A dimension reduction strategy for improving the efficiency of computer-aided detection for CT colonography

NASA Astrophysics Data System (ADS)

Song, Bowen; Zhang, Guopeng; Wang, Huafeng; Zhu, Wei; Liang, Zhengrong

2013-02-01

Various types of features, e.g., geometric features, texture features, projection features etc., have been introduced for polyp detection and differentiation tasks via computer aided detection and diagnosis (CAD) for computed tomography colonography (CTC). Although these features together cover more information of the data, some of them are statistically highly-related to others, which made the feature set redundant and burdened the computation task of CAD. In this paper, we proposed a new dimension reduction method which combines hierarchical clustering and principal component analysis (PCA) for false positives (FPs) reduction task. First, we group all the features based on their similarity using hierarchical clustering, and then PCA is employed within each group. Different numbers of principal components are selected from each group to form the final feature set. Support vector machine is used to perform the classification. The results show that when three principal components were chosen from each group we can achieve an area under the curve of receiver operating characteristics of 0.905, which is as high as the original dataset. Meanwhile, the computation time is reduced by 70% and the feature set size is reduce by 77%. It can be concluded that the proposed method captures the most important information of the feature set and the classification accuracy is not affected after the dimension reduction. The result is promising and further investigation, such as automatically threshold setting, are worthwhile and are under progress.

Positive–Negative Asymmetry in the Evaluations of Political Candidates. The Role of Features of Similarity and Affect in Voter Behavior

PubMed Central

Falkowski, Andrzej; Jabłońska, Magdalena

2018-01-01

In this study we followed the extension of Tversky’s research about features of similarity with its application to open sets. Unlike the original closed-set model in which a feature was shifted between a common and a distinctive set, we investigated how addition of new features and deletion of existing features affected similarity judgments. The model was tested empirically in a political context and we analyzed how positive and negative changes in a candidate’s profile affect the similarity of the politician to his or her ideal and opposite counterpart. The results showed a positive–negative asymmetry in comparison judgments where enhancing negative features (distinctive for an ideal political candidate) had a greater effect on judgments than operations on positive (common) features. However, the effect was not observed for comparisons to a bad politician. Further analyses showed that in the case of a negative reference point, the relationship between similarity judgments and voting intention was mediated by the affective evaluation of the candidate. PMID:29535663

Family physicians' interests in special features of electronic publication

PubMed Central

Torre, Dario M.; Wright, Scott M.; Wilson, Renee F.; Diener-West, Marie; Bass, Eric B.

2003-01-01

Objective: Because many of the medical journals read by family physicians now have an electronic version, the authors conducted a survey to determine the interest of family physicians in specific features of electronic journal publications. Setting and Participants: We surveyed 175 family physicians randomly selected from the American Academy of Family Physicians. Results: The response rate was 63%. About half of family physicians reported good to excellent computer proficiency, and about one quarter used online journals sometimes or often. Many respondents reported high interest in having links to: an electronic medical text (48% for original articles, 56% for review articles), articles' list of references (52% for original articles, 56% for review articles), and health-related Websites (48% for original and review articles). Conclusion: Primary care–oriented journals should consider the interests of family physicians when developing and offering electronic features for their readers. PMID:12883561

Sorted Index Numbers for Privacy Preserving Face Recognition

NASA Astrophysics Data System (ADS)

Wang, Yongjin; Hatzinakos, Dimitrios

2009-12-01

This paper presents a novel approach for changeable and privacy preserving face recognition. We first introduce a new method of biometric matching using the sorted index numbers (SINs) of feature vectors. Since it is impossible to recover any of the exact values of the original features, the transformation from original features to the SIN vectors is noninvertible. To address the irrevocable nature of biometric signals whilst obtaining stronger privacy protection, a random projection-based method is employed in conjunction with the SIN approach to generate changeable and privacy preserving biometric templates. The effectiveness of the proposed method is demonstrated on a large generic data set, which contains images from several well-known face databases. Extensive experimentation shows that the proposed solution may improve the recognition accuracy.

Differential diagnosis of CT focal liver lesions using texture features, feature selection and ensemble driven classifiers.

PubMed

Mougiakakou, Stavroula G; Valavanis, Ioannis K; Nikita, Alexandra; Nikita, Konstantina S

2007-09-01

The aim of the present study is to define an optimally performing computer-aided diagnosis (CAD) architecture for the classification of liver tissue from non-enhanced computed tomography (CT) images into normal liver (C1), hepatic cyst (C2), hemangioma (C3), and hepatocellular carcinoma (C4). To this end, various CAD architectures, based on texture features and ensembles of classifiers (ECs), are comparatively assessed. Number of regions of interests (ROIs) corresponding to C1-C4 have been defined by experienced radiologists in non-enhanced liver CT images. For each ROI, five distinct sets of texture features were extracted using first order statistics, spatial gray level dependence matrix, gray level difference method, Laws' texture energy measures, and fractal dimension measurements. Two different ECs were constructed and compared. The first one consists of five multilayer perceptron neural networks (NNs), each using as input one of the computed texture feature sets or its reduced version after genetic algorithm-based feature selection. The second EC comprised five different primary classifiers, namely one multilayer perceptron NN, one probabilistic NN, and three k-nearest neighbor classifiers, each fed with the combination of the five texture feature sets or their reduced versions. The final decision of each EC was extracted by using appropriate voting schemes, while bootstrap re-sampling was utilized in order to estimate the generalization ability of the CAD architectures based on the available relatively small-sized data set. The best mean classification accuracy (84.96%) is achieved by the second EC using a fused feature set, and the weighted voting scheme. The fused feature set was obtained after appropriate feature selection applied to specific subsets of the original feature set. The comparative assessment of the various CAD architectures shows that combining three types of classifiers with a voting scheme, fed with identical feature sets obtained after appropriate feature selection and fusion, may result in an accurate system able to assist differential diagnosis of focal liver lesions from non-enhanced CT images.

Robust and efficient method for matching features in omnidirectional images

NASA Astrophysics Data System (ADS)

Zhu, Qinyi; Zhang, Zhijiang; Zeng, Dan

2018-04-01

Binary descriptors have been widely used in many real-time applications due to their efficiency. These descriptors are commonly designed for perspective images but perform poorly on omnidirectional images, which are severely distorted. To address this issue, this paper proposes tangent plane BRIEF (TPBRIEF) and adapted log polar grid-based motion statistics (ALPGMS). TPBRIEF projects keypoints to a unit sphere and applies the fixed test set in BRIEF descriptor on the tangent plane of the unit sphere. The fixed test set is then backprojected onto the original distorted images to construct the distortion invariant descriptor. TPBRIEF directly enables keypoint detecting and feature describing on original distorted images, whereas other approaches correct the distortion through image resampling, which introduces artifacts and adds time cost. With ALPGMS, omnidirectional images are divided into circular arches named adapted log polar grids. Whether a match is true or false is then determined by simply thresholding the match numbers in a grid pair where the two matched points located. Experiments show that TPBRIEF greatly improves the feature matching accuracy and ALPGMS robustly removes wrong matches. Our proposed method outperforms the state-of-the-art methods.

Computer-aided diagnosis of psoriasis skin images with HOS, texture and color features: A first comparative study of its kind.

PubMed

Shrivastava, Vimal K; Londhe, Narendra D; Sonawane, Rajendra S; Suri, Jasjit S

2016-04-01

Psoriasis is an autoimmune skin disease with red and scaly plaques on skin and affecting about 125 million people worldwide. Currently, dermatologist use visual and haptic methods for diagnosis the disease severity. This does not help them in stratification and risk assessment of the lesion stage and grade. Further, current methods add complexity during monitoring and follow-up phase. The current diagnostic tools lead to subjectivity in decision making and are unreliable and laborious. This paper presents a first comparative performance study of its kind using principal component analysis (PCA) based CADx system for psoriasis risk stratification and image classification utilizing: (i) 11 higher order spectra (HOS) features, (ii) 60 texture features, and (iii) 86 color feature sets and their seven combinations. Aggregate 540 image samples (270 healthy and 270 diseased) from 30 psoriasis patients of Indian ethnic origin are used in our database. Machine learning using PCA is used for dominant feature selection which is then fed to support vector machine classifier (SVM) to obtain optimized performance. Three different protocols are implemented using three kinds of feature sets. Reliability index of the CADx is computed. Among all feature combinations, the CADx system shows optimal performance of 100% accuracy, 100% sensitivity and specificity, when all three sets of feature are combined. Further, our experimental result with increasing data size shows that all feature combinations yield high reliability index throughout the PCA-cutoffs except color feature set and combination of color and texture feature sets. HOS features are powerful in psoriasis disease classification and stratification. Even though, independently, all three set of features HOS, texture, and color perform competitively, but when combined, the machine learning system performs the best. The system is fully automated, reliable and accurate. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Freely available compound data sets and software tools for chemoinformatics and computational medicinal chemistry applications

PubMed Central

Bajorath, Jurgen

2012-01-01

We have generated a number of  compound data sets and programs for different types of applications in pharmaceutical research. These data sets and programs were originally designed for our research projects and are made publicly available. Without consulting original literature sources, it is difficult to understand specific features of data sets and software tools, basic ideas underlying their design, and applicability domains. Currently, 30 different entries are available for download from our website. In this data article, we provide an overview of the data and tools we make available and designate the areas of research for which they should be useful. For selected data sets and methods/programs, detailed descriptions are given. This article should help interested readers to select data and tools for specific computational investigations. PMID:24358818

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

Ogden, K; O’Dwyer, R; Bradford, T

Purpose: To reduce differences in features calculated from MRI brain scans acquired at different field strengths with or without Gadolinium contrast. Methods: Brain scans were processed for 111 epilepsy patients to extract hippocampus and thalamus features. Scans were acquired on 1.5 T scanners with Gadolinium contrast (group A), 1.5T scanners without Gd (group B), and 3.0 T scanners without Gd (group C). A total of 72 features were extracted. Features were extracted from original scans and from scans where the image pixel values were rescaled to the mean of the hippocampi and thalami values. For each data set, cluster analysismore » was performed on the raw feature set and for feature sets with normalization (conversion to Z scores). Two methods of normalization were used: The first was over all values of a given feature, and the second by normalizing within the patient group membership. The clustering software was configured to produce 3 clusters. Group fractions in each cluster were calculated. Results: For features calculated from both the non-rescaled and rescaled data, cluster membership was identical for both the non-normalized and normalized data sets. Cluster 1 was comprised entirely of Group A data, Cluster 2 contained data from all three groups, and Cluster 3 contained data from only groups 1 and 2. For the categorically normalized data sets there was a more uniform distribution of group data in the three Clusters. A less pronounced effect was seen in the rescaled image data features. Conclusion: Image Rescaling and feature renormalization can have a significant effect on the results of clustering analysis. These effects are also likely to influence the results of supervised machine learning algorithms. It may be possible to partly remove the influence of scanner field strength and the presence of Gadolinium based contrast in feature extraction for radiomics applications.« less

Rotation, scale, and translation invariant pattern recognition using feature extraction

NASA Astrophysics Data System (ADS)

Prevost, Donald; Doucet, Michel; Bergeron, Alain; Veilleux, Luc; Chevrette, Paul C.; Gingras, Denis J.

1997-03-01

A rotation, scale and translation invariant pattern recognition technique is proposed.It is based on Fourier- Mellin Descriptors (FMD). Each FMD is taken as an independent feature of the object, and a set of those features forms a signature. FMDs are naturally rotation invariant. Translation invariance is achieved through pre- processing. A proper normalization of the FMDs gives the scale invariance property. This approach offers the double advantage of providing invariant signatures of the objects, and a dramatic reduction of the amount of data to process. The compressed invariant feature signature is next presented to a multi-layered perceptron neural network. This final step provides some robustness to the classification of the signatures, enabling good recognition behavior under anamorphically scaled distortion. We also present an original feature extraction technique, adapted to optical calculation of the FMDs. A prototype optical set-up was built, and experimental results are presented.

Developing a radiomics framework for classifying non-small cell lung carcinoma subtypes

NASA Astrophysics Data System (ADS)

Yu, Dongdong; Zang, Yali; Dong, Di; Zhou, Mu; Gevaert, Olivier; Fang, Mengjie; Shi, Jingyun; Tian, Jie

2017-03-01

Patient-targeted treatment of non-small cell lung carcinoma (NSCLC) has been well documented according to the histologic subtypes over the past decade. In parallel, recent development of quantitative image biomarkers has recently been highlighted as important diagnostic tools to facilitate histological subtype classification. In this study, we present a radiomics analysis that classifies the adenocarcinoma (ADC) and squamous cell carcinoma (SqCC). We extract 52-dimensional, CT-based features (7 statistical features and 45 image texture features) to represent each nodule. We evaluate our approach on a clinical dataset including 324 ADCs and 110 SqCCs patients with CT image scans. Classification of these features is performed with four different machine-learning classifiers including Support Vector Machines with Radial Basis Function kernel (RBF-SVM), Random forest (RF), K-nearest neighbor (KNN), and RUSBoost algorithms. To improve the classifiers' performance, optimal feature subset is selected from the original feature set by using an iterative forward inclusion and backward eliminating algorithm. Extensive experimental results demonstrate that radiomics features achieve encouraging classification results on both complete feature set (AUC=0.89) and optimal feature subset (AUC=0.91).

Discovery of microscopic evidence for shock metamorphism at the Serpent Mound structure, south-central Ohio: Confirmation of an origin by impact

USGS Publications Warehouse

Carlton, R.W.; Koeberl, C.; Baranoski, M.T.; SchuMacHer, G.A.

1998-01-01

The origin of the Serpent Mound structure in south-central Ohio has been disputed for many years. Clearly, more evidence was needed to resolve the confusion concerning the origin of the Serpent Mound feature either by endogenic processes or by hypervelocity impact. A petrographic study of 21 samples taken from a core 903 m long drilled in the central uplift of the structure provides evidence of shock metamorphism in the form of multiple sets of planar deformation features in quartz grains, as well as the presence of clasts of altered impact-melt rock. Crystallographic orientations of the planar deformation features show maxima at the shock-characteristic planes of {101??3} and {101??2} and additional maxima at {101??1}, {213??1}, and {516??1}. Geochemical analyses of impact breccias show minor enrichments in the abundances of the siderophile elements Cr, Co, Ni, and Ir, indicating the presence of a minor meteoritic component.

Two-dimensional wavelet analysis based classification of gas chromatogram differential mobility spectrometry signals.

PubMed

Zhao, Weixiang; Sankaran, Shankar; Ibáñez, Ana M; Dandekar, Abhaya M; Davis, Cristina E

2009-08-04

This study introduces two-dimensional (2-D) wavelet analysis to the classification of gas chromatogram differential mobility spectrometry (GC/DMS) data which are composed of retention time, compensation voltage, and corresponding intensities. One reported method to process such large data sets is to convert 2-D signals to 1-D signals by summing intensities either across retention time or compensation voltage, but it can lose important signal information in one data dimension. A 2-D wavelet analysis approach keeps the 2-D structure of original signals, while significantly reducing data size. We applied this feature extraction method to 2-D GC/DMS signals measured from control and disordered fruit and then employed two typical classification algorithms to testify the effects of the resultant features on chemical pattern recognition. Yielding a 93.3% accuracy of separating data from control and disordered fruit samples, 2-D wavelet analysis not only proves its feasibility to extract feature from original 2-D signals but also shows its superiority over the conventional feature extraction methods including converting 2-D to 1-D and selecting distinguishable pixels from training set. Furthermore, this process does not require coupling with specific pattern recognition methods, which may help ensure wide applications of this method to 2-D spectrometry data.

A fuzzy-based data transformation for feature extraction to increase classification performance with small medical data sets.

PubMed

Li, Der-Chiang; Liu, Chiao-Wen; Hu, Susan C

2011-05-01

Medical data sets are usually small and have very high dimensionality. Too many attributes will make the analysis less efficient and will not necessarily increase accuracy, while too few data will decrease the modeling stability. Consequently, the main objective of this study is to extract the optimal subset of features to increase analytical performance when the data set is small. This paper proposes a fuzzy-based non-linear transformation method to extend classification related information from the original data attribute values for a small data set. Based on the new transformed data set, this study applies principal component analysis (PCA) to extract the optimal subset of features. Finally, we use the transformed data with these optimal features as the input data for a learning tool, a support vector machine (SVM). Six medical data sets: Pima Indians' diabetes, Wisconsin diagnostic breast cancer, Parkinson disease, echocardiogram, BUPA liver disorders dataset, and bladder cancer cases in Taiwan, are employed to illustrate the approach presented in this paper. This research uses the t-test to evaluate the classification accuracy for a single data set; and uses the Friedman test to show the proposed method is better than other methods over the multiple data sets. The experiment results indicate that the proposed method has better classification performance than either PCA or kernel principal component analysis (KPCA) when the data set is small, and suggest creating new purpose-related information to improve the analysis performance. This paper has shown that feature extraction is important as a function of feature selection for efficient data analysis. When the data set is small, using the fuzzy-based transformation method presented in this work to increase the information available produces better results than the PCA and KPCA approaches. Copyright © 2011 Elsevier B.V. All rights reserved.

Subsurface failure in spherical bodies. A formation scenario for linear troughs on Vesta’s surface

DOE PAGES

Stickle, Angela M.; Schultz, P. H.; Crawford, D. A.

2014-10-13

Many asteroids in the Solar System exhibit unusual, linear features on their surface. The Dawn mission recently observed two sets of linear features on the surface of the asteroid 4 Vesta. Geologic observations indicate that these features are related to the two large impact basins at the south pole of Vesta, though no specific mechanism of origin has been determined. Furthermore, the orientation of the features is offset from the center of the basins. Experimental and numerical results reveal that the offset angle is a natural consequence of oblique impacts into a spherical target. We demonstrate that a set ofmore » shear planes develops in the subsurface of the body opposite to the point of first contact. Moreover, these subsurface failure zones then propagate to the surface under combined tensile-shear stress fields after the impact to create sets of approximately linear faults on the surface. Comparison between the orientation of damage structures in the laboratory and failure regions within Vesta can be used to constrain impact parameters (e.g., the approximate impact point and likely impact trajectory).« less

TU-G-204-05: The Effects of CT Acquisition and Reconstruction Conditions On Computed Texture Feature Values of Lung Lesions

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

Lo, P; Young, S; Kim, G

2015-06-15

Purpose: Texture features have been investigated as a biomarker of response and malignancy. Because these features reflect local differences in density, they may be influenced by acquisition and reconstruction parameters. The purpose of this study was to investigate the effects of radiation dose level and reconstruction method on features derived from lung lesions. Methods: With IRB approval, 33 lung tumor cases were identified from clinically indicated thoracic CT scans in which the raw projection (sinogram) data were available. Based on a previously-published technique, noise was added to the raw data to simulate reduced-dose versions of each case at 25%, 10%more » and 3% of the original dose. Original and simulated reduced dose projection data were reconstructed with conventional and two iterative-reconstruction settings, yielding 12 combinations of dose/recon conditions. One lesion from each case was contoured. At the reference condition (full dose, conventional recon), 17 lesions were randomly selected for repeat contouring (repeatability). For each lesion at each dose/recon condition, 151 texture measures were calculated. A paired differences approach was employed to compare feature variation from repeat contours at the reference condition to the variation observed in other dose/recon conditions (reproducibility). The ratio of standard deviation of the reproducibility to repeatability was used as the variation measure for each feature. Results: The mean variation (standard deviation) across dose levels and kernel was significantly different with a ratio of 2.24 (±5.85) across texture features (p=0.01). The mean variation (standard deviation) across dose levels with conventional recon was also significantly different with 2.30 (7.11) (p=0.025). The mean variation across reconstruction settings of original dose has a trend in showing difference with 1.35 (2.60) among all features (p=0.09). Conclusion: Texture features varied considerably with variations in dose and reconstruction condition. Care should be taken to standardize these conditions when using texture as a quantitative feature. This effort supported in part by a grant from the National Cancer Institute’s Quantitative Imaging Network (QIN): U01 CA181156; The UCLA Department of Radiology has a Master Research Agreement with Siemens Healthcare; Dr. McNitt-Gray has previously received research support from Siemens Healthcare.« less

Extracting topographic structure from digital elevation data for geographic information-system analysis

USGS Publications Warehouse

Jenson, Susan K.; Domingue, Julia O.

1988-01-01

The first phase of analysis is a conditioning phase that generates three data sets: the original OEM with depressions filled, a data set indicating the flow direction for each cell, and a flow accumulation data set in which each cell receives a value equal to the total number of cells that drain to it. The original OEM and these three derivative data sets can then be processed in a variety of ways to optionally delineate drainage networks, overland paths, watersheds for userspecified locations, sub-watersheds for the major tributaries of a drainage network, or pour point linkages between watersheds. The computer-generated drainage lines and watershed polygons and the pour point linkage information can be transferred to vector-based geographic information systems for futher analysis. Comparisons between these computergenerated features and their manually delineated counterparts generally show close agreement, indicating that these software tools will save analyst time spent in manual interpretation and digitizing.

Use of fuzzy sets in modeling of GIS objects

NASA Astrophysics Data System (ADS)

Mironova, Yu N.

2018-05-01

The paper discusses modeling and methods of data visualization in geographic information systems. Information processing in Geoinformatics is based on the use of models. Therefore, geoinformation modeling is a key in the chain of GEODATA processing. When solving problems, using geographic information systems often requires submission of the approximate or insufficient reliable information about the map features in the GIS database. Heterogeneous data of different origin and accuracy have some degree of uncertainty. In addition, not all information is accurate: already during the initial measurements, poorly defined terms and attributes (e.g., "soil, well-drained") are used. Therefore, there are necessary methods for working with uncertain requirements, classes, boundaries. The author proposes using spatial information fuzzy sets. In terms of a characteristic function, a fuzzy set is a natural generalization of ordinary sets, when one rejects the binary nature of this feature and assumes that it can take any value in the interval.

Wrappers for Performance Enhancement and Oblivious Decision Graphs

DTIC Science & Technology

1995-09-01

always select all relevant features. We test di erent search engines to search the space of feature subsets and introduce compound operators to speed...distinct instances from the original dataset appearing in the test set is thus 0:632m. The 0i accuracy estimate is derived by using bootstrap sample...i for training and the rest of the instances for testing . Given a number b, the number of bootstrap samples, let 0i be the accuracy estimate for

Improved classification accuracy by feature extraction using genetic algorithms

NASA Astrophysics Data System (ADS)

Patriarche, Julia; Manduca, Armando; Erickson, Bradley J.

2003-05-01

A feature extraction algorithm has been developed for the purposes of improving classification accuracy. The algorithm uses a genetic algorithm / hill-climber hybrid to generate a set of linearly recombined features, which may be of reduced dimensionality compared with the original set. The genetic algorithm performs the global exploration, and a hill climber explores local neighborhoods. Hybridizing the genetic algorithm with a hill climber improves both the rate of convergence, and the final overall cost function value; it also reduces the sensitivity of the genetic algorithm to parameter selection. The genetic algorithm includes the operators: crossover, mutation, and deletion / reactivation - the last of these effects dimensionality reduction. The feature extractor is supervised, and is capable of deriving a separate feature space for each tissue (which are reintegrated during classification). A non-anatomical digital phantom was developed as a gold standard for testing purposes. In tests with the phantom, and with images of multiple sclerosis patients, classification with feature extractor derived features yielded lower error rates than using standard pulse sequences, and with features derived using principal components analysis. Using the multiple sclerosis patient data, the algorithm resulted in a mean 31% reduction in classification error of pure tissues.

Observational learning from a radical-behavioristic viewpoint

PubMed Central

Deguchi, Hikaru

1984-01-01

Bandura (1972, 1977b) has argued that observational learning has some distinctive features that set it apart from the operant paradigm: (1) acquisition simply through observation, (2) delayed performance through cognitive mediation, and (3) vicarious reinforcement. The present paper first redefines those three features at the descriptive level, and then adopts a radical-behavioristic viewpoint to show how those redefined distinctive features can be explained and tested experimentally. Finally, the origin of observational learning is discussed in terms of recent data of neonatal imitation. The present analysis offers a consistent theoretical and practical understanding of observational learning from a radical-behavioristic viewpoint. PMID:22478602

Forest School: Reclaiming It from Scandinavia

ERIC Educational Resources Information Center

Shields, Polly

2010-01-01

"Forest schools" are an increasingly well-known feature of the educational landscape, having been adopted by many local authorities across the United Kingdom in an effort to build children's confidence and self-esteem through learning outdoors in a woodland setting. Their origins are usually described as deriving from a Scandinavian…

Human Factors Research Task 2006-8722;111: AIMSsim Feature Development II

DTIC Science & Technology

2008-01-01

originally scheduled to end in May 2007. The SOW was amended in May to set the target end date to August 31st, 2007, without any change to the budget...work task identified in the SOW is described below. More details can be found in the User and System manuals. 1. Multiple USB joysticks: This consisted...machine for changing the course of the experiment or saving responses/data. 9. SGE cleanup: This was originally included in the SOW to allow for

Identification of important image features for pork and turkey ham classification using colour and wavelet texture features and genetic selection.

PubMed

Jackman, Patrick; Sun, Da-Wen; Allen, Paul; Valous, Nektarios A; Mendoza, Fernando; Ward, Paddy

2010-04-01

A method to discriminate between various grades of pork and turkey ham was developed using colour and wavelet texture features. Image analysis methods originally developed for predicting the palatability of beef were applied to rapidly identify the ham grade. With high quality digital images of 50-94 slices per ham it was possible to identify the greyscale that best expressed the differences between the various ham grades. The best 10 discriminating image features were then found with a genetic algorithm. Using the best 10 image features, simple linear discriminant analysis models produced 100% correct classifications for both pork and turkey on both calibration and validation sets. 2009 Elsevier Ltd. All rights reserved.

Feature generation using genetic programming with application to fault classification.

PubMed

Guo, Hong; Jack, Lindsay B; Nandi, Asoke K

2005-02-01

One of the major challenges in pattern recognition problems is the feature extraction process which derives new features from existing features, or directly from raw data in order to reduce the cost of computation during the classification process, while improving classifier efficiency. Most current feature extraction techniques transform the original pattern vector into a new vector with increased discrimination capability but lower dimensionality. This is conducted within a predefined feature space, and thus, has limited searching power. Genetic programming (GP) can generate new features from the original dataset without prior knowledge of the probabilistic distribution. In this paper, a GP-based approach is developed for feature extraction from raw vibration data recorded from a rotating machine with six different conditions. The created features are then used as the inputs to a neural classifier for the identification of six bearing conditions. Experimental results demonstrate the ability of GP to discover autimatically the different bearing conditions using features expressed in the form of nonlinear functions. Furthermore, four sets of results--using GP extracted features with artificial neural networks (ANN) and support vector machines (SVM), as well as traditional features with ANN and SVM--have been obtained. This GP-based approach is used for bearing fault classification for the first time and exhibits superior searching power over other techniques. Additionaly, it significantly reduces the time for computation compared with genetic algorithm (GA), therefore, makes a more practical realization of the solution.

Dimensionality reduction for the quantitative evaluation of a smartphone-based Timed Up and Go test.

PubMed

Palmerini, Luca; Mellone, Sabato; Rocchi, Laura; Chiari, Lorenzo

2011-01-01

The Timed Up and Go is a clinical test to assess mobility in the elderly and in Parkinson's disease. Lately instrumented versions of the test are being considered, where inertial sensors assess motion. To improve the pervasiveness, ease of use, and cost, we consider a smartphone's accelerometer as the measurement system. Several parameters (usually highly correlated) can be computed from the signals recorded during the test. To avoid redundancy and obtain the features that are most sensitive to the locomotor performance, a dimensionality reduction was performed through principal component analysis (PCA). Forty-nine healthy subjects of different ages were tested. PCA was performed to extract new features (principal components) which are not redundant combinations of the original parameters and account for most of the data variability. They can be useful for exploratory analysis and outlier detection. Then, a reduced set of the original parameters was selected through correlation analysis with the principal components. This set could be recommended for studies based on healthy adults. The proposed procedure could be used as a first-level feature selection in classification studies (i.e. healthy-Parkinson's disease, fallers-non fallers) and could allow, in the future, a complete system for movement analysis to be incorporated in a smartphone.

Learning Semantic Tags from Big Data for Clinical Text Representation.

PubMed

Li, Yanpeng; Liu, Hongfang

2015-01-01

In clinical text mining, it is one of the biggest challenges to represent medical terminologies and n-gram terms in sparse medical reports using either supervised or unsupervised methods. Addressing this issue, we propose a novel method for word and n-gram representation at semantic level. We first represent each word by its distance with a set of reference features calculated by reference distance estimator (RDE) learned from labeled and unlabeled data, and then generate new features using simple techniques of discretization, random sampling and merging. The new features are a set of binary rules that can be interpreted as semantic tags derived from word and n-grams. We show that the new features significantly outperform classical bag-of-words and n-grams in the task of heart disease risk factor extraction in i2b2 2014 challenge. It is promising to see that semantics tags can be used to replace the original text entirely with even better prediction performance as well as derive new rules beyond lexical level.

Unsupervised texture image segmentation by improved neural network ART2

NASA Technical Reports Server (NTRS)

Wang, Zhiling; Labini, G. Sylos; Mugnuolo, R.; Desario, Marco

1994-01-01

We here propose a segmentation algorithm of texture image for a computer vision system on a space robot. An improved adaptive resonance theory (ART2) for analog input patterns is adapted to classify the image based on a set of texture image features extracted by a fast spatial gray level dependence method (SGLDM). The nonlinear thresholding functions in input layer of the neural network have been constructed by two parts: firstly, to reduce the effects of image noises on the features, a set of sigmoid functions is chosen depending on the types of the feature; secondly, to enhance the contrast of the features, we adopt fuzzy mapping functions. The cluster number in output layer can be increased by an autogrowing mechanism constantly when a new pattern happens. Experimental results and original or segmented pictures are shown, including the comparison between this approach and K-means algorithm. The system written in C language is performed on a SUN-4/330 sparc-station with an image board IT-150 and a CCD camera.

A framework for semisupervised feature generation and its applications in biomedical literature mining.

PubMed

Li, Yanpeng; Hu, Xiaohua; Lin, Hongfei; Yang, Zhihao

2011-01-01

Feature representation is essential to machine learning and text mining. In this paper, we present a feature coupling generalization (FCG) framework for generating new features from unlabeled data. It selects two special types of features, i.e., example-distinguishing features (EDFs) and class-distinguishing features (CDFs) from original feature set, and then generalizes EDFs into higher-level features based on their coupling degrees with CDFs in unlabeled data. The advantage is: EDFs with extreme sparsity in labeled data can be enriched by their co-occurrences with CDFs in unlabeled data so that the performance of these low-frequency features can be greatly boosted and new information from unlabeled can be incorporated. We apply this approach to three tasks in biomedical literature mining: gene named entity recognition (NER), protein-protein interaction extraction (PPIE), and text classification (TC) for gene ontology (GO) annotation. New features are generated from over 20 GB unlabeled PubMed abstracts. The experimental results on BioCreative 2, AIMED corpus, and TREC 2005 Genomics Track show that 1) FCG can utilize well the sparse features ignored by supervised learning. 2) It improves the performance of supervised baselines by 7.8 percent, 5.0 percent, and 5.8 percent, respectively, in the tree tasks. 3) Our methods achieve 89.1, 64.5 F-score, and 60.1 normalized utility on the three benchmark data sets.

Engaging Children through the Use of Cartoons and Comics

ERIC Educational Resources Information Center

Weitkamp, Emma; Featherstone, Helen

2010-01-01

ScienceComics project originally involved a set of theatre performances that sought to highlight the importance of materials by exploring what happens when one uses the "wrong" material. As part of this early work, two plays were created that featured a young alien girl, called Selenia, who could change materials. In this article, the…

Adult Education in India & Abroad.

ERIC Educational Resources Information Center

Roy, Nikhil Ranjan

A survey is made of various aspects of adult education in India since 1947, together with comparative accounts of the origin, development, and notable features of adult education in Denmark, Great Britain, the Soviet Union, and the United States. Needs and objectives in India, largely in the eradication of illiteracy, are set forth, and pertinent…

The Sanctuary Model of Trauma-Informed Organizational Change

ERIC Educational Resources Information Center

Bloom, Sandra L.; Sreedhar, Sarah Yanosy

2008-01-01

This article features the Sanctuary Model[R], a trauma-informed method for creating or changing an organizational culture. Although the model is based on trauma theory, its tenets have application in working with children and adults across a wide diagnostic spectrum. Originally developed in a short-term, acute inpatient psychiatric setting for…

Unit: Where Humans Came From, Inspection Pack, First Trial Print.

ERIC Educational Resources Information Center

Australian Science Education Project, Toorak, Victoria.

"Where Humans Came From" is a set of materials designed for use by students (aged 15-16) to assist them in investigating the problem posed in the title. The student book briefly outlines the essential features of four explanations of human origin: special creation (Judeo-Christian, Greek, Australian Aboriginal, American Indian accounts);…

Origins of DNA Replication and Amplification in the Breast Cancer Genome

DTIC Science & Technology

2013-09-01

three peaks from the uncorrected data set remain in the corrected set. As shown in Figure 6, the peak on the left disappears as it was caused by Lexo...distribution (p. 14) Shuffled Inter‐peak distance distribution (p. 15) Peak length distribution (p. 16) Number of features per chromosome (p. 17) Correlation...with G4 ( Chromosomes 1, 3, 6, 7, 11, 19 and genome‐wide)(pp. 18-24 Proximity distribution of peaks near Delino ORC sites (p. 25) GC content of

A regularized approach for geodesic-based semisupervised multimanifold learning.

PubMed

Fan, Mingyu; Zhang, Xiaoqin; Lin, Zhouchen; Zhang, Zhongfei; Bao, Hujun

2014-05-01

Geodesic distance, as an essential measurement for data dissimilarity, has been successfully used in manifold learning. However, most geodesic distance-based manifold learning algorithms have two limitations when applied to classification: 1) class information is rarely used in computing the geodesic distances between data points on manifolds and 2) little attention has been paid to building an explicit dimension reduction mapping for extracting the discriminative information hidden in the geodesic distances. In this paper, we regard geodesic distance as a kind of kernel, which maps data from linearly inseparable space to linear separable distance space. In doing this, a new semisupervised manifold learning algorithm, namely regularized geodesic feature learning algorithm, is proposed. The method consists of three techniques: a semisupervised graph construction method, replacement of original data points with feature vectors which are built by geodesic distances, and a new semisupervised dimension reduction method for feature vectors. Experiments on the MNIST, USPS handwritten digit data sets, MIT CBCL face versus nonface data set, and an intelligent traffic data set show the effectiveness of the proposed algorithm.

Sparse Contextual Activation for Efficient Visual Re-Ranking.

PubMed

Bai, Song; Bai, Xiang

2016-03-01

In this paper, we propose an extremely efficient algorithm for visual re-ranking. By considering the original pairwise distance in the contextual space, we develop a feature vector called sparse contextual activation (SCA) that encodes the local distribution of an image. Hence, re-ranking task can be simply accomplished by vector comparison under the generalized Jaccard metric, which has its theoretical meaning in the fuzzy set theory. In order to improve the time efficiency of re-ranking procedure, inverted index is successfully introduced to speed up the computation of generalized Jaccard metric. As a result, the average time cost of re-ranking for a certain query can be controlled within 1 ms. Furthermore, inspired by query expansion, we also develop an additional method called local consistency enhancement on the proposed SCA to improve the retrieval performance in an unsupervised manner. On the other hand, the retrieval performance using a single feature may not be satisfactory enough, which inspires us to fuse multiple complementary features for accurate retrieval. Based on SCA, a robust feature fusion algorithm is exploited that also preserves the characteristic of high time efficiency. We assess our proposed method in various visual re-ranking tasks. Experimental results on Princeton shape benchmark (3D object), WM-SRHEC07 (3D competition), YAEL data set B (face), MPEG-7 data set (shape), and Ukbench data set (image) manifest the effectiveness and efficiency of SCA.

CURE-SMOTE algorithm and hybrid algorithm for feature selection and parameter optimization based on random forests.

PubMed

Ma, Li; Fan, Suohai

2017-03-14

The random forests algorithm is a type of classifier with prominent universality, a wide application range, and robustness for avoiding overfitting. But there are still some drawbacks to random forests. Therefore, to improve the performance of random forests, this paper seeks to improve imbalanced data processing, feature selection and parameter optimization. We propose the CURE-SMOTE algorithm for the imbalanced data classification problem. Experiments on imbalanced UCI data reveal that the combination of Clustering Using Representatives (CURE) enhances the original synthetic minority oversampling technique (SMOTE) algorithms effectively compared with the classification results on the original data using random sampling, Borderline-SMOTE1, safe-level SMOTE, C-SMOTE, and k-means-SMOTE. Additionally, the hybrid RF (random forests) algorithm has been proposed for feature selection and parameter optimization, which uses the minimum out of bag (OOB) data error as its objective function. Simulation results on binary and higher-dimensional data indicate that the proposed hybrid RF algorithms, hybrid genetic-random forests algorithm, hybrid particle swarm-random forests algorithm and hybrid fish swarm-random forests algorithm can achieve the minimum OOB error and show the best generalization ability. The training set produced from the proposed CURE-SMOTE algorithm is closer to the original data distribution because it contains minimal noise. Thus, better classification results are produced from this feasible and effective algorithm. Moreover, the hybrid algorithm's F-value, G-mean, AUC and OOB scores demonstrate that they surpass the performance of the original RF algorithm. Hence, this hybrid algorithm provides a new way to perform feature selection and parameter optimization.

Reading Researchers in Search of Common Ground: The Expert Study Revisited. 2nd Edition

ERIC Educational Resources Information Center

Flippo, Rona F., Ed.

2011-01-01

In "Reading Researchers in Search of Common Ground, Second Edition", Rona F. Flippo revisits her study, in which she set out to find common ground among experts in the much-fragmented field of reading research. The original edition, featuring contributions from participants in the study, commentary from additional distinguished literacy scholars…

ROC analysis of lesion descriptors in breast ultrasound images

NASA Astrophysics Data System (ADS)

Andre, Michael P.; Galperin, Michael; Phan, Peter; Chiu, Peter

2003-05-01

Breast biopsy serves as the key diagnostic tool in the evaluation of breast masses for malignancy, yet the procedure affects patients physically and emotionally and may obscure results of future mammograms. Studies show that high quality ultrasound can distinguish a benign from malignant lesions with accuracy, however, it has proven difficult to teach and clinical results are highly variable. The purpose of this study is to develop a means to optimize an automated Computer Aided Imaging System (CAIS) to assess Level of Suspicion (LOS) of a breast mass. We examine the contribution of 15 object features to lesion classification by calculating the Wilcoxon area under the ROC curve, AW, for all combinations in a set of 146 masses with known findings. For each interval A, the frequency of appearance of each feature and its combinations with others was computed as a means to find an "optimum" feature vector. The original set of 15 was reduced to 6 (area, perimeter, diameter ferret Y, relief, homogeneity, average energy) with an improvement from Aw=0.82-/+0.04 for the original 15 to Aw=0.93-/+0.02 for the subset of 6, p=0.03. For comparison, two sub-specialty mammography radiologists also scored the images for LOS resulting in Az of 0.90 and 0.87. The CAIS performed significantly higher, p=0.02.

Defining value through quantity and quality-Chimpanzees (Pan troglodytes) undervalue food quantities when items are broken.

PubMed

Parrish, Audrey E; Evans, Theodore A; Beran, Michael J

2015-02-01

Decision-making largely is influenced by the relative value of choice options, and the value of such options can be determined by a combination of different factors (e.g., the quantity, size, or quality of a stimulus). In this study, we examined the competing influences of quantity (i.e., the number of food items in a set) and quality (i.e., the original state of a food item) of choice items on chimpanzees' food preferences in a two-option natural choice paradigm. In Experiment 1, chimpanzees chose between sets of food items that were either entirely whole or included items that were broken into pieces before being shown to the chimpanzees. Chimpanzees exhibited a bias for whole food items even when such choice options consisted of a smaller overall quantity of food than the sets containing broken items. In Experiment 2, chimpanzees chose between sets of entirely whole food items and sets of initially whole items that were subsequently broken in view of the chimpanzees just before choice time. Chimpanzees continued to exhibit a bias for sets of whole items. In Experiment 3, chimpanzees chose between sets of new food items that were initially discrete but were subsequently transformed into a larger cohesive unit. Here, chimpanzees were biased to choose the discrete sets that retained their original qualitative state rather than toward the cohesive or clumped sets. These results demonstrate that beyond a food set's quantity (i.e., the value dimension that accounts for maximization in terms of caloric intake), other seemingly non-relevant features (i.e., quality in terms of a set's original state) affect how chimpanzees assign value to their choice options. Copyright © 2014 Elsevier B.V. All rights reserved.

Machine-assisted verification of latent fingerprints: first results for nondestructive contact-less optical acquisition techniques with a CWL sensor

NASA Astrophysics Data System (ADS)

Hildebrandt, Mario; Kiltz, Stefan; Krapyvskyy, Dmytro; Dittmann, Jana; Vielhauer, Claus; Leich, Marcus

2011-11-01

A machine-assisted analysis of traces from crime scenes might be possible with the advent of new high-resolution non-destructive contact-less acquisition techniques for latent fingerprints. This requires reliable techniques for the automatic extraction of fingerprint features from latent and exemplar fingerprints for matching purposes using pattern recognition approaches. Therefore, we evaluate the NIST Biometric Image Software for the feature extraction and verification of contact-lessly acquired latent fingerprints to determine potential error rates. Our exemplary test setup includes 30 latent fingerprints from 5 people in two test sets that are acquired from different surfaces using a chromatic white light sensor. The first test set includes 20 fingerprints on two different surfaces. It is used to determine the feature extraction performance. The second test set includes one latent fingerprint on 10 different surfaces and an exemplar fingerprint to determine the verification performance. This utilized sensing technique does not require a physical or chemical visibility enhancement of the fingerprint residue, thus the original trace remains unaltered for further investigations. No particular feature extraction and verification techniques have been applied to such data, yet. Hence, we see the need for appropriate algorithms that are suitable to support forensic investigations.

The McIntosh Archive: A solar feature database spanning four solar cycles

NASA Astrophysics Data System (ADS)

Gibson, S. E.; Malanushenko, A. V.; Hewins, I.; McFadden, R.; Emery, B.; Webb, D. F.; Denig, W. F.

2016-12-01

The McIntosh Archive consists of a set of hand-drawn solar Carrington maps created by Patrick McIntosh from 1964 to 2009. McIntosh used mainly H-alpha, He-1 10830 and photospheric magnetic measurements from both ground-based and NASA satellite observations. With these he traced coronal holes, polarity inversion lines, filaments, sunspots and plage, yielding a unique 45-year record of the features associated with the large-scale solar magnetic field. We will present the results of recent efforts to preserve and digitize this archive. Most of the original hand-drawn maps have been scanned, a method for processing these scans into digital, searchable format has been developed and streamlined, and an archival repository at NOAA's National Centers for Environmental Information (NCEI) has been created. We will demonstrate how Solar Cycle 23 data may now be accessed and how it may be utilized for scientific applications. In addition, we will discuss how this database of human-recognized features, which overlaps with the onset of high-resolution, continuous modern solar data, may act as a training set for computer feature recognition algorithms.

Curve Set Feature-Based Robust and Fast Pose Estimation Algorithm

PubMed Central

Hashimoto, Koichi

2017-01-01

Bin picking refers to picking the randomly-piled objects from a bin for industrial production purposes, and robotic bin picking is always used in automated assembly lines. In order to achieve a higher productivity, a fast and robust pose estimation algorithm is necessary to recognize and localize the randomly-piled parts. This paper proposes a pose estimation algorithm for bin picking tasks using point cloud data. A novel descriptor Curve Set Feature (CSF) is proposed to describe a point by the surface fluctuation around this point and is also capable of evaluating poses. The Rotation Match Feature (RMF) is proposed to match CSF efficiently. The matching process combines the idea of the matching in 2D space of origin Point Pair Feature (PPF) algorithm with nearest neighbor search. A voxel-based pose verification method is introduced to evaluate the poses and proved to be more than 30-times faster than the kd-tree-based verification method. Our algorithm is evaluated against a large number of synthetic and real scenes and proven to be robust to noise, able to detect metal parts, more accurately and more than 10-times faster than PPF and Oriented, Unique and Repeatable (OUR)-Clustered Viewpoint Feature Histogram (CVFH). PMID:28771216

Computed Tomography Image Origin Identification Based on Original Sensor Pattern Noise and 3-D Image Reconstruction Algorithm Footprints.

PubMed

Duan, Yuping; Bouslimi, Dalel; Yang, Guanyu; Shu, Huazhong; Coatrieux, Gouenou

2017-07-01

In this paper, we focus on the "blind" identification of the computed tomography (CT) scanner that has produced a CT image. To do so, we propose a set of noise features derived from the image chain acquisition and which can be used as CT-scanner footprint. Basically, we propose two approaches. The first one aims at identifying a CT scanner based on an original sensor pattern noise (OSPN) that is intrinsic to the X-ray detectors. The second one identifies an acquisition system based on the way this noise is modified by its three-dimensional (3-D) image reconstruction algorithm. As these reconstruction algorithms are manufacturer dependent and kept secret, our features are used as input to train a support vector machine (SVM) based classifier to discriminate acquisition systems. Experiments conducted on images issued from 15 different CT-scanner models of 4 distinct manufacturers demonstrate that our system identifies the origin of one CT image with a detection rate of at least 94% and that it achieves better performance than sensor pattern noise (SPN) based strategy proposed for general public camera devices.

Genetic Programming and Frequent Itemset Mining to Identify Feature Selection Patterns of iEEG and fMRI Epilepsy Data

PubMed Central

Smart, Otis; Burrell, Lauren

2014-01-01

Pattern classification for intracranial electroencephalogram (iEEG) and functional magnetic resonance imaging (fMRI) signals has furthered epilepsy research toward understanding the origin of epileptic seizures and localizing dysfunctional brain tissue for treatment. Prior research has demonstrated that implicitly selecting features with a genetic programming (GP) algorithm more effectively determined the proper features to discern biomarker and non-biomarker interictal iEEG and fMRI activity than conventional feature selection approaches. However for each the iEEG and fMRI modalities, it is still uncertain whether the stochastic properties of indirect feature selection with a GP yield (a) consistent results within a patient data set and (b) features that are specific or universal across multiple patient data sets. We examined the reproducibility of implicitly selecting features to classify interictal activity using a GP algorithm by performing several selection trials and subsequent frequent itemset mining (FIM) for separate iEEG and fMRI epilepsy patient data. We observed within-subject consistency and across-subject variability with some small similarity for selected features, indicating a clear need for patient-specific features and possible need for patient-specific feature selection or/and classification. For the fMRI, using nearest-neighbor classification and 30 GP generations, we obtained over 60% median sensitivity and over 60% median selectivity. For the iEEG, using nearest-neighbor classification and 30 GP generations, we obtained over 65% median sensitivity and over 65% median selectivity except one patient. PMID:25580059

Classification of Microarray Data Using Kernel Fuzzy Inference System

PubMed Central

Kumar Rath, Santanu

2014-01-01

The DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function ϕ through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function. PMID:27433543

What do you think of my picture? Investigating factors of influence in profile images context perception

NASA Astrophysics Data System (ADS)

Mazza, F.; Da Silva, M. P.; Le Callet, P.; Heynderickx, I. E. J.

2015-03-01

Multimedia quality assessment has been an important research topic during the last decades. The original focus on artifact visibility has been extended during the years to aspects as image aesthetics, interestingness and memorability. More recently, Fedorovskaya proposed the concept of 'image psychology': this concept focuses on additional quality dimensions related to human content processing. While these additional dimensions are very valuable in understanding preferences, it is very hard to define, isolate and measure their effect on quality. In this paper we continue our research on face pictures investigating which image factors influence context perception. We collected perceived fit of a set of images to various content categories. These categories were selected based on current typologies in social networks. Logistic regression was adopted to model category fit based on images features. In this model we used both low level and high level features, the latter focusing on complex features related to image content. In order to extract these high level features, we relied on crowdsourcing, since computer vision algorithms are not yet sufficiently accurate for the features we needed. Our results underline the importance of some high level content features, e.g. the dress of the portrayed person and scene setting, in categorizing image.

Reduced multiple empirical kernel learning machine.

PubMed

Wang, Zhe; Lu, MingZhe; Gao, Daqi

2015-02-01

Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3) this paper adopts the Gauss Elimination, one of the on-the-shelf techniques, to generate a basis of the original feature space, which is stable and efficient.

Visualization of Penile Suspensory Ligamentous System Based on Visible Human Data Sets

PubMed Central

Chen, Xianzhuo; Wu, Yi; Tao, Ling; Yan, Yan; Pang, Jun; Zhang, Shaoxiang; Li, Shirong

2017-01-01

Background The aim of this study was to use a three-dimensional (3D) visualization technology to illustrate and describe the anatomical features of the penile suspensory ligamentous system based on the Visible Human data sets and to explore the suspensory mechanism of the penis for the further improvement of the penis-lengthening surgery. Material/Methods Cross-sectional images retrieved from the first Chinese Visible Human (CVH-1), third Chinese Visible Human (CVH-3), and Visible Human Male (VHM) data sets were used to segment the suspensory ligamentous system and its adjacent structures. The magnetic resonance imaging (MRI) images of this system were studied and compared with those from the Visible Human data sets. The 3D models reconstructed from the Visible Human data sets were used to provide morphological features of the penile suspensory ligamentous system and its related structures. Results The fundiform ligament was a superficial, loose, fibro-fatty tissue which originated from Scarpa’s fascia superiorly and continued to the scrotal septum inferiorly. The suspensory ligament and arcuate pubic ligament were dense fibrous connective tissues which started from the pubic symphysis and terminated by attaching to the tunica albuginea of the corpora cavernosa. Furthermore, the arcuate pubic ligament attached to the inferior rami of the pubis laterally. Conclusions The 3D model based on Visible Human data sets can be used to clarify the anatomical features of the suspensory ligamentous system, thereby contributing to the improvement of penis-lengthening surgery. PMID:28530218

Visualization of Penile Suspensory Ligamentous System Based on Visible Human Data Sets.

PubMed

Chen, Xianzhuo; Wu, Yi; Tao, Ling; Yan, Yan; Pang, Jun; Zhang, Shaoxiang; Li, Shirong

2017-05-22

BACKGROUND The aim of this study was to use a three-dimensional (3D) visualization technology to illustrate and describe the anatomical features of the penile suspensory ligamentous system based on the Visible Human data sets and to explore the suspensory mechanism of the penis for the further improvement of the penis-lengthening surgery. MATERIAL AND METHODS Cross-sectional images retrieved from the first Chinese Visible Human (CVH-1), third Chinese Visible Human (CVH-3), and Visible Human Male (VHM) data sets were used to segment the suspensory ligamentous system and its adjacent structures. The magnetic resonance imaging (MRI) images of this system were studied and compared with those from the Visible Human data sets. The 3D models reconstructed from the Visible Human data sets were used to provide morphological features of the penile suspensory ligamentous system and its related structures. RESULTS The fundiform ligament was a superficial, loose, fibro-fatty tissue which originated from Scarpa's fascia superiorly and continued to the scrotal septum inferiorly. The suspensory ligament and arcuate pubic ligament were dense fibrous connective tissues which started from the pubic symphysis and terminated by attaching to the tunica albuginea of the corpora cavernosa. Furthermore, the arcuate pubic ligament attached to the inferior rami of the pubis laterally. CONCLUSIONS The 3D model based on Visible Human data sets can be used to clarify the anatomical features of the suspensory ligamentous system, thereby contributing to the improvement of penis-lengthening surgery.

Using qualitative comparative analysis in a systematic review of a complex intervention.

PubMed

Kahwati, Leila; Jacobs, Sara; Kane, Heather; Lewis, Megan; Viswanathan, Meera; Golin, Carol E

2016-05-04

Systematic reviews evaluating complex interventions often encounter substantial clinical heterogeneity in intervention components and implementation features making synthesis challenging. Qualitative comparative analysis (QCA) is a non-probabilistic method that uses mathematical set theory to study complex phenomena; it has been proposed as a potential method to complement traditional evidence synthesis in reviews of complex interventions to identify key intervention components or implementation features that might explain effectiveness or ineffectiveness. The objective of this study was to describe our approach in detail and examine the suitability of using QCA within the context of a systematic review. We used data from a completed systematic review of behavioral interventions to improve medication adherence to conduct two substantive analyses using QCA. The first analysis sought to identify combinations of nine behavior change techniques/components (BCTs) found among effective interventions, and the second analysis sought to identify combinations of five implementation features (e.g., agent, target, mode, time span, exposure) found among effective interventions. For each substantive analysis, we reframed the review's research questions to be designed for use with QCA, calibrated sets (i.e., transformed raw data into data used in analysis), and identified the necessary and/or sufficient combinations of BCTs and implementation features found in effective interventions. Our application of QCA for each substantive analysis is described in detail. We extended the original review findings by identifying seven combinations of BCTs and four combinations of implementation features that were sufficient for improving adherence. We found reasonable alignment between several systematic review steps and processes used in QCA except that typical approaches to study abstraction for some intervention components and features did not support a robust calibration for QCA. QCA was suitable for use within a systematic review of medication adherence interventions and offered insights beyond the single dimension stratifications used in the original completed review. Future prospective use of QCA during a review is needed to determine the optimal way to efficiently integrate QCA into existing approaches to evidence synthesis of complex interventions.

A simulation framework for auditory discrimination experiments: Revealing the importance of across-frequency processing in speech perception.

PubMed

Schädler, Marc René; Warzybok, Anna; Ewert, Stephan D; Kollmeier, Birger

2016-05-01

A framework for simulating auditory discrimination experiments, based on an approach from Schädler, Warzybok, Hochmuth, and Kollmeier [(2015). Int. J. Audiol. 54, 100-107] which was originally designed to predict speech recognition thresholds, is extended to also predict psychoacoustic thresholds. The proposed framework is used to assess the suitability of different auditory-inspired feature sets for a range of auditory discrimination experiments that included psychoacoustic as well as speech recognition experiments in noise. The considered experiments were 2 kHz tone-in-broadband-noise simultaneous masking depending on the tone length, spectral masking with simultaneously presented tone signals and narrow-band noise maskers, and German Matrix sentence test reception threshold in stationary and modulated noise. The employed feature sets included spectro-temporal Gabor filter bank features, Mel-frequency cepstral coefficients, logarithmically scaled Mel-spectrograms, and the internal representation of the Perception Model from Dau, Kollmeier, and Kohlrausch [(1997). J. Acoust. Soc. Am. 102(5), 2892-2905]. The proposed framework was successfully employed to simulate all experiments with a common parameter set and obtain objective thresholds with less assumptions compared to traditional modeling approaches. Depending on the feature set, the simulated reference-free thresholds were found to agree with-and hence to predict-empirical data from the literature. Across-frequency processing was found to be crucial to accurately model the lower speech reception threshold in modulated noise conditions than in stationary noise conditions.

National Hydrography Dataset (NHD)

USGS Publications Warehouse

,

2001-01-01

The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000 scale and exists at that scale for the whole country. High resolution NHD adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Like the 1:100,000-scale NHD, high resolution NHD contains reach codes for networked features and isolated lakes, flow direction, names, stream level, and centerline representations for areal water bodies. Reaches are also defined to represent waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria set out by the Federal Geographic Data Committee.

Upheaval Dome, Utah, USA: Impact origin confirmed

NASA Astrophysics Data System (ADS)

Buchner, Elmar; Kenkmann, Thomas

2008-03-01

Upheaval Dome is a unique circular structure on the ColoradoPlateau in SE Utah, the origin of which has been controversiallydiscussed for decades. It has been interpreted as a crypto volcanicfeature, a salt diapir, a pinched-off salt diapir, and an erodedimpact crater. While recent structural mapping, modeling, andanalyses of deformation mechanisms strongly support an impactorigin, ultimate proof, namely the documentation of unambiguousshock features, has yet to be successfully provided. In thisstudy, we document, for the first time, shocked quartz grainsfrom this crater in sandstones of the Jurassic Kayenta Formation.The investigated grains contain multiple sets of decorated planardeformation features. Transmission electron microscopy (TEM)reveals that the amorphous lamellae are annealed and exhibitdense tangles of dislocations as well as trails of fluid inclusions.The shocked quartz grains were found in the periphery of thecentral uplift in the northeastern sector of the crater, whichmost likely represents the cross range crater sector.

Using partially labeled data for normal mixture identification with application to class definition

NASA Technical Reports Server (NTRS)

Shahshahani, Behzad M.; Landgrebe, David A.

1992-01-01

The problem of estimating the parameters of a normal mixture density when, in addition to the unlabeled samples, sets of partially labeled samples are available is addressed. The density of the multidimensional feature space is modeled with a normal mixture. It is assumed that the set of components of the mixture can be partitioned into several classes and that training samples are available from each class. Since for any training sample the class of origin is known but the exact component of origin within the corresponding class is unknown, the training samples as considered to be partially labeled. The EM iterative equations are derived for estimating the parameters of the normal mixture in the presence of partially labeled samples. These equations can be used to combine the supervised and nonsupervised learning processes.

Use of MAGSAT anomaly data for crustal structure and mineral resources in the US midcontinent

NASA Technical Reports Server (NTRS)

Carmichael, R. S. (Principal Investigator)

1981-01-01

While the preliminary magnetic anomaly map for the centra midcontinent is only in the hand-drawn stage, it agrees in broad aspects with the preliminary global MAGSAT map provided by NASA. Because of data evaluation and finer scale averaging, there are more detailed features which hold promise for eventual geological/crustal interpretation. Some current analysis is directed at examining whether a map data feature such as an elongated anomaly or trend, which seems parallel to satellite data tracks, is likely of crustal origin or is an artifact of the data set.

T-ray relevant frequencies for osteosarcoma classification

NASA Astrophysics Data System (ADS)

Withayachumnankul, W.; Ferguson, B.; Rainsford, T.; Findlay, D.; Mickan, S. P.; Abbott, D.

2006-01-01

We investigate the classification of the T-ray response of normal human bone cells and human osteosarcoma cells, grown in culture. Given the magnitude and phase responses within a reliable spectral range as features for input vectors, a trained support vector machine can correctly classify the two cell types to some extent. Performance of the support vector machine is deteriorated by the curse of dimensionality, resulting from the comparatively large number of features in the input vectors. Feature subset selection methods are used to select only an optimal number of relevant features for inputs. As a result, an improvement in generalization performance is attainable, and the selected frequencies can be used for further describing different mechanisms of the cells, responding to T-rays. We demonstrate a consistent classification accuracy of 89.6%, while the only one fifth of the original features are retained in the data set.

Classification of Normal and Apoptotic Cells from Fluorescence Microscopy Images Using Generalized Polynomial Chaos and Level Set Function.

PubMed

Du, Yuncheng; Budman, Hector M; Duever, Thomas A

2016-06-01

Accurate automated quantitative analysis of living cells based on fluorescence microscopy images can be very useful for fast evaluation of experimental outcomes and cell culture protocols. In this work, an algorithm is developed for fast differentiation of normal and apoptotic viable Chinese hamster ovary (CHO) cells. For effective segmentation of cell images, a stochastic segmentation algorithm is developed by combining a generalized polynomial chaos expansion with a level set function-based segmentation algorithm. This approach provides a probabilistic description of the segmented cellular regions along the boundary, from which it is possible to calculate morphological changes related to apoptosis, i.e., the curvature and length of a cell's boundary. These features are then used as inputs to a support vector machine (SVM) classifier that is trained to distinguish between normal and apoptotic viable states of CHO cell images. The use of morphological features obtained from the stochastic level set segmentation of cell images in combination with the trained SVM classifier is more efficient in terms of differentiation accuracy as compared with the original deterministic level set method.

Ripples in Rocks Point to Water

NASA Technical Reports Server (NTRS)

2004-01-01

This image taken by the Mars Exploration Rover Opportunity's panoramic camera shows the rock nicknamed 'Last Chance,' which lies within the outcrop near the rover's landing site at Meridiani Planum, Mars. The image provides evidence for a geologic feature known as ripple cross-stratification. At the base of the rock, layers can be seen dipping downward to the right. The bedding that contains these dipping layers is only one to two centimeters (0.4 to 0.8 inches) thick. In the upper right corner of the rock, layers also dip to the right, but exhibit a weak 'concave-up' geometry. These two features -- the thin, cross-stratified bedding combined with the possible concave geometry -- suggest small ripples with sinuous crest lines. Although wind can produce ripples, they rarely have sinuous crest lines and never form steep, dipping layers at this small scale. The most probable explanation for these ripples is that they were formed in the presence of moving water.

Crossbedding Evidence for Underwater Origin Interpretations of cross-lamination patterns presented as clues to this martian rock's origin under flowing water are marked on images taken by the panoramic camera and microscopic imager on NASA's Opportunity.

[figure removed for brevity, see original site] [figure removed for brevity, see original site] Figure 1Figure 2

The red arrows (Figure 1) point to features suggesting cross-lamination within the rock called 'Last Chance' taken at a distance of 4.5 meters (15 feet) during Opportunity's 17th sol (February 10, 2004). The inferred sets of fine layers at angles to each other (cross-laminae) are up to 1.4 centimeters (half an inch) thick. For scale, the distance between two vertical cracks in the rock is about 7 centimeters (2.8 inches). The feature indicated by the middle red arrow suggests a pattern called trough cross-lamination, likely produced when flowing water shaped sinuous ripples in underwater sediment and pushed the ripples to migrate in one direction. The direction of the ancient flow would have been either toward or away from the line of sight from this perspective. The lower and upper red arrows point to cross-lamina sets that are consistent with underwater ripples in the sediment having moved in water that was flowing left to right from this perspective.

The yellow arrows (Figure 2) indicate places in the panoramic camera view that correlate with places in the microscope's view of the same rock.

[figure removed for brevity, see original site] Figure 3

The microscopic view (Figure 3) is a mosaic of some of the 152 microscopic imager frames of 'Last Chance' that Opportunity took on sols 39 and 40 (March 3 and 4, 2004).

[figure removed for brevity, see original site] Figure 4

Figure 4 shows cross-lamination expressed by lines that trend downward from left to right, traced with black lines in the interpretive overlay. These cross-lamination lines are consistent with dipping planes that would have formed surfaces on the down-current side of migrating ripples. Interpretive blue lines indicate boundaries between possible sets of cross-laminae.

oPOSSUM: integrated tools for analysis of regulatory motif over-representation

PubMed Central

Ho Sui, Shannan J.; Fulton, Debra L.; Arenillas, David J.; Kwon, Andrew T.; Wasserman, Wyeth W.

2007-01-01

The identification of over-represented transcription factor binding sites from sets of co-expressed genes provides insights into the mechanisms of regulation for diverse biological contexts. oPOSSUM, an internet-based system for such studies of regulation, has been improved and expanded in this new release. New features include a worm-specific version for investigating binding sites conserved between Caenorhabditis elegans and C. briggsae, as well as a yeast-specific version for the analysis of co-expressed sets of Saccharomyces cerevisiae genes. The human and mouse applications feature improvements in ortholog mapping, sequence alignments and the delineation of multiple alternative promoters. oPOSSUM2, introduced for the analysis of over-represented combinations of motifs in human and mouse genes, has been integrated with the original oPOSSUM system. Analysis using user-defined background gene sets is now supported. The transcription factor binding site models have been updated to include new profiles from the JASPAR database. oPOSSUM is available at http://www.cisreg.ca/oPOSSUM/ PMID:17576675

The oceanic islands - Azores. [geological, geophysical and geochemical features

NASA Technical Reports Server (NTRS)

Ridley, W. I.; Watkins, N. D.; Macfarlane, D. J.

1974-01-01

A presentation is made of the known geological, geophysical, and geochemical data on the Azores. The regional setting of the islands is described; under the geological heading, surface geology and petrochemistry are discussed; and paleomagnetism, marine magnetic surveys, gravity, seismology, and heat flow are treated in the geophysics category. A model for the origin of the Azores is constructed on the basis of these observations.

Geology and regional setting of the Al Masane ancient mine area, southeastern Arabian Shield, Kingdom of Saudi Arabia

USGS Publications Warehouse

Conway, Clay M.

