifying differentiating residues. Similarity between protein pairs according to various properties is interpreted as functional similarity evidence. Data are integrated using genetic programming and provided to a spectral clustering algorithm, which creates clusters of similar proteins. The proposed framework was applied to well-known protein families and to a family of unknown function, then compared to ASMC. Results showed our fully automated technique obtained better clusters than ASMC for two families, besides equivalent results for other two, including one whose clusters were manually defined. Clusters produced by our framework showed great correspondence with the known subfamilies, besides being more contrasting than those produced by ASMC. Additionally, for the families whose specificity determining positions are known, such residues were among those our technique considered most important to differentiate a given group. When run with the crotonase and enolase SFLD superfamilies, the results showed great agreement with this gold-standard. Best results consistently involved multiple data types, thus confirming our hypothesis that similarities according to different knowledge domains may be used as functional similarity evidence. Our main contributions are the proposed strategy for selecting and integrating data types, along with the ability to work with noisy and incomplete data; domain knowledge usage for detecting subfamilies in a family with different specificities, thus reducing the complexity of the experimental function characterization problem; and the identification of residues responsible for specificity.

Isofunctional Protein Subfamily Detection Using Data Integration and Spectral Clustering

PubMed Central

Boari de Lima, Elisa; Meira, Wagner; de Melo-Minardi, Raquel Cardoso

2016-01-01

As increasingly more genomes are sequenced, the vast majority of proteins may only be annotated computationally, given experimental investigation is extremely costly. This highlights the need for computational methods to determine protein functions quickly and reliably. We believe dividing a protein family into subtypes which share specific functions uncommon to the whole family reduces the function annotation problem’s complexity. Hence, this work’s purpose is to detect isofunctional subfamilies inside a family of unknown function, while identifying differentiating residues. Similarity between protein pairs according to various properties is interpreted as functional similarity evidence. Data are integrated using genetic programming and provided to a spectral clustering algorithm, which creates clusters of similar proteins. The proposed framework was applied to well-known protein families and to a family of unknown function, then compared to ASMC. Results showed our fully automated technique obtained better clusters than ASMC for two families, besides equivalent results for other two, including one whose clusters were manually defined. Clusters produced by our framework showed great correspondence with the known subfamilies, besides being more contrasting than those produced by ASMC. Additionally, for the families whose specificity determining positions are known, such residues were among those our technique considered most important to differentiate a given group. When run with the crotonase and enolase SFLD superfamilies, the results showed great agreement with this gold-standard. Best results consistently involved multiple data types, thus confirming our hypothesis that similarities according to different knowledge domains may be used as functional similarity evidence. Our main contributions are the proposed strategy for selecting and integrating data types, along with the ability to work with noisy and incomplete data; domain knowledge usage for detecting subfamilies in a family with different specificities, thus reducing the complexity of the experimental function characterization problem; and the identification of residues responsible for specificity. PMID:27348631

Soft-landing ion mobility of silver clusters for small-molecule matrix-assisted laser desorption ionization mass spectrometry and imaging of latent fingerprints.

PubMed

Walton, Barbara L; Verbeck, Guido F

2014-08-19

Matrix-assisted laser desorption ionization (MALDI) imaging is gaining popularity, but matrix effects such as mass spectral interference and damage to the sample limit its applications. Replacing traditional matrices with silver particles capable of equivalent or increased photon energy absorption from the incoming laser has proven to be beneficial for low mass analysis. Not only can silver clusters be advantageous for low mass compound detection, but they can be used for imaging as well. Conventional matrix application methods can obstruct samples, such as fingerprints, rendering them useless after mass analysis. The ability to image latent fingerprints without causing damage to the ridge pattern is important as it allows for further characterization of the print. The application of silver clusters by soft-landing ion mobility allows for enhanced MALDI and preservation of fingerprint integrity.

Hyperspectral remote sensing for advanced detection of early blight (Alternaria solani) disease in potato (Solanum tuberosum) plants

NASA Astrophysics Data System (ADS)

Atherton, Daniel

Early detection of disease and insect infestation within crops and precise application of pesticides can help reduce potential production losses, reduce environmental risk, and reduce the cost of farming. The goal of this study was the advanced detection of early blight (Alternaria solani) in potato (Solanum tuberosum) plants using hyperspectral remote sensing data captured with a handheld spectroradiometer. Hyperspectral reflectance spectra were captured 10 times over five weeks from plants grown to the vegetative and tuber bulking growth stages. The spectra were analyzed using principal component analysis (PCA), spectral change (ratio) analysis, partial least squares (PLS), cluster analysis, and vegetative indices. PCA successfully distinguished more heavily diseased plants from healthy and minimally diseased plants using two principal components. Spectral change (ratio) analysis provided wavelengths (490-510, 640, 665-670, 690, 740-750, and 935 nm) most sensitive to early blight infection followed by ANOVA results indicating a highly significant difference (p < 0.0001) between disease rating group means. In the majority of the experiments, comparisons of diseased plants with healthy plants using Fisher's LSD revealed more heavily diseased plants were significantly different from healthy plants. PLS analysis demonstrated the feasibility of detecting early blight infected plants, finding four optimal factors for raw spectra with the predictor variation explained ranging from 93.4% to 94.6% and the response variation explained ranging from 42.7% to 64.7%. Cluster analysis successfully distinguished healthy plants from all diseased plants except for the most mildly diseased plants, showing clustering analysis was an effective method for detection of early blight. Analysis of the reflectance spectra using the simple ratio (SR) and the normalized difference vegetative index (NDVI) was effective at differentiating all diseased plants from healthy plants, except for the most mildly diseased plants. Of the analysis methods attempted, cluster analysis and vegetative indices were the most promising. The results show the potential of hyperspectral remote sensing for the detection of early blight in potato plants.

High- and low-level hierarchical classification algorithm based on source separation process

NASA Astrophysics Data System (ADS)

Loghmari, Mohamed Anis; Karray, Emna; Naceur, Mohamed Saber

2016-10-01

High-dimensional data applications have earned great attention in recent years. We focus on remote sensing data analysis on high-dimensional space like hyperspectral data. From a methodological viewpoint, remote sensing data analysis is not a trivial task. Its complexity is caused by many factors, such as large spectral or spatial variability as well as the curse of dimensionality. The latter describes the problem of data sparseness. In this particular ill-posed problem, a reliable classification approach requires appropriate modeling of the classification process. The proposed approach is based on a hierarchical clustering algorithm in order to deal with remote sensing data in high-dimensional space. Indeed, one obvious method to perform dimensionality reduction is to use the independent component analysis process as a preprocessing step. The first particularity of our method is the special structure of its cluster tree. Most of the hierarchical algorithms associate leaves to individual clusters, and start from a large number of individual classes equal to the number of pixels; however, in our approach, leaves are associated with the most relevant sources which are represented according to mutually independent axes to specifically represent some land covers associated with a limited number of clusters. These sources contribute to the refinement of the clustering by providing complementary rather than redundant information. The second particularity of our approach is that at each level of the cluster tree, we combine both a high-level divisive clustering and a low-level agglomerative clustering. This approach reduces the computational cost since the high-level divisive clustering is controlled by a simple Boolean operator, and optimizes the clustering results since the low-level agglomerative clustering is guided by the most relevant independent sources. Then at each new step we obtain a new finer partition that will participate in the clustering process to enhance semantic capabilities and give good identification rates.

A New Method to Constrain Supernova Fractions Using X-ray Observations of Clusters of Galaxies

NASA Technical Reports Server (NTRS)

Bulbul, Esra; Smith, Randall K.; Loewenstein, Michael

2012-01-01

Supernova (SN) explosions enrich the intracluster medium (ICM) both by creating and dispersing metals. We introduce a method to measure the number of SNe and relative contribution of Type Ia supernovae (SNe Ia) and core-collapse supernovae (SNe cc) by directly fitting X-ray spectral observations. The method has been implemented as an XSPEC model called snapec. snapec utilizes a single-temperature thermal plasma code (apec) to model the spectral emission based on metal abundances calculated using the latest SN yields from SN Ia and SN cc explosion models. This approach provides a self-consistent single set of uncertainties on the total number of SN explosions and relative fraction of SN types in the ICM over the cluster lifetime by directly allowing these parameters to be determined by SN yields provided by simulations. We apply our approach to XMM-Newton European Photon Imaging Camera (EPIC), Reflection Grating Spectrometer (RGS), and 200 ks simulated Astro-H observations of a cooling flow cluster, A3112.We find that various sets of SN yields present in the literature produce an acceptable fit to the EPIC and RGS spectra of A3112. We infer that 30.3% plus or minus 5.4% to 37.1% plus or minus 7.1% of the total SN explosions are SNe Ia, and the total number of SN explosions required to create the observed metals is in the range of (1.06 plus or minus 0.34) x 10(exp 9), to (1.28 plus or minus 0.43) x 10(exp 9), fromsnapec fits to RGS spectra. These values may be compared to the enrichment expected based on well-established empirically measured SN rates per star formed. The proportions of SNe Ia and SNe cc inferred to have enriched the ICM in the inner 52 kiloparsecs of A3112 is consistent with these specific rates, if one applies a correction for the metals locked up in stars. At the same time, the inferred level of SN enrichment corresponds to a star-to-gas mass ratio that is several times greater than the 10% estimated globally for clusters in the A3112 mass range.

Portfolio Decisions and Brain Reactions via the CEAD method.

PubMed

Majer, Piotr; Mohr, Peter N C; Heekeren, Hauke R; Härdle, Wolfgang K

2016-09-01

Decision making can be a complex process requiring the integration of several attributes of choice options. Understanding the neural processes underlying (uncertain) investment decisions is an important topic in neuroeconomics. We analyzed functional magnetic resonance imaging (fMRI) data from an investment decision study for stimulus-related effects. We propose a new technique for identifying activated brain regions: cluster, estimation, activation, and decision method. Our analysis is focused on clusters of voxels rather than voxel units. Thus, we achieve a higher signal-to-noise ratio within the unit tested and a smaller number of hypothesis tests compared with the often used General Linear Model (GLM). We propose to first conduct the brain parcellation by applying spatially constrained spectral clustering. The information within each cluster can then be extracted by the flexible dynamic semiparametric factor model (DSFM) dimension reduction technique and finally be tested for differences in activation between conditions. This sequence of Cluster, Estimation, Activation, and Decision admits a model-free analysis of the local fMRI signal. Applying a GLM on the DSFM-based time series resulted in a significant correlation between the risk of choice options and changes in fMRI signal in the anterior insula and dorsomedial prefrontal cortex. Additionally, individual differences in decision-related reactions within the DSFM time series predicted individual differences in risk attitudes as modeled with the framework of the mean-variance model.

Non-thermal emission and dynamical state of massive galaxy clusters from CLASH sample

NASA Astrophysics Data System (ADS)

Pandey-Pommier, M.; Richard, J.; Combes, F.; Edge, A.; Guiderdoni, B.; Narasimha, D.; Bagchi, J.; Jacob, J.

2016-12-01

Massive galaxy clusters are the most violent large scale structures undergoing merger events in the Universe. Based upon their morphological properties in X-rays, they are classified as un-relaxed and relaxed clusters and often host (a fraction of them) different types of non-thermal radio emitting components, viz., 'haloes', 'mini-haloes', 'relics' and 'phoenix' within their Intra Cluster Medium (ICM). The radio haloes show steep (α = -1.2) and ultra steep (α < -1.5) spectral properties at low radio frequencies, giving important insights on the merger (pre or post) state of the cluster. Ultra steep spectrum radio halo emissions are rare and expected to be the dominating population to be discovered via LOFAR and SKA in the future. Further, the distribution of matter (morphological information), alignment of hot X-ray emitting gas from the ICM with the total mass (dark + baryonic matter) and the bright cluster galaxy (BCG) is generally used to study the dynamical state of the cluster. We present here a multi wavelength study on 14 massive clusters from the CLASH survey and show the correlation between the state of their merger in X-ray and spectral properties (1.4 GHz - 150 MHz) at radio wavelengths. Using the optical data we also discuss about the gas-mass alignment, in order to understand the interplay between dark and baryonic matter in massive galaxy clusters.

Band structures in coupled-cluster singles-and-doubles Green's function (GFCCSD)

NASA Astrophysics Data System (ADS)

Furukawa, Yoritaka; Kosugi, Taichi; Nishi, Hirofumi; Matsushita, Yu-ichiro

2018-05-01

We demonstrate that the coupled-cluster singles-and-doubles Green's function (GFCCSD) method is a powerful and prominent tool drawing the electronic band structures and the total energies, which many theoretical techniques struggle to reproduce. We have calculated single-electron energy spectra via the GFCCSD method for various kinds of systems, ranging from ionic to covalent and van der Waals, for the first time: the one-dimensional LiH chain, one-dimensional C chain, and one-dimensional Be chain. We have found that the bandgap becomes narrower than in HF due to the correlation effect. We also show that the band structures obtained from the GFCCSD method include both quasiparticle and satellite peaks successfully. Besides, taking one-dimensional LiH as an example, we discuss the validity of restricting the active space to suppress the computational cost of the GFCCSD method. We show that the calculated results without bands that do not contribute to the chemical bonds are in good agreement with full-band calculations. With the GFCCSD method, we can calculate the total energies and spectral functions for periodic systems in an explicitly correlated manner.

A tripartite clustering analysis on microRNA, gene and disease model.

PubMed

Shen, Chengcheng; Liu, Ying

2012-02-01

Alteration of gene expression in response to regulatory molecules or mutations could lead to different diseases. MicroRNAs (miRNAs) have been discovered to be involved in regulation of gene expression and a wide variety of diseases. In a tripartite biological network of human miRNAs, their predicted target genes and the diseases caused by altered expressions of these genes, valuable knowledge about the pathogenicity of miRNAs, involved genes and related disease classes can be revealed by co-clustering miRNAs, target genes and diseases simultaneously. Tripartite co-clustering can lead to more informative results than traditional co-clustering with only two kinds of members and pass the hidden relational information along the relation chain by considering multi-type members. Here we report a spectral co-clustering algorithm for k-partite graph to find clusters with heterogeneous members. We use the method to explore the potential relationships among miRNAs, genes and diseases. The clusters obtained from the algorithm have significantly higher density than randomly selected clusters, which means members in the same cluster are more likely to have common connections. Results also show that miRNAs in the same family based on the hairpin sequences tend to belong to the same cluster. We also validate the clustering results by checking the correlation of enriched gene functions and disease classes in the same cluster. Finally, widely studied miR-17-92 and its paralogs are analyzed as a case study to reveal that genes and diseases co-clustered with the miRNAs are in accordance with current research findings.

The DAFT/FADA survey. I.Photometric redshifts along lines of sight to clusters in the z=[0.4,0.9] interval

DOE Office of Scientific and Technical Information (OSTI.GOV)

Guennou, L.; /Northwestern U. /Marseille, Lab. Astrophys.; Adami, C.

2010-08-01

As a contribution to the understanding of the dark energy concept, the Dark energy American French Team (DAFT, in French FADA) has started a large project to characterize statistically high redshift galaxy clusters, infer cosmological constraints from Weak Lensing Tomography, and understand biases relevant for constraining dark energy and cluster physics in future cluster and cosmological experiments. Aims. The purpose of this paper is to establish the basis of reference for the photo-z determination used in all our subsequent papers, including weak lensing tomography studies. This project is based on a sample of 91 high redshift (z {ge} 0.4), massivemore » ({approx}> 3 x 10{sup 14} M{sub {circle_dot}}) clusters with existing HST imaging, for which we are presently performing complementary multi-wavelength imaging. This allows us in particular to estimate spectral types and determine accurate photometric redshifts for galaxies along the lines of sight to the first ten clusters for which all the required data are available down to a limit of I{sub AB} = 24./24.5 with the LePhare software. The accuracy in redshift is of the order of 0.05 for the range 0.2 {le} z {le} 1.5. We verified that the technique applied to obtain photometric redshifts works well by comparing our results to with previous works. In clusters, photo-z accuracy is degraded for bright absolute magnitudes and for the latest and earliest type galaxies. The photo-z accuracy also only slightly varies as a function of the spectral type for field galaxies. As a consequence, we find evidence for an environmental dependence of the photo-z accuracy, interpreted as the standard used Spectral Energy Distributions being not very well suited to cluster galaxies. Finally, we modeled the LCDCS 0504 mass with the strong arcs detected along this line of sight.« less

Automatic segmentation of fluorescence lifetime microscopy images of cells using multiresolution community detection--a first study.

PubMed

Hu, D; Sarder, P; Ronhovde, P; Orthaus, S; Achilefu, S; Nussinov, Z

2014-01-01

Inspired by a multiresolution community detection based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Furthermore, using the proposed method, the mean-square error in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The multiresolution community detection method appeared to perform better than a popular spectral clustering-based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in mean-square error with increasing resolution. © 2013 The Authors Journal of Microscopy © 2013 Royal Microscopical Society.

Automatic Segmentation of Fluorescence Lifetime Microscopy Images of Cells Using Multi-Resolution Community Detection -A First Study

PubMed Central

Hu, Dandan; Sarder, Pinaki; Ronhovde, Peter; Orthaus, Sandra; Achilefu, Samuel; Nussinov, Zohar

2014-01-01

Inspired by a multi-resolution community detection (MCD) based network segmentation method, we suggest an automatic method for segmenting fluorescence lifetime (FLT) imaging microscopy (FLIM) images of cells in a first pilot investigation on two selected images. The image processing problem is framed as identifying segments with respective average FLTs against the background in FLIM images. The proposed method segments a FLIM image for a given resolution of the network defined using image pixels as the nodes and similarity between the FLTs of the pixels as the edges. In the resulting segmentation, low network resolution leads to larger segments, and high network resolution leads to smaller segments. Further, using the proposed method, the mean-square error (MSE) in estimating the FLT segments in a FLIM image was found to consistently decrease with increasing resolution of the corresponding network. The MCD method appeared to perform better than a popular spectral clustering based method in performing FLIM image segmentation. At high resolution, the spectral segmentation method introduced noisy segments in its output, and it was unable to achieve a consistent decrease in MSE with increasing resolution. PMID:24251410

Spectral biclustering of microarray data: coclustering genes and conditions.

PubMed

Kluger, Yuval; Basri, Ronen; Chang, Joseph T; Gerstein, Mark

2003-04-01

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).

Clustering the Orion B giant molecular cloud based on its molecular emission

NASA Astrophysics Data System (ADS)

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H.; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R.; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

Context. Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). Aims: We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. Methods: We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional probability density function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. Results: A clustering analysis based only on the J = 1-0 lines of three isotopologues of CO proves sufficient to reveal distinct density/column density regimes (nH 100 cm-3, 500 cm-3, and >1000 cm-3), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1-0 line of HCO+ and the N = 1-0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO+ and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO+ intensity ratio in UV-illuminated regions. Finer distinctions in density classes (nH 7 × 103 cm-3, 4 × 104 cm-3) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO+ (1-0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Conclusions: Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers. Data products associated with this paper are available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/610/A12 and at http://www.iram.fr/ pety/ORION-B

Inductive Sensor Performance in Partial Discharges and Noise Separation by Means of Spectral Power Ratios

PubMed Central

Ardila-Rey, Jorge Alfredo; Rojas-Moreno, Mónica Victoria; Martínez-Tarifa, Juan Manuel; Robles, Guillermo

2014-01-01

Partial discharge (PD) detection is a standardized technique to qualify electrical insulation in machines and power cables. Several techniques that analyze the waveform of the pulses have been proposed to discriminate noise from PD activity. Among them, spectral power ratio representation shows great flexibility in the separation of the sources of PD. Mapping spectral power ratios in two-dimensional plots leads to clusters of points which group pulses with similar characteristics. The position in the map depends on the nature of the partial discharge, the setup and the frequency response of the sensors. If these clusters are clearly separated, the subsequent task of identifying the source of the discharge is straightforward so the distance between clusters can be a figure of merit to suggest the best option for PD recognition. In this paper, two inductive sensors with different frequency responses to pulsed signals, a high frequency current transformer and an inductive loop sensor, are analyzed to test their performance in detecting and separating the sources of partial discharges. PMID:24556674

A First Estimate of the X-Ray Binary Frequency as a Function of Star Cluster Mass in a Single Galactic System

NASA Astrophysics Data System (ADS)

Clark, D. M.; Eikenberry, S. S.; Brandl, B. R.; Wilson, J. C.; Carson, J. C.; Henderson, C. P.; Hayward, T. L.; Barry, D. J.; Ptak, A. F.; Colbert, E. J. M.

2008-05-01

We use the previously identified 15 infrared star cluster counterparts to X-ray point sources in the interacting galaxies NGC 4038/4039 (the Antennae) to study the relationship between total cluster mass and X-ray binary number. This significant population of X-Ray/IR associations allows us to perform, for the first time, a statistical study of X-ray point sources and their environments. We define a quantity, η, relating the fraction of X-ray sources per unit mass as a function of cluster mass in the Antennae. We compute cluster mass by fitting spectral evolutionary models to Ks luminosity. Considering that this method depends on cluster age, we use four different age distributions to explore the effects of cluster age on the value of η and find it varies by less than a factor of 4. We find a mean value of η for these different distributions of η = 1.7 × 10-8 M-1⊙ with ση = 1.2 × 10-8 M-1⊙. Performing a χ2 test, we demonstrate η could exhibit a positive slope, but that it depends on the assumed distribution in cluster ages. While the estimated uncertainties in η are factors of a few, we believe this is the first estimate made of this quantity to "order of magnitude" accuracy. We also compare our findings to theoretical models of open and globular cluster evolution, incorporating the X-ray binary fraction per cluster.

Binaries at Birth: Stellar multiplicity in embedded clusters from radial velocity variations in the IN-SYNC survey

NASA Astrophysics Data System (ADS)

Oskar Jaehnig, Karl; Stassun, Keivan; Tan, Jonathan C.; Covey, Kevin R.; Da Rio, Nicola

2016-01-01

We study the nature of stellar multiplicity in young stellar systems using the INfrared Spectroscopy of Young Nebulous Clusters (IN-SYNC) survey, carried out in SDSS III with the APOGEE spectrograph. Multi-epoch observations of thousands of low-mass stars in Orion A, NGC2264, NGC1333 and IC348 have been carried out, yielding H-band spectra with R=22,500 for sources with H<12 mag. Radial velocity sensitivities ~0.3 km/s can be achieved, depending on the spectral type of the star. We search the IN-SYNC radial velocity catalog to identify sources with radial velocity variations indicative of spectroscopically undetected companions, analyze their spectral properties and discuss the implications for the overall multiplicity of stellar populations in young, embedded star clusters.

Blue Stragglers in Clusters and Integrated Spectral Properties of Stellar Populations

NASA Astrophysics Data System (ADS)

Xin, Yu; Deng, Licai

Blue straggler stars are the most prominent bright objects in the colour-magnitude diagram of a star cluster that challenges the theory of stellar evolution. Star clusters are the closest counterparts of the theoretical concept of simple stellar populations (SSPs) in the Universe. SSPs are widely used as the basic building blocks to interpret stellar contents in galaxies. The concept of an SSP is a group of coeval stars which follows a given distribution in mass, and has the same chemical property and age. In practice, SSPs are more conveniently made by the latest stellar evolutionary models of single stars. In reality, however, stars can be more complicated than just single either at birth time or during the course of evolution in a typical environment. Observations of star clusters show that there are always exotic objects which do not follow the predictions of standard theory of stellar evolution. Blue straggler stars (BSSs), as discussed intensively in this book both observationally and theoretically, are very important in our context when considering the integrated spectral properties of a cluster, or a simple stellar population. In this chapter, we are going to describe how important the contribution of BSSs is to the total light of a cluster.

Fingerprinting of Non-resolved Three-axis Stabilized Space Objects Using a Two-Facet Analytical Model

DTIC Science & Technology

2011-09-01

Crawford, K., " Time - Resolved Infrared Spectrophotometric Observations of IRIDIUM satellites and related Resident Space Objects", IAC-09-A6.1.17...Figure 10 for a geosynchronous (GEO) satellite . The figure shows three sets of multi-spectral signatures were collected at different times of the...provides a simple method to determine suitable observation conditions for the cluster of satellites . For instance, on Day 0, the times of the

Characterization and identification of microorganisms by FT-IR microspectrometry

NASA Astrophysics Data System (ADS)

Ngo-Thi, N. A.; Kirschner, C.; Naumann, D.

2003-12-01

We report on a novel FT-IR approach for microbial characterization/identification based on a light microscope coupled to an infrared spectrometer which offers the possibility to acquire IR-spectra of microcolonies containing only few hundred cells. Microcolony samples suitable for FT-IR microspectroscopic measurements were obtained by a replica technique with a stamping device that transfers spatially accurate cells of microcolonies growing on solid culture plates to a special, IR-transparent or reflecting stamping plate. High quality spectra could be recorded either by applying the transmission/absorbance or the reflectance/absorbance mode of the infrared microscope. Signal to noise ratios higher than 1000 were obtained for microcolonies as small as 40 μm in diameter. Reproducibility levels were established that allowed species and strain identification. The differentiation and classification capacity of the FT-IR microscopic technique was tested for different selected microorganisms. Cluster and factor analysis methods were used to evaluate the complex spectral data. Excellent discrimination between bacteria and yeasts, and at the same time Gram-negative and Gram-positive bacterial strains was obtained. Twenty-two selected strains of different species within the genus Staphylococcus were repetitively measured and could be grouped into correct species cluster. Moreover, the results indicated that the method allows also identifications at the subspecies level. Additionally, the new approach allowed spectral mapping analysis of single colonies which provided spatially resolved characterization of growth heterogeneity within complex microbial populations such as colonies.

Surface mapping via unsupervised classification of remote sensing: application to MESSENGER/MASCS and DAWN/VIRS data.

NASA Astrophysics Data System (ADS)

D'Amore, M.; Le Scaon, R.; Helbert, J.; Maturilli, A.

2017-12-01

Machine-learning achieved unprecedented results in high-dimensional data processing tasks with wide applications in various fields. Due to the growing number of complex nonlinear systems that have to be investigated in science and the bare raw size of data nowadays available, ML offers the unique ability to extract knowledge, regardless the specific application field. Examples are image segmentation, supervised/unsupervised/ semi-supervised classification, feature extraction, data dimensionality analysis/reduction.The MASCS instrument has mapped Mercury surface in the 400-1145 nm wavelength range during orbital observations by the MESSENGER spacecraft. We have conducted k-means unsupervised hierarchical clustering to identify and characterize spectral units from MASCS observations. The results display a dichotomy: a polar and equatorial units, possibly linked to compositional differences or weathering due to irradiation. To explore possible relations between composition and spectral behavior, we have compared the spectral provinces with elemental abundance maps derived from MESSENGER's X-Ray Spectrometer (XRS).For the Vesta application on DAWN Visible and infrared spectrometer (VIR) data, we explored several Machine Learning techniques: image segmentation method, stream algorithm and hierarchical clustering.The algorithm successfully separates the Olivine outcrops around two craters on Vesta's surface [1]. New maps summarizing the spectral and chemical signature of the surface could be automatically produced.We conclude that instead of hand digging in data, scientist could choose a subset of algorithms with well known feature (i.e. efficacy on the particular problem, speed, accuracy) and focus their effort in understanding what important characteristic of the groups found in the data mean. [1] E Ammannito et al. "Olivine in an unexpected location on Vesta's surface". In: Nature 504.7478 (2013), pp. 122-125.

Searching for the 3.5 keV Line in the Stacked Suzaku Observations of Galaxy Clusters

NASA Technical Reports Server (NTRS)

Bulbul, Esra; Markevitch, Maxim; Foster, Adam; Miller, Eric; Bautz, Mark; Lowenstein, Mike; Randall, Scott W.; Smith, Randall K.

2016-01-01

We perform a detailed study of the stacked Suzaku observations of 47 galaxy clusters, spanning a redshift range of 0.01-0.45, to search for the unidentified 3.5 keV line. This sample provides an independent test for the previously detected line. We detect a 2sigma-significant spectral feature at 3.5 keV in the spectrum of the full sample. When the sample is divided into two subsamples (cool-core and non-cool core clusters), the cool-core subsample shows no statistically significant positive residuals at the line energy. A very weak (approx. 2sigma confidence) spectral feature at 3.5 keV is permitted by the data from the non-cool-core clusters sample. The upper limit on a neutrino decay mixing angle of sin(sup 2)(2theta) = 6.1 x 10(exp -11) from the full Suzaku sample is consistent with the previous detections in the stacked XMM-Newton sample of galaxy clusters (which had a higher statistical sensitivity to faint lines), M31, and Galactic center, at a 90% confidence level. However, the constraint from the present sample, which does not include the Perseus cluster, is in tension with previously reported line flux observed in the core of the Perseus cluster with XMM-Newton and Suzaku.

X-ray spectral observations of clusters of galaxies undergoing merger events

NASA Astrophysics Data System (ADS)

Henriksen, Mark J.

1993-09-01

We have analyzed the HEAO 1 A2 observations of two clusters whose optical and X-ray isophotes are suggestive of merging subclusters, A119 and A754, and find evidence of nonisothermal X-ray emission from both clusters. The X-ray spectrum of both clusters, when fitted with a single isothermal model, shows residual soft X-ray emission. There is a statistically significant reduction in chi-squared (98 percent probability based on the F-test) when a second temperature component is added. If the asymmetric isophotes seen in the soft X-ray image are indicative of merging subclusters, then our analysis of the Einstein IPC spectra and Solid State Spectrometer observations of A754, which provide some spatial and spectral resolution, suggests that the two temperature components seen in the HEAO 1 A2 spectra are associated with gas trapped in the subcluster potential wells. The implied subcluster isothermal masses suggest that a more massive cluster is accreting a less massive companion in A754. The present observations cannot rule out the alternative possibility that the cooler gas is associated with the outer cluster atmosphere rather than individual subclusters, as appears to be the case for A119. Astro D observations will be necessary to distinguish between these two possibilities for both clusters.

Spectral characteristics and the extent of paleosols of the Palouse formation

NASA Technical Reports Server (NTRS)

Frazier, B. E.; Busacca, A.; Cheng, Y.; Wherry, D.; Hart, J.; Gill, S.

1986-01-01

Spectral relationships were investigated for several bare soil fields which were in summer fallow rotation on the date of the imagery. Printouts of each band were examined and compared to aerial photography. Bands with dissimilar reflectance patterns for known areas were then combined using ratio techniques which were proven useful in other studies (Williams, 1983). Selected ratios were Thematic Mapper (TM) 1/TM4, TM3/TM4, and TM5/TM4. Cluster analyses and Baysian and Fastclass classifier images were produced using the three ratio images. Plots of cluster analysis outputs revealed distinct groupings of reflectance data representing green crops, ripened crops, soil and green plants, and bare soil. Bare soil was represented by a line of clusters on plots of the ratios TM5/TM4 and TM3/TM4. The soil line was investigated further to determine factors involved in the distributin of clusters alone the line. The clusters representing the bare soil line were also studied by plotting the Tm5/TM4, TM1/TM4 dimension. A total of 76 soil samples were gathered and analyzed for organic carbon.

Absorption line indices in the UV. I. Empirical and theoretical stellar population models

NASA Astrophysics Data System (ADS)

Maraston, C.; Nieves Colmenárez, L.; Bender, R.; Thomas, D.

2009-01-01

Aims: Stellar absorption lines in the optical (e.g. the Lick system) have been extensively studied and constitute an important stellar population diagnostic for galaxies in the local universe and up to moderate redshifts. Proceeding towards higher look-back times, galaxies are younger and the ultraviolet becomes the relevant spectral region where the dominant stellar populations shine. A comprehensive study of ultraviolet absorption lines of stellar population models is however still lacking. With this in mind, we study absorption line indices in the far and mid-ultraviolet in order to determine age and metallicity indicators for UV-bright stellar populations in the local universe as well as at high redshift. Methods: We explore empirical and theoretical spectral libraries and use evolutionary population synthesis to compute synthetic line indices of stellar population models. From the empirical side, we exploit the IUE-low resolution library of stellar spectra and system of absorption lines, from which we derive analytical functions (fitting functions) describing the strength of stellar line indices as a function of gravity, temperature and metallicity. The fitting functions are entered into an evolutionary population synthesis code in order to compute the integrated line indices of stellar populations models. The same line indices are also directly evaluated on theoretical spectral energy distributions of stellar population models based on Kurucz high-resolution synthetic spectra, In order to select indices that can be used as age and/or metallicity indicators for distant galaxies and globular clusters, we compare the models to data of template globular clusters from the Magellanic Clouds with independently known ages and metallicities. Results: We provide synthetic line indices in the wavelength range ~1200 Å to ~3000 Å for stellar populations of various ages and metallicities.This adds several new indices to the already well-studied CIV and SiIV absorptions. Based on the comparison with globular cluster data, we select a set of 11 indices blueward of the 2000 Å rest-frame that allows us to recover well the ages and the metallicities of the clusters. These indices are ideal to study ages and metallicities of young galaxies at high redshift. We also provide the synthetic high-resolution stellar population SEDs.

A comprehensive comparative test of seven widely used spectral synthesis models against multi-band photometry of young massive-star clusters

NASA Astrophysics Data System (ADS)

Wofford, A.; Charlot, S.; Bruzual, G.; Eldridge, J. J.; Calzetti, D.; Adamo, A.; Cignoni, M.; de Mink, S. E.; Gouliermis, D. A.; Grasha, K.; Grebel, E. K.; Lee, J. C.; Östlin, G.; Smith, L. J.; Ubeda, L.; Zackrisson, E.

2016-04-01

We test the predictions of spectral synthesis models based on seven different massive-star prescriptions against Legacy ExtraGalactic UV Survey (LEGUS) observations of eight young massive clusters in two local galaxies, NGC 1566 and NGC 5253, chosen because predictions of all seven models are available at the published galactic metallicities. The high angular resolution, extensive cluster inventory, and full near-ultraviolet to near-infrared photometric coverage make the LEGUS data set excellent for this study. We account for both stellar and nebular emission in the models and try two different prescriptions for attenuation by dust. From Bayesian fits of model libraries to the observations, we find remarkably low dispersion in the median E(B - V) (˜0.03 mag), stellar masses (˜104 M⊙), and ages (˜1 Myr) derived for individual clusters using different models, although maximum discrepancies in these quantities can reach 0.09 mag and factors of 2.8 and 2.5, respectively. This is for ranges in median properties of 0.05-0.54 mag, 1.8-10 × 104 M⊙, and 1.6-40 Myr spanned by the clusters in our sample. In terms of best fit, the observations are slightly better reproduced by models with interacting binaries and least well reproduced by models with single rotating stars. Our study provides a first quantitative estimate of the accuracies and uncertainties of the most recent spectral synthesis models of young stellar populations, demonstrates the good progress of models in fitting high-quality observations, and highlights the needs for a larger cluster sample and more extensive tests of the model parameter space.

Six-port optical switch for cluster-mesh photonic network-on-chip

NASA Astrophysics Data System (ADS)

Jia, Hao; Zhou, Ting; Zhao, Yunchou; Xia, Yuhao; Dai, Jincheng; Zhang, Lei; Ding, Jianfeng; Fu, Xin; Yang, Lin

2018-05-01

Photonic network-on-chip for high-performance multi-core processors has attracted substantial interest in recent years as it offers a systematic method to meet the demand of large bandwidth, low latency and low power dissipation. In this paper we demonstrate a non-blocking six-port optical switch for cluster-mesh photonic network-on-chip. The architecture is constructed by substituting three optical switching units of typical Spanke-Benes network to optical waveguide crossings. Compared with Spanke-Benes network, the number of optical switching units is reduced by 20%, while the connectivity of routing path is maintained. By this way the footprint and power consumption can be reduced at the expense of sacrificing the network latency performance in some cases. The device is realized by 12 thermally tuned silicon Mach-Zehnder optical switching units. Its theoretical spectral responses are evaluated by establishing a numerical model. The experimental spectral responses are also characterized, which indicates that the optical signal-to-noise ratios of the optical switch are larger than 13.5 dB in the wavelength range from 1525 nm to 1565 nm. Data transmission experiment with the data rate of 32 Gbps is implemented for each optical link.

Spectroscopic studies of clusterization of methanol molecules isolated in a nitrogen matrix

NASA Astrophysics Data System (ADS)

Vaskivskyi, Ye.; Doroshenko, I.; Chernolevska, Ye.; Pogorelov, V.; Pitsevich, G.

2017-12-01

IR absorption spectra of methanol isolated in a nitrogen matrix are recorded at temperatures ranging from 9 to 34 K. The changes in the spectra with increasing matrix temperature are analyzed. Based on quantum-chemical calculations of the geometric and spectral parameters of different methanol clusters, the observed absorption bands are identified. The cluster composition of the sample is determined at each temperature. It is shown that as the matrix is heated there is a redistribution among the different cluster structures in the sample, from smaller to larger clusters.

Multi-band morpho-Spectral Component Analysis Deblending Tool (MuSCADeT): Deblending colourful objects

NASA Astrophysics Data System (ADS)

Joseph, R.; Courbin, F.; Starck, J.-L.

2016-05-01

We introduce a new algorithm for colour separation and deblending of multi-band astronomical images called MuSCADeT which is based on Morpho-spectral Component Analysis of multi-band images. The MuSCADeT algorithm takes advantage of the sparsity of astronomical objects in morphological dictionaries such as wavelets and their differences in spectral energy distribution (SED) across multi-band observations. This allows us to devise a model independent and automated approach to separate objects with different colours. We show with simulations that we are able to separate highly blended objects and that our algorithm is robust against SED variations of objects across the field of view. To confront our algorithm with real data, we use HST images of the strong lensing galaxy cluster MACS J1149+2223 and we show that MuSCADeT performs better than traditional profile-fitting techniques in deblending the foreground lensing galaxies from background lensed galaxies. Although the main driver for our work is the deblending of strong gravitational lenses, our method is fit to be used for any purpose related to deblending of objects in astronomical images. An example of such an application is the separation of the red and blue stellar populations of a spiral galaxy in the galaxy cluster Abell 2744. We provide a python package along with all simulations and routines used in this paper to contribute to reproducible research efforts. Codes can be found at http://lastro.epfl.ch/page-126973.html

Functional Connectivity Changes in Resting-State EEG as Potential Biomarker for Amyotrophic Lateral Sclerosis.

PubMed

Iyer, Parameswaran Mahadeva; Egan, Catriona; Pinto-Grau, Marta; Burke, Tom; Elamin, Marwa; Nasseroleslami, Bahman; Pender, Niall; Lalor, Edmund C; Hardiman, Orla

2015-01-01

Amyotrophic Lateral Sclerosis (ALS) is heterogeneous and overlaps with frontotemporal dementia. Spectral EEG can predict damage in structural and functional networks in frontotemporal dementia but has never been applied to ALS. 18 incident ALS patients with normal cognition and 17 age matched controls underwent 128 channel EEG and neuropsychology assessment. The EEG data was analyzed using FieldTrip software in MATLAB to calculate simple connectivity measures and scalp network measures. sLORETA was used in nodal analysis for source localization and same methods were applied as above to calculate nodal network measures. Graph theory measures were used to assess network integrity. Cross spectral density in alpha band was higher in patients. In ALS patients, increased degree values of the network nodes was noted in the central and frontal regions in the theta band across seven of the different connectivity maps (p<0.0005). Among patients, clustering coefficient in alpha and gamma bands was increased in all regions of the scalp and connectivity were significantly increased (p=0.02). Nodal network showed increased assortativity in alpha band in the patients group. The Clustering Coefficient in Partial Directed Connectivity (PDC) showed significantly higher values for patients in alpha, beta, gamma, theta and delta frequencies (p=0.05). There is increased connectivity in the fronto-central regions of the scalp and areas corresponding to Salience and Default Mode network in ALS, suggesting a pathologic disruption of neuronal networking in early disease states. Spectral EEG has potential utility as a biomarker in ALS.