1985-01-01

Chemical characteristics of volcanic rocks at Al Masane and elsewhere, along with features such as zinc-copper-iron sulfide mineralization, rhyolite-basalt bimodality, and the quartz phenocryst-rich nature of the felsic rocks, are compatible with an unusually primitive tholeiitic island-arc origin for the strata and mineral deposits of the Habawnah mineral belt.

Intrapartum fetal heart rate classification from trajectory in Sparse SVM feature space.

PubMed

Spilka, J; Frecon, J; Leonarduzzi, R; Pustelnik, N; Abry, P; Doret, M

2015-01-01

Intrapartum fetal heart rate (FHR) constitutes a prominent source of information for the assessment of fetal reactions to stress events during delivery. Yet, early detection of fetal acidosis remains a challenging signal processing task. The originality of the present contribution are three-fold: multiscale representations and wavelet leader based multifractal analysis are used to quantify FHR variability ; Supervised classification is achieved by means of Sparse-SVM that aim jointly to achieve optimal detection performance and to select relevant features in a multivariate setting ; Trajectories in the feature space accounting for the evolution along time of features while labor progresses are involved in the construction of indices quantifying fetal health. The classification performance permitted by this combination of tools are quantified on a intrapartum FHR large database (≃ 1250 subjects) collected at a French academic public hospital.

Calibration of Wide-Field Deconvolution Microscopy for Quantitative Fluorescence Imaging

PubMed Central

Lee, Ji-Sook; Wee, Tse-Luen (Erika); Brown, Claire M.

2014-01-01

Deconvolution enhances contrast in fluorescence microscopy images, especially in low-contrast, high-background wide-field microscope images, improving characterization of features within the sample. Deconvolution can also be combined with other imaging modalities, such as confocal microscopy, and most software programs seek to improve resolution as well as contrast. Quantitative image analyses require instrument calibration and with deconvolution, necessitate that this process itself preserves the relative quantitative relationships between fluorescence intensities. To ensure that the quantitative nature of the data remains unaltered, deconvolution algorithms need to be tested thoroughly. This study investigated whether the deconvolution algorithms in AutoQuant X3 preserve relative quantitative intensity data. InSpeck Green calibration microspheres were prepared for imaging, z-stacks were collected using a wide-field microscope, and the images were deconvolved using the iterative deconvolution algorithms with default settings. Afterwards, the mean intensities and volumes of microspheres in the original and the deconvolved images were measured. Deconvolved data sets showed higher average microsphere intensities and smaller volumes than the original wide-field data sets. In original and deconvolved data sets, intensity means showed linear relationships with the relative microsphere intensities given by the manufacturer. Importantly, upon normalization, the trend lines were found to have similar slopes. In original and deconvolved images, the volumes of the microspheres were quite uniform for all relative microsphere intensities. We were able to show that AutoQuant X3 deconvolution software data are quantitative. In general, the protocol presented can be used to calibrate any fluorescence microscope or image processing and analysis procedure. PMID:24688321

Magnetic Signature of the Lunar South Pole-Aitken Basin: Character, Origin, and Age

NASA Technical Reports Server (NTRS)

Purucker, Michael E.; Head, James W., III; Wilson, Lionel

2012-01-01

A new magnetic map of the Moon, based on Lunar Prospector (LP) magnetometer observations, sheds light on the origin of the South Pole-Aitken Basin (SPA), the largest and oldest of the recognized lunar basins. A set of WNW-trending linear to arcuate magnetic features, evident in both the radial and scalar observations, covers much of a 1000 km wide region centered on the NW portion of SPA. The source bodies are not at the surface because the magnetic features show no first-order correspondence to any surface topographic or structural feature. Patchy mare basalts of possible late Imbrianage are emplaced within SPA and are inferred to have been emplaced through dikes, directly from mantle sources. We infer that the magnetic features represent dike swarms that served as feeders for these mare basalts, as evident from the location of the Thomson/ Mare Ingenii, Van de Graaff, and Leeuwenhoek mare basalts on the two largest magnetic features in the region. Modeling suggests that the dike zone is between 25 and 50 km wide at the surface, and dike magnetization contrasts are in the range of 0.2 A/m. We theorize that the basaltic dikes were emplaced in the lunar crust when a long-lived dynamo was active. Based on pressure, temperature, and stress conditions prevalent in the lunar crust, dikes are expected to be a dominantly subsurface phenomenon, consistent with the observations reported here.

Dexter: Data Extractor for scanned graphs

NASA Astrophysics Data System (ADS)

Demleitner, Markus

2011-12-01

The NASA Astrophysics Data System (ADS) now holds 1.3 million scanned pages, containing numerous plots and figures for which the original data sets are lost or inaccessible. The availability of scans of the figures can significantly ease the regeneration of the data sets. For this purpose, the ADS has developed Dexter, a Java applet that supports the user in this process. Dexter's basic functionality is to let the user manually digitize a plot by marking points and defining the coordinate transformation from the logical to the physical coordinate system. Advanced features include automatic identification of axes, tracing lines and finding points matching a template.

Matching by linear programming and successive convexification.

PubMed

Jiang, Hao; Drew, Mark S; Li, Ze-Nian

2007-06-01

We present a novel convex programming scheme to solve matching problems, focusing on the challenging problem of matching in a large search range and with cluttered background. Matching is formulated as metric labeling with L1 regularization terms, for which we propose a novel linear programming relaxation method and an efficient successive convexification implementation. The unique feature of the proposed relaxation scheme is that a much smaller set of basis labels is used to represent the original label space. This greatly reduces the size of the searching space. A successive convexification scheme solves the labeling problem in a coarse to fine manner. Importantly, the original cost function is reconvexified at each stage, in the new focus region only, and the focus region is updated so as to refine the searching result. This makes the method well-suited for large label set matching. Experiments demonstrate successful applications of the proposed matching scheme in object detection, motion estimation, and tracking.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

NASA Astrophysics Data System (ADS)

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods.

Extracting features from protein sequences to improve deep extreme learning machine for protein fold recognition.

PubMed

Ibrahim, Wisam; Abadeh, Mohammad Saniee

2017-05-21

Protein fold recognition is an important problem in bioinformatics to predict three-dimensional structure of a protein. One of the most challenging tasks in protein fold recognition problem is the extraction of efficient features from the amino-acid sequences to obtain better classifiers. In this paper, we have proposed six descriptors to extract features from protein sequences. These descriptors are applied in the first stage of a three-stage framework PCA-DELM-LDA to extract feature vectors from the amino-acid sequences. Principal Component Analysis PCA has been implemented to reduce the number of extracted features. The extracted feature vectors have been used with original features to improve the performance of the Deep Extreme Learning Machine DELM in the second stage. Four new features have been extracted from the second stage and used in the third stage by Linear Discriminant Analysis LDA to classify the instances into 27 folds. The proposed framework is implemented on the independent and combined feature sets in SCOP datasets. The experimental results show that extracted feature vectors in the first stage could improve the performance of DELM in extracting new useful features in second stage. Copyright © 2017 Elsevier Ltd. All rights reserved.

Kernel-based Joint Feature Selection and Max-Margin Classification for Early Diagnosis of Parkinson’s Disease

PubMed Central

Adeli, Ehsan; Wu, Guorong; Saghafi, Behrouz; An, Le; Shi, Feng; Shen, Dinggang

2017-01-01

Feature selection methods usually select the most compact and relevant set of features based on their contribution to a linear regression model. Thus, these features might not be the best for a non-linear classifier. This is especially crucial for the tasks, in which the performance is heavily dependent on the feature selection techniques, like the diagnosis of neurodegenerative diseases. Parkinson’s disease (PD) is one of the most common neurodegenerative disorders, which progresses slowly while affects the quality of life dramatically. In this paper, we use the data acquired from multi-modal neuroimaging data to diagnose PD by investigating the brain regions, known to be affected at the early stages. We propose a joint kernel-based feature selection and classification framework. Unlike conventional feature selection techniques that select features based on their performance in the original input feature space, we select features that best benefit the classification scheme in the kernel space. We further propose kernel functions, specifically designed for our non-negative feature types. We use MRI and SPECT data of 538 subjects from the PPMI database, and obtain a diagnosis accuracy of 97.5%, which outperforms all baseline and state-of-the-art methods. PMID:28120883

Dissuasive exit signage for building fire evacuation.

PubMed

Olander, Joakim; Ronchi, Enrico; Lovreglio, Ruggiero; Nilsson, Daniel

2017-03-01

This work presents the result of a questionnaire study which investigates the design of dissuasive emergency signage, i.e. signage conveying a message of not utilizing a specific exit door. The work analyses and tests a set of key features of dissuasive emergency signage using the Theory of Affordances. The variables having the largest impact on observer preference, interpretation and noticeability of the signage have been identified. Results show that features which clearly negate the exit-message of the original positive exit signage are most effective, for instance a red X-marking placed across the entirety of the exit signage conveys a clear dissuasive message. Other features of note are red flashing lights and alternation of colour. The sense of urgency conveyed by the sign is largely affected by sensory inputs such as red flashing lights or other features which cause the signs to break the tendencies of normalcy. Copyright © 2016 Elsevier Ltd. All rights reserved.

Statistical Features of Deep-ocean Tsunamis Based on 30 Years of Bottom Pressure Observations in the Northeast Pacific

NASA Astrophysics Data System (ADS)

Fine, I.; Thomson, R.; Chadwick, W. W., Jr.; Davis, E. E.; Fox, C. G.

2016-12-01

We have used a set of high-resolution bottom pressure recorder (BPR) time series collected at Axial Seamount on the Juan de Fuca Ridge beginning in 1986 to examine tsunami waves of seismological origin in the northeast Pacific. These data are a combination of autonomous, internally-recording battery-powered instruments and cabled instruments on the OOI Cabled Array. Of the total of 120 tsunami events catalogued for the coasts of Japan, Alaska, western North America and Hawaii, we found evidence for 38 events in the Axial Seamount BPR records. Many of these tsunamis were not observed along the adjacent west coast of the USA and Canada because of the much higher noise level of coastal locations and the lack of digital tide gauge data prior to 2000. We have also identified several tsunamis of apparent seismological origin that were observed at coastal stations but not at the deep ocean site. Careful analysis of these observations suggests that they were likely of meteorological origin. Analysis of the pressure measurements from Axial Seamount, along with BPR measurements from a nearby ODP CORK (Ocean Drilling Program Circulation Obviation Retrofit Kit) borehole and DART (Deep-ocean Assessment and Reporting of Tsunamis) locations, reveals features of deep-ocean tsunamis that are markedly different from features observed at coastal locations. Results also show that the energy of deep-ocean tsunamis can differ significantly among the three sets of stations despite their close spatial spacing and that this difference is strongly dependent on the direction of the incoming tsunami waves. These deep-ocean observations provide the most comprehensive statistics possible for tsunamis in the Pacific Ocean over the past 30 years. New insight into the distribution of tsunami amplitudes and wave energy derived from the deep-ocean sites should prove useful for long-term tsunami prediction and mitigation for coastal communities along the west coast of the USA and Canada.

Decoding spectrotemporal features of overt and covert speech from the human cortex

PubMed Central

Martin, Stéphanie; Brunner, Peter; Holdgraf, Chris; Heinze, Hans-Jochen; Crone, Nathan E.; Rieger, Jochem; Schalk, Gerwin; Knight, Robert T.; Pasley, Brian N.

2014-01-01

Auditory perception and auditory imagery have been shown to activate overlapping brain regions. We hypothesized that these phenomena also share a common underlying neural representation. To assess this, we used electrocorticography intracranial recordings from epileptic patients performing an out loud or a silent reading task. In these tasks, short stories scrolled across a video screen in two conditions: subjects read the same stories both aloud (overt) and silently (covert). In a control condition the subject remained in a resting state. We first built a high gamma (70–150 Hz) neural decoding model to reconstruct spectrotemporal auditory features of self-generated overt speech. We then evaluated whether this same model could reconstruct auditory speech features in the covert speech condition. Two speech models were tested: a spectrogram and a modulation-based feature space. For the overt condition, reconstruction accuracy was evaluated as the correlation between original and predicted speech features, and was significant in each subject (p < 10−5; paired two-sample t-test). For the covert speech condition, dynamic time warping was first used to realign the covert speech reconstruction with the corresponding original speech from the overt condition. Reconstruction accuracy was then evaluated as the correlation between original and reconstructed speech features. Covert reconstruction accuracy was compared to the accuracy obtained from reconstructions in the baseline control condition. Reconstruction accuracy for the covert condition was significantly better than for the control condition (p < 0.005; paired two-sample t-test). The superior temporal gyrus, pre- and post-central gyrus provided the highest reconstruction information. The relationship between overt and covert speech reconstruction depended on anatomy. These results provide evidence that auditory representations of covert speech can be reconstructed from models that are built from an overt speech data set, supporting a partially shared neural substrate. PMID:24904404

Comparative genomics of Mortierella elongata and its bacterial endosymbiont Mycoavidus cysteinexigens

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

Uehling, J.; Gryganskyi, A.; Hameed, K.

Endosymbiosis of bacteria by eukaryotes is a defining feature of cellular evolution. In addition to well-known bacterial origins for mitochondria and chloroplasts, multiple origins of bacterial endosymbiosis are known within the cells of diverse animals, plants and fungi. Early-diverging lineages of terrestrial fungi harbor endosymbiotic bacteria belonging to the Burkholderiaceae. Furthermore, we sequenced the metagenome of the soil-inhabiting fungus Mortierella elongata and assembled the complete circular chromosome of its endosymbiont, Mycoavidus cysteinexigens, which we place within a lineage of endofungal symbionts that are sister clade to Burkholderia. The genome of M. elongata strain AG77 features a core set of primarymore » metabolic pathways for degradation of simple carbohydrates and lipid biosynthesis, while the M. cysteinexigens (AG77) genome is reduced in size and function. Experiments using antibiotics to cure the endobacterium from the host demonstrate that the fungal host metabolism is highly modulated by presence/ absence of M. cysteinexigens. In independent comparative phylogenomic analyses of fungal and bacterial genomes we find that they are consistent with an ancient origin for M. elongata M. cysteinexigens symbiosis, most likely over 350 million years ago and concomitant with the terrestrialization of Earth and diversification of land fungi and plants.« less

Comparative genomics of Mortierella elongata and its bacterial endosymbiont Mycoavidus cysteinexigens

DOE PAGES

Uehling, J.; Gryganskyi, A.; Hameed, K.; ...

2017-01-11

Endosymbiosis of bacteria by eukaryotes is a defining feature of cellular evolution. In addition to well-known bacterial origins for mitochondria and chloroplasts, multiple origins of bacterial endosymbiosis are known within the cells of diverse animals, plants and fungi. Early-diverging lineages of terrestrial fungi harbor endosymbiotic bacteria belonging to the Burkholderiaceae. Furthermore, we sequenced the metagenome of the soil-inhabiting fungus Mortierella elongata and assembled the complete circular chromosome of its endosymbiont, Mycoavidus cysteinexigens, which we place within a lineage of endofungal symbionts that are sister clade to Burkholderia. The genome of M. elongata strain AG77 features a core set of primarymore » metabolic pathways for degradation of simple carbohydrates and lipid biosynthesis, while the M. cysteinexigens (AG77) genome is reduced in size and function. Experiments using antibiotics to cure the endobacterium from the host demonstrate that the fungal host metabolism is highly modulated by presence/ absence of M. cysteinexigens. In independent comparative phylogenomic analyses of fungal and bacterial genomes we find that they are consistent with an ancient origin for M. elongata M. cysteinexigens symbiosis, most likely over 350 million years ago and concomitant with the terrestrialization of Earth and diversification of land fungi and plants.« less

Broken Ergodicity in Two-Dimensional Homogeneous Magnetohydrodynamic Turbulence

NASA Technical Reports Server (NTRS)

Shebalin, John V.

2010-01-01

Two-dimensional (2-D) homogeneous magnetohydrodynamic (MHD) turbulence has many of the same qualitative features as three-dimensional (3-D) homogeneous MHD turbulence.The se features include several ideal invariants, along with the phenomenon of broken ergodicity. Broken ergodicity appears when certain modes act like random variables with mean values that are large compared to their standard deviations, indicating a coherent structure or dynamo.Recently, the origin of broken ergodicity in 3-D MHD turbulence that is manifest in the lowest wavenumbers was explained. Here, a detailed description of the origins of broken ergodicity in 2-D MHD turbulence is presented. It will be seen that broken ergodicity in ideal 2-D MHD turbulence can be manifest in the lowest wavenumbers of a finite numerical model for certain initial conditions or in the highest wavenumbers for another set of initial conditions.T he origins of broken ergodicity in ideal 2-D homogeneous MHD turbulence are found through an eigen analysis of the covariance matrices of the modal probability density functions.It will also be shown that when the lowest wavenumber magnetic field becomes quasi-stationary, the higher wavenumber modes can propagate as Alfven waves on these almost static large-scale magnetic structures

A Scalable Distributed Approach to Mobile Robot Vision

NASA Technical Reports Server (NTRS)

Kuipers, Benjamin; Browning, Robert L.; Gribble, William S.

1997-01-01

This paper documents our progress during the first year of work on our original proposal entitled 'A Scalable Distributed Approach to Mobile Robot Vision'. We are pursuing a strategy for real-time visual identification and tracking of complex objects which does not rely on specialized image-processing hardware. In this system perceptual schemas represent objects as a graph of primitive features. Distributed software agents identify and track these features, using variable-geometry image subwindows of limited size. Active control of imaging parameters and selective processing makes simultaneous real-time tracking of many primitive features tractable. Perceptual schemas operate independently from the tracking of primitive features, so that real-time tracking of a set of image features is not hurt by latency in recognition of the object that those features make up. The architecture allows semantically significant features to be tracked with limited expenditure of computational resources, and allows the visual computation to be distributed across a network of processors. Early experiments are described which demonstrate the usefulness of this formulation, followed by a brief overview of our more recent progress (after the first year).

A Novel Anti-classification Approach for Knowledge Protection.

PubMed

Lin, Chen-Yi; Chen, Tung-Shou; Tsai, Hui-Fang; Lee, Wei-Bin; Hsu, Tien-Yu; Kao, Yuan-Hung

2015-10-01

Classification is the problem of identifying a set of categories where new data belong, on the basis of a set of training data whose category membership is known. Its application is wide-spread, such as the medical science domain. The issue of the classification knowledge protection has been paid attention increasingly in recent years because of the popularity of cloud environments. In the paper, we propose a Shaking Sorted-Sampling (triple-S) algorithm for protecting the classification knowledge of a dataset. The triple-S algorithm sorts the data of an original dataset according to the projection results of the principal components analysis so that the features of the adjacent data are similar. Then, we generate noise data with incorrect classes and add those data to the original dataset. In addition, we develop an effective positioning strategy, determining the added positions of noise data in the original dataset, to ensure the restoration of the original dataset after removing those noise data. The experimental results show that the disturbance effect of the triple-S algorithm on the CLC, MySVM, and LibSVM classifiers increases when the noise data ratio increases. In addition, compared with existing methods, the disturbance effect of the triple-S algorithm is more significant on MySVM and LibSVM when a certain amount of the noise data added to the original dataset is reached.

New insights on the origin of the High Velocity Peaks in the Galactic Bulge

NASA Astrophysics Data System (ADS)

Fernández-Trincado, J. G.; Robin, A. C.; Moreno, E.; Pérez-Villegas, A.; Pichardo, B.

2017-12-01

We provide new insight on the origin of the cold high-V_{los} peaks (˜200 kms^{-1}) in the Milky Way bulge discovered in the APOGEE commissioning data (Nidever et al. 2012). Here we show that such kinematic behaviour present in the field regions towards the Galactic bulge is not likely associated with orbits that build the boxy/peanut (B/P) bulge. To this purpose, a new set of test particle simulations of a kinematically cold stellar disk evolved in a 3D steady-state barred Milky Way galactic potential, has been analysed in detail. Especially bar particles trapped into the bar are identified through the orbital Jacobi energy E_{J}, which allows us to identify the building blocks of the B/P feature and investigate their kinematic properties. Finally, we present preliminary results showing that the high-V_{los} features observed towards the Milky Way bulge are a natural consequence of a large-scale midplane particle structure, which is unlikely associated with the Galactic bar.

Structure of random discrete spacetime

NASA Technical Reports Server (NTRS)

Brightwell, Graham; Gregory, Ruth

1991-01-01

The usual picture of spacetime consists of a continuous manifold, together with a metric of Lorentzian signature which imposes a causal structure on the spacetime. A model, first suggested by Bombelli et al., is considered in which spacetime consists of a discrete set of points taken at random from a manifold, with only the causal structure on this set remaining. This structure constitutes a partially ordered set (or poset). Working from the poset alone, it is shown how to construct a metric on the space which closely approximates the metric on the original spacetime manifold, how to define the effective dimension of the spacetime, and how such quantities may depend on the scale of measurement. Possible desirable features of the model are discussed.

The structure of random discrete spacetime

NASA Technical Reports Server (NTRS)

Brightwell, Graham; Gregory, Ruth

1990-01-01

The usual picture of spacetime consists of a continuous manifold, together with a metric of Lorentzian signature which imposes a causal structure on the spacetime. A model, first suggested by Bombelli et al., is considered in which spacetime consists of a discrete set of points taken at random from a manifold, with only the causal structure on this set remaining. This structure constitutes a partially ordered set (or poset). Working from the poset alone, it is shown how to construct a metric on the space which closely approximates the metric on the original spacetime manifold, how to define the effective dimension of the spacetime, and how such quantities may depend on the scale of measurement. Possible desirable features of the model are discussed.

Probabilistic Modeling of Aircraft Trajectories for Dynamic Separation Volumes

NASA Technical Reports Server (NTRS)

Lewis, Timothy A.

2016-01-01

With a proliferation of new and unconventional vehicles and operations expected in the future, the ab initio airspace design will require new approaches to trajectory prediction for separation assurance and other air traffic management functions. This paper presents an approach to probabilistic modeling of the trajectory of an aircraft when its intent is unknown. The approach uses a set of feature functions to constrain a maximum entropy probability distribution based on a set of observed aircraft trajectories. This model can be used to sample new aircraft trajectories to form an ensemble reflecting the variability in an aircraft's intent. The model learning process ensures that the variability in this ensemble reflects the behavior observed in the original data set. Computational examples are presented.

Heroes and Legends Exhibit

NASA Image and Video Library

2016-11-07

Inside the Heroes and Legends attraction at the Kennedy Space Center Visitor Complex, interactive features include the original consoles of the Mercury Mission Control room with the world map where capsules paths were followed between tracking stations. The new facility looks back to the pioneering efforts of Mercury, Gemini and Apollo. It sets the stage by providing the background and context for space exploration and the legendary men and women who pioneered the nation's journey into space.

Goldstonic pseudoscalar mesons in Bethe-Salpeter-inspired setting

NASA Astrophysics Data System (ADS)

Lucha, Wolfgang; Schöberl, Franz F.

2018-03-01

For a two-particle bound-state equation closer to its Bethe-Salpeter origins than Salpeter’s equation, with effective interaction kernel deliberately forged such as to ensure, in the limit of zero mass of the bound-state constituents, the vanishing of the arising bound-state mass, we scrutinize the emerging features of the lightest pseudoscalar mesons for their agreement with the behavior predicted by a generalization of the Gell-Mann-Oakes-Renner relation.

Enhanced Regulatory Sequence Prediction Using Gapped k-mer Features

PubMed Central

Mohammad-Noori, Morteza; Beer, Michael A.

2014-01-01

Abstract Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem. PMID:25033408

Enhanced regulatory sequence prediction using gapped k-mer features.

PubMed

Ghandi, Mahmoud; Lee, Dongwon; Mohammad-Noori, Morteza; Beer, Michael A

2014-07-01

Oligomers of length k, or k-mers, are convenient and widely used features for modeling the properties and functions of DNA and protein sequences. However, k-mers suffer from the inherent limitation that if the parameter k is increased to resolve longer features, the probability of observing any specific k-mer becomes very small, and k-mer counts approach a binary variable, with most k-mers absent and a few present once. Thus, any statistical learning approach using k-mers as features becomes susceptible to noisy training set k-mer frequencies once k becomes large. To address this problem, we introduce alternative feature sets using gapped k-mers, a new classifier, gkm-SVM, and a general method for robust estimation of k-mer frequencies. To make the method applicable to large-scale genome wide applications, we develop an efficient tree data structure for computing the kernel matrix. We show that compared to our original kmer-SVM and alternative approaches, our gkm-SVM predicts functional genomic regulatory elements and tissue specific enhancers with significantly improved accuracy, increasing the precision by up to a factor of two. We then show that gkm-SVM consistently outperforms kmer-SVM on human ENCODE ChIP-seq datasets, and further demonstrate the general utility of our method using a Naïve-Bayes classifier. Although developed for regulatory sequence analysis, these methods can be applied to any sequence classification problem.

Large scale analysis of protein-binding cavities using self-organizing maps and wavelet-based surface patches to describe functional properties, selectivity discrimination, and putative cross-reactivity.

PubMed

Kupas, Katrin; Ultsch, Alfred; Klebe, Gerhard

2008-05-15

A new method to discover similar substructures in protein binding pockets, independently of sequence and folding patterns or secondary structure elements, is introduced. The solvent-accessible surface of a binding pocket, automatically detected as a depression on the protein surface, is divided into a set of surface patches. Each surface patch is characterized by its shape as well as by its physicochemical characteristics. Wavelets defined on surfaces are used for the description of the shape, as they have the great advantage of allowing a comparison at different resolutions. The number of coefficients to describe the wavelets can be chosen with respect to the size of the considered data set. The physicochemical characteristics of the patches are described by the assignment of the exposed amino acid residues to one or more of five different properties determinant for molecular recognition. A self-organizing neural network is used to project the high-dimensional feature vectors onto a two-dimensional layer of neurons, called a map. To find similarities between the binding pockets, in both geometrical and physicochemical features, a clustering of the projected feature vector is performed using an automatic distance- and density-based clustering algorithm. The method was validated with a small training data set of 109 binding cavities originating from a set of enzymes covering 12 different EC numbers. A second test data set of 1378 binding cavities, extracted from enzymes of 13 different EC numbers, was then used to prove the discriminating power of the algorithm and to demonstrate its applicability to large scale analyses. In all cases, members of the data set with the same EC number were placed into coherent regions on the map, with small distances between them. Different EC numbers are separated by large distances between the feature vectors. A third data set comprising three subfamilies of endopeptidases is used to demonstrate the ability of the algorithm to detect similar substructures between functionally related active sites. The algorithm can also be used to predict the function of novel proteins not considered in training data set. 2007 Wiley-Liss, Inc.

Significance of northeast-trending features in Canada Basin, Arctic Ocean

USGS Publications Warehouse

Hutchinson, Deborah; Jackson, H.R.; Houseknecht, David W.; Li, Q.; Shimeld, J.W.; Mosher, D.C.; Chian, D.; Saltus, Richard; Oakey, G.N.

2017-01-01

Synthesis of seismic velocity, potential field, and geological data from Canada Basin and its surrounding continental margins suggests that a northeast-trending structural fabric has influenced the origin, evolution, and current tectonics of the basin. This structural fabric has a crustal origin, based on the persistence of these trends in upward continuation of total magnetic intensity data and vertical derivative analysis of free-air gravity data. Three subparallel northeast-trending features are described. Northwind Escarpment, bounding the east side of the Chukchi Borderland, extends ∼600 km and separates continental crust of Northwind Ridge from high-velocity transitional crust in Canada Basin. A second, shorter northeast-trending zone extends ∼300 km in northern Canada Basin and separates inferred continental crust of Sever Spur from magmatically intruded crust of the High Arctic Large Igneous Province. A third northeast-trending feature, here called the Alaska-Prince Patrick magnetic lineament (APPL) is inferred from magnetic data and its larger regional geologic setting. Analysis of these three features suggests strike slip or transtensional deformation played a role in the opening of Canada Basin. These features can be explained by initial Jurassic-Early Cretaceous strike slip deformation (phase 1) followed in the Early Cretaceous (∼134 to ∼124 Ma) by rotation of Arctic Alaska with seafloor spreading orthogonal to the fossil spreading axis preserved in the central Canada Basin (phase 2). In this model, the Chukchi Borderland is part of Arctic Alaska.

Significance of Northeast-Trending Features in Canada Basin, Arctic Ocean

NASA Astrophysics Data System (ADS)

Hutchinson, D. R.; Jackson, H. R.; Houseknecht, D. W.; Li, Q.; Shimeld, J. W.; Mosher, D. C.; Chian, D.; Saltus, R. W.; Oakey, G. N.

2017-11-01

Synthesis of seismic velocity, potential field, and geological data from Canada Basin and its surrounding continental margins suggests that a northeast-trending structural fabric has influenced the origin, evolution, and current tectonics of the basin. This structural fabric has a crustal origin, based on the persistence of these trends in upward continuation of total magnetic intensity data and vertical derivative analysis of free-air gravity data. Three subparallel northeast-trending features are described. Northwind Escarpment, bounding the east side of the Chukchi Borderland, extends ˜600 km and separates continental crust of Northwind Ridge from high-velocity transitional crust in Canada Basin. A second, shorter northeast-trending zone extends ˜300 km in northern Canada Basin and separates inferred continental crust of Sever Spur from magmatically intruded crust of the High Arctic Large Igneous Province. A third northeast-trending feature, here called the Alaska-Prince Patrick magnetic lineament (APPL) is inferred from magnetic data and its larger regional geologic setting. Analysis of these three features suggests strike slip or transtensional deformation played a role in the opening of Canada Basin. These features can be explained by initial Jurassic-Early Cretaceous strike slip deformation (phase 1) followed in the Early Cretaceous (˜134 to ˜124 Ma) by rotation of Arctic Alaska with seafloor spreading orthogonal to the fossil spreading axis preserved in the central Canada Basin (phase 2). In this model, the Chukchi Borderland is part of Arctic Alaska.

Metastatic prostate adenocarcinoma to the penis: a series of 29 cases with predilection for ductal adenocarcinoma.

PubMed

Ellis, Carla L; Epstein, Jonathan I

2015-01-01

Twenty-nine men with metastatic prostate adenocarcinoma to the penis were identified at our institution between 1993 and 2013. Of the 29 patients, 19 had a prior history of adenocarcinoma of the prostate, and 8 of those had ductal features in the primary lesion. Sixteen of 29 revealed ductal features in the metastasis. Seven of the 8 cases with ductal features in the primary had ductal features in the penile metastasis. Seven penile metastases were proven to be of prostatic origin solely by immunohistochemistry. Three cases were originally misdiagnosed as urothelial carcinoma upon review of the penile lesion. Other variant morphologies in the metastases included sarcomatoid carcinoma, small cell carcinoma, and adenosquamous carcinoma. In summary, prostate carcinoma involving the penis displays ductal features considerably more often than prostate cancer in general. Features that can cause difficulty in recognizing metastatic prostate adenocarcinoma to the penis include the unusual anatomic site for prostate cancer, poor differentiation, an increased prevalence of variant morphology, a long interval from the primary lesion, and, in some cases, no documented history of a primary prostatic lesion. Immunohistochemical analysis should be performed to rule out prostate carcinoma in penile/penile urethral tumors with morphology that differs from typical squamous or urothelial carcinoma. Even in the setting of metastatic disease, there is a critical need for an accurate diagnosis so that the appropriate therapy can be initiated, symptomatic relief can be provided, and long-term survival achieved in some cases, while at the same time avoiding penectomy for a misdiagnosis of a primary penile cancer.

The FRUITY database on AGB stars: past, present and future

NASA Astrophysics Data System (ADS)

Cristallo, S.; Piersanti, L.; Straniero, O.

2016-01-01

We present and show the features of the FRUITY database, an interactive web- based interface devoted to the nucleosynthesis in AGB stars. We describe the current available set of AGB models (largely expanded with respect to the original one) with masses in the range 1.3≤M/M⊙≤3.0 and metallicities -2.15 ≤[Fe/H]≤+0.15. We illustrate the details of our s-process surface distributions and we compare our results to observations. Moreover, we introduce a new set of models where the effects of rotation are taken into account. Finally, we shortly describe next planned upgrades.

Modeling Far-UV Fluorescent Emission Features of Warm Molecular Hydrogen in the Inner Regions of Protoplanetary Disks

NASA Astrophysics Data System (ADS)

Hoadley, Keri; France, Kevin

2015-01-01

Probing the surviving molecular gas within the inner regions of protoplanetary disks (PPDs) around T Tauri stars (1 - 10 Myr) provides insight into the conditions in which planet formation and migration occurs while the gas disk is still present. We model observed far ultraviolet (FUV) molecular hydrogen (H₂) fluorescent emission lines that originate within the inner regions (< 10 AU) of 9 well-studied Classic T Tauri stars, using the Hubble Space Telescope Cosmic Origins Spectrograph (COS), to explore the physical structure of the molecular disk at different PPD dust evolutionary stages. We created a 2D radiative transfer model that estimates the density and temperature distributions of warm, inner radial H₂ (T > 1500 K) with a set of 6 free parameters and produces a data cube of expected emission line profiles that describe the physical structure of the inner molecular disk atmosphere. By comparing the modeled emission lines with COS H₂ fluorescence emission features, we estimate the physical structure of the molecular disk atmosphere for each target with the set of free parameters that best replicate the observed lines. First results suggest that, for all dust evolutionary stages of disks considered, ground-state H₂ populations are described by a roughly constant temperature T(H₂) = 2500 +/- 1000 K. Possible evolution of the density structure of the H₂ atmosphere between intact and depleting dust disks may be distinguishable, but large errors in the inferred best-fit parameter sets prevent us from making this conclusion. Further improvements to the modeling framework and statistical comparison in determining the best-fit model-to-data parameter sets are ongoing, beginning with improvements to the radiative transfer model and use of up-to-date HI Lyman α absorption optical depths (see McJunkin in posters) to better estimate disk structural parameters. Once improvements are implemented, we will investigate the possible presence of a molecular wind component in the observed H₂ fluorescence features by determining blue-shifted flux residuals in the data after best-fit model-to-data comparisons are complete.

Predicting protein amidation sites by orchestrating amino acid sequence features

NASA Astrophysics Data System (ADS)

Zhao, Shuqiu; Yu, Hua; Gong, Xiujun

2017-08-01

Amidation is the fourth major category of post-translational modifications, which plays an important role in physiological and pathological processes. Identifying amidation sites can help us understanding the amidation and recognizing the original reason of many kinds of diseases. But the traditional experimental methods for predicting amidation sites are often time-consuming and expensive. In this study, we propose a computational method for predicting amidation sites by orchestrating amino acid sequence features. Three kinds of feature extraction methods are used to build a feature vector enabling to capture not only the physicochemical properties but also position related information of the amino acids. An extremely randomized trees algorithm is applied to choose the optimal features to remove redundancy and dependence among components of the feature vector by a supervised fashion. Finally the support vector machine classifier is used to label the amidation sites. When tested on an independent data set, it shows that the proposed method performs better than all the previous ones with the prediction accuracy of 0.962 at the Matthew's correlation coefficient of 0.89 and area under curve of 0.964.

Characterizing chaotic melodies in automatic music composition

NASA Astrophysics Data System (ADS)

Coca, Andrés E.; Tost, Gerard O.; Zhao, Liang

2010-09-01

In this paper, we initially present an algorithm for automatic composition of melodies using chaotic dynamical systems. Afterward, we characterize chaotic music in a comprehensive way as comprising three perspectives: musical discrimination, dynamical influence on musical features, and musical perception. With respect to the first perspective, the coherence between generated chaotic melodies (continuous as well as discrete chaotic melodies) and a set of classical reference melodies is characterized by statistical descriptors and melodic measures. The significant differences among the three types of melodies are determined by discriminant analysis. Regarding the second perspective, the influence of dynamical features of chaotic attractors, e.g., Lyapunov exponent, Hurst coefficient, and correlation dimension, on melodic features is determined by canonical correlation analysis. The last perspective is related to perception of originality, complexity, and degree of melodiousness (Euler's gradus suavitatis) of chaotic and classical melodies by nonparametric statistical tests.

Electrical structure of the central Cascadia subduction zone: The EMSLAB Lincoln Line revisited

NASA Astrophysics Data System (ADS)

Evans, Rob L.; Wannamaker, Philip E.; McGary, R. Shane; Elsenbeck, Jimmy

2014-09-01

The EMSLAB experiment was an ambitious onshore-offshore magnetotelluric (MT) transect of the Cascadia subduction zone. When completed (1985-1988), it was the largest experiment of its kind. Modeling and inversion capabilities at the time were, however, not sufficiently sophisticated to handle a fully regularized inversion of the data, including the seafloor data and bathymetric constraints, with the main final model presented based on trial and error forward modeling of the responses. Moreover, new data collected as part of the Earthscope USArray program are of higher quality due to improvements in instrument technology, and augment the original EMSLAB data set, presenting an opportunity to revisit the structure in this part of the subduction system. We have integrated the original wide-band MT data as well as several long-period stations from the original EMSLAB data set and invert these in conjunction with EMSLAB seafloor responses and new Earthscope data on land. This new composite data set has been analyzed in several ways, within a two-dimensional geometry in which conductivity is assumed to be invariant along a strike direction roughly coincident with that of the subduction zone. We have solved for fully smooth regularized models, as well as solutions that allow discontinuities in conductivity along the top surface of the descending slab. Finally, we have tested specific features in the EMSLAB model, notably a moderately shallow ( 30 km depth) forearc conductor. A feature similar to this shallow conductor is a consistent and required feature in our new inversion models, but the new models highlight the connection between the slab and what is interpreted to be an accumulation of aqueous fluids in the deep crust. The depth ( 40 km) at which the conductor intersects the slab suggests that the fluids are released by the transition of hydrous basalt to eclogite at upper greenschist facies and higher metamorphic grade. The nose of the mantle wedge has a conductivity consistent with a dry peridotite composition and thermal models of the system. At a depth of around 80 km the mantle intersecting the slab shows a slight increase in conductivity. This increase is not sufficient to require the presence of melt, but a conductor indicative of melt can be inserted into the model at this depth without compromising the fit.

Methodology for classification of geographical features with remote sensing images: Application to tidal flats

NASA Astrophysics Data System (ADS)

Revollo Sarmiento, G. N.; Cipolletti, M. P.; Perillo, M. M.; Delrieux, C. A.; Perillo, Gerardo M. E.

2016-03-01

Tidal flats generally exhibit ponds of diverse size, shape, orientation and origin. Studying the genesis, evolution, stability and erosive mechanisms of these geographic features is critical to understand the dynamics of coastal wetlands. However, monitoring these locations through direct access is hard and expensive, not always feasible, and environmentally damaging. Processing remote sensing images is a natural alternative for the extraction of qualitative and quantitative data due to their non-invasive nature. In this work, a robust methodology for automatic classification of ponds and tidal creeks in tidal flats using Google Earth images is proposed. The applicability of our method is tested in nine zones with different morphological settings. Each zone is processed by a segmentation stage, where ponds and tidal creeks are identified. Next, each geographical feature is measured and a set of shape descriptors is calculated. This dataset, together with a-priori classification of each geographical feature, is used to define a regression model, which allows an extensive automatic classification of large volumes of data discriminating ponds and tidal creeks against other various geographical features. In all cases, we identified and automatically classified different geographic features with an average accuracy over 90% (89.7% in the worst case, and 99.4% in the best case). These results show the feasibility of using freely available Google Earth imagery for the automatic identification and classification of complex geographical features. Also, the presented methodology may be easily applied in other wetlands of the world and perhaps employing other remote sensing imagery.

Fort Leonard Wood - Building 2101: Interior Character-Defining Features, Inventory and Assessment

DTIC Science & Technology

2014-04-01

the original wood wain- scot and original wallboard above (Figure 12 and Figure 13), and a large room with the original wood wainscot and original...ER D C/ CE RL S R- 14 -3 Fort Leonard Wood – Building 2101 Interior Character-Defining Features, Inventory and Assessment Co ns tr uc...2014 Fort Leonard Wood – Building 2101 Interior Character-Defining Features, Inventory and Assessment Adam D. Smith Construction Engineering

Beyond intensity: Spectral features effectively predict music-induced subjective arousal.

PubMed

Gingras, Bruno; Marin, Manuela M; Fitch, W Tecumseh

2014-01-01

Emotions in music are conveyed by a variety of acoustic cues. Notably, the positive association between sound intensity and arousal has particular biological relevance. However, although amplitude normalization is a common procedure used to control for intensity in music psychology research, direct comparisons between emotional ratings of original and amplitude-normalized musical excerpts are lacking. In this study, 30 nonmusicians retrospectively rated the subjective arousal and pleasantness induced by 84 six-second classical music excerpts, and an additional 30 nonmusicians rated the same excerpts normalized for amplitude. Following the cue-redundancy and Brunswik lens models of acoustic communication, we hypothesized that arousal and pleasantness ratings would be similar for both versions of the excerpts, and that arousal could be predicted effectively by other acoustic cues besides intensity. Although the difference in mean arousal and pleasantness ratings between original and amplitude-normalized excerpts correlated significantly with the amplitude adjustment, ratings for both sets of excerpts were highly correlated and shared a similar range of values, thus validating the use of amplitude normalization in music emotion research. Two acoustic parameters, spectral flux and spectral entropy, accounted for 65% of the variance in arousal ratings for both sets, indicating that spectral features can effectively predict arousal. Additionally, we confirmed that amplitude-normalized excerpts were adequately matched for loudness. Overall, the results corroborate our hypotheses and support the cue-redundancy and Brunswik lens models.

Spectral-Spatial Shared Linear Regression for Hyperspectral Image Classification.

PubMed

Haoliang Yuan; Yuan Yan Tang

2017-04-01

Classification of the pixels in hyperspectral image (HSI) is an important task and has been popularly applied in many practical applications. Its major challenge is the high-dimensional small-sized problem. To deal with this problem, lots of subspace learning (SL) methods are developed to reduce the dimension of the pixels while preserving the important discriminant information. Motivated by ridge linear regression (RLR) framework for SL, we propose a spectral-spatial shared linear regression method (SSSLR) for extracting the feature representation. Comparing with RLR, our proposed SSSLR has the following two advantages. First, we utilize a convex set to explore the spatial structure for computing the linear projection matrix. Second, we utilize a shared structure learning model, which is formed by original data space and a hidden feature space, to learn a more discriminant linear projection matrix for classification. To optimize our proposed method, an efficient iterative algorithm is proposed. Experimental results on two popular HSI data sets, i.e., Indian Pines and Salinas demonstrate that our proposed methods outperform many SL methods.

Mining Feature of Data Fusion in the Classification of Beer Flavor Information Using E-Tongue and E-Nose

PubMed Central

Men, Hong; Shi, Yan; Fu, Songlin; Jiao, Yanan; Qiao, Yu; Liu, Jingjing

2017-01-01

Multi-sensor data fusion can provide more comprehensive and more accurate analysis results. However, it also brings some redundant information, which is an important issue with respect to finding a feature-mining method for intuitive and efficient analysis. This paper demonstrates a feature-mining method based on variable accumulation to find the best expression form and variables’ behavior affecting beer flavor. First, e-tongue and e-nose were used to gather the taste and olfactory information of beer, respectively. Second, principal component analysis (PCA), genetic algorithm-partial least squares (GA-PLS), and variable importance of projection (VIP) scores were applied to select feature variables of the original fusion set. Finally, the classification models based on support vector machine (SVM), random forests (RF), and extreme learning machine (ELM) were established to evaluate the efficiency of the feature-mining method. The result shows that the feature-mining method based on variable accumulation obtains the main feature affecting beer flavor information, and the best classification performance for the SVM, RF, and ELM models with 96.67%, 94.44%, and 98.33% prediction accuracy, respectively. PMID:28753917

Incipient fault feature extraction of rolling bearings based on the MVMD and Teager energy operator.

PubMed

Ma, Jun; Wu, Jiande; Wang, Xiaodong

2018-06-04

Aiming at the problems that the incipient fault of rolling bearings is difficult to recognize and the number of intrinsic mode functions (IMFs) decomposed by variational mode decomposition (VMD) must be set in advance and can not be adaptively selected, taking full advantages of the adaptive segmentation of scale spectrum and Teager energy operator (TEO) demodulation, a new method for early fault feature extraction of rolling bearings based on the modified VMD and Teager energy operator (MVMD-TEO) is proposed. Firstly, the vibration signal of rolling bearings is analyzed by adaptive scale space spectrum segmentation to obtain the spectrum segmentation support boundary, and then the number K of IMFs decomposed by VMD is adaptively determined. Secondly, the original vibration signal is adaptively decomposed into K IMFs, and the effective IMF components are extracted based on the correlation coefficient criterion. Finally, the Teager energy spectrum of the reconstructed signal of the effective IMF components is calculated by the TEO, and then the early fault features of rolling bearings are extracted to realize the fault identification and location. Comparative experiments of the proposed method and the existing fault feature extraction method based on Local Mean Decomposition and Teager energy operator (LMD-TEO) have been implemented using experimental data-sets and a measured data-set. The results of comparative experiments in three application cases show that the presented method can achieve a fairly or slightly better performance than LMD-TEO method, and the validity and feasibility of the proposed method are proved. Copyright © 2018. Published by Elsevier Ltd.

Upper-mantle origin of the Yellowstone hotspot

USGS Publications Warehouse

Christiansen, R.L.; Foulger, G.R.; Evans, J.R.

2002-01-01

Fundamental features of the geology and tectonic setting of the northeast-propagating Yellowstone hotspot are not explained by a simple deep-mantle plume hypothesis and, within that framework, must be attributed to coincidence or be explained by auxiliary hypotheses. These features include the persistence of basaltic magmatism along the hotspot track, the origin of the hotspot during a regional middle Miocene tectonic reorganization, a similar and coeval zone of northwestward magmatic propagation, the occurrence of both zones of magmatic propagation along a first-order tectonic boundary, and control of the hotspot track by preexisting structures. Seismic imaging provides no evidence for, and several contraindications of, a vertically extensive plume-like structure beneath Yellowstone or a broad trailing plume head beneath the eastern Snake River Plain. The high helium isotope ratios observed at Yellowstone and other hotspots are commonly assumed to arise from the lower mantle, but upper-mantle processes can explain the observations. The available evidence thus renders an upper-mantle origin for the Yellowstone system the preferred model; there is no evidence that the system extends deeper than ???200 km, and some evidence that it does not. A model whereby the Yellowstone system reflects feedback between upper-mantle convection and regional lithospheric tectonics is able to explain the observations better than a deep-mantle plume hypothesis.

Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples.

PubMed

Men, Hong; Fu, Songlin; Yang, Jialin; Cheng, Meiqi; Shi, Yan; Liu, Jingjing

2018-01-18

Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33-100%, and ELM, with an accuracy rate of 98.01-100%. For level assessment, the R² related to the training set was above 0.97 and the R² related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016-0.3494, lower than the error of 0.5-1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level.

[Fast discrimination of edible vegetable oil based on Raman spectroscopy].

PubMed

Zhou, Xiu-Jun; Dai, Lian-Kui; Li, Sheng

2012-07-01

A novel method to fast discriminate edible vegetable oils by Raman spectroscopy is presented. The training set is composed of different edible vegetable oils with known classes. Based on their original Raman spectra, baseline correction and normalization were applied to obtain standard spectra. Two characteristic peaks describing the unsaturated degree of vegetable oil were selected as feature vectors; then the centers of all classes were calculated. For an edible vegetable oil with unknown class, the same pretreatment and feature extraction methods were used. The Euclidian distances between the feature vector of the unknown sample and the center of each class were calculated, and the class of the unknown sample was finally determined by the minimum distance. For 43 edible vegetable oil samples from seven different classes, experimental results show that the clustering effect of each class was more obvious and the class distance was much larger with the new feature extraction method compared with PCA. The above classification model can be applied to discriminate unknown edible vegetable oils rapidly and accurately.

Measurements of Cuspal Slope Inclination Angles in Palaeoanthropological Applications

NASA Astrophysics Data System (ADS)

Gaboutchian, A. V.; Knyaz, V. A.; Leybova, N. A.

2017-05-01

Tooth crown morphological features, studied in palaeoanthropology, provide valuable information about human evolution and development of civilization. Tooth crown morphology represents biological and historical data of high taxonomical value as it characterizes genetically conditioned tooth relief features averse to substantial changes under environmental factors during lifetime. Palaeoanthropological studies are still based mainly on descriptive techniques and manual measurements of limited number of morphological parameters. Feature evaluation and measurement result analysis are expert-based. Development of new methods and techniques in 3D imaging creates a background provides for better value of palaeoanthropological data processing, analysis and distribution. The goals of the presented research are to propose new features for automated odontometry and to explore their applicability to paleoanthropological studies. A technique for automated measuring of given morphological tooth parameters needed for anthropological study is developed. It is based on using original photogrammetric system as a teeth 3D models acquisition device and on a set of algorithms for given tooth parameters estimation.

Time-reversal symmetric resolution of unity without background integrals in open quantum systems

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

Hatano, Naomichi, E-mail: hatano@iis.u-tokyo.ac.jp; Ordonez, Gonzalo, E-mail: gordonez@butler.edu

2014-12-15

We present a new complete set of states for a class of open quantum systems, to be used in expansion of the Green’s function and the time-evolution operator. A remarkable feature of the complete set is that it observes time-reversal symmetry in the sense that it contains decaying states (resonant states) and growing states (anti-resonant states) parallelly. We can thereby pinpoint the occurrence of the breaking of time-reversal symmetry at the choice of whether we solve Schrödinger equation as an initial-condition problem or a terminal-condition problem. Another feature of the complete set is that in the subspace of the centralmore » scattering area of the system, it consists of contributions of all states with point spectra but does not contain any background integrals. In computing the time evolution, we can clearly see contribution of which point spectrum produces which time dependence. In the whole infinite state space, the complete set does contain an integral but it is over unperturbed eigenstates of the environmental area of the system and hence can be calculated analytically. We demonstrate the usefulness of the complete set by computing explicitly the survival probability and the escaping probability as well as the dynamics of wave packets. The origin of each term of matrix elements is clear in our formulation, particularly, the exponential decays due to the resonance poles.« less

Fractography applied to investigations of cores, outcrops, and fractured reservoirs

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

Kulander, B.

1995-11-01

Fractography focuses investigations on the topography of fracture surfaces. This topography is composed of fractographic features produced by changing stress magnitudes and directions along the advancing crack tip. Fractographic features commonly useful in core and outcrop analysis include the origin, twist hackle, inclusion hackle, and rib marks. These structures develop during brittle failure by Mode I loading at the crack tip and act together to form a hackle plume. Fractographic components throughout the plume record the dynamic history of fracture development. Components show, to the limit of visual scale, the principal stress directions, as well as relative stress magnitudes andmore » propagation velocities, that existed at the advancing fracture front. This information contributes to more meaningful conclusions in fracture investigations. In core studies, fractography aids identification of induced and natural fractures. Induced fractures and fractographic features show distinct geometry with that of the core and reflect the effects of the core boundary, in-situ stresses, drilling stresses, and rock anisotropies. Certain drilling- and coring-induced fractures possess orientations and fractographic features that suggest the direction of minimum in-situ stress and that this direction may change abruptly within the drilled volume of rock. Cored natural fractures generally originated away from the bit and possess fractographic features that bear no geometerical relationship to core parameters. Abrupt changes of natural fracture strike and development of twist hackle suggest locally complex paleostress distributions. A combined knowledge of in-situ stress and natural fracture trends is useful in predicting reservoir permeability. In outcrop, fractographic features, including abutting relationships between joints, more readily depict order of development, intrastratum distribution of fracturing stress, and size for joints in any set.« less

Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE.

PubMed

Chen, Qi; Meng, Zhaopeng; Liu, Xinyi; Jin, Qianguo; Su, Ran

2018-06-15

Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE.

Application of digital mapping technology to the display of hydrologic information; a proof-of-concept test in the Fox-Wolf River Basin, Wisconsin

USGS Publications Warehouse

Moore, G.K.; Baten, L.G.; Allord, G.J.; Robinove, C.J.

1983-01-01

The Fox-Wolf River basin in east-central Wisconsin was selected to test concepts for a water-resources information system using digital mapping technology. This basin of 16,800 sq km is typical of many areas in the country. Fifty digital data sets were included in the Fox-Wolf information system. Many data sets were digitized from 1:500,000 scale maps and overlays. Some thematic data were acquired from WATSTORE and other digital data files. All data were geometrically transformed into a Lambert Conformal Conic map projection and converted to a raster format with a 1-km resolution. The result of this preliminary processing was a group of spatially registered, digital data sets in map form. Parameter evaluation, areal stratification, data merging, and data integration were used to achieve the processing objectives and to obtain analysis results for the Fox-Wolf basin. Parameter evaluation includes the visual interpretation of single data sets and digital processing to obtain new derived data sets. In the areal stratification stage, masks were used to extract from one data set all features that are within a selected area on another data set. Most processing results were obtained by data merging. Merging is the combination of two or more data sets into a composite product, in which the contribution of each original data set is apparent and can be extracted from the composite. One processing result was also obtained by data integration. Integration is the combination of two or more data sets into a single new product, from which the original data cannot be separated or calculated. (USGS)

Cross-indexing of binary SIFT codes for large-scale image search.

PubMed

Liu, Zhen; Li, Houqiang; Zhang, Liyan; Zhou, Wengang; Tian, Qi

2014-05-01

In recent years, there has been growing interest in mapping visual features into compact binary codes for applications on large-scale image collections. Encoding high-dimensional data as compact binary codes reduces the memory cost for storage. Besides, it benefits the computational efficiency since the computation of similarity can be efficiently measured by Hamming distance. In this paper, we propose a novel flexible scale invariant feature transform (SIFT) binarization (FSB) algorithm for large-scale image search. The FSB algorithm explores the magnitude patterns of SIFT descriptor. It is unsupervised and the generated binary codes are demonstrated to be dispreserving. Besides, we propose a new searching strategy to find target features based on the cross-indexing in the binary SIFT space and original SIFT space. We evaluate our approach on two publicly released data sets. The experiments on large-scale partial duplicate image retrieval system demonstrate the effectiveness and efficiency of the proposed algorithm.

Stick-Shape, Rice-Size Features on Martian Rock "Haroldswick"

NASA Image and Video Library

2018-02-08

The dark, stick-shaped features clustered on this Martian rock are about the size of grains of rice. This is a focus-merged view from the Mars Hand Lens Imager (MAHLI) camera on NASA's Curiosity Mars rover. It covers an area about 2 inches (5 centimeters) across. The focus-merged product was generated autonomously by MAHLI combining the in-focus portions of a few separate images taken at different focus settings on Jan. 1, 2018, during the 1,922nd Martian day, or sol, of Curiosity's work on Mars. This rock target, called "Haroldswick," is near the southern, uphill edge of "Vera Rubin Ridge" on lower Mount Sharp. The origin of the stick-shaped features is uncertain. One possibility is that they are erosion-resistant bits of dark material from mineral veins cutting through rocks in this area. https://photojournal.jpl.nasa.gov/catalog/PIA22213

Centimeter to Decimeter Size Spherical and Cylindrical Features in Gale Crater Sediments

NASA Technical Reports Server (NTRS)

Wiens, R. C.; Maurice, S.; Gasnault, O.; Clegg, S.; Fabre, C.; Nachon, M.; Rubin, D.; Goetz, W.; Mangold, N.; Schroeder, S.;

Identification and mitigation of Advanced LIGO noise sources

NASA Astrophysics Data System (ADS)

Berger, Beverly K.

2018-02-01

In order to increase the reach of the astrophysical searches, various sources of instrumental and environmental noise must be identified and ameliorated. Here we discuss efforts to understand the origin of noise manifested as short-duration bursts (glitches) and/or range-impacting features at LIGO Hanford. Several examples found at LIGO Hanford Observatory in O1 and O2 were identified including glitches due to an air compressor, ringing phone, airplanes, and an incorrect servo setting, and a decrease in detector sensitivity due to truck traffic.

Letter "To the Yakut Intelligentsia" by A. E. Kulakovsky: Setting and Features of the Problem of Survival of Indigenous Peoples of the North in the Early 20th Century

ERIC Educational Resources Information Center

Sivtseva-Maksimova, Praskovia Vasilevna

2016-01-01

The relevance of the study is determined by the increasing interest in the new interpretations of social issues of living in the early 20th century, and from this perspective, in the scientific heritage of A. E. Kulakovsky (1877-1926) as an original thinker, who worried about the fate of the indigenous people inhabiting a large territory of the…

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification.

PubMed

Fan, Jianqing; Feng, Yang; Jiang, Jiancheng; Tong, Xin

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing.

Feature Augmentation via Nonparametrics and Selection (FANS) in High-Dimensional Classification

PubMed Central

Feng, Yang; Jiang, Jiancheng; Tong, Xin

2015-01-01

We propose a high dimensional classification method that involves nonparametric feature augmentation. Knowing that marginal density ratios are the most powerful univariate classifiers, we use the ratio estimates to transform the original feature measurements. Subsequently, penalized logistic regression is invoked, taking as input the newly transformed or augmented features. This procedure trains models equipped with local complexity and global simplicity, thereby avoiding the curse of dimensionality while creating a flexible nonlinear decision boundary. The resulting method is called Feature Augmentation via Nonparametrics and Selection (FANS). We motivate FANS by generalizing the Naive Bayes model, writing the log ratio of joint densities as a linear combination of those of marginal densities. It is related to generalized additive models, but has better interpretability and computability. Risk bounds are developed for FANS. In numerical analysis, FANS is compared with competing methods, so as to provide a guideline on its best application domain. Real data analysis demonstrates that FANS performs very competitively on benchmark email spam and gene expression data sets. Moreover, FANS is implemented by an extremely fast algorithm through parallel computing. PMID:27185970

The Distribution and Behaviour of Photospheric Magnetic Features

NASA Astrophysics Data System (ADS)

Parnell, C. E.; Lamb, D. A.; DeForest, C. E.

2014-12-01

Over the past two decades enormous amounts of data on the magnetic fields of the solar photosphere have been produced by both ground-based (Kitt Peak & SOLIS), as well as space-based instruments (MDI, Hinode & HMI). In order to study the behaviour and distribution of photospheric magnetic features, efficient automated detection routines need to be utilised to identify and track magnetic features. In this talk, I will discuss the pros and cons of different automated magnetic feature identification and tracking routines with a special focus on the requirements of these codes to deal with the large data sets produced by HMI. By patching together results from Hinode and MDI (high-res & full-disk), the fluxes of magnetic features were found to follow a power-law over 5 orders of magnitude. At the strong flux tail of this distribution, the power law was found to fall off at solar minimum, but was maintained over all fluxes during solar maximum. However, the point of deflection in the power-law distribution occurs at a patching point between instruments and so questions remain over the reasons for the deflection. The feature fluxes determined from the superb high-resolution HMI data covers almost all of the 5 orders of magnitude. Considering both solar mimimum and solar maximum HMI data sets, we investigate whether the power-law over 5 orders of magnitude in flux still holds. Furthermore, we investigate the behaviour of magnetic features in order to probe the nature of their origin. In particular, we analyse small-scale flux emergence events using HMI data to investigate the existence of a small-scale dynamo just below the solar photosphere.

NIMEFI: gene regulatory network inference using multiple ensemble feature importance algorithms.

PubMed

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available.

Rhinoplasty Results Are Influenced by Non-nasal Features.

PubMed

Wang, Frederick; Xu, Ginger; Gruber, Ronald Peter

2017-04-01

Rhinoplasty results are evaluated both objectively and subjectively following any procedure by plastic surgeons and nonplastic surgeons at meetings, in publications, and online. We aim to evaluate whether subjective aesthetics of non-nasal features, such as the eyes and lips, would influence the overall evaluation of rhinoplasty results. We matched pairs of photographs of patients who had undergone aesthetic rhinoplasty by sex, age, and skin tone. We transferred the eyes/eyebrows and lips from the photographs of the donor patient onto the photographs of the original patient to create composite photographs. Plastic surgeons were asked to rate the rhinoplasty results objectively, and non-plastic surgeons were asked to rate the overall attractiveness of 16 sets of photographs (8 originals and 8 composites). Postoperative photographs that were deemed to be more attractive were associated with higher ratings of rhinoplasty improvement. The objective nasal result may be influenced by non-nasal aesthetic factors as rhinoplasty surgeons gave higher ratings to more attractive faces. Greater emphasis on neutralizing non-nasal factors in pre- and postoperative photographs should be considered. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Automatic crack detection and classification method for subway tunnel safety monitoring.

PubMed

Zhang, Wenyu; Zhang, Zhenjiang; Qi, Dapeng; Liu, Yun

2014-10-16

Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification.

Automatic Crack Detection and Classification Method for Subway Tunnel Safety Monitoring

PubMed Central

Zhang, Wenyu; Zhang, Zhenjiang; Qi, Dapeng; Liu, Yun

2014-01-01

Cracks are an important indicator reflecting the safety status of infrastructures. This paper presents an automatic crack detection and classification methodology for subway tunnel safety monitoring. With the application of high-speed complementary metal-oxide-semiconductor (CMOS) industrial cameras, the tunnel surface can be captured and stored in digital images. In a next step, the local dark regions with potential crack defects are segmented from the original gray-scale images by utilizing morphological image processing techniques and thresholding operations. In the feature extraction process, we present a distance histogram based shape descriptor that effectively describes the spatial shape difference between cracks and other irrelevant objects. Along with other features, the classification results successfully remove over 90% misidentified objects. Also, compared with the original gray-scale images, over 90% of the crack length is preserved in the last output binary images. The proposed approach was tested on the safety monitoring for Beijing Subway Line 1. The experimental results revealed the rules of parameter settings and also proved that the proposed approach is effective and efficient for automatic crack detection and classification. PMID:25325337

Hierarchical learning architecture with automatic feature selection for multiclass protein fold classification.

PubMed

Huang, Chuen-Der; Lin, Chin-Teng; Pal, Nikhil Ranjan

2003-12-01

The structure classification of proteins plays a very important role in bioinformatics, since the relationships and characteristics among those known proteins can be exploited to predict the structure of new proteins. The success of a classification system depends heavily on two things: the tools being used and the features considered. For the bioinformatics applications, the role of appropriate features has not been paid adequate importance. In this investigation we use three novel ideas for multiclass protein fold classification. First, we use the gating neural network, where each input node is associated with a gate. This network can select important features in an online manner when the learning goes on. At the beginning of the training, all gates are almost closed, i.e., no feature is allowed to enter the network. Through the training, gates corresponding to good features are completely opened while gates corresponding to bad features are closed more tightly, and some gates may be partially open. The second novel idea is to use a hierarchical learning architecture (HLA). The classifier in the first level of HLA classifies the protein features into four major classes: all alpha, all beta, alpha + beta, and alpha/beta. And in the next level we have another set of classifiers, which further classifies the protein features into 27 folds. The third novel idea is to induce the indirect coding features from the amino-acid composition sequence of proteins based on the N-gram concept. This provides us with more representative and discriminative new local features of protein sequences for multiclass protein fold classification. The proposed HLA with new indirect coding features increases the protein fold classification accuracy by about 12%. Moreover, the gating neural network is found to reduce the number of features drastically. Using only half of the original features selected by the gating neural network can reach comparable test accuracy as that using all the original features. The gating mechanism also helps us to get a better insight into the folding process of proteins. For example, tracking the evolution of different gates we can find which characteristics (features) of the data are more important for the folding process. And, of course, it also reduces the computation time.

Atomic and vibrational origins of mechanical toughness in bioactive cement during setting

PubMed Central

Tian, Kun V.; Yang, Bin; Yue, Yuanzheng; Bowron, Daniel T.; Mayers, Jerry; Donnan, Robert S.; Dobó-Nagy, Csaba; Nicholson, John W.; Fang, De-Cai; Greer, A. Lindsay; Chass, Gregory A.; Greaves, G. Neville

2015-01-01

Bioactive glass ionomer cements (GICs) have been in widespread use for ∼40 years in dentistry and medicine. However, these composites fall short of the toughness needed for permanent implants. Significant impediment to improvement has been the requisite use of conventional destructive mechanical testing, which is necessarily retrospective. Here we show quantitatively, through the novel use of calorimetry, terahertz (THz) spectroscopy and neutron scattering, how GIC's developing fracture toughness during setting is related to interfacial THz dynamics, changing atomic cohesion and fluctuating interfacial configurations. Contrary to convention, we find setting is non-monotonic, characterized by abrupt features not previously detected, including a glass–polymer coupling point, an early setting point, where decreasing toughness unexpectedly recovers, followed by stress-induced weakening of interfaces. Subsequently, toughness declines asymptotically to long-term fracture test values. We expect the insight afforded by these in situ non-destructive techniques will assist in raising understanding of the setting mechanisms and associated dynamics of cementitious materials. PMID:26548704

Toward optimal feature and time segment selection by divergence method for EEG signals classification.

PubMed

Wang, Jie; Feng, Zuren; Lu, Na; Luo, Jing

2018-06-01

Feature selection plays an important role in the field of EEG signals based motor imagery pattern classification. It is a process that aims to select an optimal feature subset from the original set. Two significant advantages involved are: lowering the computational burden so as to speed up the learning procedure and removing redundant and irrelevant features so as to improve the classification performance. Therefore, feature selection is widely employed in the classification of EEG signals in practical brain-computer interface systems. In this paper, we present a novel statistical model to select the optimal feature subset based on the Kullback-Leibler divergence measure, and automatically select the optimal subject-specific time segment. The proposed method comprises four successive stages: a broad frequency band filtering and common spatial pattern enhancement as preprocessing, features extraction by autoregressive model and log-variance, the Kullback-Leibler divergence based optimal feature and time segment selection and linear discriminate analysis classification. More importantly, this paper provides a potential framework for combining other feature extraction models and classification algorithms with the proposed method for EEG signals classification. Experiments on single-trial EEG signals from two public competition datasets not only demonstrate that the proposed method is effective in selecting discriminative features and time segment, but also show that the proposed method yields relatively better classification results in comparison with other competitive methods. Copyright © 2018 Elsevier Ltd. All rights reserved.

VARS-TOOL: A Comprehensive, Efficient, and Robust Sensitivity Analysis Toolbox

NASA Astrophysics Data System (ADS)

Razavi, S.; Sheikholeslami, R.; Haghnegahdar, A.; Esfahbod, B.

2016-12-01

VARS-TOOL is an advanced sensitivity and uncertainty analysis toolbox, applicable to the full range of computer simulation models, including Earth and Environmental Systems Models (EESMs). The toolbox was developed originally around VARS (Variogram Analysis of Response Surfaces), which is a general framework for Global Sensitivity Analysis (GSA) that utilizes the variogram/covariogram concept to characterize the full spectrum of sensitivity-related information, thereby providing a comprehensive set of "global" sensitivity metrics with minimal computational cost. VARS-TOOL is unique in that, with a single sample set (set of simulation model runs), it generates simultaneously three philosophically different families of global sensitivity metrics, including (1) variogram-based metrics called IVARS (Integrated Variogram Across a Range of Scales - VARS approach), (2) variance-based total-order effects (Sobol approach), and (3) derivative-based elementary effects (Morris approach). VARS-TOOL is also enabled with two novel features; the first one being a sequential sampling algorithm, called Progressive Latin Hypercube Sampling (PLHS), which allows progressively increasing the sample size for GSA while maintaining the required sample distributional properties. The second feature is a "grouping strategy" that adaptively groups the model parameters based on their sensitivity or functioning to maximize the reliability of GSA results. These features in conjunction with bootstrapping enable the user to monitor the stability, robustness, and convergence of GSA with the increase in sample size for any given case study. VARS-TOOL has been shown to achieve robust and stable results within 1-2 orders of magnitude smaller sample sizes (fewer model runs) than alternative tools. VARS-TOOL, available in MATLAB and Python, is under continuous development and new capabilities and features are forthcoming.

A naive Bayes algorithm for tissue origin diagnosis (TOD-Bayes) of synchronous multifocal tumors in the hepatobiliary and pancreatic system.

PubMed

Jiang, Weiqin; Shen, Yifei; Ding, Yongfeng; Ye, Chuyu; Zheng, Yi; Zhao, Peng; Liu, Lulu; Tong, Zhou; Zhou, Linfu; Sun, Shuo; Zhang, Xingchen; Teng, Lisong; Timko, Michael P; Fan, Longjiang; Fang, Weijia

2018-01-15

Synchronous multifocal tumors are common in the hepatobiliary and pancreatic system but because of similarities in their histological features, oncologists have difficulty in identifying their precise tissue clonal origin through routine histopathological methods. To address this problem and assist in more precise diagnosis, we developed a computational approach for tissue origin diagnosis based on naive Bayes algorithm (TOD-Bayes) using ubiquitous RNA-Seq data. Massive tissue-specific RNA-Seq data sets were first obtained from The Cancer Genome Atlas (TCGA) and ∼1,000 feature genes were used to train and validate the TOD-Bayes algorithm. The accuracy of the model was >95% based on tenfold cross validation by the data from TCGA. A total of 18 clinical cancer samples (including six negative controls) with definitive tissue origin were subsequently used for external validation and 17 of the 18 samples were classified correctly in our study (94.4%). Furthermore, we included as cases studies seven tumor samples, taken from two individuals who suffered from synchronous multifocal tumors across tissues, where the efforts to make a definitive primary cancer diagnosis by traditional diagnostic methods had failed. Using our TOD-Bayes analysis, the two clinical test cases were successfully diagnosed as pancreatic cancer (PC) and cholangiocarcinoma (CC), respectively, in agreement with their clinical outcomes. Based on our findings, we believe that the TOD-Bayes algorithm is a powerful novel methodology to accurately identify the tissue origin of synchronous multifocal tumors of unknown primary cancers using RNA-Seq data and an important step toward more precision-based medicine in cancer diagnosis and treatment. © 2017 UICC.

Expert and novice categorization of introductory physics problems

NASA Astrophysics Data System (ADS)

Wolf, Steven Frederick

Since it was first published 30 years ago, Chi et al.'s seminal paper on expert and novice categorization of introductory problems led to a plethora of follow-up studies within and outside of the area of physics [Chi et al. Cognitive Science 5, 121 -- 152 (1981)]. These studies frequently encompass "card-sorting" exercises whereby the participants group problems. The study firmly established the paradigm that novices categorize physics problems by "surface features" (e.g. "incline," "pendulum," "projectile motion,"... ), while experts use "deep structure" (e.g. "energy conservation," "Newton 2,"... ). While this technique certainly allows insights into problem solving approaches, simple descriptive statistics more often than not fail to find significant differences between experts and novices. In most experiments, the clean-cut outcome of the original study cannot be reproduced. Given the widespread implications of the original study, the frequent failure to reproduce its findings warrants a closer look. We developed a less subjective statistical analysis method for the card sorting outcome and studied how the "successful" outcome of the experiment depends on the choice of the original card set. Thus, in a first step, we are moving beyond descriptive statistics, and develop a novel microscopic approach that takes into account the individual identity of the cards and uses graph theory and models to visualize, analyze, and interpret problem categorization experiments. These graphs are compared macroscopically, using standard graph theoretic statistics, and microscopically, using a distance metric that we have developed. This macroscopic sorting behavior is described using our Cognitive Categorization Model. The microscopic comparison allows us to visualize our sorters using Principal Components Analysis and compare the expert sorters to the novice sorters as a group. In the second step, we ask the question: Which properties of problems are most important in problem sets that discriminate experts from novices in a measurable way? We are describing a method to characterize problems along several dimensions, and then study the effectiveness of differently composed problem sets in differentiating experts from novices, using our analysis method. Both components of our study are based on an extensive experiment using a large problem set, which known physics experts and novices categorized according to the original experimental protocol. Both the size of the card set and the size of the sorter pool were larger than in comparable experiments. Based on our analysis method, we find that most of the variation in sorting outcome is not due to the sorter being an expert versus a novice, but rather due to an independent characteristic that we named "stacker" versus "spreader." The fact that the expert-novice distinction only accounts for a smaller amount of the variation may partly explain the frequent null-results when conducting these experiments. In order to study how the outcome depends on the original problem set, our problem set needed to be large so that we could determine how well experts and novices could be discriminated by considering both small subsets using a Monte Carlo approach and larger subsets using Simulated Annealing. This computationally intense study relied on our objective analysis method, as the large combinatorics did not allow for manual analysis of the outcomes from the subsets. We found that the number of questions required to accurately classify experts and novices could be surprisingly small so long as the problem set was carefully crafted to be composed of problems with particular pedagogical and contextual features. In order to discriminate experts from novices in a categorization task, it is important that the problem sets carefully consider three problem properties: The chapters that problems are in (the problems need to be from a wide spectrum of chapters to allow for the original "deep structure" categorization), the processes required to solve the problems (the problems must required different solving strategies), and the difficulty of the problems (the problems must be "easy"). In other words, for the experiment to be "successful," the card set needs to be carefully "rigged" across three property dimensions.

Low-rank matrix fitting based on subspace perturbation analysis with applications to structure from motion.

PubMed

Jia, Hongjun; Martinez, Aleix M

2009-05-01

The task of finding a low-rank (r) matrix that best fits an original data matrix of higher rank is a recurring problem in science and engineering. The problem becomes especially difficult when the original data matrix has some missing entries and contains an unknown additive noise term in the remaining elements. The former problem can be solved by concatenating a set of r-column matrices that share a common single r-dimensional solution space. Unfortunately, the number of possible submatrices is generally very large and, hence, the results obtained with one set of r-column matrices will generally be different from that captured by a different set. Ideally, we would like to find that solution that is least affected by noise. This requires that we determine which of the r-column matrices (i.e., which of the original feature points) are less influenced by the unknown noise term. This paper presents a criterion to successfully carry out such a selection. Our key result is to formally prove that the more distinct the r vectors of the r-column matrices are, the less they are swayed by noise. This key result is then combined with the use of a noise model to derive an upper bound for the effect that noise and occlusions have on each of the r-column matrices. It is shown how this criterion can be effectively used to recover the noise-free matrix of rank r. Finally, we derive the affine and projective structure-from-motion (SFM) algorithms using the proposed criterion. Extensive validation on synthetic and real data sets shows the superiority of the proposed approach over the state of the art.

DD-HDS: A method for visualization and exploration of high-dimensional data.

PubMed

Lespinats, Sylvain; Verleysen, Michel; Giron, Alain; Fertil, Bernard

2007-09-01

Mapping high-dimensional data in a low-dimensional space, for example, for visualization, is a problem of increasingly major concern in data analysis. This paper presents data-driven high-dimensional scaling (DD-HDS), a nonlinear mapping method that follows the line of multidimensional scaling (MDS) approach, based on the preservation of distances between pairs of data. It improves the performance of existing competitors with respect to the representation of high-dimensional data, in two ways. It introduces (1) a specific weighting of distances between data taking into account the concentration of measure phenomenon and (2) a symmetric handling of short distances in the original and output spaces, avoiding false neighbor representations while still allowing some necessary tears in the original distribution. More precisely, the weighting is set according to the effective distribution of distances in the data set, with the exception of a single user-defined parameter setting the tradeoff between local neighborhood preservation and global mapping. The optimization of the stress criterion designed for the mapping is realized by "force-directed placement" (FDP). The mappings of low- and high-dimensional data sets are presented as illustrations of the features and advantages of the proposed algorithm. The weighting function specific to high-dimensional data and the symmetric handling of short distances can be easily incorporated in most distance preservation-based nonlinear dimensionality reduction methods.

On the origin of Triton and Pluto

NASA Technical Reports Server (NTRS)

Mckinnon, W. B.

1984-01-01

Lyttleton's (1936) hypothesis that Triton and Pluto originated as adjacent prograde satellites of Neptune is evaluated, and it is shown that with the presently accepted masses of Triton and Pluto-Charon, the momentum and energy exchange required to set Triton on a retrograde orbit is impossible. The Pluto-Charon system could not have acquired its present angular momentum state during an ejection event unless a physical collision was involved, which is quite unlikely. The simplest hypothesis is that Triton and Pluto are independent representatives of large outer solar system planetesimals. Triton is simply captured, with spectacular consequences that include runaway melting of interior ices and release to the surface of clathrated CH4, CO, and N2. Condensed remnants of this protoatmosphere could account for features in Triton's unique spectrum.

Significance of MPEG-7 textural features for improved mass detection in mammography.

PubMed

Eltonsy, Nevine H; Tourassi, Georgia D; Fadeev, Aleksey; Elmaghraby, Adel S

2006-01-01

The purpose of the study is to investigate the significance of MPEG-7 textural features for improving the detection of masses in screening mammograms. The detection scheme was originally based on morphological directional neighborhood features extracted from mammographic regions of interest (ROIs). Receiver Operating Characteristics (ROC) was performed to evaluate the performance of each set of features independently and merged into a back-propagation artificial neural network (BPANN) using the leave-one-out sampling scheme (LOOSS). The study was based on a database of 668 mammographic ROIs (340 depicting cancer regions and 328 depicting normal parenchyma). Overall, the ROC area index of the BPANN using the directional morphological features was Az=0.85+/-0.01. The MPEG-7 edge histogram descriptor-based BPNN showed an ROC area index of Az=0.71+/-0.01 while homogeneous textural descriptors using 30 and 120 channels helped the BPNN achieve similar ROC area indexes of Az=0.882+/-0.02 and Az=0.877+/-0.01 respectively. After merging the MPEG-7 homogeneous textural features with the directional neighborhood features the performance of the BPANN increased providing an ROC area index of Az=0.91+/-0.01. MPEG-7 homogeneous textural descriptor significantly improved the morphology-based detection scheme.

Lymph nodes fine needle cytology in the diagnosis of infectious diseases: clinical settings.

PubMed

Natella, Valentina; Cozzolino, Immacolata; Sosa Fernandez, Laura Virginia; Vigliar, Elena

2012-01-01

Lymph node reactive hyperplasia, caused by specific infectious etiologic factors, represents the most frequent cause of enlarged peripheral lymph nodes. The main infectious agents are viruses, pyogenic bacteria, mycobacteria, fungi and protozoa that may determine unspecific or specific pathological entities, such as cat-scratch disease, toxoplasmosis or infectious mononucleosis. Lymph node fine needle cytology (FNC) is a safe, simple, cost-effective and efficient technique that quickly provides information about the cell population and the nature of the process. FNC can also provide suitable material for ancillary techniques, such as flow cytometry, immunocytochemistry, molecular biology and microbiological examinations. This study focuses on the cytological features of benign lymphadenopathy of infectious origin and their possible contribution to the clinical setting definition of corresponding patients.

ADS's Dexter Data Extraction Applet

NASA Astrophysics Data System (ADS)

Demleitner, M.; Accomazzi, A.; Eichhorn, G.; Grant, C. S.; Kurtz, M. J.; Murray, S. S.

The NASA Astrophysics Data System (ADS) now holds 1.3 million scanned pages, containing numerous plots and figures for which the original data sets are lost or inaccessible. The availability of scans of the figures can significantly ease the regeneration of the data sets. For this purpose, the ADS has developed Dexter, a Java applet that supports the user in this process. Dexter's basic functionality is to let the user manually digitize a plot by marking points and defining the coordinate transformation from the logical to the physical coordinate system. Advanced features include automatic identification of axes, tracing lines and finding points matching a template. This contribution both describes the operation of Dexter from a user's point of view and discusses some of the architectural issues we faced during implementation.

Martian North Polar Impacts and Volcanoes: Feature Discrimination and Comparisons to Global Trends

NASA Technical Reports Server (NTRS)

Sakimoto, E. H.; Weren, S. L.

2003-01-01

The recent Mars Global Surveyor and Mars Odyssey Missions have greatly improved our available data for the north polar region of Mars. Pre- MGS and MO studies proposed possible volcanic features, and have revealed numerous volcanoes and impact craters in a range of weathering states that were poorly visible or not visible in prior data sets. This new data has helped in the reassessment of the polar deposits. From images or shaded Mars Orbiter Laser Altimeter (MOLA) topography grids alone, it has proved to be difficult to differentiate cratered cones of probable volcanic origins from impact craters that appear to have been filled. It is important that the distinction is made if possible, as the relative ages of the polar deposits hinge on small numbers of craters, and the local volcanic regime originally only proposed small numbers of volcanoes. Therefore, we have expanded prior work on detailed topographic parameter measurements and modeling for the polar volcanic landforms and mapped and measured all of the probable volcanic and impact features for the north polar region as well as other midlatitude fields, and suggest that: 1) The polar volcanic edifices are significantly different topographically from midlatitude edifices, and have steeper slopes and larger craters as a group; 2) The impact craters are actually distinct from the volcanoes in terms of the feature volume that is cavity compared to feature volume that is positive relief; 3) There are actually several distinct types of volcanic edifices present; 4) These types tend to be spatially grouped by edifice. This is a contrast to many of the other small volcanic fields around Mars, where small edifices tend to be mixed types within a field.

Comparison of SVM, RF and ELM on an Electronic Nose for the Intelligent Evaluation of Paraffin Samples

PubMed Central

Men, Hong; Fu, Songlin; Yang, Jialin; Cheng, Meiqi; Shi, Yan

2018-01-01

Paraffin odor intensity is an important quality indicator when a paraffin inspection is performed. Currently, paraffin odor level assessment is mainly dependent on an artificial sensory evaluation. In this paper, we developed a paraffin odor analysis system to classify and grade four kinds of paraffin samples. The original feature set was optimized using Principal Component Analysis (PCA) and Partial Least Squares (PLS). Support Vector Machine (SVM), Random Forest (RF), and Extreme Learning Machine (ELM) were applied to three different feature data sets for classification and level assessment of paraffin. For classification, the model based on SVM, with an accuracy rate of 100%, was superior to that based on RF, with an accuracy rate of 98.33–100%, and ELM, with an accuracy rate of 98.01–100%. For level assessment, the R2 related to the training set was above 0.97 and the R2 related to the test set was above 0.87. Through comprehensive comparison, the generalization of the model based on ELM was superior to those based on SVM and RF. The scoring errors for the three models were 0.0016–0.3494, lower than the error of 0.5–1.0 measured by industry standard experts, meaning these methods have a higher prediction accuracy for scoring paraffin level. PMID:29346328

Anomalous Aortic Origin of Coronary Arteries in the Young: Echocardiographic Evaluation With Surgical Correlation.

PubMed

Lorber, Richard; Srivastava, Shubhika; Wilder, Travis J; McIntyre, Susan; DeCampli, William M; Williams, William G; Frommelt, Peter C; Parness, Ira A; Blackstone, Eugene H; Jacobs, Marshall L; Mertens, Luc; Brothers, Julie A; Herlong, J René

2015-11-01

This study sought to compare findings from institutional echocardiographic reports with imaging core laboratory (ICL) review of corresponding echocardiographic images and operative reports in 159 patients with anomalous aortic origin of a coronary artery (AAOCA). The study also sought to develop a "best practice" protocol for imaging and interpreting images in establishing the diagnosis of AAOCA. AAOCA is associated with sudden death in the young. Underlying anatomic risk factors that can cause ischemia-related events include coronary arterial ostial stenosis, intramural course of the proximal coronary within the aortic wall, interarterial course, and potential compression between the great arteries. Consistent protocols for diagnosing and evaluating these features are lacking, potentially precluding the ability to risk stratify patients based on evidence and plan surgical strategy. For a prescribed set of anatomic AAOCA features, percentages of missing data in institutional echocardiographic reports were calculated. For each feature, agreement among institutional echocardiographic reports, ICL review of images, and surgical reports was evaluated using the weighted kappa statistic. An echocardiographic imaging protocol was developed heuristically to reduce differences between institutional reports and ICL review. A total of 13%, 33%, and 62% of echocardiograms were missing images enabling diagnosis of intra-arterial course, proximal intramural course, and high ostial takeoff, respectively. There was poor agreement between institutional reports and ICL review for diagnosis of origin of coronary artery, interarterial course, intramural course, and acute angle takeoff (kappa = 0.74, 0.11, -0.03, 0.13, respectively). Surgical findings were also significantly different from those of reports, and to a lesser extent ICL reviews. The resulting protocol contains technical recommendations for imaging each of these features. Poor agreement between institutional reports and ICL review for AAOCA suggests need for an imaging protocol to permit evidence-based risk stratification and surgical planning. Even then, delineation of echocardiographic details in AAOCA will remain imperfect. Copyright © 2015 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Improvement to the scanning electron microscope image adaptive Canny optimization colorization by pseudo-mapping.

PubMed

Lo, T Y; Sim, K S; Tso, C P; Nia, M E

2014-01-01

An improvement to the previously proposed adaptive Canny optimization technique for scanning electron microscope image colorization is reported. The additional feature, called pseudo-mapping technique, is that the grayscale markings are temporarily mapped to a set of pre-defined pseudo-color map as a mean to instill color information for grayscale colors in chrominance channels. This allows the presence of grayscale markings to be identified; hence optimization colorization of grayscale colors is made possible. This additional feature enhances the flexibility of scanning electron microscope image colorization by providing wider range of possible color enhancement. Furthermore, the nature of this technique also allows users to adjust the luminance intensities of selected region from the original image within certain extent. © 2014 Wiley Periodicals, Inc.

Trident: A Universal Tool for Generating Synthetic Absorption Spectra from Astrophysical Simulations

NASA Astrophysics Data System (ADS)

Hummels, Cameron B.; Smith, Britton D.; Silvia, Devin W.

2017-09-01

Hydrodynamical simulations are increasingly able to accurately model physical systems on stellar, galactic, and cosmological scales; however, the utility of these simulations is often limited by our ability to directly compare them with the data sets produced by observers: spectra, photometry, etc. To address this problem, we have created trident, a Python-based open-source tool for post-processing hydrodynamical simulations to produce synthetic absorption spectra and related data. trident can (I) create absorption-line spectra for any trajectory through a simulated data set mimicking both background quasar and down-the-barrel configurations; (II) reproduce the spectral characteristics of common instruments like the Cosmic Origins Spectrograph; (III) operate across the ultraviolet, optical, and infrared using customizable absorption-line lists; (IV) trace simulated physical structures directly to spectral features; (v) approximate the presence of ion species absent from the simulation outputs; (VI) generate column density maps for any ion; and (vii) provide support for all major astrophysical hydrodynamical codes. trident was originally developed to aid in the interpretation of observations of the circumgalactic medium and intergalactic medium, but it remains a general tool applicable in other contexts.

Nemesis reconsidered

NASA Astrophysics Data System (ADS)

Melott, Adrian L.; Bambach, Richard K.

2010-09-01

The hypothesis of a companion object (Nemesis) orbiting the Sun was motivated by the claim of a terrestrial extinction periodicity, thought to be mediated by comet showers. The orbit of a distant companion to the Sun is expected to be perturbed by the Galactic tidal field and encounters with passing stars, which will induce variation in the period. We examine the evidence for the previously proposed periodicity, using two modern, greatly improved paleontological data sets of fossil biodiversity. We find that there is a narrow peak at 27 Myr in the cross-spectrum of extinction intensity time series between these independent data sets. This periodicity extends over a time period nearly twice that for which it was originally noted. An excess of extinction events is associated with this periodicity at 99 per cent confidence. In this sense we confirm the originally noted feature in the time series for extinction. However, we find that it displays extremely regular timing for about 0.5 Gyr. The regularity of the timing compared with earlier calculations of orbital perturbation would seem to exclude the Nemesis hypothesis as a causal factor.

Stable isotope study of antimony deposits in the Muratdagi region, western Turkey

NASA Astrophysics Data System (ADS)

Gokçe, A.; Spiro, B.

1994-09-01

The Muratdagi region is rich in antimony deposits having the following common characteristics: post Miocene age, location on the down-thrown blocks next to normal faults, in the vicinity of active or fossil thermal springs, and in contact with carbonate rocks. The isotopic composition of — 7‰. SMOW of the mineralizing fluid calculated from the measured ° 18O of quartz and the fluid inclusion microthermometry, is indicative of meteoric water origin. The ° 13C of the inclusion CO2 of — 19.1 to — 25.4‰ PDB is indicative of interaction with organic material-graphite. The ° 34S of stibnite — 3.6 to — 0.7‰ is, in view of the mineral assemblage, indicative of magmatic origin of the sulphur. A tightly confined set of structural, lithological, hydrological and geochemical features define a sequence of geochemical processes; formation of acid and reducing fluid, leaching and transport of antimony complexes and precipitation of stibnite within defined lithological units. The set of processes seems to have taken place within a space of 5000 m lateral and 1000 m vertical extension.

The Plausibility of a String Quartet Performance in Virtual Reality.

PubMed

Bergstrom, Ilias; Azevedo, Sergio; Papiotis, Panos; Saldanha, Nuno; Slater, Mel

2017-04-01

We describe an experiment that explores the contribution of auditory and other features to the illusion of plausibility in a virtual environment that depicts the performance of a string quartet. 'Plausibility' refers to the component of presence that is the illusion that the perceived events in the virtual environment are really happening. The features studied were: Gaze (the musicians ignored the participant, the musicians sometimes looked towards and followed the participant's movements), Sound Spatialization (Mono, Stereo, Spatial), Auralization (no sound reflections, reflections corresponding to a room larger than the one perceived, reflections that exactly matched the virtual room), and Environment (no sound from outside of the room, birdsong and wind corresponding to the outside scene). We adopted the methodology based on color matching theory, where 20 participants were first able to assess their feeling of plausibility in the environment with each of the four features at their highest setting. Then five times participants started from a low setting on all features and were able to make transitions from one system configuration to another until they matched their original feeling of plausibility. From these transitions a Markov transition matrix was constructed, and also probabilities of a match conditional on feature configuration. The results show that Environment and Gaze were individually the most important factors influencing the level of plausibility. The highest probability transitions were to improve Environment and Gaze, and then Auralization and Spatialization. We present this work as both a contribution to the methodology of assessing presence without questionnaires, and showing how various aspects of a musical performance can influence plausibility.

A standardised protocol for texture feature analysis of endoscopic images in gynaecological cancer.

PubMed

Neofytou, Marios S; Tanos, Vasilis; Pattichis, Marios S; Pattichis, Constantinos S; Kyriacou, Efthyvoulos C; Koutsouris, Dimitris D

2007-11-29

In the development of tissue classification methods, classifiers rely on significant differences between texture features extracted from normal and abnormal regions. Yet, significant differences can arise due to variations in the image acquisition method. For endoscopic imaging of the endometrium, we propose a standardized image acquisition protocol to eliminate significant statistical differences due to variations in: (i) the distance from the tissue (panoramic vs close up), (ii) difference in viewing angles and (iii) color correction. We investigate texture feature variability for a variety of targets encountered in clinical endoscopy. All images were captured at clinically optimum illumination and focus using 720 x 576 pixels and 24 bits color for: (i) a variety of testing targets from a color palette with a known color distribution, (ii) different viewing angles, (iv) two different distances from a calf endometrial and from a chicken cavity. Also, human images from the endometrium were captured and analysed. For texture feature analysis, three different sets were considered: (i) Statistical Features (SF), (ii) Spatial Gray Level Dependence Matrices (SGLDM), and (iii) Gray Level Difference Statistics (GLDS). All images were gamma corrected and the extracted texture feature values were compared against the texture feature values extracted from the uncorrected images. Statistical tests were applied to compare images from different viewing conditions so as to determine any significant differences. For the proposed acquisition procedure, results indicate that there is no significant difference in texture features between the panoramic and close up views and between angles. For a calibrated target image, gamma correction provided an acquired image that was a significantly better approximation to the original target image. In turn, this implies that the texture features extracted from the corrected images provided for better approximations to the original images. Within the proposed protocol, for human ROIs, we have found that there is a large number of texture features that showed significant differences between normal and abnormal endometrium. This study provides a standardized protocol for avoiding any significant texture feature differences that may arise due to variability in the acquisition procedure or the lack of color correction. After applying the protocol, we have found that significant differences in texture features will only be due to the fact that the features were extracted from different types of tissue (normal vs abnormal).

Computer-aided diagnostic method for classification of Alzheimer's disease with atrophic image features on MR images

NASA Astrophysics Data System (ADS)

Arimura, Hidetaka; Yoshiura, Takashi; Kumazawa, Seiji; Tanaka, Kazuhiro; Koga, Hiroshi; Mihara, Futoshi; Honda, Hiroshi; Sakai, Shuji; Toyofuku, Fukai; Higashida, Yoshiharu

2008-03-01

Our goal for this study was to attempt to develop a computer-aided diagnostic (CAD) method for classification of Alzheimer's disease (AD) with atrophic image features derived from specific anatomical regions in three-dimensional (3-D) T1-weighted magnetic resonance (MR) images. Specific regions related to the cerebral atrophy of AD were white matter and gray matter regions, and CSF regions in this study. Cerebral cortical gray matter regions were determined by extracting a brain and white matter regions based on a level set based method, whose speed function depended on gradient vectors in an original image and pixel values in grown regions. The CSF regions in cerebral sulci and lateral ventricles were extracted by wrapping the brain tightly with a zero level set determined from a level set function. Volumes of the specific regions and the cortical thickness were determined as atrophic image features. Average cortical thickness was calculated in 32 subregions, which were obtained by dividing each brain region. Finally, AD patients were classified by using a support vector machine, which was trained by the image features of AD and non-AD cases. We applied our CAD method to MR images of whole brains obtained from 29 clinically diagnosed AD cases and 25 non-AD cases. As a result, the area under a receiver operating characteristic (ROC) curve obtained by our computerized method was 0.901 based on a leave-one-out test in identification of AD cases among 54 cases including 8 AD patients at early stages. The accuracy for discrimination between 29 AD patients and 25 non-AD subjects was 0.840, which was determined at the point where the sensitivity was the same as the specificity on the ROC curve. This result showed that our CAD method based on atrophic image features may be promising for detecting AD patients by using 3-D MR images.

SU-F-T-450: The Investigation of Radiotherapy Quality Assurance and Automatic Treatment Planning Based On the Kernel Density Estimation Method

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

Fan, J; Fan, J; Hu, W

Purpose: To develop a fast automatic algorithm based on the two dimensional kernel density estimation (2D KDE) to predict the dose-volume histogram (DVH) which can be employed for the investigation of radiotherapy quality assurance and automatic treatment planning. Methods: We propose a machine learning method that uses previous treatment plans to predict the DVH. The key to the approach is the framing of DVH in a probabilistic setting. The training consists of estimating, from the patients in the training set, the joint probability distribution of the dose and the predictive features. The joint distribution provides an estimation of the conditionalmore » probability of the dose given the values of the predictive features. For the new patient, the prediction consists of estimating the distribution of the predictive features and marginalizing the conditional probability from the training over this. Integrating the resulting probability distribution for the dose yields an estimation of the DVH. The 2D KDE is implemented to predict the joint probability distribution of the training set and the distribution of the predictive features for the new patient. Two variables, including the signed minimal distance from each OAR (organs at risk) voxel to the target boundary and its opening angle with respect to the origin of voxel coordinate, are considered as the predictive features to represent the OAR-target spatial relationship. The feasibility of our method has been demonstrated with the rectum, breast and head-and-neck cancer cases by comparing the predicted DVHs with the planned ones. Results: The consistent result has been found between these two DVHs for each cancer and the average of relative point-wise differences is about 5% within the clinical acceptable extent. Conclusion: According to the result of this study, our method can be used to predict the clinical acceptable DVH and has ability to evaluate the quality and consistency of the treatment planning.« less

Experimental Applications of Automatic Test Markup Language (ATML)

NASA Technical Reports Server (NTRS)

Lansdowne, Chatwin A.; McCartney, Patrick; Gorringe, Chris

2012-01-01

The authors describe challenging use-cases for Automatic Test Markup Language (ATML), and evaluate solutions. The first case uses ATML Test Results to deliver active features to support test procedure development and test flow, and bridging mixed software development environments. The second case examines adding attributes to Systems Modelling Language (SysML) to create a linkage for deriving information from a model to fill in an ATML document set. Both cases are outside the original concept of operations for ATML but are typical when integrating large heterogeneous systems with modular contributions from multiple disciplines.

Optical design and testing: introduction.

PubMed

Liang, Chao-Wen; Koshel, John; Sasian, Jose; Breault, Robert; Wang, Yongtian; Fang, Yi Chin

2014-10-10

Optical design and testing has numerous applications in industrial, military, consumer, and medical settings. Assembling a complete imaging or nonimage optical system may require the integration of optics, mechatronics, lighting technology, optimization, ray tracing, aberration analysis, image processing, tolerance compensation, and display rendering. This issue features original research ranging from the optical design of image and nonimage optical stimuli for human perception, optics applications, bio-optics applications, 3D display, solar energy system, opto-mechatronics to novel imaging or nonimage modalities in visible and infrared spectral imaging, modulation transfer function measurement, and innovative interferometry.

Multiconfigurational quantum propagation with trajectory-guided generalized coherent states

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

Grigolo, Adriano, E-mail: agrigolo@ifi.unicamp.br; Aguiar, Marcus A. M. de, E-mail: aguiar@ifi.unicamp.br; Viscondi, Thiago F., E-mail: viscondi@if.usp.br

2016-03-07

A generalized version of the coupled coherent states method for coherent states of arbitrary Lie groups is developed. In contrast to the original formulation, which is restricted to frozen-Gaussian basis sets, the extended method is suitable for propagating quantum states of systems featuring diversified physical properties, such as spin degrees of freedom or particle indistinguishability. The approach is illustrated with simple models for interacting bosons trapped in double- and triple-well potentials, most adequately described in terms of SU(2) and SU(3) bosonic coherent states, respectively.

The construction of support vector machine classifier using the firefly algorithm.

PubMed

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy.

The Construction of Support Vector Machine Classifier Using the Firefly Algorithm

PubMed Central

Chao, Chih-Feng; Horng, Ming-Huwi

2015-01-01

The setting of parameters in the support vector machines (SVMs) is very important with regard to its accuracy and efficiency. In this paper, we employ the firefly algorithm to train all parameters of the SVM simultaneously, including the penalty parameter, smoothness parameter, and Lagrangian multiplier. The proposed method is called the firefly-based SVM (firefly-SVM). This tool is not considered the feature selection, because the SVM, together with feature selection, is not suitable for the application in a multiclass classification, especially for the one-against-all multiclass SVM. In experiments, binary and multiclass classifications are explored. In the experiments on binary classification, ten of the benchmark data sets of the University of California, Irvine (UCI), machine learning repository are used; additionally the firefly-SVM is applied to the multiclass diagnosis of ultrasonic supraspinatus images. The classification performance of firefly-SVM is also compared to the original LIBSVM method associated with the grid search method and the particle swarm optimization based SVM (PSO-SVM). The experimental results advocate the use of firefly-SVM to classify pattern classifications for maximum accuracy. PMID:25802511

CNN-SVM for Microvascular Morphological Type Recognition with Data Augmentation.

PubMed

Xue, Di-Xiu; Zhang, Rong; Feng, Hui; Wang, Ya-Lei

2016-01-01

This paper focuses on the problem of feature extraction and the classification of microvascular morphological types to aid esophageal cancer detection. We present a patch-based system with a hybrid SVM model with data augmentation for intraepithelial papillary capillary loop recognition. A greedy patch-generating algorithm and a specialized CNN named NBI-Net are designed to extract hierarchical features from patches. We investigate a series of data augmentation techniques to progressively improve the prediction invariance of image scaling and rotation. For classifier boosting, SVM is used as an alternative to softmax to enhance generalization ability. The effectiveness of CNN feature representation ability is discussed for a set of widely used CNN models, including AlexNet, VGG-16, and GoogLeNet. Experiments are conducted on the NBI-ME dataset. The recognition rate is up to 92.74% on the patch level with data augmentation and classifier boosting. The results show that the combined CNN-SVM model beats models of traditional features with SVM as well as the original CNN with softmax. The synthesis results indicate that our system is able to assist clinical diagnosis to a certain extent.

Sediment features at the grounding zone and beneath Ekström Ice Shelf, East Antarctica, imaged using on-ice vibroseis.

NASA Astrophysics Data System (ADS)

Smith, Emma C.; Eisen, Olaf; Hofstede, Coen; Lambrecht, Astrid; Mayer, Christoph

2017-04-01

The grounding zone, where an ice sheet becomes a floating ice shelf, is known to be a key threshold region for ice flow and stability. A better understanding of ice dynamics and sediment transport across such zones will improve knowledge about contemporary and palaeo ice flow, as well as past ice extent. Here we present a set of seismic reflection profiles crossing the grounding zone and continuing to the shelf edge of Ekström Ice Shelf, East Antarctica. Using an on-ice vibroseis source combined with a snowstreamer we have imaged a range of sub-glacial and sub-shelf sedimentary and geomorphological features; from layered sediment deposits to elongated flow features. The acoustic properties of the features as well as their morphology allow us to draw conclusions as to their material properties and origin. These results will eventually be integrated with numerical models of ice dynamics to quantify past and present interactions between ice and the solid Earth in East Antarctica; leading to a better understanding of future contributions of this region to sea-level rise.

Fuzzy automata and pattern matching

NASA Technical Reports Server (NTRS)

Setzer, C. B.; Warsi, N. A.

1986-01-01

A wide-ranging search for articles and books concerned with fuzzy automata and syntactic pattern recognition is presented. A number of survey articles on image processing and feature detection were included. Hough's algorithm is presented to illustrate the way in which knowledge about an image can be used to interpret the details of the image. It was found that in hand generated pictures, the algorithm worked well on following the straight lines, but had great difficulty turning corners. An algorithm was developed which produces a minimal finite automaton recognizing a given finite set of strings. One difficulty of the construction is that, in some cases, this minimal automaton is not unique for a given set of strings and a given maximum length. This algorithm compares favorably with other inference algorithms. More importantly, the algorithm produces an automaton with a rigorously described relationship to the original set of strings that does not depend on the algorithm itself.

Cell-of-Origin Patterns Dominate the Molecular Classification of 10,000 Tumors from 33 Types of Cancer.

PubMed

Hoadley, Katherine A; Yau, Christina; Hinoue, Toshinori; Wolf, Denise M; Lazar, Alexander J; Drill, Esther; Shen, Ronglai; Taylor, Alison M; Cherniack, Andrew D; Thorsson, Vésteinn; Akbani, Rehan; Bowlby, Reanne; Wong, Christopher K; Wiznerowicz, Maciej; Sanchez-Vega, Francisco; Robertson, A Gordon; Schneider, Barbara G; Lawrence, Michael S; Noushmehr, Houtan; Malta, Tathiane M; Stuart, Joshua M; Benz, Christopher C; Laird, Peter W

2018-04-05

We conducted comprehensive integrative molecular analyses of the complete set of tumors in The Cancer Genome Atlas (TCGA), consisting of approximately 10,000 specimens and representing 33 types of cancer. We performed molecular clustering using data on chromosome-arm-level aneuploidy, DNA hypermethylation, mRNA, and miRNA expression levels and reverse-phase protein arrays, of which all, except for aneuploidy, revealed clustering primarily organized by histology, tissue type, or anatomic origin. The influence of cell type was evident in DNA-methylation-based clustering, even after excluding sites with known preexisting tissue-type-specific methylation. Integrative clustering further emphasized the dominant role of cell-of-origin patterns. Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which in turn may inform strategies for future therapeutic development. Copyright © 2018 Elsevier Inc. All rights reserved.

Actively learning to distinguish suspicious from innocuous anomalies in a batch of vehicle tracks

NASA Astrophysics Data System (ADS)

Qiu, Zhicong; Miller, David J.; Stieber, Brian; Fair, Tim

2014-06-01

We investigate the problem of actively learning to distinguish between two sets of anomalous vehicle tracks, innocuous" and suspicious", starting from scratch, without any initial examples of suspicious" and with no prior knowledge of what an operator would deem suspicious. This two-class problem is challenging because it is a priori unknown which track features may characterize the suspicious class. Furthermore, there is inherent imbalance in the sizes of the labeled innocuous" and suspicious" sets, even after some suspicious examples are identified. We present a comprehensive solution wherein a classifier learns to discriminate suspicious from innocuous based on derived p-value track features. Through active learning, our classifier thus learns the types of anomalies on which to base its discrimination. Our solution encompasses: i) judicious choice of kinematic p-value based features conditioned on the road of origin, along with more explicit features that capture unique vehicle behavior (e.g. U-turns); ii) novel semi-supervised learning that exploits information in the unlabeled (test batch) tracks, and iii) evaluation of several classifier models (logistic regression, SVMs). We find that two active labeling streams are necessary in practice in order to have efficient classifier learning while also forwarding (for labeling) the most actionable tracks. Experiments on wide-area motion imagery (WAMI) tracks, extracted via a system developed by Toyon Research Corporation, demonstrate the strong ROC AUC performance of our system, with sparing use of operator-based active labeling.

Inefficient conjunction search made efficient by concurrent spoken delivery of target identity.

PubMed

Reali, Florencia; Spivey, Michael J; Tyler, Melinda J; Terranova, Joseph

2006-08-01

Visual search based on a conjunction of two features typically elicits reaction times that increase linearly as a function of the number of distractors, whereas search based on a single feature is essentially unaffected by set size. These and related findings have often been interpreted as evidence of a serial search stage that follows a parallel search stage. However, a wide range of studies has been showing a form of blending of these two processes. For example, when a spoken instruction identifies the conjunction target concurrently with the visual display, the effect of set size is significantly reduced, suggesting that incremental linguistic processing of the first feature adjective and then the second feature adjective may facilitate something approximating a parallel extraction of objects during search for the target. Here, we extend these results to a variety of experimental designs. First, we replicate the result with a mixed-trials design (ruling out potential strategies associated with the blocked design of the original study). Second, in a mixed-trials experiment, the order of adjective types in the spoken query varies randomly across conditions. In a third experiment, we extend the effect to a triple-conjunction search task. A fourth (control) experiment demonstrates that these effects are not due to an efficient odd-one-out search that ignores the linguistic input. This series of experiments, along with attractor-network simulations of the phenomena, provide further evidence toward understanding linguistically mediated influences in real-time visual search processing.

Unravelling the depositional origins and diagenetic alteration of carbonate breccias

NASA Astrophysics Data System (ADS)

Madden, Robert H. C.; Wilson, Moyra E. J.; Mihaljević, Morana; Pandolfi, John M.; Welsh, Kevin

2017-07-01

Carbonate breccias dissociated from their platform top counterparts are little studied despite their potential to reveal the nature of past shallow-water carbonate systems and the sequential alteration of such systems. A petrographic and stable isotopic study allowed evaluation of the sedimentological and diagenetic variability of the Cenozoic Batu Gading Limestone breccia of Borneo. Sixteen lithofacies representing six facies groups have been identified mainly from the breccia clasts on the basis of shared textural and compositional features. Clasts of the breccia are representative of shallow carbonate platform top and associated flank to basinal deposits. Dominant inputs are from rocky (karstic) shorelines or localised seagrass environments, coral patch reef and larger foraminiferal-rich deposits. Early, pre-brecciation alteration (including micritisation, rare dissolution of bioclasts, minor syntaxial overgrowth cementation, pervasive neomorphism and calcitisation of bioclasts and matrix) was mainly associated with marine fluids in a near surface to shallow burial environment. The final stages of pre-brecciation diagenesis include mechanical compaction and cementation of open porosity in a shallow to moderate depth burial environment. Post-brecciation diagenesis took place at increasingly moderate to deep burial depths under the influence of dominantly marine burial fluids. Extensive compaction, circum-clast dissolution seams and stylolites have resulted in a tightly fitted breccia fabric, with some development of fractures and calcite cements. A degree of facies-specific controls are evident for the pre-brecciation diagenesis. Pervasive mineralogical stabilisation and cementation have, however, led to a broad similarity of diagenetic features in the breccia clasts thereby effectively preserving depositional features of near-original platform top and margin environments. There is little intra-clast alteration overprint associated with subsequent clast reworking and post-brecciation diagenesis. The diagenetic-, and to an extent depositional- and clast-characteristics of the Batu Gading deposits are diagnostic of breccia origins. The predominance of: early and pervasive stabilisation of calcitic components, pervasive compaction resulting in a fitted texture, and paucity of meteoric dissolution or cementation effects are collectively all indicators of slope deposition and lithification. These features are comparable with other regional and global examples of submarine slope breccias, and in particular those also from syntectonic settings (Wannier, 2009). The results of this study, along with regional analogues, suggest the potential for reworked carbonate debris in slope settings to be a viable way of investigating carbonate platform variability and their subsequent alteration in the absence of preserved platform top or margin deposits.

Exploring the color feature power for psoriasis risk stratification and classification: A data mining paradigm.

PubMed

Shrivastava, Vimal K; Londhe, Narendra D; Sonawane, Rajendra S; Suri, Jasjit S

2015-10-01

A large percentage of dermatologist׳s decision in psoriasis disease assessment is based on color. The current computer-aided diagnosis systems for psoriasis risk stratification and classification lack the vigor of color paradigm. The paper presents an automated psoriasis computer-aided diagnosis (pCAD) system for classification of psoriasis skin images into psoriatic lesion and healthy skin, which solves the two major challenges: (i) fulfills the color feature requirements and (ii) selects the powerful dominant color features while retaining high classification accuracy. Fourteen color spaces are discovered for psoriasis disease analysis leading to 86 color features. The pCAD system is implemented in a support vector-based machine learning framework where the offline image data set is used for computing machine learning offline color machine learning parameters. These are then used for transformation of the online color features to predict the class labels for healthy vs. diseased cases. The above paradigm uses principal component analysis for color feature selection of dominant features, keeping the original color feature unaltered. Using the cross-validation protocol, the above machine learning protocol is compared against the standalone grayscale features with 60 features and against the combined grayscale and color feature set of 146. Using a fixed data size of 540 images with equal number of healthy and diseased, 10 fold cross-validation protocol, and SVM of polynomial kernel of type two, pCAD system shows an accuracy of 99.94% with sensitivity and specificity of 99.93% and 99.96%. Using a varying data size protocol, the mean classification accuracies for color, grayscale, and combined scenarios are: 92.85%, 93.83% and 93.99%, respectively. The reliability of the system in these three scenarios are: 94.42%, 97.39% and 96.00%, respectively. We conclude that pCAD system using color space alone is compatible to grayscale space or combined color and grayscale spaces. We validated our pCAD system against facial color databases and the results are consistent in accuracy and reliability. Copyright © 2015 Elsevier Ltd. All rights reserved.

Comparative analysis and visualization of multiple collinear genomes

PubMed Central

2012-01-01

Background Genome browsers are a common tool used by biologists to visualize genomic features including genes, polymorphisms, and many others. However, existing genome browsers and visualization tools are not well-suited to perform meaningful comparative analysis among a large number of genomes. With the increasing quantity and availability of genomic data, there is an increased burden to provide useful visualization and analysis tools for comparison of multiple collinear genomes such as the large panels of model organisms which are the basis for much of the current genetic research. Results We have developed a novel web-based tool for visualizing and analyzing multiple collinear genomes. Our tool illustrates genome-sequence similarity through a mosaic of intervals representing local phylogeny, subspecific origin, and haplotype identity. Comparative analysis is facilitated through reordering and clustering of tracks, which can vary throughout the genome. In addition, we provide local phylogenetic trees as an alternate visualization to assess local variations. Conclusions Unlike previous genome browsers and viewers, ours allows for simultaneous and comparative analysis. Our browser provides intuitive selection and interactive navigation about features of interest. Dynamic visualizations adjust to scale and data content making analysis at variable resolutions and of multiple data sets more informative. We demonstrate our genome browser for an extensive set of genomic data sets composed of almost 200 distinct mouse laboratory strains. PMID:22536897

Integration of g4tools in Geant4

NASA Astrophysics Data System (ADS)

Hřivnáčová, Ivana

2014-06-01

g4tools, that is originally part of the inlib and exlib packages, provides a very light and easy to install set of C++ classes that can be used to perform analysis in a Geant4 batch program. It allows to create and manipulate histograms and ntuples, and write them in supported file formats (ROOT, AIDA XML, CSV and HBOOK). It is integrated in Geant4 through analysis manager classes, thus providing a uniform interface to the g4tools objects and also hiding the differences between the classes for different supported output formats. Moreover, additional features, such as for example histogram activation or support for Geant4 units, are implemented in the analysis classes following users requests. A set of Geant4 user interface commands allows the user to create histograms and set their properties interactively or in Geant4 macros. g4tools was first introduced in the Geant4 9.5 release where its use was demonstrated in one basic example, and it is already used in a majority of the Geant4 examples within the Geant4 9.6 release. In this paper, we will give an overview and the present status of the integration of g4tools in Geant4 and report on upcoming new features.

Neurochemical, morphologic, and laminar characterization of cortical projection neurons in the cingulate motor areas of the macaque monkey

NASA Technical Reports Server (NTRS)

Nimchinsky, E. A.; Hof, P. R.; Young, W. G.; Morrison, J. H.; Bloom, F. E. (Principal Investigator)

1996-01-01

The primate cingulate gyrus contains multiple cortical areas that can be distinguished by several neurochemical features, including the distribution of neurofilament protein-enriched pyramidal neurons. In addition, connectivity and functional properties indicate that there are multiple motor areas in the cortex lining the cingulate sulcus. These motor areas were targeted for analysis of potential interactions among regional specialization, connectivity, and cellular characteristics such as neurochemical profile and morphology. Specifically, intracortical injections of retrogradely transported dyes and intracellular injection were combined with immunocytochemistry to investigate neurons projecting from the cingulate motor areas to the putative forelimb region of the primary motor cortex, area M1. Two separate groups of neurons projecting to area M1 emanated from the cingulate sulcus, one anterior and one posterior, both of which furnished commissural and ipsilateral connections with area M1. The primary difference between the two populations was laminar origin, with the anterior projection originating largely in deep layers, and the posterior projection taking origin equally in superficial and deep layers. With regard to cellular morphology, the anterior projection exhibited more morphologic diversity than the posterior projection. Commissural projections from both anterior and posterior fields originated largely in layer VI. Neurofilament protein distribution was a reliable tool for localizing the two projections and for discriminating between them. Comparable proportions of the two sets of projection neurons contained neurofilament protein, although the density and distribution of the total population of neurofilament protein-enriched neurons was very different in the two subareas of origin. Within a projection, the participating neurons exhibited a high degree of morphologic heterogeneity, and no correlation was observed between somatodendritic morphology and neurofilament protein content. Thus, although the neurons that provide the anterior and posterior cingulate motor projections to area M1 differ morphologically and in laminar origin, their neurochemical profiles are similar with respect to neurofilament protein. This suggests that neurochemical phenotype may be a more important unifying feature for corticocortical projections than morphology.

Evaluation of the effects of the seasonal variation of solar elevation angle and azimuth on the processes of digital filtering and thematic classification of relief units

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Novo, E. M. L. M.

1983-01-01

The effects of the seasonal variation of illumination over digital processing of LANDSAT images are evaluated. Two sets of LANDSAT data referring to the orbit 150 and row 28 were selected with illumination parameters varying from 43 deg to 64 deg for azimuth and from 30 deg to 36 deg for solar elevation respectively. IMAGE-100 system permitted the digital processing of LANDSAT data. Original images were transformed by means of digital filtering so as to enhance their spatial features. The resulting images were used to obtain an unsupervised classification of relief units. Topographic variables (declivity, altitude, relief range and slope length) were used to identify the true relief units existing on the ground. The LANDSAT over pass data show that digital processing is highly affected by illumination geometry, and there is no correspondence between relief units as defined by spectral features and those resulting from topographic features.

Prevalence of neuropathic features of back pain in clinical populations: implications for the diagnostic triage paradigm.

PubMed

Hush, Julia M; Marcuzzi, Anna

2012-07-01

SUMMARY Contemporary clinical assessment of back pain is based on the diagnostic triage paradigm. The most common diagnostic classification is nonspecific back pain, considered to be of nociceptive etiology. A small proportion are diagnosed with radicular pain, of neuropathic origin. In this study we review the body of literature on the prevalence of neuropathic features of back pain, revealing that the point prevalence is 17% in primary care, 34% in mixed clinical settings and 53% in tertiary care. There is evidence that neuropathic features of back pain are not restricted to typical clinical radicular pain phenotypes and may be under-recognized, particularly in primary care. The consequence of this is that in the clinic, diagnostic triage may erroneously classify patients with nonspecific back pain or radicular pain. A promising alternative is the development of mechanism-based pain phenotyping in patients with back pain. Timely identification of contributory pain mechanisms may enable greater opportunity to select appropriate therapeutic targets and improve patient outcomes.

REM Sleep Behavior Disorder and Narcoleptic Features in Anti–Ma2-associated Encephalitis

PubMed Central

Compta, Yaroslau; Iranzo, Alex; Santamaría, Joan; Casamitjana, Roser; Graus, Francesc

2007-01-01

A 69-year-old man with anti-Ma2 paraneoplastic encephalitis presented with subacute onset of severe hypersomnia, memory loss, parkinsonism, and gaze palsy. A brain magnetic resonance imaging study showed bilateral damage in the dorsolateral midbrain, amygdala, and paramedian thalami. Videopolysomnography disclosed rapid eye movement (REM) sleep behavior disorder, and a Multiple Sleep Latency Test showed a mean sleep latency of 7 minutes and 4 sleep-onset REM periods. The level of hypocretin-1 in the cerebrospinal fluid was low (49 pg/mL). This observation illustrates that REM sleep behavior disorder and narcoleptic features are 2 REM-sleep abnormalities that (1) may share the same autoimmune-mediated origin affecting the brainstem, limbic, and diencephalic structures and (2) may occur in the setting of the paraneoplastic anti–Ma2-associated encephalitis. Citation: Compta Y; Iranzo A; Santamaría J et al. REM Sleep Behavior Disorder and Narcoleptic Features in Anti–Ma2-associated Encephalitis. SLEEP 2007;30(6):767-769. PMID:17580598

Writers Identification Based on Multiple Windows Features Mining

NASA Astrophysics Data System (ADS)

Fadhil, Murad Saadi; Alkawaz, Mohammed Hazim; Rehman, Amjad; Saba, Tanzila

2016-03-01

Now a days, writer identification is at high demand to identify the original writer of the script at high accuracy. The one of the main challenge in writer identification is how to extract the discriminative features of different authors' scripts to classify precisely. In this paper, the adaptive division method on the offline Latin script has been implemented using several variant window sizes. Fragments of binarized text a set of features are extracted and classified into clusters in the form of groups or classes. Finally, the proposed approach in this paper has been tested on various parameters in terms of text division and window sizes. It is observed that selection of the right window size yields a well positioned window division. The proposed approach is tested on IAM standard dataset (IAM, Institut für Informatik und angewandte Mathematik, University of Bern, Bern, Switzerland) that is a constraint free script database. Finally, achieved results are compared with several techniques reported in the literature.

Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features

PubMed Central

Zhang, Yaoyun; Jiang, Min; Wang, Jingqi; Xu, Hua

2016-01-01

Semantic role labeling (SRL), which extracts shallow semantic relation representation from different surface textual forms of free text sentences, is important for understanding clinical narratives. Since semantic roles are formed by syntactic constituents in the sentence, an effective parser, as well as an effective syntactic feature set are essential to build a practical SRL system. Our study initiates a formal evaluation and comparison of SRL performance on a clinical text corpus MiPACQ, using three state-of-the-art parsers, the Stanford parser, the Berkeley parser, and the Charniak parser. First, the original parsers trained on the open domain syntactic corpus Penn Treebank were employed. Next, those parsers were retrained on the clinical Treebank of MiPACQ for further comparison. Additionally, state-of-the-art syntactic features from open domain SRL were also examined for clinical text. Experimental results showed that retraining the parsers on clinical Treebank improved the performance significantly, with an optimal F1 measure of 71.41% achieved by the Berkeley parser. PMID:28269926

Operator for object recognition and scene analysis by estimation of set occupancy with noisy and incomplete data sets

NASA Astrophysics Data System (ADS)

Rees, S. J.; Jones, Bryan F.

1992-11-01

Once feature extraction has occurred in a processed image, the recognition problem becomes one of defining a set of features which maps sufficiently well onto one of the defined shape/object models to permit a claimed recognition. This process is usually handled by aggregating features until a large enough weighting is obtained to claim membership, or an adequate number of located features are matched to the reference set. A requirement has existed for an operator or measure capable of a more direct assessment of membership/occupancy between feature sets, particularly where the feature sets may be defective representations. Such feature set errors may be caused by noise, by overlapping of objects, and by partial obscuration of features. These problems occur at the point of acquisition: repairing the data would then assume a priori knowledge of the solution. The technique described in this paper offers a set theoretical measure for partial occupancy defined in terms of the set of minimum additions to permit full occupancy and the set of locations of occupancy if such additions are made. As is shown, this technique permits recognition of partial feature sets with quantifiable degrees of uncertainty. A solution to the problems of obscuration and overlapping is therefore available.

Sublake geologic structure from high-resolution seismic-reflection data from four sinkhole lakes in the Lake Wales Ridge, Central Florida

USGS Publications Warehouse

Tihansky, A.B.; Arthur, J.D.; DeWitt, D.W.

1996-01-01

Seismic-reflection profiles from Lake Wales, Blue Lake, Lake Letta, and Lake Apthorp located along the Lake Wales Ridge in central Florida provide local detail within the regional hydrogeologic framework as described by litho- and hydrostratigraphic cross sections. Lakes located with the mantled karst region have long been considered to be sinkhole lakes, originating from subsidence activity. High-resolution seismic- reflection data confirm this origin for these four lakes. The geologic framework of the Lake Wales Ridge has proven to be a suitable geologic setting for continuous high-resolution seismic-reflection profiling in lakes; however, the nature of the lake-bottom sediments largely controls the quality of the seismic data. In lakes with significant organic-rich bottom deposits, interpretable record was limited to areas where organic deposits were minimal. In lakes with clean, sandy bottoms, the seismic-reflection methods were highly successful in obtaining data that can be correlated with sublake subsidence features. These techniques are useful in examining sublake geology and providing a better understanding of how confining units are affected by subsidence in a region where their continuity is of significant importance to local lake hydrology. Although local geologic control around each lake generally corresponds to the regional geologic framework, local deviations from regional geologic trends occur in sublake areas affected by subsidence activity. Each of the four lakes examined represents a unique set of geologic controls and provides some degree of structural evidence of subsidence activity. Sublake geologic structures identified include: (1) marginal lake sediments dipping into bathymetric lows, (2) lateral discontinuity of confining units including sags and breaches, (3) the disruption and reworking of overlying unconsolidated siliciclastic sediments as they subside into the underlying irregular limestone surface, and (4) sublake regions where confining units appear to remain intact and unaffected by nearby subsidence activity. Each lake likely is underlain by several piping features rather than one large subsidence feature.

Tempest: Accelerated MS/MS database search software for heterogeneous computing platforms

PubMed Central

Adamo, Mark E.; Gerber, Scott A.

2017-01-01

MS/MS database search algorithms derive a set of candidate peptide sequences from in-silico digest of a protein sequence database, and compute theoretical fragmentation patterns to match these candidates against observed MS/MS spectra. The original Tempest publication described these operations mapped to a CPU-GPU model, in which the CPU generates peptide candidates that are asynchronously sent to a discrete GPU to be scored against experimental spectra in parallel (Milloy et al., 2012). The current version of Tempest expands this model, incorporating OpenCL to offer seamless parallelization across multicore CPUs, GPUs, integrated graphics chips, and general-purpose coprocessors. Three protocols describe how to configure and run a Tempest search, including discussion of how to leverage Tempest's unique feature set to produce optimal results. PMID:27603022

REM sleep behavior disorder and narcoleptic features in anti-Ma2-associated encephalitis.

PubMed

Compta, Yaroslau; Iranzo, Alex; Santamaría, Joan; Casamitjana, Roser; Graus, Francesc

2007-06-01

A 69-year-old man with anti-Ma2 paraneoplastic encephalitis presented with subacute onset of severe hypersomnia, memory loss, parkinsonism, and gaze palsy. A brain magnetic resonance imaging study showed bilateral damage in the dorsolateral midbrain, amygdala, and paramedian thalami. Videopolysomnography disclosed rapid eye movement (REM) sleep behavior disorder, and a Multiple Sleep Latency Test showed a mean sleep latency of 7 minutes and 4 sleep-onset REM periods. The level of hypocretin-1 in the cerebrospinal fluid was low (49 pg/mL). This observation illustrates that REM sleep behavior disorder and narcoleptic features are 2 REM-sleep abnormalities that (1) may share the same autoimmune-mediated origin affecting the brainstem, limbic, and diencephalic structures and (2) may occur in the setting of the paraneoplastic anti-Ma2-associated encephalitis.

NIMEFI: Gene Regulatory Network Inference using Multiple Ensemble Feature Importance Algorithms

PubMed Central

Ruyssinck, Joeri; Huynh-Thu, Vân Anh; Geurts, Pierre; Dhaene, Tom; Demeester, Piet; Saeys, Yvan

2014-01-01

One of the long-standing open challenges in computational systems biology is the topology inference of gene regulatory networks from high-throughput omics data. Recently, two community-wide efforts, DREAM4 and DREAM5, have been established to benchmark network inference techniques using gene expression measurements. In these challenges the overall top performer was the GENIE3 algorithm. This method decomposes the network inference task into separate regression problems for each gene in the network in which the expression values of a particular target gene are predicted using all other genes as possible predictors. Next, using tree-based ensemble methods, an importance measure for each predictor gene is calculated with respect to the target gene and a high feature importance is considered as putative evidence of a regulatory link existing between both genes. The contribution of this work is twofold. First, we generalize the regression decomposition strategy of GENIE3 to other feature importance methods. We compare the performance of support vector regression, the elastic net, random forest regression, symbolic regression and their ensemble variants in this setting to the original GENIE3 algorithm. To create the ensemble variants, we propose a subsampling approach which allows us to cast any feature selection algorithm that produces a feature ranking into an ensemble feature importance algorithm. We demonstrate that the ensemble setting is key to the network inference task, as only ensemble variants achieve top performance. As second contribution, we explore the effect of using rankwise averaged predictions of multiple ensemble algorithms as opposed to only one. We name this approach NIMEFI (Network Inference using Multiple Ensemble Feature Importance algorithms) and show that this approach outperforms all individual methods in general, although on a specific network a single method can perform better. An implementation of NIMEFI has been made publicly available. PMID:24667482

A novel deep learning-based approach to high accuracy breast density estimation in digital mammography

NASA Astrophysics Data System (ADS)

Ahn, Chul Kyun; Heo, Changyong; Jin, Heongmin; Kim, Jong Hyo

2017-03-01

Mammographic breast density is a well-established marker for breast cancer risk. However, accurate measurement of dense tissue is a difficult task due to faint contrast and significant variations in background fatty tissue. This study presents a novel method for automated mammographic density estimation based on Convolutional Neural Network (CNN). A total of 397 full-field digital mammograms were selected from Seoul National University Hospital. Among them, 297 mammograms were randomly selected as a training set and the rest 100 mammograms were used for a test set. We designed a CNN architecture suitable to learn the imaging characteristic from a multitudes of sub-images and classify them into dense and fatty tissues. To train the CNN, not only local statistics but also global statistics extracted from an image set were used. The image set was composed of original mammogram and eigen-image which was able to capture the X-ray characteristics in despite of the fact that CNN is well known to effectively extract features on original image. The 100 test images which was not used in training the CNN was used to validate the performance. The correlation coefficient between the breast estimates by the CNN and those by the expert's manual measurement was 0.96. Our study demonstrated the feasibility of incorporating the deep learning technology into radiology practice, especially for breast density estimation. The proposed method has a potential to be used as an automated and quantitative assessment tool for mammographic breast density in routine practice.

Automatic Identification of Messages Related to Adverse Drug Reactions from Online User Reviews using Feature-based Classification.

PubMed

Liu, Jingfang; Zhang, Pengzhu; Lu, Yingjie

2014-11-01

User-generated medical messages on Internet contain extensive information related to adverse drug reactions (ADRs) and are known as valuable resources for post-marketing drug surveillance. The aim of this study was to find an effective method to identify messages related to ADRs automatically from online user reviews. We conducted experiments on online user reviews using different feature set and different classification technique. Firstly, the messages from three communities, allergy community, schizophrenia community and pain management community, were collected, the 3000 messages were annotated. Secondly, the N-gram-based features set and medical domain-specific features set were generated. Thirdly, three classification techniques, SVM, C4.5 and Naïve Bayes, were used to perform classification tasks separately. Finally, we evaluated the performance of different method using different feature set and different classification technique by comparing the metrics including accuracy and F-measure. In terms of accuracy, the accuracy of SVM classifier was higher than 0.8, the accuracy of C4.5 classifier or Naïve Bayes classifier was lower than 0.8; meanwhile, the combination feature sets including n-gram-based feature set and domain-specific feature set consistently outperformed single feature set. In terms of F-measure, the highest F-measure is 0.895 which was achieved by using combination feature sets and a SVM classifier. In all, we can get the best classification performance by using combination feature sets and SVM classifier. By using combination feature sets and SVM classifier, we can get an effective method to identify messages related to ADRs automatically from online user reviews.