A comparison of autonomous techniques for multispectral image analysis and classification

NASA Astrophysics Data System (ADS)

Valdiviezo-N., Juan C.; Urcid, Gonzalo; Toxqui-Quitl, Carina; Padilla-Vivanco, Alfonso

2012-10-01

Multispectral imaging has given place to important applications related to classification and identification of objects from a scene. Because of multispectral instruments can be used to estimate the reflectance of materials in the scene, these techniques constitute fundamental tools for materials analysis and quality control. During the last years, a variety of algorithms has been developed to work with multispectral data, whose main purpose has been to perform the correct classification of the objects in the scene. The present study introduces a brief review of some classical as well as a novel technique that have been used for such purposes. The use of principal component analysis and K-means clustering techniques as important classification algorithms is here discussed. Moreover, a recent method based on the min-W and max-M lattice auto-associative memories, that was proposed for endmember determination in hyperspectral imagery, is introduced as a classification method. Besides a discussion of their mathematical foundation, we emphasize their main characteristics and the results achieved for two exemplar images conformed by objects similar in appearance, but spectrally different. The classification results state that the first components computed from principal component analysis can be used to highlight areas with different spectral characteristics. In addition, the use of lattice auto-associative memories provides good results for materials classification even in the cases where some spectral similarities appears in their spectral responses.

Electrolytic synthesis of aqueous aluminum nanoclusters and in situ characterization by femtosecond Raman spectroscopy and computations

PubMed Central

Wang, Wei; Liu, Weimin; Chang, I-Ya; Wills, Lindsay A.; Zakharov, Lev N.; Boettcher, Shannon W.; Cheong, Paul Ha-Yeon; Fang, Chong; Keszler, Douglas A.

2013-01-01

The selective synthesis and in situ characterization of aqueous Al-containing clusters is a long-standing challenge. We report a newly developed integrated platform that combines (i) a selective, atom-economical, step-economical, scalable synthesis of Al-containing nanoclusters in water via precision electrolysis with strict pH control and (ii) an improved femtosecond stimulated Raman spectroscopic method covering a broad spectral range of ca. 350–1,400 cm−1 with high sensitivity, aided by ab initio computations, to elucidate Al aqueous cluster structures and formation mechanisms in real time. Using this platform, a unique view of flat [Al13(μ3-OH)6(μ2-OH)18(H2O)24](NO3)15 nanocluster formation is observed in water, in which three distinct reaction stages are identified. The initial stage involves the formation of an [Al7(μ3-OH)6(μ2-OH)6(H2O)12]9+ cluster core as an important intermediate toward the flat Al13 aqueous cluster. PMID:24167254

A Catalog of Galaxy Clusters Observed by XMM-Newton

NASA Technical Reports Server (NTRS)

Snowden, S. L.; Mushotzky, R. M.; Kuntz, K. D.; Davis, David S.

2007-01-01

Images and the radial profiles of the temperature, abundance, and brightness for 70 clusters of galaxies observed by XMM-Newton are presented along with a detailed discussion of the data reduction and analysis methods, including background modeling, which were used in the processing. Proper consideration of the various background components is vital to extend the reliable determination of cluster parameters to the largest possible cluster radii. The various components of the background including the quiescent particle background, cosmic diffuse emission, soft proton contamination, and solar wind charge exchange emission are discussed along with suggested means of their identification, filtering, and/or their modeling and subtraction. Every component is spectrally variable, sometimes significantly so, and all components except the cosmic background are temporally variable as well. The distributions of the events over the FOV vary between the components, and some distributions vary with energy. The scientific results from observations of low surface brightness objects and the diffuse background itself can be strongly affected by these background components and therefore great care should be taken in their consideration.

A novel combined approach of diffuse reflectance UV-Vis-NIR spectroscopy and multivariate analysis for non-destructive examination of blue ballpoint pen inks in forensic application

NASA Astrophysics Data System (ADS)

Kumar, Raj; Sharma, Vishal

2017-03-01

The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%).

Détection des transitions lithologiques par l'analyse de la composante fractale des diagraphies par transformée continue en ondelettes

NASA Astrophysics Data System (ADS)

Zaourar, Naima; Hamoudi, Mohamed; Briqueu, Louis

2006-06-01

The frequency analysis of many log data permits to verify that their stochastic component show 'power-law-type' spectral densities, characteristic of 1/f noise. They can be modelled by fractional Brownian motions. Continuous Wavelet Transformation (CWT) provides us with very efficient methods to determine the local spectral exponents of these scaling laws. These new attributes are related to the local fractality of these signals. We first present some theoretical results and an application to a fractional Brownian motion. The second application concerns a dataset recorded in the MAR203 borehole. We show that clustering of these new pseudo-logs leads to a good resolution between different lithofacies. To cite this article: N. Zaourar et al., C. R. Geoscience 338 (2006).

Density-Aware Clustering Based on Aggregated Heat Kernel and Its Transformation

DOE PAGES

Huang, Hao; Yoo, Shinjae; Yu, Dantong; ...

2015-06-01

Current spectral clustering algorithms suffer from the sensitivity to existing noise, and parameter scaling, and may not be aware of different density distributions across clusters. If these problems are left untreated, the consequent clustering results cannot accurately represent true data patterns, in particular, for complex real world datasets with heterogeneous densities. This paper aims to solve these problems by proposing a diffusion-based Aggregated Heat Kernel (AHK) to improve the clustering stability, and a Local Density Affinity Transformation (LDAT) to correct the bias originating from different cluster densities. AHK statistically\\ models the heat diffusion traces along the entire time scale, somore » it ensures robustness during clustering process, while LDAT probabilistically reveals local density of each instance and suppresses the local density bias in the affinity matrix. Our proposed framework integrates these two techniques systematically. As a result, not only does it provide an advanced noise-resisting and density-aware spectral mapping to the original dataset, but also demonstrates the stability during the processing of tuning the scaling parameter (which usually controls the range of neighborhood). Furthermore, our framework works well with the majority of similarity kernels, which ensures its applicability to many types of data and problem domains. The systematic experiments on different applications show that our proposed algorithms outperform state-of-the-art clustering algorithms for the data with heterogeneous density distributions, and achieve robust clustering performance with respect to tuning the scaling parameter and handling various levels and types of noise.« less

Ir Spectroscopic Studies on Microsolvation of HCl by Water

NASA Astrophysics Data System (ADS)

Mani, Devendra; Schwan, Raffael; Fischer, Theo; Dey, Arghya; Kaufmann, Matin; Redlich, Britta; van der Meer, Lex; Schwaab, Gerhard; Havenith, Martina

2016-06-01

Acid dissociation reactions are at the heart of chemistry. These reactions are well understood at the macroscopic level. However, a microscopic level understanding is still in the early stages of development. Questions such as 'how many H_2O molecules are needed to dissociate one HCl molecule?' have been posed and explored both theoretically and experimentally.1-5 Most of the theoretical calculations predict that four H_2O molecules are sufficient to dissociate one HCl molecule, resulting in the formation of a solvent separated H_3O+(H_2O)3Cl- cluster.1-3 IR spectroscopy in helium nanodroplets has earlier been used to study this dissociation process.3-5 However, these studies were carried out in the region of O-H and H-Cl stretch, which is dominated by the spectral features of undissociated (HCl)m-(H_2O)n clusters. This contributed to the ambiguity in assigning the spectral features arising from the dissociated cluster.4,5 Recent predictions from Bowman's group, suggest the presence of a broad spectral feature (1300-1360 wn) for the H_3O+(H_2O)3Cl- cluster, corresponding to the umbrella motion of H_3O+ moiety.6 This region is expected to be free from the spectral features due to the undissociated clusters. In conjunction with the FELIX laboratory, we have performed experiments on the (HCl)m(H_2O)n (m=1-2, n≥4) clusters, aggregated in helium nanodroplets, in the 900-1700 wn region. Mass selective measurements on these clusters revealed the presence of a weak-broad feature which spans between 1000-1450 wn and depends on both HCl as well as H_2O concentration. Measurements are in progress for the different deuterated species. The details will be presented in the talk. References: 1) C.T. Lee et al., J. Chem. Phys., 104, 7081 (1996). 2) H. Forbert et al., J. Am. Chem. Soc., 133, 4062 (2011). 3) A. Gutberlet et al., Science, 324, 1545 (2009). 4) S. D. Flynn et al., J. Phys. Chem. Lett., 1, 2233 (2010). 5) M. Letzner et al., J. Chem. Phys., 139, 154304 (2013). 6) J. M. Bowman et al., Phys. Chem. Chem. Phys., 17, 6222 (2015).

Improving Spectral Image Classification through Band-Ratio Optimization and Pixel Clustering

NASA Astrophysics Data System (ADS)

O'Neill, M.; Burt, C.; McKenna, I.; Kimblin, C.

2017-12-01

The Underground Nuclear Explosion Signatures Experiment (UNESE) seeks to characterize non-prompt observables from underground nuclear explosions (UNE). As part of this effort, we evaluated the ability of DigitalGlobe's WorldView-3 (WV3) to detect and map UNE signatures. WV3 is the current state-of-the-art, commercial, multispectral imaging satellite; however, it has relatively limited spectral and spatial resolutions. These limitations impede image classifiers from detecting targets that are spatially small and lack distinct spectral features. In order to improve classification results, we developed custom algorithms to reduce false positive rates while increasing true positive rates via a band-ratio optimization and pixel clustering front-end. The clusters resulting from these algorithms were processed with standard spectral image classifiers such as Mixture-Tuned Matched Filter (MTMF) and Adaptive Coherence Estimator (ACE). WV3 and AVIRIS data of Cuprite, Nevada, were used as a validation data set. These data were processed with a standard classification approach using MTMF and ACE algorithms. They were also processed using the custom front-end prior to the standard approach. A comparison of the results shows that the custom front-end significantly increases the true positive rate and decreases the false positive rate.This work was done by National Security Technologies, LLC, under Contract No. DE-AC52-06NA25946 with the U.S. Department of Energy. DOE/NV/25946-3283.

The software and algorithms for hyperspectral data processing

NASA Astrophysics Data System (ADS)

Shyrayeva, Anhelina; Martinov, Anton; Ivanov, Victor; Katkovsky, Leonid

2017-04-01

Hyperspectral remote sensing technique is widely used for collecting and processing -information about the Earth's surface objects. Hyperspectral data are combined to form a three-dimensional (x, y, λ) data cube. Department of Aerospace Research of the Institute of Applied Physical Problems of the Belarusian State University presents a general model of the software for hyperspectral image data analysis and processing. The software runs in Windows XP/7/8/8.1/10 environment on any personal computer. This complex has been has been written in C++ language using QT framework and OpenGL for graphical data visualization. The software has flexible structure that consists of a set of independent plugins. Each plugin was compiled as Qt Plugin and represents Windows Dynamic library (dll). Plugins can be categorized in terms of data reading types, data visualization (3D, 2D, 1D) and data processing The software has various in-built functions for statistical and mathematical analysis, signal processing functions like direct smoothing function for moving average, Savitzky-Golay smoothing technique, RGB correction, histogram transformation, and atmospheric correction. The software provides two author's engineering techniques for the solution of atmospheric correction problem: iteration method of refinement of spectral albedo's parameters using Libradtran and analytical least square method. The main advantages of these methods are high rate of processing (several minutes for 1 GB data) and low relative error in albedo retrieval (less than 15%). Also, the software supports work with spectral libraries, region of interest (ROI) selection, spectral analysis such as cluster-type image classification and automatic hypercube spectrum comparison by similarity criterion with similar ones from spectral libraries, and vice versa. The software deals with different kinds of spectral information in order to identify and distinguish spectrally unique materials. Also, the following advantages should be noted: fast and low memory hypercube manipulation features, user-friendly interface, modularity, and expandability.

Validating clustering of molecular dynamics simulations using polymer models.

PubMed

Phillips, Joshua L; Colvin, Michael E; Newsam, Shawn

2011-11-14

Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers.

Validating clustering of molecular dynamics simulations using polymer models

PubMed Central

2011-01-01

Background Molecular dynamics (MD) simulation is a powerful technique for sampling the meta-stable and transitional conformations of proteins and other biomolecules. Computational data clustering has emerged as a useful, automated technique for extracting conformational states from MD simulation data. Despite extensive application, relatively little work has been done to determine if the clustering algorithms are actually extracting useful information. A primary goal of this paper therefore is to provide such an understanding through a detailed analysis of data clustering applied to a series of increasingly complex biopolymer models. Results We develop a novel series of models using basic polymer theory that have intuitive, clearly-defined dynamics and exhibit the essential properties that we are seeking to identify in MD simulations of real biomolecules. We then apply spectral clustering, an algorithm particularly well-suited for clustering polymer structures, to our models and MD simulations of several intrinsically disordered proteins. Clustering results for the polymer models provide clear evidence that the meta-stable and transitional conformations are detected by the algorithm. The results for the polymer models also help guide the analysis of the disordered protein simulations by comparing and contrasting the statistical properties of the extracted clusters. Conclusions We have developed a framework for validating the performance and utility of clustering algorithms for studying molecular biopolymer simulations that utilizes several analytic and dynamic polymer models which exhibit well-behaved dynamics including: meta-stable states, transition states, helical structures, and stochastic dynamics. We show that spectral clustering is robust to anomalies introduced by structural alignment and that different structural classes of intrinsically disordered proteins can be reliably discriminated from the clustering results. To our knowledge, our framework is the first to utilize model polymers to rigorously test the utility of clustering algorithms for studying biopolymers. PMID:22082218

Spectral reflectance of surface soils - A statistical analysis

NASA Technical Reports Server (NTRS)

Crouse, K. R.; Henninger, D. L.; Thompson, D. R.

1983-01-01

The relationship of the physical and chemical properties of soils to their spectral reflectance as measured at six wavebands of Thematic Mapper (TM) aboard NASA's Landsat-4 satellite was examined. The results of performing regressions of over 20 soil properties on the six TM bands indicated that organic matter, water, clay, cation exchange capacity, and calcium were the properties most readily predicted from TM data. The middle infrared bands, bands 5 and 7, were the best bands for predicting soil properties, and the near infrared band, band 4, was nearly as good. Clustering 234 soil samples on the TM bands and characterizing the clusters on the basis of soil properties revealed several clear relationships between properties and reflectance. Discriminant analysis found organic matter, fine sand, base saturation, sand, extractable acidity, and water to be significant in discriminating among clusters.

Peak clustering in two-dimensional gas chromatography with mass spectrometric detection based on theoretical calculation of two-dimensional peak shapes: the 2DAid approach.

PubMed

van Stee, Leo L P; Brinkman, Udo A Th

2011-10-28

A method is presented to facilitate the non-target analysis of data obtained in temperature-programmed comprehensive two-dimensional (2D) gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-ToF-MS). One main difficulty of GC×GC data analysis is that each peak is usually modulated several times and therefore appears as a series of peaks (or peaklets) in the one-dimensionally recorded data. The proposed method, 2DAid, uses basic chromatographic laws to calculate the theoretical shape of a 2D peak (a cluster of peaklets originating from the same analyte) in order to define the area in which the peaklets of each individual compound can be expected to show up. Based on analyte-identity information obtained by means of mass spectral library searching, the individual peaklets are then combined into a single 2D peak. The method is applied, amongst others, to a complex mixture containing 362 analytes. It is demonstrated that the 2D peak shapes can be accurately predicted and that clustering and further processing can reduce the final peak list to a manageable size. Copyright © 2011 Elsevier B.V. All rights reserved.

Survey of the spectral properties of turbulence in the solar wind, the magnetospheres of Venus and Earth, at solar minimum and maximum

NASA Astrophysics Data System (ADS)

Echim, Marius M.

2014-05-01

In the framework of the European FP7 project STORM ("Solar system plasma Turbulence: Observations, inteRmittency and Multifractals") we analyze the properties of turbulence in various regions of the solar system, for the minimum and respectively maximum of the solar activity. The main scientific objective of STORM is to advance the understanding of the turbulent energy transfer, intermittency and multifractals in space plasmas. Specific analysis methods are applied on magnetic field and plasma data provided by Ulysses, Venus Express and Cluster, as well as other solar system missions (e.g. Giotto, Cassini). In this paper we provide an overview of the spectral properties of turbulence derived from Power Spectral Densities (PSD) computed in the solar wind (from Ulysses, Cluster, Venus Express) and at the interface of planetary magnetospheres with the solar wind (from Venus Express, Cluster). Ulysses provides data in the solar wind between 1992 and 2008, out of the ecliptic, at radial distances ranging between 1.3 and 5.4 AU. We selected only those Ulysses data that satisfy a consolidated set of selection criteria able to identify "pure" fast and slow wind. We analyzed Venus Express data close to the orbital apogee, in the solar wind, at 0.72 AU, and in the Venus magnetosheath. We investigated Cluster data in the solar wind (for time intervals not affected by planetary ions effects), the magnetosheath and few crossings of other key magnetospheric regions (cusp, plasma sheet). We organize our PSD results in three solar wind data bases (one for the solar maximum, 1999-2001, two for the solar minimum, 1997-1998 and respectively, 2007-2008), and two planetary databases (one for the solar maximum, 2000-2001, that includes PSD obtained in the terrestrial magnetosphere, and one for the solar minimum, 2007-2008, that includes PSD obtained in the terrestrial and Venus magnetospheres and magnetosheaths). In addition to investigating the properties of turbulence for the minimum and maximum of the solar cycle we also analyze the spectral similarities and differences between fast and slow wind turbulence. We emphasize the importance of our data survey and analysis in the context of understanding the solar wind turbulence, the exploitation of data bases and as a first step towards developing a (virtual) laboratory for studying solar system plasma turbulence. Research supported by the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement no 313038/STORM, and a grant of the Romanian Ministry of National Education, CNCS - UEFISCDI, project number PN-II-ID-PCE-2012-4-0418.

Simulated MERTIS observation of the Rudaki-Kuiper craters area on Mercury

NASA Astrophysics Data System (ADS)

D'Amore, M.; Helbert, J.; Maturilli, A.; Ferrari, S.; Bauch, K.; D'Incecco, P.; Hiesinger, H.; Head, J. W.; Holsclaw, G. M.; Lorin, D. D.; Denevi, B. W.; Stockstill-Cahill, K. R.

2013-12-01

The MErcury Radiometer and Thermal infrared Imaging Spectrometer (MERTIS) is part of the payload of the BepiColombo mission. The mission is scheduled for launch in 2015 with arrival at Mercury in 2021. To achieve MERTIS's scientific goals the instrument maps the surface of Mercury with a spatial resolution of 500m for the spectrometer channel and 2km for the radiometer channel. MERTIS spans wavelength ranges of 7-14 and 7-40 μm with its two channels. Among it scientific goals, MERTIS will infer rock-forming minerals, map surface composition, and study surface temperature variations on Mercury with an uncooled microbolometer detector. To exploit the full potential of the unique MERTIS dataset, an extensive calibration campaign has been performed. This includes radiometric, spectral, and geometric calibration. In addition we have performed measurement of analog materials at temperatures of up to 500°C - similar to the peak temperatures expected at Mercury - with the MERTIS qualification model in the Planetary Emissivity Laboratory. These measurements allow for the evaluation of the MERTIS performance in direct comparison with the laboratory spectrometer. They also enable the creation of synthetic MERTIS datasets. For this purpose we use data from the MErcury Surface, Space ENvironment, GEochemistry, and Ranging (MESSENGER) spacecraft as baseline. MESSENGER can provide geological information as well as spectral information in the UV, visible and near-infrared wavelengths range. For a first test we have selected the Kuiper-Rudaki region. The region has been extensively covered by measurements from the MESSENGER spacecraft. Recent analysis of observations by the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) instrument on the MESSENGER spacecraft with an unsupervised hierarchical clustering method shows at global scales two major units: a Polar region (PR) spectrally flat and redder than the equatorial region (ER). The study area is primarily classified as a homogeneous expanse of the equatorial region (ER) cluster. Further clustering shows that the study area belongs to the 'core' ER, in opposition to some smaller patches of a transitional sub-unit, that are transitional region between global ER and the polar region (PR) cluster. Assuming a set of several potential mineralogies for the study area and modeled surface temperature at different local times we can obtain at PEL the spectra in the mid-infrared spectral range. Combining these with our knowledge of the MERTIS performance we can produce simulated MERTIS datasets of the study region at different point of the mission.

The radio relics and halo of El Gordo, a massive z = 0.870 cluster merger

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lindner, Robert R.; Baker, Andrew J.; Hughes, John P.

We present 610 MHz and 2.1 GHz imaging of the massive Sunyaev-Zel'dovich Effect selected z = 0.870 cluster merger ACT-CL J0102–4915 ({sup E}l Gordo{sup )}, obtained with the Giant Metrewave Radio Telescope and the Australia Telescope Compact Array (ATCA), respectively. We detect two complexes of radio relics separated by 3.'4 (1.6 Mpc) along the system's northwest-to-southeast collision axis that have high integrated polarization fractions (33%) and steep spectral indices (α between 1 and 2; S {sub ν}∝ν{sup –α}), consistent with creation via Fermi acceleration by shocks in the intracluster medium triggered by the cluster collision. From the spectral index ofmore » the relics, we compute a Mach number M=2.5{sub −0.3}{sup +0.7} and shock speed of 2500{sub −300}{sup +400} km s{sup −1}. With our wide-bandwidth, full-polarization ATCA data, we compute the Faraday depth φ across the northwest relic and find a range of values spanning Δφ = 30 rad m{sup –2}, with a mean value of (φ) = 11 rad m{sup –2} and standard deviation σ{sub φ} = 6 rad m{sup –2}. With the integrated line-of-sight gas density derived from new Chandra X-ray observations, our Faraday depth measurement implies B {sub ∥} ∼ 0.01 μG in the cluster outskirts. The extremely narrow shock widths in the relics (d {sub shock} ≤ 23 kpc), caused by the short synchrotron cooling timescale of relativistic electrons at z = 0.870, prevent us from placing a meaningful constraint on the magnetic field strength B using cooling time arguments. In addition to the relics, we detect a large (r {sub H} ≅ 1.1 Mpc radius), powerful (log (L {sub 1.4}/W Hz{sup –1}) = 25.66 ± 0.12) radio halo with a shape similar to El Gordo's 'bullet'-like X-ray morphology. The spatially resolved spectral-index map of the halo shows the synchrotron spectrum is flattest near the relics, along the system's collision axis, and in regions of high T {sub gas}, all locations associated with recent energy injection. The spatial and spectral correlation between the halo emission and cluster X-ray properties supports primary-electron processes like turbulent reacceleration as the halo production mechanism. The halo's integrated 610 MHz to 2.1 GHz spectral index is a relatively flat α = 1.2 ± 0.1, consistent with the cluster's high T {sub gas} in view of previously established global scaling relations. El Gordo is the highest-redshift cluster known to host a radio halo and/or radio relics, and provides new constraints on the non-thermal physics in clusters at z > 0.6.« less

MRI-alone radiation therapy planning for prostate cancer: Automatic fiducial marker detection.

PubMed

Ghose, Soumya; Mitra, Jhimli; Rivest-Hénault, David; Fazlollahi, Amir; Stanwell, Peter; Pichler, Peter; Sun, Jidi; Fripp, Jurgen; Greer, Peter B; Dowling, Jason A

2016-05-01

The feasibility of radiation therapy treatment planning using substitute computed tomography (sCT) generated from magnetic resonance images (MRIs) has been demonstrated by a number of research groups. One challenge with an MRI-alone workflow is the accurate identification of intraprostatic gold fiducial markers, which are frequently used for prostate localization prior to each dose delivery fraction. This paper investigates a template-matching approach for the detection of these seeds in MRI. Two different gradient echo T1 and T2* weighted MRI sequences were acquired from fifteen prostate cancer patients and evaluated for seed detection. For training, seed templates from manual contours were selected in a spectral clustering manifold learning framework. This aids in clustering "similar" gold fiducial markers together. The marker with the minimum distance to a cluster centroid was selected as the representative template of that cluster during training. During testing, Gaussian mixture modeling followed by a Markovian model was used in automatic detection of the probable candidates. The probable candidates were rigidly registered to the templates identified from spectral clustering, and a similarity metric is computed for ranking and detection. A fiducial detection accuracy of 95% was obtained compared to manual observations. Expert radiation therapist observers were able to correctly identify all three implanted seeds on 11 of the 15 scans (the proposed method correctly identified all seeds on 10 of the 15). An novel automatic framework for gold fiducial marker detection in MRI is proposed and evaluated with detection accuracies comparable to manual detection. When radiation therapists are unable to determine the seed location in MRI, they refer back to the planning CT (only available in the existing clinical framework); similarly, an automatic quality control is built into the automatic software to ensure that all gold seeds are either correctly detected or a warning is raised for further manual intervention.

A single population of red globular clusters around the massive compact galaxy NGC 1277

NASA Astrophysics Data System (ADS)

Beasley, Michael A.; Trujillo, Ignacio; Leaman, Ryan; Montes, Mireia

2018-03-01

Massive galaxies are thought to form in two phases: an initial collapse of gas and giant burst of central star formation, followed by the later accretion of material that builds up their stellar and dark-matter haloes. The systems of globular clusters within such galaxies are believed to form in a similar manner. The initial central burst forms metal-rich (spectrally red) clusters, whereas more metal-poor (spectrally blue) clusters are brought in by the later accretion of less-massive satellites. This formation process is thought to result in the multimodal optical colour distributions that are seen in the globular cluster systems of massive galaxies. Here we report optical observations of the massive relic-galaxy candidate NGC 1277—a nearby, un-evolved example of a high-redshift ‘red nugget’ galaxy. We find that the optical colour distribution of the cluster system of NGC 1277 is unimodal and entirely red. This finding is in strong contrast to other galaxies of similar and larger stellar mass, the cluster systems of which always exhibit (and are generally dominated by) blue clusters. We argue that the colour distribution of the cluster system of NGC 1277 indicates that the galaxy has undergone little (if any) mass accretion after its initial collapse, and use simulations of possible merger histories to show that the stellar mass due to accretion is probably at most ten per cent of the total stellar mass of the galaxy. These results confirm that NGC 1277 is a genuine relic galaxy and demonstrate that blue clusters constitute an accreted population in present-day massive galaxies.

A single population of red globular clusters around the massive compact galaxy NGC 1277.

PubMed

Beasley, Michael A; Trujillo, Ignacio; Leaman, Ryan; Montes, Mireia

2018-03-22

Massive galaxies are thought to form in two phases: an initial collapse of gas and giant burst of central star formation, followed by the later accretion of material that builds up their stellar and dark-matter haloes. The systems of globular clusters within such galaxies are believed to form in a similar manner. The initial central burst forms metal-rich (spectrally red) clusters, whereas more metal-poor (spectrally blue) clusters are brought in by the later accretion of less-massive satellites. This formation process is thought to result in the multimodal optical colour distributions that are seen in the globular cluster systems of massive galaxies. Here we report optical observations of the massive relic-galaxy candidate NGC 1277-a nearby, un-evolved example of a high-redshift 'red nugget' galaxy. We find that the optical colour distribution of the cluster system of NGC 1277 is unimodal and entirely red. This finding is in strong contrast to other galaxies of similar and larger stellar mass, the cluster systems of which always exhibit (and are generally dominated by) blue clusters. We argue that the colour distribution of the cluster system of NGC 1277 indicates that the galaxy has undergone little (if any) mass accretion after its initial collapse, and use simulations of possible merger histories to show that the stellar mass due to accretion is probably at most ten per cent of the total stellar mass of the galaxy. These results confirm that NGC 1277 is a genuine relic galaxy and demonstrate that blue clusters constitute an accreted population in present-day massive galaxies.

Rethinking CMB foregrounds: systematic extension of foreground parametrizations

NASA Astrophysics Data System (ADS)

Chluba, Jens; Hill, James Colin; Abitbol, Maximilian H.

2017-11-01

Future high-sensitivity measurements of the cosmic microwave background (CMB) anisotropies and energy spectrum will be limited by our understanding and modelling of foregrounds. Not only does more information need to be gathered and combined, but also novel approaches for the modelling of foregrounds, commensurate with the vast improvements in sensitivity, have to be explored. Here, we study the inevitable effects of spatial averaging on the spectral shapes of typical foreground components, introducing a moment approach, which naturally extends the list of foreground parameters that have to be determined through measurements or constrained by theoretical models. Foregrounds are thought of as a superposition of individual emitting volume elements along the line of sight and across the sky, which then are observed through an instrumental beam. The beam and line-of-sight averages are inevitable. Instead of assuming a specific model for the distributions of physical parameters, our method identifies natural new spectral shapes for each foreground component that can be used to extract parameter moments (e.g. mean, dispersion, cross terms, etc.). The method is illustrated for the superposition of power laws, free-free spectra, grey-body and modified blackbody spectra, but can be applied to more complicated fundamental spectral energy distributions. Here, we focus on intensity signals but the method can be extended to the case of polarized emission. The averaging process automatically produces scale-dependent spectral shapes and the moment method can be used to propagate the required information across scales in power spectrum estimates. The approach is not limited to applications to CMB foregrounds, but could also be useful for the modelling of X-ray emission in clusters of galaxies.

STAR FORMATION ACTIVITY IN A YOUNG GALAXY CLUSTER AT Z = 0.866

DOE Office of Scientific and Technical Information (OSTI.GOV)

Laganá, T. F.; Martins, L. P.; Ulmer, M. P.

2016-07-10

The galaxy cluster RX J1257+4738 at z = 0.866 is one of the highest redshift clusters with a richness of multi-wavelength data, and is thus a good target to study the star formation–density relation at early epochs. Using a sample of spectroscopically confirmed cluster members, we derive the star-formation rates (SFRs) of our galaxies using two methods: (1) the relation between SFR and total infrared luminosity extrapolated from the observed Spitzer Multiband Imaging Photometer for Spitzer 24 μ m imaging data; and (2) spectral energy distribution fitting using the MAGPHYS code, including eight different bands. We show that, for thismore » cluster, the SFR–density relation is very weak and seems to be dominated by the two central galaxies and the SFR presents a mild dependence on stellar mass, with more massive galaxies having higher SFR. However, the specific SFR (SSFR) decreases with stellar mass, meaning that more massive galaxies are forming fewer stars per unit of mass, and thus suggesting that the increase in star-forming members is driven by cluster assembly and infall. If the environment is somehow driving the star formation, one would expect a relation between the SSFR and the cluster centric distance, but that is not the case. A possible scenario to explain this lack of correlation is the contamination by infalling galaxies in the inner part of the cluster, which may be on their initial pass through the cluster center. As these galaxies have higher SFRs for their stellar mass, they enhance the mean SSFR in the center of the cluster.« less

Covariance descriptor fusion for target detection

NASA Astrophysics Data System (ADS)

Cukur, Huseyin; Binol, Hamidullah; Bal, Abdullah; Yavuz, Fatih

2016-05-01

Target detection is one of the most important topics for military or civilian applications. In order to address such detection tasks, hyperspectral imaging sensors provide useful images data containing both spatial and spectral information. Target detection has various challenging scenarios for hyperspectral images. To overcome these challenges, covariance descriptor presents many advantages. Detection capability of the conventional covariance descriptor technique can be improved by fusion methods. In this paper, hyperspectral bands are clustered according to inter-bands correlation. Target detection is then realized by fusion of covariance descriptor results based on the band clusters. The proposed combination technique is denoted Covariance Descriptor Fusion (CDF). The efficiency of the CDF is evaluated by applying to hyperspectral imagery to detect man-made objects. The obtained results show that the CDF presents better performance than the conventional covariance descriptor.

Multiple object redshift determinations in clusters of galaxies using OCTOPUS

NASA Astrophysics Data System (ADS)

Mazure, A.; Proust, D.; Sodre, L.; Capelato, H. V.; Lund, G.

1988-04-01

The ESO multiobject facility, Octopus, was used to observe a sample of galaxy clusters such as SC2008-565 in an attempt to collect a large set of individual radial velocities. A dispersion of 114 A/mm was used, providing spectral coverage from 3800 to 5180 A. Octopus was found to be a well-adapted instrument for the rapid and simultaneous determination of redshifts in cataloged galaxy clusters.

Multiple object redshift determinations in clusters of galaxies using OCTOPUS

NASA Astrophysics Data System (ADS)

Mazure, A.; Proust, D.; Sodre, L.; Lund, G.; Capelato, H.

1987-03-01

The ESO multiobject facility, Octopus, was used to observe a sample of galaxy clusters such as SC2008-565 in an attempt to collect a large set of individual radial velocities. A dispersion of 114 A/mm was used, providing spectral coverage from 3800 to 5180 A. Octopus was found to be a well-adapted instrument for the rapid and simultaneous determination of redshifts in cataloged galaxy clusters.

Discussion of CoSA: Clustering of Sparse Approximations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, Derek Elswick

2017-03-07

The purpose of this talk is to discuss the possible applications of CoSA (Clustering of Sparse Approximations) to the exploitation of HSI (HyperSpectral Imagery) data. CoSA is presented by Moody et al. in the Journal of Applied Remote Sensing (“Land cover classification in multispectral imagery using clustering of sparse approximations over learned feature dictionaries”, Vol. 8, 2014) and is based on machine learning techniques.

1997 Technical Digest Series. Volume 7: Applications of High Field and Short Wavelength Sources VII

DTIC Science & Technology

1997-03-01

clusters irradiated with ultrashort , high intensity laser pulses can exhibit "ionization ig- nition" which leads...8, 9]. 25-atom Ne clusters and 25-atom Ar clusters are modelled as irradiated by a 800 nm, 15 fs (fwhm) laser pulse with peak intensities ranging...Measurements of the spatial and spectral properties of ultrashort , intense laser pulses propagating in underdense plasmas demonstrate

Data Acquisition and Analysis for Camouflage Design

DTIC Science & Technology

1981-04-01

were clustered to produce a facsimile of the original scene in 39 49 or 5 average representative colors in CIELAB notation with spectral reflectance...result of the Euclidean clustering or averaging carried out in 1976 CIELAB color space. The size and shape of these domains, along with color, provide...Reflectance Calibration .... ...... 49 Figure O-i CIE 1976 (L*a*b*) Uniform Color Coordinate System (ClELAO) 53 Figure B-2 CIELAB Clustering

Spectral Target Detection using Schroedinger Eigenmaps

NASA Astrophysics Data System (ADS)

Dorado-Munoz, Leidy P.

Applications of optical remote sensing processes include environmental monitoring, military monitoring, meteorology, mapping, surveillance, etc. Many of these tasks include the detection of specific objects or materials, usually few or small, which are surrounded by other materials that clutter the scene and hide the relevant information. This target detection process has been boosted lately by the use of hyperspectral imagery (HSI) since its high spectral dimension provides more detailed spectral information that is desirable in data exploitation. Typical spectral target detectors rely on statistical or geometric models to characterize the spectral variability of the data. However, in many cases these parametric models do not fit well HSI data that impacts the detection performance. On the other hand, non-linear transformation methods, mainly based on manifold learning algorithms, have shown a potential use in HSI transformation, dimensionality reduction and classification. In target detection, non-linear transformation algorithms are used as preprocessing techniques that transform the data to a more suitable lower dimensional space, where the statistical or geometric detectors are applied. One of these non-linear manifold methods is the Schroedinger Eigenmaps (SE) algorithm that has been introduced as a technique for semi-supervised classification. The core tool of the SE algorithm is the Schroedinger operator that includes a potential term that encodes prior information about the materials present in a scene, and enables the embedding to be steered in some convenient directions in order to cluster similar pixels together. A completely novel target detection methodology based on SE algorithm is proposed for the first time in this thesis. The proposed methodology does not just include the transformation of the data to a lower dimensional space but also includes the definition of a detector that capitalizes on the theory behind SE. The fact that target pixels and those similar pixels are clustered in a predictable region of the low-dimensional representation is used to define a decision rule that allows one to identify target pixels over the rest of pixels in a given image. In addition, a knowledge propagation scheme is used to combine spectral and spatial information as a means to propagate the "potential constraints" to nearby points. The propagation scheme is introduced to reinforce weak connections and improve the separability between most of the target pixels and the background. Experiments using different HSI data sets are carried out in order to test the proposed methodology. The assessment is performed from a quantitative and qualitative point of view, and by comparing the SE-based methodology against two other detection methodologies that use linear/non-linear algorithms as transformations and the well-known Adaptive Coherence/Cosine Estimator (ACE) detector. Overall results show that the SE-based detector outperforms the other two detection methodologies, which indicates the usefulness of the SE transformation in spectral target detection problems.

A Marker-Based Approach for the Automated Selection of a Single Segmentation from a Hierarchical Set of Image Segmentations

NASA Technical Reports Server (NTRS)

Tarabalka, Y.; Tilton, J. C.; Benediktsson, J. A.; Chanussot, J.

2012-01-01

The Hierarchical SEGmentation (HSEG) algorithm, which combines region object finding with region object clustering, has given good performances for multi- and hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. Two classification-based approaches for automatic marker selection are adapted and compared for this purpose. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. Three different implementations of the M-HSEG method are proposed and their performances in terms of classification accuracies are compared. The experimental results, presented for three hyperspectral airborne images, demonstrate that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for remote sensing image analysis.

Deriving photometric redshifts using fuzzy archetypes and self-organizing maps - I. Methodology

NASA Astrophysics Data System (ADS)

Speagle, Joshua S.; Eisenstein, Daniel J.

2017-07-01

We propose a method to substantially increase the flexibility and power of template fitting-based photometric redshifts by transforming a large number of galaxy spectral templates into a corresponding collection of 'fuzzy archetypes' using a suitable set of perturbative priors designed to account for empirical variation in dust attenuation and emission-line strengths. To bypass widely separated degeneracies in parameter space (e.g. the redshift-reddening degeneracy), we train self-organizing maps (SOMs) on large 'model catalogues' generated from Monte Carlo sampling of our fuzzy archetypes to cluster the predicted observables in a topologically smooth fashion. Subsequent sampling over the SOM then allows full reconstruction of the relevant probability distribution functions (PDFs). This combined approach enables the multimodal exploration of known variation among galaxy spectral energy distributions with minimal modelling assumptions. We demonstrate the power of this approach to recover full redshift PDFs using discrete Markov chain Monte Carlo sampling methods combined with SOMs constructed from Large Synoptic Survey Telescope ugrizY and Euclid YJH mock photometry.

OSO-8 X-ray spectra of clusters of galaxies. 2: Discussion. [hot intracluster gas structures

NASA Technical Reports Server (NTRS)

Smith, B. W.; Mushotzky, R. F.; Serlemitsos, P. J.

1978-01-01

X-ray spectral parameters obtained from 2 to 20 keV OSO-8 data on X-ray clusters and optical cluster properties were examined to obtain information for restricting models for hot intracluster gas structures. Topics discussed include the radius of the X-ray core in relation to the galaxy core radius, the viral mass of hotter clusters, and galaxy density and optical central cluster properties. A population of cool, dim X-ray clusters which have not been observed is predicted. The iron abundance determinations recently quoted for intracluster gas are uncertain by 50 to greater than 100 percent from this nonstatistical cause alone.