Automatic detection of wheezes by evaluation of multiple acoustic feature extraction methods and C-weighted SVM

NASA Astrophysics Data System (ADS)

Sosa, Germán. D.; Cruz-Roa, Angel; González, Fabio A.

2015-01-01

This work addresses the problem of lung sound classification, in particular, the problem of distinguishing between wheeze and normal sounds. Wheezing sound detection is an important step to associate lung sounds with an abnormal state of the respiratory system, usually associated with tuberculosis or another chronic obstructive pulmonary diseases (COPD). The paper presents an approach for automatic lung sound classification, which uses different state-of-the-art sound features in combination with a C-weighted support vector machine (SVM) classifier that works better for unbalanced data. Feature extraction methods used here are commonly applied in speech recognition and related problems thanks to the fact that they capture the most informative spectral content from the original signals. The evaluated methods were: Fourier transform (FT), wavelet decomposition using Wavelet Packet Transform bank of filters (WPT) and Mel Frequency Cepstral Coefficients (MFCC). For comparison, we evaluated and contrasted the proposed approach against previous works using different combination of features and/or classifiers. The different methods were evaluated on a set of lung sounds including normal and wheezing sounds. A leave-two-out per-case cross-validation approach was used, which, in each fold, chooses as validation set a couple of cases, one including normal sounds and the other including wheezing sounds. Experimental results were reported in terms of traditional classification performance measures: sensitivity, specificity and balanced accuracy. Our best results using the suggested approach, C-weighted SVM and MFCC, achieve a 82.1% of balanced accuracy obtaining the best result for this problem until now. These results suggest that supervised classifiers based on kernel methods are able to learn better models for this challenging classification problem even using the same feature extraction methods.

A compression algorithm for the combination of PDF sets.

PubMed

Carrazza, Stefano; Latorre, José I; Rojo, Juan; Watt, Graeme

The current PDF4LHC recommendation to estimate uncertainties due to parton distribution functions (PDFs) in theoretical predictions for LHC processes involves the combination of separate predictions computed using PDF sets from different groups, each of which comprises a relatively large number of either Hessian eigenvectors or Monte Carlo (MC) replicas. While many fixed-order and parton shower programs allow the evaluation of PDF uncertainties for a single PDF set at no additional CPU cost, this feature is not universal, and, moreover, the a posteriori combination of the predictions using at least three different PDF sets is still required. In this work, we present a strategy for the statistical combination of individual PDF sets, based on the MC representation of Hessian sets, followed by a compression algorithm for the reduction of the number of MC replicas. We illustrate our strategy with the combination and compression of the recent NNPDF3.0, CT14 and MMHT14 NNLO PDF sets. The resulting compressed Monte Carlo PDF sets are validated at the level of parton luminosities and LHC inclusive cross sections and differential distributions. We determine that around 100 replicas provide an adequate representation of the probability distribution for the original combined PDF set, suitable for general applications to LHC phenomenology.

The Origin of Faint Tidal Features around Galaxies in the RESOLVE Survey

NASA Astrophysics Data System (ADS)

Hood, Callie E.; Kannappan, Sheila J.; Stark, David V.; Dell’Antonio, Ian P.; Moffett, Amanda J.; Eckert, Kathleen D.; Norris, Mark A.; Hendel, David

2018-04-01

We study tidal features around galaxies in the REsolved Spectroscopy Of a Local VolumE (RESOLVE) survey. Our sample consists of 1048 RESOLVE galaxies that overlap with the DECam Legacy Survey, which reaches an r-band 3σ depth of ∼27.9 mag arcsec‑2 for a 100 arcsec2 feature. Images were masked, smoothed, and inspected for tidal features such as streams, shells, or tails/arms. We find tidal features in 17±2% of our galaxies, setting a lower limit on the true frequency. The frequency of tidal features in the gas-poor (gas-to-stellar mass ratio <0.1) subsample is lower than in the gas-rich subsample (13±3% versus 19±2%). Within the gas-poor subsample, galaxies with tidal features have higher stellar and halo masses, ∼3× closer distances to nearest neighbors (in the same group), and possibly fewer group members at fixed halo mass than galaxies without tidal features, but similar specific star formation rates. These results suggest tidal features in gas-poor galaxies are typically streams/shells from dry mergers or satellite disruption. In contrast, the presence of tidal features around gas-rich galaxies does not correlate with stellar or halo mass, suggesting these tidal features are often tails/arms from resonant interactions. Similar to tidal features in gas-poor galaxies, tidal features in gas-rich galaxies imply 1.7× closer nearest neighbors in the same group; however, they are associated with diskier morphologies, higher star formation rates, and higher gas content. In addition to interactions with known neighbors, we suggest that tidal features in gas-rich galaxies may arise from accretion of cosmic gas and/or gas-rich satellites below the survey limit.

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions

PubMed Central

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject’s face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject’s face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network. PMID:26859884

Quality of clinical brain tumor MR spectra judged by humans and machine learning tools.

PubMed

Kyathanahally, Sreenath P; Mocioiu, Victor; Pedrosa de Barros, Nuno; Slotboom, Johannes; Wright, Alan J; Julià-Sapé, Margarida; Arús, Carles; Kreis, Roland

2018-05-01

To investigate and compare human judgment and machine learning tools for quality assessment of clinical MR spectra of brain tumors. A very large set of 2574 single voxel spectra with short and long echo time from the eTUMOUR and INTERPRET databases were used for this analysis. Original human quality ratings from these studies as well as new human guidelines were used to train different machine learning algorithms for automatic quality control (AQC) based on various feature extraction methods and classification tools. The performance was compared with variance in human judgment. AQC built using the RUSBoost classifier that combats imbalanced training data performed best. When furnished with a large range of spectral and derived features where the most crucial ones had been selected by the TreeBagger algorithm it showed better specificity (98%) in judging spectra from an independent test-set than previously published methods. Optimal performance was reached with a virtual three-class ranking system. Our results suggest that feature space should be relatively large for the case of MR tumor spectra and that three-class labels may be beneficial for AQC. The best AQC algorithm showed a performance in rejecting spectra that was comparable to that of a panel of human expert spectroscopists. Magn Reson Med 79:2500-2510, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions.

PubMed

Maruthapillai, Vasanthan; Murugappan, Murugappan

2016-01-01

In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject's face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject's face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network.

Toward Optimal Manifold Hashing via Discrete Locally Linear Embedding.

PubMed

Rongrong Ji; Hong Liu; Liujuan Cao; Di Liu; Yongjian Wu; Feiyue Huang

2017-11-01

Binary code learning, also known as hashing, has received increasing attention in large-scale visual search. By transforming high-dimensional features to binary codes, the original Euclidean distance is approximated via Hamming distance. More recently, it is advocated that it is the manifold distance, rather than the Euclidean distance, that should be preserved in the Hamming space. However, it retains as an open problem to directly preserve the manifold structure by hashing. In particular, it first needs to build the local linear embedding in the original feature space, and then quantize such embedding to binary codes. Such a two-step coding is problematic and less optimized. Besides, the off-line learning is extremely time and memory consuming, which needs to calculate the similarity matrix of the original data. In this paper, we propose a novel hashing algorithm, termed discrete locality linear embedding hashing (DLLH), which well addresses the above challenges. The DLLH directly reconstructs the manifold structure in the Hamming space, which learns optimal hash codes to maintain the local linear relationship of data points. To learn discrete locally linear embeddingcodes, we further propose a discrete optimization algorithm with an iterative parameters updating scheme. Moreover, an anchor-based acceleration scheme, termed Anchor-DLLH, is further introduced, which approximates the large similarity matrix by the product of two low-rank matrices. Experimental results on three widely used benchmark data sets, i.e., CIFAR10, NUS-WIDE, and YouTube Face, have shown superior performance of the proposed DLLH over the state-of-the-art approaches.

Landscape of Fluid Sets of Hairpin-Derived 21-/24-nt-Long Small RNAs at Seed Set Uncovers Special Epigenetic Features in Picea glauca.

PubMed

Liu, Yang; El-Kassaby, Yousry A

2017-01-01

Conifers' exceptionally large genome (20-30 Gb) is scattered with 60% retrotransposon (RT) components and we have little knowledge on their origin and evolutionary implications. RTs may impede the expression of flanking genes and provide sources of the formation of novel small RNA (sRNAs) populations to constrain events of transposon (TE) proliferation/transposition. Here we show a declining expression of 24-nt-long sRNAs and low expression levels of their key processing gene, pgRTL2 (RNASE THREE LIKE 2) at seed set in Picea glauca. The sRNAs in 24-nt size class are significantly less enriched in type and read number than 21-nt sRNAs and have not been documented in other species. The architecture of MIR loci generating highly expressed 24-/21-nt sRNAs is featured by long terminal repeat-retrotransposons (LTR-RTs) in families of Ty3/Gypsy and Ty1/Copia elements. This implies that the production of sRNAs may be predominantly originated from TE fragments on chromosomes. Furthermore, a large proportion of highly expressed 24-nt sRNAs does not have predictable targets against unique genes in Picea, suggestive of their potential pathway in DNA methylation modifications on, for instance, TEs. Additionally, the classification of computationally predicted sRNAs suggests that 24-nt sRNA targets may bear particular functions in metabolic processes while 21-nt sRNAs target genes involved in many different biological processes. This study, therefore, directs our attention to a possible extrapolation that lacking of 24-nt sRNAs at the late conifer seed developmental phase may result in less constraints in TE activities, thus contributing to the massive expansion of genome size. © The Author(s) 2017. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution.

Computer-Aided Breast Cancer Diagnosis with Optimal Feature Sets: Reduction Rules and Optimization Techniques.

PubMed

Mathieson, Luke; Mendes, Alexandre; Marsden, John; Pond, Jeffrey; Moscato, Pablo

2017-01-01

This chapter introduces a new method for knowledge extraction from databases for the purpose of finding a discriminative set of features that is also a robust set for within-class classification. Our method is generic and we introduce it here in the field of breast cancer diagnosis from digital mammography data. The mathematical formalism is based on a generalization of the k-Feature Set problem called (α, β)-k-Feature Set problem, introduced by Cotta and Moscato (J Comput Syst Sci 67(4):686-690, 2003). This method proceeds in two steps: first, an optimal (α, β)-k-feature set of minimum cardinality is identified and then, a set of classification rules using these features is obtained. We obtain the (α, β)-k-feature set in two phases; first a series of extremely powerful reduction techniques, which do not lose the optimal solution, are employed; and second, a metaheuristic search to identify the remaining features to be considered or disregarded. Two algorithms were tested with a public domain digital mammography dataset composed of 71 malignant and 75 benign cases. Based on the results provided by the algorithms, we obtain classification rules that employ only a subset of these features.

Validation of the NIMH-ChEFS adolescent face stimulus set in an adolescent, parent, and health professional sample

PubMed Central

COFFMAN, MARIKA C.; TRUBANOVA, ANDREA; RICHEY, J. ANTHONY; WHITE, SUSAN W.; KIM-SPOON, JUNGMEEN; OLLENDICK, THOMAS H.; PINE, DANIEL S.

2016-01-01

Attention to faces is a fundamental psychological process in humans, with atypical attention to faces noted across several clinical disorders. Although many clinical disorders onset in adolescence, there is a lack of well-validated stimulus sets containing adolescent faces available for experimental use. Further, the images comprising most available sets are not controlled for high- and low-level visual properties. Here, we present a cross-site validation of the National Institute of Mental Health Child Emotional Faces Picture Set (NIMH-ChEFS), comprised of 257 photographs of adolescent faces displaying angry, fearful, happy, sad, and neutral expressions. All of the direct facial images from the NIMH-ChEFS set were adjusted in terms of location of facial features and standardized for luminance, size, and smoothness. Although overall agreement between raters in this study and the original development-site raters was high (89.52%), this differed by group such that agreement was lower for adolescents relative to mental health professionals in the current study. These results suggest that future research using this face set or others of adolescent/child faces should base comparisons on similarly-aged validation data. PMID:26359940

Performance comparison of phenomenology-based features to generic features for false alarm reduction in UWB SAR imagery

NASA Astrophysics Data System (ADS)

Marble, Jay A.; Gorman, John D.

1999-08-01

A feature based approach is taken to reduce the occurrence of false alarms in foliage penetrating, ultra-wideband, synthetic aperture radar data. A set of 'generic' features is defined based on target size, shape, and pixel intensity. A second set of features is defined that contains generic features combined with features based on scattering phenomenology. Each set is combined using a quadratic polynomial discriminant (QPD), and performance is characterized by generating a receiver operating characteristic (ROC) curve. Results show that the feature set containing phenomenological features improves performance against both broadside and end-on targets. Performance against end-on targets, however, is especially pronounced.

Comparison of Clinical Features and Outcomes in Patients with Extraskeletal Versus Skeletal Localized Ewing Sarcoma: A Report from the Children’s Oncology Group

PubMed Central

Cash, Thomas; McIlvaine, Elizabeth; Krailo, Mark D.; Lessnick, Stephen L.; Lawlor, Elizabeth R.; Laack, Nadia; Sorger, Joel; Marina, Neyssa; Grier, Holcombe E.; Granowetter, Linda; Womer, Richard B.; DuBois, Steven G.

2016-01-01

BACKGROUND The prognostic significance of having extraskeletal vs. skeletal Ewing sarcoma in the setting of modern chemotherapy protocols is unknown. The purpose of this study was to compare the clinical characteristics, biologic features, and outcomes for patients with extraskeletal and skeletal Ewing sarcoma. METHODS Patients had localized Ewing sarcoma (ES) and were treated on two consecutive protocols using 5-drug chemotherapy (INT-0154 and AEWS0031). Patients were analyzed based on having an extraskeletal (n=213) or skeletal (n=826) site of tumor origin. Event-free survival (EFS) was estimated using the Kaplan-Meier method, compared using the log-rank test, and modeled using Cox multivariate regression. RESULTS Patients with extraskeletal Ewing Sarcoma (EES) were more likely to have axial tumors (72% vs. 55%; P < 0.001), less likely to have tumors > 8 cm (9% vs. 17%; P < 0.01), and less likely to be white (81% vs. 87%; P < 0.001) compared to patients with skeletal ES. There was no difference in key genomic features (type of EWSR1 translocation, TP53 mutation, CDKN2A mutation/loss) between groups. After controlling for age, race, and primary site, EES was associated with superior EFS [hazard ratio = 0.69; 95% CI: 0.50–0.95; P = 0.02]. Among patients with EES, age ≥ 18 years, non-white race, and elevated baseline erythrocyte sedimentation rate (ESR) were independently associated with inferior EFS. CONCLUSION Clinical characteristics, but not key tumor genomic features, differ between EES and skeletal ES. Extraskeletal origin is a favorable prognostic factor, independent of age, race, and primary site. PMID:27297500

Universal dynamical properties preclude standard clustering in a large class of biochemical data.

PubMed

Gomez, Florian; Stoop, Ralph L; Stoop, Ruedi

2014-09-01

Clustering of chemical and biochemical data based on observed features is a central cognitive step in the analysis of chemical substances, in particular in combinatorial chemistry, or of complex biochemical reaction networks. Often, for reasons unknown to the researcher, this step produces disappointing results. Once the sources of the problem are known, improved clustering methods might revitalize the statistical approach of compound and reaction search and analysis. Here, we present a generic mechanism that may be at the origin of many clustering difficulties. The variety of dynamical behaviors that can be exhibited by complex biochemical reactions on variation of the system parameters are fundamental system fingerprints. In parameter space, shrimp-like or swallow-tail structures separate parameter sets that lead to stable periodic dynamical behavior from those leading to irregular behavior. We work out the genericity of this phenomenon and demonstrate novel examples for their occurrence in realistic models of biophysics. Although we elucidate the phenomenon by considering the emergence of periodicity in dependence on system parameters in a low-dimensional parameter space, the conclusions from our simple setting are shown to continue to be valid for features in a higher-dimensional feature space, as long as the feature-generating mechanism is not too extreme and the dimension of this space is not too high compared with the amount of available data. For online versions of super-paramagnetic clustering see http://stoop.ini.uzh.ch/research/clustering. Supplementary data are available at Bioinformatics online. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Ultrasound based computer-aided-diagnosis of kidneys for pediatric hydronephrosis

NASA Astrophysics Data System (ADS)

Cerrolaza, Juan J.; Peters, Craig A.; Martin, Aaron D.; Myers, Emmarie; Safdar, Nabile; Linguraru, Marius G.

2014-03-01

Ultrasound is the mainstay of imaging for pediatric hydronephrosis, though its potential as diagnostic tool is limited by its subjective assessment, and lack of correlation with renal function. Therefore, all cases showing signs of hydronephrosis undergo further invasive studies, like diuretic renogram, in order to assess the actual renal function. Under the hypothesis that renal morphology is correlated with renal function, a new ultrasound based computer-aided diagnosis (CAD) tool for pediatric hydronephrosis is presented. From 2D ultrasound, a novel set of morphological features of the renal collecting systems and the parenchyma, is automatically extracted using image analysis techniques. From the original set of features, including size, geometric and curvature descriptors, a subset of ten features are selected as predictive variables, combining a feature selection technique and area under the curve filtering. Using the washout half time (T1/2) as indicative of renal obstruction, two groups are defined. Those cases whose T1/2 is above 30 minutes are considered to be severe, while the rest would be in the safety zone, where diuretic renography could be avoided. Two different classification techniques are evaluated (logistic regression, and support vector machines). Adjusting the probability decision thresholds to operate at the point of maximum sensitivity, i.e., preventing any severe case be misclassified, specificities of 53%, and 75% are achieved, for the logistic regression and the support vector machine classifier, respectively. The proposed CAD system allows to establish a link between non-invasive non-ionizing imaging techniques and renal function, limiting the need for invasive and ionizing diuretic renography.

3-D seismic study into the origin of a large seafloor depression on the Chatham Rise, New Zealand

NASA Astrophysics Data System (ADS)

Pecher, I. A.; Waghorn, K. A.; Strachan, L. J.; Crutchley, G. J.; Bialas, J.; Sarkar, S.; Davy, B. W.; Papenberg, C. A.; Koch, S.; Eckardt, T.; Kroeger, K. F.; Rose, P. S.; Coffin, R. B.

2014-12-01

Vast areas of the Chatham Rise, east of New Zealand's South Island, are covered by circular to elliptical seafloor depressions. Distribution and size of these seafloor depressions appear to be linked to bathymetry: Small depressions several hundred meters in diameter are found in a depth range of ~500-800 m while two types of larger depressions with 2-5 km and >10 km in diameter, respectively, are present in water depths of 800-1100 m. Here we evaluate 3-D seismic reflection data acquired off the R/V Sonne in 2013 over one of the 2-5 km large depressions. We interpret that the seafloor bathymetry associated with the 2-5 km depressions was most likely created by contour current erosion and deposition. These contourite features are underlain by structures that indicate upward fluid flow, including polygonal fault networks and a conical feature that we interpret to result from sediment re-mobilization. We also discovered a set of smaller buried depressions immediately beneath the contourites. These features are directly connected to the stratigraphy containing the conical feature through sets of polygonal faults which truncate against the base of the paleo-depressions. We interpret these depressions as paleo-pockmarks resulting from fluid expulsion, presumably including gas. Based on interpretation and age correlation of a regional-scale seismic line, the paleo-pockmarks could be as old as 5.5 Ma. We suggest the resulting paleo-topography provided the initial roughness required to form mounded contourite deposits that lead to depressions in seafloor bathymetry.

Trident: A Universal Tool for Generating Synthetic Absorption Spectra from Astrophysical Simulations

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

Hummels, Cameron B.; Smith, Britton D.; Silvia, Devin W.

Hydrodynamical simulations are increasingly able to accurately model physical systems on stellar, galactic, and cosmological scales; however, the utility of these simulations is often limited by our ability to directly compare them with the data sets produced by observers: spectra, photometry, etc. To address this problem, we have created trident, a Python-based open-source tool for post-processing hydrodynamical simulations to produce synthetic absorption spectra and related data. trident can (i) create absorption-line spectra for any trajectory through a simulated data set mimicking both background quasar and down-the-barrel configurations; (ii) reproduce the spectral characteristics of common instruments like the Cosmic Origins Spectrograph;more » (iii) operate across the ultraviolet, optical, and infrared using customizable absorption-line lists; (iv) trace simulated physical structures directly to spectral features; (v) approximate the presence of ion species absent from the simulation outputs; (vi) generate column density maps for any ion; and (vii) provide support for all major astrophysical hydrodynamical codes. trident was originally developed to aid in the interpretation of observations of the circumgalactic medium and intergalactic medium, but it remains a general tool applicable in other contexts.« less

Signature properties of water: Their molecular electronic origins

PubMed Central

Jones, Andrew P.; Cipcigan, Flaviu S.; Crain, Jason; Martyna, Glenn J.

2015-01-01

Water challenges our fundamental understanding of emergent materials properties from a molecular perspective. It exhibits a uniquely rich phenomenology including dramatic variations in behavior over the wide temperature range of the liquid into water’s crystalline phases and amorphous states. We show that many-body responses arising from water’s electronic structure are essential mechanisms harnessed by the molecule to encode for the distinguishing features of its condensed states. We treat the complete set of these many-body responses nonperturbatively within a coarse-grained electronic structure derived exclusively from single-molecule properties. Such a “strong coupling” approach generates interaction terms of all symmetries to all orders, thereby enabling unique transferability to diverse local environments such as those encountered along the coexistence curve. The symmetries of local motifs that can potentially emerge are not known a priori. Consequently, electronic responses unfiltered by artificial truncation are then required to embody the terms that tip the balance to the correct set of structures. Therefore, our fully responsive molecular model produces, a simple, accurate, and intuitive picture of water’s complexity and its molecular origin, predicting water’s signature physical properties from ice, through liquid–vapor coexistence, to the critical point. PMID:25941394

Detection of explosive cough events in audio recordings by internal sound analysis.

PubMed

Rocha, B M; Mendes, L; Couceiro, R; Henriques, J; Carvalho, P; Paiva, R P

2017-07-01

We present a new method for the discrimination of explosive cough events, which is based on a combination of spectral content descriptors and pitch-related features. After the removal of near-silent segments, a vector of event boundaries is obtained and a proposed set of 9 features is extracted for each event. Two data sets, recorded using electronic stethoscopes and comprising a total of 46 healthy subjects and 13 patients, were employed to evaluate the method. The proposed feature set is compared to three other sets of descriptors: a baseline, a combination of both sets, and an automatic selection of the best 10 features from both sets. The combined feature set yields good results on the cross-validated database, attaining a sensitivity of 92.3±2.3% and a specificity of 84.7±3.3%. Besides, this feature set seems to generalize well when it is trained on a small data set of patients, with a variety of respiratory and cardiovascular diseases, and tested on a bigger data set of mostly healthy subjects: a sensitivity of 93.4% and a specificity of 83.4% are achieved in those conditions. These results demonstrate that complementing the proposed feature set with a baseline set is a promising approach.

Development of a smart, anti-water polyurethane polymer hair coating for style setting.

PubMed

Liu, Y; Liu, Y J; Hu, J; Ji, F L; Lv, J; Chen, S J; Zhu, Y

2016-06-01

The goal of this work was to develop a novel polyurethane polymer coating for the surface of the hair that could be used for style setting via the shape memory effect (SME). The features of the films are in accordance with conventional hair styling methods used in the laboratory. In this study, a new polyurethane polymer was synthesized; the morphology and mechanical behaviour of the coated hair were systematically investigated using a scanning electron microscope (SEM) and an Instron 5566 (with a temperature oven). The SME of the hair was tested using a 35-g weight and over five washing and drying cycles. The experimental result shows that the polyurethane polymer has effects on the mechanical behaviour of the hair. It indicates that the fixed shape (at 22°C) and recover rate (at 60°C) of different casted thickness films are similar. And the stress of the film becomes larger with increasing film thickness. Furthermore, the shape memory ability could be endowed with the hair styling using this polymer; the hair fibre could recover to the 65% of its original shape after five cycle deformation by 35 g mass under the heat-treated condition; it could recover its original setting styling even after 5th water washing and drying. The SEM results indicated that the microsurface of the hair is coated with the polymer membrane; it contributes to the shape memory ability of the coated hair to keep and recover to the original setting styling. The styling hair can return to the original hair because the polyurethane polymer can be washed out by water with suitable strength and shampoo totally which does not leave any flake. The polyurethane polymer-based hair setting agent has been developed successfully, and it could be coated evenly on the human hair with good hand feeling and SMEs. The SME is highly related to the quantity of polyurethane polymer solution, and the effect could be improved by increasing the solution quantity. The maximum deformation of the coated hair could be recovered 94% at 75°C, once its shape is changed by an external force. The treated hair can withstand warm water rinsing for at least five cycles, and it can keep 65% of its original setting style after water rinsing. The polyurethane polymer could be totally removed by shampooing the hair and hot towel covering for 5-10 min. This research provides an effective way for the development of new intelligent shaping agents. © 2015 Society of Cosmetic Scientists and the Société Française de Cosmétologie.

Generative adversarial networks recover features in astrophysical images of galaxies beyond the deconvolution limit

NASA Astrophysics Data System (ADS)

Schawinski, Kevin; Zhang, Ce; Zhang, Hantian; Fowler, Lucas; Santhanam, Gokula Krishnan

2017-05-01

Observations of astrophysical objects such as galaxies are limited by various sources of random and systematic noise from the sky background, the optical system of the telescope and the detector used to record the data. Conventional deconvolution techniques are limited in their ability to recover features in imaging data by the Shannon-Nyquist sampling theorem. Here, we train a generative adversarial network (GAN) on a sample of 4550 images of nearby galaxies at 0.01 < z < 0.02 from the Sloan Digital Sky Survey and conduct 10× cross-validation to evaluate the results. We present a method using a GAN trained on galaxy images that can recover features from artificially degraded images with worse seeing and higher noise than the original with a performance that far exceeds simple deconvolution. The ability to better recover detailed features such as galaxy morphology from low signal to noise and low angular resolution imaging data significantly increases our ability to study existing data sets of astrophysical objects as well as future observations with observatories such as the Large Synoptic Sky Telescope (LSST) and the Hubble and James Webb space telescopes.

Understanding user intents in online health forums.

PubMed

Zhang, Thomas; Cho, Jason H D; Zhai, Chengxiang

2015-07-01

Online health forums provide a convenient way for patients to obtain medical information and connect with physicians and peers outside of clinical settings. However, large quantities of unstructured and diversified content generated on these forums make it difficult for users to digest and extract useful information. Understanding user intents would enable forums to find and recommend relevant information to users by filtering out threads that do not match particular intents. In this paper, we derive a taxonomy of intents to capture user information needs in online health forums and propose novel pattern-based features for use with a multiclass support vector machine (SVM) classifier to classify original thread posts according to their underlying intents. Since no dataset existed for this task, we employ three annotators to manually label a dataset of 1192 HealthBoards posts spanning four forum topics. Experimental results show that a SVM using pattern-based features is highly capable of identifying user intents in forum posts, reaching a maximum precision of 75%, and that a SVM-based hierarchical classifier using both pattern and word features outperforms its SVM counterpart that uses only word features. Furthermore, comparable classification performance can be achieved by training and testing on posts from different forum topics.

AN INFRARED DIFFUSE CIRCUMSTELLAR BAND? THE UNUSUAL 1.5272 μm DIB IN THE RED SQUARE NEBULA

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

Zasowski, G.; Chojnowski, S. Drew; Whelan, D. G.

The molecular carriers of the ubiquitous absorption features called the diffuse interstellar bands (DIBs) have eluded identification for many decades, in part because of the enormous parameter space spanned by the candidates and the limited set of empirical constraints afforded by observations in the diffuse interstellar medium. Detection of these features in circumstellar regions, where the environmental properties are more easily measured, is thus a promising approach to understanding the chemical nature of the carriers themselves. Here, using high-resolution spectra from the Apache Point Observatory Galactic Evolution Experiment survey, we present an analysis of the unusually asymmetric 1.5272 μm DIBmore » feature along the sightline to the Red Square Nebula (RSN) and demonstrate the likely circumstellar origin of about half of the DIB absorption in this line of sight. This interpretation is supported both by the velocities of the feature components and by the ratio of foreground to total reddening along the line of sight. The RSN sightline offers the unique opportunity to study the behavior of DIB carriers in a constrained environment and thus to shed new light on the carriers themselves.« less

The effect of sequential information on consumers' willingness to pay for credence food attributes.

PubMed

Botelho, A; Dinis, I; Lourenço-Gomes, L; Moreira, J; Costa Pinto, L; Simões, O

2017-11-01

The use of experimental methods to determine consumers' willingness to pay for "quality" food has been gaining importance in scientific research. In most of the empirical literature on this issue the experimental design starts with blind tasting, after which information is introduced. It is assumed that this approach allows consumers to elicit the real value that they attach to each of the features added through specific information. In this paper, the starting hypothesis is that this technique overestimates the weight of the features introduced by information in consumers' willingness to pay when compared to a real market situation, in which consumers are confronted with all the information at once. The data obtained through contingent valuation in an in-store setting was used to estimate a hedonic model aiming at assessing consumers' willingness to pay (WTP) for the feature "geographical origin of the variety" of pears and apples in different information scenarios: i) blind tasting followed by extrinsic information and ii) full information provided at once. The results show that, in fact, features are more valued when gradually added to background information than when consumers receive all the information from the beginning. Copyright © 2017 Elsevier Ltd. All rights reserved.

Dynamic modeling of the neck muscles during horizontal head movement. Part II: Model construction in Pro/Engineer.

PubMed

Haapala, Stephenie A; Enderle, John D

2003-01-01

This paper describes the next phase of research on a parametric model of the head-neck system for dynamic simulation of horizontal head rotation. A skull has been imported into Pro/Engineer software and has been assigned mass properties such as density, surface area and moments of inertia. The origin of a universal coordinate system has been located at the center of gravity of the T1 vertebrae. Identification of this origin allows insertion and attachment points of the sternocleidomastoid (SCOM) and splenius capitis to be located. An assembly has been created, marking the location of both muscle sets. This paper will also explore the obstacles encountered when working with an imported feature in Pro/E and attempts to resolve some of these issues. The goal of this work involves the creation of a 3D homeomorphic saccadic eye and head movement system.

Clustering approaches to feature change detection

NASA Astrophysics Data System (ADS)

G-Michael, Tesfaye; Gunzburger, Max; Peterson, Janet

2018-05-01

The automated detection of changes occurring between multi-temporal images is of significant importance in a wide range of medical, environmental, safety, as well as many other settings. The usage of k-means clustering is explored as a means for detecting objects added to a scene. The silhouette score for the clustering is used to define the optimal number of clusters that should be used. For simple images having a limited number of colors, new objects can be detected by examining the change between the optimal number of clusters for the original and modified images. For more complex images, new objects may need to be identified by examining the relative areas covered by corresponding clusters in the original and modified images. Which method is preferable depends on the composition and range of colors present in the images. In addition to describing the clustering and change detection methodology of our proposed approach, we provide some simple illustrations of its application.

Titan I propulsion system modeling and possible performance improvements

NASA Astrophysics Data System (ADS)

Giusti, Oreste

This thesis features the Titan I propulsion systems and offers data-supported suggestions for improvements to increase performance. The original propulsion systems were modeled both graphically in CAD and via equations. Due to the limited availability of published information, it was necessary to create a more detailed, secondary set of models. Various engineering equations---pertinent to rocket engine design---were implemented in order to generate the desired extra detail. This study describes how these new models were then imported into the ESI CFD Suite. Various parameters are applied to these imported models as inputs that include, for example, bi-propellant combinations, pressure, temperatures, and mass flow rates. The results were then processed with ESI VIEW, which is visualization software. The output files were analyzed for forces in the nozzle, and various results were generated, including sea level thrust and ISP. Experimental data are provided to compare the original engine configuration models to the derivative suggested improvement models.

Amyloid Fibrils as Building Blocks for Natural and Artificial Functional Materials.

PubMed

Knowles, Tuomas P J; Mezzenga, Raffaele

2016-08-01

Proteinaceous materials based on the amyloid core structure have recently been discovered at the origin of biological functionality in a remarkably diverse set of roles, and attention is increasingly turning towards such structures as the basis of artificial self-assembling materials. These roles contrast markedly with the original picture of amyloid fibrils as inherently pathological structures. Here we outline the salient features of this class of functional materials, both in the context of the functional roles that have been revealed for amyloid fibrils in nature, as well as in relation to their potential as artificial materials. We discuss how amyloid materials exemplify the emergence of function from protein self-assembly at multiple length scales. We focus on the connections between mesoscale structure and material function, and demonstrate how the natural examples of functional amyloids illuminate the potential applications for future artificial protein based materials. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images

PubMed Central

Raza, Shan-e-Ahmed; Prince, Gillian; Clarkson, John P.; Rajpoot, Nasir M.

2015-01-01

Accurate and timely detection of plant diseases can help mitigate the worldwide losses experienced by the horticulture and agriculture industries each year. Thermal imaging provides a fast and non-destructive way of scanning plants for diseased regions and has been used by various researchers to study the effect of disease on the thermal profile of a plant. However, thermal image of a plant affected by disease has been known to be affected by environmental conditions which include leaf angles and depth of the canopy areas accessible to the thermal imaging camera. In this paper, we combine thermal and visible light image data with depth information and develop a machine learning system to remotely detect plants infected with the tomato powdery mildew fungus Oidium neolycopersici. We extract a novel feature set from the image data using local and global statistics and show that by combining these with the depth information, we can considerably improve the accuracy of detection of the diseased plants. In addition, we show that our novel feature set is capable of identifying plants which were not originally inoculated with the fungus at the start of the experiment but which subsequently developed disease through natural transmission. PMID:25861025

Mechanical assembly of complex, 3D mesostructures from releasable multilayers of advanced materials.

PubMed

Yan, Zheng; Zhang, Fan; Liu, Fei; Han, Mengdi; Ou, Dapeng; Liu, Yuhao; Lin, Qing; Guo, Xuelin; Fu, Haoran; Xie, Zhaoqian; Gao, Mingye; Huang, Yuming; Kim, JungHwan; Qiu, Yitao; Nan, Kewang; Kim, Jeonghyun; Gutruf, Philipp; Luo, Hongying; Zhao, An; Hwang, Keh-Chih; Huang, Yonggang; Zhang, Yihui; Rogers, John A

2016-09-01

Capabilities for assembly of three-dimensional (3D) micro/nanostructures in advanced materials have important implications across a broad range of application areas, reaching nearly every class of microsystem technology. Approaches that rely on the controlled, compressive buckling of 2D precursors are promising because of their demonstrated compatibility with the most sophisticated planar technologies, where materials include inorganic semiconductors, polymers, metals, and various heterogeneous combinations, spanning length scales from submicrometer to centimeter dimensions. We introduce a set of fabrication techniques and design concepts that bypass certain constraints set by the underlying physics and geometrical properties of the assembly processes associated with the original versions of these methods. In particular, the use of releasable, multilayer 2D precursors provides access to complex 3D topologies, including dense architectures with nested layouts, controlled points of entanglement, and other previously unobtainable layouts. Furthermore, the simultaneous, coordinated assembly of additional structures can enhance the structural stability and drive the motion of extended features in these systems. The resulting 3D mesostructures, demonstrated in a diverse set of more than 40 different examples with feature sizes from micrometers to centimeters, offer unique possibilities in device design. A 3D spiral inductor for near-field communication represents an example where these ideas enable enhanced quality ( Q ) factors and broader working angles compared to those of conventional 2D counterparts.

Mechanical assembly of complex, 3D mesostructures from releasable multilayers of advanced materials

PubMed Central

Yan, Zheng; Zhang, Fan; Liu, Fei; Han, Mengdi; Ou, Dapeng; Liu, Yuhao; Lin, Qing; Guo, Xuelin; Fu, Haoran; Xie, Zhaoqian; Gao, Mingye; Huang, Yuming; Kim, JungHwan; Qiu, Yitao; Nan, Kewang; Kim, Jeonghyun; Gutruf, Philipp; Luo, Hongying; Zhao, An; Hwang, Keh-Chih; Huang, Yonggang; Zhang, Yihui; Rogers, John A.

2016-01-01

Capabilities for assembly of three-dimensional (3D) micro/nanostructures in advanced materials have important implications across a broad range of application areas, reaching nearly every class of microsystem technology. Approaches that rely on the controlled, compressive buckling of 2D precursors are promising because of their demonstrated compatibility with the most sophisticated planar technologies, where materials include inorganic semiconductors, polymers, metals, and various heterogeneous combinations, spanning length scales from submicrometer to centimeter dimensions. We introduce a set of fabrication techniques and design concepts that bypass certain constraints set by the underlying physics and geometrical properties of the assembly processes associated with the original versions of these methods. In particular, the use of releasable, multilayer 2D precursors provides access to complex 3D topologies, including dense architectures with nested layouts, controlled points of entanglement, and other previously unobtainable layouts. Furthermore, the simultaneous, coordinated assembly of additional structures can enhance the structural stability and drive the motion of extended features in these systems. The resulting 3D mesostructures, demonstrated in a diverse set of more than 40 different examples with feature sizes from micrometers to centimeters, offer unique possibilities in device design. A 3D spiral inductor for near-field communication represents an example where these ideas enable enhanced quality (Q) factors and broader working angles compared to those of conventional 2D counterparts. PMID:27679820

Customization of home closed-loop insulin delivery in adult patients with type 1 diabetes, assisted with structured remote monitoring: the pilot WP7 Diabeloop study.

PubMed

Benhamou, Pierre Yves; Huneker, Erik; Franc, Sylvia; Doron, Maeva; Charpentier, Guillaume

2018-06-01

Improvement in closed-loop insulin delivery systems could result from customization of settings to individual needs and remote monitoring. This pilot home study evaluated the efficacy and relevance of this approach. A bicentric clinical trial was conducted for 3 weeks, using an MPC-based algorithm (Diabeloop Artificial Pancreas system) featuring five settings designed to modulate the reactivity of regulation. Remote monitoring was ensured by expert nurses with a web platform generating automatic Secured Information Messages (SIMs) and with a structured procedure. Endpoints were glucose metrics and description of impact of monitoring on regulation parameters. Eight patients with type 1 diabetes (six men, age 41.8 ± 11.4 years, HbA1c 7.7 ± 1.0%) were included. Time spent in the 70-180 mg/dl range was 70.2% [67.5; 76.9]. Time in hypoglycemia < 70 mg/dl was 2.9% [2.1; 3.4]. Eleven SIMs led to phone intervention. Original default settings were modified in all patients by the intervention of the nurses. This pilot trial suggests that the Diabeloop closed-loop system could be efficient regarding metabolic outcomes, whereas its telemedical monitoring feature could contribute to enhanced efficacy and safety. This study is registered at ClinicalTrials.gov with trial registration number NCT02987556.

Algorithms for Learning Preferences for Sets of Objects

NASA Technical Reports Server (NTRS)

Wagstaff, Kiri L.; desJardins, Marie; Eaton, Eric

2010-01-01

A method is being developed that provides for an artificial-intelligence system to learn a user's preferences for sets of objects and to thereafter automatically select subsets of objects according to those preferences. The method was originally intended to enable automated selection, from among large sets of images acquired by instruments aboard spacecraft, of image subsets considered to be scientifically valuable enough to justify use of limited communication resources for transmission to Earth. The method is also applicable to other sets of objects: examples of sets of objects considered in the development of the method include food menus, radio-station music playlists, and assortments of colored blocks for creating mosaics. The method does not require the user to perform the often-difficult task of quantitatively specifying preferences; instead, the user provides examples of preferred sets of objects. This method goes beyond related prior artificial-intelligence methods for learning which individual items are preferred by the user: this method supports a concept of setbased preferences, which include not only preferences for individual items but also preferences regarding types and degrees of diversity of items in a set. Consideration of diversity in this method involves recognition that members of a set may interact with each other in the sense that when considered together, they may be regarded as being complementary, redundant, or incompatible to various degrees. The effects of such interactions are loosely summarized in the term portfolio effect. The learning method relies on a preference representation language, denoted DD-PREF, to express set-based preferences. In DD-PREF, a preference is represented by a tuple that includes quality (depth) functions to estimate how desired a specific value is, weights for each feature preference, the desired diversity of feature values, and the relative importance of diversity versus depth. The system applies statistical concepts to estimate quantitative measures of the user s preferences from training examples (preferred subsets) specified by the user. Once preferences have been learned, the system uses those preferences to select preferred subsets from new sets. The method was found to be viable when tested in computational experiments on menus, music playlists, and rover images. Contemplated future development efforts include further tests on more diverse sets and development of a sub-method for (a) estimating the parameter that represents the relative importance of diversity versus depth, and (b) incorporating background knowledge about the nature of quality functions, which are special functions that specify depth preferences for features.

The effect of feature-based attention on flanker interference processing: An fMRI-constrained source analysis.

PubMed

Siemann, Julia; Herrmann, Manfred; Galashan, Daniela

2018-01-25

The present study examined whether feature-based cueing affects early or late stages of flanker conflict processing using EEG and fMRI. Feature cues either directed participants' attention to the upcoming colour of the target or were neutral. Validity-specific modulations during interference processing were investigated using the N200 event-related potential (ERP) component and BOLD signal differences. Additionally, both data sets were integrated using an fMRI-constrained source analysis. Finally, the results were compared with a previous study in which spatial instead of feature-based cueing was applied to an otherwise identical flanker task. Feature-based and spatial attention recruited a common fronto-parietal network during conflict processing. Irrespective of attention type (feature-based; spatial), this network responded to focussed attention (valid cueing) as well as context updating (invalid cueing), hinting at domain-general mechanisms. However, spatially and non-spatially directed attention also demonstrated domain-specific activation patterns for conflict processing that were observable in distinct EEG and fMRI data patterns as well as in the respective source analyses. Conflict-specific activity in visual brain regions was comparable between both attention types. We assume that the distinction between spatially and non-spatially directed attention types primarily applies to temporal differences (domain-specific dynamics) between signals originating in the same brain regions (domain-general localization).

Ultraviolet divergences in non-renormalizable supersymmetric theories

NASA Astrophysics Data System (ADS)

Smilga, A.

2017-03-01

We present a pedagogical review of our current understanding of the ultraviolet structure of N = (1,1) 6D supersymmetric Yang-Mills theory and of N = 8 4 D supergravity. These theories are not renormalizable, they involve power ultraviolet divergences and, in all probability, an infinite set of higherdimensional counterterms that contribute to on-mass-shell scattering amplitudes. A specific feature of supersymmetric theories (especially, of extended supersymmetric theories) is that these counterterms may not be invariant off shell under the full set of supersymmetry transformations. The lowest-dimensional nontrivial counterterm is supersymmetric on shell. Still higher counterterms may lose even the on-shell invariance. On the other hand, the full effective Lagrangian, generating the amplitudes and representing an infinite sum of counterterms, still enjoys the complete symmetry of original theory. We also discuss simple supersymmetric quantum-mechanical models that exhibit the same behaviour.

BEAMLINE-CONTROLLED STEERING OF SOURCE-POINT ANGLE AT THE ADVANCED PHOTON SOURCE

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

Emery, L.; Fystro, G.; Shang, H.

An EPICS-based steering software system has been implemented for beamline personnel to directly steer the angle of the synchrotron radiation sources at the Advanced Photon Source. A script running on a workstation monitors "start steering" beamline EPICS records, and effects a steering given by the value of the "angle request" EPICS record. The new system makes the steering process much faster than before, although the older steering protocols can still be used. The robustness features of the original steering remain. Feedback messages are provided to the beamlines and the accelerator operators. Underpinning this new steering protocol is the recent refinementmore » of the global orbit feedback process whereby feedforward of dipole corrector set points and orbit set points are used to create a local steering bump in a rapid and seamless way.« less

A numerical relativity scheme for cosmological simulations

NASA Astrophysics Data System (ADS)

Daverio, David; Dirian, Yves; Mitsou, Ermis

2017-12-01

Cosmological simulations involving the fully covariant gravitational dynamics may prove relevant in understanding relativistic/non-linear features and, therefore, in taking better advantage of the upcoming large scale structure survey data. We propose a new 3  +  1 integration scheme for general relativity in the case where the matter sector contains a minimally-coupled perfect fluid field. The original feature is that we completely eliminate the fluid components through the constraint equations, thus remaining with a set of unconstrained evolution equations for the rest of the fields. This procedure does not constrain the lapse function and shift vector, so it holds in arbitrary gauge and also works for arbitrary equation of state. An important advantage of this scheme is that it allows one to define and pass an adaptation of the robustness test to the cosmological context, at least in the case of pressureless perfect fluid matter, which is the relevant one for late-time cosmology.

Culture and biology in the origins of linguistic structure.

PubMed

Kirby, Simon

2017-02-01

Language is systematically structured at all levels of description, arguably setting it apart from all other instances of communication in nature. In this article, I survey work over the last 20 years that emphasises the contributions of individual learning, cultural transmission, and biological evolution to explaining the structural design features of language. These 3 complex adaptive systems exist in a network of interactions: individual learning biases shape the dynamics of cultural evolution; universal features of linguistic structure arise from this cultural process and form the ultimate linguistic phenotype; the nature of this phenotype affects the fitness landscape for the biological evolution of the language faculty; and in turn this determines individuals' learning bias. Using a combination of computational simulation, laboratory experiments, and comparison with real-world cases of language emergence, I show that linguistic structure emerges as a natural outcome of cultural evolution once certain minimal biological requirements are in place.

Policy Driven Development: Flexible Policy Insertion for Large Scale Systems.

PubMed

Demchak, Barry; Krüger, Ingolf

2012-07-01

The success of a software system depends critically on how well it reflects and adapts to stakeholder requirements. Traditional development methods often frustrate stakeholders by creating long latencies between requirement articulation and system deployment, especially in large scale systems. One source of latency is the maintenance of policy decisions encoded directly into system workflows at development time, including those involving access control and feature set selection. We created the Policy Driven Development (PDD) methodology to address these development latencies by enabling the flexible injection of decision points into existing workflows at runtime , thus enabling policy composition that integrates requirements furnished by multiple, oblivious stakeholder groups. Using PDD, we designed and implemented a production cyberinfrastructure that demonstrates policy and workflow injection that quickly implements stakeholder requirements, including features not contemplated in the original system design. PDD provides a path to quickly and cost effectively evolve such applications over a long lifetime.

A software suite for the generation and comparison of peptide arrays from sets of data collected by liquid chromatography-mass spectrometry.

PubMed

Li, Xiao-jun; Yi, Eugene C; Kemp, Christopher J; Zhang, Hui; Aebersold, Ruedi

2005-09-01

There is an increasing interest in the quantitative proteomic measurement of the protein contents of substantially similar biological samples, e.g. for the analysis of cellular response to perturbations over time or for the discovery of protein biomarkers from clinical samples. Technical limitations of current proteomic platforms such as limited reproducibility and low throughput make this a challenging task. A new LC-MS-based platform is able to generate complex peptide patterns from the analysis of proteolyzed protein samples at high throughput and represents a promising approach for quantitative proteomics. A crucial component of the LC-MS approach is the accurate evaluation of the abundance of detected peptides over many samples and the identification of peptide features that can stratify samples with respect to their genetic, physiological, or environmental origins. We present here a new software suite, SpecArray, that generates a peptide versus sample array from a set of LC-MS data. A peptide array stores the relative abundance of thousands of peptide features in many samples and is in a format identical to that of a gene expression microarray. A peptide array can be subjected to an unsupervised clustering analysis to stratify samples or to a discriminant analysis to identify discriminatory peptide features. We applied the SpecArray to analyze two sets of LC-MS data: one was from four repeat LC-MS analyses of the same glycopeptide sample, and another was from LC-MS analysis of serum samples of five male and five female mice. We demonstrate through these two study cases that the SpecArray software suite can serve as an effective software platform in the LC-MS approach for quantitative proteomics.

Rule Extraction Based on Extreme Learning Machine and an Improved Ant-Miner Algorithm for Transient Stability Assessment.

PubMed

Li, Yang; Li, Guoqing; Wang, Zhenhao

2015-01-01

In order to overcome the problems of poor understandability of the pattern recognition-based transient stability assessment (PRTSA) methods, a new rule extraction method based on extreme learning machine (ELM) and an improved Ant-miner (IAM) algorithm is presented in this paper. First, the basic principles of ELM and Ant-miner algorithm are respectively introduced. Then, based on the selected optimal feature subset, an example sample set is generated by the trained ELM-based PRTSA model. And finally, a set of classification rules are obtained by IAM algorithm to replace the original ELM network. The novelty of this proposal is that transient stability rules are extracted from an example sample set generated by the trained ELM-based transient stability assessment model by using IAM algorithm. The effectiveness of the proposed method is shown by the application results on the New England 39-bus power system and a practical power system--the southern power system of Hebei province.

Fault-tolerant feature-based estimation of space debris rotational motion during active removal missions

NASA Astrophysics Data System (ADS)

Biondi, Gabriele; Mauro, Stefano; Pastorelli, Stefano; Sorli, Massimo

2018-05-01

One of the key functionalities required by an Active Debris Removal mission is the assessment of the target kinematics and inertial properties. Passive sensors, such as stereo cameras, are often included in the onboard instrumentation of a chaser spacecraft for capturing sequential photographs and for tracking features of the target surface. A plenty of methods, based on Kalman filtering, are available for the estimation of the target's state from feature positions; however, to guarantee the filter convergence, they typically require continuity of measurements and the capability of tracking a fixed set of pre-defined features of the object. These requirements clash with the actual tracking conditions: failures in feature detection often occur and the assumption of having some a-priori knowledge about the shape of the target could be restrictive in certain cases. The aim of the presented work is to propose a fault-tolerant alternative method for estimating the angular velocity and the relative magnitudes of the principal moments of inertia of the target. Raw data regarding the positions of the tracked features are processed to evaluate corrupted values of a 3-dimentional parameter which entirely describes the finite screw motion of the debris and which primarily is invariant on the particular set of considered features of the object. Missing values of the parameter are completely restored exploiting the typical periodicity of the rotational motion of an uncontrolled satellite: compressed sensing techniques, typically adopted for recovering images or for prognostic applications, are herein used in a completely original fashion for retrieving a kinematic signal that appears sparse in the frequency domain. Due to its invariance about the features, no assumptions are needed about the target's shape and continuity of the tracking. The obtained signal is useful for the indirect evaluation of an attitude signal that feeds an unscented Kalman filter for the estimation of the global rotational state of the target. The results of the computer simulations showed a good robustness of the method and its potential applicability for general motion conditions of the target.

Geological remote sensing signatures of terrestrial impact craters

NASA Technical Reports Server (NTRS)

Garvin, J. B.; Schnetzler, C.; Grieve, R. A. F.

1988-01-01

Geological remote sensing techniques can be used to investigate structural, depositional, and shock metamorphic effects associated with hypervelocity impact structures, some of which may be linked to global Earth system catastrophies. Although detailed laboratory and field investigations are necessary to establish conclusive evidence of an impact origin for suspected crater landforms, the synoptic perspective provided by various remote sensing systems can often serve as a pathfinder to key deposits which can then be targetted for intensive field study. In addition, remote sensing imagery can be used as a tool in the search for impact and other catastrophic explosion landforms on the basis of localized disruption and anomaly patterns. In order to reconstruct original dimensions of large, complex impact features in isolated, inaccessible regions, remote sensing imagery can be used to make preliminary estimates in the absence of field geophysical surveys. The experienced gained from two decades of planetary remote sensing of impact craters on the terrestrial planets, as well as the techniques developed for recognizing stages of degradation and initial crater morphology, can now be applied to the problem of discovering and studying eroded impact landforms on Earth. Preliminary results of remote sensing analyses of a set of terrestrial impact features in various states of degradation, geologic settings, and for a broad range of diameters and hence energies of formation are summarized. The intention is to develop a database of remote sensing signatures for catastrophic impact landforms which can then be used in EOS-era global surveys as the basis for locating the possibly hundreds of missing impact structures. In addition, refinement of initial dimensions of extremely recent structures such as Zhamanshin and Bosumtwi is an important objective in order to permit re-evaluation of global Earth system responses associated with these types of events.

A new approach to pre-processing digital image for wavelet-based watermark

NASA Astrophysics Data System (ADS)

Agreste, Santa; Andaloro, Guido

2008-11-01

The growth of the Internet has increased the phenomenon of digital piracy, in multimedia objects, like software, image, video, audio and text. Therefore it is strategic to individualize and to develop methods and numerical algorithms, which are stable and have low computational cost, that will allow us to find a solution to these problems. We describe a digital watermarking algorithm for color image protection and authenticity: robust, not blind, and wavelet-based. The use of Discrete Wavelet Transform is motivated by good time-frequency features and a good match with Human Visual System directives. These two combined elements are important for building an invisible and robust watermark. Moreover our algorithm can work with any image, thanks to the step of pre-processing of the image that includes resize techniques that adapt to the size of the original image for Wavelet transform. The watermark signal is calculated in correlation with the image features and statistic properties. In the detection step we apply a re-synchronization between the original and watermarked image according to the Neyman-Pearson statistic criterion. Experimentation on a large set of different images has been shown to be resistant against geometric, filtering, and StirMark attacks with a low rate of false alarm.

Erosion-tectonics feedbacks in shaping the landscape: An example from the Mekele Outlier (Tigray, Ethiopia)

NASA Astrophysics Data System (ADS)

Sembroni, Andrea; Molin, Paola; Dramis, Francesco; Faccenna, Claudio; Abebe, Bekele

2017-05-01

An outlier consists of an area of younger rocks surrounded by older ones. Its formation is mainly related to the erosion of surrounding rocks which causes the interruption of the original continuity of the rocks. Because of its origin, an outlier is an important witness of the paleogeography of a region and, therefore, essential to understand its topographic and geological evolution. The Mekele Outlier (N Ethiopia) is characterized by poorly incised Mesozoic marine sediments and dolerites (∼2000 m in elevation), surrounded by strongly eroded Precambrian and Paleozoic rocks and Tertiary volcanic deposits in a context of a mantle supported topography. In the past, studies about the Mekele outlier focused mainly in the mere description of the stratigraphic and tectonic settings without taking into account the feedback between surface and deep processes in shaping such peculiar feature. In this study we present the geological and geomorphometric analyses of the Mekele Outlier taking into account the general topographic features (slope map, swath profiles, local relief), the river network and the principal tectonic lineaments of the outlier. The results trace the evolution of the study area as related not only to the mere erosion of the surrounding rocks but to a complex interaction between surface and deep processes where the lithology played a crucial role.

Glioblastomas with oligodendroglial component - common origin of the different histological parts and genetic subclassification.

PubMed

Klink, Barbara; Schlingelhof, Ben; Klink, Martin; Stout-Weider, Karen; Patt, Stephan; Schrock, Evelin

2010-01-01

Glioblastomas are the most common and most malignant brain tumors in adults. A small subgroup of glioblastomas contains areas with histological features of oligodendroglial differentiation (GBMO). Our objective was to genetically characterize the oligodendroglial and the astrocytic parts of GBMOs and correlate morphologic and genetic features with clinical data. The oligodendroglial and the "classic" glioblastoma parts of 13 GBMO were analyzed separately by interphase fluorescence in situ hybridization (FISH) on paraffin sections using a custom probe set (regions 1p, 1q, 7q, 10q, 17p, 19q, cen18, 21q) and by comparative genomic hybridization (CGH) of microdissected paraffin embedded tumor tissue. We identified four distinct genetic subtypes in 13 GBMOs: an "astrocytic" subtype (9/13) characterized by +7/-10; an "oligodendroglial" subtype with -1p/-19q (1/13); an "intermediate" subtype showing +7/-1p (1/13), and an "other" subtype having none of the former aberrations typical for gliomas (2/13). The different histological tumor parts of GBMO revealed common genetic changes in all tumors and showed additional aberrations specific for each part. Our findings demonstrate the monoclonal origin of GBMO followed by the development of the astrocytic and oligodendroglial components. The diagnostic determination of the genetic signatures may allow for a better prognostication of the patients.

On the polymer physics origins of protein folding thermodynamics.

PubMed

Taylor, Mark P; Paul, Wolfgang; Binder, Kurt

2016-11-07

A remarkable feature of the spontaneous folding of many small proteins is the striking similarity in the thermodynamics of the folding process. This process is characterized by simple two-state thermodynamics with large and compensating changes in entropy and enthalpy and a funnel-like free energy landscape with a free-energy barrier that varies linearly with temperature. One might attribute the commonality of this two-state folding behavior to features particular to these proteins (e.g., chain length, hydrophobic/hydrophilic balance, attributes of the native state) or one might suspect that this similarity in behavior has a more general polymer-physics origin. Here we show that this behavior is also typical for flexible homopolymer chains with sufficiently short range interactions. Two-state behavior arises from the presence of a low entropy ground (folded) state separated from a set of high entropy disordered (unfolded) states by a free energy barrier. This homopolymer model exhibits a funneled free energy landscape that reveals a complex underlying dynamics involving competition between folding and non-folding pathways. Despite the presence of multiple pathways, this simple physics model gives the robust result of two-state thermodynamics for both the cases of folding from a basin of expanded coil states and from a basin of compact globule states.

On the polymer physics origins of protein folding thermodynamics

NASA Astrophysics Data System (ADS)

Taylor, Mark P.; Paul, Wolfgang; Binder, Kurt

2016-11-01

A remarkable feature of the spontaneous folding of many small proteins is the striking similarity in the thermodynamics of the folding process. This process is characterized by simple two-state thermodynamics with large and compensating changes in entropy and enthalpy and a funnel-like free energy landscape with a free-energy barrier that varies linearly with temperature. One might attribute the commonality of this two-state folding behavior to features particular to these proteins (e.g., chain length, hydrophobic/hydrophilic balance, attributes of the native state) or one might suspect that this similarity in behavior has a more general polymer-physics origin. Here we show that this behavior is also typical for flexible homopolymer chains with sufficiently short range interactions. Two-state behavior arises from the presence of a low entropy ground (folded) state separated from a set of high entropy disordered (unfolded) states by a free energy barrier. This homopolymer model exhibits a funneled free energy landscape that reveals a complex underlying dynamics involving competition between folding and non-folding pathways. Despite the presence of multiple pathways, this simple physics model gives the robust result of two-state thermodynamics for both the cases of folding from a basin of expanded coil states and from a basin of compact globule states.

Composition of Façon de Venise glass from early 17th century London in comparison with luxury glass of the same age

NASA Astrophysics Data System (ADS)

Cagno, S.; De Raedt, I.; Jeffries, T.; Janssens, K.

SEM-EDX and LA-ICP-MS analyses were performed on a set of early 17th century London glass fragments. The samples originate from two archaeological sites (Aldgate and Old Broad Street) where glass workshops were active in this period. The great majority of the samples are made of soda glass. Two distinct compositional groups are observed, each typical of one site of provenance. The samples originating from the Old Broad Street excavation feature a silica-soda-lime composition, with a moderate amount of potash. The samples from Aldgate are richer in potassium and feature higher amounts of trace elements such as Rb, Zr and Cu. The distinction between the two groups stems from different flux and silica sources used for glassmaking. A comparison with different European glass compositions of that time reveals no resemblance with genuine Venetian production, yet the composition of the Old Broad Street glass shows a close similarity to that of fragments produced `à la façon de Venise' in Antwerp at the end of the 16th century. This coincides with historical sources attesting the arrival of glassworkers from the Low Countries in England and suggests that a transfer of technology took place near the turn of the century.

MEVTV Workshop on Tectonic Features on Mars

NASA Technical Reports Server (NTRS)

Watters, Thomas R. (Editor); Golombek, Matthew P. (Editor)

1989-01-01

The state of knowledge of tectonic features on Mars was determined and kinematic and mechanical models were assessed for their origin. Three sessions were held: wrinkle ridges and compressional structure; strike-slip faults; and extensional structures. Each session began with an overview of the features under discussion. In the case of wrinkle ridges and extensional structures, the overview was followed by keynote addresses by specialists working on similar structures on the Earth. The first session of the workshop focused on the controversy over the relative importance of folding, faulting, and intrusive volcanism in the origin of wrinkle ridges. The session ended with discussions of the origin of compressional flank structures associated with Martian volcanoes and the relationship between the volcanic complexes and the inferred regional stress field. The second day of the workshop began with the presentation and discussion of evidence for strike-slip faults on Mars at various scales. In the last session, the discussion of extensional structures ranged from the origin of grabens, tension cracks, and pit-crater chains to the origin of Valles Marineris canyons. Shear and tensile modes of brittle failure in the formation of extensional features and the role of these failure modes in the formation of pit-crater chains and the canyons of Valles Marineris were debated. The relationship of extensional features to other surface processes, such as carbonate dissolution (karst) were also discussed.

Geomorphology of the Iberian Continental Margin

NASA Astrophysics Data System (ADS)

Maestro, Adolfo; López-Martínez, Jerónimo; Llave, Estefanía; Bohoyo, Fernando; Acosta, Juan; Hernández-Molina, F. Javier; Muñoz, Araceli; Jané, Gloria

2013-08-01

The submarine features and processes around the Iberian Peninsula are the result of a complex and diverse geological and oceanographical setting. This paper presents an overview of the seafloor geomorphology of the Iberian Continental Margin and the adjacent abyssal plains. The study covers an area of approximately 2.3 million km2, including a 50 to 400 km wide band adjacent to the coastline. The main morphological characteristics of the seafloor features on the Iberian continental shelf, continental slope, continental rise and the surrounding abyssal plains are described. Individual seafloor features existing on the Iberian Margin have been classified into three main groups according to their origin: tectonic and/or volcanic, depositional and erosional. Major depositional and erosional features around the Iberian Margin developed in late Pleistocene-Holocene times and have been controlled by tectonic movements and eustatic fluctuations. The distribution of the geomorphological features is discussed in relation to their genetic processes and the evolution of the margin. The prevalence of one or several specific processes in certain areas reflects the dominant morphotectonic and oceanographic controlling factors. Sedimentary processes and the resulting depositional products are dominant on the Valencia-Catalán Margin and in the northern part of the Balearic Promontory. Strong tectonic control is observed in the geomorphology of the Betic and the Gulf of Cádiz margins. The role of bottom currents is especially evident throughout the Iberian Margin. The Galicia, Portuguese and Cantabrian margins show a predominance of erosional features and tectonically-controlled linear features related to faults.

High performance computing to support multiscale representation of hydrography for the conterminous United States

USGS Publications Warehouse

Stanislawski, Larry V.; Liu, Yan; Buttenfield, Barbara P.; Survila, Kornelijus; Wendel, Jeffrey; Okok, Abdurraouf

2016-01-01

The National Hydrography Dataset (NHD) for the United States furnishes a comprehensive set of vector features representing the surface-waters in the country (U.S. Geological Survey 2000). The high-resolution (HR) layer of the NHD is largely comprised of hydrographic features originally derived from 1:24,000-scale (24K) U.S. Topographic maps. However, in recent years (2009 to present) densified hydrographic feature content, from sources as large as 1:2,400, have been incorporated into some watersheds of the HR NHD within the conterminous United States to better support the needs of various local and state organizations. As such, the HR NHD is a multiresolution dataset with obvious data density variations because of scale changes. In addition, data density variations exist within the HR NHD that are particularly evident in the surface-water flow network (NHD flowlines) because of natural variations of local geographic conditions; and also because of unintentional compilation inconsistencies due to variations in data collection standards and climate conditions over the many years of 24K hydrographic data collection (US Geological Survey 1955).

Robust iterative closest point algorithm based on global reference point for rotation invariant registration.

PubMed

Du, Shaoyi; Xu, Yiting; Wan, Teng; Hu, Huaizhong; Zhang, Sirui; Xu, Guanglin; Zhang, Xuetao

2017-01-01

The iterative closest point (ICP) algorithm is efficient and accurate for rigid registration but it needs the good initial parameters. It is easily failed when the rotation angle between two point sets is large. To deal with this problem, a new objective function is proposed by introducing a rotation invariant feature based on the Euclidean distance between each point and a global reference point, where the global reference point is a rotation invariant. After that, this optimization problem is solved by a variant of ICP algorithm, which is an iterative method. Firstly, the accurate correspondence is established by using the weighted rotation invariant feature distance and position distance together. Secondly, the rigid transformation is solved by the singular value decomposition method. Thirdly, the weight is adjusted to control the relative contribution of the positions and features. Finally this new algorithm accomplishes the registration by a coarse-to-fine way whatever the initial rotation angle is, which is demonstrated to converge monotonically. The experimental results validate that the proposed algorithm is more accurate and robust compared with the original ICP algorithm.

Robust iterative closest point algorithm based on global reference point for rotation invariant registration

PubMed Central

Du, Shaoyi; Xu, Yiting; Wan, Teng; Zhang, Sirui; Xu, Guanglin; Zhang, Xuetao

2017-01-01

The iterative closest point (ICP) algorithm is efficient and accurate for rigid registration but it needs the good initial parameters. It is easily failed when the rotation angle between two point sets is large. To deal with this problem, a new objective function is proposed by introducing a rotation invariant feature based on the Euclidean distance between each point and a global reference point, where the global reference point is a rotation invariant. After that, this optimization problem is solved by a variant of ICP algorithm, which is an iterative method. Firstly, the accurate correspondence is established by using the weighted rotation invariant feature distance and position distance together. Secondly, the rigid transformation is solved by the singular value decomposition method. Thirdly, the weight is adjusted to control the relative contribution of the positions and features. Finally this new algorithm accomplishes the registration by a coarse-to-fine way whatever the initial rotation angle is, which is demonstrated to converge monotonically. The experimental results validate that the proposed algorithm is more accurate and robust compared with the original ICP algorithm. PMID:29176780

Efficient iris recognition by characterizing key local variations.

PubMed

Ma, Li; Tan, Tieniu; Wang, Yunhong; Zhang, Dexin

2004-06-01

Unlike other biometrics such as fingerprints and face, the distinct aspect of iris comes from randomly distributed features. This leads to its high reliability for personal identification, and at the same time, the difficulty in effectively representing such details in an image. This paper describes an efficient algorithm for iris recognition by characterizing key local variations. The basic idea is that local sharp variation points, denoting the appearing or vanishing of an important image structure, are utilized to represent the characteristics of the iris. The whole procedure of feature extraction includes two steps: 1) a set of one-dimensional intensity signals is constructed to effectively characterize the most important information of the original two-dimensional image; 2) using a particular class of wavelets, a position sequence of local sharp variation points in such signals is recorded as features. We also present a fast matching scheme based on exclusive OR operation to compute the similarity between a pair of position sequences. Experimental results on 2255 iris images show that the performance of the proposed method is encouraging and comparable to the best iris recognition algorithm found in the current literature.

Overview of the observations of symbiotic stars

NASA Technical Reports Server (NTRS)

Viotti, Roberto

1993-01-01

The term Symbiotic stars commonly denotes variable stars whose optical spectra simultaneously present a cool absorption spectrum (typically TiO absorption bands) and emission lines of high ionization energy. This term is now used for the category of variable stars with composite spectrum. The main spectral features of these objects are: (1) the presence of the red continuum typical of a cool star, (2) the rich emission line spectrum, and (3) the UV excess, frequently with the Balmer continuum in emission. In addition to the peculiar spectrum, the very irregular photometric and spectroscopic variability is the major feature of the symbiotic stars. Moreover, the light curve is basic to identify the different phases of activity in a symbiotic star. The physical mechanisms that cause the symbiotic phenomenon and its variety are the focus of this paper. An astronomical phenomenon characterized by a composite stellar spectrum with two apparently conflicting features, and large variability has been observed. Our research set out to find the origin of this behavior and, in particular, to identify and measure the physical mechanism(s) responsible for the observed phenomena.

A fast, automated, polynomial-based cosmic ray spike-removal method for the high-throughput processing of Raman spectra.

PubMed

Schulze, H Georg; Turner, Robin F B

2013-04-01

Raman spectra often contain undesirable, randomly positioned, intense, narrow-bandwidth, positive, unidirectional spectral features generated when cosmic rays strike charge-coupled device cameras. These must be removed prior to analysis, but doing so manually is not feasible for large data sets. We developed a quick, simple, effective, semi-automated procedure to remove cosmic ray spikes from spectral data sets that contain large numbers of relatively homogenous spectra. Although some inhomogeneous spectral data sets can be accommodated--it requires replacing excessively modified spectra with the originals and removing their spikes with a median filter instead--caution is advised when processing such data sets. In addition, the technique is suitable for interpolating missing spectra or replacing aberrant spectra with good spectral estimates. The method is applied to baseline-flattened spectra and relies on fitting a third-order (or higher) polynomial through all the spectra at every wavenumber. Pixel intensities in excess of a threshold of 3× the noise standard deviation above the fit are reduced to the threshold level. Because only two parameters (with readily specified default values) might require further adjustment, the method is easily implemented for semi-automated processing of large spectral sets.

XMM-Newton Proposal 03033401

NASA Astrophysics Data System (ADS)

Ghosh, Kajal

2004-10-01

We have detected a highly blueshifted (7.6 keV at the source frame) emission feature in the ASCA spectra of the unusual Narrow-line Seyfert 1 galaxy RX J0136.9-3510. At ASCA resolution it is impossible to tell if the feature is a single line or a combination of lines nor if the feature is due to He-like or H-like Fe. The line profile can tell us where the bulk of the emission origin- ates: A low velocity dispersion would favor a wind/outflow origin, while a hi- gher dispersion may allow for an ionized disk reflection origin. Strong absor- ption and resonant scattering could also produce blueshifted line. To acquire better resolution spectrum to constrain the origin of the line via detailed physical modeling, we propose 50 ks XMM-Newton observations of RXJ0136.9-3510.

Sensor-oriented feature usability evaluation in fingerprint segmentation

NASA Astrophysics Data System (ADS)

Li, Ying; Yin, Yilong; Yang, Gongping

2013-06-01

Existing fingerprint segmentation methods usually process fingerprint images captured by different sensors with the same feature or feature set. We propose to improve the fingerprint segmentation result in view of an important fact that images from different sensors have different characteristics for segmentation. Feature usability evaluation, which means to evaluate the usability of features to find the personalized feature or feature set for different sensors to improve the performance of segmentation. The need for feature usability evaluation for fingerprint segmentation is raised and analyzed as a new issue. To address this issue, we present a decision-tree-based feature-usability evaluation method, which utilizes a C4.5 decision tree algorithm to evaluate and pick the best suitable feature or feature set for fingerprint segmentation from a typical candidate feature set. We apply the novel method on the FVC2002 database of fingerprint images, which are acquired by four different respective sensors and technologies. Experimental results show that the accuracy of segmentation is improved, and time consumption for feature extraction is dramatically reduced with selected feature(s).

2-DE combined with two-layer feature selection accurately establishes the origin of oolong tea.

PubMed

Chien, Han-Ju; Chu, Yen-Wei; Chen, Chi-Wei; Juang, Yu-Min; Chien, Min-Wei; Liu, Chih-Wei; Wu, Chia-Chang; Tzen, Jason T C; Lai, Chien-Chen

2016-11-15

Taiwan is known for its high quality oolong tea. Because of high consumer demand, some tea manufactures mix lower quality leaves with genuine Taiwan oolong tea in order to increase profits. Robust scientific methods are, therefore, needed to verify the origin and quality of tea leaves. In this study, we investigated whether two-dimensional gel electrophoresis (2-DE) and nanoscale liquid chromatography/tandem mass spectroscopy (nano-LC/MS/MS) coupled with a two-layer feature selection mechanism comprising information gain attribute evaluation (IGAE) and support vector machine feature selection (SVM-FS) are useful in identifying characteristic proteins that can be used as markers of the original source of oolong tea. Samples in this study included oolong tea leaves from 23 different sources. We found that our method had an accuracy of 95.5% in correctly identifying the origin of the leaves. Overall, our method is a novel approach for determining the origin of oolong tea leaves. Copyright © 2016 Elsevier Ltd. All rights reserved.

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 7 Agriculture 2 2014-01-01 2014-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 7 Agriculture 2 2013-01-01 2013-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

7 CFR 28.107 - Original cotton standards and reserve sets.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 7 Agriculture 2 2012-01-01 2012-01-01 false Original cotton standards and reserve sets. 28.107... CONTAINER REGULATIONS COTTON CLASSING, TESTING, AND STANDARDS Regulations Under the United States Cotton Standards Act Practical Forms of Cotton Standards § 28.107 Original cotton standards and reserve sets. (a...

Bacterially induced precipitation of CaCO 3: An example from studies of cyanobacterial mats. Final report

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

Chafetz, Henry S.

1990-04-30

Bacteria induce the precipitation of calcium carbonate in the laboratory and in nature by altering their chemical environment. Geologists are recognizing the possibility that bacterially induced precipitates may form significant mineral deposits, unfortunately, there are currently no sound criteria by which they can be recognized in recent sediments, or in the rock record. Cultures of aerobic and facultative bacteria from cyanobacterial mats on Andros Island, Bahamas, and Baffin Bay, Texas, induced the precipitation of calcium carbonate under controlled conditions. Crusts, the largest features formed, are composed of 5--200μm diameter bundles which are, in turn, composed of numerous individual crystals. Themore » smallest observed features are 0.1--0.4μm spheres and rods which comprise some individual crystals and crystal bundles. Crystal bundles resembling rhombohedra, tetragonal disphenoids, tetragonal dipyramids, and calcite dumbbells appear to be uniquely bacterial in origin, and they have all been observed in recent sediments. Swollen rods, discs, curved dumbbells, and 50--200μm optically continuous crystals resembling brushes may be uniquely bacterial in origin, however, they have not been reported by other laboratories nor observed in natural settings. Presence of any of these forms in recent sediments should be taken as strong evidence for bacterial influence. Spheres and aragonite dumbbells have also been observed in natural environments, however, they are not always bacterial in origin. Precipitation of calcium carbonate occurs preferentially on dead cyanobacteria in the presence of bacteria. Lithification of algal mats to form stromatolites may take place in the zone of decaying organic matter due to bacterial activity.« less

Bacterially induced precipitation of CaCO sub 3 : An example from studies of cyanobacterial mats

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

Chafetz, H.S.

1990-04-30

Bacteria induce the precipitation of calcium carbonate in the laboratory and in nature by altering their chemical environment. Geologists are recognizing the possibility that bacterially induced precipitates may form significant mineral deposits, unfortunately, there are currently no sound criteria by which they can be recognized in recent sediments, or in the rock record. Cultures of aerobic and facultative bacteria from cyanobacterial mats on Andros Island, Bahamas, and Baffin Bay, Texas, induced the precipitation of calcium carbonate under controlled conditions. Crusts, the largest features formed, are composed of 5--200{mu}m diameter bundles which are, in turn, composed of numerous individual crystals. Themore » smallest observed features are 0.1--0.4{mu}m spheres and rods which comprise some individual crystals and crystal bundles. Crystal bundles resembling rhombohedra, tetragonal disphenoids, tetragonal dipyramids, and calcite dumbbells appear to be uniquely bacterial in origin, and they have all been observed in recent sediments. Swollen rods, discs, curved dumbbells, and 50--200{mu}m optically continuous crystals resembling brushes may be uniquely bacterial in origin, however, they have not been reported by other laboratories nor observed in natural settings. Presence of any of these forms in recent sediments should be taken as strong evidence for bacterial influence. Spheres and aragonite dumbbells have also been observed in natural environments, however, they are not always bacterial in origin. Precipitation of calcium carbonate occurs preferentially on dead cyanobacteria in the presence of bacteria. Lithification of algal mats to form stromatolites may take place in the zone of decaying organic matter due to bacterial activity.« less

Landslides Identification Using Airborne Laser Scanning Data Derived Topographic Terrain Attributes and Support Vector Machine Classification

NASA Astrophysics Data System (ADS)

Pawłuszek, Kamila; Borkowski, Andrzej

2016-06-01

Since the availability of high-resolution Airborne Laser Scanning (ALS) data, substantial progress in geomorphological research, especially in landslide analysis, has been carried out. First and second order derivatives of Digital Terrain Model (DTM) have become a popular and powerful tool in landslide inventory mapping. Nevertheless, an automatic landslide mapping based on sophisticated classifiers including Support Vector Machine (SVM), Artificial Neural Network or Random Forests is often computationally time consuming. The objective of this research is to deeply explore topographic information provided by ALS data and overcome computational time limitation. For this reason, an extended set of topographic features and the Principal Component Analysis (PCA) were used to reduce redundant information. The proposed novel approach was tested on a susceptible area affected by more than 50 landslides located on Rożnów Lake in Carpathian Mountains, Poland. The initial seven PCA components with 90% of the total variability in the original topographic attributes were used for SVM classification. Comparing results with landslide inventory map, the average user's accuracy (UA), producer's accuracy (PA), and overall accuracy (OA) were calculated for two models according to the classification results. Thereby, for the PCA-feature-reduced model UA, PA, and OA were found to be 72%, 76%, and 72%, respectively. Similarly, UA, PA, and OA in the non-reduced original topographic model, was 74%, 77% and 74%, respectively. Using the initial seven PCA components instead of the twenty original topographic attributes does not significantly change identification accuracy but reduce computational time.

Structure and origin of Australian ring and dome features with reference to the search for asteroid impact events

NASA Astrophysics Data System (ADS)

Glikson, Andrew

2018-01-01

Ring, dome and crater features on the Australian continent and shelf include (A) 38 structures of confirmed or probable asteroid and meteorite impact origin and (B) numerous buried and exposed ring, dome and crater features of undefined origin. A large number of the latter include structural and geophysical elements consistent with impact structures, pending test by field investigations and/or drilling. This paper documents and briefly describes 43 ring and dome features with the aim of appraising their similarities and differences from those of impact structures. Discrimination between impact structures and igneous plugs, volcanic caldera and salt domes require field work and/or drilling. Where crater-like morphological patterns intersect pre-existing linear structural features and contain central morphological highs and unique thrust and fault patterns an impact connection needs to tested in the field. Hints of potential buried impact structures may be furnished by single or multi-ring TMI patterns, circular TMI quiet zones, corresponding gravity patterns, low velocity and non-reflective seismic zones.

Data Exploration using Unsupervised Feature Extraction for Mixed Micro-Seismic Signals

NASA Astrophysics Data System (ADS)

Meyer, Matthias; Weber, Samuel; Beutel, Jan

2017-04-01

We present a system for the analysis of data originating in a multi-sensor and multi-year experiment focusing on slope stability and its underlying processes in fractured permafrost rock walls undertaken at 3500m a.s.l. on the Matterhorn Hörnligrat, (Zermatt, Switzerland). This system incorporates facilities for the transmission, management and storage of large-scales of data ( 7 GB/day), preprocessing and aggregation of multiple sensor types, machine-learning based automatic feature extraction for micro-seismic and acoustic emission data and interactive web-based visualization of the data. Specifically, a combination of three types of sensors are used to profile the frequency spectrum from 1 Hz to 80 kHz with the goal to identify the relevant destructive processes (e.g. micro-cracking and fracture propagation) leading to the eventual destabilization of large rock masses. The sensors installed for this profiling experiment (2 geophones, 1 accelerometers and 2 piezo-electric sensors for detecting acoustic emission), are further augmented with sensors originating from a previous activity focusing on long-term monitoring of temperature evolution and rock kinematics with the help of wireless sensor networks (crackmeters, cameras, weather station, rock temperature profiles, differential GPS) [Hasler2012]. In raw format, the data generated by the different types of sensors, specifically the micro-seismic and acoustic emission sensors, is strongly heterogeneous, in part unsynchronized and the storage and processing demand is large. Therefore, a purpose-built signal preprocessing and event-detection system is used. While the analysis of data from each individual sensor follows established methods, the application of all these sensor types in combination within a field experiment is unique. Furthermore, experience and methods from using such sensors in laboratory settings cannot be readily transferred to the mountain field site setting with its scale and full exposure to the natural environment. Consequently, many state-of-the-art algorithms for big data analysis and event classification requiring a ground truth dataset cannot be applied. The above mentioned challenges require a tool for data exploration. In the presented system, data exploration is supported by unsupervised feature learning based on convolutional neural networks, which is used to automatically extract common features for preliminary clustering and outlier detection. With this information, an interactive web-tool allows for a fast identification of interesting time segments on which segment-selective algorithms for visualization, feature extraction and statistics can be applied. The combination of manual labeling based and unsupervised feature extraction provides an event catalog for classification of different characteristic events related to internal progression of micro-crack in steep fractured bedrock permafrost. References Hasler, A., S. Gruber, and J. Beutel (2012), Kinematics of steep bedrock permafrost, J. Geophys. Res., 117, F01016, doi:10.1029/2011JF001981.

Comparing Pattern Recognition Feature Sets for Sorting Triples in the FIRST Database

NASA Astrophysics Data System (ADS)

Proctor, D. D.

2006-07-01

Pattern recognition techniques have been used with increasing success for coping with the tremendous amounts of data being generated by automated surveys. Usually this process involves construction of training sets, the typical examples of data with known classifications. Given a feature set, along with the training set, statistical methods can be employed to generate a classifier. The classifier is then applied to process the remaining data. Feature set selection, however, is still an issue. This paper presents techniques developed for accommodating data for which a substantive portion of the training set cannot be classified unambiguously, a typical case for low-resolution data. Significance tests on the sort-ordered, sample-size-normalized vote distribution of an ensemble of decision trees is introduced as a method of evaluating relative quality of feature sets. The technique is applied to comparing feature sets for sorting a particular radio galaxy morphology, bent-doubles, from the Faint Images of the Radio Sky at Twenty Centimeters (FIRST) database. Also examined are alternative functional forms for feature sets. Associated standard deviations provide the means to evaluate the effect of the number of folds, the number of classifiers per fold, and the sample size on the resulting classifications. The technique also may be applied to situations for which, although accurate classifications are available, the feature set is clearly inadequate, but is desired nonetheless to make the best of available information.

Feature Selection for Ridge Regression with Provable Guarantees.

PubMed

Paul, Saurabh; Drineas, Petros

2016-04-01

We introduce single-set spectral sparsification as a deterministic sampling-based feature selection technique for regularized least-squares classification, which is the classification analog to ridge regression. The method is unsupervised and gives worst-case guarantees of the generalization power of the classification function after feature selection with respect to the classification function obtained using all features. We also introduce leverage-score sampling as an unsupervised randomized feature selection method for ridge regression. We provide risk bounds for both single-set spectral sparsification and leverage-score sampling on ridge regression in the fixed design setting and show that the risk in the sampled space is comparable to the risk in the full-feature space. We perform experiments on synthetic and real-world data sets; a subset of TechTC-300 data sets, to support our theory. Experimental results indicate that the proposed methods perform better than the existing feature selection methods.

Method for discovering relationships in data by dynamic quantum clustering

DOEpatents

Weinstein, Marvin; Horn, David

2017-05-09

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

Method for discovering relationships in data by dynamic quantum clustering

DOEpatents

Weinstein, Marvin; Horn, David

2014-10-28

Data clustering is provided according to a dynamical framework based on quantum mechanical time evolution of states corresponding to data points. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data-points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering.

Incorporating Edge Information into Best Merge Region-Growing Segmentation

NASA Technical Reports Server (NTRS)

Tilton, James C.; Pasolli, Edoardo

2014-01-01

We have previously developed a best merge region-growing approach that integrates nonadjacent region object aggregation with the neighboring region merge process usually employed in region growing segmentation approaches. This approach has been named HSeg, because it provides a hierarchical set of image segmentation results. Up to this point, HSeg considered only global region feature information in the region growing decision process. We present here three new versions of HSeg that include local edge information into the region growing decision process at different levels of rigor. We then compare the effectiveness and processing times of these new versions HSeg with each other and with the original version of HSeg.

Empty test section streamlining of the transonic self-streamlining wind tunnel fitted with new walls

NASA Technical Reports Server (NTRS)

Lewis, M. C.

1988-01-01

The original flexible top and bottom walls of the Transonic Self-Streamlining Wind Tunnel (TSWT), at the University of Southampton, have been replaced with new walls featuring a larger number of static pressure tappings and detailed mechanical improvements. This report describes the streamling method, results, and conclusions of a series of tests aimed at defining sets of aerodynamically straight wall contours for the new flexible walls. This procedure is a necessary prelude to model testing. The quality of data obtained compares favorably with the aerodynamically straight data obtained with the old walls. No operational difficulties were experienced with the new walls.

Channel Access in Erlang

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

Nicklaus, Dennis J.

2013-10-13

We have developed an Erlang language implementation of the Channel Access protocol. Included are low-level functions for encoding and decoding Channel Access protocol network packets as well as higher level functions for monitoring or setting EPICS process variables. This provides access to EPICS process variables for the Fermilab Acnet control system via our Erlang-based front-end architecture without having to interface to C/C++ programs and libraries. Erlang is a functional programming language originally developed for real-time telecommunications applications. Its network programming features and list management functions make it particularly well-suited for the task of managing multiple Channel Access circuits and PVmore » monitors.« less

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data.

PubMed

Wang, Kung-Jeng; Makond, Bunjira; Wang, Kung-Min

2013-11-09

Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE), cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR.

An improved survivability prognosis of breast cancer by using sampling and feature selection technique to solve imbalanced patient classification data

PubMed Central

2013-01-01

Background Breast cancer is one of the most critical cancers and is a major cause of cancer death among women. It is essential to know the survivability of the patients in order to ease the decision making process regarding medical treatment and financial preparation. Recently, the breast cancer data sets have been imbalanced (i.e., the number of survival patients outnumbers the number of non-survival patients) whereas the standard classifiers are not applicable for the imbalanced data sets. The methods to improve survivability prognosis of breast cancer need for study. Methods Two well-known five-year prognosis models/classifiers [i.e., logistic regression (LR) and decision tree (DT)] are constructed by combining synthetic minority over-sampling technique (SMOTE) ,cost-sensitive classifier technique (CSC), under-sampling, bagging, and boosting. The feature selection method is used to select relevant variables, while the pruning technique is applied to obtain low information-burden models. These methods are applied on data obtained from the Surveillance, Epidemiology, and End Results database. The improvements of survivability prognosis of breast cancer are investigated based on the experimental results. Results Experimental results confirm that the DT and LR models combined with SMOTE, CSC, and under-sampling generate higher predictive performance consecutively than the original ones. Most of the time, DT and LR models combined with SMOTE and CSC use less informative burden/features when a feature selection method and a pruning technique are applied. Conclusions LR is found to have better statistical power than DT in predicting five-year survivability. CSC is superior to SMOTE, under-sampling, bagging, and boosting to improve the prognostic performance of DT and LR. PMID:24207108

Synthesis of Joint Volumes, Visualization of Paths, and Revision of Viewing Sequences in a Multi-dimensional Seismic Data Viewer

NASA Astrophysics Data System (ADS)

Chen, D. M.; Clapp, R. G.; Biondi, B.

2006-12-01

Ricksep is a freely-available interactive viewer for multi-dimensional data sets. The viewer is very useful for simultaneous display of multiple data sets from different viewing angles, animation of movement along a path through the data space, and selection of local regions for data processing and information extraction. Several new viewing features are added to enhance the program's functionality in the following three aspects. First, two new data synthesis algorithms are created to adaptively combine information from a data set with mostly high-frequency content, such as seismic data, and another data set with mainly low-frequency content, such as velocity data. Using the algorithms, these two data sets can be synthesized into a single data set which resembles the high-frequency data set on a local scale and at the same time resembles the low- frequency data set on a larger scale. As a result, the originally separated high and low-frequency details can now be more accurately and conveniently studied together. Second, a projection algorithm is developed to display paths through the data space. Paths are geophysically important because they represent wells into the ground. Two difficulties often associated with tracking paths are that they normally cannot be seen clearly inside multi-dimensional spaces and depth information is lost along the direction of projection when ordinary projection techniques are used. The new algorithm projects samples along the path in three orthogonal directions and effectively restores important depth information by using variable projection parameters which are functions of the distance away from the path. Multiple paths in the data space can be generated using different character symbols as positional markers, and users can easily create, modify, and view paths in real time. Third, a viewing history list is implemented which enables Ricksep's users to create, edit and save a recipe for the sequence of viewing states. Then, the recipe can be loaded into an active Ricksep session, after which the user can navigate to any state in the sequence and modify the sequence from that state. Typical uses of this feature are undoing and redoing viewing commands and animating a sequence of viewing states. The theoretical discussion are carried out and several examples using real seismic data are provided to show how these new Ricksep features provide more convenient, accurate ways to manipulate multi-dimensional data sets.

Compositions of Low Albedo Intracrater Materials and Wind Streaks on Mars: Examination of MGS TES Data in Western Arabia Terra

NASA Technical Reports Server (NTRS)

Bandfield, J. L.; Wyatt, M. B.; Christensen, P.; McSween, H. Y., Jr.

2001-01-01

Basalt and andesite surface compositions are identified within individual low albedo intracrater features and adjacent dark wind streaks. High resolution mapping of compositional heterogeneities may help constrain origin hypotheses for these features. Additional information is contained in the original extended abstract.

Origin of Sinuous Channels on the SW Apron of Ascraeus Mons and the Surrounding Plains, Mars

NASA Technical Reports Server (NTRS)

Schierl, Z.; Signorella, J.; Collins, A.; Schwans, B.; de Wet, A. P.; Bleacher, J. E.

2012-01-01

Ascraeus Mons is one of three large shield volcanoes located along a NE-SW trending lineament atop the Tharsis Bulge on Mars. Spacecraft images, beginning with Viking in the 1970 s, revealed that the SW rift apron of Ascraeus Mons is cut by numerous sinuous channels, many of which originate from large, elongated, bowl shaped amphitheaters known as the Ascraeus Chasmata. A number of these channels can be traced onto the flatter plains to the east of the rift apron. These features have been interpreted as either fluvial [1] or volcanic [2] in origin. Most recently, it has been shown that one of the longest channels on the Ascraeus rift apron appears to transition into a roofed-over lava channel or lava tube at its distal end, and thus the entire feature is likely of a volcanic origin [2]. In addition, field observations of recent lava flows on Hawaii have shown that lava is capable of producing features such as the complex braided and anastomosing channels and streamlined islands that are observed in the Ascraeus features [2].

The anatomy and development of normal and abnormal coronary arteries.

PubMed

Spicer, Diane E; Henderson, Deborah J; Chaudhry, Bill; Mohun, Timothy J; Anderson, Robert H

2015-12-01

At present, there is significant interest in the morphology of the coronary arteries, not least due to the increasingly well-recognised association between anomalous origin of the arteries and sudden cardiac death. Much has also been learnt over the last decade regarding the embryology of the arteries. In this review, therefore, we provide a brief introduction into the recent findings regarding their development. In particular, we emphasise that new evidence, derived using the developing murine heart, points to the arterial stems growing out from the adjacent sinuses of the aortic root, rather than the arteries growing in, as is currently assumed. As we show, the concept of outgrowth provides an excellent explanation for several of the abnormal arrangements encountered in the clinical setting. Before summarising these abnormal features, we draw attention to the need to describe the heart in an attitudinally appropriate manner, following the basic rule of human anatomy, rather than describing the cardiac components with the heart in the "Valentine" orientation. We then show how the major abnormalities involving the coronary arteries in humans can be summarised in terms of abnormal origin from the pulmonary circulation, abnormal aortic origin, or fistulous communications between the coronary arteries and the cardiac cavities. In the case of abnormal aortic origin, we highlight those malformations known to be associated with sudden cardiac death.

A novel feature extraction approach for microarray data based on multi-algorithm fusion

PubMed Central

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions. PMID:25780277

A novel feature extraction approach for microarray data based on multi-algorithm fusion.

PubMed

Jiang, Zhu; Xu, Rong

2015-01-01

Feature extraction is one of the most important and effective method to reduce dimension in data mining, with emerging of high dimensional data such as microarray gene expression data. Feature extraction for gene selection, mainly serves two purposes. One is to identify certain disease-related genes. The other is to find a compact set of discriminative genes to build a pattern classifier with reduced complexity and improved generalization capabilities. Depending on the purpose of gene selection, two types of feature extraction algorithms including ranking-based feature extraction and set-based feature extraction are employed in microarray gene expression data analysis. In ranking-based feature extraction, features are evaluated on an individual basis, without considering inter-relationship between features in general, while set-based feature extraction evaluates features based on their role in a feature set by taking into account dependency between features. Just as learning methods, feature extraction has a problem in its generalization ability, which is robustness. However, the issue of robustness is often overlooked in feature extraction. In order to improve the accuracy and robustness of feature extraction for microarray data, a novel approach based on multi-algorithm fusion is proposed. By fusing different types of feature extraction algorithms to select the feature from the samples set, the proposed approach is able to improve feature extraction performance. The new approach is tested against gene expression dataset including Colon cancer data, CNS data, DLBCL data, and Leukemia data. The testing results show that the performance of this algorithm is better than existing solutions.

Validation of the NIMH-ChEFS adolescent face stimulus set in an adolescent, parent, and health professional sample.

PubMed

Coffman, Marika C; Trubanova, Andrea; Richey, J Anthony; White, Susan W; Kim-Spoon, Jungmeen; Ollendick, Thomas H; Pine, Daniel S

2015-12-01

Attention to faces is a fundamental psychological process in humans, with atypical attention to faces noted across several clinical disorders. Although many clinical disorders onset in adolescence, there is a lack of well-validated stimulus sets containing adolescent faces available for experimental use. Further, the images comprising most available sets are not controlled for high- and low-level visual properties. Here, we present a cross-site validation of the National Institute of Mental Health Child Emotional Faces Picture Set (NIMH-ChEFS), comprised of 257 photographs of adolescent faces displaying angry, fearful, happy, sad, and neutral expressions. All of the direct facial images from the NIMH-ChEFS set were adjusted in terms of location of facial features and standardized for luminance, size, and smoothness. Although overall agreement between raters in this study and the original development-site raters was high (89.52%), this differed by group such that agreement was lower for adolescents relative to mental health professionals in the current study. These results suggest that future research using this face set or others of adolescent/child faces should base comparisons on similarly-aged validation data. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

Exploring nonlinear feature space dimension reduction and data representation in breast Cadx with Laplacian eigenmaps and t-SNE.

PubMed

Jamieson, Andrew R; Giger, Maryellen L; Drukker, Karen; Li, Hui; Yuan, Yading; Bhooshan, Neha

2010-01-01

In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, "Laplacian eigenmaps for dimensionality reduction and data representation," Neural Comput. 15, 1373-1396 (2003)] and t-distributed stochastic neighbor embedding (t-SNE) [L. van der Maaten and G. Hinton, "Visualizing data using t-SNE," J. Mach. Learn. Res. 9, 2579-2605 (2008)]. These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis (CADx) were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network (MCMC-BANN) and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise (LSW) feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+bootstrap validation, 95% empirical confidence intervals were computed for the each classifier's AUC performance. In the large U.S. data set, sample high performance results include, AUC0.632+ = 0.88 with 95% empirical bootstrap interval [0.787;0.895] for 13 ARD selected features and AUC0.632+ = 0.87 with interval [0.817;0.906] for four LSW selected features compared to 4D t-SNE mapping (from the original 81D feature space) giving AUC0.632+ = 0.90 with interval [0.847;0.919], all using the MCMC-BANN. Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space.

Diagnostic and prognostic histopathology system using morphometric indices

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

Parvin, Bahram; Chang, Hang; Han, Ju

Determining at least one of a prognosis or a therapy for a patient based on a stained tissue section of the patient. An image of a stained tissue section of a patient is processed by a processing device. A set of features values for a set of cell-based features is extracted from the processed image, and the processed image is associated with a particular cluster of a plurality of clusters based on the set of feature values, where the plurality of clusters is defined with respect to a feature space corresponding to the set of features.

The compressed average image intensity metric for stereoscopic video quality assessment

NASA Astrophysics Data System (ADS)

Wilczewski, Grzegorz

2016-09-01

The following article depicts insights towards design, creation and testing of a genuine metric designed for a 3DTV video quality evaluation. The Compressed Average Image Intensity (CAII) mechanism is based upon stereoscopic video content analysis, setting its core feature and functionality to serve as a versatile tool for an effective 3DTV service quality assessment. Being an objective type of quality metric it may be utilized as a reliable source of information about the actual performance of a given 3DTV system, under strict providers evaluation. Concerning testing and the overall performance analysis of the CAII metric, the following paper presents comprehensive study of results gathered across several testing routines among selected set of samples of stereoscopic video content. As a result, the designed method for stereoscopic video quality evaluation is investigated across the range of synthetic visual impairments injected into the original video stream.

Oscillations in rational economies.

PubMed

Mishchenko, Yuriy

2014-01-01

Economic (business) cycles are some of the most noted features of market economies, also ranked among the most serious of economic problems. Despite long historical persistence, the nature and the origin of business cycles remain controversial. In this paper we investigate the problem of the nature of business cycles from the positions of the market systems viewed as complex systems of many interacting market agents. We show that the development of cyclic instabilities in these settings can be traced down to just two fundamental factors - the competition of market agents for market shares in the settings of an open market, and the depression of market caused by accumulation of durable overproduced commodities on the market. These findings present the problem of business cycles in a new light as a systemic property of efficient market systems emerging directly from the free market competition itself, and existing in market economies at a very fundamental level.

DCTune Perceptual Optimization of Compressed Dental X-Rays

NASA Technical Reports Server (NTRS)

Watson, Andrew B.; Null, Cynthia H. (Technical Monitor)

1997-01-01

In current dental practice, x-rays of completed dental work are often sent to the insurer for verification. It is faster and cheaper to transmit instead digital scans of the x-rays. Further economies result if the images are sent in compressed form. DCtune is a technology for optimizing DCT quantization matrices to yield maximum perceptual quality for a given bit-rate, or minimum bit-rate for a given perceptual quality. In addition, the technology provides a means of setting the perceptual quality of compressed imagery in a systematic way. The purpose of this research was, with respect to dental x-rays: (1) to verify the advantage of DCTune over standard JPEG; (2) to verify the quality control feature of DCTune; and (3) to discover regularities in the optimized matrices of a set of images. Additional information is contained in the original extended abstract.

Oscillations in Rational Economies

PubMed Central

Mishchenko, Yuriy

2014-01-01

Economic (business) cycles are some of the most noted features of market economies, also ranked among the most serious of economic problems. Despite long historical persistence, the nature and the origin of business cycles remain controversial. In this paper we investigate the problem of the nature of business cycles from the positions of the market systems viewed as complex systems of many interacting market agents. We show that the development of cyclic instabilities in these settings can be traced down to just two fundamental factors – the competition of market agents for market shares in the settings of an open market, and the depression of market caused by accumulation of durable overproduced commodities on the market. These findings present the problem of business cycles in a new light as a systemic property of efficient market systems emerging directly from the free market competition itself, and existing in market economies at a very fundamental level. PMID:24505319

Tempest: Accelerated MS/MS Database Search Software for Heterogeneous Computing Platforms.

PubMed

Adamo, Mark E; Gerber, Scott A

2016-09-07

MS/MS database search algorithms derive a set of candidate peptide sequences from in silico digest of a protein sequence database, and compute theoretical fragmentation patterns to match these candidates against observed MS/MS spectra. The original Tempest publication described these operations mapped to a CPU-GPU model, in which the CPU (central processing unit) generates peptide candidates that are asynchronously sent to a discrete GPU (graphics processing unit) to be scored against experimental spectra in parallel. The current version of Tempest expands this model, incorporating OpenCL to offer seamless parallelization across multicore CPUs, GPUs, integrated graphics chips, and general-purpose coprocessors. Three protocols describe how to configure and run a Tempest search, including discussion of how to leverage Tempest's unique feature set to produce optimal results. © 2016 by John Wiley & Sons, Inc. Copyright © 2016 John Wiley & Sons, Inc.

Lessons Learned from OSIRIS-Rex Autonomous Navigation Using Natural Feature Tracking

NASA Technical Reports Server (NTRS)

Lorenz, David A.; Olds, Ryan; May, Alexander; Mario, Courtney; Perry, Mark E.; Palmer, Eric E.; Daly, Michael

2017-01-01

The Origins, Spectral Interpretation, Resource Identification, Security-Regolith Explorer (Osiris-REx) spacecraft is scheduled to launch in September, 2016 to embark on an asteroid sample return mission. It is expected to rendezvous with the asteroid, Bennu, navigate to the surface, collect a sample (July 20), and return the sample to Earth (September 23). The original mission design called for using one of two Flash Lidar units to provide autonomous navigation to the surface. Following Preliminary design and initial development of the Lidars, reliability issues with the hardware and test program prompted the project to begin development of an alternative navigation technique to be used as a backup to the Lidar. At the critical design review, Natural Feature Tracking (NFT) was added to the mission. NFT is an onboard optical navigation system that compares observed images to a set of asteroid terrain models which are rendered in real-time from a catalog stored in memory on the flight computer. Onboard knowledge of the spacecraft state is then updated by a Kalman filter using the measured residuals between the rendered reference images and the actual observed images. The asteroid terrain models used by NFT are built from a shape model generated from observations collected during earlier phases of the mission and include both terrain shape and albedo information about the asteroid surface. As a result, the success of NFT is highly dependent on selecting a set of topographic features that can be both identified during descent as well as reliably rendered using the shape model data available. During development, the OSIRIS-REx team faced significant challenges in developing a process conducive to robust operation. This was especially true for terrain models to be used as the spacecraft gets close to the asteroid and higher fidelity models are required for reliable image correlation. This paper will present some of the challenges and lessons learned from the development of the NFT system which includes not just the flight hardware and software but the development of the terrain models used to generate the onboard rendered images.

On the Origin of Wind Line Variability in O Stars

NASA Astrophysics Data System (ADS)

Massa, D.; Prinja, R. K.

2015-08-01

We analyze 10 UV time series for five stars that fulfill specific sampling and spectral criteria to constrain the origin of large-scale wind structure in O stars. We argue that excited state lines must arise close to the stellar surface and are an excellent diagnostic complement to resonance lines which, due to radiative transfer effects, rarely show variability at low velocity. Consequently, we splice dynamic spectra of the excited state line N iv λ1718 at low velocity with those of Si iv λ λ 1400 at high velocity in order to examine the temporal evolution of wind line features. These spliced time series reveal that nearly all of the features observed in the time series originate at or very near the stellar surface. Furthermore, we positively identify the observational signature of equatorial corotating interaction regions in two of the five stars and possibly two others. In addition, we see no evidence of features originating further out in the wind. We use our results to confirm the fact that the features seen in dynamic spectra must be huge in order to remain in the line of sight for days, persisting to very large velocity, and that the photospheric footprint of the features must also be quite large, ˜15%-20% of the stellar diameter.

Learning Compact Binary Face Descriptor for Face Recognition.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Xiuzhuang; Zhou, Jie

2015-10-01

Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors.

Improved facial affect recognition in schizophrenia following an emotion intervention, but not training attention-to-facial-features or treatment-as-usual.

PubMed

Tsotsi, Stella; Kosmidis, Mary H; Bozikas, Vasilis P

2017-08-01

In schizophrenia, impaired facial affect recognition (FAR) has been associated with patients' overall social functioning. Interventions targeting attention or FAR per se have invariably yielded improved FAR performance in these patients. Here, we compared the effects of two interventions, one targeting FAR and one targeting attention-to-facial-features, with treatment-as-usual on patients' FAR performance. Thirty-nine outpatients with schizophrenia were randomly assigned to one of three groups: FAR intervention (training to recognize emotional information, conveyed by changes in facial features), attention-to-facial-features intervention (training to detect changes in facial features), and treatment-as-usual. Also, 24 healthy controls, matched for age and education, were assigned to one of the two interventions. Two FAR measurements, baseline and post-intervention, were conducted using an original experimental procedure with alternative sets of stimuli. We found improved FAR performance following the intervention targeting FAR in comparison to the other patient groups, which in fact was comparable to the pre-intervention performance of healthy controls in the corresponding intervention group. This improvement was more pronounced in recognizing fear. Our findings suggest that compared to interventions targeting attention, and treatment-as-usual, training programs targeting FAR can be more effective in improving FAR in patients with schizophrenia, particularly assisting them in perceiving threat-related information more accurately. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Anomaly Detection Using an Ensemble of Feature Models

PubMed Central

Noto, Keith; Brodley, Carla; Slonim, Donna

2011-01-01

We present a new approach to semi-supervised anomaly detection. Given a set of training examples believed to come from the same distribution or class, the task is to learn a model that will be able to distinguish examples in the future that do not belong to the same class. Traditional approaches typically compare the position of a new data point to the set of “normal” training data points in a chosen representation of the feature space. For some data sets, the normal data may not have discernible positions in feature space, but do have consistent relationships among some features that fail to appear in the anomalous examples. Our approach learns to predict the values of training set features from the values of other features. After we have formed an ensemble of predictors, we apply this ensemble to new data points. To combine the contribution of each predictor in our ensemble, we have developed a novel, information-theoretic anomaly measure that our experimental results show selects against noisy and irrelevant features. Our results on 47 data sets show that for most data sets, this approach significantly improves performance over current state-of-the-art feature space distance and density-based approaches. PMID:22020249

The Influence of Changes in Size and Proportion of Selected Facial Features (Eyes, Nose, Mouth) on Assessment of Similarity between Female Faces.

PubMed

Lewandowski, Zdzisław

2015-09-01

The project aimed at finding the answers to the following two questions: to what extent does a change in size, height or width of the selected facial features influence the assessment of likeness between an original female composite portrait and a modified one? And how does the sex of the person who judges the images have an impact on the perception of likeness of facial features? The first stage of the project consisted of creating the image of the averaged female faces. Then the basic facial features like eyes, nose and mouth were cut out of the averaged face and each of these features was transformed in three ways: its size was changed by reduction or enlargement, its height was modified through reduction or enlargement of the above-mentioned features and its width was altered through widening or narrowing. In each out of six feature alternation methods, intensity of modification reached up to 20% of the original size with changes every 2%. The features altered in such a way were again stuck onto the original faces and retouched. The third stage consisted of the assessment, performed by the judges of both sexes, of the extent of likeness between the averaged composite portrait (without any changes) and the modified portraits. The results indicate that there are significant differences in the assessment of likeness of the portraits with some features modified to the original ones. The images with changes in the size and height of the nose received the lowest scores on the likeness scale, which indicates that these changes were perceived by the subjects as the most important. The photos with changes in the height of lip vermillion thickness (the lip height), lip width and the height and width of eye slit, in turn, received high scores of likeness, in spite of big changes, which signifies that these modifications were perceived as less important when compared to the other features investigated.

Mass wasting features in Juventae Chasma, Mars

NASA Astrophysics Data System (ADS)

Sarkar, Ranjan; Singh, Pragya; Porwal, Alok; Ganesh, Indujaa

2016-07-01

Introduction : We report mass-wasting features preserved as debris aprons from Juventae Chasma. Diverse lines of evidence and associated geomorphological features indicate that fluidized ice or water within the wall rocks of the chasma could be responsible for mobilizing the debris. Description : The distinctive features of the landslides in Juvenate Chasma are: (1) lack of a well-defined crown or a clear-cut section at their point of origin and instead the presence of amphitheatre-headed tributary canyons; (2) absence of slump blocks; (3) overlapping of debris aprons; (4) a variety of surface textures from fresh and grooved to degraded and chaotic; (5) rounded lobes of debris aprons; (6) large variation of sizes from small lumps (~0.52 m2) to large tongue shaped ones (~ 80 m2); (7) smaller average size of landslides as compared to other chasmas; and (8) occasional preservation of fresh surficial features indicating recent emplacement. Discussion : Amphitheatre-headed tributary canyons, which are formed due to ground water sapping, indicate that the same was responsible for wall-section collapse, although a structural control cannot be completely ruled out. The emplacement of the mass wasting features preferentially at the mouths of amphitheatre-headed tributary canyons along with the rounded flow fronts of the debris suggest fluids may have played a vital role in their emplacement. The mass-wasting features in Juventae Chasma are unique compared to other landslides in Valles Marineris despite commonalities such as the radial furrows, fan-shaped outlines, overlapping aprons and overtopped obstacles. The unique set of features and close association with amphitheatre-headed tributary canyons imply that the trigger of the landslides was not structural or tectonic but possibly weakness imparted by the presence of water or ice in the pore-spaces of the wall. Craters with fluidized ejecta blankets and scalloped depressions in the surrounding plateau also support this possibility. Depending on the amounts of fluids involved at the time of emplacement, these mass movements may also qualify as debris flows. The role of fluids in the Valles Marineris landslides is still debated; however, in the Juventae Chasma landslides we see unique features which set these apart from other landslides in Valles Marineris. Further study is required to fully investigate the mechanism of emplacement of these debris.

Fast detection of vascular plaque in optical coherence tomography images using a reduced feature set

NASA Astrophysics Data System (ADS)

Prakash, Ammu; Ocana Macias, Mariano; Hewko, Mark; Sowa, Michael; Sherif, Sherif

2018-03-01

Optical coherence tomography (OCT) images are capable of detecting vascular plaque by using the full set of 26 Haralick textural features and a standard K-means clustering algorithm. However, the use of the full set of 26 textural features is computationally expensive and may not be feasible for real time implementation. In this work, we identified a reduced set of 3 textural feature which characterizes vascular plaque and used a generalized Fuzzy C-means clustering algorithm. Our work involves three steps: 1) the reduction of a full set 26 textural feature to a reduced set of 3 textural features by using genetic algorithm (GA) optimization method 2) the implementation of an unsupervised generalized clustering algorithm (Fuzzy C-means) on the reduced feature space, and 3) the validation of our results using histology and actual photographic images of vascular plaque. Our results show an excellent match with histology and actual photographic images of vascular tissue. Therefore, our results could provide an efficient pre-clinical tool for the detection of vascular plaque in real time OCT imaging.

Color image definition evaluation method based on deep learning method

NASA Astrophysics Data System (ADS)

Liu, Di; Li, YingChun

2018-01-01

In order to evaluate different blurring levels of color image and improve the method of image definition evaluation, this paper proposed a method based on the depth learning framework and BP neural network classification model, and presents a non-reference color image clarity evaluation method. Firstly, using VGG16 net as the feature extractor to extract 4,096 dimensions features of the images, then the extracted features and labeled images are employed in BP neural network to train. And finally achieve the color image definition evaluation. The method in this paper are experimented by using images from the CSIQ database. The images are blurred at different levels. There are 4,000 images after the processing. Dividing the 4,000 images into three categories, each category represents a blur level. 300 out of 400 high-dimensional features are trained in VGG16 net and BP neural network, and the rest of 100 samples are tested. The experimental results show that the method can take full advantage of the learning and characterization capability of deep learning. Referring to the current shortcomings of the major existing image clarity evaluation methods, which manually design and extract features. The method in this paper can extract the images features automatically, and has got excellent image quality classification accuracy for the test data set. The accuracy rate is 96%. Moreover, the predicted quality levels of original color images are similar to the perception of the human visual system.

Diagnosis of multiple sclerosis from EEG signals using nonlinear methods.

PubMed

Torabi, Ali; Daliri, Mohammad Reza; Sabzposhan, Seyyed Hojjat

2017-12-01

EEG signals have essential and important information about the brain and neural diseases. The main purpose of this study is classifying two groups of healthy volunteers and Multiple Sclerosis (MS) patients using nonlinear features of EEG signals while performing cognitive tasks. EEG signals were recorded when users were doing two different attentional tasks. One of the tasks was based on detecting a desired change in color luminance and the other task was based on detecting a desired change in direction of motion. EEG signals were analyzed in two ways: EEG signals analysis without rhythms decomposition and EEG sub-bands analysis. After recording and preprocessing, time delay embedding method was used for state space reconstruction; embedding parameters were determined for original signals and their sub-bands. Afterwards nonlinear methods were used in feature extraction phase. To reduce the feature dimension, scalar feature selections were done by using T-test and Bhattacharyya criteria. Then, the data were classified using linear support vector machines (SVM) and k-nearest neighbor (KNN) method. The best combination of the criteria and classifiers was determined for each task by comparing performances. For both tasks, the best results were achieved by using T-test criterion and SVM classifier. For the direction-based and the color-luminance-based tasks, maximum classification performances were 93.08 and 79.79% respectively which were reached by using optimal set of features. Our results show that the nonlinear dynamic features of EEG signals seem to be useful and effective in MS diseases diagnosis.

Application of all-relevant feature selection for the failure analysis of parameter-induced simulation crashes in climate models

NASA Astrophysics Data System (ADS)

Paja, Wiesław; Wrzesien, Mariusz; Niemiec, Rafał; Rudnicki, Witold R.

2016-03-01

Climate models are extremely complex pieces of software. They reflect the best knowledge on the physical components of the climate; nevertheless, they contain several parameters, which are too weakly constrained by observations, and can potentially lead to a simulation crashing. Recently a study by Lucas et al. (2013) has shown that machine learning methods can be used for predicting which combinations of parameters can lead to the simulation crashing and hence which processes described by these parameters need refined analyses. In the current study we reanalyse the data set used in this research using different methodology. We confirm the main conclusion of the original study concerning the suitability of machine learning for the prediction of crashes. We show that only three of the eight parameters indicated in the original study as relevant for prediction of the crash are indeed strongly relevant, three others are relevant but redundant and two are not relevant at all. We also show that the variance due to the split of data between training and validation sets has a large influence both on the accuracy of predictions and on the relative importance of variables; hence only a cross-validated approach can deliver a robust prediction of performance and relevance of variables.

Combining various types of classifiers and features extracted from magnetic resonance imaging data in schizophrenia recognition.

PubMed

Janousova, Eva; Schwarz, Daniel; Kasparek, Tomas

2015-06-30

We investigated a combination of three classification algorithms, namely the modified maximum uncertainty linear discriminant analysis (mMLDA), the centroid method, and the average linkage, with three types of features extracted from three-dimensional T1-weighted magnetic resonance (MR) brain images, specifically MR intensities, grey matter densities, and local deformations for distinguishing 49 first episode schizophrenia male patients from 49 healthy male subjects. The feature sets were reduced using intersubject principal component analysis before classification. By combining the classifiers, we were able to obtain slightly improved results when compared with single classifiers. The best classification performance (81.6% accuracy, 75.5% sensitivity, and 87.8% specificity) was significantly better than classification by chance. We also showed that classifiers based on features calculated using more computation-intensive image preprocessing perform better; mMLDA with classification boundary calculated as weighted mean discriminative scores of the groups had improved sensitivity but similar accuracy compared to the original MLDA; reducing a number of eigenvectors during data reduction did not always lead to higher classification accuracy, since noise as well as the signal important for classification were removed. Our findings provide important information for schizophrenia research and may improve accuracy of computer-aided diagnostics of neuropsychiatric diseases. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Ion Mobility-Mass Spectrometry Analysis of Serum N-linked Glycans from Esophageal Adenocarcinoma Phenotypes

PubMed Central

Gaye, M. M.; Valentine, S. J.; Hu, Y.; Mirjankar, N.; Hammoud, Z. T.; Mechref, Y.; Lavine, B. K.; Clemmer, D. E.

2012-01-01

Three disease phenotypes, Barrett’s esophagus (BE), high-grade dysplasia (HGD), esophageal adenocarcinoma (EAC), and a set of normal control (NC) serum samples are examined using a combination of ion mobility spectrometry (IMS), mass spectrometry (MS) and principal component analysis (PCA) techniques. Samples from a total of 136 individuals were examined, including: 7 characterized as BE, 12 as HGD, 56 as EAC and 61 as NC. In typical datasets it was possible to assign ~20 to 30 glycan ions based on MS measurements. Ion mobility distributions for these ions show multiple features. In some cases, such as the [S1H5N4+3Na]3+ and [S1F1H5N4+3Na]3+ glycan ions, the ratio of intensities of high-mobility features to low-mobility features vary significantly for different groups. The degree to which such variations in mobility profiles can be used to distinguish phenotypes is evaluated for eleven N-linked glycan ions. An outlier analysis on each sample class followed by an unsupervised PCA using a genetic algorithm for pattern recognition reveals that EAC samples are separated from NC samples based on 46 features originating from the 11-glycan composite IMS distribution. PMID:23126309

Robust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification

NASA Astrophysics Data System (ADS)

He, Zhi; Liu, Lin

2016-11-01

Empirical mode decomposition (EMD) and its variants have recently been applied for hyperspectral image (HSI) classification due to their ability to extract useful features from the original HSI. However, it remains a challenging task to effectively exploit the spectral-spatial information by the traditional vector or image-based methods. In this paper, a three-dimensional (3D) extension of EMD (3D-EMD) is proposed to naturally treat the HSI as a cube and decompose the HSI into varying oscillations (i.e. 3D intrinsic mode functions (3D-IMFs)). To achieve fast 3D-EMD implementation, 3D Delaunay triangulation (3D-DT) is utilized to determine the distances of extrema, while separable filters are adopted to generate the envelopes. Taking the extracted 3D-IMFs as features of different tasks, robust multitask learning (RMTL) is further proposed for HSI classification. In RMTL, pairs of low-rank and sparse structures are formulated by trace-norm and l1,2 -norm to capture task relatedness and specificity, respectively. Moreover, the optimization problems of RMTL can be efficiently solved by the inexact augmented Lagrangian method (IALM). Compared with several state-of-the-art feature extraction and classification methods, the experimental results conducted on three benchmark data sets demonstrate the superiority of the proposed methods.

Optimized spatio-temporal descriptors for real-time fall detection: comparison of support vector machine and Adaboost-based classification

NASA Astrophysics Data System (ADS)

Charfi, Imen; Miteran, Johel; Dubois, Julien; Atri, Mohamed; Tourki, Rached

2013-10-01

We propose a supervised approach to detect falls in a home environment using an optimized descriptor adapted to real-time tasks. We introduce a realistic dataset of 222 videos, a new metric allowing evaluation of fall detection performance in a video stream, and an automatically optimized set of spatio-temporal descriptors which fed a supervised classifier. We build the initial spatio-temporal descriptor named STHF using several combinations of transformations of geometrical features (height and width of human body bounding box, the user's trajectory with her/his orientation, projection histograms, and moments of orders 0, 1, and 2). We study the combinations of usual transformations of the features (Fourier transform, wavelet transform, first and second derivatives), and we show experimentally that it is possible to achieve high performance using support vector machine and Adaboost classifiers. Automatic feature selection allows to show that the best tradeoff between classification performance and processing time is obtained by combining the original low-level features with their first derivative. Hence, we evaluate the robustness of the fall detection regarding location changes. We propose a realistic and pragmatic protocol that enables performance to be improved by updating the training in the current location with normal activities records.

Feature Selection Methods for Zero-Shot Learning of Neural Activity.

PubMed

Caceres, Carlos A; Roos, Matthew J; Rupp, Kyle M; Milsap, Griffin; Crone, Nathan E; Wolmetz, Michael E; Ratto, Christopher R

2017-01-01

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy.

Reduction of Marine Magnetic Data for Modeling the Main Field of the Earth

NASA Technical Reports Server (NTRS)

Baldwin, R. T.; Ridgway, J. R.; Davis, W. M.

1992-01-01

The marine data set archived at the National Geophysical Data Center (NGDC) consists of shipborne surveys conducted by various institutes worldwide. This data set spans four decades (1953, 1958, 1960-1987), and contains almost 13 million total intensity observations. These are often less than 1 km apart. These typically measure seafloor spreading anomalies with amplitudes of several hundred nanotesla (nT) which, since they originate in the crust, interfere with main field modeling. The source for these short wavelength features are confined within the magnetic crust (i.e., sources above the Curie isotherm). The main field, on the other hand, is of much longer wavelengths and originates within the earth's core. It is desirable to extract the long wavelength information from the marine data set for use in modeling the main field. This can be accomplished by averaging the data along the track. In addition, those data which are measured during periods of magnetic disturbance can be identified and eliminated. Thus, it should be possible to create a data set which has worldwide data distribution, spans several decades, is not contaminated with short wavelengths of the crustal field or with magnetic storm noise, and which is limited enough in size to be manageable for the main field modeling. The along track filtering described above has proved to be an effective means of condensing large numbers of shipborne magnetic data into a manageable and meaningful data set for main field modeling. Its simplicity and ability to adequately handle varying spatial and sampling constraints has outweighed consideration of more sophisticated approaches. This filtering technique also provides the benefits of smoothing out short wavelength crustal anomalies, discarding data recorded during magnetically noisy periods, and assigning reasonable error estimates to be used in the least square modeling. A useful data set now exists which spans 1953-1987.

Spasm of the near reflex: A case report.

PubMed

Rhatigan, Maedbh; Byrne, Caroline; Logan, Patricia

2017-06-01

Spasm of the near reflex (SNR) is a triad of miosis, excess accommodation and excess convergence. Primary SNR is most often functional in origin We aim to highlight the clinical features which distinguish primary convergence from other conditions with a similar presentation but more sinister underlying aetiology, for example bilateral abducens nerve palsy. There is a paucity of published data on SNR, in particular diagnostic criteria and treatment. We report a case of SNR of functional origin in an otherwise healthy young female and discuss the clinical features that differentiate this condition from similar conditions with underlying neurological origin. SNR is predominantly a clinical diagnosis, and often leads to patients undergoing unnecessary investigations and sometimes treatment. Recognising the salient features that differentiate it could potentially avoid this.

An automated procedure to identify biomedical articles that contain cancer-associated gene variants.

PubMed

McDonald, Ryan; Scott Winters, R; Ankuda, Claire K; Murphy, Joan A; Rogers, Amy E; Pereira, Fernando; Greenblatt, Marc S; White, Peter S

2006-09-01

The proliferation of biomedical literature makes it increasingly difficult for researchers to find and manage relevant information. However, identifying research articles containing mutation data, a requisite first step in integrating large and complex mutation data sets, is currently tedious, time-consuming and imprecise. More effective mechanisms for identifying articles containing mutation information would be beneficial both for the curation of mutation databases and for individual researchers. We developed an automated method that uses information extraction, classifier, and relevance ranking techniques to determine the likelihood of MEDLINE abstracts containing information regarding genomic variation data suitable for inclusion in mutation databases. We targeted the CDKN2A (p16) gene and the procedure for document identification currently used by CDKN2A Database curators as a measure of feasibility. A set of abstracts was manually identified from a MEDLINE search as potentially containing specific CDKN2A mutation events. A subset of these abstracts was used as a training set for a maximum entropy classifier to identify text features distinguishing "relevant" from "not relevant" abstracts. Each document was represented as a set of indicative word, word pair, and entity tagger-derived genomic variation features. When applied to a test set of 200 candidate abstracts, the classifier predicted 88 articles as being relevant; of these, 29 of 32 manuscripts in which manual curation found CDKN2A sequence variants were positively predicted. Thus, the set of potentially useful articles that a manual curator would have to review was reduced by 56%, maintaining 91% recall (sensitivity) and more than doubling precision (positive predictive value). Subsequent expansion of the training set to 494 articles yielded similar precision and recall rates, and comparison of the original and expanded trials demonstrated that the average precision improved with the larger data set. Our results show that automated systems can effectively identify article subsets relevant to a given task and may prove to be powerful tools for the broader research community. This procedure can be readily adapted to any or all genes, organisms, or sets of documents. Published 2006 Wiley-Liss, Inc.

River meanders and channel size

USGS Publications Warehouse

Williams, G.P.

1986-01-01

This study uses an enlarged data set to (1) compare measured meander geometry to that predicted by the Langbein and Leopold (1966) theory, (2) examine the frequency distribution of the ratio radius of curvature/channel width, and (3) derive 40 empirical equations (31 of which are original) involving meander and channel size features. The data set, part of which comes from publications by other authors, consists of 194 sites from a large variety of physiographic environments in various countries. The Langbein-Leopold sine-generated-curve theory for predicting radius of curvature agrees very well with the field data (78 sites). The ratio radius of curvature/channel width has a modal value in the range of 2 to 3, in accordance with earlier work; about one third of the 79 values is less than 2.0. The 40 empirical relations, most of which include only two variables, involve channel cross-section dimensions (bankfull area, width, and mean depth) and meander features (wavelength, bend length, radius of curvature, and belt width). These relations have very high correlation coefficients, most being in the range of 0.95-0.99. Although channel width traditionally has served as a scale indicator, bankfull cross-sectional area and mean depth also can be used for this purpose. ?? 1986.

The origins of duality of patterning in artificial whistled languages

PubMed Central

Verhoef, Tessa

2012-01-01

In human speech, a finite set of basic sounds is combined into a (potentially) unlimited set of well-formed morphemes. Hockett (1960) placed this phenomenon under the term ‘duality of patterning’ and included it as one of the basic design features of human language. Of the thirteen basic design features Hockett proposed, duality of patterning is the least studied and it is still unclear how it evolved in language. Recent work shedding light on this is summarized in this paper and experimental data is presented. This data shows that combinatorial structure can emerge in an artificial whistled language through cultural transmission as an adaptation to human cognitive biases and learning. In this work the method of experimental iterated learning (Kirby et al. 2008) is used, in which a participant is trained on the reproductions of the utterances the previous participant learned. Participants learn and recall a system of sounds that are produced with a slide whistle. Transmission from participant to participant causes the whistle systems to change and become more learnable and more structured. These findings follow from qualitative observations, quantitative measures and a follow-up experiment that tests how well participants can learn the emerged whistled languages by generalizing from a few examples. PMID:23637710

Machine learning-based diagnosis of melanoma using macro images.

PubMed

Gautam, Diwakar; Ahmed, Mushtaq; Meena, Yogesh Kumar; Ul Haq, Ahtesham

2018-05-01

Cancer bears a poisoning threat to human society. Melanoma, the skin cancer, originates from skin layers and penetrates deep into subcutaneous layers. There exists an extensive research in melanoma diagnosis using dermatoscopic images captured through a dermatoscope. While designing a diagnostic model for general handheld imaging systems is an emerging trend, this article proposes a computer-aided decision support system for macro images captured by a general-purpose camera. General imaging conditions are adversely affected by nonuniform illumination, which further affects the extraction of relevant information. To mitigate it, we process an image to define a smooth illumination surface using the multistage illumination compensation approach, and the infected region is extracted using the proposed multimode segmentation method. The lesion information is numerated as a feature set comprising geometry, photometry, border series, and texture measures. The redundancy in feature set is reduced using information theory methods, and a classification boundary is modeled to distinguish benign and malignant samples using support vector machine, random forest, neural network, and fast discriminative mixed-membership-based naive Bayesian classifiers. Moreover, the experimental outcome is supported by hypothesis testing and boxplot representation for classification losses. The simulation results prove the significance of the proposed model that shows an improved performance as compared with competing arts. Copyright © 2017 John Wiley & Sons, Ltd.

Remote Sensing Image Analysis Without Expert Knowledge - A Web-Based Classification Tool On Top of Taverna Workflow Management System

NASA Astrophysics Data System (ADS)

Selsam, Peter; Schwartze, Christian

2016-10-01

Providing software solutions via internet has been known for quite some time and is now an increasing trend marketed as "software as a service". A lot of business units accept the new methods and streamlined IT strategies by offering web-based infrastructures for external software usage - but geospatial applications featuring very specialized services or functionalities on demand are still rare. Originally applied in desktop environments, the ILMSimage tool for remote sensing image analysis and classification was modified in its communicating structures and enabled for running on a high-power server and benefiting from Tavema software. On top, a GIS-like and web-based user interface guides the user through the different steps in ILMSimage. ILMSimage combines object oriented image segmentation with pattern recognition features. Basic image elements form a construction set to model for large image objects with diverse and complex appearance. There is no need for the user to set up detailed object definitions. Training is done by delineating one or more typical examples (templates) of the desired object using a simple vector polygon. The template can be large and does not need to be homogeneous. The template is completely independent from the segmentation. The object definition is done completely by the software.

Fusion of shallow and deep features for classification of high-resolution remote sensing images

NASA Astrophysics Data System (ADS)

Gao, Lang; Tian, Tian; Sun, Xiao; Li, Hang

2018-02-01

Effective spectral and spatial pixel description plays a significant role for the classification of high resolution remote sensing images. Current approaches of pixel-based feature extraction are of two main kinds: one includes the widelyused principal component analysis (PCA) and gray level co-occurrence matrix (GLCM) as the representative of the shallow spectral and shape features, and the other refers to the deep learning-based methods which employ deep neural networks and have made great promotion on classification accuracy. However, the former traditional features are insufficient to depict complex distribution of high resolution images, while the deep features demand plenty of samples to train the network otherwise over fitting easily occurs if only limited samples are involved in the training. In view of the above, we propose a GLCM-based convolution neural network (CNN) approach to extract features and implement classification for high resolution remote sensing images. The employment of GLCM is able to represent the original images and eliminate redundant information and undesired noises. Meanwhile, taking shallow features as the input of deep network will contribute to a better guidance and interpretability. In consideration of the amount of samples, some strategies such as L2 regularization and dropout methods are used to prevent over-fitting. The fine-tuning strategy is also used in our study to reduce training time and further enhance the generalization performance of the network. Experiments with popular data sets such as PaviaU data validate that our proposed method leads to a performance improvement compared to individual involved approaches.