High Resolution X-ray Spectroscopy and Star Formation: HETG Observations of the Pre-Main Sequence Stellar Cluster IC 348

NASA Astrophysics Data System (ADS)

Principe, David; Huenemoerder, David P.; Schulz, Norbert; Kastner, Joel H.; Weintraub, David; Preibisch, Thomas

2018-01-01

We present Chandra High Energy Transmission Grating (HETG) observations of the ∼3 Myr old pre-main sequence (pre-MS) stellar cluster IC 348. With 400-500 cluster members at a distance of ∼300 pc, IC 348 is an ideal target to observe a large number of X-ray sources in a single pointing and is thus an extremely efficient use of Chandra-HETG. High resolution X-ray spectroscopy offers a means to investigate detailed spectral characteristic of X-ray emitting plasmas and their surrounding environments. We present preliminary results where we compare X-ray spectral signatures (e.g., luminosity, temperature, column density, abundance) of the X-ray brightest pre-MS stars in IC 348 with spectral type, multiwavelength signatures of accretion, and the presence of circumstellar disks at multiple stages of pre-MS stellar evolution. Assuming all IC 348 members formed from the same primordial molecular cloud, any disparity between coronal abundances of individual members, as constrained by the identification and strength of emission lines, will constrain the source(s) of coronal chemical evolution at a stage of pre-MS evolution vital to the formation of planets.

A CCD Search for Variable Stars of Spectral Type B in the Northern Hemisphere Open Clusters. IX. NGC 457

NASA Astrophysics Data System (ADS)

Moździerski, D.; Pigulski, A.; Kopacki, G.; Kołaczkowski, Z.; Stęślicki, M.

2014-06-01

We present results of a BVIC variability survey in the young open cluster NGC 457 based on observations obtained during three separate runs spanning almost 20 years. In total, we found 79 variable stars, of which 66 are new. The BVIC photometry was transformed to the standard system and used to derive cluster parameters by means of isochrone fitting. The cluster is about 20 Myr old, the mean reddening amounts to about 0.48 mag in terms of the color excess E(B-V). Depending on the metallicity, the isochrone fitting yields a distance between 2.3 kpc and 2.9 kpc, which locates the cluster in the Perseus arm of the Galaxy. Using the complementary Hα photometry carried out in two seasons separated by over 10 years, we find that the cluster is very rich in Be stars. In total, 15 stars in the observed field of which 14 are cluster members showed Hα in emission either during our observations or in the past. Most of the Be stars vary in brightness on different time scales including short-period variability related most likely to g-mode pulsations. A single-epoch spectrum of NGC 457-6 shows that this Be star is presently in the shell phase. The inventory of variable stars in the observed field consists of a single β Cep-type star, NGC 457-8, 13 Be stars, 21 slowly pulsating B stars, seven δ Sct stars, one γ Dor star, 16 unclassified periodic stars, 8 eclipsing systems and a dozen of stars with irregular variability, of which six are also B-type stars. As many as 45 variable stars are of spectral type B which is the largest number in all open clusters presented in this series of papers. The most interesting is the discovery of a large group of slowly pulsating B stars which occupy the cluster main sequence in the range between V=11 mag and 14.5 mag, corresponding to spectral types B3 to B8. They all have very low amplitudes and about half show pulsations with frequencies higher than 3 d-1. We argue that these are most likely fast-rotating slowly pulsating B stars, observed also in other open clusters.

Probing the Curious Case of a Galaxy Cluster Merger in Abell 115 with High-fidelity Chandra X-Ray Temperature and Radio Maps

NASA Astrophysics Data System (ADS)

Hallman, Eric J.; Alden, Brian; Rapetti, David; Datta, Abhirup; Burns, Jack O.

2018-05-01

We present results from an X-ray and radio study of the merging galaxy cluster Abell 115. We use the full set of five Chandra observations taken of A115 to date (360 ks total integration) to construct high-fidelity temperature and surface brightness maps. We also examine radio data from the Very Large Array at 1.5 GHz and the Giant Metrewave Radio Telescope at 0.6 GHz. We propose that the high X-ray spectral temperature between the subclusters results from the interaction of the bow shocks driven into the intracluster medium by the motion of the subclusters relative to one another. We have identified morphologically similar scenarios in Enzo numerical N-body/hydrodynamic simulations of galaxy clusters in a cosmological context. In addition, the giant radio relic feature in A115, with an arc-like structure and a relatively flat spectral index, is likely consistent with other shock-associated giant radio relics seen in other massive galaxy clusters. We suggest a dynamical scenario that is consistent with the structure of the X-ray gas, the hot region between the clusters, and the radio relic feature.

Comparative study of cluster Ag17Cu2 by instantaneous normal mode analysis and by isothermal Brownian-type molecular dynamics simulation.

PubMed

Tang, Ping-Han; Wu, Ten-Ming; Yen, Tsung-Wen; Lai, S K; Hsu, P J

2011-09-07

We perform isothermal Brownian-type molecular dynamics simulations to obtain the velocity autocorrelation function and its time Fourier-transformed power spectral density for the metallic cluster Ag(17)Cu(2). The temperature dependences of these dynamical quantities from T = 0 to 1500 K were examined and across this temperature range the cluster melting temperature T(m), which we define to be the principal maximum position of the specific heat is determined. The instantaneous normal mode analysis is then used to dissect the cluster dynamics by calculating the vibrational instantaneous normal mode density of states and hence its frequency integrated value I(j) which is an ensemble average of all vibrational projection operators for the jth atom in the cluster. In addition to comparing the results with simulation data, we look more closely at the entities I(j) of all atoms using the point group symmetry and diagnose their temperature variations. We find that I(j) exhibit features that may be used to deduce T(m), which turns out to agree very well with those inferred from the power spectral density and specific heat. © 2011 American Institute of Physics

Univariate and multivariate methods for chemical mapping of cervical cancer cells

NASA Astrophysics Data System (ADS)

Duraipandian, Shiyamala; Zheng, Wei; Huang, Zhiwei

2012-01-01

Visualization of cells and subcellular organelles are currently carried out using available microscopy methods such as cryoelectron microscopy, and fluorescence microscopy. These methods require external labeling using fluorescent dyes and extensive sample preparations to access the subcellular structures. However, Raman micro-spectroscopy provides a non-invasive, label-free method for imaging the cells with chemical specificity at sub-micrometer spatial resolutions. The scope of this paper is to image the biochemical/molecular distributions in cells associated with cancerous changes. Raman map data sets were acquired from the human cervical carcinoma cell lines (HeLa) after fixation under 785 nm excitation wavelength. The individual spectrum was recorded by raster-scanning the laser beam over the sample with 1μm step size and 10s exposure time. Images revealing nucleic acids, lipids and proteins (phenylalanine, amide I) were reconstructed using univariate methods. In near future, the small pixel to pixel variations will also be imaged using different multivariate methods (PCA, clustering (HCA, K-means, FCM)) to determine the main cellular constitutions. The hyper-spectral image of cell was reconstructed utilizing the spectral contrast at different pixels of the cell (due to the variation in the biochemical distribution) without using fluorescent dyes. Normal cervical squamous cells will also be imaged in order to differentiate normal and cancer cells of cervix using the biochemical changes in different grades of cancer. Based on the information obtained from the pseudo-color maps, constructed from the hyper-spectral cubes, the primary cellular constituents of normal and cervical cancer cells were identified.

Temporal and Spectral Characteristics of X-Ray Bright Pleiads

NASA Astrophysics Data System (ADS)

Caillault, J.-P.; Gagne, M.; Yglesias, J.; Hartmann, L.; Prosser, C.; Stauffer, J.

1993-05-01

ROSAT PSPC observations of the Pleiades have allowed us to analyze the spectral and temporal characteristics of the X-ray sources within the cluster. Of the ~ 300 sources detected within the images, ~ 20-30 of them seem to be variable at the 99% confidence level (chi (2) -test). Numerous flares have also been found, the light curves of which we display. In addition, we have fit two-temperature Raymond-Smith thermal plasma models to the spectra of the ~ 6 brightest sources and examined whether these sources behave in accordance with coronal loop models. We also demonstrate that the two-temperature fit changes during a flare. We have constructed composite spectra for both shallow and deep convective zone stars in order to see whether there is a systematic change of spectral characteristics from spectral type F to M. Finally, in an attempt to discern possible evolutionary effects, we compare our results with those from the older Hyades cluster (Stern et al. 1993). This research was supported by NASA Grants NAG5-1608 to UGA and NAG5-1849 & NAGW-2698 to the CfA.

Spectral Biclustering of Microarray Data: Coclustering Genes and Conditions

PubMed Central

Kluger, Yuval; Basri, Ronen; Chang, Joseph T.; Gerstein, Mark

2003-01-01

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find “marker genes” that are differentially expressed in particular sets of “conditions.” We have developed a method that simultaneously clusters genes and conditions, finding distinctive “checkerboard” patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data). PMID:12671006

Comparison of temporal and spectral scattering methods using acoustically large breast models derived from magnetic resonance images.

PubMed

Hesford, Andrew J; Tillett, Jason C; Astheimer, Jeffrey P; Waag, Robert C

2014-08-01

Accurate and efficient modeling of ultrasound propagation through realistic tissue models is important to many aspects of clinical ultrasound imaging. Simplified problems with known solutions are often used to study and validate numerical methods. Greater confidence in a time-domain k-space method and a frequency-domain fast multipole method is established in this paper by analyzing results for realistic models of the human breast. Models of breast tissue were produced by segmenting magnetic resonance images of ex vivo specimens into seven distinct tissue types. After confirming with histologic analysis by pathologists that the model structures mimicked in vivo breast, the tissue types were mapped to variations in sound speed and acoustic absorption. Calculations of acoustic scattering by the resulting model were performed on massively parallel supercomputer clusters using parallel implementations of the k-space method and the fast multipole method. The efficient use of these resources was confirmed by parallel efficiency and scalability studies using large-scale, realistic tissue models. Comparisons between the temporal and spectral results were performed in representative planes by Fourier transforming the temporal results. An RMS field error less than 3% throughout the model volume confirms the accuracy of the methods for modeling ultrasound propagation through human breast.

Comparative study of icy patches on comet nuclei

NASA Astrophysics Data System (ADS)

Oklay, Nilda; Pommerol, Antoine; Barucci, Maria Antonietta; Sunshine, Jessica; Sierks, Holger; Pajola, Maurizio

2016-07-01

Cometary missions Deep Impact, EPOXI and Rosetta investigated the nuclei of comets 9P/Tempel 1, 103P/Hartley 2 and 67P/Churyumov-Gerasimenko respectively. Bright patches were observed on the surfaces of each of these three comets [1-5]. Of these, the surface of 67P is mapped at the highest spatial resolution via narrow angle camera (NAC) of the Optical, Spectroscopic, and Infrared Remote Imaging System (OSIRIS, [6]) on board the Rosetta spacecraft. OSIRIS NAC is equipped with twelve filters covering the wavelength range of 250 nm to 1000 nm. Various filters combinations are used during surface mapping. With high spatial resolution data of comet 67P, three types of bright features were detected on the comet surface: Clustered, isolated and bright boulders [2]. In the visible spectral range, clustered bright features on comet 67P display bluer spectral slopes than the average surface [2, 4] while isolated bright features on comet 67P have flat spectra [4]. Icy patches observed on the surface of comets 9P and 103P display bluer spectral slopes than the average surface [1, 5]. Clustered and isolated bright features are blue in the RGB composites generated by using the images taken in NIR, visible and NUV wavelengths [2, 4]. This is valid for the icy patches observed on comets 9P and 103P [1, 5]. Spectroscopic observations of bright patches on comets 9P and 103P confirmed the existence of water [1, 5]. There were more than a hundred of bright features detected on the northern hemisphere of comet 67P [2]. Analysis of those features from both multispectral data and spectroscopic data is an ongoing work. Water ice is detected in eight of the bright features so far [7]. Additionally, spectroscopic observations of two clustered bright features on the surface of comet 67P revealed the existence of water ice [3]. The spectral properties of one of the icy patches were studied by [4] using OSIRIS NAC images and compared with the spectral properties of the active regions observed on comet 67P. Additionally jets rising from the same clustered bright feature were detected visually [4]. We analyzed bright patches on the surface of comets 9P, 103P and 67P using multispectral data obtained by the high-resolution instrument (HRI), medium- resolution instrument (MRI) and OSIRIS NAC using various spectral analysis techniques. Clustered bright features on comet 67P have similar visible spectra to the bright patches on comets 9P and 103P. The comparison of the bright patches includes the published results of the IR spectra. References: [1] Sunshine et al., 2006, Science, 311, 1453 [2] Pommerol et al., 2015, A&A, 583, A25 [3] Filacchione et al., 2016, Nature, 529, 368-372 [4] Oklay et al., 2016, A&A, 586, A80 [5] Sunshine et al. 2012, ACM [6] Keller et al., 2007, Space Sci. Rev., 128, 433 [7] Barucci et al., 2016, COSPAR, B04

Spectral properties near the Mott transition in the two-dimensional t-J model with next-nearest-neighbor hopping

NASA Astrophysics Data System (ADS)

Kohno, Masanori

2018-05-01

The single-particle spectral properties of the two-dimensional t-J model with next-nearest-neighbor hopping are investigated near the Mott transition by using cluster perturbation theory. The spectral features are interpreted by considering the effects of the next-nearest-neighbor hopping on the shift of the spectral-weight distribution of the two-dimensional t-J model. Various anomalous features observed in hole-doped and electron-doped high-temperature cuprate superconductors are collectively explained in the two-dimensional t-J model with next-nearest-neighbor hopping near the Mott transition.

The Spectral Web of stationary plasma equilibria. II. Internal modes

NASA Astrophysics Data System (ADS)

Goedbloed, J. P.

2018-03-01

The new method of the Spectral Web to calculate the spectrum of waves and instabilities of plasma equilibria with sizeable flows, developed in the preceding Paper I [Goedbloed, Phys. Plasmas 25, 032109 (2018)], is applied to a collection of classical magnetohydrodynamic instabilities operating in cylindrical plasmas with shear flow or rotation. After a review of the basic concepts of the complementary energy giving the solution path and the conjugate path, which together constitute the Spectral Web, the cylindrical model is presented and the spectral equations are derived. The first example concerns the internal kink instabilities of a cylindrical force-free magnetic field of constant α subjected to a parabolic shear flow profile. The old stability diagram and the associated growth rate calculations for static equilibria are replaced by a new intricate stability diagram and associated complex growth rates for the stationary model. The power of the Spectral Web method is demonstrated by showing that the two associated paths in the complex ω-plane nearly automatically guide to the new class of global Alfvén instabilities of the force-free configuration that would have been very hard to predict by other methods. The second example concerns the Rayleigh-Taylor instability of a rotating theta-pinch. The old literature is revisited and shown to suffer from inconsistencies that are remedied. The most global n = 1 instability and a cluster sequence of more local but much more unstable n =2 ,3 ,…∞ modes are located on separate solution paths in the hydrodynamic (HD) version of the instability, whereas they merge in the MHD version. The Spectral Web offers visual demonstration of the central position the HD flow continuum and of the MHD Alfvén and slow magneto-sonic continua in the respective spectra by connecting the discrete modes in the complex plane by physically meaningful curves towards the continua. The third example concerns the magneto-rotational instability (MRI) thought to be operating in accretion disks about black holes. The sequence n =1 ,2 ,… of unstable MRIs is located on one continuous solution path, but also on infinitely many separate loops ("pancakes") of the conjugate path with just one MRI on each of them. For narrow accretion disks, those sequences are connected with the slow magneto-sonic continuum, which is far away though from the marginal stability transition. In this case, the Spectral Web method is the first to effectively incorporate the MRIs into the general MHD spectral theory of equilibria with background flows. Together, the three examples provide compelling evidence of the computational power of the Spectral Web Method.

Statistical analyses and characteristics of volcanic tremor on Stromboli Volcano (Italy)

NASA Astrophysics Data System (ADS)

Falsaperla, S.; Langer, H.; Spampinato, S.

A study of volcanic tremor on Stromboli is carried out on the basis of data recorded daily between 1993 and 1995 by a permanent seismic station (STR) located 1.8km away from the active craters. We also consider the signal of a second station (TF1), which operated for a shorter time span. Changes in the spectral tremor characteristics can be related to modifications in volcanic activity, particularly to lava effusions and explosive sequences. Statistical analyses were carried out on a set of spectra calculated daily from seismic signals where explosion quakes were present or excluded. Principal component analysis and cluster analysis were applied to identify different classes of spectra. Three clusters of spectra are associated with two different states of volcanic activity. One cluster corresponds to a state of low to moderate activity, whereas the two other clusters are present during phases with a high magma column as inferred from the occurrence of lava fountains or effusions. We therefore conclude that variations in volcanic activity at Stromboli are usually linked to changes in the spectral characteristics of volcanic tremor. Site effects are evident when comparing the spectra calculated from signals synchronously recorded at STR and TF1. However, some major spectral peaks at both stations may reflect source properties. Statistical considerations and polarization analysis are in favor of a prevailing presence of P-waves in the tremor signal along with a position of the source northwest of the craters and at shallow depth.

CO2 in solid para-hydrogen: spectral splitting and the CO2···(o-H2)n clusters.

PubMed

Du, Jun-He; Wan, Lei; Wu, Lei; Xu, Gang; Deng, Wen-Ping; Liu, An-Wen; Chen, Yang; Hu, Shui-Ming

2011-02-17

Complicated high-resolution spectral structures are often observed for molecules doped in solid molecular hydrogen. The structures can result from miscellaneous effects and are often interpreted differently in references. The spectrum of the ν(3) band of CO(2) in solid para-H(2) presents a model system which exhibits rich spectral structures. With the help of the potential energy simulation of the CO(2) molecule doped in para-hydrogen matrix, and extensive experiments with different CO(2) isotopologues and different ortho-hydrogen concentrations in the matrix, the spectral features observed in p-H(2) matrix are assigned to the CO(2)···(o-H(2))(n) clusters and also to energy level splitting that is due to different alignments of the doped CO(2) molecules in the matrix. The assignments are further supported by the dynamics analysis and also by the spectrum recorded with sample codoped with O(2) which serves as catalyst transferring o-H(2) to p-H(2) in the matrix at 4 K temperature. The observed spectral features of CO(2)/pH(2) can potentially be used as an alternative readout of the temperature and orthohydrogen concentration in the solid para-hydrogen.

Quantitative evidence of an intrinsic luminosity spread in the Orion nebula cluster

NASA Astrophysics Data System (ADS)

Reggiani, M.; Robberto, M.; Da Rio, N.; Meyer, M. R.; Soderblom, D. R.; Ricci, L.

2011-10-01

Aims: We study the distribution of stellar ages in the Orion nebula cluster (ONC) using accurate HST photometry taken from HST Treasury Program observations of the ONC utilizing the cluster distance estimated by Menten and collaborators. We investigate whether there is an intrinsic age spread in the region and whether the age depends on the spatial distribution. Methods: We estimate the extinction and accretion luminosity towards each source by performing synthetic photometry on an empirical calibration of atmospheric models using the package Chorizos of Maiz-Apellaniz. The position of the sources in the HR-diagram is compared with different theoretical isochrones to estimate the mean cluster age and age dispersion. On the basis of Monte Carlo simulations, we quantify the amount of intrinsic age spread in the region, taking into account uncertainties in the distance, spectral type, extinction, unresolved binaries, accretion, and photometric variability. Results: According to the evolutionary models of Siess and collaborators, the mean age of the Cluster is 2.2 Myr with a scatter of few Myr. With Monte Carlo simulations, we find that the observed age spread is inconsistent with that of a coeval stellar population, but in agreement with a star formation activity between 1.5 and 3.5 Myr. We also observe some evidence that ages depends on the spatial distribution.

Characterization of Interfacial Chemistry of Adhesive/Dentin Bond Using FTIR Chemical Imaging With Univariate and Multivariate Data Processing

PubMed Central

Wang, Yong; Yao, Xiaomei; Parthasarathy, Ranganathan

2008-01-01

Fourier transform infrared (FTIR) chemical imaging can be used to investigate molecular chemical features of the adhesive/dentin interfaces. However, the information is not straightforward, and is not easily extracted. The objective of this study was to use multivariate analysis methods, principal component analysis and fuzzy c-means clustering, to analyze spectral data in comparison with univariate analysis. The spectral imaging data collected from both the adhesive/healthy dentin and adhesive/caries-affected dentin specimens were used and compared. The univariate statistical methods such as mapping of intensities of specific functional group do not always accurately identify functional group locations and concentrations due to more or less band overlapping in adhesive and dentin. Apart from the ease with which information can be extracted, multivariate methods highlight subtle and often important changes in the spectra that are difficult to observe using univariate methods. The results showed that the multivariate methods gave more satisfactory, interpretable results than univariate methods and were conclusive in showing that they can discriminate and classify differences between healthy dentin and caries-affected dentin within the interfacial regions. It is demonstrated that the multivariate FTIR imaging approaches can be used in the rapid characterization of heterogeneous, complex structure. PMID:18980198

An Unlikely Radio Halo in the Low X-Ray Luminosity Galaxy Cluster RXCJ1514.9-1523

NASA Technical Reports Server (NTRS)

Marketvitch, M.; ZuHone, J. A.; Lee, D.; Giacintucci, S.; Dallacasa, D.; Venturi, T.; Brunetti, G.; Cassano, R.; Markevitch, M.; Athreya, R. M.

2011-01-01

Aims: We report the discovery of a giant radio halo in the galaxy cluster RXCJ1514,9-1523 at z=0.22 with a relatively low X-ray luminosity, L(sub X) (0.1-2.4kev) approx. 7 x 10(exp 44) ergs/s. Methods: This faint, diffuse radio source is detected with the Giant Meterwave Radio Telescope at 327 MHz. The source is barely detected at 1.4 GHz in a NVSS pointing that we have reanalyzed. Results: The integrated radio spectrum of the halo is quite steep, with a slope alpha = 1.6 between 327 MHz and 1.4 GHz. While giant radio halos are common in more X-ray luminous cluster mergers, there is a less than 10% probability to detect a halo in systems with L(sub X) < 8 x 10(exp 44) ergs/s. The detection of a new giant halo in this borderline luminosity regime can be particularly useful for discriminating between the competing theories for the origin of ultrarelativistic electrons in clusters. Furthermore, if our steep radio spectral index is confirmed by future deeper radio observations, this cluster would provide another example of the very rare, new class of ultra-steep spectrum radio halos, predicted by the model in which the cluster cosmic ray electrons are produced by turbulent reacceleration.

Spatial Field Variability Mapping of Rice Crop using Clustering Technique from Space Borne Hyperspectral Data

NASA Astrophysics Data System (ADS)

Moharana, S.; Dutta, S.

2015-12-01

Precision farming refers to field-specific management of an agricultural crop at a spatial scale with an aim to get the highest achievable yield and to achieve this spatial information on field variability is essential. The difficulty in mapping of spatial variability occurring within an agriculture field can be revealed by employing spectral techniques in hyperspectral imagery rather than multispectral imagery. However an advanced algorithm needs to be developed to fully make use of the rich information content in hyperspectral data. In the present study, potential of hyperspectral data acquired from space platform was examined to map the field variation of paddy crop and its species discrimination. This high dimensional data comprising 242 spectral narrow bands with 30m ground resolution Hyperion L1R product acquired for Assam, India (30th Sept and 3rd Oct, 2014) were allowed for necessary pre-processing steps followed by geometric correction using Hyperion L1GST product. Finally an atmospherically corrected and spatially deduced image consisting of 112 band was obtained. By employing an advanced clustering algorithm, 12 different clusters of spectral waveforms of the crop were generated from six paddy fields for each images. The findings showed that, some clusters were well discriminated representing specific rice genotypes and some clusters were mixed treating as a single rice genotype. As vegetation index (VI) is the best indicator of vegetation mapping, three ratio based VI maps were also generated and unsupervised classification was performed for it. The so obtained 12 clusters of paddy crop were mapped spatially to the derived VI maps. From these findings, the existence of heterogeneity was clearly captured in one of the 6 rice plots (rice plot no. 1) while heterogeneity was observed in rest of the 5 rice plots. The degree of heterogeneous was found more in rice plot no.6 as compared to other plots. Subsequently, spatial variability of paddy field was observed in different plot levels in the paddy fields from the two images. However, no such significant variation in rice genotypes at growth level was observed. Hence, the spectral information acquired from space platform can be linearly scaled to map the variation in field levels of rice crop which will be act as an informative system for rice agriculture practice.

How specific Raman spectroscopic models are: a comparative study between different cancers

NASA Astrophysics Data System (ADS)

Singh, S. P.; Kumar, K. Kalyan; Chowdary, M. V. P.; Maheedhar, K.; Krishna, C. Murali

2010-02-01

Optical spectroscopic methods are being contemplated as adjunct/ alternative to existing 'Gold standard' of cancer diagnosis, histopathological examination. Several groups are actively pursuing diagnostic applications of Ramanspectroscopy in cancers. We have developed Raman spectroscopic models for diagnosis of breast, oral, stomach, colon and larynx cancers. So far, specificity and applicability of spectral- models has been limited to particular tissue origin. In this study we have evaluated explicitly of spectroscopic-models by analyzing spectra from already developed spectralmodels representing normal and malignant tissues of breast (46), cervix (52), colon (25), larynx (53), and oral (47). Spectral data was analyzed by Principal Component Analysis (PCA) using scores of factor, Mahalanobis distance and Spectral residuals as discriminating parameters. Multiparametric limit test approach was also explored. The preliminary unsupervised PCA of pooled data indicates that normal tissue types were always exclusive from their malignant counterparts. But when we consider tissue of different origin, large overlap among clusters was found. Supervised analysis by Mahalanobis distance and spectral residuals gave similar results. The 'limit test' approach where classification is based on match / mis-match of the given spectrum against all the available spectra has revealed that spectral models are very exclusive and specific. For example breast normal spectral model show matches only with breast normal spectra and mismatch to rest of the spectra. Same pattern was seen for most of spectral models. Therefore, results of the study indicate the exclusiveness and efficacy of Raman spectroscopic-models. Prospectively, these findings might open new application of Raman spectroscopic models in identifying a tumor as primary or metastatic.

A high throughput spectral image microscopy system

NASA Astrophysics Data System (ADS)

Gesley, M.; Puri, R.

2018-01-01

A high throughput spectral image microscopy system is configured for rapid detection of rare cells in large populations. To overcome flow cytometry rates and use of fluorophore tags, a system architecture integrates sample mechanical handling, signal processors, and optics in a non-confocal version of light absorption and scattering spectroscopic microscopy. Spectral images with native contrast do not require the use of exogeneous stain to render cells with submicron resolution. Structure may be characterized without restriction to cell clusters of differentiation.

Isomer-Specific IR Spectroscopy of BENZENE-(WATER)N Clusters with N=1-8: New Insights from the Water Bend Fundamentals and Isotopically Substituted Clusters

NASA Astrophysics Data System (ADS)

Kusaka, Ryoji; Walsh, Patrick S.; Zwier, Timothy S.

2014-06-01

This talk will focus on the isomer-specific IR spectra of benzene-(water)n (BWn) clusters with n = 1-8, returning to a topic studied by our group some 20 years ago, but now with higher resolution (OH stretch region), with inclusion of data from isotopically substituted clusters, and with extension into the HOH bending mode region. Spectra are recorded using resonant ion-dip infrared spectroscopy, an IR-UV double resonance method. Isomer-specific IR spectra in the regions of OH, OD stretches and HOH, HOD bend of benzene-H_2O, -D_2O, -HOD, -(H_2O)_2, -(D_2O)_2, -HOD-DOD were recorded in order to investigate in greater detail the intermolecular potential energy surface between water and benzene. These spectra show strong combination bands in addition to the OH/OD stretch fundamentals arising from large-amplitude "tumbling" and tunneling along internal rotation and torsion coordinates of water(s) on the surface of benzene. Interestingly, the number of extra bands and spectral patterns change dramatically depending on cluster size, the kind of deuterated isomer, and the spectral region probed. In larger clusters with n=3-8, the water HOH bending region is explored for the first time. The prominent bending mode transitions in BW1-8 are spread over a relatively small range (1610-1660 wn), and shift with cluster size in a way that reflects the known structural changes that accompany the increase in size. By comparison of experiment with calculation, it is possible to assign the experimentally observed 1614 wn transition of BW1 and 1615 wn of BW2 bands to the π-bound water molecule. The 1620-1660 wn bands of BW3-8 are due to water molecules that can be categorized as single-acceptor, single-donor (AD) hydrogen-bonded waters. In the case of single-acceptor, double-donor (ADD) water molecules, which are expected to be seen from BW6,a they show higher-frequency bending vibrations and weaker IR intensity, which would correspond to very weakly observed bands in 1660-1750 wn for BW6-8. R. N. Pribble and T. S. Zwier, Science, 1994, 265, 75-79.

Fano resonances of a ring-shaped "hexamer" cluster at near-infrared wavelength

NASA Astrophysics Data System (ADS)

Liu, Tong-Tong; Xia, Feng; Sun, Peng; Liu, Li-Li; Du, Wei; Li, Meng-Xue; Kong, Wei-Jin; Wan, Yong; Dong, Li-Feng; Yun, Mao-Jin

2018-03-01

Fano resonances have been studied intensely in the last decade, since it is an important way to decrease the resonance line width and enhance local electric field. However, achieving a Fano line-shape with both narrow line width and high spectral contrast ratio is still a challenge. In this paper, we theoretically predict the Fano resonance induced by the extinction of normal plane wave in a ring-shaped hexamer cluster at near-infrared wavelength. In order to obtain the narrow Fano line width and high spectral contrast ratio, the relationships between the Fano line-shape and the parameters of the nanostructure are analyzed in detail. The nanostructure is simulated by using commercial software based on finite element method. The simulation results show that when the structural parameters are optimized, the Fano line width can be narrowed down 0.028 eV with a contrast ratio of 86%, and the local electric field enhancement factor at the Fano resonance wavelength can reach to 36. Furthermore, the effective mode volume of the structure is 3.9 ×10-23m3 which is lower than the available literature. These results indicate many potential applications of the Fano resonance in multiwavelength surface-enhanced Raman scattering and biosensing.

Evaluation of Fourier transform infrared (FT-IR) spectroscopy and chemometrics as a rapid approach for sub-typing Escherichia coli O157:H7 isolates.

PubMed

Davis, R; Paoli, G; Mauer, L J

2012-09-01

The importance of tracking outbreaks of foodborne illness and the emergence of new virulent subtypes of foodborne pathogens have created the need for rapid and reliable sub-typing methods for Escherichia coli O157:H7. Fourier transform infrared (FT-IR) spectroscopy coupled with multivariate statistical analyses was used for sub-typing 30 strains of E. coli O157:H7 that had previously been typed by multilocus variable number tandem repeat analysis (MLVA) and pulsed field gel electrophoresis (PFGE). Hierarchical cluster analysis (HCA) and canonical variate analysis (CVA) of the FT-IR spectra resulted in the clustering of the same or similar MLVA types and separation of different MLVA types of E. coli O157:H7. The developed FT-IR method showed better discriminatory power than PFGE in sub-typing E. coli O157:H7. Results also indicated the spectral relatedness between different outbreak strains. However, the grouping of some strains was not in complete agreement with the clustering based on PFGE and MLVA. Additionally, HCA of the spectra differentiated the strains into 30 sub-clusters, indicating the high specificity and suitability of the method for strain level identification. Strains were also classified (97% correct) based on the type of Shiga toxin present using CVA of the spectra. This study demonstrated that FT-IR spectroscopy is suitable for rapid (≤16 h) and economical sub-typing of E. coli O157:H7 with comparable accuracy to MLVA typing. This is the first report of using an FT-IR-based method for sub-typing E. coli O157:H7. Copyright © 2012 Elsevier Ltd. All rights reserved.

The continuum spectral characteristics of gamma-ray bursts observed by BATSE

NASA Technical Reports Server (NTRS)

Pendleton, Geoffrey N.; Paciesas, William S.; Briggs, Michael S.; Mallozzi, Robert S.; Koshut, Tom M.; Fishman, Gerald J.; Meegan, Charles A.; Wilson, Robert B.; Harmon, Alan B.; Kouveliotou, Chryssa

1994-01-01

Distributions of the continuum spectral characteristics of 260 bursts in the first Burst And Transient Source Experiement (BATSE) catalog are presented. The data are derived from flux calculated from BATSE Large Area Detector (LAD) four-channel discriminator data. The data are converted from counts to protons using a direct spectral inversion technique to remove the effects of atmospheric scattering and the energy dependence of the detector angular response. Although there are intriguing clusters of bursts in the spectral hardness ratio distributions, no evidence for the presence of distinct burst classes based in spectral hardness ratios alone is found. All subsets of bursts selected for their spectral characteristics in this analysis exhibit spatial distributions consistent with isotropy. The spectral diversity of the burst population appears to be caused largely by the highly variable nature of the burst production mechanisms themselves.

A comparison of the near-infrared spectral features of early-type galaxies in the Coma Cluster, the Virgo cluster and the field

NASA Technical Reports Server (NTRS)

Houdashelt, Mark L.; Frogel, Jay A.

1993-01-01

Earlier researchers derived the relative distance between the Coma and Virgo clusters from color-magnitude relations of the early-type galaxies in each cluster. They found that the derived distance was color-dependent and concluded that the galaxies of similar luminosity in the two clusters differ in their red stellar populations. More recently, the color-dependence of the Coma-Virgo distance modulus has been called into question. However, because these two clusters differ so dramatically in their morphologies and kinematics, it is plausible that the star formation histories of the member galaxies also differed. If the conclusions of earlier researchers are indeed correct, then some signature of the resulting stellar population differences should appear in the near-infrared and/or infrared light of the respective galaxies. We have collected near-infrared spectra of 17 Virgo and 10 Coma early-type galaxies; this sample spans about four magnitudes in luminosity in each cluster. Seven field E/S0 galaxies have been observed for comparison. Pseudo-equivalent widths have been measured for all of the field galaxies, all but one of the Virgo members, and five of the Coma galaxies. The features examined are sensitive to the temperature, metallicity, and surface gravity of the reddest stars. A preliminary analysis of these spectral features has been performed, and, with a few notable exceptions, the measured pseudo-equivalent widths agree well with previously published values.

Analysis of correlated mutations in HIV-1 protease using spectral clustering.

PubMed

Liu, Ying; Eyal, Eran; Bahar, Ivet

2008-05-15

The ability of human immunodeficiency virus-1 (HIV-1) protease to develop mutations that confer multi-drug resistance (MDR) has been a major obstacle in designing rational therapies against HIV. Resistance is usually imparted by a cooperative mechanism that can be elucidated by a covariance analysis of sequence data. Identification of such correlated substitutions of amino acids may be obscured by evolutionary noise. HIV-1 protease sequences from patients subjected to different specific treatments (set 1), and from untreated patients (set 2) were subjected to sequence covariance analysis by evaluating the mutual information (MI) between all residue pairs. Spectral clustering of the resulting covariance matrices disclosed two distinctive clusters of correlated residues: the first, observed in set 1 but absent in set 2, contained residues involved in MDR acquisition; and the second, included those residues differentiated in the various HIV-1 protease subtypes, shortly referred to as the phylogenetic cluster. The MDR cluster occupies sites close to the central symmetry axis of the enzyme, which overlap with the global hinge region identified from coarse-grained normal-mode analysis of the enzyme structure. The phylogenetic cluster, on the other hand, occupies solvent-exposed and highly mobile regions. This study demonstrates (i) the possibility of distinguishing between the correlated substitutions resulting from neutral mutations and those induced by MDR upon appropriate clustering analysis of sequence covariance data and (ii) a connection between global dynamics and functional substitution of amino acids.

Probabilistic cluster labeling of imagery data

NASA Technical Reports Server (NTRS)

Chittineni, C. B. (Principal Investigator)

1980-01-01

The problem of obtaining the probabilities of class labels for the clusters using spectral and spatial information from a given set of labeled patterns and their neighbors is considered. A relationship is developed between class and clusters conditional densities in terms of probabilities of class labels for the clusters. Expressions are presented for updating the a posteriori probabilities of the classes of a pixel using information from its local neighborhood. Fixed-point iteration schemes are developed for obtaining the optimal probabilities of class labels for the clusters. These schemes utilize spatial information and also the probabilities of label imperfections. Experimental results from the processing of remotely sensed multispectral scanner imagery data are presented.

Turbulence measurements in clusters of galaxies with XMM-Newton

NASA Astrophysics Data System (ADS)

Pinto, C.; Fabian, A.; de Plaa, J.; Sanders, J.

2014-07-01

The kinematics structure of the intracluster medium (ICM) in clusters of galaxies is related to the their evolution. AGN feedback, sloshing of gas within the potential well, and galaxy mergers are thought to generate ICM velocity widths of several hundred km/s. Appropriate determinations of turbulent broadening are crucial not only to understand the effects of the central engine onto the evolution of the clusters, but are also mandatory to obtain realistic (emission) line fits and abundances estimate. We have analyzed the data from the CHEERS catalog which includes 1.5 Ms of new observations (PI: Jelle de Plaa) and archival data for a total of 29 clusters and groups of galaxies, and elliptical galaxies. This campaign provided us with a unique database that significantly improves the quality of the existing observations and the measurements of chemical abundances and turbulent broadening. We have applied the continuum-subtraction spectral-fitting method of Sanders and Fabian and measured turbulence, temperatures, and abundances for the sources in the catalog. For some sources we obtain tight estimates of velocity broadening which is related to the past AGN activity and mergers. We will show our results at the conference and their relevance in the context of future missions.

WINGS-SPE Spectroscopy in the WIde-field Nearby Galaxy-cluster Survey

NASA Astrophysics Data System (ADS)

Cava, A.; Bettoni, D.; Poggianti, B. M.; Couch, W. J.; Moles, M.; Varela, J.; Biviano, A.; D'Onofrio, M.; Dressler, A.; Fasano, G.; Fritz, J.; Kjærgaard, P.; Ramella, M.; Valentinuzzi, T.

2009-03-01

Aims: We present the results from a comprehensive spectroscopic survey of the WINGS (WIde-field Nearby Galaxy-cluster Survey) clusters, a program called WINGS-SPE. The WINGS-SPE sample consists of 48 clusters, 22 of which are in the southern sky and 26 in the north. The main goals of this spectroscopic survey are: (1) to study the dynamics and kinematics of the WINGS clusters and their constituent galaxies, (2) to explore the link between the spectral properties and the morphological evolution in different density environments and across a wide range of cluster X-ray luminosities and optical properties. Methods: Using multi-object fiber-fed spectrographs, we observed our sample of WINGS cluster galaxies at an intermediate resolution of 6-9 Å and, using a cross-correlation technique, we measured redshifts with a mean accuracy of ~45 km s-1. Results: We present redshift measurements for 6137 galaxies and their first analyses. Details of the spectroscopic observations are reported. The WINGS-SPE has ~30% overlap with previously published data sets, allowing us both to perform a complete comparison with the literature and to extend the catalogs. Conclusions: Using our redshifts, we calculate the velocity dispersion for all the clusters in the WINGS-SPE sample. We almost triple the number of member galaxies known in each cluster with respect to previous works. We also investigate the X-ray luminosity vs. velocity dispersion relation for our WINGS-SPE clusters, and find it to be consistent with the form Lx ∝ σ_v^4. Table 4, containing the complete redshift catalog, is only available in electronic form at the CDS via anonymous ftp to cdsarc.u-strasbg.fr (130.79.128.5) or via http://cdsweb.u-strasbg.fr/cgi-bin/qcat?J/A+A/495/707

A long-term space astrophysics research program: An x-ray perspective of the components and structure of galaxies

NASA Technical Reports Server (NTRS)

Fabbiano, G.

1995-01-01

X-ray studies of galaxies by the Smithsonian Astrophysical Observatory (SAO) and MIT are described. Activities at SAO include ROSAT PSPC x-ray data reduction and analysis pipeline; x-ray sources in nearby Sc galaxies; optical, x-ray, and radio study of ongoing galactic merger; a radio, far infrared, optical, and x-ray study of the Sc galaxy NGC247; and a multiparametric analysis of the Einstein sample of early-type galaxies. Activities at MIT included continued analysis of observations with ROSAT and ASCA, and continued development of new approaches to spectral analysis with ASCA and AXAF. Also, a new method for characterizing structure in galactic clusters was developed and applied to ROSAT images of a large sample of clusters. An appendix contains preprints generated by the research.

Color excesses, intrinsic colors, and absolute magnitudes of Galactic and Large Magellanic Cloud Wolf-Rayet stars

NASA Technical Reports Server (NTRS)

Vacca, William D.; Torres-Dodgen, Ana V.

1990-01-01

A new method of determining the color excesses of WR stars in the Galaxy and the LMC has been developed and is used to determine the excesses for 44 Galactic and 32 LMC WR stars. The excesses are combined with line-free, narrow-band spectrophotometry to derive intrinsic colors of the WR stars of nearly all spectral subtypes. No correlation of UV spectral index or intrinsic colors with spectral subtype is found for the samples of single WN or WC stars. There is evidence that early WN stars in the LMC have flatter UV continua and redder intrinsic colors than early WN stars in the Galaxy. No separation is found between the values derived for Galactic WC stars and those obtained for LMC WC stars. The intrinsic colors are compared with those calculated from model atmospheres of WR stars and generally good agreement is found. Absolute magnitudes are derived for WR stars in the LMC and for those Galactic WR stars located in clusters and associations for which there are reliable distance estimates.

Distributed Unmixing of Hyperspectral Datawith Sparsity Constraint

NASA Astrophysics Data System (ADS)

Khoshsokhan, S.; Rajabi, R.; Zayyani, H.

2017-09-01

Spectral unmixing (SU) is a data processing problem in hyperspectral remote sensing. The significant challenge in the SU problem is how to identify endmembers and their weights, accurately. For estimation of signature and fractional abundance matrices in a blind problem, nonnegative matrix factorization (NMF) and its developments are used widely in the SU problem. One of the constraints which was added to NMF is sparsity constraint that was regularized by L1/2 norm. In this paper, a new algorithm based on distributed optimization has been used for spectral unmixing. In the proposed algorithm, a network including single-node clusters has been employed. Each pixel in hyperspectral images considered as a node in this network. The distributed unmixing with sparsity constraint has been optimized with diffusion LMS strategy, and then the update equations for fractional abundance and signature matrices are obtained. Simulation results based on defined performance metrics, illustrate advantage of the proposed algorithm in spectral unmixing of hyperspectral data compared with other methods. The results show that the AAD and SAD of the proposed approach are improved respectively about 6 and 27 percent toward distributed unmixing in SNR=25dB.

A novel combined approach of diffuse reflectance UV-Vis-NIR spectroscopy and multivariate analysis for non-destructive examination of blue ballpoint pen inks in forensic application.

PubMed

Kumar, Raj; Sharma, Vishal

2017-03-15

The present research is focused on the analysis of writing inks using destructive UV-Vis spectroscopy (dissolution of ink by the solvent) and non-destructive diffuse reflectance UV-Vis-NIR spectroscopy along with Chemometrics. Fifty seven samples of blue ballpoint pen inks were analyzed under optimum conditions to determine the differences in spectral features of inks among same and different manufacturers. Normalization was performed on the spectroscopic data before chemometric analysis. Principal Component Analysis (PCA) and K-mean cluster analysis were used on the data to ascertain whether the blue ballpoint pen inks could be differentiated by their UV-Vis/UV-Vis NIR spectra. The discriminating power is calculated by qualitative analysis by the visual comparison of the spectra (absorbance peaks), produced by the destructive and non-destructive methods. In the latter two methods, the pairwise comparison is made by incorporating the clustering method. It is found that chemometric method provides better discriminating power (98.72% and 99.46%, in destructive and non-destructive, respectively) in comparison to the qualitative analysis (69.67%). Copyright © 2016 Elsevier B.V. All rights reserved.

Tree crown mapping in managed woodlands (parklands) of semi-arid West Africa using WorldView-2 imagery and geographic object based image analysis.

PubMed

Karlson, Martin; Reese, Heather; Ostwald, Madelene

2014-11-28

Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35-100 m(2)) and large (≥100 m(2)) trees compared to small (<35 m(2)) trees. The results indicate potential of GEOBIA and WorldView-2 imagery for tree crown mapping in parkland landscapes and similar woodland areas.

Tree Crown Mapping in Managed Woodlands (Parklands) of Semi-Arid West Africa Using WorldView-2 Imagery and Geographic Object Based Image Analysis

PubMed Central

Karlson, Martin; Reese, Heather; Ostwald, Madelene

2014-01-01

Detailed information on tree cover structure is critical for research and monitoring programs targeting African woodlands, including agroforestry parklands. High spatial resolution satellite imagery represents a potentially effective alternative to field-based surveys, but requires the development of accurate methods to automate information extraction. This study presents a method for tree crown mapping based on Geographic Object Based Image Analysis (GEOBIA) that use spectral and geometric information to detect and delineate individual tree crowns and crown clusters. The method was implemented on a WorldView-2 image acquired over the parklands of Saponé, Burkina Faso, and rigorously evaluated against field reference data. The overall detection rate was 85.4% for individual tree crowns and crown clusters, with lower accuracies in areas with high tree density and dense understory vegetation. The overall delineation error (expressed as the difference between area of delineated object and crown area measured in the field) was 45.6% for individual tree crowns and 61.5% for crown clusters. Delineation accuracies were higher for medium (35–100 m2) and large (≥100 m2) trees compared to small (<35 m2) trees. The results indicate potential of GEOBIA and WorldView-2 imagery for tree crown mapping in parkland landscapes and similar woodland areas. PMID:25460815

Potential use of MCR-ALS for the identification of coeliac-related biochemical changes in hyperspectral Raman maps from pediatric intestinal biopsies.

PubMed

Fornasaro, Stefano; Vicario, Annalisa; De Leo, Luigina; Bonifacio, Alois; Not, Tarcisio; Sergo, Valter

2018-05-14

Raman hyperspectral imaging is an emerging practice in biological and biomedical research for label free analysis of tissues and cells. Using this method, both spatial distribution and spectral information of analyzed samples can be obtained. The current study reports the first Raman microspectroscopic characterisation of colon tissues from patients with Coeliac Disease (CD). The aim was to assess if Raman imaging coupled with hyperspectral multivariate image analysis is capable of detecting the alterations in the biochemical composition of intestinal tissues associated with CD. The analytical approach was based on a multi-step methodology: duodenal biopsies from healthy and coeliac patients were measured and processed with Multivariate Curve Resolution Alternating Least Squares (MCR-ALS). Based on the distribution maps and the pure spectra of the image constituents obtained from MCR-ALS, interesting biochemical differences between healthy and coeliac patients has been derived. Noticeably, a reduced distribution of complex lipids in the pericryptic space, and a different distribution and abundance of proteins rich in beta-sheet structures was found in CD patients. The output of the MCR-ALS analysis was then used as a starting point for two clustering algorithms (k-means clustering and hierarchical clustering methods). Both methods converged with similar results providing precise segmentation over multiple Raman images of studied tissues.

Clustering of Multispectral Airborne Laser Scanning Data Using Gaussian Decomposition

NASA Astrophysics Data System (ADS)

Morsy, S.; Shaker, A.; El-Rabbany, A.

2017-09-01

With the evolution of the LiDAR technology, multispectral airborne laser scanning systems are currently available. The first operational multispectral airborne LiDAR sensor, the Optech Titan, acquires LiDAR point clouds at three different wavelengths (1.550, 1.064, 0.532 μm), allowing the acquisition of different spectral information of land surface. Consequently, the recent studies are devoted to use the radiometric information (i.e., intensity) of the LiDAR data along with the geometric information (e.g., height) for classification purposes. In this study, a data clustering method, based on Gaussian decomposition, is presented. First, a ground filtering mechanism is applied to separate non-ground from ground points. Then, three normalized difference vegetation indices (NDVIs) are computed for both non-ground and ground points, followed by histograms construction from each NDVI. The Gaussian function model is used to decompose the histograms into a number of Gaussian components. The maximum likelihood estimate of the Gaussian components is then optimized using Expectation - Maximization algorithm. The intersection points of the adjacent Gaussian components are subsequently used as threshold values, whereas different classes can be clustered. This method is used to classify the terrain of an urban area in Oshawa, Ontario, Canada, into four main classes, namely roofs, trees, asphalt and grass. It is shown that the proposed method has achieved an overall accuracy up to 95.1 % using different NDVIs.

Spatial organization of RNA polymerase II inside a mammalian cell nucleus revealed by reflected light-sheet superresolution microscopy.

PubMed

Zhao, Ziqing W; Roy, Rahul; Gebhardt, J Christof M; Suter, David M; Chapman, Alec R; Xie, X Sunney

2014-01-14

Superresolution microscopy based on single-molecule centroid determination has been widely applied to cellular imaging in recent years. However, quantitative imaging of the mammalian nucleus has been challenging due to the lack of 3D optical sectioning methods for normal-sized cells, as well as the inability to accurately count the absolute copy numbers of biomolecules in highly dense structures. Here we report a reflected light-sheet superresolution microscopy method capable of imaging inside the mammalian nucleus with superior signal-to-background ratio as well as molecular counting with single-copy accuracy. Using reflected light-sheet superresolution microscopy, we probed the spatial organization of transcription by RNA polymerase II (RNAP II) molecules and quantified their global extent of clustering inside the mammalian nucleus. Spatiotemporal clustering analysis that leverages on the blinking photophysics of specific organic dyes showed that the majority (>70%) of the transcription foci originate from single RNAP II molecules, and no significant clustering between RNAP II molecules was detected within the length scale of the reported diameter of "transcription factories." Colocalization measurements of RNAP II molecules equally labeled by two spectrally distinct dyes confirmed the primarily unclustered distribution, arguing against a prevalent existence of transcription factories in the mammalian nucleus as previously proposed. The methods developed in our study pave the way for quantitative mapping and stoichiometric characterization of key biomolecular species deep inside mammalian cells.

[Study on discrimination of varieties of fire resistive coating for steel structure based on near-infrared spectroscopy].

PubMed

Xue, Gang; Song, Wen-qi; Li, Shu-chao

2015-01-01

In order to achieve the rapid identification of fire resistive coating for steel structure of different brands in circulating, a new method for the fast discrimination of varieties of fire resistive coating for steel structure by means of near infrared spectroscopy was proposed. The raster scanning near infrared spectroscopy instrument and near infrared diffuse reflectance spectroscopy were applied to collect the spectral curve of different brands of fire resistive coating for steel structure and the spectral data were preprocessed with standard normal variate transformation(standard normal variate transformation, SNV) and Norris second derivative. The principal component analysis (principal component analysis, PCA)was used to near infrared spectra for cluster analysis. The analysis results showed that the cumulate reliabilities of PC1 to PC5 were 99. 791%. The 3-dimentional plot was drawn with the scores of PC1, PC2 and PC3 X 10, which appeared to provide the best clustering of the varieties of fire resistive coating for steel structure. A total of 150 fire resistive coating samples were divided into calibration set and validation set randomly, the calibration set had 125 samples with 25 samples of each variety, and the validation set had 25 samples with 5 samples of each variety. According to the principal component scores of unknown samples, Mahalanobis distance values between each variety and unknown samples were calculated to realize the discrimination of different varieties. The qualitative analysis model for external verification of unknown samples is a 10% recognition ration. The results demonstrated that this identification method can be used as a rapid, accurate method to identify the classification of fire resistive coating for steel structure and provide technical reference for market regulation.

Limited-angle multi-energy CT using joint clustering prior and sparsity regularization

NASA Astrophysics Data System (ADS)

Zhang, Huayu; Xing, Yuxiang

2016-03-01

In this article, we present an easy-to-implement Multi-energy CT scanning strategy and a corresponding reconstruction method, which facilitate spectral CT imaging by improving the data efficiency the number-of-energy- channel fold without introducing visible limited-angle artifacts caused by reducing projection views. Leveraging the structure coherence at different energies, we first pre-reconstruct a prior structure information image using projection data from all energy channels. Then, we perform a k-means clustering on the prior image to generate a sparse dictionary representation for the image, which severs as a structure information constraint. We com- bine this constraint with conventional compressed sensing method and proposed a new model which we referred as Joint Clustering Prior and Sparsity Regularization (CPSR). CPSR is a convex problem and we solve it by Alternating Direction Method of Multipliers (ADMM). We verify our CPSR reconstruction method with a numerical simulation experiment. A dental phantom with complicate structures of teeth and soft tissues is used. X-ray beams from three spectra of different peak energies (120kVp, 90kVp, 60kVp) irradiate the phantom to form tri-energy projections. Projection data covering only 75◦ from each energy spectrum are collected for reconstruction. Independent reconstruction for each energy will cause severe limited-angle artifacts even with the help of compressed sensing approaches. Our CPSR provides us with images free of the limited-angle artifact. All edge details are well preserved in our experimental study.

Hyperspectral remote sensing of paddy crop using insitu measurement and clustering technique

NASA Astrophysics Data System (ADS)

Moharana, S.; Dutta, S.

2014-11-01

Rice Agriculture, mainly cultivated in South Asia regions, is being monitored for extracting crop parameter, crop area, crop growth profile, crop yield using both optical and microwave remote sensing. Hyperspectral data provide more detailed information of rice agriculture. The present study was carried out at the experimental station of the Regional Rainfed Low land Rice Research Station, Assam, India (26.1400° N, 91.7700° E) and the overall climate of the study area comes under Lower Brahmaputra Valley (LBV) Agro Climatic Zones. The hyperspectral measurements were made in the year 2009 from 72 plots that include eight rice varieties along with three different level of nitrogen treatments (50, 100, 150 kg/ha) covering rice transplanting to the crop harvesting period. With an emphasis to varieties, hyperspectral measurements were taken in the year 2014 from 24 plots having 24 rice genotypes with different crop developmental ages. All the measurements were performed using a spectroradiometer with a spectral range of 350-1050 nm under direct sunlight of a cloud free sky and stable condition of the atmosphere covering more than 95 % canopy. In this study, reflectance collected from canopy of rice were expressed in terms of waveforms. Furthermore, generated waveforms were analysed for all combinations of nitrogen applications and varieties. A hierarchical clustering technique was employed to classify these waveforms into different groups. By help of agglomerative clustering algorithm a few number of clusters were finalized for different rice varieties along with nitrogen treatments. By this clustering approach, observational error in spectroradiometer reflectance was also nullified. From this hierarchical clustering, appropriate spectral signature for rice canopy were identified and will help to create rice crop classification accurately and therefore have a prospect to make improved information on rice agriculture at both local and regional scales. From this hierarchical clustering, spectral signature library for rice canopy were identified which will help to create rice crop classification maps and critical wave bands like green (519,559 nm), red (649 nm), red edge (729 nm) and NIR region (779,819 nm) were marked sensitive to nitrogen which will further help in nitrogen mapping of paddy agriculture over therefore have the prospect to make improved informed decisions.

XMM-Newton observations of NGC 3268 in the Antlia Galaxy Cluster: characterization of a hidden group of galaxies at z ≈ 0.41

NASA Astrophysics Data System (ADS)

Gargiulo, I. D.; García, F.; Combi, J. A.; Caso, J. P.; Bassino, L. P.

2018-05-01

We report on a detailed X-ray study of the extended emission of the intracluster medium (ICM) around NGC 3268, in the Antlia cluster of galaxies, together with a characterization of an extended source in the field, namely a background cluster of galaxies at z ≈ 0.41, which was previously accounted as an X-ray point source. The spectral properties of the extended emission of the gas present in Antlia were studied using data from the XMM-Newton satellite complemented with optical images of CTIO-Blanco telescope, to attain for associations of the optical sources with the X-ray emission. The XMM-Newton observations show that the intracluster gas is concentrated in a region centred in one of the main galaxies of the cluster, NGC 3268. By means of a spatially-resolved spectral analysis we derived the abundances of the ICM plasma. We found a wall-like feature in the northeast direction where the gas is characterized by a lower temperature with respect to the rest of the ICM. Furthermore, using combined optical observations we inferred the presence of an elliptical galaxy in the centre of the extended X-ray source considered as a background cluster, which favours this interpretation.

A young solar twin in the Rosette cluster NGC 2244 line of sight

NASA Astrophysics Data System (ADS)

Huber, Jeremy M.; Kielkopf, John F.; Mengel, Matthew; Carter, Bradley D.; Ferland, Gary J.; Clark, Frank O.

2018-05-01

Based on prior precision photometry and cluster age analysis, the bright star GSC 00154-01819 is a possible young pre-main sequence member of the Rosette cluster, NGC 2244. As part of a comprehensive study of the large-scale structure of the Rosette and its excitation by the cluster stars, we noted this star as a potential backlight for a probe of the interstellar medium and extinction along the sight line towards a distinctive nebular feature projected on to the cluster centre. New high-resolution spectra of the star were taken with the University College London Echelle Spectrograph of the AAT. They reveal that rather than being a reddened spectral type B or A star within the Mon OB2 association, it is a nearby, largely unreddened, solar twin of spectral type G2V less than 180 Myr old. It is about 219 pc from the Sun with a barycentric radial velocity of +14.35 ± 1.99 km s-1. The spectrum of the Rosette behind it and along this line of sight shows a barycentric radial velocity of +26.0 ± 2.4 km s-1 in H α, and a full width at half-maximum velocity dispersion of 61.94 ± 1.38 km s-1.

Superpixel Based Factor Analysis and Target Transformation Method for Martian Minerals Detection

NASA Astrophysics Data System (ADS)

Wu, X.; Zhang, X.; Lin, H.

2018-04-01

The Factor analysis and target transformation (FATT) is an effective method to test for the presence of particular mineral on Martian surface. It has been used both in thermal infrared (Thermal Emission Spectrometer, TES) and near-infrared (Compact Reconnaissance Imaging Spectrometer for Mars, CRISM) hyperspectral data. FATT derived a set of orthogonal eigenvectors from a mixed system and typically selected first 10 eigenvectors to least square fit the library mineral spectra. However, minerals present only in a limited pixels will be ignored because its weak spectral features compared with full image signatures. Here, we proposed a superpixel based FATT method to detect the mineral distributions on Mars. The simple linear iterative clustering (SLIC) algorithm was used to partition the CRISM image into multiple connected image regions with spectral homogeneous to enhance the weak signatures by increasing their proportion in a mixed system. A least square fitting was used in target transformation and performed to each region iteratively. Finally, the distribution of the specific minerals in image was obtained, where fitting residual less than a threshold represent presence and otherwise absence. We validate our method by identifying carbonates in a well analysed CRISM image in Nili Fossae on Mars. Our experimental results indicate that the proposed method work well both in simulated and real data sets.

Molecular Clustering Interrelationships and Carbohydrate Conformation in Hull and Seeds Among Barley Cultivars

DOE Office of Scientific and Technical Information (OSTI.GOV)

N Liu; P Yu

2011-12-31

The objective of this study was to use molecular spectral analyses with the diffuse reflectance Fourier transform infrared spectroscopy (DRIFT) bioanlytical technique to study carbohydrate conformation features, molecular clustering and interrelationships in hull and seed among six barley cultivars (AC Metcalfe, CDC Dolly, McLeod, CDC Helgason, CDC Trey, CDC Cowboy), which had different degradation kinetics in rumen. The molecular structure spectral analyses in both hull and seed involved the fingerprint regions of ca. 1536-1484 cm{sup -1} (attributed mainly to aromatic lignin semicircle ring stretch), ca. 1293-1212 cm{sup -1} (attributed mainly to cellulosic compounds in the hull), ca. 1269-1217 cm{sup -1}more » (attributed mainly to cellulosic compound in the seeds), and ca. 1180-800 cm{sup -1} (attributed mainly to total CHO C-O stretching vibrations) together with an agglomerative hierarchical cluster (AHCA) and principal component spectral analyses (PCA). The results showed that the DRIFT technique plus AHCA and PCA molecular analyses were able to reveal carbohydrate conformation features and identify carbohydrate molecular structure differences in both hull and seeds among the barley varieties. The carbohydrate molecular spectral analyses at the region of ca. 1185-800 cm{sup -1} together with the AHCA and PCA were able to show that the barley seed inherent structures exhibited distinguishable differences among the barley varieties. CDC Helgason had differences from AC Metcalfe, MeLeod, CDC Cowboy and CDC Dolly in carbohydrate conformation in the seed. Clear molecular cluster classes could be distinguished and identified in AHCA analysis and the separate ellipses could be grouped in PCA analysis. But CDC Helgason had no distinguished differences from CDC Trey in carbohydrate conformation. These carbohydrate conformation/structure difference could partially explain why the varieties were different in digestive behaviors in animals. The molecular spectroscopy technique used in this study could also be used for other plant-based feed and food structure studies.« less

An integrated workflow for robust alignment and simplified quantitative analysis of NMR spectrometry data.

PubMed

Vu, Trung N; Valkenborg, Dirk; Smets, Koen; Verwaest, Kim A; Dommisse, Roger; Lemière, Filip; Verschoren, Alain; Goethals, Bart; Laukens, Kris

2011-10-20

Nuclear magnetic resonance spectroscopy (NMR) is a powerful technique to reveal and compare quantitative metabolic profiles of biological tissues. However, chemical and physical sample variations make the analysis of the data challenging, and typically require the application of a number of preprocessing steps prior to data interpretation. For example, noise reduction, normalization, baseline correction, peak picking, spectrum alignment and statistical analysis are indispensable components in any NMR analysis pipeline. We introduce a novel suite of informatics tools for the quantitative analysis of NMR metabolomic profile data. The core of the processing cascade is a novel peak alignment algorithm, called hierarchical Cluster-based Peak Alignment (CluPA). The algorithm aligns a target spectrum to the reference spectrum in a top-down fashion by building a hierarchical cluster tree from peak lists of reference and target spectra and then dividing the spectra into smaller segments based on the most distant clusters of the tree. To reduce the computational time to estimate the spectral misalignment, the method makes use of Fast Fourier Transformation (FFT) cross-correlation. Since the method returns a high-quality alignment, we can propose a simple methodology to study the variability of the NMR spectra. For each aligned NMR data point the ratio of the between-group and within-group sum of squares (BW-ratio) is calculated to quantify the difference in variability between and within predefined groups of NMR spectra. This differential analysis is related to the calculation of the F-statistic or a one-way ANOVA, but without distributional assumptions. Statistical inference based on the BW-ratio is achieved by bootstrapping the null distribution from the experimental data. The workflow performance was evaluated using a previously published dataset. Correlation maps, spectral and grey scale plots show clear improvements in comparison to other methods, and the down-to-earth quantitative analysis works well for the CluPA-aligned spectra. The whole workflow is embedded into a modular and statistically sound framework that is implemented as an R package called "speaq" ("spectrum alignment and quantitation"), which is freely available from http://code.google.com/p/speaq/.

A New Method Using Single-Particle Mass Spectrometry Data to Distinguish Mineral Dust and Biological Aerosols

NASA Astrophysics Data System (ADS)

Al-Mashat, H.; Kristensen, L.; Sultana, C. M.; Prather, K. A.

2016-12-01

The ability to distinguish types of particles present within a cloud is important for determining accurate inputs to climate models. The chemical composition of particles within cloud liquid droplets and ice crystals can have a significant impact on the timing, location, and amount of precipitation that falls. Precipitation efficiency is increased by the presence of ice crystals in clouds, and both mineral dust and biological aerosols have been shown to be effective ice nucleating particles (INPs) in the atmosphere. A current challenge in aerosol science is distinguishing mineral dust and biological material in the analysis of real-time, ambient, single-particle mass spectral data. Single-particle mass spectrometers are capable of measuring the size-resolved chemical composition of individual atmospheric particles. However, there is no consistent analytical method for distinguishing dust and biological aerosols. Sampling and characterization of control samples (i.e. of known identity) of mineral dust and bacteria were performed by the Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) as part of the Fifth Ice Nucleation (FIN01) Workshop at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) facility in Karlsruhe, Germany. Using data collected by the ATOFMS of control samples, a new metric has been developed to classify single particles as dust or biological independent of spectral cluster analysis. This method, involving the use of a ratio of mass spectral peak areas for organic nitrogen and silicates, is easily reproducible and does not rely on extensive knowledge of particle chemistry or the ionization characteristics of mass spectrometers. This represents a step toward rapidly distinguishing particle types responsible for ice nucleation activity during real-time sampling in clouds. The ability to distinguish types of particles present within a cloud is important for determining accurate inputs to climate models. The chemical composition of particles within cloud liquid droplets and ice crystals can have a significant impact on the timing, location, and amount of precipitation that falls. Precipitation efficiency is increased by the presence of ice crystals in clouds, and both mineral dust and biological aerosols have been shown to be effective ice nucleating particles (INPs) in the atmosphere. A current challenge in aerosol science is distinguishing mineral dust and biological material in the analysis of real-time, ambient, single-particle mass spectral data. Single-particle mass spectrometers are capable of measuring the size-resolved chemical composition of individual atmospheric particles. However, there is no consistent analytical method for distinguishing dust and biological aerosols. Sampling and characterization of control samples (i.e. of known identity) of mineral dust and bacteria were performed by the Aerosol Time-of-Flight Mass Spectrometer (ATOFMS) as part of the Fifth Ice Nucleation (FIN01) Workshop at the Aerosol Interaction and Dynamics in the Atmosphere (AIDA) facility in Karlsruhe, Germany. Using data collected by the ATOFMS of control samples, a new metric has been developed to classify single particles as dust or biological independent of spectral cluster analysis. This method, involving the use of a ratio of mass spectral peak areas for organic nitrogen and silicates, is easily reproducible and does not rely on extensive knowledge of particle chemistry or the ionization characteristics of mass spectrometers. This represents a step toward rapidly distinguishing particle types responsible for ice nucleation activity during real-time sampling in clouds.

Cluster-enriched Yang-Baxter equation from SUSY gauge theories

NASA Astrophysics Data System (ADS)

Yamazaki, Masahito

2018-04-01

We propose a new generalization of the Yang-Baxter equation, where the R-matrix depends on cluster y-variables in addition to the spectral parameters. We point out that we can construct solutions to this new equation from the recently found correspondence between Yang-Baxter equations and supersymmetric gauge theories. The S^2 partition function of a certain 2d N=(2,2) quiver gauge theory gives an R-matrix, whereas its FI parameters can be identified with the cluster y-variables.

Electron spectra of xenon clusters irradiated with a laser-driven plasma soft-x-ray laser pulse

DOE Office of Scientific and Technical Information (OSTI.GOV)

Namba, S.; Takiyama, K.; Hasegawa, N.

Xenon clusters were irradiated with plasma soft-x-ray laser pulses (having a wavelength of 13.9 nm, time duration of 7 ps, and intensities of up to 10 GW/cm{sup 2}). The laser photon energy was high enough to photoionize 4d core electrons. The cross section is large due to a giant resonance. The interaction was investigated by measuring the electron energy spectra. The photoelectron spectra for small clusters indicate that the spectral width due to the 4d hole significantly broadens with increasing cluster size. For larger clusters, the electron energy spectra evolve into a Maxwell-Boltzmann distribution, as a strongly coupled cluster nanoplasmamore » is generated.« less

Spectral constraints on models of gas in clusters of galaxies

NASA Technical Reports Server (NTRS)

Henriksen, M. J.; Mushotzky, R.

1985-01-01

The HEAO 1A2 spectra of clusters of galaxies are used to determine the temperature profile which characterizes the X-ray emitting gas. Strong evidence of nonisothermality is found for the Coma, A85, and A1795 clusters. Properties of the cluster potential which binds the gas are calculated for a range of model parameters. The typical binding mass, if the gas is adiabatic, is 2-4E14 solar masses and is quite centrally concentrated. In addition, the Fe abundance in Coma is .26 + or - .06 solar, less than the typical value (.5) found for rich clusters. The results for the gas in Coma may imply a physical description of the cluster which is quite different from what was previously believed.

New Methods of Spectral-Density Based Graph Construction and Their Application to Hyperspectral Image Analysis

NASA Astrophysics Data System (ADS)

Stevens, Jeffrey

The past decade has seen the emergence of many hyperspectral image (HSI) analysis algorithms based on graph theory and derived manifold-coordinates. Yet, despite the growing number of algorithms, there has been limited study of the graphs constructed from spectral data themselves. Which graphs are appropriate for various HSI analyses--and why? This research aims to begin addressing these questions as the performance of graph-based techniques is inextricably tied to the graphical model constructed from the spectral data. We begin with a literature review providing a survey of spectral graph construction techniques currently used by the hyperspectral community, starting with simple constructs demonstrating basic concepts and then incrementally adding components to derive more complex approaches. Throughout this development, we discuss algorithm advantages and disadvantages for different types of hyperspectral analysis. A focus is provided on techniques influenced by spectral density through which the concept of community structure arises. Through the use of simulated and real HSI data, we demonstrate density-based edge allocation produces more uniform nearest neighbor lists than non-density based techniques through increasing the number of intracluster edges, facilitating higher k-nearest neighbor (k-NN) classification performance. Imposing the common mutuality constraint to symmetrify adjacency matrices is demonstrated to be beneficial in most circumstances, especially in rural (less cluttered) scenes. Many complex adaptive edge-reweighting techniques are shown to slightly degrade nearest-neighbor list characteristics. Analysis suggests this condition is possibly attributable to the validity of characterizing spectral density by a single variable representing data scale for each pixel. Additionally, it is shown that imposing mutuality hurts the performance of adaptive edge-allocation techniques or any technique that aims to assign a low number of edges (<10) to any pixel. A simple k bias addresses this problem. Many of the adaptive edge-reweighting techniques are based on the concept of codensity, so we explore codensity properties as they relate to density-based edge reweighting. We find that codensity may not be the best estimator of local scale due to variations in cluster density, so we introduce and compare two inherently density-weighted graph construction techniques from the data mining literature: shared nearest neighbors (SNN) and mutual proximity (MP). MP and SNN are not reliant upon a codensity measure, hence are not susceptible to its shortcomings. Neither has been used for hyperspectral analyses, so this presents the first study of these techniques on HSI data. We demonstrate MP and SNN can offer better performance, but in general none of the reweighting techniques improve the quality of these spectral graphs in our neighborhood structure tests. As such, these complex adaptive edge-reweighting techniques may need to be modified to increase their effectiveness. During this investigation, we probe deeper into properties of high-dimensional data and introduce the concept of concentration of measure (CoM)--the degradation in the efficacy of many common distance measures with increasing dimensionality--as it relates to spectral graph construction. CoM exists in pairwise distances between HSI pixels, but not to the degree experienced in random data of the same extrinsic dimension; a characteristic we demonstrate is due to the rich correlation and cluster structure present in HSI data. CoM can lead to hubness--a condition wherein some nodes have short distances (high similarities) to an exceptionally large number of nodes. We study hub presence in 49 HSI datasets of varying resolutions, altitudes, and spectral bands to demonstrate hubness effects are negligible in a k-NN classification example (generalized counting scenarios), but we note its impact on methods that use edge weights to derive manifold coordinates or splitting clusters based on spectral graph theory requires more investigation. Many of these new graph-related quantities can be exploited to demonstrate new techniques for HSI classification and anomaly detection. We present an initial exploration into this relatively new and exciting field based on an enhanced Schroedinger Eigenmap classification example and compare results to the current state-of-the-art approach. We produce equivalent results, but demonstrate different types of misclassifications, opening the door to combine the best of both approaches to achieve truly superior performance. A separate less mature hubness-assisted anomaly detector (HAAD) is also presented.

Raman spectroscopy for the characterization of different fractions of hemp essential oil extracted at 130 °C using steam distillation method

NASA Astrophysics Data System (ADS)

Hanif, Muhammad Asif; Nawaz, Haq; Naz, Saima; Mukhtar, Rubina; Rashid, Nosheen; Bhatti, Ijaz Ahmad; Saleem, Muhammad

2017-07-01

In this study, Raman spectroscopy along with Principal Component Analysis (PCA) is used for the characterization of pure essential oil (pure EO) isolated from the leaves of the Hemp (Cannabis sativa L.,) as well as its different fractions obtained by fractional distillation process. Raman spectra of pure Hemp essential oil and its different fractions show characteristic key bands of main volatile terpenes and terpenoids, which significantly differentiate them from each other. These bands provide information about the chemical composition of sample under investigation and hence can be used as Raman spectral markers for the qualitative monitoring of the pure EO and different fractions containing different active compounds. PCA differentiates the Raman spectral data into different clusters and loadings of the PCA further confirm the biological origin of the different fractions of the essential oil.

Terahertz imaging applied to cancer diagnosis.

PubMed

Brun, M-A; Formanek, F; Yasuda, A; Sekine, M; Ando, N; Eishii, Y

2010-08-21

We report on terahertz (THz) time-domain spectroscopy imaging of 10 microm thick histological sections. The sections are prepared according to standard pathological procedures and deposited on a quartz window for measurements in reflection geometry. Simultaneous acquisition of visible images enables registration of THz images and thus the use of digital pathology tools to investigate the links between the underlying cellular structure and specific THz information. An analytic model taking into account the polarization of the THz beam, its incidence angle, the beam shift between the reference and sample pulses as well as multiple reflections within the sample is employed to determine the frequency-dependent complex refractive index. Spectral images are produced through segmentation of the extracted refractive index data using clustering methods. Comparisons of visible and THz images demonstrate spectral differences not only between tumor and healthy tissues but also within tumors. Further visualization using principal component analysis suggests different mechanisms as to the origin of image contrast.

Methods of editing cloud and atmospheric layer affected pixels from satellite data

NASA Technical Reports Server (NTRS)

Nixon, P. R. (Principal Investigator); Wiegand, C. L.; Richardson, A. J.; Johnson, M. P.; Goodier, B. G.

1981-01-01

The location and migration of cloud, land and water features were examined in spectral space (reflective VIS vs. emissive IR). Daytime HCMM data showed two distinct types of cloud affected pixels in the south Texas test area. High altitude cirrus and/or cirrostratus and "subvisible cirrus" (SCi) reflected the same or only slightly more than land features. In the emissive band, the digital counts ranged from 1 to over 75 and overlapped land features. Pixels consisting of cumulus clouds, or of mixed cumulus and landscape, clustered in a different area of spectral space than the high altitude cloud pixels. Cumulus affected pixels were more reflective than land and water pixels. In August the high altitude clouds and SCi were more emissive than similar clouds were in July. Four-channel TIROS-N data were examined with the objective of developing a multispectral screening technique for removing SCi contaminated data.

Metal-to-metal charge transfer transitions - Interpretation of visible-region spectra of the moon and lunar materials

NASA Technical Reports Server (NTRS)

Loeffler, B. M.; Burns, R. G.; Tossell, J. A.

1975-01-01

Prominent bands in the spectral profiles of Fe-Ti phases in lunar samples have been attributed to charge-transfer transitions between Fe and Ti cations, and a model is presented for calculating charge transfer energies from energy levels computed by the SCF-X(alpha) scattered wave molecular orbital method for isolated MO6 octahedral coordination clusters containing Fe(2+), Fe(3+), Ti(3+), and Ti(4+) cations. The calculated charge transfer energy for the Fe(2+) to Ti(4+) transition correlates well with a measured spectral feature around 0.6 micron in ilmenite, and, since ilmenite is a major constituent of mare basalts and dark-mantling material, the observed darkness and blueness of the regolith in lunar black spots is attributed primarily to this transition. The Ti(3+) to Ti(4+) transition is thought to contribute to some phases.

Computational Performance of a Parallelized Three-Dimensional High-Order Spectral Element Toolbox

NASA Astrophysics Data System (ADS)

Bosshard, Christoph; Bouffanais, Roland; Clémençon, Christian; Deville, Michel O.; Fiétier, Nicolas; Gruber, Ralf; Kehtari, Sohrab; Keller, Vincent; Latt, Jonas

In this paper, a comprehensive performance review of an MPI-based high-order three-dimensional spectral element method C++ toolbox is presented. The focus is put on the performance evaluation of several aspects with a particular emphasis on the parallel efficiency. The performance evaluation is analyzed with help of a time prediction model based on a parameterization of the application and the hardware resources. A tailor-made CFD computation benchmark case is introduced and used to carry out this review, stressing the particular interest for clusters with up to 8192 cores. Some problems in the parallel implementation have been detected and corrected. The theoretical complexities with respect to the number of elements, to the polynomial degree, and to communication needs are correctly reproduced. It is concluded that this type of code has a nearly perfect speed up on machines with thousands of cores, and is ready to make the step to next-generation petaflop machines.

Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods

NASA Astrophysics Data System (ADS)

Juniati, D.; Khotimah, C.; Wardani, D. E. K.; Budayasa, K.

2018-01-01

The heart abnormalities can be detected from heart sound. A heart sound can be heard directly with a stethoscope or indirectly by a phonocardiograph, a machine of the heart sound recording. This paper presents the implementation of fractal dimension theory to make a classification of phonocardiograms into a normal heart sound, a murmur, or an extrasystole. The main algorithm used to calculate the fractal dimension was Higuchi’s Algorithm. There were two steps to make a classification of phonocardiograms, feature extraction, and classification. For feature extraction, we used Discrete Wavelet Transform to decompose the signal of heart sound into several sub-bands depending on the selected level. After the decomposition process, the signal was processed using Fast Fourier Transform (FFT) to determine the spectral frequency. The fractal dimension of the FFT output was calculated using Higuchi Algorithm. The classification of fractal dimension of all phonocardiograms was done with KNN and Fuzzy c-mean clustering methods. Based on the research results, the best accuracy obtained was 86.17%, the feature extraction by DWT decomposition level 3 with the value of kmax 50, using 5-fold cross validation and the number of neighbors was 5 at K-NN algorithm. Meanwhile, for fuzzy c-mean clustering, the accuracy was 78.56%.

Automated grouping of action potentials of human embryonic stem cell-derived cardiomyocytes.

PubMed

Gorospe, Giann; Zhu, Renjun; Millrod, Michal A; Zambidis, Elias T; Tung, Leslie; Vidal, Rene

2014-09-01

Methods for obtaining cardiomyocytes from human embryonic stem cells (hESCs) are improving at a significant rate. However, the characterization of these cardiomyocytes (CMs) is evolving at a relatively slower rate. In particular, there is still uncertainty in classifying the phenotype (ventricular-like, atrial-like, nodal-like, etc.) of an hESC-derived cardiomyocyte (hESC-CM). While previous studies identified the phenotype of a CM based on electrophysiological features of its action potential, the criteria for classification were typically subjective and differed across studies. In this paper, we use techniques from signal processing and machine learning to develop an automated approach to discriminate the electrophysiological differences between hESC-CMs. Specifically, we propose a spectral grouping-based algorithm to separate a population of CMs into distinct groups based on the similarity of their action potential shapes. We applied this method to a dataset of optical maps of cardiac cell clusters dissected from human embryoid bodies. While some of the nine cell clusters in the dataset are presented with just one phenotype, the majority of the cell clusters are presented with multiple phenotypes. The proposed algorithm is generally applicable to other action potential datasets and could prove useful in investigating the purification of specific types of CMs from an electrophysiological perspective.