The discovery of a new infrared emission feature at 1905 wavenumbers (5.25 microns) in the spectrum of BD + 30 deg 3639 and its relation to the polycyclic aromatic hydrocarbon model

NASA Technical Reports Server (NTRS)

Allamandola, L. J.; Bregman, J. D.; Sandford, S. A.; Tielens, A. G. G. M.; Witteborn, F. C.

1989-01-01

A new IR emission feature at 1905/cm (5.25 microns) has been discovered in the spectrum of BD + 30 deg 3639. This feature joins the family of well-known IR emission features at 3040, 2940, 1750, 1610, '1310', 1160, and 890/cm. The origin of this new feature is discussed and it is assigned to an overtone or combination band involving C-H bending modes of polycyclic aromatic hydrocarbons (PAHs). Laboratory work suggests that spectral studies of the 2000-1650/cm region may be very useful in elucidating the molecular structure of interstellar PAHs. The new feature, in conjunction with other recently discovered spectral structures, suggests that the narrow IR emission features originate in PAH molecules rather than large carbon grains.

The Role of Type and Source of Uncertainty on the Processing of Climate Models Projections.

PubMed

Benjamin, Daniel M; Budescu, David V

2018-01-01

Scientists agree that the climate is changing due to human activities, but there is less agreement about the specific consequences and their timeline. Disagreement among climate projections is attributable to the complexity of climate models that differ in their structure, parameters, initial conditions, etc. We examine how different sources of uncertainty affect people's interpretation of, and reaction to, information about climate change by presenting participants forecasts from multiple experts. Participants viewed three types of sets of sea-level rise projections: (1) precise, but conflicting ; (2) imprecise , but agreeing, and (3) hybrid that were both conflicting and imprecise. They estimated the most likely sea-level rise, provided a range of possible values and rated the sets on several features - ambiguity, credibility, completeness, etc. In Study 1, everyone saw the same hybrid set. We found that participants were sensitive to uncertainty between sources, but not to uncertainty about which model was used. The impacts of conflict and imprecision were combined for estimation tasks and compromised for feature ratings . Estimates were closer to the experts' original projections, and sets were rated more favorably under imprecision. Estimates were least consistent with (narrower than) the experts in the hybrid condition, but participants rated the conflicting set least favorably. In Study 2, we investigated the hybrid case in more detail by creating several distinct interval sets that combine conflict and imprecision. Two factors drive perceptual differences: overlap - the structure of the forecast set (whether intersecting, nested, tangent, or disjoint) - and a symmetry - the balance of the set. Estimates were primarily driven by asymmetry, and preferences were primarily driven by overlap. Asymmetric sets were least consistent with the experts: estimated ranges were narrower, and estimates of the most likely value were shifted further below the set mean. Intersecting and nested sets were rated similarly to imprecision, and ratings of disjoint and tangent sets were rated like conflict. Our goal was to determine which underlying factors of information sets drive perceptions of uncertainty in consistent, predictable ways. The two studies lead us to conclude that perceptions of agreement require intersection and balance, and overly precise forecasts lead to greater perceptions of disagreement and a greater likelihood of the public discrediting and misinterpreting information.

The Role of Type and Source of Uncertainty on the Processing of Climate Models Projections

PubMed Central

Benjamin, Daniel M.; Budescu, David V.

2018-01-01

Scientists agree that the climate is changing due to human activities, but there is less agreement about the specific consequences and their timeline. Disagreement among climate projections is attributable to the complexity of climate models that differ in their structure, parameters, initial conditions, etc. We examine how different sources of uncertainty affect people’s interpretation of, and reaction to, information about climate change by presenting participants forecasts from multiple experts. Participants viewed three types of sets of sea-level rise projections: (1) precise, but conflicting; (2) imprecise, but agreeing, and (3) hybrid that were both conflicting and imprecise. They estimated the most likely sea-level rise, provided a range of possible values and rated the sets on several features – ambiguity, credibility, completeness, etc. In Study 1, everyone saw the same hybrid set. We found that participants were sensitive to uncertainty between sources, but not to uncertainty about which model was used. The impacts of conflict and imprecision were combined for estimation tasks and compromised for feature ratings. Estimates were closer to the experts’ original projections, and sets were rated more favorably under imprecision. Estimates were least consistent with (narrower than) the experts in the hybrid condition, but participants rated the conflicting set least favorably. In Study 2, we investigated the hybrid case in more detail by creating several distinct interval sets that combine conflict and imprecision. Two factors drive perceptual differences: overlap – the structure of the forecast set (whether intersecting, nested, tangent, or disjoint) – and asymmetry – the balance of the set. Estimates were primarily driven by asymmetry, and preferences were primarily driven by overlap. Asymmetric sets were least consistent with the experts: estimated ranges were narrower, and estimates of the most likely value were shifted further below the set mean. Intersecting and nested sets were rated similarly to imprecision, and ratings of disjoint and tangent sets were rated like conflict. Our goal was to determine which underlying factors of information sets drive perceptions of uncertainty in consistent, predictable ways. The two studies lead us to conclude that perceptions of agreement require intersection and balance, and overly precise forecasts lead to greater perceptions of disagreement and a greater likelihood of the public discrediting and misinterpreting information. PMID:29636717

Rift brittle deformation of SE-Brazilian continental margin: Kinematic analysis of onshore structures relative to the transfer and accommodation zones of southern Campos Basin

NASA Astrophysics Data System (ADS)

Savastano, Vítor Lamy Mesiano; Schmitt, Renata da Silva; Araújo, Mário Neto Cavalcanti de; Inocêncio, Leonardo Campos

2017-01-01

High-resolution drone-supported mapping and traditional field work were used to refine the hierarchy and kinematics of rift-related faults in the basement rocks and Early Cretaceous mafic dikes onshore of the Campos Basin, SE-Brazil. Two sets of structures were identified. The most significant fault set is NE-SW oriented with predominantly normal displacement. At mesoscale, this fault set is arranged in a rhombic pattern, interpreted here as a breached relay ramp system. The rhombic pattern is a penetrative fabric from the thin-section to regional scale. The second-order set of structures is an E-W/ESE-WNW system of normal faults with sinistral component. These E-W structures are oriented parallel with regional intrabasinal transfer zones associated with the earliest stages of Campos Basin's rift system. The crosscutting relationship between the two fault sets and tholeiitic dikes implies that the NE-SW fault set is the older feature, but remained active until the final stages of rifting in this region as the second-order fault set is older than the tholeiitic dikes. Paleostresses estimated from fault slip inversion method indicated that extension was originally NW-SE, with formation of the E-W transfer, followed by ESE-WNW oblique opening associated with a relay ramp system and related accommodation zones.

Development of a New Optical Measuring Set-Up

NASA Astrophysics Data System (ADS)

Miroshnichenko, I. P.; Parinov, I. A.

2018-06-01

The paper proposes a description of the developed optical measuring set-up for the contactless recording and processing of measurement results for small spatial (linear and angular) displacements of control surfaces based on the use of laser technologies and optical interference methods. The proposed set-up is designed to solve all the arising measurement tasks in the study of the physical and mechanical properties of new materials and in the process of diagnosing the state of structural materials by acoustic active methods of nondestructive testing. The structure of the set-up, its constituent parts are described, and the features of construction and functioning during measurements are discussed. New technical solutions for the implementation of the components of the set-up under consideration are obtained. The purpose and description of the original specialized software, used to perform a priori analysis of measurement results, are present, while performing measurements, for a posteriori analysis of measurement results. Moreover, the influences of internal and external disturbance effects on the measurement results and correcting measurement results directly in their implementation are determined. The technical solutions, used in the set-up, are protected by the patents of the Russian Federation for inventions, and software is protected by the certificates of state registration of computer programs. The proposed set-up is intended for use in instrumentation, mechanical engineering, shipbuilding, aviation, energy sector, etc.

19 CFR 10.770 - Originating goods.

Code of Federal Regulations, 2010 CFR

2010-04-01

... Rules of Origin § 10.770 Originating goods. (a) General. A good will be considered an originating good... provided for in a heading or subheading of the HTSUS that is not covered by the product-specific rules set... the product-specific rules set forth in General Note 27(h), HTSUS, and: (i)(A) Each of the non...

Precraniate origin of cranial motoneurons

PubMed Central

Dufour, Héloïse D.; Chettouh, Zoubida; Deyts, Carole; de Rosa, Renaud; Goridis, Christo; Joly, Jean-Stéphane; Brunet, Jean-François

2006-01-01

The craniate head is innervated by cranial sensory and motor neurons. Cranial sensory neurons stem from the neurogenic placodes and neural crest and are seen as evolutionary innovations crucial in fulfilling the feeding and respiratory needs of the craniate “new head.” In contrast, cranial motoneurons that are located in the hindbrain and motorize the head have an unclear phylogenetic status. Here we show that these motoneurons are in fact homologous to the motoneurons of the sessile postmetamorphic form of ascidians. The motoneurons of adult Ciona intestinalis, located in the cerebral ganglion and innervating muscles associated with the huge “branchial basket,” express the transcription factors CiPhox2 and CiTbx20, whose vertebrate orthologues collectively define cranial motoneurons of the branchiovisceral class. Moreover, Ciona's postmetamorphic motoneurons arise from a hindbrain set aside during larval life and defined as such by its position (caudal to the prosensephalic sensory vesicle) and coexpression of CiPhox2 and CiHox1, whose orthologues collectively mark the vertebrate hindbrain. These data unveil that the postmetamorphic ascidian brain, assumed to be a derived feature, in fact corresponds to the vertebrate hindbrain and push back the evolutionary origin of cranial nerves to before the origin of craniates. PMID:16735475

Nature, distribution, and origin of Titan’s Undifferentiated Plains

USGS Publications Warehouse

Lopes, Rosaly; Malaska, M. J.; Solomonidou, A.; Le, Gall A.; Janssen, M.A.; Neish, Catherine D.; Turtle, E.P.; Birch, S. P. D.; Hayes, A.G.; Radebaugh, J.; Coustenis, A.; Schoenfeld, A.; Stiles, B.W.; Kirk, Randolph L.; Mitchell, K.L.; Stofan, E.R.; Lawrence, K. J.; ,

2016-01-01

The Undifferentiated Plains on Titan, first mapped by Lopes et al. (Lopes, R.M.C. et al., 2010. Icarus, 205, 540–588), are vast expanses of terrains that appear radar-dark and fairly uniform in Cassini Synthetic Aperture Radar (SAR) images. As a result, these terrains are often referred to as “blandlands”. While the interpretation of several other geologic units on Titan – such as dunes, lakes, and well-preserved impact craters – has been relatively straightforward, the origin of the Undifferentiated Plains has remained elusive. SAR images show that these “blandlands” are mostly found at mid-latitudes and appear relatively featureless at radar wavelengths, with no major topographic features. Their gradational boundaries and paucity of recognizable features in SAR data make geologic interpretation particularly challenging. We have mapped the distribution of these terrains using SAR swaths up to flyby T92 (July 2013), which cover >50% of Titan’s surface. We compared SAR images with other data sets where available, including topography derived from the SARTopo method and stereo DEMs, the response from RADAR radiometry, hyperspectral imaging data from Cassini’s Visual and Infrared Mapping Spectrometer (VIMS), and near infrared imaging from the Imaging Science Subsystem (ISS). We examined and evaluated different formation mechanisms, including (i) cryovolcanic origin, consisting of overlapping flows of low relief or (ii) sedimentary origins, resulting from fluvial/lacustrine or aeolian deposition, or accumulation of photolysis products created in the atmosphere. Our analysis indicates that the Undifferentiated Plains unit is consistent with a composition predominantly containing organic rather than icy materials and formed by depositional and/or sedimentary processes. We conclude that aeolian processes played a major part in the formation of the Undifferentiated Plains; however, other processes (fluvial, deposition of photolysis products) are likely to have contributed, possibly in differing proportions depending on location.

Use of software tools in the development of real time software systems

NASA Technical Reports Server (NTRS)

Garvey, R. C.

1981-01-01

The transformation of a preexisting software system into a larger and more versatile system with different mission requirements is discussed. The history of this transformation is used to illustrate the use of structured real time programming techniques and tools to produce maintainable and somewhat transportable systems. The predecessor system is a single ground diagnostic system; its purpose is to exercise a computer controlled hardware set prior to its deployment in its functional environment, as well as test the equipment set by supplying certain well known stimulas. The successor system (FTE) is required to perform certain testing and control functions while this hardware set is in its functional environment. Both systems must deal with heavy user input/output loads and a new I/O requirement is included in the design of the FTF system. Human factors are enhanced by adding an improved console interface and special function keyboard handler. The additional features require the inclusion of much new software to the original set from which FTF was developed. As a result, it is necessary to split the system into a duel programming configuration with high rates of interground communications. A generalized information routing mechanism is used to support this configuration.

Glioblastomas with Oligodendroglial Component – Common Origin of the Different Histological Parts and Genetic Subclassification

PubMed Central

Klink, Barbara; Schlingelhof, Ben; Klink, Martin; Stout-Weider, Karen; Patt, Stephan; Schrock, Evelin

2010-01-01

Background: Glioblastomas are the most common and most malignant brain tumors in adults. A small subgroup of glioblastomas contains areas with histological features of oligodendroglial differentiation (GBMO). Our objective was to genetically characterize the oligodendroglial and the astrocytic parts of GBMOs and correlate morphologic and genetic features with clinical data. Methods: The oligodendroglial and the “classic” glioblastoma parts of 13 GBMO were analyzed separately by interphase fluorescence in situ hybridization (FISH) on paraffin sections using a custom probe set (regions 1p, 1q, 7q, 10q, 17p, 19q, cen18, 21q) and by comparative genomic hybridization (CGH) of microdissected paraffin embedded tumor tissue. Results: We identified four distinct genetic subtypes in 13 GBMOs: an “astrocytic” subtype (9/13) characterized by +7/−10; an “oligodendroglial” subtype with −1p/−19q (1/13); an “intermediate” subtype showing +7/−1p (1/13), and an “other” subtype having none of the former aberrations typical for gliomas (2/13). The different histological tumor parts of GBMO revealed common genetic changes in all tumors and showed additional aberrations specific for each part. Conclusion: Our findings demonstrate the monoclonal origin of GBMO followed by the development of the astrocytic and oligodendroglial components. The diagnostic determination of the genetic signatures may allow for a better prognostication of the patients. PMID:20966543

Multiple ancient origins of neoteny in Lycidae (Coleoptera): consequences for ecology and macroevolution

PubMed Central

Bocak, Ladislav; Bocakova, Milada; Hunt, Toby; Vogler, Alfried P

2008-01-01

Neoteny, the maintenance of larval features in sexually mature adults, is a radical way of generating evolutionary novelty through shifts in relative timing of developmental programmes. While controlled by the environment in facultative neotenics, retention of larval features is obligatory in many species of Lycidae (net-winged beetles). They are studied here as an example of how developmental shifts and ecology interact to produce macroevolutionary impacts. We conducted a phylogenetic analysis of Lycidae based on DNA sequences from nuclear (18S and 28S rRNA) and mitochondrial (rrnL, cox1, cob and nad5) genes from a representative set of lineages (73 species), including 17 neotenic taxa. Major changes of basal relationships compared with those implied in the current classification generally supported three independent origins of neotenics in Lycidae. The southeast Asian Lyropaeinae and Ateliinae were in basal positions indicating evolutionary antiquity, also confirmed by molecular clock estimates, unlike the neotropical leptolycines nested within Calopterini and presumably much younger. neotenics exhibit typical K-selected traits including slow development, large body size, high investment in offspring and low dispersal. This correlated with low species richness and restricted ranges of neotenic lineages compared with their sisters. Yet, these factors did not impede the evolutionary persistence of affected lineages, even without reversals to fully metamorphosed forms, contradicting earlier suggestions of recent evolution from dispersive non-neotenics. PMID:18477542

Design, Implementation and Case Study of WISEMAN: WIreless Sensors Employing Mobile AgeNts

NASA Astrophysics Data System (ADS)

González-Valenzuela, Sergio; Chen, Min; Leung, Victor C. M.

We describe the practical implementation of Wiseman: our proposed scheme for running mobile agents in Wireless Sensor Networks. Wiseman’s architecture derives from a much earlier agent system originally conceived for distributed process coordination in wired networks. Given the memory constraints associated with small sensor devices, we revised the architecture of the original agent system to make it applicable to this type of networks. Agents are programmed as compact text scripts that are interpreted at the sensor nodes. Wiseman is currently implemented in TinyOS ver. 1, its binary image occupies 19Kbytes of ROM memory, and it occupies 3Kbytes of RAM to operate. We describe the rationale behind Wiseman’s interpreter architecture and unique programming features that can help reduce packet overhead in sensor networks. In addition, we gauge the proposed system’s efficiency in terms of task duration with different network topologies through a case study that involves an early-fire-detection application in a fictitious forest setting.

Reinterpreting features of the advertisement call of Dermatonotus muelleri (Boettger, 1885; Anura, Microhylidae).

PubMed

Giaretta, Ariovaldo Antonio; Vo, Pacific; Herche, Jesse; Tang, Justine Nicole; Gridi-Papp, Marcos

2015-06-13

The advertisement call of Dermatonotus muelleri was originally described by Nelson (1973) in a brief section of a review on the mating calls of the Microhylinae. He used two calls from São Leopoldo, state of Minas Gerais, in Brazil to determine that they have i) dominant frequency between 1.500-2.200 kHz (mean 1.854 + 0.216 kHz), and ii) harmonic intervals between 0.140 and 0.150 kHz (0.146 +/- 0.005 kHz). Nelson (1973) based his description on an audiospectrogram produced with high frequency resolution and did not quantify the pulse structure of the calls. More recently, Giaretta and colleagues (2013) expanded on the original description using a larger set of calls recorded from Gurinhat, state of Minas Gerais, in Brazil. They quantified the temporal structure of the call and confirmed that the dominant frequency is around 1.8 kHz. In addition, they identified a secondary low frequency band at 667 Hz.

Integrating dimension reduction and out-of-sample extension in automated classification of ex vivo human patellar cartilage on phase contrast X-ray computed tomography.

PubMed

Nagarajan, Mahesh B; Coan, Paola; Huber, Markus B; Diemoz, Paul C; Wismüller, Axel

2015-01-01

Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns.

Combining Feature Extraction Methods to Assist the Diagnosis of Alzheimer's Disease.

PubMed

Segovia, F; Górriz, J M; Ramírez, J; Phillips, C

2016-01-01

Neuroimaging data as (18)F-FDG PET is widely used to assist the diagnosis of Alzheimer's disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database) of the selected feature extraction methods.

Feature Selection Methods for Zero-Shot Learning of Neural Activity

PubMed Central

Caceres, Carlos A.; Roos, Matthew J.; Rupp, Kyle M.; Milsap, Griffin; Crone, Nathan E.; Wolmetz, Michael E.; Ratto, Christopher R.

2017-01-01

Dimensionality poses a serious challenge when making predictions from human neuroimaging data. Across imaging modalities, large pools of potential neural features (e.g., responses from particular voxels, electrodes, and temporal windows) have to be related to typically limited sets of stimuli and samples. In recent years, zero-shot prediction models have been introduced for mapping between neural signals and semantic attributes, which allows for classification of stimulus classes not explicitly included in the training set. While choices about feature selection can have a substantial impact when closed-set accuracy, open-set robustness, and runtime are competing design objectives, no systematic study of feature selection for these models has been reported. Instead, a relatively straightforward feature stability approach has been adopted and successfully applied across models and imaging modalities. To characterize the tradeoffs in feature selection for zero-shot learning, we compared correlation-based stability to several other feature selection techniques on comparable data sets from two distinct imaging modalities: functional Magnetic Resonance Imaging and Electrocorticography. While most of the feature selection methods resulted in similar zero-shot prediction accuracies and spatial/spectral patterns of selected features, there was one exception; A novel feature/attribute correlation approach was able to achieve those accuracies with far fewer features, suggesting the potential for simpler prediction models that yield high zero-shot classification accuracy. PMID:28690513

Scaling laws describe memories of host-pathogen riposte in the HIV population.

PubMed

Barton, John P; Kardar, Mehran; Chakraborty, Arup K

2015-02-17

The enormous genetic diversity and mutability of HIV has prevented effective control of this virus by natural immune responses or vaccination. Evolution of the circulating HIV population has thus occurred in response to diverse, ultimately ineffective, immune selection pressures that randomly change from host to host. We show that the interplay between the diversity of human immune responses and the ways that HIV mutates to evade them results in distinct sets of sequences defined by similar collectively coupled mutations. Scaling laws that relate these sets of sequences resemble those observed in linguistics and other branches of inquiry, and dynamics reminiscent of neural networks are observed. Like neural networks that store memories of past stimulation, the circulating HIV population stores memories of host-pathogen combat won by the virus. We describe an exactly solvable model that captures the main qualitative features of the sets of sequences and a simple mechanistic model for the origin of the observed scaling laws. Our results define collective mutational pathways used by HIV to evade human immune responses, which could guide vaccine design.

Impact of experimental design on PET radiomics in predicting somatic mutation status.

PubMed

Yip, Stephen S F; Parmar, Chintan; Kim, John; Huynh, Elizabeth; Mak, Raymond H; Aerts, Hugo J W L

2017-12-01

PET-based radiomic features have demonstrated great promises in predicting genetic data. However, various experimental parameters can influence the feature extraction pipeline, and hence, Here, we investigated how experimental settings affect the performance of radiomic features in predicting somatic mutation status in non-small cell lung cancer (NSCLC) patients. 348 NSCLC patients with somatic mutation testing and diagnostic PET images were included in our analysis. Radiomic feature extractions were analyzed for varying voxel sizes, filters and bin widths. 66 radiomic features were evaluated. The performance of features in predicting mutations status was assessed using the area under the receiver-operating-characteristic curve (AUC). The influence of experimental parameters on feature predictability was quantified as the relative difference between the minimum and maximum AUC (δ). The large majority of features (n=56, 85%) were significantly predictive for EGFR mutation status (AUC≥0.61). 29 radiomic features significantly predicted EGFR mutations and were robust to experimental settings with δ Overall <5%. The overall influence (δ Overall ) of the voxel size, filter and bin width for all features ranged from 5% to 15%, respectively. For all features, none of the experimental designs was predictive of KRAS+ from KRAS- (AUC≤0.56). The predictability of 29 radiomic features was robust to the choice of experimental settings; however, these settings need to be carefully chosen for all other features. The combined effect of the investigated processing methods could be substantial and must be considered. Optimized settings that will maximize the predictive performance of individual radiomic features should be investigated in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

Viewpoints: A High-Performance High-Dimensional Exploratory Data Analysis Tool

NASA Astrophysics Data System (ADS)

Gazis, P. R.; Levit, C.; Way, M. J.

2010-12-01

Scientific data sets continue to increase in both size and complexity. In the past, dedicated graphics systems at supercomputing centers were required to visualize large data sets, but as the price of commodity graphics hardware has dropped and its capability has increased, it is now possible, in principle, to view large complex data sets on a single workstation. To do this in practice, an investigator will need software that is written to take advantage of the relevant graphics hardware. The Viewpoints visualization package described herein is an example of such software. Viewpoints is an interactive tool for exploratory visual analysis of large high-dimensional (multivariate) data. It leverages the capabilities of modern graphics boards (GPUs) to run on a single workstation or laptop. Viewpoints is minimalist: it attempts to do a small set of useful things very well (or at least very quickly) in comparison with similar packages today. Its basic feature set includes linked scatter plots with brushing, dynamic histograms, normalization, and outlier detection/removal. Viewpoints was originally designed for astrophysicists, but it has since been used in a variety of fields that range from astronomy, quantum chemistry, fluid dynamics, machine learning, bioinformatics, and finance to information technology server log mining. In this article, we describe the Viewpoints package and show examples of its usage.

Reproducibility of radiomics for deciphering tumor phenotype with imaging

NASA Astrophysics Data System (ADS)

Zhao, Binsheng; Tan, Yongqiang; Tsai, Wei-Yann; Qi, Jing; Xie, Chuanmiao; Lu, Lin; Schwartz, Lawrence H.

2016-03-01

Radiomics (radiogenomics) characterizes tumor phenotypes based on quantitative image features derived from routine radiologic imaging to improve cancer diagnosis, prognosis, prediction and response to therapy. Although radiomic features must be reproducible to qualify as biomarkers for clinical care, little is known about how routine imaging acquisition techniques/parameters affect reproducibility. To begin to fill this knowledge gap, we assessed the reproducibility of a comprehensive, commonly-used set of radiomic features using a unique, same-day repeat computed tomography data set from lung cancer patients. Each scan was reconstructed at 6 imaging settings, varying slice thicknesses (1.25 mm, 2.5 mm and 5 mm) and reconstruction algorithms (sharp, smooth). Reproducibility was assessed using the repeat scans reconstructed at identical imaging setting (6 settings in total). In separate analyses, we explored differences in radiomic features due to different imaging parameters by assessing the agreement of these radiomic features extracted from the repeat scans reconstructed at the same slice thickness but different algorithms (3 settings in total). Our data suggest that radiomic features are reproducible over a wide range of imaging settings. However, smooth and sharp reconstruction algorithms should not be used interchangeably. These findings will raise awareness of the importance of properly setting imaging acquisition parameters in radiomics/radiogenomics research.

Discovery of a Plains Caldera Complex and Extinct Lava Lake in Arabia Terra, Mars: Implications for the Discovery of Additional Highland Volcanic Source Regions

NASA Technical Reports Server (NTRS)

Bleacher, Jacob; Michalski, Joseph

2012-01-01

Several irregularly shaped topographic depressions occur near the dichotomy boundary in northern Arabia Terra, Mars. The geomorphology of these features suggests that they formed by collapse, opposed to meteor impact. At least one depression (approx.55 by 85 km) displays geologic features indicating a complex, multi-stage collapse history. Features within and around the collapse structure indicate volcanic processes. The complex occurs within Hesperian ridged plains of likely volcanic origin and displays no crater rim or evidence for ejecta. Instead the depression consists of a series of circumferential graben and down-dropped blocks which also display upper surfaces similar to ridged plain lavas. Large blocks within the depression are tilted towards the crater center, and display graben that appear to have originally been linked with circumferential graben outside of the complex related to earlier collapse events. A nearly 700 m high mound exists along a graben within the complex that might be a vent. The deepest depression displays two sets of nearly continuous terraces, which we interpret as high-stands of a drained lava lake. These features appear similar to the black ledge described during the Kilauea Iki eruption in 1959. A lacustrine origin for the terraces seems unlikely because of the paucity of channels found in or around the depression that could be linked to aqueous surface processes. In addition, there is no obvious evidence for lacustrine sediments within the basin. Together with the presence of significant faulting that is indicative of collapse we conclude that this crater complex represents a large caldera formed in the Late Noachian to Early Hesperian. Other linear and irregular depressions in the region also might be linked to ancient volcanism. If that hypothesis is correct, it suggests that northern Arabia Terra could contain a large, previously unrecognized highland igneous province. Evacuation of magma via explosive and effusive activity produced localized collapse, might have contributed to nearby ridged plains, and pyroclastic materials erupted from these vents might have supplied sediments in fretted terrain and other deposits. The recognition of volcanoes within Arabia Terra expands the known extent of Noachian-Hesperian volcanism to cover much of the preserved martian highland crust.

Automatic markerless registration of point clouds with semantic-keypoint-based 4-points congruent sets

NASA Astrophysics Data System (ADS)

Ge, Xuming

2017-08-01

The coarse registration of point clouds from urban building scenes has become a key topic in applications of terrestrial laser scanning technology. Sampling-based algorithms in the random sample consensus (RANSAC) model have emerged as mainstream solutions to address coarse registration problems. In this paper, we propose a novel combined solution to automatically align two markerless point clouds from building scenes. Firstly, the method segments non-ground points from ground points. Secondly, the proposed method detects feature points from each cross section and then obtains semantic keypoints by connecting feature points with specific rules. Finally, the detected semantic keypoints from two point clouds act as inputs to a modified 4PCS algorithm. Examples are presented and the results compared with those of K-4PCS to demonstrate the main contributions of the proposed method, which are the extension of the original 4PCS to handle heavy datasets and the use of semantic keypoints to improve K-4PCS in relation to registration accuracy and computational efficiency.

Visual memory performance for color depends on spatiotemporal context.

PubMed

Olivers, Christian N L; Schreij, Daniel

2014-10-01

Performance on visual short-term memory for features has been known to depend on stimulus complexity, spatial layout, and feature context. However, with few exceptions, memory capacity has been measured for abruptly appearing, single-instance displays. In everyday life, objects often have a spatiotemporal history as they or the observer move around. In three experiments, we investigated the effect of spatiotemporal history on explicit memory for color. Observers saw a memory display emerge from behind a wall, after which it disappeared again. The test display then emerged from either the same side as the memory display or the opposite side. In the first two experiments, memory improved for intermediate set sizes when the test display emerged in the same way as the memory display. A third experiment then showed that the benefit was tied to the original motion trajectory and not to the display object per se. The results indicate that memory for color is embedded in a richer episodic context that includes the spatiotemporal history of the display.

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

Jurrus, Elizabeth R.; Hodas, Nathan O.; Baker, Nathan A.

Forensic analysis of nanoparticles is often conducted through the collection and identifi- cation of electron microscopy images to determine the origin of suspected nuclear material. Each image is carefully studied by experts for classification of materials based on texture, shape, and size. Manually inspecting large image datasets takes enormous amounts of time. However, automatic classification of large image datasets is a challenging problem due to the complexity involved in choosing image features, the lack of training data available for effective machine learning methods, and the availability of user interfaces to parse through images. Therefore, a significant need exists for automatedmore » and semi-automated methods to help analysts perform accurate image classification in large image datasets. We present INStINCt, our Intelligent Signature Canvas, as a framework for quickly organizing image data in a web based canvas framework. Images are partitioned using small sets of example images, chosen by users, and presented in an optimal layout based on features derived from convolutional neural networks.« less

Strongly coupled gauge theories: What can lattice calculations teach us?

NASA Astrophysics Data System (ADS)

Hasenfratz, A.; Brower, R. C.; Rebbi, C.; Weinberg, E.; Witzel, O.

2017-12-01

The dynamical origin of electroweak symmetry breaking is an open question with many possible theoretical explanations. Strongly coupled systems predicting the Higgs boson as a bound state of a new gauge-fermion interaction form one class of candidate models. Due to increased statistics, LHC run II will further constrain the phenomenologically viable models in the near future. In the meanwhile it is important to understand the general properties and specific features of the different competing models. In this work we discuss many-flavor gauge-fermion systems that contain both massless (light) and massive fermions. The former provide Goldstone bosons and trigger electroweak symmetry breaking, while the latter indirectly influence the infrared dynamics. Numerical results reveal that such systems can exhibit a light 0++ isosinglet scalar, well separated from the rest of the spectrum. Further, when we set the scale via the vev of electroweak symmetry breaking, we predict a 2 TeV vector resonance which could be a generic feature of SU(3) gauge theories.

Similar Ring Structures on Mars and Tibetan Plateau confirm recent tectonism on Martian Northern polar region

NASA Astrophysics Data System (ADS)

Anglés, A.; Li, Y. L.

2017-10-01

The polar regions of Mars feature layered deposits, some of which exist as enclosed zoning structures. These deposits raised strong interest since their discovery and still remain one of the most controversial features on Mars. Zoning structures that are enclosed only appear in the Northern polar region, where the disappearance of water bodies may have left behind huge deposits of evaporate salts. The origin of the layered deposits has been widely debated. Here we propose that the enclosed nature of the zoning structures indicates the result of recent tectonism. We compared similar structures at an analogue site located in the western Qaidam Basin of Tibetan Plateau, a unique tectonic setting with abundant saline deposits. The enclosed structures, which we term Ring Structures, in both the analogue site and in the Northern polar region of Mars, were formed by uplift induced pressurization and buoyancy of salts as the result of recent tectonic activity.

Robust Joint Graph Sparse Coding for Unsupervised Spectral Feature Selection.

PubMed

Zhu, Xiaofeng; Li, Xuelong; Zhang, Shichao; Ju, Chunhua; Wu, Xindong

2017-06-01

In this paper, we propose a new unsupervised spectral feature selection model by embedding a graph regularizer into the framework of joint sparse regression for preserving the local structures of data. To do this, we first extract the bases of training data by previous dictionary learning methods and, then, map original data into the basis space to generate their new representations, by proposing a novel joint graph sparse coding (JGSC) model. In JGSC, we first formulate its objective function by simultaneously taking subspace learning and joint sparse regression into account, then, design a new optimization solution to solve the resulting objective function, and further prove the convergence of the proposed solution. Furthermore, we extend JGSC to a robust JGSC (RJGSC) via replacing the least square loss function with a robust loss function, for achieving the same goals and also avoiding the impact of outliers. Finally, experimental results on real data sets showed that both JGSC and RJGSC outperformed the state-of-the-art algorithms in terms of k -nearest neighbor classification performance.

Ankle-like feature in the energy spectrum of light elements of cosmic rays observed with KASCADE-Grande

NASA Astrophysics Data System (ADS)

Apel, W. D.; Arteaga-Velàzquez, J. C.; Bekk, K.; Bertaina, M.; Blümer, J.; Bozdog, H.; Brancus, I. M.; Cantoni, E.; Chiavassa, A.; Cossavella, F.; Daumiller, K.; de Souza, V.; Di Pierro, F.; Doll, P.; Engel, R.; Engler, J.; Finger, M.; Fuchs, B.; Fuhrmann, D.; Gils, H. J.; Glasstetter, R.; Grupen, C.; Haungs, A.; Heck, D.; Hörandel, J. R.; Huber, D.; Huege, T.; Kampert, K.-H.; Kang, D.; Klages, H. O.; Link, K.; Łuczak, P.; Ludwig, M.; Mathes, H. J.; Mayer, H. J.; Melissas, M.; Milke, J.; Mitrica, B.; Morello, C.; Oehlschläger, J.; Ostapchenko, S.; Palmieri, N.; Petcu, M.; Pierog, T.; Rebel, H.; Roth, M.; Schieler, H.; Schoo, S.; Schröder, F. G.; Sima, O.; Toma, G.; Trinchero, G. C.; Ulrich, H.; Weindl, A.; Wochele, J.; Wommer, M.; Zabierowski, J.

2013-04-01

Recent results of the KASCADE-Grande experiment provided evidence for a mild knee-like structure in the all-particle spectrum of cosmic rays at E=1016.92±0.10eV, which was found to be due to a steepening in the flux of heavy primary particles. The spectrum of the combined components of light and intermediate masses was found to be compatible with a single power law in the energy range from 1016.3 to 1018eV. In this paper, we present an update of this analysis by using data with increased statistics, originating both from a larger data set including more recent measurements and by using a larger fiducial area. In addition, optimized selection criteria for enhancing light primaries are applied. We find a spectral feature for light elements, namely, a hardening at E=1017.08±0.08eV with a change of the power law index from -3.25±0.05 to -2.79±0.08.

High-angular-resolution stellar imaging with occultations from the Cassini spacecraft - III. Mira

NASA Astrophysics Data System (ADS)

Stewart, Paul N.; Tuthill, Peter G.; Nicholson, Philip D.; Hedman, Matthew M.

2016-04-01

We present an analysis of spectral and spatial data of Mira obtained by the Cassini spacecraft, which not only observed the star's spectra over a broad range of near-infrared wavelengths, but was also able to obtain high-resolution spatial information by watching the star pass behind Saturn's rings. The observed spectral range of 1-5 microns reveals the stellar atmosphere in the crucial water-bands which are unavailable to terrestrial observers, and the simultaneous spatial sampling allows the origin of spectral features to be located in the stellar environment. Models are fitted to the data, revealing the spectral and spatial structure of molecular layers surrounding the star. High-resolution imagery is recovered revealing the layered and asymmetric nature of the stellar atmosphere. The observational data set is also used to confront the state-of-the-art cool opacity-sampling dynamic extended atmosphere models of Mira variables through a detailed spectral and spatial comparison, revealing in general a good agreement with some specific departures corresponding to particular spectral features.

Multiple Schwannomas of the Spine: Review of the Schwannomatosis or Congenital Neurilemmomatosis: A Case Report.

PubMed

Lee, Sang-Hoon; Kim, Se-Hoon; Kim, Bum-Joon; Lim, Dong-Jun

2015-06-01

Schwannomas are the most common benign nerve sheath tumors originating in Schwann cells. With special conditions like neurofibromatosis type 2 or entity called schwannomatosis, patients develop multiple schwannomas. But in clinical setting, distinguishing schwannomatosis from neurofibromatosis type 2 is challengeable. We describe 58-year-old male who presented with severe neuropathic pain, from schwannomatosis featuring multiple schwannomas of spine and trunk, and underwent surgical treatment. We demonstrate his radiologic and clinical findings, and discuss about important clinical features of this condition. To confirm schwannomatosis, we performed brain magnetic resonance imaging, and took his familial history. Staged surgery was done for pathological confirmation and relief of the pain. Schwannomatosis and neurofibromatosis type 2 are similar but different disease. There are diagnostic hallmarks of these conditions, including familial history, pathology, and brain imaging. Because of different prognosis, the two diseases must be distinguished, so diagnostic tests that are mentioned above should be performed in caution.

Multiple Schwannomas of the Spine: Review of the Schwannomatosis or Congenital Neurilemmomatosis: A Case Report

PubMed Central

Lee, Sang-Hoon; Kim, Bum-Joon; Lim, Dong-Jun

2015-01-01

Schwannomas are the most common benign nerve sheath tumors originating in Schwann cells. With special conditions like neurofibromatosis type 2 or entity called schwannomatosis, patients develop multiple schwannomas. But in clinical setting, distinguishing schwannomatosis from neurofibromatosis type 2 is challengeable. We describe 58-year-old male who presented with severe neuropathic pain, from schwannomatosis featuring multiple schwannomas of spine and trunk, and underwent surgical treatment. We demonstrate his radiologic and clinical findings, and discuss about important clinical features of this condition. To confirm schwannomatosis, we performed brain magnetic resonance imaging, and took his familial history. Staged surgery was done for pathological confirmation and relief of the pain. Schwannomatosis and neurofibromatosis type 2 are similar but different disease. There are diagnostic hallmarks of these conditions, including familial history, pathology, and brain imaging. Because of different prognosis, the two diseases must be distinguished, so diagnostic tests that are mentioned above should be performed in caution. PMID:26217390

Silica deposits on Mars with features resembling hot spring biosignatures at El Tatio in Chile

PubMed Central

Ruff, Steven W.; Farmer, Jack D.

2016-01-01

The Mars rover Spirit encountered outcrops and regolith composed of opaline silica (amorphous SiO2·nH2O) in an ancient volcanic hydrothermal setting in Gusev crater. An origin via either fumarole-related acid-sulfate leaching or precipitation from hot spring fluids was suggested previously. However, the potential significance of the characteristic nodular and mm-scale digitate opaline silica structures was not recognized. Here we report remarkably similar features within active hot spring/geyser discharge channels at El Tatio in northern Chile, where halite-encrusted silica yields infrared spectra that are the best match yet to spectra from Spirit. Furthermore, we show that the nodular and digitate silica structures at El Tatio that most closely resemble those on Mars include complex sedimentary structures produced by a combination of biotic and abiotic processes. Although fully abiotic processes are not ruled out for the Martian silica structures, they satisfy an a priori definition of potential biosignatures. PMID:27853166

On the BRST Quantization of the Massless Bosonic Particle in Twistor-Like Formulation

NASA Astrophysics Data System (ADS)

Bandos, Igor; Maznytsia, Alexey; Rudychev, Igor; Sorokin, Dmitri

We study some features of bosonic-particle path-integral quantization in a twistor-like approach by the use of the BRST-BFV-quantization prescription. In the course of the Hamiltonian analysis we observe links between various formulations of the twistor-like particle by performing a conversion of the Hamiltonian constraints of one formulation to another. A particular feature of the conversion procedure applied to turn the second-class constraints into first-class constraints is that the simplest Lorentz-covariant way to do this is to convert a full mixed set of the initial first- and second-class constraints rather than explicitly extracting and converting only the second-class constraints. Another novel feature of the conversion procedure applied below is that in the case of the D = 4 and D = 6 twistor-like particle the number of new auxiliary Lorentz-covariant coordinates, which one introduces to get a system of first-class constraints in an extended phase space, exceeds the number of independent second-class constraints of the original dynamical system. We calculate the twistor-like particle propagator in D = 3,4,6 space-time dimensions and show that it coincides with that of a conventional massless bosonic particle.

Parametric dictionary learning for modeling EAP and ODF in diffusion MRI.

PubMed

Merlet, Sylvain; Caruyer, Emmanuel; Deriche, Rachid

2012-01-01

In this work, we propose an original and efficient approach to exploit the ability of Compressed Sensing (CS) to recover diffusion MRI (dMRI) signals from a limited number of samples while efficiently recovering important diffusion features such as the ensemble average propagator (EAP) and the orientation distribution function (ODF). Some attempts to sparsely represent the diffusion signal have already been performed. However and contrarly to what has been presented in CS dMRI, in this work we propose and advocate the use of a well adapted learned dictionary and show that it leads to a sparser signal estimation as well as to an efficient reconstruction of very important diffusion features. We first propose to learn and design a sparse and parametric dictionary from a set of training diffusion data. Then, we propose a framework to analytically estimate in closed form two important diffusion features: the EAP and the ODF. Various experiments on synthetic, phantom and human brain data have been carried out and promising results with reduced number of atoms have been obtained on diffusion signal reconstruction, thus illustrating the added value of our method over state-of-the-art SHORE and SPF based approaches.

Texture and color features for tile classification

NASA Astrophysics Data System (ADS)

Baldrich, Ramon; Vanrell, Maria; Villanueva, Juan J.

1999-09-01

In this paper we present the results of a preliminary computer vision system to classify the production of a ceramic tile industry. We focus on the classification of a specific type of tiles whose production can be affected by external factors, such as humidity, temperature, origin of clays and pigments. Variations on these uncontrolled factors provoke small differences in the color and the texture of the tiles that force to classify all the production. A constant and non- subjective classification would allow avoiding devolution from customers and unnecessary stock fragmentation. The aim of this work is to simulate the human behavior on this classification task by extracting a set of features from tile images. These features are induced by definitions from experts. To compute them we need to mix color and texture information and to define global and local measures. In this work, we do not seek a general texture-color representation, we only deal with textures formed by non-oriented colored-blobs randomly distributed. New samples are classified using Discriminant Analysis functions derived from known class tile samples. The last part of the paper is devoted to explain the correction of acquired images in order to avoid time and geometry illumination changes.

Utility of Ward-Based Retinal Photography in Stroke Patients.

PubMed

Frost, Shaun; Brown, Michael; Stirling, Verity; Vignarajan, Janardhan; Prentice, David; Kanagasingam, Yogesan

2017-03-01

Improvements in acute care of stroke patients have decreased mortality, but survivors are still at increased risk of future vascular events and mitigation of this risk requires thorough assessment of the underlying factors leading to the stroke. The brain and eye share a common embryological origin and numerous similarities exist between the small vessels of the retina and brain. Recent population-based studies have demonstrated a close link between retinal vascular changes and stroke, suggesting that retinal photography could have utility in assessing underlying stroke risk factors and prognosis after stroke. Modern imaging equipment can facilitate precise measurement and monitoring of vascular features. However, use of this equipment is a challenge in the stroke ward setting as patients are frequently unable to maintain the required seated position, and pupil dilatation is often not feasible as it could potentially obscure important neurological signs of stroke progression. This small study investigated the utility of a novel handheld, nonmydriatic retinal camera in the stroke ward and explored associations between retinal vascular features and stroke risk factors. This camera circumvented the practical limitations of conducting retinal photography in the stroke ward setting. A positive correlation was found between carotid disease and both mean width of arterioles (r = .40, P = .00571) and venules (r = .30, P = .0381). The results provide further evidence that retinal vascular features are clinically informative about underlying stroke risk factors and demonstrate the utility of handheld retinal photography in the stroke ward. Copyright © 2017 National Stroke Association. Published by Elsevier Inc. All rights reserved.

The impact of configural superiority on the processing of spatial information.

PubMed

Bratch, Alexander; Barr, Shawn; Bromfield, W Drew; Srinath, Aparna; Zhang, Jack; Gold, Jason M

2016-09-01

The impact of context on perception has been well documented for over a century. In some cases, the introduction of context to a set of target features may produce a unified percept, leading to a quicker and more accurate classification; a configural superiority effect (Pomerantz, Sager, & Stoever, 1977). Although this effect has been well characterized in terms of the stimulus features that produce the effect, the specific impact context has on the spatial strategies adopted by observers when making perceptual judgments remains unclear. Here, we sought to address this question by using the methods of response classification and ideal observer analysis. In our main experiment, we used a stimulus set known to produce the configural superiority effect and found that although observers were faster in the presence of context, they were actually less efficient at extracting stimulus information. This surprising result was attributable to the use of a spatial strategy in which observers relied on redundant, noninformative features in the presence of context. A control experiment ruled out the possibility that the mere presence of added context led to these strategic shifts. Our results support previous notions about the nature of the perceptual shifts that are induced by the configural superiority effect. However, they also show that configural processing is more nuanced than originally thought: Although observers may be faster at making judgments when context induces the percept of a configural whole, there appears to be a hidden cost in terms of the efficiency with which information is used. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

3D synchrotron x-ray microtomography of paint samples

NASA Astrophysics Data System (ADS)

Ferreira, Ester S. B.; Boon, Jaap J.; van der Horst, Jerre; Scherrer, Nadim C.; Marone, Federica; Stampanoni, Marco

2009-07-01

Synchrotron based X-ray microtomography is a novel way to examine paint samples. The three dimensional distribution of pigment particles, binding media and their deterioration products as well as other features such as voids, are made visible in their original context through a computing environment without the need of physical sectioning. This avoids manipulation related artefacts. Experiments on paint chips (approximately 500 micron wide) were done on the TOMCAT beam line (TOmographic Microscopy and Coherent rAdiology experimenTs) at the Paul Scherrer Institute in Villigen, CH, using an x-ray energy of up to 40 keV. The x-ray absorption images are obtained at a resolution of 350 nm. The 3D dataset was analysed using the commercial 3D imaging software Avizo 5.1. Through this process, virtual sections of the paint sample can be obtained in any orientation. One of the topics currently under research are the ground layers of paintings by Cuno Amiet (1868- 1961), one of the most important Swiss painters of classical modernism, whose early work is currently the focus of research at the Swiss Institute for Art Research (SIK-ISEA). This technique gives access to information such as sample surface morphology, porosity, particle size distribution and even particle identification. In the case of calcium carbonate grounds for example, features like microfossils present in natural chalks, can be reconstructed and their species identified, thus potentially providing information towards the mineral origin. One further elegant feature of this technique is that a target section can be selected within the 3D data set, before exposing it to obtain chemical data. Virtual sections can then be compared with cross sections of the same samples made in the traditional way.

Entropyology: the application of bioinformatics and data modeling to digital virus and malware recognition

NASA Astrophysics Data System (ADS)

Jaenisch, Holger M.; Handley, James W.

2010-04-01

Malware are analogs of viruses. Viruses are comprised of large numbers of polypeptide proteins. The shape and function of the protein strands determines the functionality of the segment, similar to a subroutine in malware. The full combination of subroutines is the malware organism, in analogous fashion as a collection of polypeptides forms protein structures that are information bearing. We propose to apply the methods of Bioinformatics to analyze malware to provide a rich feature set for creating a unique and novel detection and classification scheme that is originally applied to Bioinformatics amino acid sequencing. Our proposed methods enable real time in situ (in contrast to in vivo) detection applications.

Risk of coinfection outbreaks in temporal networks: a case study of a hospital contact network

NASA Astrophysics Data System (ADS)

Rodríguez, Jorge P.; Ghanbarnejad, Fakhteh; Eguíluz, Víctor M.

2017-10-01

We study the spreading of cooperative infections in an empirical temporal network of contacts between people, including health care workers and patients, in a hospital. The system exhibits a phase transition leading to one or several endemic branches, depending on the connectivity pattern and the temporal correlations. There are two endemic branches in the original setting and the non-cooperative case. However, the cooperative interaction between infections reinforces the upper branch, leading to a smaller epidemic threshold and a higher probability for having a big outbreak. We show the microscopic mechanisms leading to these differences, characterize three different risks, and use the influenza features as an example for this dynamics.

Spontaneous Raman scattering as a high resolution XUV radiation source

NASA Technical Reports Server (NTRS)

Rothenberg, J. E.; Young, J. F.; Harris, S. E.

1983-01-01

A type of high resolution XUV radiation source is described which is based upon spontaneous anti-Stokes scattering of tunable incident laser radiation from atoms excited to metastable levels. The theory of the source is summarized and two sets of experiments using He (1s2s)(1)S atoms, produced in a cw hollow cathode and in a pulsed high power microwave discharge, are discussed. The radiation source is used to examine transitions originating from the 3p(6) shell of potassium. The observed features include four previously unreported absorption lines and several sharp interferences of closely spaced autoionizing lines. A source linewidth of about 1.9 cm(-1) at 185,000 cm(-1) is demonstrated.

The Use of Photo-projects and Term Projects in Large-Format (200+ Students) Introductory Geology Courses.

NASA Astrophysics Data System (ADS)

Giles, A. N.; Wilkie, K. M.

2008-12-01

Photo-projects have long been utilized as a way of getting students in introductory geology courses to apply what they have learned in lecture to the outcrop and landscape. While the projects have many benefits, we have found that with large-format classes of 200+ students, where a mandatory field trip is logistically impossible, many problems can arise. One problem has been that of consistent and timely grading, which can be addressed by a project that can be turned in throughout the course of the semester and by utilizing a grading rubric. Also, in many cases, students simply take photographs of "scenery" and then try to identify features/processes with little thought as to whether that particular feature/process can occur in that geologic setting (such as identifying features as having a glacial origin in a non-glaciated terrain.) These types of problem can be attributed to the student's lack of knowledge of the geology of the area within which the photographs were taken and having little to no field instruction. Many of these problems can be addressed by utilizing a term project that combines elements of both research and the traditional photo project. The student chooses a specific area/region (i.e. a national park) that the student will/has actually visit(ed) and is then required to do background research before attempting to identify features and processes in photographs they have taken from the area. Here we present details of such a project that involves students performing research activities in three stages: The history/geologic setting of the area, the specific lithology of the area, and then the hydrology of the area, with each being completed at specified times throughout the semester. The final stage is the photo project component where the student identifies and interprets the features/processes in photographs from the area. The research provides the student with a framework within which they can identify and interpret the features/processes that are likely to be seen in their area.

Cognitive and Affective Aspects of Creative Option Generation in Everyday Life Situations

PubMed Central

Schweizer, T. Sophie; Schmalenberger, Katja M.; Eisenlohr-Moul, Tory A.; Mojzisch, Andreas; Kaiser, Stefan; Funke, Joachim

2016-01-01

Which factors influence a human being’s ability to develop new perspectives and be creative? This ability is pivotal for any context in which new cognitions are required, such as innovative endeavors in science and art, or psychotherapeutic settings. In this article, we seek to bring together two research programs investigating the generation of creative options: On the one hand, research on option generation in the decision-making literature and, on the other hand, cognitive and clinical creativity research. Previous decision-making research has largely neglected the topic of generating creative options. Experiments typically provided participants with a clear set of options to choose from, but everyday life situations are less structured and allow countless ways to react. Before choosing an option, agents have to self-generate a set of options to choose from. Such option generation processes have only recently moved to the center of attention. The present study examines the creative quality of self-generated options in daily life situations. A student sample (N = 48) generated options for action in 70 briefly described everyday life scenarios. We rated the quality of the options on three dimensions of creativity- originality, feasibility, and divergence -and linked these qualities to option generation fluency (speed and number of generated options), situational features like the familiarity and the affective valence of the situation in which the options were generated, and trait measures of cognitive performance. We found that when situations were familiar to the participant, greater negative affective valence of the situation was associated with more originality and divergence of generated options. We also found that a higher option generation fluency was associated with a greater maximal originality of options. We complete our article with a joint research agenda for researchers in the decision-making field focusing on option generation and, on the other hand, researchers working on the cognitive and clinical aspects of creativity. PMID:27536258

Cognitive and Affective Aspects of Creative Option Generation in Everyday Life Situations.

PubMed

Schweizer, T Sophie; Schmalenberger, Katja M; Eisenlohr-Moul, Tory A; Mojzisch, Andreas; Kaiser, Stefan; Funke, Joachim

2016-01-01

Which factors influence a human being's ability to develop new perspectives and be creative? This ability is pivotal for any context in which new cognitions are required, such as innovative endeavors in science and art, or psychotherapeutic settings. In this article, we seek to bring together two research programs investigating the generation of creative options: On the one hand, research on option generation in the decision-making literature and, on the other hand, cognitive and clinical creativity research. Previous decision-making research has largely neglected the topic of generating creative options. Experiments typically provided participants with a clear set of options to choose from, but everyday life situations are less structured and allow countless ways to react. Before choosing an option, agents have to self-generate a set of options to choose from. Such option generation processes have only recently moved to the center of attention. The present study examines the creative quality of self-generated options in daily life situations. A student sample (N = 48) generated options for action in 70 briefly described everyday life scenarios. We rated the quality of the options on three dimensions of creativity- originality, feasibility, and divergence -and linked these qualities to option generation fluency (speed and number of generated options), situational features like the familiarity and the affective valence of the situation in which the options were generated, and trait measures of cognitive performance. We found that when situations were familiar to the participant, greater negative affective valence of the situation was associated with more originality and divergence of generated options. We also found that a higher option generation fluency was associated with a greater maximal originality of options. We complete our article with a joint research agenda for researchers in the decision-making field focusing on option generation and, on the other hand, researchers working on the cognitive and clinical aspects of creativity.

Feature Selection for Chemical Sensor Arrays Using Mutual Information

PubMed Central

Wang, X. Rosalind; Lizier, Joseph T.; Nowotny, Thomas; Berna, Amalia Z.; Prokopenko, Mikhail; Trowell, Stephen C.

2014-01-01

We address the problem of feature selection for classifying a diverse set of chemicals using an array of metal oxide sensors. Our aim is to evaluate a filter approach to feature selection with reference to previous work, which used a wrapper approach on the same data set, and established best features and upper bounds on classification performance. We selected feature sets that exhibit the maximal mutual information with the identity of the chemicals. The selected features closely match those found to perform well in the previous study using a wrapper approach to conduct an exhaustive search of all permitted feature combinations. By comparing the classification performance of support vector machines (using features selected by mutual information) with the performance observed in the previous study, we found that while our approach does not always give the maximum possible classification performance, it always selects features that achieve classification performance approaching the optimum obtained by exhaustive search. We performed further classification using the selected feature set with some common classifiers and found that, for the selected features, Bayesian Networks gave the best performance. Finally, we compared the observed classification performances with the performance of classifiers using randomly selected features. We found that the selected features consistently outperformed randomly selected features for all tested classifiers. The mutual information filter approach is therefore a computationally efficient method for selecting near optimal features for chemical sensor arrays. PMID:24595058

DNA replication origins—where do we begin?

PubMed Central

Prioleau, Marie-Noëlle; MacAlpine, David M.

2016-01-01

For more than three decades, investigators have sought to identify the precise locations where DNA replication initiates in mammalian genomes. The development of molecular and biochemical approaches to identify start sites of DNA replication (origins) based on the presence of defining and characteristic replication intermediates at specific loci led to the identification of only a handful of mammalian replication origins. The limited number of identified origins prevented a comprehensive and exhaustive search for conserved genomic features that were capable of specifying origins of DNA replication. More recently, the adaptation of origin-mapping assays to genome-wide approaches has led to the identification of tens of thousands of replication origins throughout mammalian genomes, providing an unprecedented opportunity to identify both genetic and epigenetic features that define and regulate their distribution and utilization. Here we summarize recent advances in our understanding of how primary sequence, chromatin environment, and nuclear architecture contribute to the dynamic selection and activation of replication origins across diverse cell types and developmental stages. PMID:27542827

Spatial scale and distribution of neurovascular signals underlying decoding of orientation and eye of origin from fMRI data

PubMed Central

Harrison, Charlotte; Jackson, Jade; Oh, Seung-Mock; Zeringyte, Vaida

2016-01-01

Multivariate pattern analysis of functional magnetic resonance imaging (fMRI) data is widely used, yet the spatial scales and origin of neurovascular signals underlying such analyses remain unclear. We compared decoding performance for stimulus orientation and eye of origin from fMRI measurements in human visual cortex with predictions based on the columnar organization of each feature and estimated the spatial scales of patterns driving decoding. Both orientation and eye of origin could be decoded significantly above chance in early visual areas (V1–V3). Contrary to predictions based on a columnar origin of response biases, decoding performance for eye of origin in V2 and V3 was not significantly lower than that in V1, nor did decoding performance for orientation and eye of origin differ significantly. Instead, response biases for both features showed large-scale organization, evident as a radial bias for orientation, and a nasotemporal bias for eye preference. To determine whether these patterns could drive classification, we quantified the effect on classification performance of binning voxels according to visual field position. Consistent with large-scale biases driving classification, binning by polar angle yielded significantly better decoding performance for orientation than random binning in V1–V3. Similarly, binning by hemifield significantly improved decoding performance for eye of origin. Patterns of orientation and eye preference bias in V2 and V3 showed a substantial degree of spatial correlation with the corresponding patterns in V1, suggesting that response biases in these areas originate in V1. Together, these findings indicate that multivariate classification results need not reflect the underlying columnar organization of neuronal response selectivities in early visual areas. NEW & NOTEWORTHY Large-scale response biases can account for decoding of orientation and eye of origin in human early visual areas V1–V3. For eye of origin this pattern is a nasotemporal bias; for orientation it is a radial bias. Differences in decoding performance across areas and stimulus features are not well predicted by differences in columnar-scale organization of each feature. Large-scale biases in extrastriate areas are spatially correlated with those in V1, suggesting biases originate in primary visual cortex. PMID:27903637

Integrating Dimension Reduction and Out-of-Sample Extension in Automated Classification of Ex Vivo Human Patellar Cartilage on Phase Contrast X-Ray Computed Tomography

PubMed Central

Nagarajan, Mahesh B.; Coan, Paola; Huber, Markus B.; Diemoz, Paul C.; Wismüller, Axel

2015-01-01

Phase contrast X-ray computed tomography (PCI-CT) has been demonstrated as a novel imaging technique that can visualize human cartilage with high spatial resolution and soft tissue contrast. Different textural approaches have been previously investigated for characterizing chondrocyte organization on PCI-CT to enable classification of healthy and osteoarthritic cartilage. However, the large size of feature sets extracted in such studies motivates an investigation into algorithmic feature reduction for computing efficient feature representations without compromising their discriminatory power. For this purpose, geometrical feature sets derived from the scaling index method (SIM) were extracted from 1392 volumes of interest (VOI) annotated on PCI-CT images of ex vivo human patellar cartilage specimens. The extracted feature sets were subject to linear and non-linear dimension reduction techniques as well as feature selection based on evaluation of mutual information criteria. The reduced feature set was subsequently used in a machine learning task with support vector regression to classify VOIs as healthy or osteoarthritic; classification performance was evaluated using the area under the receiver-operating characteristic (ROC) curve (AUC). Our results show that the classification performance achieved by 9-D SIM-derived geometric feature sets (AUC: 0.96 ± 0.02) can be maintained with 2-D representations computed from both dimension reduction and feature selection (AUC values as high as 0.97 ± 0.02). Thus, such feature reduction techniques can offer a high degree of compaction to large feature sets extracted from PCI-CT images while maintaining their ability to characterize the underlying chondrocyte patterns. PMID:25710875

Machinery Bearing Fault Diagnosis Using Variational Mode Decomposition and Support Vector Machine as a Classifier

NASA Astrophysics Data System (ADS)

Rama Krishna, K.; Ramachandran, K. I.

2018-02-01

Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. Fault diagnosis is an established concept in identifying the faults, for observing the non-linear behaviour of the vibration signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed vibrational signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the faults in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw vibrational signals.

Speech recognition features for EEG signal description in detection of neonatal seizures.

PubMed

Temko, A; Boylan, G; Marnane, W; Lightbody, G

2010-01-01

In this work, features which are usually employed in automatic speech recognition (ASR) are used for the detection of neonatal seizures in newborn EEG. Three conventional ASR feature sets are compared to the feature set which has been previously developed for this task. The results indicate that the thoroughly-studied spectral envelope based ASR features perform reasonably well on their own. Additionally, the SVM Recursive Feature Elimination routine is applied to all extracted features pooled together. It is shown that ASR features consistently appear among the top-rank features.

Automatic feature design for optical character recognition using an evolutionary search procedure.

PubMed

Stentiford, F W

1985-03-01

An automatic evolutionary search is applied to the problem of feature extraction in an OCR application. A performance measure based on feature independence is used to generate features which do not appear to suffer from peaking effects [17]. Features are extracted from a training set of 30 600 machine printed 34 class alphanumeric characters derived from British mail. Classification results on the training set and a test set of 10 200 characters are reported for an increasing number of features. A 1.01 percent forced decision error rate is obtained on the test data using 316 features. The hardware implementation should be cheap and fast to operate. The performance compares favorably with current low cost OCR page readers.

FSR: feature set reduction for scalable and accurate multi-class cancer subtype classification based on copy number.

PubMed

Wong, Gerard; Leckie, Christopher; Kowalczyk, Adam

2012-01-15

Feature selection is a key concept in machine learning for microarray datasets, where features represented by probesets are typically several orders of magnitude larger than the available sample size. Computational tractability is a key challenge for feature selection algorithms in handling very high-dimensional datasets beyond a hundred thousand features, such as in datasets produced on single nucleotide polymorphism microarrays. In this article, we present a novel feature set reduction approach that enables scalable feature selection on datasets with hundreds of thousands of features and beyond. Our approach enables more efficient handling of higher resolution datasets to achieve better disease subtype classification of samples for potentially more accurate diagnosis and prognosis, which allows clinicians to make more informed decisions in regards to patient treatment options. We applied our feature set reduction approach to several publicly available cancer single nucleotide polymorphism (SNP) array datasets and evaluated its performance in terms of its multiclass predictive classification accuracy over different cancer subtypes, its speedup in execution as well as its scalability with respect to sample size and array resolution. Feature Set Reduction (FSR) was able to reduce the dimensions of an SNP array dataset by more than two orders of magnitude while achieving at least equal, and in most cases superior predictive classification performance over that achieved on features selected by existing feature selection methods alone. An examination of the biological relevance of frequently selected features from FSR-reduced feature sets revealed strong enrichment in association with cancer. FSR was implemented in MATLAB R2010b and is available at http://ww2.cs.mu.oz.au/~gwong/FSR.

Cheminformatic comparison of approved drugs from natural product versus synthetic origins.

PubMed

Stratton, Christopher F; Newman, David J; Tan, Derek S

2015-11-01

Despite the recent decline of natural product discovery programs in the pharmaceutical industry, approximately half of all new drug approvals still trace their structural origins to a natural product. Herein, we use principal component analysis to compare the structural and physicochemical features of drugs from natural product-based versus completely synthetic origins that were approved between 1981 and 2010. Drugs based on natural product structures display greater chemical diversity and occupy larger regions of chemical space than drugs from completely synthetic origins. Notably, synthetic drugs based on natural product pharmacophores also exhibit lower hydrophobicity and greater stereochemical content than drugs from completely synthetic origins. These results illustrate that structural features found in natural products can be successfully incorporated into synthetic drugs, thereby increasing the chemical diversity available for small-molecule drug discovery. Copyright © 2015 Elsevier Ltd. All rights reserved.