Automated Grouping of Action Potentials of Human Embryonic Stem Cell-Derived Cardiomyocytes

PubMed Central

Gorospe, Giann; Zhu, Renjun; Millrod, Michal A.; Zambidis, Elias T.; Tung, Leslie; Vidal, René

2015-01-01

Methods for obtaining cardiomyocytes from human embryonic stem cells (hESCs) are improving at a significant rate. However, the characterization of these cardiomyocytes is evolving at a relatively slower rate. In particular, there is still uncertainty in classifying the phenotype (ventricular-like, atrial-like, nodal-like, etc.) of an hESC-derived cardiomyocyte (hESC-CM). While previous studies identified the phenotype of a cardiomyocyte based on electrophysiological features of its action potential, the criteria for classification were typically subjective and differed across studies. In this paper, we use techniques from signal processing and machine learning to develop an automated approach to discriminate the electrophysiological differences between hESC-CMs. Specifically, we propose a spectral grouping-based algorithm to separate a population of cardiomyocytes into distinct groups based on the similarity of their action potential shapes. We applied this method to a dataset of optical maps of cardiac cell clusters dissected from human embryoid bodies (hEBs). While some of the 9 cell clusters in the dataset presented with just one phenotype, the majority of the cell clusters presented with multiple phenotypes. The proposed algorithm is generally applicable to other action potential datasets and could prove useful in investigating the purification of specific types of cardiomyocytes from an electrophysiological perspective. PMID:25148658

Using LUCAS topsoil database to estimate soil organic carbon content in local spectral libraries

NASA Astrophysics Data System (ADS)

Castaldi, Fabio; van Wesemael, Bas; Chabrillat, Sabine; Chartin, Caroline

2017-04-01

The quantification of the soil organic carbon (SOC) content over large areas is mandatory to obtain accurate soil characterization and classification, which can improve site specific management at local or regional scale exploiting the strong relationship between SOC and crop growth. The estimation of the SOC is not only important for agricultural purposes: in recent years, the increasing attention towards global warming highlighted the crucial role of the soil in the global carbon cycle. In this context, soil spectroscopy is a well consolidated and widespread method to estimate soil variables exploiting the interaction between chromophores and electromagnetic radiation. The importance of spectroscopy in soil science is reflected by the increasing number of large soil spectral libraries collected in the world. These large libraries contain soil samples derived from a consistent number of pedological regions and thus from different parent material and soil types; this heterogeneity entails, in turn, a large variability in terms of mineralogical and organic composition. In the light of the huge variability of the spectral responses to SOC content and composition, a rigorous classification process is necessary to subset large spectral libraries and to avoid the calibration of global models failing to predict local variation in SOC content. In this regard, this study proposes a method to subset the European LUCAS topsoil database into soil classes using a clustering analysis based on a large number of soil properties. The LUCAS database was chosen to apply a standardized multivariate calibration approach valid for large areas without the need for extensive field and laboratory work for calibration of local models. Seven soil classes were detected by the clustering analyses and the samples belonging to each class were used to calibrate specific partial least square regression (PLSR) models to estimate SOC content of three local libraries collected in Belgium (Loam belt and Wallonia) and Luxembourg. The three local libraries only consist of spectral data (199 samples) acquired using the same protocol as the one used for the LUCAS database. SOC was estimated with a good accuracy both within each local library (RMSE: 1.2 ÷ 5.4 g kg-1; RPD: 1.41 ÷ 2.06) and for the samples of the three libraries together (RMSE: 3.9 g kg-1; RPD: 2.47). The proposed approach could allow to estimate SOC everywhere in Europe only collecting spectra, without the need for chemical laboratory analyses, exploiting the potentiality of the LUCAS database and specific PLSR models.

The continuum spectral characteristics of gamma ray bursts observed by BATSE

NASA Technical Reports Server (NTRS)

Pendleton, Geoffrey N.; Paciesas, William S.; Briggs, Michael S.; Mallozzi, Robert S.; Koshut, Tom M.; Fishman, Gerald J.; Meegan, Charles A.; Wilson, Robert B.; Harmon, Alan B.; Kouveliotou, Chryssa

1994-01-01

Distributions of the continuum spectral characteristics of 260 bursts in the first Burst and Transient Source Experiment (BATSE) catalog are presented. The data are derived from flux ratios calculated from the BATSE Large Area Detector (LAD) four channel discriminator data. The data are converted from counts to photons using a direct spectral inversion technique to remove the effects of atmospheric scattering and the energy dependence of the detector angular response. Although there are intriguing clusterings of bursts in the spectral hardness ratio distributions, no evidence for the presence of distinct burst classes based on spectral hardness ratios alone is found. All subsets of bursts selected for their spectral characteristics in this analysis exhibit spatial distributions consistent with isotropy. The spectral diversity of the burst population appears to be caused largely by the highly variable nature of the burst production mechanisms themselves.

Constraining hydrostatic mass bias of galaxy clusters with high-resolution X-ray spectroscopy

NASA Astrophysics Data System (ADS)

Ota, Naomi; Nagai, Daisuke; Lau, Erwin T.

2018-04-01

Gas motions in galaxy clusters play important roles in determining the properties of the intracluster medium (ICM) and in the constraint of cosmological parameters via X-ray and Sunyaev-Zel'dovich effect observations of galaxy clusters. The Hitomi measurements of gas motions in the core of the Perseus Cluster have provided new insights into the physics in galaxy clusters. The XARM mission, equipped with the Resolve X-ray micro-calorimeter, will continue Hitomi's legacy by measuring ICM motions through Doppler shifting and broadening of emission lines in a larger number of galaxy clusters, and at larger radii. In this work, we investigate how well we can measure bulk and turbulent gas motions in the ICM with XARM, by analyzing mock XARM simulations of galaxy clusters extracted from cosmological hydrodynamic simulations. We assess how photon counts, spectral fitting methods, multiphase ICM structure, deprojections, and region selection affect the measurements of gas motions. We first show that XARM is capable of recovering the underlying spherically averaged turbulent and bulk velocity profiles for dynamically relaxed clusters to within ˜50% with a reasonable amount of photon counts in the X-ray emission lines. We also find that there are considerable azimuthal variations in the ICM velocities, where the velocities measured in a single azimuthal direction can significantly deviate from the true value even in dynamically relaxed systems. Such variation must be taken into account when interpreting data and developing observing strategies. We will discuss the prospect of using the upcoming XARM mission to measure non-thermal pressure and to correct for the hydrostatic mass bias of galaxy clusters. Our results are broadly applicable for future X-ray missions, such as Athena and Lynx.

Constraining hydrostatic mass bias of galaxy clusters with high-resolution X-ray spectroscopy

NASA Astrophysics Data System (ADS)

Ota, Naomi; Nagai, Daisuke; Lau, Erwin T.

2018-06-01

Gas motions in galaxy clusters play important roles in determining the properties of the intracluster medium (ICM) and in the constraint of cosmological parameters via X-ray and Sunyaev-Zel'dovich effect observations of galaxy clusters. The Hitomi measurements of gas motions in the core of the Perseus Cluster have provided new insights into the physics in galaxy clusters. The XARM mission, equipped with the Resolve X-ray micro-calorimeter, will continue Hitomi's legacy by measuring ICM motions through Doppler shifting and broadening of emission lines in a larger number of galaxy clusters, and at larger radii. In this work, we investigate how well we can measure bulk and turbulent gas motions in the ICM with XARM, by analyzing mock XARM simulations of galaxy clusters extracted from cosmological hydrodynamic simulations. We assess how photon counts, spectral fitting methods, multiphase ICM structure, deprojections, and region selection affect the measurements of gas motions. We first show that XARM is capable of recovering the underlying spherically averaged turbulent and bulk velocity profiles for dynamically relaxed clusters to within ˜50% with a reasonable amount of photon counts in the X-ray emission lines. We also find that there are considerable azimuthal variations in the ICM velocities, where the velocities measured in a single azimuthal direction can significantly deviate from the true value even in dynamically relaxed systems. Such variation must be taken into account when interpreting data and developing observing strategies. We will discuss the prospect of using the upcoming XARM mission to measure non-thermal pressure and to correct for the hydrostatic mass bias of galaxy clusters. Our results are broadly applicable for future X-ray missions, such as Athena and Lynx.

Hyperspectral Image Classification for Land Cover Based on an Improved Interval Type-II Fuzzy C-Means Approach

PubMed Central

Li, Zhao-Liang

2018-01-01

Few studies have examined hyperspectral remote-sensing image classification with type-II fuzzy sets. This paper addresses image classification based on a hyperspectral remote-sensing technique using an improved interval type-II fuzzy c-means (IT2FCM*) approach. In this study, in contrast to other traditional fuzzy c-means-based approaches, the IT2FCM* algorithm considers the ranking of interval numbers and the spectral uncertainty. The classification results based on a hyperspectral dataset using the FCM, IT2FCM, and the proposed improved IT2FCM* algorithms show that the IT2FCM* method plays the best performance according to the clustering accuracy. In this paper, in order to validate and demonstrate the separability of the IT2FCM*, four type-I fuzzy validity indexes are employed, and a comparative analysis of these fuzzy validity indexes also applied in FCM and IT2FCM methods are made. These four indexes are also applied into different spatial and spectral resolution datasets to analyze the effects of spectral and spatial scaling factors on the separability of FCM, IT2FCM, and IT2FCM* methods. The results of these validity indexes from the hyperspectral datasets show that the improved IT2FCM* algorithm have the best values among these three algorithms in general. The results demonstrate that the IT2FCM* exhibits good performance in hyperspectral remote-sensing image classification because of its ability to handle hyperspectral uncertainty. PMID:29373548

[Research on developping the spectral dataset for Dunhuang typical colors based on color constancy].

PubMed

Liu, Qiang; Wan, Xiao-Xia; Liu, Zhen; Li, Chan; Liang, Jin-Xing

2013-11-01

The present paper aims at developping a method to reasonably set up the typical spectral color dataset for different kinds of Chinese cultural heritage in color rendering process. The world famous wall paintings dating from more than 1700 years ago in Dunhuang Mogao Grottoes was taken as typical case in this research. In order to maintain the color constancy during the color rendering workflow of Dunhuang culture relics, a chromatic adaptation based method for developping the spectral dataset of typical colors for those wall paintings was proposed from the view point of human vision perception ability. Under the help and guidance of researchers in the art-research institution and protection-research institution of Dunhuang Academy and according to the existing research achievement of Dunhuang Research in the past years, 48 typical known Dunhuang pigments were chosen and 240 representative color samples were made with reflective spectral ranging from 360 to 750 nm was acquired by a spectrometer. In order to find the typical colors of the above mentioned color samples, the original dataset was devided into several subgroups by clustering analysis. The grouping number, together with the most typical samples for each subgroup which made up the firstly built typical color dataset, was determined by wilcoxon signed rank test according to the color inconstancy index comprehensively calculated under 6 typical illuminating conditions. Considering the completeness of gamut of Dunhuang wall paintings, 8 complementary colors was determined and finally the typical spectral color dataset was built up which contains 100 representative spectral colors. The analytical calculating results show that the median color inconstancy index of the built dataset in 99% confidence level by wilcoxon signed rank test was 3.28 and the 100 colors are distributing in the whole gamut uniformly, which ensures that this dataset can provide reasonable reference for choosing the color with highest color constancy during the color rendering process of Dunhuang cultural heritage.

PCA/HEXTE Observations of Coma and A2319

NASA Technical Reports Server (NTRS)

Rephaeli, Yoel

1998-01-01

The Coma cluster was observed in 1996 for 90 ks by the PCA and HEXTE instruments aboard the RXTE satellite, the first simultaneous, pointing measurement of Coma in the broad, 2-250 keV, energy band. The high sensitivity achieved during this long observation allows precise determination of the spectrum. Our analysis of the measurements clearly indicates that in addition to the main thermal emission from hot intracluster gas at kT=7.5 keV, a second spectral component is required to best-fit the data. If thermal, it can be described with a temperature of 4.7 keV contributing about 20% of the total flux. The additional spectral component can also be described by a power-law, possibly due to Compton scattering of relativistic electrons by the CMB. This interpretation is based on the diffuse radio synchrotron emission, which has a spectral index of 2.34, within the range allowed by fits to the RXTE spectral data. A Compton origin of the measured nonthermal component would imply that the volume-averaged magnetic field in the central region of Coma is B =0.2 micro-Gauss, a value deduced directly from the radio and X-ray measurements (and thus free of the usual assumption of energy equipartition). Barring the presence of unknown systematic errors in the RXTE source or background measurements, our spectral analysis yields considerable evidence for Compton X-ray emission in the Coma cluster.

Numerical trials of HISSE

NASA Technical Reports Server (NTRS)

Peters, C.; Kampe, F. (Principal Investigator)

1980-01-01

The mathematical description and implementation of the statistical estimation procedure known as the Houston integrated spatial/spectral estimator (HISSE) is discussed. HISSE is based on a normal mixture model and is designed to take advantage of spectral and spatial information of LANDSAT data pixels, utilizing the initial classification and clustering information provided by the AMOEBA algorithm. The HISSE calculates parametric estimates of class proportions which reduce the error inherent in estimates derived from typical classify and count procedures common to nonparametric clustering algorithms. It also singles out spatial groupings of pixels which are most suitable for labeling classes. These calculations are designed to aid the analyst/interpreter in labeling patches with a crop class label. Finally, HISSE's initial performance on an actual LANDSAT agricultural ground truth data set is reported.

Hyperspectral imaging with deformable gratings fabricated with metal-elastomer nanocomposites

NASA Astrophysics Data System (ADS)

Potenza, Marco A. C.; Nazzari, Daniele; Cremonesi, Llorenç; Denti, Ilaria; Milani, Paolo

2017-11-01

We report the fabrication and characterization of a simple and compact hyperspectral imaging setup based on a stretchable diffraction grating made with a metal-polymer nanocomposite. The nanocomposite is produced by implanting Ag clusters in a poly(dimethylsiloxane) film by supersonic cluster beam implantation. The deformable grating has curved grooves and is imposed on a concave cylindrical surface, thus obtaining optical power in two orthogonal directions. Both diffractive and optical powers are obtained by reflection, thus realizing a diffractive-catoptric optical device. This makes it easier to minimize aberrations. We prove that, despite the extended spectral range and the simplified optical scheme, it is actually possible to work with a traditional CCD sensor and achieve a good spectral and spatial resolution.

Multivariate Statistical Analysis of MSL APXS Bulk Geochemical Data

NASA Astrophysics Data System (ADS)

Hamilton, V. E.; Edwards, C. S.; Thompson, L. M.; Schmidt, M. E.

2014-12-01

We apply cluster and factor analyses to bulk chemical data of 130 soil and rock samples measured by the Alpha Particle X-ray Spectrometer (APXS) on the Mars Science Laboratory (MSL) rover Curiosity through sol 650. Multivariate approaches such as principal components analysis (PCA), cluster analysis, and factor analysis compliment more traditional approaches (e.g., Harker diagrams), with the advantage of simultaneously examining the relationships between multiple variables for large numbers of samples. Principal components analysis has been applied with success to APXS, Pancam, and Mössbauer data from the Mars Exploration Rovers. Factor analysis and cluster analysis have been applied with success to thermal infrared (TIR) spectral data of Mars. Cluster analyses group the input data by similarity, where there are a number of different methods for defining similarity (hierarchical, density, distribution, etc.). For example, without any assumptions about the chemical contributions of surface dust, preliminary hierarchical and K-means cluster analyses clearly distinguish the physically adjacent rock targets Windjana and Stephen as being distinctly different than lithologies observed prior to Curiosity's arrival at The Kimberley. In addition, they are separated from each other, consistent with chemical trends observed in variation diagrams but without requiring assumptions about chemical relationships. We will discuss the variation in cluster analysis results as a function of clustering method and pre-processing (e.g., log transformation, correction for dust cover) and implications for interpreting chemical data. Factor analysis shares some similarities with PCA, and examines the variability among observed components of a dataset so as to reveal variations attributable to unobserved components. Factor analysis has been used to extract the TIR spectra of components that are typically observed in mixtures and only rarely in isolation; there is the potential for similar results with data from APXS. These techniques offer new ways to understand the chemical relationships between the materials interrogated by Curiosity, and potentially their relation to materials observed by APXS instruments on other landed missions.

Measuring spatially varying, multispectral, ultraviolet bidirectional reflectance distribution function with an imaging spectrometer

NASA Astrophysics Data System (ADS)

Li, Hongsong; Lyu, Hang; Liao, Ningfang; Wu, Wenmin

2016-12-01

The bidirectional reflectance distribution function (BRDF) data in the ultraviolet (UV) band are valuable for many applications including cultural heritage, material analysis, surface characterization, and trace detection. We present a BRDF measurement instrument working in the near- and middle-UV spectral range. The instrument includes a collimated UV light source, a rotation stage, a UV imaging spectrometer, and a control computer. The data captured by the proposed instrument describe spatial, spectral, and angular variations of the light scattering from a sample surface. Such a multidimensional dataset of an example sample is captured by the proposed instrument and analyzed by a k-mean clustering algorithm to separate surface regions with same material but different surface roughnesses. The clustering results show that the angular dimension of the dataset can be exploited for surface roughness characterization. The two clustered BRDFs are fitted to a theoretical BRDF model. The fitting results show good agreement between the measurement data and the theoretical model.

Open cluster Dolidze 25: Stellar parameters and the metallicity in the Galactic anticentre

NASA Astrophysics Data System (ADS)

Negueruela, I.; Simón-Díaz, S.; Lorenzo, J.; Castro, N.; Herrero, A.

2015-12-01

Context. The young open cluster Dolidze 25, in the direction of the Galactic anticentre, has been attributed a very low metallicity, with typical abundances between -0.5 and -0.7 dex below solar. Aims: We intend to derive accurate cluster parameters and accurate stellar abundances for some of its members. Methods: We have obtained a large sample of intermediate- and high-resolution spectra for stars in and around Dolidze 25. We used the fastwind code to generate stellar atmosphere models to fit the observed spectra. We derive stellar parameters for a large number of OB stars in the area, and abundances of oxygen and silicon for a number of stars with spectral types around B0. Results: We measure low abundances in stars of Dolidze 25. For the three stars with spectral types around B0, we find 0.3 dex (Si) and 0.5 dex (O) below the values typical in the solar neighbourhood. These values, even though not as low as those given previously, confirm Dolidze 25 and the surrounding H ii region Sh2-284 as the most metal-poor star-forming environment known in the Milky Way. We derive a distance 4.5 ± 0.3 kpc to the cluster (rG ≈ 12.3 kpc). The cluster cannot be older than ~3 Myr, and likely is not much younger. One star in its immediate vicinity, sharing the same distance, has Si and O abundances at most 0.15 dex below solar. Conclusions: The low abundances measured in Dolidze 25 are compatible with currently accepted values for the slope of the Galactic metallicity gradient, if we take into account that variations of at least ±0.15 dex are observed at a given radius. The area traditionally identified as Dolidze 25 is only a small part of a much larger star-forming region that comprises the whole dust shell associated with Sh2-284 and very likely several other smaller H ii regions in its vicinity. Based on observations made with the Nordic Optical Telescope, the Mercator Telescope, and the telescopes of the Isaac Newton Group.

Effect of manmade pixels on the inherent dimension of natural material distributions

NASA Astrophysics Data System (ADS)

Schlamm, Ariel; Messinger, David; Basener, William

2009-05-01

The inherent dimension of hyperspectral data may be a useful metric for discriminating between the presence of manmade and natural materials in a scene without reliance on spectral signatures take from libraries. Previously, a simple geometric method for approximating the inherent dimension was introduced along with results from application to single material clusters. This method uses an estimate of the slope from a graph based on the point density estimation in the spectral space. Other information can be gathered from the plot which may aid in the discrimination between manmade and natural materials. In order to use these measures to differentiate between the two material types, the effect of the inclusion of manmade pixels on the phenomenology of the background distribution must be evaluated. Here, a procedure for injecting manmade pixels into a natural region of a scene is discussed. The results of dimension estimation on natural scenes with varying amounts of manmade pixels injected are presented here, indicating that these metrics can be sensitive to the presence of manmade phenomenology in an image.

Model Order Reduction Algorithm for Estimating the Absorption Spectrum

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Beeumen, Roel; Williams-Young, David B.; Kasper, Joseph M.

The ab initio description of the spectral interior of the absorption spectrum poses both a theoretical and computational challenge for modern electronic structure theory. Due to the often spectrally dense character of this domain in the quantum propagator’s eigenspectrum for medium-to-large sized systems, traditional approaches based on the partial diagonalization of the propagator often encounter oscillatory and stagnating convergence. Electronic structure methods which solve the molecular response problem through the solution of spectrally shifted linear systems, such as the complex polarization propagator, offer an alternative approach which is agnostic to the underlying spectral density or domain location. This generality comesmore » at a seemingly high computational cost associated with solving a large linear system for each spectral shift in some discretization of the spectral domain of interest. In this work, we present a novel, adaptive solution to this high computational overhead based on model order reduction techniques via interpolation. Model order reduction reduces the computational complexity of mathematical models and is ubiquitous in the simulation of dynamical systems and control theory. The efficiency and effectiveness of the proposed algorithm in the ab initio prediction of X-ray absorption spectra is demonstrated using a test set of challenging water clusters which are spectrally dense in the neighborhood of the oxygen K-edge. On the basis of a single, user defined tolerance we automatically determine the order of the reduced models and approximate the absorption spectrum up to the given tolerance. We also illustrate that, for the systems studied, the automatically determined model order increases logarithmically with the problem dimension, compared to a linear increase of the number of eigenvalues within the energy window. Furthermore, we observed that the computational cost of the proposed algorithm only scales quadratically with respect to the problem dimension.« less

Mesoscale, Radiometrically Referenced, Multi-Temporal Hyperspectral Data for Co2 Leak Detection by Locating Spatial Variation of Biophysically Relevant Parameters

NASA Astrophysics Data System (ADS)

McCann, Cooper Patrick

Low-cost flight-based hyperspectral imaging systems have the potential to provide valuable information for ecosystem and environmental studies as well as aide in land management and land health monitoring. This thesis describes (1) a bootstrap method of producing mesoscale, radiometrically-referenced hyperspectral data using the Landsat surface reflectance (LaSRC) data product as a reference target, (2) biophysically relevant basis functions to model the reflectance spectra, (3) an unsupervised classification technique based on natural histogram splitting of these biophysically relevant parameters, and (4) local and multi-temporal anomaly detection. The bootstrap method extends standard processing techniques to remove uneven illumination conditions between flight passes, allowing the creation of radiometrically self-consistent data. Through selective spectral and spatial resampling, LaSRC data is used as a radiometric reference target. Advantages of the bootstrap method include the need for minimal site access, no ancillary instrumentation, and automated data processing. Data from a flight on 06/02/2016 is compared with concurrently collected ground based reflectance spectra as a means of validation achieving an average error of 2.74%. Fitting reflectance spectra using basis functions, based on biophysically relevant spectral features, allows both noise and data reductions while shifting information from spectral bands to biophysical features. Histogram splitting is used to determine a clustering based on natural splittings of these fit parameters. The Indian Pines reference data enabled comparisons of the efficacy of this technique to established techniques. The splitting technique is shown to be an improvement over the ISODATA clustering technique with an overall accuracy of 34.3/19.0% before merging and 40.9/39.2% after merging. This improvement is also seen as an improvement of kappa before/after merging of 24.8/30.5 for the histogram splitting technique compared to 15.8/28.5 for ISODATA. Three hyperspectral flights over the Kevin Dome area, covering 1843 ha, acquired 06/21/2014, 06/24/2015 and 06/26/2016 are examined with different methods of anomaly detection. Detection of anomalies within a single data set is examined to determine, on a local scale, areas that are significantly different from the surrounding area. Additionally, the detection and identification of persistent anomalies and non-persistent anomalies was investigated across multiple data sets.

Clustering for unsupervised fault diagnosis in nuclear turbine shut-down transients

NASA Astrophysics Data System (ADS)

Baraldi, Piero; Di Maio, Francesco; Rigamonti, Marco; Zio, Enrico; Seraoui, Redouane

2015-06-01

Empirical methods for fault diagnosis usually entail a process of supervised training based on a set of examples of signal evolutions "labeled" with the corresponding, known classes of fault. However, in practice, the signals collected during plant operation may be, very often, "unlabeled", i.e., the information on the corresponding type of occurred fault is not available. To cope with this practical situation, in this paper we develop a methodology for the identification of transient signals showing similar characteristics, under the conjecture that operational/faulty transient conditions of the same type lead to similar behavior in the measured signals evolution. The methodology is founded on a feature extraction procedure, which feeds a spectral clustering technique, embedding the unsupervised fuzzy C-means (FCM) algorithm, which evaluates the functional similarity among the different operational/faulty transients. A procedure for validating the plausibility of the obtained clusters is also propounded based on physical considerations. The methodology is applied to a real industrial case, on the basis of 148 shut-down transients of a Nuclear Power Plant (NPP) steam turbine.

Structural motifs of pre-nucleation clusters.

PubMed

Zhang, Y; Türkmen, I R; Wassermann, B; Erko, A; Rühl, E

2013-10-07

Structural motifs of pre-nucleation clusters prepared in single, optically levitated supersaturated aqueous aerosol microparticles containing CaBr2 as a model system are reported. Cluster formation is identified by means of X-ray absorption in the Br K-edge regime. The salt concentration beyond the saturation point is varied by controlling the humidity in the ambient atmosphere surrounding the 15-30 μm microdroplets. This leads to the formation of metastable supersaturated liquid particles. Distinct spectral shifts in near-edge spectra as a function of salt concentration are observed, in which the energy position of the Br K-edge is red-shifted by up to 7.1 ± 0.4 eV if the dilute solution is compared to the solid. The K-edge positions of supersaturated solutions are found between these limits. The changes in electronic structure are rationalized in terms of the formation of pre-nucleation clusters. This assumption is verified by spectral simulations using first-principle density functional theory and molecular dynamics calculations, in which structural motifs are considered, explaining the experimental results. These consist of solvated CaBr2 moieties, rather than building blocks forming calcium bromide hexahydrates, the crystal system that is formed by drying aqueous CaBr2 solutions.

Intra-operative on-line discrimination of kidney cancer from normal tissue by IR ATR spectroscopy of extracellular fluid

NASA Astrophysics Data System (ADS)

Urboniene, V.; Velicka, M.; Ceponkus, J.; Pucetaite, M.; Jankevicius, F.; Sablinskas, V.; Steiner, G.

2016-03-01

Determination of cancerous and normal kidney tissues during partial, simple or radical nephrectomy surgery was performed by using differences in the IR absorption spectra of extracellular fluid taken from the corresponding tissue areas. The samples were prepared by stamping of the kidney tissue on ATR diamond crystal. The spectral measurements were performed directly in the OR during surgery for 58 patients. It was found that intensities of characteristic spectral bands of glycogen (880-1200 cm-1) in extracellular fluid are sensitive to the type of the tissue and can be used as spectral markers of tumours. Characteristic spectral band of lactic acid (1730 cm-1) - product of the anaerobic glycolysis, taking place in the cancer cells is not suitable for use as a spectral marker of cancerous tissue, since it overlaps with the band of carbonyl stretch in phospholipids and fatty acids. Results of hierarchical cluster analysis of the spectra show that the spectra of healthy and tumour tissue films can be reliably separated into two groups. On the other hand, possibility to differentiate between tumours of different types and grades remains in question. While the fluid from highly malignant G3 tumour tissue contains highly pronounced glycogen spectral bands and can be well separated from benign and G1 tumours by principal component analysis, the variations between spectra from sample to sample prevent from obtaining conclusive results about the grouping between different tumour types and grades. The proposed method is instant and can be used in situ and even in vivo.

Open clusters. II. Fundamental parameters of B stars in Collinder 223, Hogg 16, NGC 2645, NGC 3114, and NGC 6025

NASA Astrophysics Data System (ADS)

Aidelman, Y.; Cidale, L. S.; Zorec, J.; Panei, J. A.

2015-05-01

Context. The knowledge of accurate values of effective temperature, surface gravity, and luminosity of stars in open clusters is very important not only to derive cluster distances and ages but also to discuss the stellar structure and evolution. Unfortunately, stellar parameters are still very scarce. Aims: Our goal is to study five open clusters to derive stellar parameters of the B and Be star population and discuss the cluster properties. In a near future, we intend to gather a statistically relevant samples of Be stars to discuss their origin and evolution. Methods: We use the Barbier-Chalonge-Divan spectrophotometric system, based on the study of low-resolution spectra around the Balmer discontinuity, since it is independent of the interstellar and circumstellar extinction and provides accurate Hertzsprung-Russell diagrams and stellar parameters. Results: We determine stellar fundamental parameters, such as effective temperatures, surface gravities, spectral types, luminosity classes, absolute and bolometric magnitudes and colour gradient excesses of the stars in the field of Collinder 223, Hogg 16, NGC 2645, NGC 3114, and NGC 6025. Additional information, mainly masses and ages of cluster stellar populations, is obtained using stellar evolution models. In most cases, stellar fundamental parameters have been derived for the first time. We also discuss the derived cluster properties of reddening, age and distance. Conclusions: Collinder 223 cluster parameters are overline{E(B-V) = 0.25 ± 0.03} mag and overline{(mv - M_v)0 = 11.21 ± 0.25} mag. In Hogg 16, we clearly distinguish two groups of stars (Hogg 16a and Hogg 16b) with very different mean true distance moduli (8.91 ± 0.26 mag and 12.51 ± 0.38 mag), mean colour excesses (0.26 ± 0.03 mag and 0.63 ± 0.08 mag), and spectral types (B early-type and B late-/A-type stars, respectively). The farthest group could be merged with Collinder 272. NGC 2645 is a young cluster (<14 Myr) with overline{E(B-V) = 0.58 ± 0.05} mag and overline{(mv - M_v)0 = 12.18 ± 0.30} mag. The cluster parameters of NGC 3114 are overline{E(B-V) = 0.10 ± 0.01} mag and overline{(mv - M_v)0 = 9.20 ± 0.15} mag. This cluster presents an important population of Be star, but it is difficult to define the cluster membership of stars because of the high contamination by field stars or the possible overlapping with a nearby cluster. Finally, we derive the following cluster parameters of NGC 6025: overline{E(B-V) = 0.34 ± 0.02} mag, overline{(mv - M_v)0 = 9.25 ± 0.17} mag, and an age between 40 Myr and 69 Myr. In all the cases, new Be candidate stars are reported based on the appearance of a second Balmer discontinuity. Observations taken at CASLEO, operating under agreement of CONICET and the Universities of La Plata, Córdoba and San Juan, Argentina.

Spectral analysis of two-signed microarray expression data.

PubMed

Higham, Desmond J; Kalna, Gabriela; Vass, J Keith

2007-06-01

We give a simple and informative derivation of a spectral algorithm for clustering and reordering complementary DNA microarray expression data. Here, expression levels of a set of genes are recorded simultaneously across a number of samples, with a positive weight reflecting up-regulation and a negative weight reflecting down-regulation. We give theoretical support for the algorithm based on a biologically justified hypothesis about the structure of the data, and illustrate its use on public domain data in the context of unsupervised tumour classification. The algorithm is derived by considering a discrete optimization problem and then relaxing to the continuous realm. We prove that in the case where the data have an inherent 'checkerboard' sign pattern, the algorithm will automatically reveal that pattern. Further, our derivation shows that the algorithm may be regarded as imposing a random graph model on the expression levels and then clustering from a maximum likelihood perspective. This indicates that the output will be tolerant to perturbations and will reveal 'near-checkerboard' patterns when these are present in the data. It is interesting to note that the checkerboard structure is revealed by the first (dominant) singular vectors--previous work on spectral methods has focussed on the case of nonnegative edge weights, where only the second and higher singular vectors are relevant. We illustrate the algorithm on real and synthetic data, and then use it in a tumour classification context on three different cancer data sets. Our results show that respecting the two-signed nature of the data (thereby distinguishing between up-regulation and down-regulation) reveals structures that cannot be gleaned from the absolute value data (where up- and down-regulation are both regarded as 'changes').

Schneiderian First Rank Symptoms and Gamma Oscillatory Activity in Neuroleptic Naïve First Episode Schizophrenia: A 192 Channel EEG Study

PubMed Central

Yadav, Shailly; Nizamie, Shamusul Haque; Das, Basudeb; Tikka, Deyashini Lahiri; Goyal, Nishant

2014-01-01

Objective Schneiderian first-rank symptoms (FRS) and abnormal EEG gamma activity in schizophrenia have been reported independently to have a neurodevelopmental basis. We aimed to investigate spontaneous gamma power in two groups of first episode schizophrenia patients (those who experience FRS and those who do not). Methods A comparative hospital based study having 37neuroleptic naïve male patients with schizophrenia divided into two groups-FRS(+) and FRS(-) groups based on the presence of FRS. Thirty age, sex, education and handedness matched individuals served as controls (N). All participants underwent a 192-channel resting Electroencephalography (EEG) recording. Gamma spectral power was calculated for low- (30-50 Hz) and high-gamma 1 & 2 (51-70 and 71-100 Hz) bands. Spectral power was compared between three groups using MANOVA and supplementary one-way ANOVA with Bonferroni test controlling for multiple comparisons. Linear regression was used to identifying predictor variables for FRS. Pearson correlation coefficient was computed between spectral power parameters and various clinical variables. Results Significantly higher high gamma band-1 power was observed over right frontal (p<0.05), parietal (p<0.05) and temporal (p<0.05) regions in FRS(+) than FRS(-) group and normal controls. Right parietal high gamma-1 power and paranoid cluster on PANSS significantly predicted number of FRS in total schizophrenia patients; paranoid cluster on PANSS showed significant correlation with number of FRS in FRS(+) group. Conclusion Findings of our study add to the evidence that areas contained within the hetero modal association cortex are associated with FRS. The study findings also strengthen the neurodevelopmental basis of FRS in schizophrenia. PMID:25395979

A DISTANT RADIO MINI-HALO IN THE PHOENIX GALAXY CLUSTER

DOE Office of Scientific and Technical Information (OSTI.GOV)

Van Weeren, R. J.; Andrade-Santos, F.; Forman, W. R.

We report the discovery of extended radio emission in the Phoenix cluster (SPT-CL J2344-4243, z = 0.596) with the Giant Metrewave Radio Telescope (GMRT) at 610 MHz. The diffuse emission extends over a region of at least 400-500 kpc and surrounds the central radio source of the Brightest Cluster Galaxy, but does not appear to be directly associated with it. We classify the diffuse emission as a radio mini-halo, making it the currently most distant mini-halo known. Radio mini-halos have been explained by synchrotron emitting particles re-accelerated via turbulence, possibly induced by gas sloshing generated from a minor merger event. Chandra observationsmore » show a non-concentric X-ray surface brightness distribution, which is consistent with this sloshing interpretation. The mini-halo has a flux density of 17 ± 5 mJy, resulting in a 1.4 GHz radio power of (10.4 ± 3.5) × 10{sup 24} W Hz{sup –1}. The combined cluster emission, which includes the central compact radio source, is also detected in a shallow GMRT 156 MHz observation and together with the 610 MHz data we compute a spectral index of –0.84 ± 0.12 for the overall cluster radio emission. Given that mini-halos typically have steeper radio spectra than cluster radio galaxies, this spectral index should be taken as an upper limit for the mini-halo.« less

Constraining galaxy cluster velocity field with the thermal Sunyaev-Zel'dovich and kinematic Sunyaev-Zel'dovich cross-bispectrum

NASA Astrophysics Data System (ADS)

Hurier, G.

2017-08-01

The Sunyaev-Zel'dovich (SZ) effects are produced by the interaction of cosmic microwave background (CMB) photons with the ionized and diffuse gas of electrons inside galaxy clusters integrated along the line of sight. The two main effects are the thermal SZ (tSZ) produced by thermal pressure inside galaxy clusters and the kinematic SZ (kSZ) produced by peculiar motion of galaxy clusters compared to CMB rest-frame. The kSZ effect is particularly challenging to measure as it follows the same spectral behavior as the CMB, and consequently cannot be separated from the CMB using spectral considerations. In this paper, we explore the feasibility of detecting the kSZ through the computation of the tSZ-CMB-CMB cross-correlation bispectrum for current and future CMB experiments. We conclude that the next generation of CMB experiments will offer the possibility to detect the tSZ-kSZ-kSZ bispectrum at high signal-to-noise ration (S/N). This measurement will constraints the intra-cluster dynamics and the velocity field of galaxy cluster that is extremely sensitive to the growth rate of structures and thus to dark energy properties. Additionally, we also demonstrate that the tSZ-kSZ-kSZ bispectrum can be used to break the degeneracies between the mass-observable relation and the cosmological parameters to set tight constraints, up to 4%, on the Y - M relation calibration.

VizieR Online Data Catalog: CCD photometry of Pal 1 (Borissova+ 1995)

NASA Astrophysics Data System (ADS)

Borissova, J.; Spassova, N.

1997-06-01

A CCD photometry of the halo cluster Palomar 1 is presented in the Thuan-Gunn photometric system. The principal sequences of the color-magnitude diagrams are delineated in different spectral bands. The color- magnitude diagrams of the cluster show a well defined red horizontal branch, a subgiant branch and a main-sequence down to about two magnitudes below the main sequence turnoff. The giant branch is absent and the brightest stars are the horizontal branch stars. The age of the cluster determined by comparison with the isochrones of Bell & VandenBerg (1987ApJS...63..335B) is consistent with an age in the interval 12-14Gyr. A distance modulus of (m-M)g0=15.38+/-0.15 magnitude and E(g-r)=0.16 has been derived. An estimate of the cluster structural parameters such as core radius and concentration parameter gives rc=1.5pc and c=1.46. A mass estimate of 1.1x103M⊙ and a mass-to-light ratio of 1.79 have been obtained using King's (1966AJ.....71...64K) method. The morphology of color-magnitude diagrams allows Pal 1 to be interpreted as probably a globular cluster rather than an old open one. For a description of the uvgr photometric system, see e.g. (1 data file).

Slow Photoelectron Velocity-Map Imaging of Cryogenically Cooled Anions

NASA Astrophysics Data System (ADS)

Weichman, Marissa L.; Neumark, Daniel M.

2018-04-01

Slow photoelectron velocity-map imaging spectroscopy of cryogenically cooled anions (cryo-SEVI) is a powerful technique for elucidating the vibrational and electronic structure of neutral radicals, clusters, and reaction transition states. SEVI is a high-resolution variant of anion photoelectron spectroscopy based on photoelectron imaging that yields spectra with energy resolution as high as 1-2 cm‑1. The preparation of cryogenically cold anions largely eliminates hot bands and dramatically narrows the rotational envelopes of spectral features, enabling the acquisition of well-resolved photoelectron spectra for complex and spectroscopically challenging species. We review the basis and history of the SEVI method, including recent experimental developments that have improved its resolution and versatility. We then survey recent SEVI studies to demonstrate the utility of this technique in the spectroscopy of aromatic radicals, metal and metal oxide clusters, nonadiabatic interactions between excited states of small molecules, and transition states of benchmark bimolecular reactions.

Graph characterization via Ihara coefficients.

PubMed

Ren, Peng; Wilson, Richard C; Hancock, Edwin R

2011-02-01

The novel contributions of this paper are twofold. First, we demonstrate how to characterize unweighted graphs in a permutation-invariant manner using the polynomial coefficients from the Ihara zeta function, i.e., the Ihara coefficients. Second, we generalize the definition of the Ihara coefficients to edge-weighted graphs. For an unweighted graph, the Ihara zeta function is the reciprocal of a quasi characteristic polynomial of the adjacency matrix of the associated oriented line graph. Since the Ihara zeta function has poles that give rise to infinities, the most convenient numerically stable representation is to work with the coefficients of the quasi characteristic polynomial. Moreover, the polynomial coefficients are invariant to vertex order permutations and also convey information concerning the cycle structure of the graph. To generalize the representation to edge-weighted graphs, we make use of the reduced Bartholdi zeta function. We prove that the computation of the Ihara coefficients for unweighted graphs is a special case of our proposed method for unit edge weights. We also present a spectral analysis of the Ihara coefficients and indicate their advantages over other graph spectral methods. We apply the proposed graph characterization method to capturing graph-class structure and clustering graphs. Experimental results reveal that the Ihara coefficients are more effective than methods based on Laplacian spectra.

Improving Estimated Optical Constants With MSTM and DDSCAT Modeling

NASA Astrophysics Data System (ADS)

Pitman, K. M.; Wolff, M. J.