Probability mapping of scarred myocardium using texture and intensity features in CMR images

PubMed Central

2013-01-01

Background The myocardium exhibits heterogeneous nature due to scarring after Myocardial Infarction (MI). In Cardiac Magnetic Resonance (CMR) imaging, Late Gadolinium (LG) contrast agent enhances the intensity of scarred area in the myocardium. Methods In this paper, we propose a probability mapping technique using Texture and Intensity features to describe heterogeneous nature of the scarred myocardium in Cardiac Magnetic Resonance (CMR) images after Myocardial Infarction (MI). Scarred tissue and non-scarred tissue are represented with high and low probabilities, respectively. Intermediate values possibly indicate areas where the scarred and healthy tissues are interwoven. The probability map of scarred myocardium is calculated by using a probability function based on Bayes rule. Any set of features can be used in the probability function. Results In the present study, we demonstrate the use of two different types of features. One is based on the mean intensity of pixel and the other on underlying texture information of the scarred and non-scarred myocardium. Examples of probability maps computed using the mean intensity of pixel and the underlying texture information are presented. We hypothesize that the probability mapping of myocardium offers alternate visualization, possibly showing the details with physiological significance difficult to detect visually in the original CMR image. Conclusion The probability mapping obtained from the two features provides a way to define different cardiac segments which offer a way to identify areas in the myocardium of diagnostic importance (like core and border areas in scarred myocardium). PMID:24053280

Pattern recognition and feature extraction with an optical Hough transform

NASA Astrophysics Data System (ADS)

Fernández, Ariel

2016-09-01

Pattern recognition and localization along with feature extraction are image processing applications of great interest in defect inspection and robot vision among others. In comparison to purely digital methods, the attractiveness of optical processors for pattern recognition lies in their highly parallel operation and real-time processing capability. This work presents an optical implementation of the generalized Hough transform (GHT), a well-established technique for the recognition of geometrical features in binary images. Detection of a geometric feature under the GHT is accomplished by mapping the original image to an accumulator space; the large computational requirements for this mapping make the optical implementation an attractive alternative to digital- only methods. Starting from the integral representation of the GHT, it is possible to device an optical setup where the transformation is obtained, and the size and orientation parameters can be controlled, allowing for dynamic scale and orientation-variant pattern recognition. A compact system for the above purposes results from the use of an electrically tunable lens for scale control and a rotating pupil mask for orientation variation, implemented on a high-contrast spatial light modulator (SLM). Real-time (as limited by the frame rate of the device used to capture the GHT) can also be achieved, allowing for the processing of video sequences. Besides, by thresholding of the GHT (with the aid of another SLM) and inverse transforming (which is optically achieved in the incoherent system under appropriate focusing setting), the previously detected features of interest can be extracted.

Mapping Curie temperature depth in the western United States with a fractal model for crustal magnetization

USGS Publications Warehouse

Bouligand, C.; Glen, J.M.G.; Blakely, R.J.

2009-01-01

We have revisited the problem of mapping depth to the Curie temperature isotherm from magnetic anomalies in an attempt to provide a measure of crustal temperatures in the western United States. Such methods are based on the estimation of the depth to the bottom of magnetic sources, which is assumed to correspond to the temperature at which rocks lose their spontaneous magnetization. In this study, we test and apply a method based on the spectral analysis of magnetic anomalies. Early spectral analysis methods assumed that crustal magnetization is a completely uncorrelated function of position. Our method incorporates a more realistic representation where magnetization has a fractal distribution defined by three independent parameters: the depths to the top and bottom of magnetic sources and a fractal parameter related to the geology. The predictions of this model are compatible with radial power spectra obtained from aeromagnetic data in the western United States. Model parameters are mapped by estimating their value within a sliding window swept over the study area. The method works well on synthetic data sets when one of the three parameters is specified in advance. The application of this method to western United States magnetic compilations, assuming a constant fractal parameter, allowed us to detect robust long-wavelength variations in the depth to the bottom of magnetic sources. Depending on the geologic and geophysical context, these features may result from variations in depth to the Curie temperature isotherm, depth to the mantle, depth to the base of volcanic rocks, or geologic settings that affect the value of the fractal parameter. Depth to the bottom of magnetic sources shows several features correlated with prominent heat flow anomalies. It also shows some features absent in the map of heat flow. Independent geophysical and geologic data sets are examined to determine their origin, thereby providing new insights on the thermal and geologic crustal structure of the western United States.

RecceMan: an interactive recognition assistance for image-based reconnaissance: synergistic effects of human perception and computational methods for object recognition, identification, and infrastructure analysis

NASA Astrophysics Data System (ADS)

El Bekri, Nadia; Angele, Susanne; Ruckhäberle, Martin; Peinsipp-Byma, Elisabeth; Haelke, Bruno

2015-10-01

This paper introduces an interactive recognition assistance system for imaging reconnaissance. This system supports aerial image analysts on missions during two main tasks: Object recognition and infrastructure analysis. Object recognition concentrates on the classification of one single object. Infrastructure analysis deals with the description of the components of an infrastructure and the recognition of the infrastructure type (e.g. military airfield). Based on satellite or aerial images, aerial image analysts are able to extract single object features and thereby recognize different object types. It is one of the most challenging tasks in the imaging reconnaissance. Currently, there are no high potential ATR (automatic target recognition) applications available, as consequence the human observer cannot be replaced entirely. State-of-the-art ATR applications cannot assume in equal measure human perception and interpretation. Why is this still such a critical issue? First, cluttered and noisy images make it difficult to automatically extract, classify and identify object types. Second, due to the changed warfare and the rise of asymmetric threats it is nearly impossible to create an underlying data set containing all features, objects or infrastructure types. Many other reasons like environmental parameters or aspect angles compound the application of ATR supplementary. Due to the lack of suitable ATR procedures, the human factor is still important and so far irreplaceable. In order to use the potential benefits of the human perception and computational methods in a synergistic way, both are unified in an interactive assistance system. RecceMan® (Reconnaissance Manual) offers two different modes for aerial image analysts on missions: the object recognition mode and the infrastructure analysis mode. The aim of the object recognition mode is to recognize a certain object type based on the object features that originated from the image signatures. The infrastructure analysis mode pursues the goal to analyze the function of the infrastructure. The image analyst extracts visually certain target object signatures, assigns them to corresponding object features and is finally able to recognize the object type. The system offers him the possibility to assign the image signatures to features given by sample images. The underlying data set contains a wide range of objects features and object types for different domains like ships or land vehicles. Each domain has its own feature tree developed by aerial image analyst experts. By selecting the corresponding features, the possible solution set of objects is automatically reduced and matches only the objects that contain the selected features. Moreover, we give an outlook of current research in the field of ground target analysis in which we deal with partly automated methods to extract image signatures and assign them to the corresponding features. This research includes methods for automatically determining the orientation of an object and geometric features like width and length of the object. This step enables to reduce automatically the possible object types offered to the image analyst by the interactive recognition assistance system.

DNA replication origins-where do we begin?

PubMed

Prioleau, Marie-Noëlle; MacAlpine, David M

2016-08-01

For more than three decades, investigators have sought to identify the precise locations where DNA replication initiates in mammalian genomes. The development of molecular and biochemical approaches to identify start sites of DNA replication (origins) based on the presence of defining and characteristic replication intermediates at specific loci led to the identification of only a handful of mammalian replication origins. The limited number of identified origins prevented a comprehensive and exhaustive search for conserved genomic features that were capable of specifying origins of DNA replication. More recently, the adaptation of origin-mapping assays to genome-wide approaches has led to the identification of tens of thousands of replication origins throughout mammalian genomes, providing an unprecedented opportunity to identify both genetic and epigenetic features that define and regulate their distribution and utilization. Here we summarize recent advances in our understanding of how primary sequence, chromatin environment, and nuclear architecture contribute to the dynamic selection and activation of replication origins across diverse cell types and developmental stages. © 2016 Prioleau and MacAlpine; Published by Cold Spring Harbor Laboratory Press.

A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA

NASA Astrophysics Data System (ADS)

Nemoto, Mitsutaka; Hayashi, Naoto; Hanaoka, Shouhei; Nomura, Yukihiro; Miki, Soichiro; Yoshikawa, Takeharu; Ohtomo, Kuni

2016-03-01

The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.

Working memory for visual features and conjunctions in schizophrenia.

PubMed

Gold, James M; Wilk, Christopher M; McMahon, Robert P; Buchanan, Robert W; Luck, Steven J

2003-02-01

The visual working memory (WM) storage capacity of patients with schizophrenia was investigated using a change detection paradigm. Participants were presented with 2, 3, 4, or 6 colored bars with testing of both single feature (color, orientation) and feature conjunction conditions. Patients performed significantly worse than controls at all set sizes but demonstrated normal feature binding. Unlike controls, patient WM capacity declined at set size 6 relative to set size 4. Impairments with subcapacity arrays suggest a deficit in task set maintenance: Greater impairment for supercapacity set sizes suggests a deficit in the ability to selectively encode information for WM storage. Thus, the WM impairment in schizophrenia appears to be a consequence of attentional deficits rather than a reduction in storage capacity.

Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and t-SNE

PubMed Central

Jamieson, Andrew R.; Giger, Maryellen L.; Drukker, Karen; Li, Hui; Yuan, Yading; Bhooshan, Neha

2010-01-01

Purpose: In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer-extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full-field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003)] and t-distributed stochastic neighbor embedding (t-SNE) [L. van der Maaten and G. Hinton, “Visualizing data using t-SNE,” J. Mach. Learn. Res. 9, 2579–2605 (2008)]. Methods: These methods attempt to map originally high dimensional feature spaces to more human interpretable lower dimensional spaces while preserving both local and global information. The properties of these methods as applied to breast computer-aided diagnosis (CADx) were evaluated in the context of malignancy classification performance as well as in the visual inspection of the sparseness within the two-dimensional and three-dimensional mappings. Classification performance was estimated by using the reduced dimension mapped feature output as input into both linear and nonlinear classifiers: Markov chain Monte Carlo based Bayesian artificial neural network (MCMC-BANN) and linear discriminant analysis. The new techniques were compared to previously developed breast CADx methodologies, including automatic relevance determination and linear stepwise (LSW) feature selection, as well as a linear DR method based on principal component analysis. Using ROC analysis and 0.632+bootstrap validation, 95% empirical confidence intervals were computed for the each classifier’s AUC performance. Results: In the large U.S. data set, sample high performance results include, AUC0.632+=0.88 with 95% empirical bootstrap interval [0.787;0.895] for 13 ARD selected features and AUC0.632+=0.87 with interval [0.817;0.906] for four LSW selected features compared to 4D t-SNE mapping (from the original 81D feature space) giving AUC0.632+=0.90 with interval [0.847;0.919], all using the MCMC-BANN. Conclusions: Preliminary results appear to indicate capability for the new methods to match or exceed classification performance of current advanced breast lesion CADx algorithms. While not appropriate as a complete replacement of feature selection in CADx problems, DR techniques offer a complementary approach, which can aid elucidation of additional properties associated with the data. Specifically, the new techniques were shown to possess the added benefit of delivering sparse lower dimensional representations for visual interpretation, revealing intricate data structure of the feature space. PMID:20175497

Glimpsing over the event horizon: evolution of nuclear pores and envelope.

PubMed

Jékely, Gáspár

2005-02-01

The origin of eukaryotes from prokaryotic ancestors is one of the major evolutionary transitions in the history of life. The nucleus, a membrane bound compartment for confining the genome, is a central feature of eukaryotic cells and its origin also has to be a central feature of any workable theory that ventures to explain eukaryotic origins. Recent bioinformatic analyses of components of the nuclear pore complex (NPC), the nuclear envelope (NE), and the nuclear transport systems revealed exciting evolutionary connections (e.g., between NPC and coated vesicles) and provided a useful record of the phyletic distribution and history of NPC and NE components. These analyses allow us to refine theories on the origin and evolution of the nucleus, and consequently, of the eukaryotic cell.

Perceptual quality estimation of H.264/AVC videos using reduced-reference and no-reference models

NASA Astrophysics Data System (ADS)

Shahid, Muhammad; Pandremmenou, Katerina; Kondi, Lisimachos P.; Rossholm, Andreas; Lövström, Benny

2016-09-01

Reduced-reference (RR) and no-reference (NR) models for video quality estimation, using features that account for the impact of coding artifacts, spatio-temporal complexity, and packet losses, are proposed. The purpose of this study is to analyze a number of potentially quality-relevant features in order to select the most suitable set of features for building the desired models. The proposed sets of features have not been used in the literature and some of the features are used for the first time in this study. The features are employed by the least absolute shrinkage and selection operator (LASSO), which selects only the most influential of them toward perceptual quality. For comparison, we apply feature selection in the complete feature sets and ridge regression on the reduced sets. The models are validated using a database of H.264/AVC encoded videos that were subjectively assessed for quality in an ITU-T compliant laboratory. We infer that just two features selected by RR LASSO and two bitstream-based features selected by NR LASSO are able to estimate perceptual quality with high accuracy, higher than that of ridge, which uses more features. The comparisons with competing works and two full-reference metrics also verify the superiority of our models.

Evidence of tampering in watermark identification

NASA Astrophysics Data System (ADS)

McLauchlan, Lifford; Mehrübeoglu, Mehrübe

2009-08-01

In this work, watermarks are embedded in digital images in the discrete wavelet transform (DWT) domain. Principal component analysis (PCA) is performed on the DWT coefficients. Next higher order statistics based on the principal components and the eigenvalues are determined for different sets of images. Feature sets are analyzed for different types of attacks in m dimensional space. The results demonstrate the separability of the features for the tampered digital copies. Different feature sets are studied to determine more effective tamper evident feature sets. The digital forensics, the probable manipulation(s) or modification(s) performed on the digital information can be identified using the described technique.

Nozzle Extension for Safety Air Gun

NASA Technical Reports Server (NTRS)

Zumbrun, H. N.; Croom, Delwin R., Jr.

1986-01-01

New nozzle-extension design overcomes problems and incorporates original commercial nozzle, retaining intrinsic safety features. Components include extension tube, length of which made to suit application; adaptor fitting, and nozzle adaptor repinned to maintain original safety features. Design moves conical airstream to end of extension to blow machine chips away from operator. Nozzle-extension modification allows safe and efficient operation of machine tools while maintaining integrity of orginial safety-air-gun design.

Astronomical Software Directory Service

NASA Astrophysics Data System (ADS)

Hanisch, Robert J.; Payne, Harry; Hayes, Jeffrey

1997-01-01

With the support of NASA's Astrophysics Data Program (NRA 92-OSSA-15), we have developed the Astronomical Software Directory Service (ASDS): a distributed, searchable, WWW-based database of software packages and their related documentation. ASDS provides integrated access to 56 astronomical software packages, with more than 16,000 URLs indexed for full-text searching. Users are performing about 400 searches per month. A new aspect of our service is the inclusion of telescope and instrumentation manuals, which prompted us to change the name to the Astronomical Software and Documentation Service. ASDS was originally conceived to serve two purposes: to provide a useful Internet service in an area of expertise of the investigators (astronomical software), and as a research project to investigate various architectures for searching through a set of documents distributed across the Internet. Two of the co-investigators were then installing and maintaining astronomical software as their primary job responsibility. We felt that a service which incorporated our experience in this area would be more useful than a straightforward listing of software packages. The original concept was for a service based on the client/server model, which would function as a directory/referral service rather than as an archive. For performing the searches, we began our investigation with a decision to evaluate the Isite software from the Center for Networked Information Discovery and Retrieval (CNIDR). This software was intended as a replacement for Wide-Area Information Service (WAIS), a client/server technology for performing full-text searches through a set of documents. Isite had some additional features that we considered attractive, and we enjoyed the cooperation of the Isite developers, who were happy to have ASDS as a demonstration project. We ended up staying with the software throughout the project, making modifications to take advantage of new features as they came along, as well as influencing the software development. The Web interface to the search engine is provided by a gateway program written in C++ by a consultant to the project (A. Warnock).

Facial Attractiveness Assessment using Illustrated Questionnairers

PubMed Central

MESAROS, ANCA; CORNEA, DANIELA; CIOARA, LIVIU; DUDEA, DIANA; MESAROS, MICHAELA; BADEA, MINDRA

2015-01-01

Introduction. An attractive facial appearance is considered nowadays to be a decisive factor in establishing successful interactions between humans. In relation to this topic, scientific literature states that some of the facial features have more impact then others, and important authors revealed that certain proportions between different anthropometrical landmarks are mandatory for an attractive facial appearance. Aim. Our study aims to assess if certain facial features count differently in people’s opinion while assessing facial attractiveness in correlation with factors such as age, gender, specific training and culture. Material and methods. A 5-item multiple choice illustrated questionnaire was presented to 236 dental students. The Photoshop CS3 software was used in order to obtain the sets of images for the illustrated questions. The original image was handpicked from the internet by a panel of young dentists from a series of 15 pictures of people considered to have attractive faces. For each of the questions, the images presented were simulating deviations from the ideally symmetric and proportionate face. The sets of images consisted in multiple variations of deviations mixed with the original photo. Junior and sophomore year students from our dental medical school, having different nationalities were required to participate in our questionnaire. Simple descriptive statistics were used to interpret the data. Results. Assessing the results obtained from the questionnaire it was observed that a majority of students considered as unattractive the overdevelopment of the lower third, while the initial image with perfect symmetry and proportion was considered as the most attractive by only 38.9% of the subjects. Likewise, regarding the symmetry 36.86% considered unattractive the canting of the inter-commissural line. The interviewed subjects considered that for a face to be attractive it needs to have harmonious proportions between the different facial elements. Conclusions. Considering an evaluation of facial attractiveness it is important to keep in mind that such assessment is subjective and influenced by multiple factors, among which the most important are cultural background and specific training. PMID:26528052

Placebo can enhance creativity.

PubMed

Rozenkrantz, Liron; Mayo, Avraham E; Ilan, Tomer; Hart, Yuval; Noy, Lior; Alon, Uri

2017-01-01

The placebo effect is usually studied in clinical settings for decreasing negative symptoms such as pain, depression and anxiety. There is interest in exploring the placebo effect also outside the clinic, for enhancing positive aspects of performance or cognition. Several studies indicate that placebo can enhance cognitive abilities including memory, implicit learning and general knowledge. Here, we ask whether placebo can enhance creativity, an important aspect of human cognition. Subjects were randomly assigned to a control group who smelled and rated an odorant (n = 45), and a placebo group who were treated identically but were also told that the odorant increases creativity and reduces inhibitions (n = 45). Subjects completed a recently developed automated test for creativity, the creative foraging game (CFG), and a randomly chosen subset (n = 57) also completed two manual standardized creativity tests, the alternate uses test (AUT) and the Torrance test (TTCT). In all three tests, participants were asked to create as many original solutions and were scored for originality, flexibility and fluency. The placebo group showed higher originality than the control group both in the CFG (p<0.04, effect size = 0.5) and in the AUT (p<0.05, effect size = 0.4), but not in the Torrance test. The placebo group also found more shapes outside of the standard categories found by a set of 100 CFG players in a previous study, a feature termed out-of-the-boxness (p<0.01, effect size = 0.6). The findings indicate that placebo can enhance the originality aspect of creativity. This strengthens the view that placebo can be used not only to reduce negative clinical symptoms, but also to enhance positive aspects of cognition. Furthermore, we find that the impact of placebo on creativity can be tested by CFG, which can quantify multiple aspects of creative search without need for manual coding. This approach opens the way to explore the behavioral and neural mechanisms by which placebo might amplify creativity.

Placebo can enhance creativity

PubMed Central

Rozenkrantz, Liron; Mayo, Avraham E.; Ilan, Tomer; Hart, Yuval

2017-01-01

Background The placebo effect is usually studied in clinical settings for decreasing negative symptoms such as pain, depression and anxiety. There is interest in exploring the placebo effect also outside the clinic, for enhancing positive aspects of performance or cognition. Several studies indicate that placebo can enhance cognitive abilities including memory, implicit learning and general knowledge. Here, we ask whether placebo can enhance creativity, an important aspect of human cognition. Methods Subjects were randomly assigned to a control group who smelled and rated an odorant (n = 45), and a placebo group who were treated identically but were also told that the odorant increases creativity and reduces inhibitions (n = 45). Subjects completed a recently developed automated test for creativity, the creative foraging game (CFG), and a randomly chosen subset (n = 57) also completed two manual standardized creativity tests, the alternate uses test (AUT) and the Torrance test (TTCT). In all three tests, participants were asked to create as many original solutions and were scored for originality, flexibility and fluency. Results The placebo group showed higher originality than the control group both in the CFG (p<0.04, effect size = 0.5) and in the AUT (p<0.05, effect size = 0.4), but not in the Torrance test. The placebo group also found more shapes outside of the standard categories found by a set of 100 CFG players in a previous study, a feature termed out-of-the-boxness (p<0.01, effect size = 0.6). Conclusions The findings indicate that placebo can enhance the originality aspect of creativity. This strengthens the view that placebo can be used not only to reduce negative clinical symptoms, but also to enhance positive aspects of cognition. Furthermore, we find that the impact of placebo on creativity can be tested by CFG, which can quantify multiple aspects of creative search without need for manual coding. This approach opens the way to explore the behavioral and neural mechanisms by which placebo might amplify creativity. PMID:28892513

Feature selection gait-based gender classification under different circumstances

NASA Astrophysics Data System (ADS)

Sabir, Azhin; Al-Jawad, Naseer; Jassim, Sabah

2014-05-01

This paper proposes a gender classification based on human gait features and investigates the problem of two variations: clothing (wearing coats) and carrying bag condition as addition to the normal gait sequence. The feature vectors in the proposed system are constructed after applying wavelet transform. Three different sets of feature are proposed in this method. First, Spatio-temporal distance that is dealing with the distance of different parts of the human body (like feet, knees, hand, Human Height and shoulder) during one gait cycle. The second and third feature sets are constructed from approximation and non-approximation coefficient of human body respectively. To extract these two sets of feature we divided the human body into two parts, upper and lower body part, based on the golden ratio proportion. In this paper, we have adopted a statistical method for constructing the feature vector from the above sets. The dimension of the constructed feature vector is reduced based on the Fisher score as a feature selection method to optimize their discriminating significance. Finally k-Nearest Neighbor is applied as a classification method. Experimental results demonstrate that our approach is providing more realistic scenario and relatively better performance compared with the existing approaches.

Rough sets and Laplacian score based cost-sensitive feature selection

PubMed Central

Yu, Shenglong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of “good” features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms. PMID:29912884

Rough sets and Laplacian score based cost-sensitive feature selection.

PubMed

Yu, Shenglong; Zhao, Hong

2018-01-01

Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feature individually and select features one by one. Obviously, these algorithms do not consider the relationship among features. In this paper, we propose a new algorithm for minimal cost feature selection called the rough sets and Laplacian score based cost-sensitive feature selection. The importance of each feature is evaluated by both rough sets and Laplacian score. Compared with heuristic algorithms, the proposed algorithm takes into consideration the relationship among features with locality preservation of Laplacian score. We select a feature subset with maximal feature importance and minimal cost when cost is undertaken in parallel, where the cost is given by three different distributions to simulate different applications. Different from existing cost-sensitive feature selection algorithms, our algorithm simultaneously selects out a predetermined number of "good" features. Extensive experimental results show that the approach is efficient and able to effectively obtain the minimum cost subset. In addition, the results of our method are more promising than the results of other cost-sensitive feature selection algorithms.

Rome in its setting. Post-glacial aggradation history of the Tiber River alluvial deposits and tectonic origin of the Tiber Island

PubMed Central

Motta, Laura; Brock, Andrea L.; Macrì, Patrizia; Florindo, Fabio; Sadori, Laura; Terrenato, Nicola

2018-01-01

The Tiber valley is a prominent feature in the landscape of ancient Rome and an important element for understanding its urban development. However, little is known about the city’s original setting. Our research provides new data on the Holocene sedimentary history and human-environment interactions in the Forum Boarium, the location of the earliest harbor of the city. Since the Last Glacial Maximum, when the fluvial valley was incised to a depth of tens of meters below the present sea level, 14C and ceramic ages coupled with paleomagnetic analysis show the occurrence of three distinct aggradational phases until the establishment of a relatively stable alluvial plain at 6–8 m a.s.l. during the late 3rd century BCE. Moreover, we report evidence of a sudden and anomalous increase in sedimentation rate around 2600 yr BP, leading to the deposition of a 4-6m thick package of alluvial deposits in approximately one century. We discuss this datum in the light of possible tectonic activity along a morpho-structural lineament, revealed by the digital elevation model of this area, crossing the Forum Boarium and aligned with the Tiber Island. We formulate the hypothesis that fault displacement along this structural lineament may be responsible for the sudden collapse of the investigated area, which provided new space for the observed unusually large accumulation of sediments. We also posit that, as a consequence of the diversion of the Tiber course and the loss in capacity of transport by the river, this faulting activity triggered the origin of the Tiber Island. PMID:29590208

Rome in its setting. Post-glacial aggradation history of the Tiber River alluvial deposits and tectonic origin of the Tiber Island.

PubMed

Marra, Fabrizio; Motta, Laura; Brock, Andrea L; Macrì, Patrizia; Florindo, Fabio; Sadori, Laura; Terrenato, Nicola

2018-01-01

The Tiber valley is a prominent feature in the landscape of ancient Rome and an important element for understanding its urban development. However, little is known about the city's original setting. Our research provides new data on the Holocene sedimentary history and human-environment interactions in the Forum Boarium, the location of the earliest harbor of the city. Since the Last Glacial Maximum, when the fluvial valley was incised to a depth of tens of meters below the present sea level, 14C and ceramic ages coupled with paleomagnetic analysis show the occurrence of three distinct aggradational phases until the establishment of a relatively stable alluvial plain at 6-8 m a.s.l. during the late 3rd century BCE. Moreover, we report evidence of a sudden and anomalous increase in sedimentation rate around 2600 yr BP, leading to the deposition of a 4-6m thick package of alluvial deposits in approximately one century. We discuss this datum in the light of possible tectonic activity along a morpho-structural lineament, revealed by the digital elevation model of this area, crossing the Forum Boarium and aligned with the Tiber Island. We formulate the hypothesis that fault displacement along this structural lineament may be responsible for the sudden collapse of the investigated area, which provided new space for the observed unusually large accumulation of sediments. We also posit that, as a consequence of the diversion of the Tiber course and the loss in capacity of transport by the river, this faulting activity triggered the origin of the Tiber Island.

The protein folds as platonic forms: new support for the pre-Darwinian conception of evolution by natural law.

PubMed

Denton, Michael J; Marshall, Craig J; Legge, Michael

2002-12-07

Before the Darwinian revolution many biologists considered organic forms to be determined by natural law like atoms or crystals and therefore necessary, intrinsic and immutable features of the world order, which will occur throughout the cosmos wherever there is life. The search for the natural determinants of organic form-the celebrated "Laws of Form"-was seen as one of the major tasks of biology. After Darwin, this Platonic conception of form was abandoned and natural selection, not natural law, was increasingly seen to be the main, if not the exclusive, determinant of organic form. However, in the case of one class of very important organic forms-the basic protein folds-advances in protein chemistry since the early 1970s have revealed that they represent a finite set of natural forms, determined by a number of generative constructional rules, like those which govern the formation of atoms or crystals, in which functional adaptations are clearly secondary modifications of primary "givens of physics." The folds are evidently determined by natural law, not natural selection, and are "lawful forms" in the Platonic and pre-Darwinian sense of the word, which are bound to occur everywhere in the universe where the same 20 amino acids are used for their construction. We argue that this is a major discovery which has many important implications regarding the origin of proteins, the origin of life and the fundamental nature of organic form. We speculate that it is unlikely that the folds will prove to be the only case in nature where a set of complex organic forms is determined by natural law, and suggest that natural law may have played a far greater role in the origin and evolution of life than is currently assumed.

The Megamaser Cosmology Project. X. High-resolution Maps and Mass Constraints for SMBHs

NASA Astrophysics Data System (ADS)

Zhao, W.; Braatz, J. A.; Condon, J. J.; Lo, K. Y.; Reid, M. J.; Henkel, C.; Pesce, D. W.; Greene, J. E.; Gao, F.; Kuo, C. Y.; Impellizzeri, C. M. V.

2018-02-01

We present high-resolution (submas) Very Long Baseline Interferometry maps of nuclear H2O megamasers for seven galaxies. In UGC 6093, the well-aligned systemic masers and high-velocity masers originate in an edge-on, flat disk and we determine the mass of the central supermassive black holes (SMBH) to be M SMBH = 2.58 × 107 M ⊙ (±7%). For J1346+5228, the distribution of masers is consistent with a disk, but the faint high-velocity masers are only marginally detected, and we constrain the mass of the SMBH to be in the range (1.5–2.0) × 107 M ⊙. The origin of the masers in Mrk 1210 is less clear, as the systemic and high-velocity masers are misaligned and show a disorganized velocity structure. We present one possible model in which the masers originate in a tilted, warped disk, but we do not rule out the possibility of other explanations including outflow masers. In NGC 6926, we detect a set of redshifted masers, clustered within a parsec of each other, and a single blueshifted maser about 4.4 pc away, an offset that would be unusually large for a maser disk system. Nevertheless, if it is a disk system, we estimate the enclosed mass to be M SMBH < 4.8 × 107 M ⊙. For NGC 5793, we detect redshifted masers spaced about 1.4 pc from a clustered set of blueshifted features. The orientation of the structure supports a disk scenario as suggested by Hagiwara et al. We estimate the enclosed mass to be M SMBH < 1.3 × 107 M ⊙. For NGC 2824 and J0350‑0127, the masers may be associated with parsec- or subparsec-scale jets or outflows.

Clinical and Demographic Features of Vertigo: Findings from the REVERT Registry

PubMed Central

Agus, Sam; Benecke, Heike; Thum, Cornelia; Strupp, Michael

2013-01-01

Introduction: Despite being a common disease, data on vertigo management in a real-world setting are scarce. Aims: To provide information on the vertigo and its management in a real-world setting. Methods: Data were collected from 4,294 patients with vertigo in 13 countries over 28 months via a multi-national, non-interventional observational study (the so-called REVERT registry). Data included medical history and details of anti-vertigo therapy. “Clinical global impression” (CGI) of severity (CGI-S) was assessed at baseline (V1) and then at 6 months follow-up (V2) along with CGI change (CGI-C). All variables were analyzed descriptively. Results: The majority of patients were female, >40 years of age, and almost half had co-morbid cardio-vascular disease. Diagnoses were split into four categories: 37.2% “other vertigo of peripheral vestibular origin,” 26.9% benign paroxysmal positional vertigo (BPPV), 20.5% “peripheral vestibular vertigo of unknown origin,” and 15.4% Ménière’s disease (MD). Betahistine was the most commonly prescribed therapy prior to and after enrollment, and was followed by piracetam, ginkgo biloba, and diuretics. MD had the highest proportion of betahistine treated patients. Almost half of patients were “moderately ill” at V1 based on CGI-S. At V2, patient distribution moved toward “less severe illness” (91.0% improved). The greatest improvements were in the more severely ill, and those with BPPV or “other vertigo of peripheral origin.” Conclusion: There was a reduction in illness severity over the course of the study, some of which is likely to be due to pharmacological intervention. Further studies are needed to confirm these results. PMID:23675366

Nematode.net update 2011: addition of data sets and tools featuring next-generation sequencing data

PubMed Central

Martin, John; Abubucker, Sahar; Heizer, Esley; Taylor, Christina M.; Mitreva, Makedonka

2012-01-01

Nematode.net (http://nematode.net) has been a publicly available resource for studying nematodes for over a decade. In the past 3 years, we reorganized Nematode.net to provide more user-friendly navigation through the site, a necessity due to the explosion of data from next-generation sequencing platforms. Organism-centric portals containing dynamically generated data are available for over 56 different nematode species. Next-generation data has been added to the various data-mining portals hosted, including NemaBLAST and NemaBrowse. The NemaPath metabolic pathway viewer builds associations using KOs, rather than ECs to provide more accurate and fine-grained descriptions of proteins. Two new features for data analysis and comparative genomics have been added to the site. NemaSNP enables the user to perform population genetics studies in various nematode populations using next-generation sequencing data. HelmCoP (Helminth Control and Prevention) as an independent component of Nematode.net provides an integrated resource for storage, annotation and comparative genomics of helminth genomes to aid in learning more about nematode genomes, as well as drug, pesticide, vaccine and drug target discovery. With this update, Nematode.net will continue to realize its original goal to disseminate diverse bioinformatic data sets and provide analysis tools to the broad scientific community in a useful and user-friendly manner. PMID:22139919

Using argumentation to retrieve articles with similar citations: an inquiry into improving related articles search in the MEDLINE digital library.

PubMed

Tbahriti, Imad; Chichester, Christine; Lisacek, Frédérique; Ruch, Patrick

2006-06-01

The aim of this study is to investigate the relationships between citations and the scientific argumentation found abstracts. We design a related article search task and observe how the argumentation can affect the search results. We extracted citation lists from a set of 3200 full-text papers originating from a narrow domain. In parallel, we recovered the corresponding MEDLINE records for analysis of the argumentative moves. Our argumentative model is founded on four classes: PURPOSE, METHODS, RESULTS and CONCLUSION. A Bayesian classifier trained on explicitly structured MEDLINE abstracts generates these argumentative categories. The categories are used to generate four different argumentative indexes. A fifth index contains the complete abstract, together with the title and the list of Medical Subject Headings (MeSH) terms. To appraise the relationship of the moves to the citations, the citation lists were used as the criteria for determining relatedness of articles, establishing a benchmark; it means that two articles are considered as "related" if they share a significant set of co-citations. Our results show that the average precision of queries with the PURPOSE and CONCLUSION features is the highest, while the precision of the RESULTS and METHODS features was relatively low. A linear weighting combination of the moves is proposed, which significantly improves retrieval of related articles.

Detecting spam comments on Indonesia’s Instagram posts

NASA Astrophysics Data System (ADS)

Septiandri, Ali Akbar; Wibisono, Okiriza

2017-01-01

In this paper we experimented with several feature sets for detecting spam comments in social media contents authored by Indonesian public figures. We define spam comments as comments which have promotional purposes (e.g. referring other users to products and services) and thus not related to the content to which the comments are posted. Three sets of features are evaluated for detecting spams: (1) hand-engineered features such as comment length, number of capital letters, and number of emojis, (2) keyword features such as whether the comment contains advertising words or product-related words, and (3) text features, namely, bag-of-words, TF-IDF, and fastText embeddings, each combined with latent semantic analysis. With 24,000 manually-annotated comments scraped from Instagram posts authored by more than 100 Indonesian public figures, we compared the performance of these feature sets and their combinations using 3 popular classification algorithms: Na¨ıve Bayes, SVM, and XGBoost. We find that using all three feature sets (with fastText embedding for the text features) gave the best F 1-score of 0.9601 on a holdout dataset. More interestingly, fastText embedding combined with hand-engineered features (i.e. without keyword features) yield similar F 1-score of 0.9523, and McNemar’s test failed to reject the hypothesis that the two results are not significantly different. This result is important as keyword features are largely dependent on the dataset and may not be as generalisable as the other feature sets when applied to new data. For future work, we hope to collect bigger and more diverse dataset of Indonesian spam comments, improve our model’s performance and generalisability, and publish a programming package for others to reliably detect spam comments.

Contingent attentional capture across multiple feature dimensions in a temporal search task.

PubMed

Ito, Motohiro; Kawahara, Jun I

2016-01-01

The present study examined whether attention can be flexibly controlled to monitor two different feature dimensions (shape and color) in a temporal search task. Specifically, we investigated the occurrence of contingent attentional capture (i.e., interference from task-relevant distractors) and resulting set reconfiguration (i.e., enhancement of single task-relevant set). If observers can restrict searches to a specific value for each relevant feature dimension independently, the capture and reconfiguration effect should only occur when the single relevant distractor in each dimension appears. Participants identified a target letter surrounded by a non-green square or a non-square green frame. The results revealed contingent attentional capture, as target identification accuracy was lower when the distractor contained a target-defining feature than when it contained a nontarget feature. Resulting set reconfiguration was also obtained in that accuracy was superior when the current target's feature (e.g., shape) corresponded to the defining feature of the present distractor (shape) than when the current target's feature did not match the distractor's feature (color). This enhancement was not due to perceptual priming. The present study demonstrated that the principles of contingent attentional capture and resulting set reconfiguration held even when multiple target feature dimensions were monitored. Copyright © 2015 Elsevier B.V. All rights reserved.

New Features for Neuron Classification.

PubMed

Hernández-Pérez, Leonardo A; Delgado-Castillo, Duniel; Martín-Pérez, Rainer; Orozco-Morales, Rubén; Lorenzo-Ginori, Juan V

2018-04-28

This paper addresses the problem of obtaining new neuron features capable of improving results of neuron classification. Most studies on neuron classification using morphological features have been based on Euclidean geometry. Here three one-dimensional (1D) time series are derived from the three-dimensional (3D) structure of neuron instead, and afterwards a spatial time series is finally constructed from which the features are calculated. Digitally reconstructed neurons were separated into control and pathological sets, which are related to three categories of alterations caused by epilepsy, Alzheimer's disease (long and local projections), and ischemia. These neuron sets were then subjected to supervised classification and the results were compared considering three sets of features: morphological, features obtained from the time series and a combination of both. The best results were obtained using features from the time series, which outperformed the classification using only morphological features, showing higher correct classification rates with differences of 5.15, 3.75, 5.33% for epilepsy and Alzheimer's disease (long and local projections) respectively. The morphological features were better for the ischemia set with a difference of 3.05%. Features like variance, Spearman auto-correlation, partial auto-correlation, mutual information, local minima and maxima, all related to the time series, exhibited the best performance. Also we compared different evaluators, among which ReliefF was the best ranked.

Open quantum dots—probing the quantum to classical transition

NASA Astrophysics Data System (ADS)

Ferry, D. K.; Burke, A. M.; Akis, R.; Brunner, R.; Day, T. E.; Meisels, R.; Kuchar, F.; Bird, J. P.; Bennett, B. R.

2011-04-01

Quantum dots provide a natural system in which to study both quantum and classical features of transport. As a closed testbed, they provide a natural system with a very rich set of eigenstates. When coupled to the environment through a pair of quantum point contacts, each of which passes several modes, the original quantum environment evolves into a set of decoherent and coherent states, which classically would compose a mixed phase space. The manner of this breakup is governed strongly by Zurek's decoherence theory, and the remaining coherent states possess all the properties of his pointer states. These states are naturally studied via traditional magnetotransport at low temperatures. More recently, we have used scanning gate (conductance) microscopy to probe the nature of the coherent states, and have shown that families of states exist through the spectrum in a manner consistent with quantum Darwinism. In this review, we discuss the nature of the various states, how they are formed, and the signatures that appear in magnetotransport and general conductance studies.

Classification of illicit heroin by UPLC-Q-TOF analysis of acidic and neutral manufacturing impurities.

PubMed

Liu, Cuimei; Hua, Zhendong; Bai, Yanping

2015-12-01

The illicit manufacture of heroin results in the formation of trace levels of acidic and neutral manufacturing impurities that provide valuable information about the manufacturing process used. In this work, a new ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF) method; that features high resolution, mass accuracy and sensitivity for profiling neutral and acidic heroin manufacturing impurities was developed. After the UPLC-Q-TOF analysis, the retention times and m/z data pairs of acidic and neutral manufacturing impurities were detected, and 19 peaks were found to be evidently different between heroin samples from "Golden Triangle" and "Golden Crescent". Based on the data set of these 19 impurities in 150 authentic heroin samples, classification of heroin geographic origins was successfully achieved utilizing partial least squares discriminant analysis (PLS-DA). By analyzing another data set of 267 authentic heroin samples, the developed discrimiant model was validated and proved to be accurate and reliable. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Preface

NASA Astrophysics Data System (ADS)

Taran, Yuri; Tassi, Franco; Varekamp, Johan; Inguaggiato, Salvatore; Kalacheva, Elena

2017-10-01

Many volcanoes at any tectonic settings host hydrothermal systems. Volcano-hydrothermal systems (VHS) are result of interaction of the upper part of plumbing systems of active volcanoes with crust, hydrosphere and atmosphere. They are heated by magma, fed by magmatic fluids and meteoric (sea) water, transport and re-distribute magmatic and crustal material. VHS are sensitive to the activity of a host volcano. VHS may have specific features depending on the regional and local tectonic, geologic and geographic settings. The studies reported in this volume help to illustrate the diversity of the approaches and investigations that are being conducting at different volcano-hydrothermal systems over the world and the results of which will be of important value in furthering our understanding of the complex array of the processes accompanying hydrothermal activity of volcanoes. About 60 papers were submitted to a special session of "Volcano-Hydrothermal Systems" at the 2015 fall meeting of the American Geophysical Union. The papers in this special issue of the Journal of Volcanology and Geothermal Research were originally presented at that session.

Two-stage opening of the Dover Strait and the origin of island Britain

PubMed Central

Gupta, Sanjeev; Collier, Jenny S.; Garcia-Moreno, David; Oggioni, Francesca; Trentesaux, Alain; Vanneste, Kris; De Batist, Marc; Camelbeeck, Thierry; Potter, Graeme; Van Vliet-Lanoë, Brigitte; Arthur, John C. R.

2017-01-01

Late Quaternary separation of Britain from mainland Europe is considered to be a consequence of spillover of a large proglacial lake in the Southern North Sea basin. Lake spillover is inferred to have caused breaching of a rock ridge at the Dover Strait, although this hypothesis remains untested. Here we show that opening of the Strait involved at least two major episodes of erosion. Sub-bottom records reveal a remarkable set of sediment-infilled depressions that are deeply incised into bedrock that we interpret as giant plunge pools. These support a model of initial erosion of the Dover Strait by lake overspill, plunge pool erosion by waterfalls and subsequent dam breaching. Cross-cutting of these landforms by a prominent bedrock-eroded valley that is characterized by features associated with catastrophic flooding indicates final breaching of the Strait by high-magnitude flows. These events set-up conditions for island Britain during sea-level highstands and caused large-scale re-routing of NW European drainage. PMID:28375202

Two-stage opening of the Dover Strait and the origin of island Britain

NASA Astrophysics Data System (ADS)

Gupta, Sanjeev; Collier, Jenny S.; Garcia-Moreno, David; Oggioni, Francesca; Trentesaux, Alain; Vanneste, Kris; de Batist, Marc; Camelbeeck, Thierry; Potter, Graeme; van Vliet-Lanoë, Brigitte; Arthur, John C. R.

2017-04-01

Late Quaternary separation of Britain from mainland Europe is considered to be a consequence of spillover of a large proglacial lake in the Southern North Sea basin. Lake spillover is inferred to have caused breaching of a rock ridge at the Dover Strait, although this hypothesis remains untested. Here we show that opening of the Strait involved at least two major episodes of erosion. Sub-bottom records reveal a remarkable set of sediment-infilled depressions that are deeply incised into bedrock that we interpret as giant plunge pools. These support a model of initial erosion of the Dover Strait by lake overspill, plunge pool erosion by waterfalls and subsequent dam breaching. Cross-cutting of these landforms by a prominent bedrock-eroded valley that is characterized by features associated with catastrophic flooding indicates final breaching of the Strait by high-magnitude flows. These events set-up conditions for island Britain during sea-level highstands and caused large-scale re-routing of NW European drainage.

Signature of nonadiabatic coupling in excited-state vibrational modes.

PubMed

Soler, Miguel A; Nelson, Tammie; Roitberg, Adrian E; Tretiak, Sergei; Fernandez-Alberti, Sebastian

2014-11-13

Using analytical excited-state gradients, vibrational normal modes have been calculated at the minimum of the electronic excited-state potential energy surfaces for a set of extended conjugated molecules with different coupling between them. Molecular model systems composed of units of polyphenylene ethynylene (PPE), polyphenylenevinylene (PPV), and naphthacene/pentacene (NP) have been considered. In all cases except the NP model, the influence of the nonadiabatic coupling on the excited-state equilibrium normal modes is revealed as a unique highest frequency adiabatic vibrational mode that overlaps with the coupling vector. This feature is removed by using a locally diabatic representation in which the effect of NA interaction is removed. Comparison of the original adiabatic modes with a set of vibrational modes computed in the locally diabatic representation demonstrates that the effect of nonadiabaticity is confined to only a few modes. This suggests that the nonadiabatic character of a molecular system may be detected spectroscopically by identifying these unique state-specific high frequency vibrational modes.

Metabolomics for organic food authentication: Results from a long-term field study in carrots.

PubMed

Cubero-Leon, Elena; De Rudder, Olivier; Maquet, Alain

2018-01-15

Increasing demand for organic products and their premium prices make them an attractive target for fraudulent malpractices. In this study, a large-scale comparative metabolomics approach was applied to investigate the effect of the agronomic production system on the metabolite composition of carrots and to build statistical models for prediction purposes. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) was applied successfully to predict the origin of the agricultural system of the harvested carrots on the basis of features determined by liquid chromatography-mass spectrometry. When the training set used to build the OPLS-DA models contained samples representative of each harvest year, the models were able to classify unknown samples correctly (100% correct classification). If a harvest year was left out of the training sets and used for predictions, the correct classification rates achieved ranged from 76% to 100%. The results therefore highlight the potential of metabolomic fingerprinting for organic food authentication purposes. Copyright © 2017 The Author(s). Published by Elsevier Ltd.. All rights reserved.

[Origin and morphological features of small supernumerary marker chromosomes in Turner syndrome].

PubMed

Liu, Nan; Tong, Tong; Chen, Yue; Chen, Yanling; Cai, Chunquan

2018-02-10

OBJECTIVE To explore the origin and morphological features of small supernumerary marker chromosomes (sSMCs) in Turner syndrome. METHODS For 5 cases of Turner syndrome with a sSMC identified by conventional G-banding, dual-color fluorescence in situ hybridization (FISH) was applied to explore their origin and morphological features. RESULTS Among the 5 cases, 3 have derived from the X chromosome, which included 2 ring chromosomes and 1 centric minute. For the 2 sSMCs derived from the Y chromosome, 1 was ring or isodicentric chromosome, while the other was an isodicentric chromosome. CONCLUSION The sSMCs found in Turner syndrome have almost all derived from sex chromosomes. The majority of sSMCs derived from the X chromosome will form ring chromosomes, while a minority will form centric minute. While most sSMC derived from Y chromosome may exist as isodicentric chromosomes, and a small number may exist as rings. For Turner syndrome patients with sSMCs, dual-color FISH may be used to delineate their origins to facilitate genetic counseling and selection of clinical regime.

Pre-coding method and apparatus for multiple source or time-shifted single source data and corresponding inverse post-decoding method and apparatus

NASA Technical Reports Server (NTRS)

Yeh, Pen-Shu (Inventor)

1997-01-01

A pre-coding method and device for improving data compression performance by removing correlation between a first original data set and a second original data set, each having M members, respectively. The pre-coding method produces a compression-efficiency-enhancing double-difference data set. The method and device produce a double-difference data set, i.e., an adjacent-delta calculation performed on a cross-delta data set or a cross-delta calculation performed on two adjacent-delta data sets, from either one of (1) two adjacent spectral bands coming from two discrete sources, respectively, or (2) two time-shifted data sets coming from a single source. The resulting double-difference data set is then coded using either a distortionless data encoding scheme (entropy encoding) or a lossy data compression scheme. Also, a post-decoding method and device for recovering a second original data set having been represented by such a double-difference data set.

Pre-coding method and apparatus for multiple source or time-shifted single source data and corresponding inverse post-decoding method and apparatus

NASA Technical Reports Server (NTRS)

Yeh, Pen-Shu (Inventor)

1998-01-01

A pre-coding method and device for improving data compression performance by removing correlation between a first original data set and a second original data set, each having M members, respectively. The pre-coding method produces a compression-efficiency-enhancing double-difference data set. The method and device produce a double-difference data set, i.e., an adjacent-delta calculation performed on a cross-delta data set or a cross-delta calculation performed on two adjacent-delta data sets, from either one of (1) two adjacent spectral bands coming from two discrete sources, respectively, or (2) two time-shifted data sets coming from a single source. The resulting double-difference data set is then coded using either a distortionless data encoding scheme (entropy encoding) or a lossy data compression scheme. Also, a post-decoding method and device for recovering a second original data set having been represented by such a double-difference data set.

CARSVM: a class association rule-based classification framework and its application to gene expression data.

PubMed

Kianmehr, Keivan; Alhajj, Reda

2008-09-01

In this study, we aim at building a classification framework, namely the CARSVM model, which integrates association rule mining and support vector machine (SVM). The goal is to benefit from advantages of both, the discriminative knowledge represented by class association rules and the classification power of the SVM algorithm, to construct an efficient and accurate classifier model that improves the interpretability problem of SVM as a traditional machine learning technique and overcomes the efficiency issues of associative classification algorithms. In our proposed framework: instead of using the original training set, a set of rule-based feature vectors, which are generated based on the discriminative ability of class association rules over the training samples, are presented to the learning component of the SVM algorithm. We show that rule-based feature vectors present a high-qualified source of discrimination knowledge that can impact substantially the prediction power of SVM and associative classification techniques. They provide users with more conveniences in terms of understandability and interpretability as well. We have used four datasets from UCI ML repository to evaluate the performance of the developed system in comparison with five well-known existing classification methods. Because of the importance and popularity of gene expression analysis as real world application of the classification model, we present an extension of CARSVM combined with feature selection to be applied to gene expression data. Then, we describe how this combination will provide biologists with an efficient and understandable classifier model. The reported test results and their biological interpretation demonstrate the applicability, efficiency and effectiveness of the proposed model. From the results, it can be concluded that a considerable increase in classification accuracy can be obtained when the rule-based feature vectors are integrated in the learning process of the SVM algorithm. In the context of applicability, according to the results obtained from gene expression analysis, we can conclude that the CARSVM system can be utilized in a variety of real world applications with some adjustments.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine.

PubMed

Monakhova, Yulia B; Godelmann, Rolf; Kuballa, Thomas; Mushtakova, Svetlana P; Rutledge, Douglas N

2015-08-15

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements. Copyright © 2015 Elsevier B.V. All rights reserved.

Persistence of urban organic aerosols composition: Decoding their structural complexity and seasonal variability.

PubMed

Matos, João T V; Duarte, Regina M B O; Lopes, Sónia P; Silva, Artur M S; Duarte, Armando C

2017-12-01

Organic Aerosols (OAs) are typically defined as highly complex matrices whose composition changes in time and space. Focusing on time vector, this work uses two-dimensional nuclear magnetic resonance (2D NMR) techniques to examine the structural features of water-soluble (WSOM) and alkaline-soluble organic matter (ASOM) sequentially extracted from fine atmospheric aerosols collected in an urban setting during cold and warm seasons. This study reveals molecular signatures not previously decoded in NMR-related studies of OAs as meaningful source markers. Although the ASOM is less hydrophilic and structurally diverse than its WSOM counterpart, both fractions feature a core with heteroatom-rich branched aliphatics from both primary (natural and anthropogenic) and secondary origin, aromatic secondary organics originated from anthropogenic aromatic precursors, as well as primary saccharides and amino sugar derivatives from biogenic emissions. These common structures represent those 2D NMR spectral signatures that are present in both seasons and can thus be seen as an "annual background" profile of the structural composition of OAs at the urban location. Lignin-derived structures, nitroaromatics, disaccharides, and anhydrosaccharides signatures were also identified in the WSOM samples only from periods identified as smoke impacted, which reflects the influence of biomass-burning sources. The NMR dataset on the H-C molecules backbone was also used to propose a semi-quantitative structural model of urban WSOM, which will aid efforts for more realistic studies relating the chemical properties of OAs with their atmospheric behavior. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stratification of co-evolving genomic groups using ranked phylogenetic profiles

PubMed Central

Freilich, Shiri; Goldovsky, Leon; Gottlieb, Assaf; Blanc, Eric; Tsoka, Sophia; Ouzounis, Christos A

2009-01-01

Background Previous methods of detecting the taxonomic origins of arbitrary sequence collections, with a significant impact to genome analysis and in particular metagenomics, have primarily focused on compositional features of genomes. The evolutionary patterns of phylogenetic distribution of genes or proteins, represented by phylogenetic profiles, provide an alternative approach for the detection of taxonomic origins, but typically suffer from low accuracy. Herein, we present rank-BLAST, a novel approach for the assignment of protein sequences into genomic groups of the same taxonomic origin, based on the ranking order of phylogenetic profiles of target genes or proteins across the reference database. Results The rank-BLAST approach is validated by computing the phylogenetic profiles of all sequences for five distinct microbial species of varying degrees of phylogenetic proximity, against a reference database of 243 fully sequenced genomes. The approach - a combination of sequence searches, statistical estimation and clustering - analyses the degree of sequence divergence between sets of protein sequences and allows the classification of protein sequences according to the species of origin with high accuracy, allowing taxonomic classification of 64% of the proteins studied. In most cases, a main cluster is detected, representing the corresponding species. Secondary, functionally distinct and species-specific clusters exhibit different patterns of phylogenetic distribution, thus flagging gene groups of interest. Detailed analyses of such cases are provided as examples. Conclusion Our results indicate that the rank-BLAST approach can capture the taxonomic origins of sequence collections in an accurate and efficient manner. The approach can be useful both for the analysis of genome evolution and the detection of species groups in metagenomics samples. PMID:19860884

Distinction between epigenic and hypogenic maze caves

NASA Astrophysics Data System (ADS)

Palmer, Arthur N.

2011-11-01

Certain caves formed by dissolution of bedrock have maze patterns composed of closed loops in which many intersecting fractures or pores have enlarged simultaneously. Their origin can be epigenic (by shallow circulation of meteoric groundwater) or hypogenic (by rising groundwater or production of deep-seated solutional aggressiveness). Epigenic mazes form by diffuse infiltration through a permeable insoluble caprock or by floodwater supplied by sinking streams. Most hypogenic caves involve deep sources of aggressiveness. Transverse hypogenic cave origin is a recently proposed concept in which groundwater of mainly meteoric origin rises across strata in the distal portions of large flow systems, to form mazes in soluble rock sandwiched between permeable but insoluble strata. The distinction between maze types is debated and is usually based on examination of diagnostic cave features and relation of caves to their regional setting. In this paper, the principles of mass transfer are applied to clarify the limits of each model, to show how cave origin is related to groundwater discharge, dissolution rate, and time. The results show that diffuse infiltration and floodwater can each form maze caves at geologically feasible rates (typically within 500 ka). Transverse hypogenic mazes in limestone, to enlarge significantly within 1 Ma, require an unusually high permeability of the non-carbonate beds (generally ≥ 10-4 cm/s), large discharge, and calcite saturation no greater than 90%, which is rare in deep diffuse flow in sedimentary rocks. Deep sources of aggressiveness are usually required. The origin of caves by transverse hypogenic flow is much more favorable in evaporite rocks than in carbonate rocks.

Anteriorly located zonular fibres as a tool for fine regulation in accommodation

PubMed Central

Flügel-Koch, Cassandra; Croft, Mary Ann; Kaufman, Paul L.; Lütjen-Drecoll, Elke

2015-01-01

Purpose To describe an anteriorly located system of zonular fibres that could be involved in fine-tuning of accommodation Methods Forty six human and 28 rhesus monkey eyes were dissected and special preparations were processed for scanning electron microscopy and reflected-light microscopy. Additional series of frontal and sagittal histological and ultrathin sections were analysed in respect to the origin and insertion of anteriorly located zonules. The presence of sensory terminals at the site of the originating zonules within the connective tissue of the ciliary body was studied by immunohistochemistry. For in-vivo visualization ultrasound biomicroscopy (UBM) was performed on 12 human subjects. Results Fine zonular fibres originated from the valleys and lateral walls of the most anterior pars plicata that covers the anterior and inner circular ciliary muscle portion. These most anterior zonules (MAZ) showed attachments either to the anterior or posterior tines or they inserted directly onto the surface of the lens. At the site of origin, the course of the MAZ merged into the connective tissue fibres connecting the adjacent pigmented epithelium to the ciliary muscle. Numerous afferent terminals directly at the site of this MAZ-origin were connected to the intrinsic nervous network of the ciliary muscle. Conclusions A newly described set of zonular fibres features the capabilities to register the tensions of the zonular fork and lens capsule. The close location and neural connection towards the circular ciliary muscle portion could provide the basis for stabilization and readjustment of focusing that serves fast and fine-tuned accommodation and disaccommodation. PMID:26490669

A general prediction model for the detection of ADHD and Autism using structural and functional MRI.

PubMed

Sen, Bhaskar; Borle, Neil C; Greiner, Russell; Brown, Matthew R G

2018-01-01

This work presents a novel method for learning a model that can diagnose Attention Deficit Hyperactivity Disorder (ADHD), as well as Autism, using structural texture and functional connectivity features obtained from 3-dimensional structural magnetic resonance imaging (MRI) and 4-dimensional resting-state functional magnetic resonance imaging (fMRI) scans of subjects. We explore a series of three learners: (1) The LeFMS learner first extracts features from the structural MRI images using the texture-based filters produced by a sparse autoencoder. These filters are then convolved with the original MRI image using an unsupervised convolutional network. The resulting features are used as input to a linear support vector machine (SVM) classifier. (2) The LeFMF learner produces a diagnostic model by first computing spatial non-stationary independent components of the fMRI scans, which it uses to decompose each subject's fMRI scan into the time courses of these common spatial components. These features can then be used with a learner by themselves or in combination with other features to produce the model. Regardless of which approach is used, the final set of features are input to a linear support vector machine (SVM) classifier. (3) Finally, the overall LeFMSF learner uses the combined features obtained from the two feature extraction processes in (1) and (2) above as input to an SVM classifier, achieving an accuracy of 0.673 on the ADHD-200 holdout data and 0.643 on the ABIDE holdout data. Both of these results, obtained with the same LeFMSF framework, are the best known, over all hold-out accuracies on these datasets when only using imaging data-exceeding previously-published results by 0.012 for ADHD and 0.042 for Autism. Our results show that combining multi-modal features can yield good classification accuracy for diagnosis of ADHD and Autism, which is an important step towards computer-aided diagnosis of these psychiatric diseases and perhaps others as well.

Feature-Based Statistical Analysis of Combustion Simulation Data

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

Bennett, J; Krishnamoorthy, V; Liu, S

2011-11-18

We present a new framework for feature-based statistical analysis of large-scale scientific data and demonstrate its effectiveness by analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion. Turbulent flows are ubiquitous and account for transport and mixing processes in combustion, astrophysics, fusion, and climate modeling among other disciplines. They are also characterized by coherent structure or organized motion, i.e. nonlocal entities whose geometrical features can directly impact molecular mixing and reactive processes. While traditional multi-point statistics provide correlative information, they lack nonlocal structural information, and hence, fail to provide mechanistic causality information between organized fluid motion and mixing andmore » reactive processes. Hence, it is of great interest to capture and track flow features and their statistics together with their correlation with relevant scalar quantities, e.g. temperature or species concentrations. In our approach we encode the set of all possible flow features by pre-computing merge trees augmented with attributes, such as statistical moments of various scalar fields, e.g. temperature, as well as length-scales computed via spectral analysis. The computation is performed in an efficient streaming manner in a pre-processing step and results in a collection of meta-data that is orders of magnitude smaller than the original simulation data. This meta-data is sufficient to support a fully flexible and interactive analysis of the features, allowing for arbitrary thresholds, providing per-feature statistics, and creating various global diagnostics such as Cumulative Density Functions (CDFs), histograms, or time-series. We combine the analysis with a rendering of the features in a linked-view browser that enables scientists to interactively explore, visualize, and analyze the equivalent of one terabyte of simulation data. We highlight the utility of this new framework for combustion science; however, it is applicable to many other science domains.« less

Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators

PubMed Central

Bai, Xiangzhi

2015-01-01

The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion. PMID:26184229

Infrared and Visual Image Fusion through Fuzzy Measure and Alternating Operators.

PubMed

Bai, Xiangzhi

2015-07-15

The crucial problem of infrared and visual image fusion is how to effectively extract the image features, including the image regions and details and combine these features into the final fusion result to produce a clear fused image. To obtain an effective fusion result with clear image details, an algorithm for infrared and visual image fusion through the fuzzy measure and alternating operators is proposed in this paper. Firstly, the alternating operators constructed using the opening and closing based toggle operator are analyzed. Secondly, two types of the constructed alternating operators are used to extract the multi-scale features of the original infrared and visual images for fusion. Thirdly, the extracted multi-scale features are combined through the fuzzy measure-based weight strategy to form the final fusion features. Finally, the final fusion features are incorporated with the original infrared and visual images using the contrast enlargement strategy. All the experimental results indicate that the proposed algorithm is effective for infrared and visual image fusion.

Patterns of public participation.

PubMed

Slutsky, Jean; Tumilty, Emma; Max, Catherine; Lu, Lanting; Tantivess, Sripen; Hauegen, Renata Curi; Whitty, Jennifer A; Weale, Albert; Pearson, Steven D; Tugendhaft, Aviva; Wang, Hufeng; Staniszewska, Sophie; Weerasuriya, Krisantha; Ahn, Jeonghoon; Cubillos, Leonardo

2016-08-15

Purpose - The paper summarizes data from 12 countries, chosen to exhibit wide variation, on the role and place of public participation in the setting of priorities. The purpose of this paper is to exhibit cross-national patterns in respect of public participation, linking those differences to institutional features of the countries concerned. Design/methodology/approach - The approach is an example of case-orientated qualitative assessment of participation practices. It derives its data from the presentation of country case studies by experts on each system. The country cases are located within the historical development of democracy in each country. Findings - Patterns of participation are widely variable. Participation that is effective through routinized institutional processes appears to be inversely related to contestatory participation that uses political mobilization to challenge the legitimacy of the priority setting process. No system has resolved the conceptual ambiguities that are implicit in the idea of public participation. Originality/value - The paper draws on a unique collection of country case studies in participatory practice in prioritization, supplementing existing published sources. In showing that contestatory participation plays an important role in a sub-set of these countries it makes an important contribution to the field because it broadens the debate about public participation in priority setting beyond the use of minipublics and the observation of public representatives on decision-making bodies.

Learning better deep features for the prediction of occult invasive disease in ductal carcinoma in situ through transfer learning

NASA Astrophysics Data System (ADS)

Shi, Bibo; Hou, Rui; Mazurowski, Maciej A.; Grimm, Lars J.; Ren, Yinhao; Marks, Jeffrey R.; King, Lorraine M.; Maley, Carlo C.; Hwang, E. Shelley; Lo, Joseph Y.

2018-02-01

Purpose: To determine whether domain transfer learning can improve the performance of deep features extracted from digital mammograms using a pre-trained deep convolutional neural network (CNN) in the prediction of occult invasive disease for patients with ductal carcinoma in situ (DCIS) on core needle biopsy. Method: In this study, we collected digital mammography magnification views for 140 patients with DCIS at biopsy, 35 of which were subsequently upstaged to invasive cancer. We utilized a deep CNN model that was pre-trained on two natural image data sets (ImageNet and DTD) and one mammographic data set (INbreast) as the feature extractor, hypothesizing that these data sets are increasingly more similar to our target task and will lead to better representations of deep features to describe DCIS lesions. Through a statistical pooling strategy, three sets of deep features were extracted using the CNNs at different levels of convolutional layers from the lesion areas. A logistic regression classifier was then trained to predict which tumors contain occult invasive disease. The generalization performance was assessed and compared using repeated random sub-sampling validation and receiver operating characteristic (ROC) curve analysis. Result: The best performance of deep features was from CNN model pre-trained on INbreast, and the proposed classifier using this set of deep features was able to achieve a median classification performance of ROC-AUC equal to 0.75, which is significantly better (p<=0.05) than the performance of deep features extracted using ImageNet data set (ROCAUC = 0.68). Conclusion: Transfer learning is helpful for learning a better representation of deep features, and improves the prediction of occult invasive disease in DCIS.

Real-Time Feature Tracking Using Homography

NASA Technical Reports Server (NTRS)

Clouse, Daniel S.; Cheng, Yang; Ansar, Adnan I.; Trotz, David C.; Padgett, Curtis W.