2015-12-01

We present numerical experiments to determine quantitatively the effects of mineral particle clustering on Mars spacecraft spectral signatures and to improve upon the values of refractive indices (optical constants n, k) derived from Mars dust laboratory analog spectra such as those from RELAB and MRO CRISM libraries. Whereas spectral properties for Mars analog minerals and actual Mars soil are dominated by aggregates of particles smaller than the size of martian atmospheric dust, the analytic radiative transfer (RT) solutions used to interpret planetary surfaces assume that individual, well-separated particles dominate the spectral signature. Both in RT models and in the refractive index derivation methods that include analytic RT approximations, spheres are also over-used to represent nonspherical particles. Part of the motivation is that the integrated effect over randomly oriented particles on quantities such as single scattering albedo and phase function are relatively less than for single particles. However, we have seen in previous numerical experiments that when varying the shape and size of individual grains within a cluster, the phase function changes in both magnitude and slope, thus the "relatively less" effect is more significant than one might think. Here we examine the wavelength dependence of the forward scattering parameter with multisphere T-matrix (MSTM) and discrete dipole approximation (DDSCAT) codes that compute light scattering by layers of particles on planetary surfaces to see how albedo is affected and integrate our model results into refractive index calculations to remove uncertainties in approximations and parameters that can lower the accuracy of optical constants. By correcting the single scattering albedo and phase function terms in the refractive index determinations, our data will help to improve the understanding of Mars in identifying, mapping the distributions, and quantifying abundances for these minerals and will address long-standing questions on fundamental physics in the martian surface (e.g., what is the fundamental scattering unit for closely packed dust or regolith grains?). This work was supported by NASA's Mars Fundamental Research Program and performed with the Pleiades cluster courtesy of NASA's Advanced Supercomputing Division.

High-resolution Spectroscopic Observations of Single Red Giants in Three Open Clusters: NGC 2360, NGC 3680, and NGC 5822

NASA Astrophysics Data System (ADS)

Peña Suárez, V. J.; Sales Silva, J. V.; Katime Santrich, O. J.; Drake, N. A.; Pereira, C. B.

2018-02-01

Single stars in open clusters with known distances are important targets in constraining the nucleosynthesis process since their ages and luminosities are also known. In this work, we analyze a sample of 29 single red giants of the open clusters NGC 2360, NGC 3680, and NGC 5822 using high-resolution spectroscopy. We obtained atmospheric parameters, abundances of the elements C, N, O, Na, Mg, Al, Ca, Si, Ti, Ni, Cr, Y, Zr, La, Ce, and Nd, as well as radial and rotational velocities. We employed the local thermodynamic equilibrium atmospheric models of Kurucz and the spectral analysis code MOOG. Rotational velocities and light-element abundances were derived using spectral synthesis. Based on our analysis of the single red giants in these three open clusters, we could compare, for the first time, their abundance pattern with that of the binary stars of the same clusters previously studied. Our results show that the abundances of both single and binary stars of the open clusters NGC 2360, NGC 3680, and NGC 5822 do not have significant differences. For the elements created by the s-process, we observed that the open clusters NGC 2360, NGC 3680, and NGC 5822 also follow the trend already raised in the literature that young clusters have higher s-process element abundances than older clusters. Finally, we observed that the three clusters of our sample exhibit a trend in the [Y/Mg]-age relation, which may indicate the ability of the [Y/Mg] ratio to be used as a clock for the giants. Based on the observations made with the 2.2 m telescope at the European Southern Observatory (La Silla, Chile) under an agreement with Observatório Nacional and under an agreement between Observatório Nacional and Max-Planck Institute für Astronomie.

Phytoplankton Pigment Communities Can be Modeled Using Unique Relationships With Spectral Absorption Signatures in a Dynamic Coastal Environment

NASA Astrophysics Data System (ADS)

Catlett, D.; Siegel, D. A.

2018-01-01

Understanding the roles of phytoplankton community composition in the functioning of marine ecosystems and ocean biogeochemical cycles is important for many ocean science problems of societal relevance. Remote sensing currently offers the only feasible method for continuously assessing phytoplankton community structure on regional to global scales. However, methods are presently hindered by the limited spectral resolution of most satellite sensors and by uncertainties associated with deriving quantitative indices of phytoplankton community structure from phytoplankton pigment concentrations. Here we analyze a data set of concurrent phytoplankton pigment concentrations and phytoplankton absorption coefficient spectra from the Santa Barbara Channel, California, to develop novel optical oceanographic models for retrieving metrics of phytoplankton community composition. Cluster and Empirical Orthogonal Function analyses of phytoplankton pigment concentrations are used to define up to five phytoplankton pigment communities as a representation of phytoplankton functional types. Unique statistical relationships are found between phytoplankton pigment communities and absorption features isolated using spectral derivative analysis and are the basis of predictive models. Model performance is substantially better for phytoplankton pigment community indices compared with determinations of the contributions of individual pigments or taxa to chlorophyll a. These results highlight the application of data-driven chemotaxonomic approaches for developing and validating bio-optical algorithms and illustrate the potential and limitations for retrieving phytoplankton community composition from hyperspectral satellite ocean color observations.

Deriving temperature, mass, and age of evolved stars from high-resolution spectra. Application to field stars and the open cluster IC 4651

NASA Astrophysics Data System (ADS)

Biazzo, K.; Pasquini, L.; Girardi, L.; Frasca, A.; da Silva, L.; Setiawan, J.; Marilli, E.; Hatzes, A. P.; Catalano, S.

2007-12-01

Aims:We test our capability of deriving stellar physical parameters of giant stars by analysing a sample of field stars and the well studied open cluster IC 4651 with different spectroscopic methods. Methods: The use of a technique based on line-depth ratios (LDRs) allows us to determine with high precision the effective temperature of the stars and to compare the results with those obtained with a classical LTE abundance analysis. Results: (i) For the field stars we find that the temperatures derived by means of the LDR method are in excellent agreement with those found by the spectral synthesis. This result is extremely encouraging because it shows that spectra can be used to firmly derive population characteristics (e.g., mass and age) of the observed stars. (ii) For the IC 4651 stars we use the determined effective temperature to derive the following results. a) The reddening E(B-V) of the cluster is 0.12±0.02, largely independent of the color-temperature calibration used. b) The age of the cluster is 1.2±0.2 Gyr. c) The typical mass of the analysed giant stars is 2.0±0.2~M⊙. Moreover, we find a systematic difference of about 0.2 dex in log g between spectroscopic and evolutionary values. Conclusions: We conclude that, in spite of known limitations, a classical spectroscopic analysis of giant stars may indeed result in very reliable stellar parameters. We caution that the quality of the agreement, on the other hand, depends on the details of the adopted spectroscopic analysis. Based on observations collected at the ESO telescopes at the Paranal and La Silla Observatories, Chile.

Signature extraction of ocean pollutants by eigenvector transformation of remote spectra

NASA Technical Reports Server (NTRS)

Grew, G. W.

1978-01-01

Spectral signatures of suspended matter in the ocean are being extracted through characteristic vector analysis of remote ocean color data collected with MOCS (Multichannel Ocean Color Sensor). Spectral signatures appear to be obtainable through analyses of 'linear' clusters that appear on scatter diagrams associated with eigenvectors. Signatures associated with acid waste, sewage sludge, oil, and algae are presented. The application of vector analysis to two acid waste dumps overflown two years apart is examined in some detail. The relationships between eigenvectors and spectral signatures for these examples are analyzed. These cases demonstrate the value of characteristic vector analysis in remotely identifying pollutants in the ocean and in determining the consistency of their spectral signatures.

Unsupervised classification of multivariate geostatistical data: Two algorithms

NASA Astrophysics Data System (ADS)

Romary, Thomas; Ors, Fabien; Rivoirard, Jacques; Deraisme, Jacques

2015-12-01

With the increasing development of remote sensing platforms and the evolution of sampling facilities in mining and oil industry, spatial datasets are becoming increasingly large, inform a growing number of variables and cover wider and wider areas. Therefore, it is often necessary to split the domain of study to account for radically different behaviors of the natural phenomenon over the domain and to simplify the subsequent modeling step. The definition of these areas can be seen as a problem of unsupervised classification, or clustering, where we try to divide the domain into homogeneous domains with respect to the values taken by the variables in hand. The application of classical clustering methods, designed for independent observations, does not ensure the spatial coherence of the resulting classes. Image segmentation methods, based on e.g. Markov random fields, are not adapted to irregularly sampled data. Other existing approaches, based on mixtures of Gaussian random functions estimated via the expectation-maximization algorithm, are limited to reasonable sample sizes and a small number of variables. In this work, we propose two algorithms based on adaptations of classical algorithms to multivariate geostatistical data. Both algorithms are model free and can handle large volumes of multivariate, irregularly spaced data. The first one proceeds by agglomerative hierarchical clustering. The spatial coherence is ensured by a proximity condition imposed for two clusters to merge. This proximity condition relies on a graph organizing the data in the coordinates space. The hierarchical algorithm can then be seen as a graph-partitioning algorithm. Following this interpretation, a spatial version of the spectral clustering algorithm is also proposed. The performances of both algorithms are assessed on toy examples and a mining dataset.

Identification of segregated regions in the functional brain connectome of autistic patients by a combination of fuzzy spectral clustering and entropy analysis

PubMed Central

Sato, João Ricardo; Balardin, Joana; Vidal, Maciel Calebe; Fujita, André

2016-01-01

Background Several neuroimaging studies support the model of abnormal development of brain connectivity in patients with autism-spectrum disorders (ASD). In this study, we aimed to test the hypothesis of reduced functional network segregation in autistic patients compared with controls. Methods Functional MRI data from children acquired under a resting-state protocol (Autism Brain Imaging Data Exchange [ABIDE]) were submitted to both fuzzy spectral clustering (FSC) with entropy analysis and graph modularity analysis. Results We included data from 814 children in our analysis. We identified 5 regions of interest comprising the motor, temporal and occipito-temporal cortices with increased entropy (p < 0.05) in the clustering structure (i.e., more segregation in the controls). Moreover, we noticed a statistically reduced modularity (p < 0.001) in the autistic patients compared with the controls. Significantly reduced eigenvector centrality values (p < 0.05) in the patients were observed in the same regions that were identified in the FSC analysis. Limitations There is considerable heterogeneity in the fMRI acquisition protocols among the sites that contributed to the ABIDE data set (e.g., scanner type, pulse sequence, duration of scan and resting-state protocol). Moreover, the sites differed in many variables related to sample characterization (e.g., age, IQ and ASD diagnostic criteria). Therefore, we cannot rule out the possibility that additional differences in functional network organization would be found in a more homogeneous data sample of individuals with ASD. Conclusion Our results suggest that the organization of the whole-brain functional network in patients with ASD is different from that observed in controls, which implies a reduced modularity of the brain functional networks involved in sensorimotor, social, affective and cognitive processing. PMID:26505141

Search and characterization of T-type planetary mass candidates in the σ Orionis cluster

NASA Astrophysics Data System (ADS)

Peña Ramírez, K.; Zapatero Osorio, M. R.; Béjar, V. J. S.; Rebolo, R.; Bihain, G.

2011-08-01

Context. The proper characterization of the least massive population of the young σ Orionis star cluster is required to understand the form of the cluster mass function and its impact on our comprehension of the substellar formation processes. S Ori 70 (T5.5 ± 1) and 73, two T-type cluster member candidates, are likely to have masses between 3 and 7 MJup if their age is 3 Myr. It awaits confirmation whether S Ori 73 has a methane atmosphere. Aims: We aim to: i) confirm the presence of methane absorption in S Ori 73 by performing methane imaging; ii) study S Ori 70 and 73 cluster membership via photometric colors and accurate proper motion analysis; and iii) perform a new search to identify additional T-type σ Orionis member candidates. Methods: We obtained HAWK-I (VLT) J, H, and CH4off photometry of an area of 119.15 arcmin2 in σ Orionis down to Jcomp = 21.7 and Hcomp = 21 mag. S Ori 70 and 73 are contained in the explored area. Near-infrared data were complemented with optical photometry using images acquired with OSIRIS (GTC) and VISTA as part of the VISTA Orion survey. Color-magnitude and color-color diagrams were constructed to characterize S Ori 70 and 73 photometrically, and to identify new objects with methane absorption and masses below 7 MJup. We derived proper motions by comparing of the new HAWK-I and VISTA images with published near-infrared data taken 3.4 - 7.9 yr ago. Results.S Ori 73 has a red H - CH4off color indicating methane absorption in the H-band and a spectral type of T4 ± 1. S Ori 70 displays a redder methane color than S Ori 73 in agreement with its latter spectral classification. Our proper motion measurements (μα cos δ = 26.7 ± 6.1, μδ = 21.3 ± 6.1 mas yr-1 for S Ori 70, and μα cos δ = 46.7 ± 4.9, μδ = -6.3 ± 4.7 mas yr-1 for S Ori 73) are larger than the motion of σ Orionis, rendering S Ori 70 and 73 cluster membership uncertain. From our survey, we identified one new photometric candidate with J = 21.69 ± 0.12 mag and methane color consistent with spectral type ≥ T8. Conclusions.S Ori 73 has colors similar to those of T3-T5 field dwarfs, which in addition to its high proper motion suggests that it is probably a field dwarf located at 170-200 pc. The origin of S Ori 70 remains unclear: it can be a field, foreground mid- to late-T free-floating dwarf with peculiar colors, or an orphan planet ejected through strong dynamical interactions from σ Orionis or from a nearby star-forming region in Orion.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cameron, Maria K., E-mail: cameron@math.umd.edu

We develop computational tools for spectral analysis of stochastic networks representing energy landscapes of atomic and molecular clusters. Physical meaning and some properties of eigenvalues, left and right eigenvectors, and eigencurrents are discussed. We propose an approach to compute a collection of eigenpairs and corresponding eigencurrents describing the most important relaxation processes taking place in the system on its way to the equilibrium. It is suitable for large and complex stochastic networks where pairwise transition rates, given by the Arrhenius law, vary by orders of magnitude. The proposed methodology is applied to the network representing the Lennard-Jones-38 cluster created bymore » Wales's group. Its energy landscape has a double funnel structure with a deep and narrow face-centered cubic funnel and a shallower and wider icosahedral funnel. However, the complete spectrum of the generator matrix of the Lennard-Jones-38 network has no appreciable spectral gap separating the eigenvalue corresponding to the escape from the icosahedral funnel. We provide a detailed description of the escape process from the icosahedral funnel using the eigencurrent and demonstrate a superexponential growth of the corresponding eigenvalue. The proposed spectral approach is compared to the methodology of the Transition Path Theory. Finally, we discuss whether the Lennard-Jones-38 cluster is metastable from the points of view of a mathematician and a chemical physicist, and make a connection with experimental works.« less

Detection of cracks on concrete surfaces by hyperspectral image processing

NASA Astrophysics Data System (ADS)

Santos, Bruno O.; Valença, Jonatas; Júlio, Eduardo

2017-06-01

All large infrastructures worldwide must have a suitable monitoring and maintenance plan, aiming to evaluate their behaviour and predict timely interventions. In the particular case of concrete infrastructures, the detection and characterization of crack patterns is a major indicator of their structural response. In this scope, methods based on image processing have been applied and presented. Usually, methods focus on image binarization followed by applications of mathematical morphology to identify cracks on concrete surface. In most cases, publications are focused on restricted areas of concrete surfaces and in a single crack. On-site, the methods and algorithms have to deal with several factors that interfere with the results, namely dirt and biological colonization. Thus, the automation of a procedure for on-site characterization of crack patterns is of great interest. This advance may result in an effective tool to support maintenance strategies and interventions planning. This paper presents a research based on the analysis and processing of hyper-spectral images for detection and classification of cracks on concrete structures. The objective of the study is to evaluate the applicability of several wavelengths of the electromagnetic spectrum for classification of cracks in concrete surfaces. An image survey considering highly discretized wavelengths between 425 nm and 950 nm was performed on concrete specimens, with bandwidths of 25 nm. The concrete specimens were produced with a crack pattern induced by applying a load with displacement control. The tests were conducted to simulate usual on-site drawbacks. In this context, the surface of the specimen was subjected to biological colonization (leaves and moss). To evaluate the results and enhance crack patterns a clustering method, namely k-means algorithm, is being applied. The research conducted allows to define the suitability of using clustering k-means algorithm combined with hyper-spectral images highly discretized for crack detection on concrete surfaces, considering cracking combined with the most usual concrete anomalies, namely biological colonization.

Discovery of large-scale diffuse radio emission in low-mass galaxy cluster Abell 1931

NASA Astrophysics Data System (ADS)

Brüggen, M.; Rafferty, D.; Bonafede, A.; van Weeren, R. J.; Shimwell, T.; Intema, H.; Röttgering, H.; Brunetti, G.; Di Gennaro, G.; Savini, F.; Wilber, A.; O'Sullivan, S.; Ensslin, T. A.; De Gasperin, F.; Hoeft, M.

2018-04-01

Extended, steep-spectrum radio synchrotron sources are pre-dominantly found in massive galaxy clusters as opposed to groups. LOFAR Two-Metre Sky Survey images have revealed a diffuse, ultra-steep spectrum radio source in the low-mass cluster Abell 1931. The source has a fairly irregular morphology with a largest linear size of about 550 kpc. The source is only seen in LOFAR observations at 143 MHz and GMRT observations at 325 MHz. The spectral index of the total source between 143 MHz and 325 MHz is α _{143}^{325} = -2.86 ± 0.36. The source remains invisible in Very Large Array (1-2 GHz) observations as expected given the spectral index. Chandra X-ray observations of the cluster revealed a bolometric luminosity of LX = (1.65 ± 0.39) × 1043 erg s-1 and a temperature of 2.92_{-0.87}^{+1.89} keV which implies a mass of around ˜1014M⊙. We conclude that the source is a remnant radio galaxy that has shut off around 200 Myr ago. The brightest cluster galaxy, a radio-loud elliptical galaxy, could be the source for this extinct source. Unlike remnant sources studied in the literature, our source has a steep spectrum at low radio frequencies. Studying such remnant radio galaxies at low radio frequencies is important for understanding the scarcity of such sources and their role in feedback processes.

Human-Robot Interaction: Does Robotic Guidance Force Affect Gait-Related Brain Dynamics during Robot-Assisted Treadmill Walking?

PubMed

Knaepen, Kristel; Mierau, Andreas; Swinnen, Eva; Fernandez Tellez, Helio; Michielsen, Marc; Kerckhofs, Eric; Lefeber, Dirk; Meeusen, Romain

2015-01-01

In order to determine optimal training parameters for robot-assisted treadmill walking, it is essential to understand how a robotic device interacts with its wearer, and thus, how parameter settings of the device affect locomotor control. The aim of this study was to assess the effect of different levels of guidance force during robot-assisted treadmill walking on cortical activity. Eighteen healthy subjects walked at 2 km.h-1 on a treadmill with and without assistance of the Lokomat robotic gait orthosis. Event-related spectral perturbations and changes in power spectral density were investigated during unassisted treadmill walking as well as during robot-assisted treadmill walking at 30%, 60% and 100% guidance force (with 0% body weight support). Clustering of independent components revealed three clusters of activity in the sensorimotor cortex during treadmill walking and robot-assisted treadmill walking in healthy subjects. These clusters demonstrated gait-related spectral modulations in the mu, beta and low gamma bands over the sensorimotor cortex related to specific phases of the gait cycle. Moreover, mu and beta rhythms were suppressed in the right primary sensory cortex during treadmill walking compared to robot-assisted treadmill walking with 100% guidance force, indicating significantly larger involvement of the sensorimotor area during treadmill walking compared to robot-assisted treadmill walking. Only marginal differences in the spectral power of the mu, beta and low gamma bands could be identified between robot-assisted treadmill walking with different levels of guidance force. From these results it can be concluded that a high level of guidance force (i.e., 100% guidance force) and thus a less active participation during locomotion should be avoided during robot-assisted treadmill walking. This will optimize the involvement of the sensorimotor cortex which is known to be crucial for motor learning.

Stabilization of flat aromatic Si6 rings analogous to benzene: ab initio theoretical prediction.

PubMed

Zdetsis, Aristides D

2007-12-07

It is shown by ab initio calculations, based on density functional (DFT/B3LYP), and high level coupled-cluster [CCSD(T)] and quadratic CI [QCISD(T)] methods, that flat aromatic silicon structures analogous to benzene (C6H6) can be stabilized in the presence of lithium. The resulting planar Si6Li6 structure is both stable and aromatic, sharing many key characteristics with benzene. To facilitate possible synthesis and characterization of these species, routes of formation with high exothermicity are suggested and several spectral properties (including optical absorption, infrared, and Raman) are calculated.

Spatial organization of RNA polymerase II inside a mammalian cell nucleus revealed by reflected light-sheet superresolution microscopy

PubMed Central

Zhao, Ziqing W.; Roy, Rahul; Gebhardt, J. Christof M.; Suter, David M.; Chapman, Alec R.; Xie, X. Sunney

2014-01-01

Superresolution microscopy based on single-molecule centroid determination has been widely applied to cellular imaging in recent years. However, quantitative imaging of the mammalian nucleus has been challenging due to the lack of 3D optical sectioning methods for normal-sized cells, as well as the inability to accurately count the absolute copy numbers of biomolecules in highly dense structures. Here we report a reflected light-sheet superresolution microscopy method capable of imaging inside the mammalian nucleus with superior signal-to-background ratio as well as molecular counting with single-copy accuracy. Using reflected light-sheet superresolution microscopy, we probed the spatial organization of transcription by RNA polymerase II (RNAP II) molecules and quantified their global extent of clustering inside the mammalian nucleus. Spatiotemporal clustering analysis that leverages on the blinking photophysics of specific organic dyes showed that the majority (>70%) of the transcription foci originate from single RNAP II molecules, and no significant clustering between RNAP II molecules was detected within the length scale of the reported diameter of “transcription factories.” Colocalization measurements of RNAP II molecules equally labeled by two spectrally distinct dyes confirmed the primarily unclustered distribution, arguing against a prevalent existence of transcription factories in the mammalian nucleus as previously proposed. The methods developed in our study pave the way for quantitative mapping and stoichiometric characterization of key biomolecular species deep inside mammalian cells. PMID:24379392

Van Der Waals Clusters of Aromatic Molecules Studied Using Supersonic Molecular Jet Spectroscopy.

DTIC Science & Technology

1987-01-01

i n iie t ri 166 TABLE 7.5 Out-or-Plane Elgenvector Normal Modes Calculated for H2PC. Mode Elgenvector in Terms of Symmetry Coordinates a Bu1...clusters exhibit spectra and calculated geomet- ries which demonstrate that the solvent OH groups are large contributors to the spectral shifts and...10’ cluster structure. We calculate that 0.005 cm-’ resolution N-C 1.725 x 10’ I 575< 10’ would be required to resolve rotational structure for N-H

X-radiation from clusters of galaxies: Spectral evidence for a hot evolved gas

NASA Technical Reports Server (NTRS)

Serlemitsos, P. J.; Smith, B. W.; Boldt, E. A.; Holt, S. S.; Swank, J. H.

1976-01-01

OSO-8 observations of the X-ray flux in the range 2-60 keV from the Virgo, Perseus, and Coma Clusters provide strong evidence for the thermal origin of the radiation, including iron line emission. The data are adequately described by emission from an isothermal plasma with an iron abundance in near agreement with cosmic levels. A power law description is generally less acceptable and is ruled out in the case of Perseus. Implications on the origin of the cluster gas are discussed.

ASCA observations of distant clusters of galaxies.

NASA Astrophysics Data System (ADS)

Tsuru, T.; Koyama, K.; Hughes, J. P.; Arimoto, N.; Kii, T.; Hattori, M.

It is important not only in studies of clusters of galaxies but also in cosmological aspects to investigate the evolution of X-ray properties of clusters of galaxies. ASCA enables detailed spectral studies on distant clusters and the evolution of temperature for the first time. The authors present here "preliminary" results of ASCA observation of 17 distant (z = 0.14 - 0.55) clusters of galaxies. The sample includes: Cl0016+16 Abell 370, Abell 1995, Abell 959, ACGG 118, Zw 3136, EMSS 1305.4+2941, Abell 1851, Abell 963, Abell 2163, EMSS 0839.8+2938, Abell 665, Abell 1689, Abell 2218, Abell 586, Abell 1413, Abell 1895. The cosmological constants of H0 = 50 km/s/Mpc and q0 = 0.5 are adopted in this paper.

A theoretical and observational study of the Red Giant Branch phase transition in Magellanic Cloud clusters - A progress report

NASA Technical Reports Server (NTRS)

Buonanno, R.; Corsi, C. E.; Fusi Pecci, F.; Greggio, L.; Renzini, A.; Sweigart, A. V.

1986-01-01

Preliminary results are reported for an investigation comparing theoretical models of the sudden appearance of an extended RGB (and its effects on the spectral energy distributions of stellar populations) with data from ESO CCD observations of clusters in the LMC and SMC. Isochrones for the entire RGB are being constructed on the basis of 100 new evolutionary sequences (calculated using the evolution code of Sweigart and Gross, 1976 and 1978) to permit determination of synthetic colors and spectral energy distributions. The observations so far indicate a main sequence about 0.1 mag redder than that predicted by the present models or by the isochrones of VandenBerg and Bell (1985), and fail to show a B-V color difference at the RGB phase transition.

Deep data: discovery and visualization Application to hyperspectral ALMA imagery

NASA Astrophysics Data System (ADS)

Merényi, Erzsébet; Taylor, Joshua; Isella, Andrea

2017-06-01

Leading-edge telescopes such as the Atacama Large Millimeter and sub-millimeter Array (ALMA), and near-future ones, are capable of imaging the same sky area at hundreds-to-thousands of frequencies with both high spectral and spatial resolution. This provides unprecedented opportunities for discovery about the spatial, kinematical and compositional structure of sources such as molecular clouds or protoplanetary disks, and more. However, in addition to enormous volume, the data also exhibit unprecedented complexity, mandating new approaches for extracting and summarizing relevant information. Traditional techniques such as examining images at selected frequencies become intractable while tools that integrate data across frequencies or pixels (like moment maps) can no longer fully exploit and visualize the rich information. We present a neural map-based machine learning approach that can handle all spectral channels simultaneously, utilizing the full depth of these data for discovery and visualization of spectrally homogeneous spatial regions (spectral clusters) that characterize distinct kinematic behaviors. We demonstrate the effectiveness on an ALMA image cube of the protoplanetary disk HD142527. The tools we collectively name ``NeuroScope'' are efficient for ``Big Data'' due to intelligent data summarization that results in significant sparsity and noise reduction. We also demonstrate a new approach to automate our clustering for fast distillation of large data cubes.

LOFAR, VLA, and Chandra observations of the Toothbrush Galaxy Cluster

DOE PAGES

van Weeren, R. J.; Brunetti, G.; Bruggen, M.; ...

2016-02-22

We present deep LOFAR observations between 120 {181 MHz of the `Toothbrush' (RX J0603.3+4214), a cluster that contains one of the brightest radio relic sources known. Our LOFAR observations exploit a new and novel calibration scheme to probe 10 times deeper than any previous study in this relatively unexplored part of the spectrum. The LOFAR observations, when combined with VLA, GMRT, and Chandra X-ray data, provide new information about the nature of cluster merger shocks and their role in re-accelerating relativistic particles. We derive a spectral index of α = -0:8±0:1 at the northern edge of the main radio relic,more » steepening towards the south to α ≈ -2. The spectral index of the radio halo is remarkably uniform (α = -1:16, with an intrinsic scatter of ≤ 0:04). The observed radio relic spectral index gives a Mach number of M = 2:8 +0:5 -0:3, assuming diffusive shock acceleration (DSA). However, the gas density jump at the northern edge of the large radio relic implies a much weaker shock (M≈1:2, with an upper limit ofM≈1:5). The discrepancy between the Mach numbers calculated from the radio and X-rays can be explained if either (i) the relic traces a complex shock surface along the line of sight, or (ii) if the radio relic emission is produced by a re-accelerated population of fossil particles from a radio galaxy. Our results highlight the need for additional theoretical work and numerical simulations of particle acceleration and re-acceleration at cluster merger shocks.« less

Application of self-organizing feature maps to analyze the relationships between ignitable liquids and selected mass spectral ions.

PubMed

Frisch-Daiello, Jessica L; Williams, Mary R; Waddell, Erin E; Sigman, Michael E

2014-03-01

The unsupervised artificial neural networks method of self-organizing feature maps (SOFMs) is applied to spectral data of ignitable liquids to visualize the grouping of similar ignitable liquids with respect to their American Society for Testing and Materials (ASTM) class designations and to determine the ions associated with each group. The spectral data consists of extracted ion spectra (EIS), defined as the time-averaged mass spectrum across the chromatographic profile for select ions, where the selected ions are a subset of ions from Table 2 of the ASTM standard E1618-11. Utilization of the EIS allows for inter-laboratory comparisons without the concern of retention time shifts. The trained SOFM demonstrates clustering of the ignitable liquid samples according to designated ASTM classes. The EIS of select samples designated as miscellaneous or oxygenated as well as ignitable liquid residues from fire debris samples are projected onto the SOFM. The results indicate the similarities and differences between the variables of the newly projected data compared to those of the data used to train the SOFM. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

The Clusters-in-a-Liquid Approach for Solvation: New Insights from the Conformer Specific Gas Phase Spectroscopy and Vibrational Optical Activity Spectroscopy

PubMed Central

Perera, Angelo S.; Thomas, Javix; Poopari, Mohammad R.; Xu, Yunjie

2016-01-01

Vibrational optical activity spectroscopies, namely vibrational circular dichroism (VCD) and Raman optical activity (ROA), have been emerged in the past decade as powerful spectroscopic tools for stereochemical information of a wide range of chiral compounds in solution directly. More recently, their applications in unveiling solvent effects, especially those associated with water solvent, have been explored. In this review article, we first select a few examples to demonstrate the unique sensitivity of VCD spectral signatures to both bulk solvent effects and explicit hydrogen-bonding interactions in solution. Second, we discuss the induced solvent chirality, or chiral transfer, VCD spectral features observed in the water bending band region in detail. From these chirality transfer spectral data, the related conformer specific gas phase spectroscopic studies of small chiral hydration clusters, and the associated matrix isolation VCD experiments of hydrogen-bonded complexes in cold rare gas matrices, a general picture of solvation in aqueous solution emerges. In such an aqueous solution, some small chiral hydration clusters, rather than the chiral solutes themselves, are the dominant species and are the ones that contribute mainly to the experimentally observed VCD features. We then review a series of VCD studies of amino acids and their derivatives in aqueous solution under different pHs to emphasize the importance of the inclusion of the bulk solvent effects. These experimental data and the associated theoretical analyses are the foundation for the proposed “clusters-in-a-liquid” approach to account for solvent effects effectively. We present several approaches to identify and build such representative chiral hydration clusters. Recent studies which applied molecular dynamics simulations and the subsequent snapshot averaging approach to generate the ROA, VCD, electronic CD, and optical rotatory dispersion spectra are also reviewed. Challenges associated with the molecular dynamics snapshot approach are discussed and the successes of the seemingly random “ad hoc explicit solvation” reported before are also explained. To further test and improve the “clusters-in-a-liquid” model in practice, future work in terms of conformer specific gas phase spectroscopy of sequential solvation of a chiral solute, matrix isolation VCD measurements of small chiral hydration clusters, and more sophisticated models for the bulk solvent effects would be highly valuable. PMID:26942177

Object Manifold Alignment for Multi-Temporal High Resolution Remote Sensing Images Classification

NASA Astrophysics Data System (ADS)

Gao, G.; Zhang, M.; Gu, Y.

2017-05-01

Multi-temporal remote sensing images classification is very useful for monitoring the land cover changes. Traditional approaches in this field mainly face to limited labelled samples and spectral drift of image information. With spatial resolution improvement, "pepper and salt" appears and classification results will be effected when the pixelwise classification algorithms are applied to high-resolution satellite images, in which the spatial relationship among the pixels is ignored. For classifying the multi-temporal high resolution images with limited labelled samples, spectral drift and "pepper and salt" problem, an object-based manifold alignment method is proposed. Firstly, multi-temporal multispectral images are cut to superpixels by simple linear iterative clustering (SLIC) respectively. Secondly, some features obtained from superpixels are formed as vector. Thirdly, a majority voting manifold alignment method aiming at solving high resolution problem is proposed and mapping the vector data to alignment space. At last, all the data in the alignment space are classified by using KNN method. Multi-temporal images from different areas or the same area are both considered in this paper. In the experiments, 2 groups of multi-temporal HR images collected by China GF1 and GF2 satellites are used for performance evaluation. Experimental results indicate that the proposed method not only has significantly outperforms than traditional domain adaptation methods in classification accuracy, but also effectively overcome the problem of "pepper and salt".

[Research on spectra recognition method for cabbages and weeds based on PCA and SIMCA].

PubMed

Zu, Qin; Deng, Wei; Wang, Xiu; Zhao, Chun-Jiang

2013-10-01

In order to improve the accuracy and efficiency of weed identification, the difference of spectral reflectance was employed to distinguish between crops and weeds. Firstly, the different combinations of Savitzky-Golay (SG) convolutional derivation and multiplicative scattering correction (MSC) method were applied to preprocess the raw spectral data. Then the clustering analysis of various types of plants was completed by using principal component analysis (PCA) method, and the feature wavelengths which were sensitive for classifying various types of plants were extracted according to the corresponding loading plots of the optimal principal components in PCA results. Finally, setting the feature wavelengths as the input variables, the soft independent modeling of class analogy (SIMCA) classification method was used to identify the various types of plants. The experimental results of classifying cabbages and weeds showed that on the basis of the optimal pretreatment by a synthetic application of MSC and SG convolutional derivation with SG's parameters set as 1rd order derivation, 3th degree polynomial and 51 smoothing points, 23 feature wavelengths were extracted in accordance with the top three principal components in PCA results. When SIMCA method was used for classification while the previously selected 23 feature wavelengths were set as the input variables, the classification rates of the modeling set and the prediction set were respectively up to 98.6% and 100%.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adam, R.; Ade, P. A. R.; Aghanim, N.

Although infrared (IR) overall dust emission from clusters of galaxies has been statistically detected using data from the Infrared Astronomical Satellite (IRAS), it has not been possible to sample the spectral energy distribution (SED) of this emission over its peak, and thus to break the degeneracy between dust temperature and mass. By complementing the IRAS spectral coverage with Planck satellite data from 100 to 857 GHz, we provide in this paper new constraints on the IR spectrum of thermal dust emission in clusters of galaxies. We achieve this by using a stacking approach for a sample of several hundred objectsmore » from the Planck cluster sample. This procedure averages out fluctuations from the IR sky, allowing us to reach a significant detection of the faint cluster contribution. We also use the large frequency range probed by Planck, together with component-separation techniques, to remove the contamination from both cosmic microwave background anisotropies and the thermal Sunyaev-Zeldovich effect (tSZ) signal, which dominate at ν ≤ 353 GHz. By excluding dominant spurious signals or systematic effects, averaged detections are reported at frequencies 353 GHz ≤ ν ≤ 5000 GHz. We confirm the presence of dust in clusters of galaxies at low and intermediate redshifts, yielding an SED with a shape similar to that of the Milky Way. Planck’s resolution does not allow us to investigate the detailed spatial distribution of this emission (e.g. whether it comes from intergalactic dust or simply the dust content of the cluster galaxies), but the radial distribution of the emission appears to follow that of the stacked SZ signal, and thus the extent of the clusters. Finally, the recovered SED allows us to constrain the dust mass responsible for the signal and its temperature.« less

SEARCHING FOR BULK MOTIONS IN THE INTRACLUSTER MEDIUM OF MASSIVE, MERGING CLUSTERS WITH CHANDRA CCD DATA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Liu, Ang; Yu, Heng; Tozzi, Paolo

2016-04-10

We search for bulk motions in the intracluster medium (ICM) of massive clusters showing evidence of an ongoing or recent major merger with spatially resolved spectroscopy in Chandra CCD data. We identify a sample of six merging clusters with >150 ks Chandra exposure in the redshift range 0.1 < z < 0.3. By performing X-ray spectral analysis of projected ICM regions selected according to their surface brightness, we obtain the projected redshift maps for all of these clusters. After performing a robust analysis of the statistical and systematic uncertainties in the measured X-ray redshift z{sub X}, we check whether or not themore » global z{sub X} distribution differs from that expected when the ICM is at rest. We find evidence of significant bulk motions at more than 3σ in A2142 and A115, and less than 2σ in A2034 and A520. Focusing on single regions, we identify significant localized velocity differences in all of the merger clusters. We also perform the same analysis on two relaxed clusters with no signatures of recent mergers, finding no signs of bulk motions, as expected. Our results indicate that deep Chandra CCD data enable us to identify the presence of bulk motions at the level of v{sub BM} > 1000 km s{sup −1} in the ICM of massive merging clusters at 0.1 < z < 0.3. Although the CCD spectral resolution is not sufficient for a detailed analysis of the ICM dynamics, Chandra CCD data constitute a key diagnostic tool complementing X-ray bolometers on board future X-ray missions.« less

Constraints on axion-like particles from non-observation of spectral modulations for X-ray point sources

DOE Office of Scientific and Technical Information (OSTI.GOV)

Conlon, Joseph P.; Day, Francesca; Jennings, Nicholas

We extend previous searches for X-ray spectral modulations induced by ALP-photon conversion to a variety of new sources, all consisting of quasars or AGNs located in or behind galaxy clusters. We consider a total of seven new sources, with data drawn from the Chandra archive. In all cases the spectrum is well fit by an absorbed power-law with no evidence for spectral modulations, allowing constraints to be placed on the ALP-photon coupling parameter g {sub a} {sub γγ}. Two sources are particularly good: the Seyfert galaxy 2E3140 in A1795 and the AGN NGC3862 within the cluster A1367, leading to 95%more » bounds for light ALPs ( m {sub a} ∼< 10{sup −12} eV) of g {sub a} {sub γγ} ∼< 1.5 × 10{sup −12} GeV{sup −1} and g {sub a} {sub γγ} ∼< 2.4 × 10{sup −12} GeV{sup −1} respectively.« less

FIREFLY (Fitting IteRativEly For Likelihood analYsis): a full spectral fitting code

NASA Astrophysics Data System (ADS)

Wilkinson, David M.; Maraston, Claudia; Goddard, Daniel; Thomas, Daniel; Parikh, Taniya

2017-12-01

We present a new spectral fitting code, FIREFLY, for deriving the stellar population properties of stellar systems. FIREFLY is a chi-squared minimization fitting code that fits combinations of single-burst stellar population models to spectroscopic data, following an iterative best-fitting process controlled by the Bayesian information criterion. No priors are applied, rather all solutions within a statistical cut are retained with their weight. Moreover, no additive or multiplicative polynomials are employed to adjust the spectral shape. This fitting freedom is envisaged in order to map out the effect of intrinsic spectral energy distribution degeneracies, such as age, metallicity, dust reddening on galaxy properties, and to quantify the effect of varying input model components on such properties. Dust attenuation is included using a new procedure, which was tested on Integral Field Spectroscopic data in a previous paper. The fitting method is extensively tested with a comprehensive suite of mock galaxies, real galaxies from the Sloan Digital Sky Survey and Milky Way globular clusters. We also assess the robustness of the derived properties as a function of signal-to-noise ratio (S/N) and adopted wavelength range. We show that FIREFLY is able to recover age, metallicity, stellar mass, and even the star formation history remarkably well down to an S/N ∼ 5, for moderately dusty systems. Code and results are publicly available.1

Kmeans-ICA based automatic method for ocular artifacts removal in a motorimagery classification.

PubMed

Bou Assi, Elie; Rihana, Sandy; Sawan, Mohamad

2014-01-01

Electroencephalogram (EEG) recordings aroused as inputs of a motor imagery based BCI system. Eye blinks contaminate the spectral frequency of the EEG signals. Independent Component Analysis (ICA) has been already proved for removing these artifacts whose frequency band overlap with the EEG of interest. However, already ICA developed methods, use a reference lead such as the ElectroOculoGram (EOG) to identify the ocular artifact components. In this study, artifactual components were identified using an adaptive thresholding by means of Kmeans clustering. The denoised EEG signals have been fed into a feature extraction algorithm extracting the band power, the coherence and the phase locking value and inserted into a linear discriminant analysis classifier for a motor imagery classification.

Study of Deformation Phenomena in TRIP/TWIP Steels by Acoustic Emission and Scanning Electron Microscopy

NASA Astrophysics Data System (ADS)

Linderov, M. L.; Segel, C.; Weidner, A.; Biermann, H.; Vinogradov, A. Yu.

2018-04-01

Modern metastable steels with TRIP/TWIP effects have a unique set of physical-mechanical properties. They combine both high-strength and high-plasticity characteristics, which is governed by processes activated during deformation, namely, twinning, the formation of stacking faults, and martensitic transformations. To study the behavior of these phenomena in CrMnNi TRIP/TWIP steels and stainless CrNiMo steel, which does not have these effects in the temperature range under study, we used the method of acoustic emission and modern methods of signal processing, including the cluster analysis of spectral-density functions. The results of this study have been compared with a detailed microstructural analysis performed with a scanning electron microscope using electron backscatter diffraction (EBSD).

A Deep Chandra Observation of the Distant Galaxy Cluster MS 1137.5+6625

NASA Astrophysics Data System (ADS)

Grego, Laura; Vrtilek, J. M.; Van Speybroeck, Leon; David, Laurence P.; Forman, William; Carlstrom, John E.; Reese, Erik D.; Joy, Marshall K.

2004-06-01

We present results from a deep Chandra observation of MS 1137.5+66, a distant (z=0.783) and massive cluster of galaxies. Only a few similarly massive clusters are currently known at such high redshifts; accordingly, this observation provides much needed information on the dynamical state of these rare systems. The cluster appears both regular and symmetric in the X-ray image. However, our analysis of the spectral and spatial X-ray data in conjunction with interferometric Sunyaev-Zel'dovich effect data and published deep optical imaging suggests that the cluster has a fairly complex structure. The angular diameter distance we calculate from the Chandra and Sunyaev-Zel'dovich effect data assuming an isothermal, spherically symmetric cluster implies a low value for the Hubble constant for which we explore possible explanations.

STAR CLUSTERS IN M33: UPDATED UBVRI PHOTOMETRY, AGES, METALLICITIES, AND MASSES

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fan, Zhou; De Grijs, Richard, E-mail: zfan@bao.ac.cn, E-mail: grijs@pku.edu.cn

2014-04-01

The photometric characterization of M33 star clusters is far from complete. In this paper, we present homogeneous UBVRI photometry of 708 star clusters and cluster candidates in M33 based on archival images from the Local Group Galaxies Survey, which covers 0.8 deg{sup 2} along the galaxy's major axis. Our photometry includes 387, 563, 616, 580, and 478 objects in the UBVRI bands, respectively, of which 276, 405, 430, 457, and 363 do not have previously published UBVRI photometry. Our photometry is consistent with previous measurements (where available) in all filters. We adopted Sloan Digital Sky Survey ugriz photometry for complementarymore » purposes, as well as Two Micron All Sky Survey near-infrared JHK photometry where available. We fitted the spectral-energy distributions of 671 star clusters and candidates to derive their ages, metallicities, and masses based on the updated PARSEC simple stellar populations synthesis models. The results of our χ{sup 2} minimization routines show that only 205 of the 671 clusters (31%) are older than 2 Gyr, which represents a much smaller fraction of the cluster population than that in M31 (56%), suggesting that M33 is dominated by young star clusters (<1 Gyr). We investigate the mass distributions of the star clusters—both open and globular clusters—in M33, M31, the Milky Way, and the Large Magellanic Cloud. Their mean values are log (M {sub cl}/M {sub ☉}) = 4.25, 5.43, 2.72, and 4.18, respectively. The fraction of open to globular clusters is highest in the Milky Way and lowest in M31. Our comparisons of the cluster ages, masses, and metallicities show that our results are basically in agreement with previous studies (where objects in common are available); differences can be traced back to differences in the models adopted, the fitting methods used, and stochastic sampling effects.« less

A k-mer-based barcode DNA classification methodology based on spectral representation and a neural gas network.

PubMed

Fiannaca, Antonino; La Rosa, Massimo; Rizzo, Riccardo; Urso, Alfonso

2015-07-01

In this paper, an alignment-free method for DNA barcode classification that is based on both a spectral representation and a neural gas network for unsupervised clustering is proposed. In the proposed methodology, distinctive words are identified from a spectral representation of DNA sequences. A taxonomic classification of the DNA sequence is then performed using the sequence signature, i.e., the smallest set of k-mers that can assign a DNA sequence to its proper taxonomic category. Experiments were then performed to compare our method with other supervised machine learning classification algorithms, such as support vector machine, random forest, ripper, naïve Bayes, ridor, and classification tree, which also consider short DNA sequence fragments of 200 and 300 base pairs (bp). The experimental tests were conducted over 10 real barcode datasets belonging to different animal species, which were provided by the on-line resource "Barcode of Life Database". The experimental results showed that our k-mer-based approach is directly comparable, in terms of accuracy, recall and precision metrics, with the other classifiers when considering full-length sequences. In addition, we demonstrate the robustness of our method when a classification is performed task with a set of short DNA sequences that were randomly extracted from the original data. For example, the proposed method can reach the accuracy of 64.8% at the species level with 200-bp fragments. Under the same conditions, the best other classifier (random forest) reaches the accuracy of 20.9%. Our results indicate that we obtained a clear improvement over the other classifiers for the study of short DNA barcode sequence fragments. Copyright © 2015 Elsevier B.V. All rights reserved.

An X-Ray Survey of the Open Cluster NGC 6475 (M7) with ROSAT

NASA Technical Reports Server (NTRS)

Prosser, Charles F.; Stauffer, John R.; Caillault, J.-P.; Balachandran, Suchitra; Stern, Robert A.; Randich, Sofia

1995-01-01

A ROSAT x-ray survey, with complimentary optical photometry, of the open cluster NGC 6475 has enabled the detection of approx. 50 late-F to K0 and approx. 70 K/M dwarf new candidate members, providing the first reliable detection of low-mass stars in this low. galactic latitude, 220 Myr old cluster. The x-ray observations reported here have a typical limiting sensitivity of L(sub x) approx. equal to 10(exp 29) erg/s. The detection frequency of early type cluster members is consistent with the hypothesis that the x-ray emitting early type stars are binary systems with an unseen, low-mass secondary producing the x rays. The ratio between x-ray and bolometric luminosity among NGC 6475 members saturates at a spectral-type/color which is intermediate between that in much younger and in much older clusters, consistent with rotational spindown of solar-type stars upon their arrival on the ZAMS. The upper envelope of x-ray luminosity as a function of spectral type is comparable to that of the Pleiades, with the observed spread in x-ray luminosity among low-mass members being likely due to the presence of binaries and relatively rapid rotators. However, the list of x-ray selected candidate members is likely biased against low-mass, slowly rotating single stars. While some preliminary spectroscopic information is given in an appendix, further spectroscopic observations of the new candidate members will aid in interpreting the coronal activity among solar-type NGC 6475 members and their relation to similar stars in older and younger open clusters.

Evaluation of Thematic Mapper data for mapping forest, agricultural and soil resources

NASA Technical Reports Server (NTRS)

Degloria, S.; Benson, A.; Dummer, K.; Fakhoury, E.

1985-01-01

Color composite TM film products which include TM5, TM4, and a visible band (TM1, TM2, or TM3) are superior to composites which exclude TM4 for discriminating most forest and agricultural cover types and estimating area proportions for inventory and sampling purposes. Clustering a subset of TM data results in a spectral class map which groups diverse forest cover types into spectrally and ecologically similar areas suitable for use as a stratification base in traditional forest inventory practices. Analysis of simulated Thematic Mapper data indicate that the location and number of TM spectral bands are suitable for detecting differences in major soil properties and characterizing soil spectral curve form and magnitude.

Detection of a Double Relic in the Torpedo Cluster: SPT-CL J0245-5302

NASA Astrophysics Data System (ADS)

Zheng, Q.; Johnston-Hollitt, M.; Duchesne, S. W.; Li, W. T.

2018-06-01

The Torpedo cluster, SPT-CL J0245-5302 (S0295) is a massive, merging cluster at a redshift of z = 0.300, which exhibits a strikingly similar morphology to the Bullet cluster 1E 0657-55.8 (z = 0.296), including a classic bow shock in the cluster's intra-cluster medium revealed by Chandra X-ray observations. We present Australia Telescope Compact Array data centred at 2.1 GHz and Murchison Widefield Array data at frequencies between 72 MHz and 231 MHz which we use to study the properties of the cluster. We characterise a number of discrete and diffuse radio sources in the cluster, including the detection of two previously unknown radio relics on the cluster periphery. The average spectral index of the diffuse emission between 70 MHz and 3.1 GHz is α =-1.63_{-0.10}^{+0.10} and a radio-derived Mach number for the shock in the west of the cluster is calculated as M = 2.04. The Torpedo cluster is thus a double relic system at moderate redshift.

The Membership and Distance of the Open Cluster Collinder 419

DTIC Science & Technology

2010-09-01

distance based upon new spectral classifications of the brighter members, UBV photometry , and an analysis of astrometric and photometric data from the... photometry of the fainter cluster members in Section 4. Our results are summarized in Section 5. 2. SPECTROSCOPY AND REDDENING OF THE BRIGHTER STARS...including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing

Raman spectroscopy of normal oral buccal mucosa tissues: study on intact and incised biopsies

NASA Astrophysics Data System (ADS)

Deshmukh, Atul; Singh, S. P.; Chaturvedi, Pankaj; Krishna, C. Murali

2011-12-01

Oral squamous cell carcinoma is one of among the top 10 malignancies. Optical spectroscopy, including Raman, is being actively pursued as alternative/adjunct for cancer diagnosis. Earlier studies have demonstrated the feasibility of classifying normal, premalignant, and malignant oral ex vivo tissues. Spectral features showed predominance of lipids and proteins in normal and cancer conditions, respectively, which were attributed to membrane lipids and surface proteins. In view of recent developments in deep tissue Raman spectroscopy, we have recorded Raman spectra from superior and inferior surfaces of 10 normal oral tissues on intact, as well as incised, biopsies after separation of epithelium from connective tissue. Spectral variations and similarities among different groups were explored by unsupervised (principal component analysis) and supervised (linear discriminant analysis, factorial discriminant analysis) methodologies. Clusters of spectra from superior and inferior surfaces of intact tissues show a high overlap; whereas spectra from separated epithelium and connective tissue sections yielded clear clusters, though they also overlap on clusters of intact tissues. Spectra of all four groups of normal tissues gave exclusive clusters when tested against malignant spectra. Thus, this study demonstrates that spectra recorded from the superior surface of an intact tissue may have contributions from deeper layers but has no bearing from the classification of a malignant tissues point of view.

Preliminary Comparisons of the Information Content and Utility of TM Versus MSS Data

NASA Technical Reports Server (NTRS)

Markham, B. L.

1984-01-01

Comparisons were made between subscenes from the first TM scene acquired of the Washington, D.C. area and a MSS scene acquired approximately one year earlier. Three types of analyses were conducted to compare TM and MSS data: a water body analysis, a principal components analysis and a spectral clustering analysis. The water body analysis compared the capability of the TM to the MSS for detecting small uniform targets. Of the 59 ponds located on aerial photographs 34 (58%) were detected by the TM with six commission errors (15%) and 13 (22%) were detected by the MSS with three commission errors (19%). The smallest water body detected by the TM was 16 meters; the smallest detected by the MSS was 40 meters. For the principal components analysis, means and covariance matrices were calculated for each subscene, and principal components images generated and characterized. In the spectral clustering comparison each scene was independently clustered and the clusters were assigned to informational classes. The preliminary comparison indicated that TM data provides enhancements over MSS in terms of (1) small target detection and (2) data dimensionality (even with 4-band data). The extra dimension, partially resultant from TM band 1, appears useful for built-up/non-built-up area separation.

Site-specific vibrational spectral signatures of water molecules in the magic H3O+(H2O)20 and Cs+(H2O)20 clusters

PubMed Central

Fournier, Joseph A.; Wolke, Conrad T.; Johnson, Christopher J.; Johnson, Mark A.; Heine, Nadja; Gewinner, Sandy; Schöllkopf, Wieland; Esser, Tim K.; Fagiani, Matias R.; Knorke, Harald; Asmis, Knut R.

2014-01-01

Theoretical models of proton hydration with tens of water molecules indicate that the excess proton is embedded on the surface of clathrate-like cage structures with one or two water molecules in the interior. The evidence for these structures has been indirect, however, because the experimental spectra in the critical H-bonding region of the OH stretching vibrations have been too diffuse to provide band patterns that distinguish between candidate structures predicted theoretically. Here we exploit the slow cooling afforded by cryogenic ion trapping, along with isotopic substitution, to quench water clusters attached to the H3O+ and Cs+ ions into structures that yield well-resolved vibrational bands over the entire 215- to 3,800-cm−1 range. The magic H3O+(H2O)20 cluster yields particularly clear spectral signatures that can, with the aid of ab initio predictions, be traced to specific classes of network sites in the predicted pentagonal dodecahedron H-bonded cage with the hydronium ion residing on the surface. PMID:25489068

Site-specific vibrational spectral signatures of water molecules in the magic H 3O +(H 2O) 20 and Cs +(H 2O) 20 clusters

DOE PAGES

Fournier, Joseph A.; Wolke, Conrad T.; Johnson, Christopher J.; ...

2014-12-08

Here, theoretical models of proton hydration with tens of water molecules indicate that the excess proton is embedded on the surface of clathrate-like cage structures with one or two water molecules in the interior. The evidence for these structures has been indirect, however, because the experimental spectra in the critical H-bonding region of the OH stretching vibrations have been too diffuse to provide band patterns that distinguish between candidate structures predicted theoretically. Here we exploit the slow cooling afforded by cryogenic ion trapping, along with isotopic substitution, to quench water clusters attached to the H 3O + and Cs +more » ions into structures that yield well-resolved vibrational bands over the entire 215- to 3,800-cm -1 range. The magic H 3O +(H 2O) 20 cluster yields particularly clear spectral signatures that can, with the aid of ab initio predictions, be traced to specific classes of network sites in the predicted pentagonal dodecahedron H-bonded cage with the hydronium ion residing on the surface.« less

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks

PubMed Central

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-01-01

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks. PMID:27754380

A Hybrid Spectral Clustering and Deep Neural Network Ensemble Algorithm for Intrusion Detection in Sensor Networks.

PubMed

Ma, Tao; Wang, Fen; Cheng, Jianjun; Yu, Yang; Chen, Xiaoyun

2016-10-13

The development of intrusion detection systems (IDS) that are adapted to allow routers and network defence systems to detect malicious network traffic disguised as network protocols or normal access is a critical challenge. This paper proposes a novel approach called SCDNN, which combines spectral clustering (SC) and deep neural network (DNN) algorithms. First, the dataset is divided into k subsets based on sample similarity using cluster centres, as in SC. Next, the distance between data points in a testing set and the training set is measured based on similarity features and is fed into the deep neural network algorithm for intrusion detection. Six KDD-Cup99 and NSL-KDD datasets and a sensor network dataset were employed to test the performance of the model. These experimental results indicate that the SCDNN classifier not only performs better than backpropagation neural network (BPNN), support vector machine (SVM), random forest (RF) and Bayes tree models in detection accuracy and the types of abnormal attacks found. It also provides an effective tool of study and analysis of intrusion detection in large networks.

EEG Correlates of Ten Positive Emotions.

PubMed

Hu, Xin; Yu, Jianwen; Song, Mengdi; Yu, Chun; Wang, Fei; Sun, Pei; Wang, Daifa; Zhang, Dan

2017-01-01

Compared with the well documented neurophysiological findings on negative emotions, much less is known about positive emotions. In the present study, we explored the EEG correlates of ten different positive emotions (joy, gratitude, serenity, interest, hope, pride, amusement, inspiration, awe, and love). A group of 20 participants were invited to watch 30 short film clips with their EEGs simultaneously recorded. Distinct topographical patterns for different positive emotions were found for the correlation coefficients between the subjective ratings on the ten positive emotions per film clip and the corresponding EEG spectral powers in different frequency bands. Based on the similarities of the participants' ratings on the ten positive emotions, these emotions were further clustered into three representative clusters, as 'encouragement' for awe, gratitude, hope, inspiration, pride, 'playfulness' for amusement, joy, interest, and 'harmony' for love, serenity. Using the EEG spectral powers as features, both the binary classification on the higher and lower ratings on these positive emotions and the binary classification between the three positive emotion clusters, achieved accuracies of approximately 80% and above. To our knowledge, our study provides the first piece of evidence on the EEG correlates of different positive emotions.

Key issues in decomposing fMRI during naturalistic and continuous music experience with independent component analysis.

PubMed

Cong, Fengyu; Puoliväli, Tuomas; Alluri, Vinoo; Sipola, Tuomo; Burunat, Iballa; Toiviainen, Petri; Nandi, Asoke K; Brattico, Elvira; Ristaniemi, Tapani

2014-02-15

Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA. For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering. The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features. Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA. Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps. Copyright © 2013 Elsevier B.V. All rights reserved.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations.

PubMed

Fabelo, Himar; Ortega, Samuel; Ravi, Daniele; Kiran, B Ravi; Sosa, Coralia; Bulters, Diederik; Callicó, Gustavo M; Bulstrode, Harry; Szolna, Adam; Piñeiro, Juan F; Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O'Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area.

Mapping and modeling the urban landscape in Bangkok, Thailand: Physical-spectral-spatial relations of population-environmental interactions

NASA Astrophysics Data System (ADS)

Shao, Yang

This research focuses on the application of remote sensing, geographic information systems, statistical modeling, and spatial analysis to examine the dynamics of urban land cover, urban structure, and population-environment interactions in Bangkok, Thailand, with an emphasis on rural-to-urban migration from rural Nang Rong District, Northeast Thailand to the primate city of Bangkok. The dissertation consists of four main sections: (1) development of remote sensing image classification and change-detection methods for characterizing imperviousness for Bangkok, Thailand from 1993-2002; (2) development of 3-D urban mapping methods, using high spatial resolution IKONOS satellite images, to assess high-rises and other urban structures; (3) assessment of urban spatial structure from 2-D and 3-D perspectives; and (4) an analysis of the spatial clustering of migrants from Nang Rong District in Bangkok and the neighborhood environments of migrants' locations. Techniques are developed to improve the accuracy of the neural network classification approach for the analysis of remote sensing data, with an emphasis on the spectral unmixing problem. The 3-D building heights are derived using the shadow information on the high-resolution IKONOS image. The results from the 2-D and 3-D mapping are further examined to assess urban structure and urban feature identification. This research contributes to image processing of remotely-sensed images and urban studies. The rural-urban migration process and migrants' settlement patterns are examined using spatial statistics, GIS, and remote sensing perspectives. The results show that migrants' spatial clustering in urban space is associated with the source village and a number of socio-demographic variables. In addition, the migrants' neighborhood environments in urban setting are modeled using a set of geographic and socio-demographic variables, and the results are scale-dependent.

Spatio-spectral classification of hyperspectral images for brain cancer detection during surgical operations

PubMed Central

Kabwama, Silvester; Madroñal, Daniel; Lazcano, Raquel; J-O’Shanahan, Aruma; Bisshopp, Sara; Hernández, María; Báez, Abelardo; Yang, Guang-Zhong; Stanciulescu, Bogdan; Salvador, Rubén; Juárez, Eduardo; Sarmiento, Roberto

2018-01-01

Surgery for brain cancer is a major problem in neurosurgery. The diffuse infiltration into the surrounding normal brain by these tumors makes their accurate identification by the naked eye difficult. Since surgery is the common treatment for brain cancer, an accurate radical resection of the tumor leads to improved survival rates for patients. However, the identification of the tumor boundaries during surgery is challenging. Hyperspectral imaging is a non-contact, non-ionizing and non-invasive technique suitable for medical diagnosis. This study presents the development of a novel classification method taking into account the spatial and spectral characteristics of the hyperspectral images to help neurosurgeons to accurately determine the tumor boundaries in surgical-time during the resection, avoiding excessive excision of normal tissue or unintentionally leaving residual tumor. The algorithm proposed in this study to approach an efficient solution consists of a hybrid framework that combines both supervised and unsupervised machine learning methods. Firstly, a supervised pixel-wise classification using a Support Vector Machine classifier is performed. The generated classification map is spatially homogenized using a one-band representation of the HS cube, employing the Fixed Reference t-Stochastic Neighbors Embedding dimensional reduction algorithm, and performing a K-Nearest Neighbors filtering. The information generated by the supervised stage is combined with a segmentation map obtained via unsupervised clustering employing a Hierarchical K-Means algorithm. The fusion is performed using a majority voting approach that associates each cluster with a certain class. To evaluate the proposed approach, five hyperspectral images of surface of the brain affected by glioblastoma tumor in vivo from five different patients have been used. The final classification maps obtained have been analyzed and validated by specialists. These preliminary results are promising, obtaining an accurate delineation of the tumor area. PMID:29554126

Clusters of Insomnia Disorder: An Exploratory Cluster Analysis of Objective Sleep Parameters Reveals Differences in Neurocognitive Functioning, Quantitative EEG, and Heart Rate Variability

PubMed Central

Miller, Christopher B.; Bartlett, Delwyn J.; Mullins, Anna E.; Dodds, Kirsty L.; Gordon, Christopher J.; Kyle, Simon D.; Kim, Jong Won; D'Rozario, Angela L.; Lee, Rico S.C.; Comas, Maria; Marshall, Nathaniel S.; Yee, Brendon J.; Espie, Colin A.; Grunstein, Ronald R.

2016-01-01

Study Objectives: To empirically derive and evaluate potential clusters of Insomnia Disorder through cluster analysis from polysomnography (PSG). We hypothesized that clusters would differ on neurocognitive performance, sleep-onset measures of quantitative (q)-EEG and heart rate variability (HRV). Methods: Research volunteers with Insomnia Disorder (DSM-5) completed a neurocognitive assessment and overnight PSG measures of total sleep time (TST), wake time after sleep onset (WASO), and sleep onset latency (SOL) were used to determine clusters. Results: From 96 volunteers with Insomnia Disorder, cluster analysis derived at least two clusters from objective sleep parameters: Insomnia with normal objective sleep duration (I-NSD: n = 53) and Insomnia with short sleep duration (I-SSD: n = 43). At sleep onset, differences in HRV between I-NSD and I-SSD clusters suggest attenuated parasympathetic activity in I-SSD (P < 0.05). Preliminary work suggested three clusters by retaining the I-NSD and splitting the I-SSD cluster into two: I-SSD A (n = 29): defined by high WASO and I-SSD B (n = 14): a second I-SSD cluster with high SOL and medium WASO. The I-SSD B cluster performed worse than I-SSD A and I-NSD for sustained attention (P ≤ 0.05). In an exploratory analysis, q-EEG revealed reduced spectral power also in I-SSD B before (Delta, Alpha, Beta-1) and after sleep-onset (Beta-2) compared to I-SSD A and I-NSD (P ≤ 0.05). Conclusions: Two insomnia clusters derived from cluster analysis differ in sleep onset HRV. Preliminary data suggest evidence for three clusters in insomnia with differences for sustained attention and sleep-onset q-EEG. Clinical Trial Registration: Insomnia 100 sleep study: Australia New Zealand Clinical Trials Registry (ANZCTR) identification number 12612000049875. URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=347742. Citation: Miller CB, Bartlett DJ, Mullins AE, Dodds KL, Gordon CJ, Kyle SD, Kim JW, D'Rozario AL, Lee RS, Comas M, Marshall NS, Yee BJ, Espie CA, Grunstein RR. Clusters of Insomnia Disorder: an exploratory cluster analysis of objective sleep parameters reveals differences in neurocognitive functioning, quantitative EEG, and heart rate variability. SLEEP 2016;39(11):1993–2004. PMID:27568796

Co-clustering directed graphs to discover asymmetries and directional communities

PubMed Central

Rohe, Karl; Qin, Tai; Yu, Bin

2016-01-01

In directed graphs, relationships are asymmetric and these asymmetries contain essential structural information about the graph. Directed relationships lead to a new type of clustering that is not feasible in undirected graphs. We propose a spectral co-clustering algorithm called di-sim for asymmetry discovery and directional clustering. A Stochastic co-Blockmodel is introduced to show favorable properties of di-sim. To account for the sparse and highly heterogeneous nature of directed networks, di-sim uses the regularized graph Laplacian and projects the rows of the eigenvector matrix onto the sphere. A nodewise asymmetry score and di-sim are used to analyze the clustering asymmetries in the networks of Enron emails, political blogs, and the Caenorhabditis elegans chemical connectome. In each example, a subset of nodes have clustering asymmetries; these nodes send edges to one cluster, but receive edges from another cluster. Such nodes yield insightful information (e.g., communication bottlenecks) about directed networks, but are missed if the analysis ignores edge direction. PMID:27791058

Co-clustering directed graphs to discover asymmetries and directional communities.

PubMed

Rohe, Karl; Qin, Tai; Yu, Bin

2016-10-21

In directed graphs, relationships are asymmetric and these asymmetries contain essential structural information about the graph. Directed relationships lead to a new type of clustering that is not feasible in undirected graphs. We propose a spectral co-clustering algorithm called di-sim for asymmetry discovery and directional clustering. A Stochastic co-Blockmodel is introduced to show favorable properties of di-sim To account for the sparse and highly heterogeneous nature of directed networks, di-sim uses the regularized graph Laplacian and projects the rows of the eigenvector matrix onto the sphere. A nodewise asymmetry score and di-sim are used to analyze the clustering asymmetries in the networks of Enron emails, political blogs, and the Caenorhabditis elegans chemical connectome. In each example, a subset of nodes have clustering asymmetries; these nodes send edges to one cluster, but receive edges from another cluster. Such nodes yield insightful information (e.g., communication bottlenecks) about directed networks, but are missed if the analysis ignores edge direction.

Near infrared photometric and optical spectroscopic study of 22 low mass star clusters embedded in nebulae

NASA Astrophysics Data System (ADS)

Soares, J. B.; Bica, E.; Ahumada, A. V.; Clariá, J. J.

2008-02-01

Aims:Among the star clusters in the Galaxy, those embedded in nebulae represent the youngest group, which has only recently been explored. The analysis of a sample of 22 candidate embedded stellar systems in reflection nebulae and/or HII environments is presented. Methods: We employed optical spectroscopic observations of stars in the directions of the clusters carried out at CASLEO (Argentina) together with near infrared photometry from the 2MASS catalogue. Our analysis is based on source surface density, colour-colour diagrams and on theoretical pre-main sequence isochrones. We take into account the field star contamination by carrying out a statistical subtraction. Results: The studied objects have the characteristics of low mass systems. We derive their fundamental parameters. Most of the cluster ages are younger than 2 Myr. The studied embedded stellar systems in reflection nebulae and/or HII region complexes do not have stars of spectral types earlier than B. The total stellar masses locked in the clusters are in the range 20-220 M⊙. They are found to be gravitationally unstable and are expected to dissolve in a timescale of a few Myr. Based on observations made at Complejo Astronómico El Leoncito, which is operated under agreement between the Consejo Nacional de Investigaciones Científicas y Técnicas de la República Argentina and the National Universities of La Plata, Córdoba and San Juan, Argentina.

Dust Evolution in Galaxy Cluster Simulations

NASA Astrophysics Data System (ADS)

Gjergo, Eda; Granato, Gian Luigi; Murante, Giuseppe; Ragone-Figueroa, Cinthia; Tornatore, Luca; Borgani, Stefano

2018-06-01

We implement a state-of-the-art treatment of the processes affecting the production and Interstellar Medium (ISM) evolution of carbonaceous and silicate dust grains within SPH simulations. We trace the dust grain size distribution by means of a two-size approximation. We test our method on zoom-in simulations of four massive (M200 ≥ 3 × 1014M⊙) galaxy clusters. We predict that during the early stages of assembly of the cluster at z ≳ 3, where the star formation activity is at its maximum in our simulations, the proto-cluster regions are rich in dusty gas. Compared to the case in which only dust production in stellar ejecta is active, if we include processes occurring in the cold ISM,the dust content is enhanced by a factor 2 - 3. However, the dust properties in this stage turn out to be significantly different from those observationally derived for the average Milky Way dust, and commonly adopted in calculations of dust reprocessing. We show that these differences may have a strong impact on the predicted spectral energy distributions. At low redshift in star forming regions our model reproduces reasonably well the trend of dust abundances over metallicity as observed in local galaxies. However we under-produce by a factor of 2 to 3 the total dust content of clusters estimated observationally at low redshift, z ≲ 0.5 using IRAS, Planck and Herschel satellites data. This discrepancy does not subsist by assuming a lower sputtering efficiency, which erodes dust grains in the hot Intracluster Medium (ICM).

Discovery of large-scale diffuse radio emission in low-mass galaxy cluster Abell 1931

NASA Astrophysics Data System (ADS)

Brüggen, M.; Rafferty, D.; Bonafede, A.; van Weeren, R. J.; Shimwell, T.; Intema, H.; Röttgering, H.; Brunetti, G.; Di Gennaro, G.; Savini, F.; Wilber, A.; O'Sullivan, S.; Ensslin, T. A.; De Gasperin, F.; Hoeft, M.

2018-07-01

Extended, steep-spectrum radio synchrotron sources are pre-dominantly found in massive galaxy clusters as opposed to groups. LOFAR Two-Metre Sky Survey images have revealed a diffuse, ultra-steep-spectrum radio source in the low-mass cluster Abell 1931. The source has a fairly irregular morphology with the largest linear size of about 550 kpc. The source is only seen in LOFAR observations at 143 MHz and Giant Metre Radio Telescope observations at 325 MHz. The spectral index of the total source between 143 and 325 MHz is α _{143}^{325} = -2.86 ± 0.36. The source remains invisible in Very Large Array (1-2 GHz) observations as expected given the spectral index. Chandra X-ray observations of the cluster revealed a bolometric luminosity of LX = (1.65 ± 0.39) × 1043 erg s-1 and a temperature of 2.92_{-0.87}^{+1.89} keV which implies a mass of around ˜1014 M⊙. We conclude that the source is a remnant radio galaxy that has shut off around 200 Myr ago. The brightest cluster galaxy, a radio-loud elliptical galaxy, could be the source for this extinct source. Unlike remnant sources studied in the literature, our source has a steep spectrum at low radio frequencies. Studying such remnant radio galaxies at low radio frequencies is important for understanding the scarcity of such sources and their role in feedback processes.

Search for Gamma-Ray Emission from the Coma Cluster with Six Years of Fermi-LAT Data

NASA Technical Reports Server (NTRS)

Ackermann, M.; Ajello, M.; Albert, A.; Atwood, W. B.; Baldini, L.; Ballet, J.; Barbiellini, G.; Bastieri, D.; Bechtol, K.; Bellazzini, R.;

Applying reconfigurable hardware to the analysis of multispectral and hyperspectral imagery

NASA Astrophysics Data System (ADS)

Leeser, Miriam E.; Belanovic, Pavle; Estlick, Michael; Gokhale, Maya; Szymanski, John J.; Theiler, James P.

2002-01-01

Unsupervised clustering is a powerful technique for processing multispectral and hyperspectral images. Last year, we reported on an implementation of k-means clustering for multispectral images. Our implementation in reconfigurable hardware processed 10 channel multispectral images two orders of magnitude faster than a software implementation of the same algorithm. The advantage of using reconfigurable hardware to accelerate k-means clustering is clear; the disadvantage is the hardware implementation worked for one specific dataset. It is a non-trivial task to change this implementation to handle a dataset with different number of spectral channels, bits per spectral channel, or number of pixels; or to change the number of clusters. These changes required knowledge of the hardware design process and could take several days of a designer's time. Since multispectral data sets come in many shapes and sizes, being able to easily change the k-means implementation for these different data sets is important. For this reason, we have developed a parameterized implementation of the k-means algorithm. Our design is parameterized by the number of pixels in an image, the number of channels per pixel, and the number of bits per channel as well as the number of clusters. These parameters can easily be changed in a few minutes by someone not familiar with the design process. The resulting implementation is very close in performance to the original hardware implementation. It has the added advantage that the parameterized design compiles approximately three times faster than the original.

Search for gamma-ray emission from the Coma Cluster with six years of Fermi-LAT data

DOE PAGES

Ackermann, M.

2016-03-08

We present results from γ-ray observations of the Coma cluster incorporating 6 years of Fermi-LAT data and the newly released “Pass 8” event-level analysis. Our analysis of the region reveals low-significance residual structures within the virial radius of the cluster that are too faint for a detailed investigation with the current data. Using a likelihood approach that is free of assumptions on the spectral shape we derive upper limits on the γ-ray flux that is expected from energetic particle interactions in the cluster. We also consider a benchmark spatial and spectral template motivated by models in which the observed radiomore » halo is mostly emission by secondary electrons. In this case, the median expected and observed upper limits for the flux above 100MeV are 1.7 x 10 -9 ph cm -2 s -1 and 5.2 x 10 -9 ph cm -2 s -1 respectively (the latter corresponds to residual emission at the level of 1:8σ). These bounds are comparable to or higher than predicted levels of hadronic gamma-ray emission in cosmic-ray models with or without reacceleration of secondary electrons, although direct comparisons are sensitive to assumptions regarding the origin and propagation mode of cosmic rays and magnetic field properties. The minimal expected γ-ray flux from radio and star-forming galaxies within the Coma cluster is roughly an order of magnitude below the median sensitivity of our analysis.« less

Mpc-scale diffuse radio emission in two massive cool-core clusters of galaxies

NASA Astrophysics Data System (ADS)

Sommer, Martin W.; Basu, Kaustuv; Intema, Huib; Pacaud, Florian; Bonafede, Annalisa; Babul, Arif; Bertoldi, Frank

2017-04-01

Radio haloes are diffuse synchrotron sources on scales of ˜1 Mpc that are found in merging clusters of galaxies, and are believed to be powered by electrons re-accelerated by merger-driven turbulence. We present measurements of extended radio emission on similarly large scales in two clusters of galaxies hosting cool cores: Abell 2390 and Abell 2261. The analysis is based on interferometric imaging with the Karl G. Jansky Very Large Array, Very Large Array and Giant Metrewave Radio Telescope. We present detailed radio images of the targets, subtract the compact emission components and measure the spectral indices for the diffuse components. The radio emission in A2390 extends beyond a known sloshing-like brightness discontinuity, and has a very steep in-band spectral slope at 1.5 GHz that is similar to some known ultrasteep spectrum radio haloes. The diffuse signal in A2261 is more extended than in A2390 but has lower luminosity. X-ray morphological indicators, derived from XMM-Newton X-ray data, place these clusters in the category of relaxed or regular systems, although some asymmetric features that can indicate past minor mergers are seen in the X-ray brightness images. If these two Mpc-scale radio sources are categorized as giant radio haloes, they question the common assumption of radio haloes occurring exclusively in clusters undergoing violent merging activity, in addition to commonly used criteria for distinguishing between radio haloes and minihaloes.

Spectra of random networks in the weak clustering regime

NASA Astrophysics Data System (ADS)

Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen; Rodrigues, Francisco A.

2018-03-01

The asymptotic behavior of dynamical processes in networks can be expressed as a function of spectral properties of the corresponding adjacency and Laplacian matrices. Although many theoretical results are known for the spectra of traditional configuration models, networks generated through these models fail to describe many topological features of real-world networks, in particular non-null values of the clustering coefficient. Here we study effects of cycles of order three (triangles) in network spectra. By using recent advances in random matrix theory, we determine the spectral distribution of the network adjacency matrix as a function of the average number of triangles attached to each node for networks without modular structure and degree-degree correlations. Implications to network dynamics are discussed. Our findings can shed light in the study of how particular kinds of subgraphs influence network dynamics.

Chemical evolution of the Magellanic Clouds

NASA Astrophysics Data System (ADS)

Barbuy, B.; de Freitas Pacheco, J. A.; Idiart, T.

We have obtained integrated spectra for 14 clusters in the Magellanic Clouds, on which the spectral indices Hβ, Mg2, Fe5270, Fe5335 were measured. Selecting indices whose behaviour depends essentially on age and metallicity (Hβ and ), together with (B-V) and (V-K) colours, we were able to determine age and metallicities for these clusters, using calibrations based on single stellar population models (Borges et al. 1995). A chemical evolution model which follows a star formation history as indicated by the field population is checked with the age and metallicity data for our sample star clusters.

Detection of high-energy gamma-ray emission from the globular cluster 47 Tucanae with Fermi.

PubMed

Abdo, A A; Ackermann, M; Ajello, M; Atwood, W B; Axelsson, M; Baldini, L; Ballet, J; Barbiellini, G; Bastieri, D; Baughman, B M; Bechtol, K; Bellazzini, R; Berenji, B; Blandford, R D; Bloom, E D; Bonamente, E; Borgland, A W; Bregeon, J; Brez, A; Brigida, M; Bruel, P; Burnett, T H; Caliandro, G A; Cameron, R A; Caraveo, P A; Casandjian, J M; Cecchi, C; Celik, O; Charles, E; Chaty, S; Chekhtman, A; Cheung, C C; Chiang, J; Ciprini, S; Claus, R; Cohen-Tanugi, J; Conrad, J; Cutini, S; Dermer, C D; de Palma, F; Digel, S W; Dormody, M; do Couto e Silva, E; Drell, P S; Dubois, R; Dumora, D; Farnier, C; Favuzzi, C; Fegan, S J; Focke, W B; Frailis, M; Fukazawa, Y; Fusco, P; Gargano, F; Gasparrini, D; Gehrels, N; Germani, S; Giebels, B; Giglietto, N; Giordano, F; Glanzman, T; Godfrey, G; Grenier, I A; Grove, J E; Guillemot, L; Guiriec, S; Hanabata, Y; Harding, A K; Hayashida, M; Hays, E; Horan, D; Hughes, R E; Jóhannesson, G; Johnson, A S; Johnson, R P; Johnson, T J; Johnson, W N; Kamae, T; Katagiri, H; Kawai, N; Kerr, M; Knödlseder, J; Kuehn, F; Kuss, M; Lande, J; Latronico, L; Lemoine-Goumard, M; Longo, F; Loparco, F; Lott, B; Lovellette, M N; Lubrano, P; Makeev, A; Mazziotta, M N; McConville, W; McEnery, J E; Meurer, C; Michelson, P F; Mitthumsiri, W; Mizuno, T; Moiseev, A A; Monte, C; Monzani, M E; Morselli, A; Moskalenko, I V; Murgia, S; Nolan, P L; Norris, J P; Nuss, E; Ohsugi, T; Omodei, N; Orlando, E; Ormes, J F; Paneque, D; Panetta, J H; Parent, D; Pelassa, V; Pepe, M; Pierbattista, M; Piron, F; Porter, T A; Rainò, S; Rando, R; Razzano, M; Rea, N; Reimer, A; Reimer, O; Reposeur, T; Ritz, S; Rochester, L S; Rodriguez, A Y; Romani, R W; Roth, M; Ryde, F; Sadrozinski, H F-W; Sanchez, D; Sander, A; Saz Parkinson, P M; Sgrò, C; Smith, D A; Smith, P D; Spandre, G; Spinelli, P; Starck, J-L; Strickman, M S; Suson, D J; Tajima, H; Takahashi, H; Tanaka, T; Thayer, J B; Thayer, J G; Thompson, D J; Tibaldo, L; Torres, D F; Tosti, G; Tramacere, A; Uchiyama, Y; Usher, T L; Vasileiou, V; Vilchez, N; Vitale, V; Wang, P; Webb, N; Winer, B L; Wood, K S; Ylinen, T; Ziegler, M

2009-08-14

We report the detection of gamma-ray emissions above 200 megaelectron volts at a significance level of 17sigma from the globular cluster 47 Tucanae, using data obtained with the Large Area Telescope onboard the Fermi Gamma-ray Space Telescope. Globular clusters are expected to emit gamma rays because of the large populations of millisecond pulsars that they contain. The spectral shape of 47 Tucanae is consistent with gamma-ray emission from a population of millisecond pulsars. The observed gamma-ray luminosity implies an upper limit of 60 millisecond pulsars present in 47 Tucanae.

A GPU accelerated and error-controlled solver for the unbounded Poisson equation in three dimensions

NASA Astrophysics Data System (ADS)

Exl, Lukas

2017-12-01

An efficient solver for the three dimensional free-space Poisson equation is presented. The underlying numerical method is based on finite Fourier series approximation. While the error of all involved approximations can be fully controlled, the overall computation error is driven by the convergence of the finite Fourier series of the density. For smooth and fast-decaying densities the proposed method will be spectrally accurate. The method scales with O(N log N) operations, where N is the total number of discretization points in the Cartesian grid. The majority of the computational costs come from fast Fourier transforms (FFT), which makes it ideal for GPU computation. Several numerical computations on CPU and GPU validate the method and show efficiency and convergence behavior. Tests are performed using the Vienna Scientific Cluster 3 (VSC3). A free MATLAB implementation for CPU and GPU is provided to the interested community.

A Multi-color Optical Survey of the Orion Nebula Cluster. II. The H-R Diagram

NASA Astrophysics Data System (ADS)

Da Rio, N.; Robberto, M.; Soderblom, D. R.; Panagia, N.; Hillenbrand, L. A.; Palla, F.; Stassun, K. G.

2010-10-01

We present a new analysis of the stellar population of the Orion Nebula Cluster (ONC) based on multi-band optical photometry and spectroscopy. We study the color-color diagrams in BVI, plus a narrowband filter centered at 6200 Å, finding evidence that intrinsic color scales valid for main-sequence dwarfs are incompatible with the ONC in the M spectral-type range, while a better agreement is found employing intrinsic colors derived from synthetic photometry, constraining the surface gravity value as predicted by a pre-main-sequence isochrone. We refine these model colors even further, empirically, by comparison with a selected sample of ONC stars with no accretion and no extinction. We consider the stars with known spectral types from the literature, and extend this sample with the addition of 65 newly classified stars from slit spectroscopy and 182 M-type from narrowband photometry; in this way, we isolate a sample of about 1000 stars with known spectral type. We introduce a new method to self-consistently derive the stellar reddening and the optical excess due to accretion from the location of each star in the BVI color-color diagram. This enables us to accurately determine the extinction of the ONC members, together with an estimate of their accretion luminosities. We adopt a lower distance for the Orion Nebula than previously assumed, based on recent parallax measurements. With a careful choice of also the spectral-type-temperature transformation, we produce the new Hertzsprung-Russell diagram of the ONC population, more populated than previous works. With respect to previous works, we find higher luminosity for late-type stars and a slightly lower luminosity for early types. We determine the age distribution of the population, peaking from ~2 to ~3 Myr depending on the model. We study the distribution of the members in the mass-age plane and find that taking into account selection effects due to incompleteness, removes an apparent correlation between mass and age. We derive the initial mass function for low- and intermediate-mass members of the ONC, which turns out to be model dependent and shows a turnover at M <~ 0.2 M sun.

The experimental vibrational infrared spectrum of lemon peel and simulation of spectral properties of the plant cell wall

NASA Astrophysics Data System (ADS)

Berezin, K. V.; Shagautdinova, I. T.; Chernavina, M. L.; Novoselova, A. V.; Dvoretskii, K. N.; Likhter, A. M.

2017-09-01

The experimental vibrational IR spectra of the outer part of lemon peel are recorded in the range of 3800-650 cm-1. The effect of artificial and natural dehydration of the peel on its vibrational spectrum is studied. It is shown that the colored outer layer of lemon peel does not have a noticeable effect on the vibrational spectrum. Upon 28-day storage of a lemon under natural laboratory conditions, only sequential dehydration processes are reflected in the vibrational spectrum of the peel. Within the framework of the theoretical DFT/B3LYP/6-31G(d) method, a model of a plant cell wall is developed consisting of a number of polymeric molecules of dietary fibers like cellulose, hemicellulose, pectin, lignin, some polyphenolic compounds (hesperetin glycoside-flavonoid), and a free water cluster. Using a supermolecular approach, the spectral properties of the wall of a lemon peel cell was simulated, and a detailed theoretical interpretation of the recorded vibrational spectrum is given.

Spectral pattern classification in lidar data for rock identification in outcrops.

PubMed

Campos Inocencio, Leonardo; Veronez, Mauricio Roberto; Wohnrath Tognoli, Francisco Manoel; de Souza, Marcelo Kehl; da Silva, Reginaldo Macedônio; Gonzaga, Luiz; Blum Silveira, César Leonardo

2014-01-01

The present study aimed to develop and implement a method for detection and classification of spectral signatures in point clouds obtained from terrestrial laser scanner in order to identify the presence of different rocks in outcrops and to generate a digital outcrop model. To achieve this objective, a software based on cluster analysis was created, named K-Clouds. This software was developed through a partnership between UNISINOS and the company V3D. This tool was designed to begin with an analysis and interpretation of a histogram from a point cloud of the outcrop and subsequently indication of a number of classes provided by the user, to process the intensity return values. This classified information can then be interpreted by geologists, to provide a better understanding and identification from the existing rocks in the outcrop. Beyond the detection of different rocks, this work was able to detect small changes in the physical-chemical characteristics of the rocks, as they were caused by weathering or compositional changes.

Spectral method for the static electric potential of a charge density in a composite medium

NASA Astrophysics Data System (ADS)

Bergman, David J.; Farhi, Asaf

2018-04-01

A spectral representation for the static electric potential field in a two-constituent composite medium is presented. A theory is developed for calculating the quasistatic eigenstates of Maxwell's equations for such a composite. The local physical potential field produced in the system by a given source charge density is expanded in this set of orthogonal eigenstates for any position r. The source charges can be located anywhere, i.e., inside any of the constituents. This is shown to work even if the eigenfunctions are normalized in an infinite volume. If the microstructure consists of a cluster of separate inclusions in a uniform host medium, then the quasistatic eigenstates of all the separate isolated inclusions can be used to calculate the eigenstates of the total structure as well as the local potential field. Once the eigenstates are known for a given host and a given microstructure, then calculation of the local field only involves calculating three-dimensional integrals of known functions and solving sets of linear algebraic equations.

The Metal-poor non-Sagittarius (?) Globular Cluster NGC 5053: Orbit and Mg, Al, and Si Abundances

NASA Astrophysics Data System (ADS)

Tang, Baitian; Fernández-Trincado, J. G.; Geisler, Doug; Zamora, Olga; Mészáros, Szabolcs; Masseron, Thomas; Cohen, Roger E.; García-Hernández, D. A.; Dell’Agli, Flavia; Beers, Timothy C.; Schiavon, Ricardo P.; Sohn, Sangmo Tony; Hasselquist, Sten; Robin, Annie C.; Shetrone, Matthew; Majewski, Steven R.; Villanova, Sandro; Schiappacasse Ulloa, Jose; Lane, Richard R.; Minnti, Dante; Roman-Lopes, Alexandre; Almeida, Andres; Moreno, E.

2018-03-01

Metal-poor globular clusters (GCs) exhibit intriguing Al–Mg anti-correlations and possible Si–Al correlations, which are important clues to decipher the multiple-population phenomenon. NGC 5053 is one of the most metal-poor GCs in the nearby universe and has been suggested to be associated with the Sagittarius (Sgr) dwarf galaxy, due to its similarity in location and radial velocity with one of the Sgr arms. In this work, we simulate the orbit of NGC 5053, and argue against a physical connection between Sgr and NGC 5053. On the other hand, the Mg, Al, and Si spectral lines, which are difficult to detect in the optical spectra of NGC 5053 stars, have been detected in the near-infrared APOGEE spectra. We use three different sets of stellar parameters and codes to derive the Mg, Al, and Si abundances. Regardless of which method is adopted, we see a large Al variation, and a substantial Si spread. Along with NGC 5053, metal-poor GCs exhibit different Mg, Al, and Si variations. Moreover, NGC 5053 has the lowest cluster mass among the GCs that have been identified to exhibit an observable Si spread until now.

A new approach for evaluating flexible working hours.

PubMed

Giebel, Ole; Janssen, Daniela; Schomann, Carsten; Nachreiner, Friedhelm

2004-01-01

Recent studies on flexible working hours show at least some of these working time arrangements seem to be associated with impairing effects of health and well-being. According to available evidence, variability of working hours seems to play an important role. The question, however, is how this variability can be assessed and used to explain or predict impairments. Based on earlier methods used to assess shift-work effects, a time series analysis approach was applied to the matter of flexible working hours. Data on the working hours of 4 week's length of 137 respondents derived from a survey on flexible work hours involving 15 companies of different production and service sectors in Germany were converted to time series and analyzed by spectral analysis. A cluster analysis of the resulting power spectra yielded 5 clusters of flexible work hours. Analyzing these clusters for differences in reported impairments showed that workers who showed suppression of circadian and weekly rhythms experienced severest impairments, especially in circadian controlled functions like sleep and digestion. The results thus indicate that analyzing the periodicity of flexible working hours seems to be a promising approach for predicting impairments which should be investigated further in the future.

Graph edit distance from spectral seriation.

PubMed

Robles-Kelly, Antonio; Hancock, Edwin R

2005-03-01

This paper is concerned with computing graph edit distance. One of the criticisms that can be leveled at existing methods for computing graph edit distance is that they lack some of the formality and rigor of the computation of string edit distance. Hence, our aim is to convert graphs to string sequences so that string matching techniques can be used. To do this, we use a graph spectral seriation method to convert the adjacency matrix into a string or sequence order. We show how the serial ordering can be established using the leading eigenvector of the graph adjacency matrix. We pose the problem of graph-matching as a maximum a posteriori probability (MAP) alignment of the seriation sequences for pairs of graphs. This treatment leads to an expression in which the edit cost is the negative logarithm of the a posteriori sequence alignment probability. We compute the edit distance by finding the sequence of string edit operations which minimizes the cost of the path traversing the edit lattice. The edit costs are determined by the components of the leading eigenvectors of the adjacency matrix and by the edge densities of the graphs being matched. We demonstrate the utility of the edit distance on a number of graph clustering problems.

Rapid and direct screening of H:C ratio in Archean kerogen via microRaman Spectroscopy

NASA Astrophysics Data System (ADS)

Ferralis, N.; Matys, E. D.; Allwood, A.; Knoll, A. H.; Summons, R. E.

2015-12-01

Rapid evaluation of the preservation of biosignatures in ancient kerogens is essential for the evaluation of the usability of Earth analogues as proxies of Martian geological materials. No single, non-destructive and non-invasive technique currently exists to rapidly determine such state of preservation of the organic matter in relation to its geological and mineral environment. Due to its non-invasive nature, microRaman spectroscopy is emerging as a candidate technique for the qualitative determination maturity of organic matter, by correlating Raman spectral features and aromatic carbon cluster size. Here we will present a novel quantitative method in which before-neglected Raman spectral features are correlated directly and with excellent accuracy with the H:C ratio. In addition to providing a chemical justification of the found direct correlation, we will show its applicability and predictive capabilities in evaluating H:C in Archean kerogens. This novel method opens new opportunities for the use of Raman spectroscopy and mapping. This includes the non-invasively determination of kerogen preservation and microscale chemical diversity within a particular Earth analogue, to be potentially extended to evaluate Raman spectra acquired directly on Mars.

Young star clusters in circumnuclear starburst rings

NASA Astrophysics Data System (ADS)

de Grijs, Richard; Ma, Chao; Jia, Siyao; Ho, Luis C.; Anders, Peter

2017-03-01

We analyse the cluster luminosity functions (CLFs) of the youngest star clusters in two galaxies exhibiting prominent circumnuclear starburst rings. We focus specifically on NGC 1512 and NGC 6951, for which we have access to Hα data that allow us to unambiguously identify the youngest sample clusters. To place our results on a firm statistical footing, we first explore in detail a number of important technical issues affecting the process from converting the observational data into the spectral energy distributions of the objects in our final catalogues. The CLFs of the young clusters in both galaxies exhibit approximate power-law behaviour down to the 90 per cent observational completeness limits, thus showing that star cluster formation in the violent environments of starburst rings appears to proceed similarly as that elsewhere in the local Universe. We discuss this result in the context of the density of the interstellar medium in our starburst-ring galaxies.

Machine Learning Method for Pattern Recognition in Volcano Seismic Spectra

NASA Astrophysics Data System (ADS)

Radic, V.; Unglert, K.; Jellinek, M.

2016-12-01

Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as Self-Organizing Maps (SOM), Principal Component Analysis (PCA) and clustering methods can help to quickly and automatically identify important patterns related to impending eruptions. In this study we develop and evaluate an algorithm applied on a set of synthetic volcano seismic spectra as well as observed spectra from Kılauea Volcano, Hawai`i. Our goal is to retrieve a set of known spectral patterns that are associated with dominant phases of volcanic tremor before, during, and after periods of volcanic unrest. The algorithm is based on training a SOM on the spectra and then identifying local maxima and minima on the SOM 'topography'. The topography is derived from the first two PCA modes so that the maxima represent the SOM patterns that carry most of the variance in the spectra. Patterns identified in this way reproduce the known set of spectra. Our results show that, regardless of the level of white noise in the spectra, the algorithm can accurately reproduce the characteristic spectral patterns and their occurrence in time. The ability to rapidly classify spectra of volcano seismic data without prior knowledge of the character of the seismicity at a given volcanic system holds great potential for real time or near-real time applications, and thus ultimately for eruption forecasting.

Graph Based Models for Unsupervised High Dimensional Data Clustering and Network Analysis

DTIC Science & Technology

2015-01-01

ApprovedOMB No. 0704-0188 Public reporting burden for the collection of information is estimated to average 1 hour per response, including the time for...algorithms we proposed improve the time e ciency signi cantly for large scale datasets. In the last chapter, we also propose an incremental reseeding...plume detection in hyper-spectral video data. These graph based clustering algorithms we proposed improve the time efficiency significantly for large

Impact of Regulation on Spectral Clustering

DTIC Science & Technology

2014-07-22

the eigenvector values. The regularization parameter was taken to be n. The shaded blue and pink regions corresponds to the nodes belonging to the two...values. As before, the shaded blue and pink regions corresponds to the nodes belonging to the two strong clusters. For plots (a) & (b) the blue line... pink regions corresponds to the nodes belonging to the liberal and conservative blogs respectively. insensitive for large τ . In this case 70% of the

Probing the early stages of salt nucleation—experimental and theoretical investigations of sodium/potassium thiocyanate cluster anions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deng, Shihu; Kong, Xiangyu; Wang, Xue B.

2015-01-14

Due to fast solvent evaporation in electrospray ionization (ESI), the concentration of initially dilute electrolyte solutions rapidly increases to afford formation of supersaturated droplets and generating various pristine anhydrous salt clusters in the gas phase. The size, composition, and charge distributions of these clusters, in principle witness the nucleation evolution in solutions. Herein, we report a microscopic study on the initial stage of nucleation and crystallization of sodium/potassium thiocyanate salt solutions simulated in the ESI process. Singly charged M x(SCN)⁻ x+1, doubly charged M y(SCN)²⁻ y+2 (M = Na, K), and triply charged K z(SCN)³⁻ z+3 anion clusters were producedmore » via electrospray of the corresponding salt solutions, and were characterized by negative ion photoelectron spectroscopy (NIPES). The vertical detachment energies (VDEs) of these sodium/potassium thiocyanate cluster anions were obtained, and theoretical calculations were carried out for sodium thiocyanate clusters in assisting spectral identification. The measured VDEs of singly charged anions M x(SCN)⁻ x+1 (M = Na and K) demonstrate they are superhalogen anions. The existence of doubly charged anions M y (SCN)²⁻ y+2 (y = 2x, x ≥ 4 and 3 for M = Na and K, respectively) and triply charged anions K z(SCN)³⁻ z+3 (z = 3x, x ≥ 6) were initially discovered from the photoelectron spectra for those singly charged anions of Msub>x(SCN)⁻ x+1 with the same mass-to-charge ratio (m/z), and later independently confirmed by observation of their distinct mass spectral distributions and by taking their NIPE spectra for those pure multiply charged anions with their m/z different from the singly charged species. For large clusters, multiply charged clusters are found to become preferred, but at higher temperatures those multiply charged clusters are suppressed. The series of anion clusters investigated here range from molecular-like M₁(SCN)⁻ 2 to nano-sized K₂₂(SCN)³⁻ 25, providing a vivid molecular-level growth pattern reflecting the initial salt nucleation process.« less

The Impact of Non-Thermal Processes in the Intracluster Medium on Cosmological Cluster Observables

NASA Astrophysics Data System (ADS)

Battaglia, Nicholas Ambrose

In this thesis we describe the generation and analysis of hydrodynamical simulations of galaxy clusters and their intracluster medium (ICM), using large cosmological boxes to generate large samples, in conjunction with individual cluster computations. The main focus is the exploration of the non-thermal processes in the ICM and the effect they have on the interpretation of observations used for cosmological constraints. We provide an introduction to the cosmological structure formation framework for our computations and an overview of the numerical simulations and observations of galaxy clusters. We explore the cluster magnetic field observables through radio relics, extended entities in the ICM characterized by their of diffuse radio emission. We show that statistical quantities such as radio relic luminosity functions and rotation measure power spectra are sensitive to magnetic field models. The spectral index of the radio relic emission provides information on structure formation shocks, e.g., on their Mach number. We develop a coarse grained stochastic model of active galaxy nucleus (AGN) feed-back in clusters and show the impact of such inhomogeneous feedback on the thermal pressure profile. We explore variations in the pressure profile as a function of cluster mass, redshift, and radius and provide a constrained fitting function for this profile. We measure the degree of the non-thermal pressure in the gas from internal cluster bulk motions and show it has an impact on the slope and scatter of the Sunyaev-Zel'dovich (SZ) scaling relation. We also find that the gross shape of the ICM, as characterized by scaled moment of inertia tensors, affects the SZ scaling relation. We demonstrate that the shape and the amplitude of the SZ angular power spectrum is sensitive to AGN feedback, and this affects the cosmological parameters determined from high resolution ACT and SPT cosmic microwave background data. We compare analytic, semi-analytic, and simulation-based methods for calculating the SZ power spectrum, and characterize their differences. All the methods must rely, one way or another, on high resolution large-scale hydrodynamical simulations with varying assumptions for modelling the gas of the sort presented here. We show how our results can be used to interpret the latest ACT and SPT power spectrum results. We provide an outlook for the future, describing follow-up work we are undertaking to further advance the theory of cluster science.

Properties of Spectral Shapes of Whistler-Mode Emissions

NASA Astrophysics Data System (ADS)

Macusova, E.; Santolik, O.; Pickett, J. S.; Gurnett, D. A.; Cornilleau-Wehrlin, N.

2014-12-01

Whistler-mode emissions play an important role in wave-particle interactions occurring in the radiation belt region. Whistler mode chorus emissions consist of discrete wave packets which exhibit different spectral shapes. Rising tones (events with positive value of the frequency sweep rate) are frequently observed. Other categories of chorus spectral shapes, such as falling tones, hooks, broadband patterns, are also known. Whistler-mode emissions can additionally occur as hiss or combinations of hiss with discrete patterns. In this study, we have analyzed more than 11 years of high-time resolution measurements provided by the Wideband Data (WBD) instrument onboard four Cluster spacecraft to identify different spectral shapes of whistler mode emissions. We determine the distribution of individual groups of chorus spectral shapes in the Earth's magnetosphere and the effect of the different geomagnetic conditions on their occurrence. We focus on average polarization and propagation properties of the different types of spectral shapes, obtained during visually identified time intervals from multicomponent measurements of the STAFF-SA instrument recorded with a time resolution of 4 seconds.

Evidence of genotypic diversity among Candida auris isolates by multilocus sequence typing, matrix-assisted laser desorption ionization time-of-flight mass spectrometry and amplified fragment length polymorphism.

PubMed

Prakash, A; Sharma, C; Singh, A; Kumar Singh, P; Kumar, A; Hagen, F; Govender, N P; Colombo, A L; Meis, J F; Chowdhary, A

2016-03-01

Candida auris is a multidrug-resistant nosocomial bloodstream pathogen that has been reported from Asian countries and South Africa. Herein, we studied the population structure and genetic relatedness among 104 global C. auris isolates from India, South Africa and Brazil using multilocus sequence typing (MLST), amplified fragment length polymorphism (AFLP) fingerprinting and matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). RPB1, RPB2 and internal transcribed spacer (ITS) and D1/D2 regions of the ribosomal DNA were sequenced for MLST. Further, genetic variation and proteomic assessment was carried out using AFLP and MALDI-TOF MS, respectively. Both MLST and AFLP typing clearly demarcated two major clusters comprising Indian and Brazilian isolates. However, the South African isolates were randomly distributed, suggesting different genotypes. MALDI-TOF MS spectral profiling also revealed evidence of geographical clustering but did not correlate fully with the genotyping methods. Notably, overall the population structure of C. auris showed evidence of geographical clustering by all the three techniques analysed. Antifungal susceptibility testing by the CLSI microbroth dilution method revealed that fluconazole had limited activity against 87% of isolates (MIC90, 64 mg/L). Also, MIC90 of AMB was 4 mg/L. Candida auris is emerging as an important yeast pathogen globally and requires reproducible laboratory methods for identification and typing. Evaluation of MALDI-TOF MS as a typing method for this yeast is warranted. Copyright © 2015 European Society of Clinical Microbiology and Infectious Diseases. Published by Elsevier Ltd. All rights reserved.

Detailed abundances from integrated-light spectroscopy: Milky Way globular clusters

NASA Astrophysics Data System (ADS)

Larsen, S. S.; Brodie, J. P.; Strader, J.

2017-05-01

Context. Integrated-light spectroscopy at high spectral resolution is rapidly maturing as a powerful way to measure detailed chemical abundances for extragalactic globular clusters (GCs). Aims: We test the performance of our analysis technique for integrated-light spectra by applying it to seven well-studied Galactic GCs that span a wide range of metallicities. Methods: Integrated-light spectra were obtained by scanning the slit of the UVES spectrograph on the ESO Very Large Telescope across the half-light diameters of the clusters. We modelled the spectra using resolved Hubble Space Telescope colour-magnitude diagrams (CMDs), as well as theoretical isochrones, in combination with standard stellar atmosphere and spectral synthesis codes. The abundances of Fe, Na, Mg, Ca, Ti, Cr, and Ba were compared with literature data for individual stars in the clusters. Results: The typical differences between iron abundances derived from our integrated-light spectra and those compiled from the literature are less than 0.1 dex. A larger difference is found for one cluster (NGC 6752), and is most likely caused primarily by stochastic fluctuations in the numbers of bright red giants within the scanned area. As expected, the α-elements (Ca, Ti) are enhanced by about 0.3 dex compared to the Solar-scaled composition, while the [Cr/Fe] ratios are close to Solar. When using up-to-date line lists, our [Mg/Fe] ratios also agree well with literature data. Our [Na/Fe] ratios are, on average, 0.08-0.14 dex lower than average values quoted in the literature, and our [Ba/Fe] ratios may be overestimated by 0.20-0.35 dex at the lowest metallicities. We find that analyses based on theoretical isochrones give very similar results to those based on resolved CMDs. Conclusions: Overall, the agreement between our integrated-light abundance measurements and the literature data is satisfactory. Refinements of the modelling procedure, such as corrections for stellar evolutionary and non-LTE effects, might further reduce some of the remaining offsets. Based on observations collected at the European Organisation for Astronomical Research in the Southern Hemisphere under ESO programme(s) 095.B-0677(A).Tables A.1-A.7 are only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/601/A96

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

DOE Office of Scientific and Technical Information (OSTI.GOV)

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

DOE PAGES

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; ...

2015-07-09

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. Moreover, a range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. In this paper, we illustrate how the method can be used to: (1) distinguishmore » between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.« less

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

PubMed Central

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-01-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required. PMID:26156000

A High Throughput Ambient Mass Spectrometric Approach to Species Identification and Classification from Chemical Fingerprint Signatures

NASA Astrophysics Data System (ADS)

Musah, Rabi A.; Espinoza, Edgard O.; Cody, Robert B.; Lesiak, Ashton D.; Christensen, Earl D.; Moore, Hannah E.; Maleknia, Simin; Drijfhout, Falko P.

2015-07-01

A high throughput method for species identification and classification through chemometric processing of direct analysis in real time (DART) mass spectrometry-derived fingerprint signatures has been developed. The method entails introduction of samples to the open air space between the DART ion source and the mass spectrometer inlet, with the entire observed mass spectral fingerprint subjected to unsupervised hierarchical clustering processing. A range of both polar and non-polar chemotypes are instantaneously detected. The result is identification and species level classification based on the entire DART-MS spectrum. Here, we illustrate how the method can be used to: (1) distinguish between endangered woods regulated by the Convention for the International Trade of Endangered Flora and Fauna (CITES) treaty; (2) assess the origin and by extension the properties of biodiesel feedstocks; (3) determine insect species from analysis of puparial casings; (4) distinguish between psychoactive plants products; and (5) differentiate between Eucalyptus species. An advantage of the hierarchical clustering approach to processing of the DART-MS derived fingerprint is that it shows both similarities and differences between species based on their chemotypes. Furthermore, full knowledge of the identities of the constituents contained within the small molecule profile of analyzed samples is not required.

Automated road network extraction from high spatial resolution multi-spectral imagery

NASA Astrophysics Data System (ADS)

Zhang, Qiaoping

For the last three decades, the Geomatics Engineering and Computer Science communities have considered automated road network extraction from remotely-sensed imagery to be a challenging and important research topic. The main objective of this research is to investigate the theory and methodology of automated feature extraction for image-based road database creation, refinement or updating, and to develop a series of algorithms for road network extraction from high resolution multi-spectral imagery. The proposed framework for road network extraction from multi-spectral imagery begins with an image segmentation using the k-means algorithm. This step mainly concerns the exploitation of the spectral information for feature extraction. The road cluster is automatically identified using a fuzzy classifier based on a set of predefined road surface membership functions. These membership functions are established based on the general spectral signature of road pavement materials and the corresponding normalized digital numbers on each multi-spectral band. Shape descriptors of the Angular Texture Signature are defined and used to reduce the misclassifications between roads and other spectrally similar objects (e.g., crop fields, parking lots, and buildings). An iterative and localized Radon transform is developed for the extraction of road centerlines from the classified images. The purpose of the transform is to accurately and completely detect the road centerlines. It is able to find short, long, and even curvilinear lines. The input image is partitioned into a set of subset images called road component images. An iterative Radon transform is locally applied to each road component image. At each iteration, road centerline segments are detected based on an accurate estimation of the line parameters and line widths. Three localization approaches are implemented and compared using qualitative and quantitative methods. Finally, the road centerline segments are grouped into a road network. The extracted road network is evaluated against a reference dataset using a line segment matching algorithm. The entire process is unsupervised and fully automated. Based on extensive experimentation on a variety of remotely-sensed multi-spectral images, the proposed methodology achieves a moderate success in automating road network extraction from high spatial resolution multi-spectral imagery.

Clustering and Filtering Tandem Mass Spectra Acquired in Data-Independent Mode

NASA Astrophysics Data System (ADS)

Pak, Huisong; Nikitin, Frederic; Gluck, Florent; Lisacek, Frederique; Scherl, Alexander; Muller, Markus

2013-12-01

Data-independent mass spectrometry activates all ion species isolated within a given mass-to-charge window ( m/z) regardless of their abundance. This acquisition strategy overcomes the traditional data-dependent ion selection boosting data reproducibility and sensitivity. However, several tandem mass (MS/MS) spectra of the same precursor ion are acquired during chromatographic elution resulting in large data redundancy. Also, the significant number of chimeric spectra and the absence of accurate precursor ion masses hamper peptide identification. Here, we describe an algorithm to preprocess data-independent MS/MS spectra by filtering out noise peaks and clustering the spectra according to both the chromatographic elution profiles and the spectral similarity. In addition, we developed an approach to estimate the m/z value of precursor ions from clustered MS/MS spectra in order to improve database search performance. Data acquired using a small 3 m/z units precursor mass window and multiple injections to cover a m/z range of 400-1400 was processed with our algorithm. It showed an improvement in the number of both peptide and protein identifications by 8 % while reducing the number of submitted spectra by 18 % and the number of peaks by 55 %. We conclude that our clustering method is a valid approach for data analysis of these data-independent fragmentation spectra. The software including the source code is available for the scientific community.

Simulating the Galaxy Cluster “El Gordo”: Gas Motion, Kinetic Sunyaev–Zel’dovich Signal, and X-Ray Line Features

NASA Astrophysics Data System (ADS)

Zhang, Congyao; Yu, Qingjuan; Lu, Youjun

2018-03-01

The massive galaxy cluster “El Gordo” (ACT-CL J0102–4915) is a rare merging system with a high collision speed suggested by multi-wavelength observations and theoretical modeling. Zhang et al. propose two types of mergers, a nearly head-on merger and an off-axis merger with a large impact parameter, to reproduce most of the observational features of the cluster using numerical simulations. The different merger configurations of the two models result in different gas motion in the simulated clusters. In this paper, we predict the kinetic Sunyaev–Zel’dovich (kSZ) effect, the relativistic correction of the thermal Sunyaev–Zel’dovich (tSZ) effect, and the X-ray spectrum of this cluster, based on the two proposed models. We find that (1) the amplitudes of the kSZ effect resulting from the two models are both on the order of ΔT/T ∼ 10‑5 but their morphologies are different, which trace the different line-of-sight velocity distributions of the systems; (2) the relativistic correction of the tSZ effect around 240 GHz can be possibly used to constrain the temperature of the hot electrons heated by the shocks; and (3) the shift between the X-ray spectral lines emitted from different regions of the cluster can be significantly different in the two models. The shift and the line broadening can be up to ∼25 eV and 50 eV, respectively. We expect that future observations of the kSZ effect and the X-ray spectral lines (e.g., by ALMA, XARM) will provide a strong constraint on the gas motion and the merger configuration of ACT-CL J0102–4915.

Multistep Ionization of Argon Clusters in Intense Femtosecond Extreme Ultraviolet Pulses

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bostedt, C.; Thomas, H.; Hoener, M.

The interaction of intense extreme ultraviolet femtosecond laser pulses ({lambda}=32.8 nm) from the FLASH free electron laser (FEL) with clusters has been investigated by means of photoelectron spectroscopy and modeled by Monte Carlo simulations. For laser intensities up to 5x10{sup 13} W/cm{sup 2}, we find that the cluster ionization process is a sequence of direct electron emission events in a developing Coulomb field. A nanoplasma is formed only at the highest investigated power densities where ionization is frustrated due to the deep cluster potential. In contrast with earlier studies in the IR and vacuum ultraviolet spectral regime, we find nomore » evidence for electron emission from plasma heating processes.« less

HEAO-A2 observations of the X-ray spectra of the Centaurus and A1060 clusters of galaxies

NASA Technical Reports Server (NTRS)

Mitchell, R.; Mushotzky, R.

1979-01-01

The X-ray spectral observations of two low luminosity clusters of galaxies, Centaurus and A1060, are presented. An emission feature of the Centaurus cluster at 7.9 keV is detected at about one third of the strength of the 6.7 keV line. This higher energy line represents K sub beta emission from highly ionized iron. An isothermal model with an Fe emission line is discussed and it is shown that the model cannot fit the data of the Centaurus or the A1060 clusters. The implications of the two component nature of the continuum on the Fe abundance and the X-ray surface brightness distribution are discussed.

The mystery of the "Kite" radio source in Abell 2626: Insights from new Chandra observations

NASA Astrophysics Data System (ADS)

Ignesti, A.; Gitti, M.; Brunetti, G.; O'Sullivan, E.; Sarazin, C.; Wong, K.

2018-03-01

Context. We present the results of a new Chandra study of the galaxy cluster Abell 2626. The radio emission of the cluster shows a complex system of four symmetric arcs without known correlations with the thermal X-ray emission. The mirror symmetry of the radio arcs toward the center and the presence of two optical cores in the central galaxy suggested that they may be created by pairs of precessing radio jets powered by dual active galactic nuclei (AGNs) inside the core dominant galaxy. However, previous observations failed to observe the second jetted AGN and the spectral trend due to radiative age along the radio arcs, thus challenging this interpretation. Aim. The new Chandra observation had several scientific objectives, including the search for the second AGN that would support the jet precession model. We focus here on the detailed study of the local properties of the thermal and non-thermal emission in the proximity of the radio arcs, in order to obtain further insights into their origin. Methods: We performed a standard data reduction of the Chandra dataset deriving the radial profiles of temperature, density, pressure and cooling time of the intra-cluster medium. We further analyzed the two-dimensional (2D) distribution of the gas temperature, discovering that the south-western junction of the radio arcs surrounds the cool core of the cluster. Results: We studied the X-ray surface brightness and spectral profiles across the junction, finding a cold front spatially coincident with the radio arcs. This may suggest a connection between the sloshing of the thermal gas and the nature of the radio filaments, raising new scenarios for their origin. A tantalizing possibility is that the radio arcs trace the projection of a complex surface connecting the sites where electrons are most efficiently reaccelerated by the turbulence that is generated by the gas sloshing. In this case, diffuse emission embedded by the arcs and with extremely steep spectrum should be most visible at very low radio frequencies.

Using Cluster Analysis and ICP-MS to Identify Groups of Ecstasy Tablets in Sao Paulo State, Brazil.

PubMed

Maione, Camila; de Oliveira Souza, Vanessa Cristina; Togni, Loraine Rezende; da Costa, José Luiz; Campiglia, Andres Dobal; Barbosa, Fernando; Barbosa, Rommel Melgaço

2017-11-01

The variations found in the elemental composition in ecstasy samples result in spectral profiles with useful information for data analysis, and cluster analysis of these profiles can help uncover different categories of the drug. We provide a cluster analysis of ecstasy tablets based on their elemental composition. Twenty-five elements were determined by ICP-MS in tablets apprehended by Sao Paulo's State Police, Brazil. We employ the K-means clustering algorithm along with C4.5 decision tree to help us interpret the clustering results. We found a better number of two clusters within the data, which can refer to the approximated number of sources of the drug which supply the cities of seizures. The C4.5 model was capable of differentiating the ecstasy samples from the two clusters with high prediction accuracy using the leave-one-out cross-validation. The model used only Nd, Ni, and Pb concentration values in the classification of the samples. © 2017 American Academy of Forensic Sciences.

OpenCluster: A Flexible Distributed Computing Framework for Astronomical Data Processing

NASA Astrophysics Data System (ADS)

Wei, Shoulin; Wang, Feng; Deng, Hui; Liu, Cuiyin; Dai, Wei; Liang, Bo; Mei, Ying; Shi, Congming; Liu, Yingbo; Wu, Jingping

2017-02-01

The volume of data generated by modern astronomical telescopes is extremely large and rapidly growing. However, current high-performance data processing architectures/frameworks are not well suited for astronomers because of their limitations and programming difficulties. In this paper, we therefore present OpenCluster, an open-source distributed computing framework to support rapidly developing high-performance processing pipelines of astronomical big data. We first detail the OpenCluster design principles and implementations and present the APIs facilitated by the framework. We then demonstrate a case in which OpenCluster is used to resolve complex data processing problems for developing a pipeline for the Mingantu Ultrawide Spectral Radioheliograph. Finally, we present our OpenCluster performance evaluation. Overall, OpenCluster provides not only high fault tolerance and simple programming interfaces, but also a flexible means of scaling up the number of interacting entities. OpenCluster thereby provides an easily integrated distributed computing framework for quickly developing a high-performance data processing system of astronomical telescopes and for significantly reducing software development expenses.

Long-term survey of lion-roar emissions inside the terrestrial magnetosheath obtained from the STAFF-SA measurements onboard the Cluster spacecraft

NASA Astrophysics Data System (ADS)

Pisa, D.; Krupar, V.; Kruparova, O.; Santolik, O.

2017-12-01

Intense whistler-mode emissions known as 'lion-roars' are often observed inside the terrestrial magnetosheath, where the solar wind plasma flow slows down, and the local magnetic field increases ahead of a planetary magnetosphere. Plasma conditions in this transient region lead to the electron temperature anisotropy, which can result in the whistler-mode waves. The lion-roars are narrow-band emissions with typical frequencies between 0.1-0.5 Fce, where Fce is the electron cyclotron frequency. We present results of a long-term survey obtained by the Spatio Temporal Analysis Field Fluctuations - Spectral Analyzer (STAFF-SA) instruments on board the four Cluster spacecraft between 2001 and 2010. We have visually identified the time-frequency intervals with the intense lion-roar signature. Using the Singular Value Decomposition (SVD) method, we analyzed the wave propagation properties. We show the spatial, frequency and wave power distributions. Finally, the wave properties as a function of upstream solar wind conditions are discussed.

Community detection in complex networks using deep auto-encoded extreme learning machine

NASA Astrophysics Data System (ADS)

Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

2018-06-01

Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.

Atomically precise arrays of fluorescent silver clusters: a modular approach for metal cluster photonics on DNA nanostructures.

PubMed

Copp, Stacy M; Schultz, Danielle E; Swasey, Steven; Gwinn, Elisabeth G

2015-03-24

The remarkable precision that DNA scaffolds provide for arraying nanoscale optical elements enables optical phenomena that arise from interactions of metal nanoparticles, dye molecules, and quantum dots placed at nanoscale separations. However, control of ensemble optical properties has been limited by the difficulty of achieving uniform particle sizes and shapes. Ligand-stabilized metal clusters offer a route to atomically precise arrays that combine desirable attributes of both metals and molecules. Exploiting the unique advantages of the cluster regime requires techniques to realize controlled nanoscale placement of select cluster structures. Here we show that atomically monodisperse arrays of fluorescent, DNA-stabilized silver clusters can be realized on a prototypical scaffold, a DNA nanotube, with attachment sites separated by <10 nm. Cluster attachment is mediated by designed DNA linkers that enable isolation of specific clusters prior to assembly on nanotubes and preserve cluster structure and spectral purity after assembly. The modularity of this approach generalizes to silver clusters of diverse sizes and DNA scaffolds of many types. Thus, these silver cluster nano-optical elements, which themselves have colors selected by their particular DNA templating oligomer, bring unique dimensions of control and flexibility to the rapidly expanding field of nano-optics.

Analytic approximation for random muffin-tin alloys

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mills, R.; Gray, L.J.; Kaplan, T.

1983-03-15

The methods introduced in a previous paper under the name of ''traveling-cluster approximation'' (TCA) are applied, in a multiple-scattering approach, to the case of a random muffin-tin substitutional alloy. This permits the iterative part of a self-consistent calculation to be carried out entirely in terms of on-the-energy-shell scattering amplitudes. Off-shell components of the mean resolvent, needed for the calculation of spectral functions, are obtained by standard methods involving single-site scattering wave functions. The single-site TCA is just the usual coherent-potential approximation, expressed in a form particularly suited for iteration. A fixed-point theorem is proved for the general t-matrix TCA, ensuringmore » convergence upon iteration to a unique self-consistent solution with the physically essential Herglotz properties.« less