2010-01-01

This software finds feature point correspondences in sequences of images. It is designed for feature matching in aerial imagery. Feature matching is a fundamental step in a number of important image processing operations: calibrating the cameras in a camera array, stabilizing images in aerial movies, geo-registration of images, and generating high-fidelity surface maps from aerial movies. The method uses a Shi-Tomasi corner detector and normalized cross-correlation. This process is likely to result in the production of some mismatches. The feature set is cleaned up using the assumption that there is a large planar patch visible in both images. At high altitude, this assumption is often reasonable. A mathematical transformation, called an homography, is developed that allows us to predict the position in image 2 of any point on the plane in image 1. Any feature pair that is inconsistent with the homography is thrown out. The output of the process is a set of feature pairs, and the homography. The algorithms in this innovation are well known, but the new implementation improves the process in several ways. It runs in real-time at 2 Hz on 64-megapixel imagery. The new Shi-Tomasi corner detector tries to produce the requested number of features by automatically adjusting the minimum distance between found features. The homography-finding code now uses an implementation of the RANSAC algorithm that adjusts the number of iterations automatically to achieve a pre-set probability of missing a set of inliers. The new interface allows the caller to pass in a set of predetermined points in one of the images. This allows the ability to track the same set of points through multiple frames.

What Top-Down Task Sets Do for Us: An ERP Study on the Benefits of Advance Preparation in Visual Search

ERIC Educational Resources Information Center

Eimer, Martin; Kiss, Monika; Nicholas, Susan

2011-01-01

When target-defining features are specified in advance, attentional target selection in visual search is controlled by preparatory top-down task sets. We used ERP measures to study voluntary target selection in the absence of such feature-specific task sets, and to compare it to selection that is guided by advance knowledge about target features.…

Classification of large-scale fundus image data sets: a cloud-computing framework.

PubMed

Roychowdhury, Sohini

2016-08-01

Large medical image data sets with high dimensionality require substantial amount of computation time for data creation and data processing. This paper presents a novel generalized method that finds optimal image-based feature sets that reduce computational time complexity while maximizing overall classification accuracy for detection of diabetic retinopathy (DR). First, region-based and pixel-based features are extracted from fundus images for classification of DR lesions and vessel-like structures. Next, feature ranking strategies are used to distinguish the optimal classification feature sets. DR lesion and vessel classification accuracies are computed using the boosted decision tree and decision forest classifiers in the Microsoft Azure Machine Learning Studio platform, respectively. For images from the DIARETDB1 data set, 40 of its highest-ranked features are used to classify four DR lesion types with an average classification accuracy of 90.1% in 792 seconds. Also, for classification of red lesion regions and hemorrhages from microaneurysms, accuracies of 85% and 72% are observed, respectively. For images from STARE data set, 40 high-ranked features can classify minor blood vessels with an accuracy of 83.5% in 326 seconds. Such cloud-based fundus image analysis systems can significantly enhance the borderline classification performances in automated screening systems.

The effect of feature selection methods on computer-aided detection of masses in mammograms

NASA Astrophysics Data System (ADS)

Hupse, Rianne; Karssemeijer, Nico

2010-05-01

In computer-aided diagnosis (CAD) research, feature selection methods are often used to improve generalization performance of classifiers and shorten computation times. In an application that detects malignant masses in mammograms, we investigated the effect of using a selection criterion that is similar to the final performance measure we are optimizing, namely the mean sensitivity of the system in a predefined range of the free-response receiver operating characteristics (FROC). To obtain the generalization performance of the selected feature subsets, a cross validation procedure was performed on a dataset containing 351 abnormal and 7879 normal regions, each region providing a set of 71 mass features. The same number of noise features, not containing any information, were added to investigate the ability of the feature selection algorithms to distinguish between useful and non-useful features. It was found that significantly higher performances were obtained using feature sets selected by the general test statistic Wilks' lambda than using feature sets selected by the more specific FROC measure. Feature selection leads to better performance when compared to a system in which all features were used.

Evidence-based practice.

PubMed Central

McKibbon, K A

1998-01-01

Evidence-based practice (EBP) is spreading in popularity in many health care disciplines. One of its main features is the reliance on the partnership among hard scientific evidence, clinical expertise, and individual patient needs and choices. Librarians play an important role in the spread of EBP because of the importance of identifying and retrieving appropriate literature from various sources for use in making health care decisions. This article gives an overview of how to search for therapy, diagnosis, etiology, and prognosis both for original studies and secondary publications such as systematic reviews, meta-analyses, and clinical practice guidelines. Understanding how this research is done, how it is indexed, and how to retrieve the clinical evidence are an important set of skills that librarians can provide for clinicians interested in EBP. PMID:9681176

Applications of iQID cameras

NASA Astrophysics Data System (ADS)

Han, Ling; Miller, Brian W.; Barrett, Harrison H.; Barber, H. Bradford; Furenlid, Lars R.

2017-09-01

iQID is an intensified quantum imaging detector developed in the Center for Gamma-Ray Imaging (CGRI). Originally called BazookaSPECT, iQID was designed for high-resolution gamma-ray imaging and preclinical gamma-ray single-photon emission computed tomography (SPECT). With the use of a columnar scintillator, an image intensifier and modern CCD/CMOS sensors, iQID cameras features outstanding intrinsic spatial resolution. In recent years, many advances have been achieved that greatly boost the performance of iQID, broadening its applications to cover nuclear and particle imaging for preclinical, clinical and homeland security settings. This paper presents an overview of the recent advances of iQID technology and its applications in preclinical and clinical scintigraphy, preclinical SPECT, particle imaging (alpha, neutron, beta, and fission fragment), and digital autoradiography.

Bell's "Theorem": loopholes vs. conceptual flaws

NASA Astrophysics Data System (ADS)

Kracklauer, A. F.

2017-12-01

An historical overview and detailed explication of a critical analysis of what has become known as Bell's Theorem to the effect that, it should be impossible to extend Quantum Theory with the addition of local, real variables so as to obtain a version free of the ambiguous and preternatural features of the currently accepted interpretations is presented. The central point on which this critical analysis, due originally to Edwin Jaynes, is that Bell incorrectly applied probabilistic formulas involving conditional probabilities. In addition, mathematical technicalities that have complicated the understanding of the logical or mathematical setting in which current theory and experimentation are embedded, are discussed. Finally, some historical speculations on the sociological environment, in particular misleading aspects, in which recent generations of physicists lived and worked are mentioned.

Uniting Tricholoma sulphureum and T. bufonium.

PubMed

Comandini, Ornella; Haug, Ingeborg; Rinaldi, Andrea C; Kuyper, Thomas W

2004-10-01

The taxonomic status and relationship of Tricholoma sulphureum and the similar T. bufonium were investigated using different sets of characters. These included morphological data on fruit bodies, ecological and chorological data, and analysis of the sequence data obtained for the ITS of basidiomes of different ecological and geographic origin. Moreover, the ectomycorrhizas formed by T. bufonium on Abies alba and Quercus sp. were characterised, and anatomical features compared with those of T. sulphureum mycorrhizas on coniferous and broad-leaved host trees. Our results revealed extensive ITS variation in members of the T. sulphureum group, but this variation was not correlated with morphology, ecology, or geographical distribution. We conclude that T. bufonium cannot be maintained as an autonomous taxon and should be treated as an infraspecific variant of T. sulphureum.

The Multimodal Assessment of Adult Attachment Security: Developing the Biometric Attachment Test.

PubMed

Parra, Federico; Miljkovitch, Raphaële; Persiaux, Gwenaelle; Morales, Michelle; Scherer, Stefan

2017-04-06

Attachment theory has been proven essential for mental health, including psychopathology, development, and interpersonal relationships. Validated psychometric instruments to measure attachment abound but suffer from shortcomings common to traditional psychometrics. Recent developments in multimodal fusion and machine learning pave the way for new automated and objective psychometric instruments for adult attachment that combine psychophysiological, linguistic, and behavioral analyses in the assessment of the construct. The aim of this study was to present a new exposure-based, automatic, and objective adult-attachment assessment, the Biometric Attachment Test (BAT), which exposes participants to a short standardized set of visual and music stimuli, whereas their immediate reactions and verbal responses, captured by several computer sense modalities, are automatically analyzed for scoring and classification. We also aimed to empirically validate two of its assumptions: its capacity to measure attachment security and the viability of using themes as placeholders for rotating stimuli. A total of 59 French participants from the general population were assessed using the Adult Attachment Questionnaire (AAQ), the Adult Attachment Projective Picture System (AAP), and the Attachment Multiple Model Interview (AMMI) as ground truth for attachment security. They were then exposed to three different BAT stimuli sets, whereas their faces, voices, heart rate (HR), and electrodermal activity (EDA) were recorded. Psychophysiological features, such as skin-conductance response (SCR) and Bayevsky stress index; behavioral features, such as gaze and facial expressions; as well as linguistic and paralinguistic features, were automatically extracted. An exploratory analysis was conducted using correlation matrices to uncover the features that are most associated with attachment security. A confirmatory analysis was conducted by creating a single composite effects index and by testing it for correlations with attachment security. The stability of the theory-consistent features across three different stimuli sets was explored using repeated measures analysis of variances (ANOVAs). In total, 46 theory-consistent correlations were found during the exploration (out of 65 total significant correlations). For example, attachment security as measured by the AAP was correlated with positive facial expressions (r=.36, P=.01). AMMI's security with the father was inversely correlated with the low frequency (LF) of HRV (r=-.87, P=.03). Attachment security to partners as measured by the AAQ was inversely correlated with anger facial expression (r=-.43, P=.001). The confirmatory analysis showed that the composite effects index was significantly correlated to security in the AAP (r=.26, P=.05) and the AAQ (r=.30, P=.04) but not in the AMMI. Repeated measures ANOVAs conducted individually on each of the theory-consistent features revealed that only 7 of the 46 (15%) features had significantly different values among responses to three different stimuli sets. We were able to validate two of the instrument's core assumptions: its capacity to measure attachment security and the viability of using themes as placeholders for rotating stimuli. Future validation of other of its dimensions, as well as the ongoing development of its scoring and classification algorithms is discussed. ©Federico Parra, Raphaële Miljkovitch, Gwenaelle Persiaux, Michelle Morales, Stefan Scherer. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 06.04.2017.

Assessing the Readability of Medical Documents: A Ranking Approach.

PubMed

Zheng, Jiaping; Yu, Hong

2018-03-23

The use of electronic health record (EHR) systems with patient engagement capabilities, including viewing, downloading, and transmitting health information, has recently grown tremendously. However, using these resources to engage patients in managing their own health remains challenging due to the complex and technical nature of the EHR narratives. Our objective was to develop a machine learning-based system to assess readability levels of complex documents such as EHR notes. We collected difficulty ratings of EHR notes and Wikipedia articles using crowdsourcing from 90 readers. We built a supervised model to assess readability based on relative orders of text difficulty using both surface text features and word embeddings. We evaluated system performance using the Kendall coefficient of concordance against human ratings. Our system achieved significantly higher concordance (.734) with human annotators than did a baseline using the Flesch-Kincaid Grade Level, a widely adopted readability formula (.531). The improvement was also consistent across different disease topics. This method's concordance with an individual human user's ratings was also higher than the concordance between different human annotators (.658). We explored methods to automatically assess the readability levels of clinical narratives. Our ranking-based system using simple textual features and easy-to-learn word embeddings outperformed a widely used readability formula. Our ranking-based method can predict relative difficulties of medical documents. It is not constrained to a predefined set of readability levels, a common design in many machine learning-based systems. Furthermore, the feature set does not rely on complex processing of the documents. One potential application of our readability ranking is personalization, allowing patients to better accommodate their own background knowledge. ©Jiaping Zheng, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 23.03.2018.

Universal Spatial Correlation Functions for Describing and Reconstructing Soil Microstructure

PubMed Central

Skvortsova, Elena B.; Mallants, Dirk

2015-01-01

Structural features of porous materials such as soil define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, or gas exchange between biologically active soil root zone and atmosphere) and solute transport. To characterize soil microstructure, conventional soil science uses such metrics as pore size and pore-size distributions and thin section-derived morphological indicators. However, these descriptors provide only limited amount of information about the complex arrangement of soil structure and have limited capability to reconstruct structural features or predict physical properties. We introduce three different spatial correlation functions as a comprehensive tool to characterize soil microstructure: 1) two-point probability functions, 2) linear functions, and 3) two-point cluster functions. This novel approach was tested on thin-sections (2.21×2.21 cm2) representing eight soils with different pore space configurations. The two-point probability and linear correlation functions were subsequently used as a part of simulated annealing optimization procedures to reconstruct soil structure. Comparison of original and reconstructed images was based on morphological characteristics, cluster correlation functions, total number of pores and pore-size distribution. Results showed excellent agreement for soils with isolated pores, but relatively poor correspondence for soils exhibiting dual-porosity features (i.e. superposition of pores and micro-cracks). Insufficient information content in the correlation function sets used for reconstruction may have contributed to the observed discrepancies. Improved reconstructions may be obtained by adding cluster and other correlation functions into reconstruction sets. Correlation functions and the associated stochastic reconstruction algorithms introduced here are universally applicable in soil science, such as for soil classification, pore-scale modelling of soil properties, soil degradation monitoring, and description of spatial dynamics of soil microbial activity. PMID:26010779

Universal spatial correlation functions for describing and reconstructing soil microstructure.

PubMed

Karsanina, Marina V; Gerke, Kirill M; Skvortsova, Elena B; Mallants, Dirk

2015-01-01

Structural features of porous materials such as soil define the majority of its physical properties, including water infiltration and redistribution, multi-phase flow (e.g. simultaneous water/air flow, or gas exchange between biologically active soil root zone and atmosphere) and solute transport. To characterize soil microstructure, conventional soil science uses such metrics as pore size and pore-size distributions and thin section-derived morphological indicators. However, these descriptors provide only limited amount of information about the complex arrangement of soil structure and have limited capability to reconstruct structural features or predict physical properties. We introduce three different spatial correlation functions as a comprehensive tool to characterize soil microstructure: 1) two-point probability functions, 2) linear functions, and 3) two-point cluster functions. This novel approach was tested on thin-sections (2.21×2.21 cm2) representing eight soils with different pore space configurations. The two-point probability and linear correlation functions were subsequently used as a part of simulated annealing optimization procedures to reconstruct soil structure. Comparison of original and reconstructed images was based on morphological characteristics, cluster correlation functions, total number of pores and pore-size distribution. Results showed excellent agreement for soils with isolated pores, but relatively poor correspondence for soils exhibiting dual-porosity features (i.e. superposition of pores and micro-cracks). Insufficient information content in the correlation function sets used for reconstruction may have contributed to the observed discrepancies. Improved reconstructions may be obtained by adding cluster and other correlation functions into reconstruction sets. Correlation functions and the associated stochastic reconstruction algorithms introduced here are universally applicable in soil science, such as for soil classification, pore-scale modelling of soil properties, soil degradation monitoring, and description of spatial dynamics of soil microbial activity.

EEG-based recognition of video-induced emotions: selecting subject-independent feature set.

PubMed

Kortelainen, Jukka; Seppänen, Tapio

2013-01-01

Emotions are fundamental for everyday life affecting our communication, learning, perception, and decision making. Including emotions into the human-computer interaction (HCI) could be seen as a significant step forward offering a great potential for developing advanced future technologies. While the electrical activity of the brain is affected by emotions, offers electroencephalogram (EEG) an interesting channel to improve the HCI. In this paper, the selection of subject-independent feature set for EEG-based emotion recognition is studied. We investigate the effect of different feature sets in classifying person's arousal and valence while watching videos with emotional content. The classification performance is optimized by applying a sequential forward floating search algorithm for feature selection. The best classification rate (65.1% for arousal and 63.0% for valence) is obtained with a feature set containing power spectral features from the frequency band of 1-32 Hz. The proposed approach substantially improves the classification rate reported in the literature. In future, further analysis of the video-induced EEG changes including the topographical differences in the spectral features is needed.

Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer?

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

Fave, Xenia, E-mail: xjfave@mdanderson.org; Fried, David; Mackin, Dennis

Purpose: Increasing evidence suggests radiomics features extracted from computed tomography (CT) images may be useful in prognostic models for patients with nonsmall cell lung cancer (NSCLC). This study was designed to determine whether such features can be reproducibly obtained from cone-beam CT (CBCT) images taken using medical Linac onboard-imaging systems in order to track them through treatment. Methods: Test-retest CBCT images of ten patients previously enrolled in a clinical trial were retrospectively obtained and used to determine the concordance correlation coefficient (CCC) for 68 different texture features. The volume dependence of each feature was also measured using the Spearman rankmore » correlation coefficient. Features with a high reproducibility (CCC > 0.9) that were not due to volume dependence in the patient test-retest set were further examined for their sensitivity to differences in imaging protocol, level of scatter, and amount of motion by using two phantoms. The first phantom was a texture phantom composed of rectangular cartridges to represent different textures. Features were measured from two cartridges, shredded rubber and dense cork, in this study. The texture phantom was scanned with 19 different CBCT imagers to establish the features’ interscanner variability. The effect of scatter on these features was studied by surrounding the same texture phantom with scattering material (rice and solid water). The effect of respiratory motion on these features was studied using a dynamic-motion thoracic phantom and a specially designed tumor texture insert of the shredded rubber material. The differences between scans acquired with different Linacs and protocols, varying amounts of scatter, and with different levels of motion were compared to the mean intrapatient difference from the test-retest image set. Results: Of the original 68 features, 37 had a CCC >0.9 that was not due to volume dependence. When the Linac manufacturer and imaging protocol were kept consistent, 4–13 of these 37 features passed our criteria for reproducibility more than 50% of the time, depending on the manufacturer-protocol combination. Almost all of the features changed substantially when scatter material was added around the phantom. For the dense cork, 23 features passed in the thoracic scans and 11 features passed in the head scans when the differences between one and two layers of scatter were compared. Using the same test for the shredded rubber, five features passed the thoracic scans and eight features passed the head scans. Motion substantially impacted the reproducibility of the features. With 4 mm of motion, 12 features from the entire volume and 14 features from the center slice measurements were reproducible. With 6–8 mm of motion, three features (Laplacian of Gaussian filtered kurtosis, gray-level nonuniformity, and entropy), from the entire volume and seven features (coarseness, high gray-level run emphasis, gray-level nonuniformity, sum-average, information measure correlation, scaled mean, and entropy) from the center-slice measurements were considered reproducible. Conclusions: Some radiomics features are robust to the noise and poor image quality of CBCT images when the imaging protocol is consistent, relative changes in the features are used, and patients are limited to those with less than 1 cm of motion.« less

Using listener-based perceptual features as intermediate representations in music information retrieval.

PubMed

Friberg, Anders; Schoonderwaldt, Erwin; Hedblad, Anton; Fabiani, Marco; Elowsson, Anders

2014-10-01

The notion of perceptual features is introduced for describing general music properties based on human perception. This is an attempt at rethinking the concept of features, aiming to approach the underlying human perception mechanisms. Instead of using concepts from music theory such as tones, pitches, and chords, a set of nine features describing overall properties of the music was selected. They were chosen from qualitative measures used in psychology studies and motivated from an ecological approach. The perceptual features were rated in two listening experiments using two different data sets. They were modeled both from symbolic and audio data using different sets of computational features. Ratings of emotional expression were predicted using the perceptual features. The results indicate that (1) at least some of the perceptual features are reliable estimates; (2) emotion ratings could be predicted by a small combination of perceptual features with an explained variance from 75% to 93% for the emotional dimensions activity and valence; (3) the perceptual features could only to a limited extent be modeled using existing audio features. Results clearly indicated that a small number of dedicated features were superior to a "brute force" model using a large number of general audio features.

Systems and Methods for Correcting Optical Reflectance Measurements

NASA Technical Reports Server (NTRS)

Yang, Ye (Inventor); Shear, Michael A. (Inventor); Soller, Babs R. (Inventor); Soyemi, Olusola O. (Inventor)

2014-01-01

We disclose measurement systems and methods for measuring analytes in target regions of samples that also include features overlying the target regions. The systems include: (a) a light source; (b) a detection system; (c) a set of at least first, second, and third light ports which transmit light from the light source to a sample and receive and direct light reflected from the sample to the detection system, generating a first set of data including information corresponding to both an internal target within the sample and features overlying the internal target, and a second set of data including information corresponding to features overlying the internal target; and (d) a processor configured to remove information characteristic of the overlying features from the first set of data using the first and second sets of data to produce corrected information representing the internal target.

Systems and methods for correcting optical reflectance measurements

NASA Technical Reports Server (NTRS)

Yang, Ye (Inventor); Soller, Babs R. (Inventor); Soyemi, Olusola O. (Inventor); Shear, Michael A. (Inventor)

2009-01-01

We disclose measurement systems and methods for measuring analytes in target regions of samples that also include features overlying the target regions. The systems include: (a) a light source; (b) a detection system; (c) a set of at least first, second, and third light ports which transmit light from the light source to a sample and receive and direct light reflected from the sample to the detection system, generating a first set of data including information corresponding to both an internal target within the sample and features overlying the internal target, and a second set of data including information corresponding to features overlying the internal target; and (d) a processor configured to remove information characteristic of the overlying features from the first set of data using the first and second sets of data to produce corrected information representing the internal target.

A framework for feature extraction from hospital medical data with applications in risk prediction.

PubMed

Tran, Truyen; Luo, Wei; Phung, Dinh; Gupta, Sunil; Rana, Santu; Kennedy, Richard Lee; Larkins, Ann; Venkatesh, Svetha

2014-12-30

Feature engineering is a time consuming component of predictive modeling. We propose a versatile platform to automatically extract features for risk prediction, based on a pre-defined and extensible entity schema. The extraction is independent of disease type or risk prediction task. We contrast auto-extracted features to baselines generated from the Elixhauser comorbidities. Hospital medical records was transformed to event sequences, to which filters were applied to extract feature sets capturing diversity in temporal scales and data types. The features were evaluated on a readmission prediction task, comparing with baseline feature sets generated from the Elixhauser comorbidities. The prediction model was through logistic regression with elastic net regularization. Predictions horizons of 1, 2, 3, 6, 12 months were considered for four diverse diseases: diabetes, COPD, mental disorders and pneumonia, with derivation and validation cohorts defined on non-overlapping data-collection periods. For unplanned readmissions, auto-extracted feature set using socio-demographic information and medical records, outperformed baselines derived from the socio-demographic information and Elixhauser comorbidities, over 20 settings (5 prediction horizons over 4 diseases). In particular over 30-day prediction, the AUCs are: COPD-baseline: 0.60 (95% CI: 0.57, 0.63), auto-extracted: 0.67 (0.64, 0.70); diabetes-baseline: 0.60 (0.58, 0.63), auto-extracted: 0.67 (0.64, 0.69); mental disorders-baseline: 0.57 (0.54, 0.60), auto-extracted: 0.69 (0.64,0.70); pneumonia-baseline: 0.61 (0.59, 0.63), auto-extracted: 0.70 (0.67, 0.72). The advantages of auto-extracted standard features from complex medical records, in a disease and task agnostic manner were demonstrated. Auto-extracted features have good predictive power over multiple time horizons. Such feature sets have potential to form the foundation of complex automated analytic tasks.

A Reduced Set of Features for Chronic Kidney Disease Prediction

PubMed Central

Misir, Rajesh; Mitra, Malay; Samanta, Ranjit Kumar

2017-01-01

Chronic kidney disease (CKD) is one of the life-threatening diseases. Early detection and proper management are solicited for augmenting survivability. As per the UCI data set, there are 24 attributes for predicting CKD or non-CKD. At least there are 16 attributes need pathological investigations involving more resources, money, time, and uncertainties. The objective of this work is to explore whether we can predict CKD or non-CKD with reasonable accuracy using less number of features. An intelligent system development approach has been used in this study. We attempted one important feature selection technique to discover reduced features that explain the data set much better. Two intelligent binary classification techniques have been adopted for the validity of the reduced feature set. Performances were evaluated in terms of four important classification evaluation parameters. As suggested from our results, we may more concentrate on those reduced features for identifying CKD and thereby reduces uncertainty, saves time, and reduces costs. PMID:28706750

Ensemble methods with simple features for document zone classification

NASA Astrophysics Data System (ADS)

Obafemi-Ajayi, Tayo; Agam, Gady; Xie, Bingqing

2012-01-01

Document layout analysis is of fundamental importance for document image understanding and information retrieval. It requires the identification of blocks extracted from a document image via features extraction and block classification. In this paper, we focus on the classification of the extracted blocks into five classes: text (machine printed), handwriting, graphics, images, and noise. We propose a new set of features for efficient classifications of these blocks. We present a comparative evaluation of three ensemble based classification algorithms (boosting, bagging, and combined model trees) in addition to other known learning algorithms. Experimental results are demonstrated for a set of 36503 zones extracted from 416 document images which were randomly selected from the tobacco legacy document collection. The results obtained verify the robustness and effectiveness of the proposed set of features in comparison to the commonly used Ocropus recognition features. When used in conjunction with the Ocropus feature set, we further improve the performance of the block classification system to obtain a classification accuracy of 99.21%.

The discovery of a new infrared emission feature at 1905 wavenumbers (5.25 microns) in the spectrum of BD +30 degrees 3639 and its relation to the polycyclic aromatic hydrocarbon model

NASA Technical Reports Server (NTRS)

Allamandola, L. J.; Bregman, J. D.; Sandford, S. A.; Tielens, A. G.; Witteborn, F. C.; Wooden, D. H.; Rank, D.

1989-01-01

We have discovered a new IR emission feature at 1905 cm-1 (5.25 microns) in the spectrum of BD +30 degrees 3639. This feature joins the family of well-known IR emission features at 3040, 2940, 1750, 1610, "1310," 1160, and 890 cm-1 (3.3, 3.4, 5.7, 6.2, "7.7," 8.6, and 11.2 microns). The origin of this new feature is discussed and it is assigned to an overtone or combination band involving C-H bending modes of polycyclic aromatic hydrocarbons (PAHs). Laboratory work suggests that spectral studies of the 2000-1650 cm-1 (5.0-6.1 microns) region may be very useful in elucidating the molecular structure of interstellar PAHs. The new feature, in conjunction with other recently discovered spectral structure, suggests that the narrow IR emission features originate in PAH molecules rather than large carbon grains. Larger species are likely to be the source of the broad underlying "plateaus" seen in many of the spectra.

Automated recognition of quasi-planar ignimbrite sheets and paleo-surfaces via robust segmentation of DTM - examples from the Western Cordillera of the Central Andes

NASA Astrophysics Data System (ADS)

Székely, B.; Karátson, D.; Koma, Zs.; Dorninger, P.; Wörner, G.; Brandmeier, M.; Nothegger, C.

2012-04-01

The Western slope of the Central Andes between 22° and 17°S is characterized by large, quasi-planar landforms with tilted ignimbrite surfaces and overlying younger sedimentary deposits (e.g. Nazca, Oxaya, Huaylillas ignimbrites). These surfaces were only modified by tectonic uplift and tilting of the Western Cordillera preserving minor now fossilized drainage systems. Several deep, canyons started to form from about 5 Ma ago. Due to tectonic oversteepening in a arid region of very low erosion rates, gravitational collapses and landslides additionally modified the Andean slope and valley flanks. Large areas of fossil surfaces, however, remain. The age of these surfaces has been dated between 11 Ma and 25 Ma at elevations of 3500 m in the Precordillera and at c. 1000 m near the coast. Due to their excellent preservation, our aim is to identify, delineate, and reconstruct these original ignimbrite and sediment surfaces via a sophisticated evaluation of SRTM DEMs. The technique we use here is a robust morphological segmentation method that is insensitive to a certain amount of outliers, even if they are spatially correlated. This paves the way to identify common local planar features and combine these into larger areas of a particular surface segment. Erosional dissection and faulting, tilting and folding define subdomains, and thus the original quasi-planar surfaces are modified. Additional processes may create younger surfaces, such as sedimentary floodplains and salt pans. The procedure is tuned to provide a distinction of these features. The technique is based on the evaluation of local normal vectors (perpendicular to the actual surface) that are obtained by determination of locally fitting planes. Then, this initial set of normal vectors are gradually classified into groups with similar properties providing candidate point clouds that are quasi co-planar. The quasi co-planar sets of points are analysed further against other criteria, such as number of minimum points, maximized standard deviation of spatial scatter, maximum point-to-plane surface, etc. SRTM DEMs of selected areas of the Western slope of the Central Andes have been processed with various parameter sets. The resulting domain structure shows strong correlation with tectonic features (e.g. faulting) and younger depositional surfaces whereas other segmentation features appear or disappear depending on parameters of the analysis. For example, a fine segmentation results - for a given study area - in ca. 2500 planar features (of course not all are geologically meaningful), whereas a more meaningful result has an order of magnitude less planes, ca. 270. The latter segmentation still covers the key areas, and the dissecting features (e.g., large incised canyons) are typically identified. For the fine segmentation version an area of 3863 km2 is covered by fitted planes for the ignimbrite surfaces, whereas for the more robust segmentation this area is 2555 km2. The same values for the sedimentary surfaces are 3162 km2 and 2080 km2, respectively. The total processed area was 14498 km2. As the previous numbers and the 18,1% and 18,6% decrease in the coverage suggest, the robust segmentation remains meaningful for large parts of the area while the number of planar features decreased by an order of magnitude. This result also emphasizes the importance of the initial parameters. To verify the results in more detail, residuals (difference between measured and modelled elevation) are also evaluated, and the results are fed back to the segmentation procedure. Steeper landscapes (young volcanic edifices) are clearly separated from higher-order (long-wavelength) structures. This method allows to quantitatively identify uniform surface segments and to relate these to geologically and morphologically meaningful parameters (type of depositional surface, rock type, surface age).

Feature Selection for Speech Emotion Recognition in Spanish and Basque: On the Use of Machine Learning to Improve Human-Computer Interaction

PubMed Central

Arruti, Andoni; Cearreta, Idoia; Álvarez, Aitor; Lazkano, Elena; Sierra, Basilio

2014-01-01

Study of emotions in human–computer interaction is a growing research area. This paper shows an attempt to select the most significant features for emotion recognition in spoken Basque and Spanish Languages using different methods for feature selection. RekEmozio database was used as the experimental data set. Several Machine Learning paradigms were used for the emotion classification task. Experiments were executed in three phases, using different sets of features as classification variables in each phase. Moreover, feature subset selection was applied at each phase in order to seek for the most relevant feature subset. The three phases approach was selected to check the validity of the proposed approach. Achieved results show that an instance-based learning algorithm using feature subset selection techniques based on evolutionary algorithms is the best Machine Learning paradigm in automatic emotion recognition, with all different feature sets, obtaining a mean of 80,05% emotion recognition rate in Basque and a 74,82% in Spanish. In order to check the goodness of the proposed process, a greedy searching approach (FSS-Forward) has been applied and a comparison between them is provided. Based on achieved results, a set of most relevant non-speaker dependent features is proposed for both languages and new perspectives are suggested. PMID:25279686

Various origins of the duplicated middle cerebral artery.

PubMed

Tutar, Nihal Uslu; Töre, Hüseyin Gürkan; Kirbaş, Ismail; Tarhan, Nefise Cağla; Coşkun, Mehmet

2008-10-01

We describe the features of a duplicated middle cerebral artery identified by computed tomographic angiography that originates from a previously undefined origin, ie, from the petrous portion of the internal carotid artery. Recognition of this anomaly is important in patients with a possible aneurysm, which was not present in our patient.

A Role for Epigenetic Mechanisms in Organ-Specific Breast Cancer Metastasis

DTIC Science & Technology

2010-05-01

of origin. The neovasculature of tumors is typically leaky (Carmeliet and Jain, 2000; Rafii et al., 2003), a feature that would facilitate not only...and therapy selection in esophageal cancer. Cancer Cell 13, 441- 453. Rafii , S., Avecilla, S.T., and Jin, D.K. (2003). Tumor vasculature address book...origin. The neovasculature of tumors is typically leaky (Carmeliet and Jain, 2000; Rafii et al., 2003), a feature that would facilitate not only the

Origin of coloration in beetle scales: An optical and structural investigation

NASA Astrophysics Data System (ADS)

Nagi, Ramneet Kaur

In this thesis the origin of angle-independent yellowish-green coloration of the exoskeleton of a beetle was studied. The beetle chosen was a weevil with the Latin name Eupholus chevrolati. The origin of this weevil's coloration was investigated by optical and structural characterization techniques, including optical microscopy, scanning electron microscopy imaging and focused ion beam milling, combined with three-dimensional modeling and photonic band structure calculations. Furthermore, using color theory the pixel-like coloring of the weevil's exoskeleton was investigated and an interesting additive color mixing scheme was discovered. For optical studies, a microreflectance microscopy/spectroscopy set-up was optimized. This set-up allowed not only for imaging of individual colored exoskeleton domains with sizes ˜2-10 μm, but also for obtaining reflection spectra of these micrometer-sized domains. Spectra were analyzed in terms of reflection intensity and wavelength position and shape of the reflection features. To find the origin of these colored exoskeleton spots, a combination of focused ion beam milling and scanning electron microscopy imaging was employed. A three-dimensional photonic crystal in the form of a face-centered cubic lattice of ABC-stacked air cylinders in a biopolymeric cuticle matrix was discovered. Our photonic band structure calculations revealed the existence of different sets of stop-gaps for the lattice constant of 360, 380 and 400 nm in the main lattice directions, Gamma-L, Gamma-X, Gamma-U, Gamma-W and Gamma-K. In addition, scanning electron microscopy images were compared to the specific directional-cuts through the constructed face-centered cubic lattice-based model and the optical micrographs of individual domains to determine the photonic structure corresponding to the different lattice directions. The three-dimensional model revealed stop-gaps in the Gamma-L, Gamma-W and Gamma-K directions. Finally, the coloration of the weevil as perceived by an unaided human eye was represented (mathematically) on the xy-chromaticity diagram, based on XYZ color space developed by CIE (Commission Internationale de l'Eclairage), using the micro-reflectance spectroscopy measurements. The results confirmed the additive mixing of various colors produced by differently oriented photonic crystal domains present in the weevil's exoskeleton scales, as a reason for the angle-independent dull yellowish-green coloration of the weevil E. chevrolati.

Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images.

PubMed

Zhang, Lefei; Zhang, Qian; Du, Bo; Huang, Xin; Tang, Yuan Yan; Tao, Dacheng

2018-01-01

In hyperspectral remote sensing data mining, it is important to take into account of both spectral and spatial information, such as the spectral signature, texture feature, and morphological property, to improve the performances, e.g., the image classification accuracy. In a feature representation point of view, a nature approach to handle this situation is to concatenate the spectral and spatial features into a single but high dimensional vector and then apply a certain dimension reduction technique directly on that concatenated vector before feed it into the subsequent classifier. However, multiple features from various domains definitely have different physical meanings and statistical properties, and thus such concatenation has not efficiently explore the complementary properties among different features, which should benefit for boost the feature discriminability. Furthermore, it is also difficult to interpret the transformed results of the concatenated vector. Consequently, finding a physically meaningful consensus low dimensional feature representation of original multiple features is still a challenging task. In order to address these issues, we propose a novel feature learning framework, i.e., the simultaneous spectral-spatial feature selection and extraction algorithm, for hyperspectral images spectral-spatial feature representation and classification. Specifically, the proposed method learns a latent low dimensional subspace by projecting the spectral-spatial feature into a common feature space, where the complementary information has been effectively exploited, and simultaneously, only the most significant original features have been transformed. Encouraging experimental results on three public available hyperspectral remote sensing datasets confirm that our proposed method is effective and efficient.

Improved parallel image reconstruction using feature refinement.

PubMed

Cheng, Jing; Jia, Sen; Ying, Leslie; Liu, Yuanyuan; Wang, Shanshan; Zhu, Yanjie; Li, Ye; Zou, Chao; Liu, Xin; Liang, Dong

2018-07-01

The aim of this study was to develop a novel feature refinement MR reconstruction method from highly undersampled multichannel acquisitions for improving the image quality and preserve more detail information. The feature refinement technique, which uses a feature descriptor to pick up useful features from residual image discarded by sparsity constrains, is applied to preserve the details of the image in compressed sensing and parallel imaging in MRI (CS-pMRI). The texture descriptor and structure descriptor recognizing different types of features are required for forming the feature descriptor. Feasibility of the feature refinement was validated using three different multicoil reconstruction methods on in vivo data. Experimental results show that reconstruction methods with feature refinement improve the quality of reconstructed image and restore the image details more accurately than the original methods, which is also verified by the lower values of the root mean square error and high frequency error norm. A simple and effective way to preserve more useful detailed information in CS-pMRI is proposed. This technique can effectively improve the reconstruction quality and has superior performance in terms of detail preservation compared with the original version without feature refinement. Magn Reson Med 80:211-223, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

The interaction of feature and space based orienting within the attention set.

PubMed

Lim, Ahnate; Sinnett, Scott

2014-01-01

The processing of sensory information relies on interacting mechanisms of sustained attention and attentional capture, both of which operate in space and on object features. While evidence indicates that exogenous attentional capture, a mechanism previously understood to be automatic, can be eliminated while concurrently performing a demanding task, we reframe this phenomenon within the theoretical framework of the "attention set" (Most et al., 2005). Consequently, the specific prediction that cuing effects should reappear when feature dimensions of the cue overlap with those in the attention set (i.e., elements of the demanding task) was empirically tested and confirmed using a dual-task paradigm involving both sustained attention and attentional capture, adapted from Santangelo et al. (2007). Participants were required to either detect a centrally presented target presented in a stream of distractors (the primary task), or respond to a spatially cued target (the secondary task). Importantly, the spatial cue could either share features with the target in the centrally presented primary task, or not share any features. Overall, the findings supported the attention set hypothesis showing that a spatial cuing effect was only observed when the peripheral cue shared a feature with objects that were already in the attention set (i.e., the primary task). However, this finding was accompanied by differential attentional orienting dependent on the different types of objects within the attention set, with feature-based orienting occurring for target-related objects, and additional spatial-based orienting for distractor-related objects.

Mining SNPs from EST sequences using filters and ensemble classifiers.

PubMed

Wang, J; Zou, Q; Guo, M Z

2010-05-04

Abundant single nucleotide polymorphisms (SNPs) provide the most complete information for genome-wide association studies. However, due to the bottleneck of manual discovery of putative SNPs and the inaccessibility of the original sequencing reads, it is essential to develop a more efficient and accurate computational method for automated SNP detection. We propose a novel computational method to rapidly find true SNPs in public-available EST (expressed sequence tag) databases; this method is implemented as SNPDigger. EST sequences are clustered and aligned. SNP candidates are then obtained according to a measure of redundant frequency. Several new informative biological features, such as the structural neighbor profiles and the physical position of the SNP, were extracted from EST sequences, and the effectiveness of these features was demonstrated. An ensemble classifier, which employs a carefully selected feature set, was included for the imbalanced training data. The sensitivity and specificity of our method both exceeded 80% for human genetic data in the cross validation. Our method enables detection of SNPs from the user's own EST dataset and can be used on species for which there is no genome data. Our tests showed that this method can effectively guide SNP discovery in ESTs and will be useful to avoid and save the cost of biological analyses.

Giant Viruses of Amoebas: An Update

PubMed Central

Aherfi, Sarah; Colson, Philippe; La Scola, Bernard; Raoult, Didier

2016-01-01

During the 12 past years, five new or putative virus families encompassing several members, namely Mimiviridae, Marseilleviridae, pandoraviruses, faustoviruses, and virophages were described. In addition, Pithovirus sibericum and Mollivirus sibericum represent type strains of putative new giant virus families. All these viruses were isolated using amoebal coculture methods. These giant viruses were linked by phylogenomic analyses to other large DNA viruses. They were then proposed to be classified in a new viral order, the Megavirales, on the basis of their common origin, as shown by a set of ancestral genes encoding key viral functions, a common virion architecture, and shared major biological features including replication inside cytoplasmic factories. Megavirales is increasingly demonstrated to stand in the tree of life aside Bacteria, Archaea, and Eukarya, and the megavirus ancestor is suspected to be as ancient as cellular ancestors. In addition, giant amoebal viruses are visible under a light microscope and display many phenotypic and genomic features not found in other viruses, while they share other characteristics with parasitic microbes. Moreover, these organisms appear to be common inhabitants of our biosphere, and mimiviruses and marseilleviruses were isolated from human samples and associated to diseases. In the present review, we describe the main features and recent findings on these giant amoebal viruses and virophages. PMID:27047465

An approach of understanding acid volcanics and tuffaceous volcaniclastics from field studies: A case from Tadpatri Formation, Proterozoic Cuddapah basin, Andhra Pradesh, India

NASA Astrophysics Data System (ADS)

Goswami, Sukanta; Upadhyay, P. K.; Bhagat, Sangeeta; Zakaulla, Syed; Bhatt, A. K.; Natarajan, V.; Dey, Sukanta

2018-03-01

The lower stratigraphic part of the Cuddapah basin is marked by mafic and felsic volcanism. Tadpatri Formation consists of a greater variety of rock types due to bimodal volcanism in the upper part. Presence of bimodal volcanism is an indication of continental rift setting. Various genetic processes involved in the formation of such volcanic sequence result in original textures which are classified into volcaniclastic and coherent categories. Detailed and systematic field works in Tadpatri-Tonduru transect of SW Cuddapah basin have provided information on the physical processes producing this diversity of rock types. Felsic volcanism is manifested here with features as finger print of past rhyolite-dacite eruptions. Acid volcanics, tuffs and associated shale of Tadpatri Formation are studied and mapped in the field. With supporting subordinate studies on geochemistry, mineralogy and petrogenesis of the volcanics to validate field features accurately, it is understood that volcanism was associated with rifting and shallow marine environmental condition. Four facies (i.e., surge, flow, fall and resedimented volcaniclastic) are demarcated to describe stratigraphic units and volcanic history of the mapped area. The present contribution focuses on the fundamental characterization and categorization of field-based features diagnostic of silica-rich volcanic activities in the Tadpatri Formation.

Peer-Based Social Media Features in Behavior Change Interventions: Systematic Review.

PubMed

Elaheebocus, Sheik Mohammad Roushdat Ally; Weal, Mark; Morrison, Leanne; Yardley, Lucy

2018-02-22

Incorporating social media features into digital behavior change interventions (DBCIs) has the potential to contribute positively to their success. However, the lack of clear design principles to describe and guide the use of these features in behavioral interventions limits cross-study comparisons of their uses and effects. The aim of this study was to provide a systematic review of DBCIs targeting modifiable behavioral risk factors that have included social media features as part of their intervention infrastructure. A taxonomy of social media features is presented to inform the development, description, and evaluation of behavioral interventions. Search terms were used in 8 databases to identify DBCIs that incorporated social media features and targeted tobacco smoking, diet and nutrition, physical activities, or alcohol consumption. The screening and review process was performed by 2 independent researchers. A total of 5264 articles were screened, and 143 articles describing a total of 134 studies were retained for full review. The majority of studies (70%) reported positive outcomes, followed by 28% finding no effects with regard to their respective objectives and hypothesis, and 2% of the studies found that their interventions had negative outcomes. Few studies reported on the association between the inclusion of social media features and intervention effect. A taxonomy of social media features used in behavioral interventions has been presented with 36 social media features organized under 7 high-level categories. The taxonomy has been used to guide the analysis of this review. Although social media features are commonly included in DBCIs, there is an acute lack of information with respect to their effect on outcomes and a lack of clear guidance to inform the selection process based on the features' suitability for the different behaviors. The proposed taxonomy along with the set of recommendations included in this review will support future research aimed at isolating and reporting the effects of social media features on DBCIs, cross-study comparisons, and evaluations. ©Sheik Mohammad Roushdat Ally Elaheebocus, Mark Weal, Leanne Morrison, Lucy Yardley. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 22.02.2018.

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

PubMed

Yao, Dongren; Calhoun, Vince D; Fu, Zening; Du, Yuhui; Sui, Jing

2018-05-15

Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i.e., those who eventually convert to AD (cMCI) versus those who do not (MCI). To solve this difficult 4-way classification problem (AD, MCI, cMCI and healthy controls), a competition was hosted by Kaggle to invite the scientific community to apply their machine learning approaches on pre-processed sets of T1-weighted magnetic resonance images (MRI) data and the demographic information from the international Alzheimer's disease neuroimaging initiative (ADNI) database. This paper summarizes our competition results. We first proposed a hierarchical process by turning the 4-way classification into five binary classification problems. A new feature selection technology based on relative importance was also proposed, aiming to identify a more informative and concise subset from 426 sMRI morphometric and 3 demographic features, to ensure each binary classifier to achieve its highest accuracy. As a result, about 2% of the original features were selected to build a new feature space, which can achieve the final four-way classification with a 54.38% accuracy on testing data through hierarchical grouping, higher than several alternative methods in comparison. More importantly, the selected discriminative features such as hippocampal volume, parahippocampal surface area, and medial orbitofrontal thickness, etc. as well as the MMSE score, are reasonable and consistent with those reported in AD/MCI deficits. In summary, the proposed method provides a new framework for multi-way classification using hierarchical grouping and precise feature selection. Copyright © 2018 Elsevier B.V. All rights reserved.

Supervised learning methods for pathological arterial pulse wave differentiation: A SVM and neural networks approach.

PubMed

Paiva, Joana S; Cardoso, João; Pereira, Tânia

2018-01-01

The main goal of this study was to develop an automatic method based on supervised learning methods, able to distinguish healthy from pathologic arterial pulse wave (APW), and those two from noisy waveforms (non-relevant segments of the signal), from the data acquired during a clinical examination with a novel optical system. The APW dataset analysed was composed by signals acquired in a clinical environment from a total of 213 subjects, including healthy volunteers and non-healthy patients. The signals were parameterised by means of 39pulse features: morphologic, time domain statistics, cross-correlation features, wavelet features. Multiclass Support Vector Machine Recursive Feature Elimination (SVM RFE) method was used to select the most relevant features. A comparative study was performed in order to evaluate the performance of the two classifiers: Support Vector Machine (SVM) and Artificial Neural Network (ANN). SVM achieved a statistically significant better performance for this problem with an average accuracy of 0.9917±0.0024 and a F-Measure of 0.9925±0.0019, in comparison with ANN, which reached the values of 0.9847±0.0032 and 0.9852±0.0031 for Accuracy and F-Measure, respectively. A significant difference was observed between the performances obtained with SVM classifier using a different number of features from the original set available. The comparison between SVM and NN allowed reassert the higher performance of SVM. The results obtained in this study showed the potential of the proposed method to differentiate those three important signal outcomes (healthy, pathologic and noise) and to reduce bias associated with clinical diagnosis of cardiovascular disease using APW. Copyright © 2017 Elsevier B.V. All rights reserved.

Unsupervised MRI segmentation of brain tissues using a local linear model and level set.

PubMed

Rivest-Hénault, David; Cheriet, Mohamed

2011-02-01

Real-world magnetic resonance imaging of the brain is affected by intensity nonuniformity (INU) phenomena which makes it difficult to fully automate the segmentation process. This difficult task is accomplished in this work by using a new method with two original features: (1) each brain tissue class is locally modeled using a local linear region representative, which allows us to account for the INU in an implicit way and to more accurately position the region's boundaries; and (2) the region models are embedded in the level set framework, so that the spatial coherence of the segmentation can be controlled in a natural way. Our new method has been tested on the ground-truthed Internet Brain Segmentation Repository (IBSR) database and gave promising results, with Tanimoto indexes ranging from 0.61 to 0.79 for the classification of the white matter and from 0.72 to 0.84 for the gray matter. To our knowledge, this is the first time a region-based level set model has been used to perform the segmentation of real-world MRI brain scans with convincing results. Copyright © 2011 Elsevier Inc. All rights reserved.

Hypothesis on the origin of lineaments in the LANDSAT and SLAR images of precambrian soil in the low Contas River Valley (southern Bahia)

NASA Technical Reports Server (NTRS)

Liu, C. C. (Principal Investigator); Rodrigues, J. E.

1984-01-01

Examination of LANDSAT and SLAR images in southern Bahia reveals numerous linear features, which are grouped in five sets, based on their trends: N65 degrees E, N70 degrees W, N45 degrees E and NS/N15 degrees E. Owing to their topographic expressions, distributive patterns, spacing between individual lineaments and their mutual relationships, the lineament sets of N65 degrees E and N70 degrees W, as well as the sets of N40 degrees E and N45 degrees W, are considered as two groups of conjugate shear fractures and the former is older and is always cut by the latter. Their conjugate shear angles are 45 degrees and 85 degrees and their bisector lines are approximately in east-west and north-south directions, respectively. According to Badgeley's argumentation on the conjugate shear angles, the former conjugate shear fractures would be caused by: (1) vertical movements, and the bisector of their conjugate angle would be parallel to the long axis of horsting or folding, or (2) by a compressive force in the east-west direction and under a condition of low confining pressure and temperature.

A keyword spotting model using perceptually significant energy features

NASA Astrophysics Data System (ADS)

Umakanthan, Padmalochini

The task of a keyword recognition system is to detect the presence of certain words in a conversation based on the linguistic information present in human speech. Such keyword spotting systems have applications in homeland security, telephone surveillance and human-computer interfacing. General procedure of a keyword spotting system involves feature generation and matching. In this work, new set of features that are based on the psycho-acoustic masking nature of human speech are proposed. After developing these features a time aligned pattern matching process was implemented to locate the words in a set of unknown words. A word boundary detection technique based on frame classification using the nonlinear characteristics of speech is also addressed in this work. Validation of this keyword spotting model was done using widely acclaimed Cepstral features. The experimental results indicate the viability of using these perceptually significant features as an augmented feature set in keyword spotting.

A random forest model based classification scheme for neonatal amplitude-integrated EEG.

PubMed

Chen, Weiting; Wang, Yu; Cao, Guitao; Chen, Guoqiang; Gu, Qiufang

2014-01-01

Modern medical advances have greatly increased the survival rate of infants, while they remain in the higher risk group for neurological problems later in life. For the infants with encephalopathy or seizures, identification of the extent of brain injury is clinically challenging. Continuous amplitude-integrated electroencephalography (aEEG) monitoring offers a possibility to directly monitor the brain functional state of the newborns over hours, and has seen an increasing application in neonatal intensive care units (NICUs). This paper presents a novel combined feature set of aEEG and applies random forest (RF) method to classify aEEG tracings. To that end, a series of experiments were conducted on 282 aEEG tracing cases (209 normal and 73 abnormal ones). Basic features, statistic features and segmentation features were extracted from both the tracing as a whole and the segmented recordings, and then form a combined feature set. All the features were sent to a classifier afterwards. The significance of feature, the data segmentation, the optimization of RF parameters, and the problem of imbalanced datasets were examined through experiments. Experiments were also done to evaluate the performance of RF on aEEG signal classifying, compared with several other widely used classifiers including SVM-Linear, SVM-RBF, ANN, Decision Tree (DT), Logistic Regression(LR), ML, and LDA. The combined feature set can better characterize aEEG signals, compared with basic features, statistic features and segmentation features respectively. With the combined feature set, the proposed RF-based aEEG classification system achieved a correct rate of 92.52% and a high F1-score of 95.26%. Among all of the seven classifiers examined in our work, the RF method got the highest correct rate, sensitivity, specificity, and F1-score, which means that RF outperforms all of the other classifiers considered here. The results show that the proposed RF-based aEEG classification system with the combined feature set is efficient and helpful to better detect the brain disorders in newborns.

Web-based newborn screening system for metabolic diseases: machine learning versus clinicians.

PubMed

Chen, Wei-Hsin; Hsieh, Sheau-Ling; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

2013-05-23

A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. This SOA Web service-based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically.

Web-Based Newborn Screening System for Metabolic Diseases: Machine Learning Versus Clinicians

PubMed Central

Chen, Wei-Hsin; Hsu, Kai-Ping; Chen, Han-Ping; Su, Xing-Yu; Tseng, Yi-Ju; Chien, Yin-Hsiu; Hwu, Wuh-Liang; Lai, Feipei

2013-01-01

Background A hospital information system (HIS) that integrates screening data and interpretation of the data is routinely requested by hospitals and parents. However, the accuracy of disease classification may be low because of the disease characteristics and the analytes used for classification. Objective The objective of this study is to describe a system that enhanced the neonatal screening system of the Newborn Screening Center at the National Taiwan University Hospital. The system was designed and deployed according to a service-oriented architecture (SOA) framework under the Web services .NET environment. The system consists of sample collection, testing, diagnosis, evaluation, treatment, and follow-up services among collaborating hospitals. To improve the accuracy of newborn screening, machine learning and optimal feature selection mechanisms were investigated for screening newborns for inborn errors of metabolism. Methods The framework of the Newborn Screening Hospital Information System (NSHIS) used the embedded Health Level Seven (HL7) standards for data exchanges among heterogeneous platforms integrated by Web services in the C# language. In this study, machine learning classification was used to predict phenylketonuria (PKU), hypermethioninemia, and 3-methylcrotonyl-CoA-carboxylase (3-MCC) deficiency. The classification methods used 347,312 newborn dried blood samples collected at the Center between 2006 and 2011. Of these, 220 newborns had values over the diagnostic cutoffs (positive cases) and 1557 had values that were over the screening cutoffs but did not meet the diagnostic cutoffs (suspected cases). The original 35 analytes and the manifested features were ranked based on F score, then combinations of the top 20 ranked features were selected as input features to support vector machine (SVM) classifiers to obtain optimal feature sets. These feature sets were tested using 5-fold cross-validation and optimal models were generated. The datasets collected in year 2011 were used as predicting cases. Results The feature selection strategies were implemented and the optimal markers for PKU, hypermethioninemia, and 3-MCC deficiency were obtained. The results of the machine learning approach were compared with the cutoff scheme. The number of the false positive cases were reduced from 21 to 2 for PKU, from 30 to 10 for hypermethioninemia, and 209 to 46 for 3-MCC deficiency. Conclusions This SOA Web service–based newborn screening system can accelerate screening procedures effectively and efficiently. An SVM learning methodology for PKU, hypermethioninemia, and 3-MCC deficiency metabolic diseases classification, including optimal feature selection strategies, is presented. By adopting the results of this study, the number of suspected cases could be reduced dramatically. PMID:23702487

GPR identification of an early monument at Los Morteros in the Peruvian coastal desert

NASA Astrophysics Data System (ADS)

Sandweiss, Daniel H.; Kelley, Alice R.; Belknap, Daniel F.; Kelley, Joseph T.; Rademaker, Kurt; Reid, David A.

2010-05-01

Los Morteros (8˚39'54″S, 78˚42 '00″W) is located in coastal, northern Peru, one of the six original centers of world civilization. The site consists of a large, sand-covered, isolated prominence situated on a Mid-Holocene shoreline, ˜ 5 km from the present coast. Preceramic archaeological deposits (4040 ± 75 to 4656 ± 60 14C yr BP or ˜ 3600-5500 cal yr BP) cap this feature, which has been identified by prior researchers as a sand-draped, bedrock-cored landform or a relict dune deposit. Because neither explanation is geomorphologically probable, we used ground-penetrating radar (GPR) and high-resolution mapping to assess the mound's interior structure. Our results indicate an anthropogenic origin for Los Morteros, potentially placing it among the earliest monumental structures in prehistoric South America. The extremely arid setting raises new questions about the purpose and the logistics of early mound construction in this region. This work demonstrates the value of an integrated Quaternary sciences approach to assess long-term landscape change and to understand the interaction between humans and the environment.

The recognition of graphical patterns invariant to geometrical transformation of the models

NASA Astrophysics Data System (ADS)

Ileană, Ioan; Rotar, Corina; Muntean, Maria; Ceuca, Emilian

2010-11-01

In case that a pattern recognition system is used for images recognition (in robot vision, handwritten recognition etc.), the system must have the capacity to identify an object indifferently of its size or position in the image. The problem of the invariance of recognition can be approached in some fundamental modes. One may apply the similarity criterion used in associative recall. The original pattern is replaced by a mathematical transform that assures some invariance (e.g. the value of two-dimensional Fourier transformation is translation invariant, the value of Mellin transformation is scale invariant). In a different approach the original pattern is represented through a set of features, each of them being coded indifferently of the position, orientation or position of the pattern. Generally speaking, it is easy to obtain invariance in relation with one transformation group, but is difficult to obtain simultaneous invariance at rotation, translation and scale. In this paper we analyze some methods to achieve invariant recognition of images, particularly for digit images. A great number of experiments are due and the conclusions are underplayed in the paper.

Estuarial fingerprinting through multidimensional fluorescence and multivariate analysis.

PubMed

Hall, Gregory J; Clow, Kerin E; Kenny, Jonathan E

2005-10-01

As part of a strategy for preventing the introduction of aquatic nuisance species (ANS) to U.S. estuaries, ballast water exchange (BWE) regulations have been imposed. Enforcing these regulations requires a reliable method for determining the port of origin of water in the ballast tanks of ships entering U.S. waters. This study shows that a three-dimensional fluorescence fingerprinting technique, excitation emission matrix (EEM) spectroscopy, holds great promise as a ballast water analysis tool. In our technique, EEMs are analyzed by multivariate classification and curve resolution methods, such as N-way partial least squares Regression-discriminant analysis (NPLS-DA) and parallel factor analysis (PARAFAC). We demonstrate that classification techniques can be used to discriminate among sampling sites less than 10 miles apart, encompassing Boston Harbor and two tributaries in the Mystic River Watershed. To our knowledge, this work is the first to use multivariate analysis to classify water as to location of origin. Furthermore, it is shown that curve resolution can show seasonal features within the multidimensional fluorescence data sets, which correlate with difficulty in classification.

Isomap transform for segmenting human body shapes.

PubMed

Cerveri, P; Sarro, K J; Marchente, M; Barros, R M L

2011-09-01

Segmentation of the 3D human body is a very challenging problem in applications exploiting volume capture data. Direct clustering in the Euclidean space is usually complex or even unsolvable. This paper presents an original method based on the Isomap (isometric feature mapping) transform of the volume data-set. The 3D articulated posture is mapped by Isomap in the pose of Da Vinci's Vitruvian man. The limbs are unrolled from each other and separated from the trunk and pelvis, and the topology of the human body shape is recovered. In such a configuration, Hoshen-Kopelman clustering applied to concentric spherical shells is used to automatically group points into the labelled principal curves. Shepard interpolation is utilised to back-map points of the principal curves into the original volume space. The experimental results performed on many different postures have proved the validity of the proposed method. Reliability of less than 2 cm and 3° in the location of the joint centres and direction axes of rotations has been obtained, respectively, which qualifies this procedure as a potential tool for markerless motion analysis.

Feature Selection for Classification of Polar Regions Using a Fuzzy Expert System

NASA Technical Reports Server (NTRS)

Penaloza, Mauel A.; Welch, Ronald M.

1996-01-01

Labeling, feature selection, and the choice of classifier are critical elements for classification of scenes and for image understanding. This study examines several methods for feature selection in polar regions, including the list, of a fuzzy logic-based expert system for further refinement of a set of selected features. Six Advanced Very High Resolution Radiometer (AVHRR) Local Area Coverage (LAC) arctic scenes are classified into nine classes: water, snow / ice, ice cloud, land, thin stratus, stratus over water, cumulus over water, textured snow over water, and snow-covered mountains. Sixty-seven spectral and textural features are computed and analyzed by the feature selection algorithms. The divergence, histogram analysis, and discriminant analysis approaches are intercompared for their effectiveness in feature selection. The fuzzy expert system method is used not only to determine the effectiveness of each approach in classifying polar scenes, but also to further reduce the features into a more optimal set. For each selection method,features are ranked from best to worst, and the best half of the features are selected. Then, rules using these selected features are defined. The results of running the fuzzy expert system with these rules show that the divergence method produces the best set features, not only does it produce the highest classification accuracy, but also it has the lowest computation requirements. A reduction of the set of features produced by the divergence method using the fuzzy expert system results in an overall classification accuracy of over 95 %. However, this increase of accuracy has a high computation cost.

Comparison of Topographic Profiles Across Venus' Coronae and Craters: Implications for Corona Origin Hypothesis

NASA Astrophysics Data System (ADS)

Stoddard, P. R.; Jurdy, D. M.

2006-12-01

Venus' surface hosts nearly 1000 unambiguous impact craters, ranging in diameter from 1.5 to 280 km. Although the majority of these are pristine, slightly less than 200 have been modified by either volcanic or tectonic activity or both. In addition, numerous researchers have identified hundreds of ring-like features of varying morphology, termed "coronae." These have typically been thought of as having a diapiric or volcanic origin. Recently, however, based on the circular to quasi-circular nature of coronae, an alternative origin - impact - has been proposed. We compare the profiles across agreed-upon craters to several coronae that have been suggested as impact sites. For each feature, 36 profiles (taken every ten degrees) are aligned and then averaged together. For Mead, Cleopatra, Meitner, and Isabella craters, the profiles display the typical rim and basin structure expected for craters, but for Klenova crater the average is more domal, with only a few of the individual profiles looking crater-like. Among the "contested" coronae, the average profiles for Eurynome, Maya, and C21 appear crater-like, albeit with more variation among the individual profiles than seen in the agreed-upon craters. Anquet has a rim-and-basin structure, but unlike typical craters, the basin is elevated above the surrounding plains. Acrea appears to be a small hill in a large depression, again with a high degree of variability among the profiles. Ninhursag is clearly domal, and cannot be taken as a crater. A summary of the variability of the profiles - where 100% correlation would indicate perfect circular symmetry - indicates that, with the exception of Klenova, those features universally agreed-upon as craters have the highest correlation percentages - all at or above 80%. The disputed features are not as circular, although C21 is close. Based on this analysis, we conclude that Klenova has been mischaracterized as an impact crater, and that C21 and some other features previously classified as coronae may indeed be of impact origin. More careful analyses will be necessary to assess the origin of similar features.

Genomic characterization of biliary tract cancers identifies driver genes and predisposing mutations.

PubMed

Wardell, Christopher P; Fujita, Masashi; Yamada, Toru; Simbolo, Michele; Fassan, Matteo; Karlic, Rosa; Polak, Paz; Kim, Jaegil; Hatanaka, Yutaka; Maejima, Kazuhiro; Lawlor, Rita T; Nakanishi, Yoshitsugu; Mitsuhashi, Tomoko; Fujimoto, Akihiro; Furuta, Mayuko; Ruzzenente, Andrea; Conci, Simone; Oosawa, Ayako; Sasaki-Oku, Aya; Nakano, Kaoru; Tanaka, Hiroko; Yamamoto, Yujiro; Michiaki, Kubo; Kawakami, Yoshiiku; Aikata, Hiroshi; Ueno, Masaki; Hayami, Shinya; Gotoh, Kunihito; Ariizumi, Shun-Ichi; Yamamoto, Masakazu; Yamaue, Hiroki; Chayama, Kazuaki; Miyano, Satoru; Getz, Gad; Scarpa, Aldo; Hirano, Satoshi; Nakamura, Toru; Nakagawa, Hidewaki

2018-05-01

Biliary tract cancers (BTCs) are clinically and pathologically heterogeneous and respond poorly to treatment. Genomic profiling can offer a clearer understanding of their carcinogenesis, classification and treatment strategy. We performed large-scale genome sequencing analyses on BTCs to investigate their somatic and germline driver events and characterize their genomic landscape. We analyzed 412 BTC samples from Japanese and Italian populations, 107 by whole-exome sequencing (WES), 39 by whole-genome sequencing (WGS), and a further 266 samples by targeted sequencing. The subtypes were 136 intrahepatic cholangiocarcinomas (ICCs), 101 distal cholangiocarcinomas (DCCs), 109 peri-hilar type cholangiocarcinomas (PHCs), and 66 gallbladder or cystic duct cancers (GBCs/CDCs). We identified somatic alterations and searched for driver genes in BTCs, finding pathogenic germline variants of cancer-predisposing genes. We predicted cell-of-origin for BTCs by combining somatic mutation patterns and epigenetic features. We identified 32 significantly and commonly mutated genes including TP53, KRAS, SMAD4, NF1, ARID1A, PBRM1, and ATR, some of which negatively affected patient prognosis. A novel deletion of MUC17 at 7q22.1 affected patient prognosis. Cell-of-origin predictions using WGS and epigenetic features suggest hepatocyte-origin of hepatitis-related ICCs. Deleterious germline mutations of cancer-predisposing genes such as BRCA1, BRCA2, RAD51D, MLH1, or MSH2 were detected in 11% (16/146) of BTC patients. BTCs have distinct genetic features including somatic events and germline predisposition. These findings could be useful to establish treatment and diagnostic strategies for BTCs based on genetic information. We here analyzed genomic features of 412 BTC samples from Japanese and Italian populations. A total of 32 significantly and commonly mutated genes were identified, some of which negatively affected patient prognosis, including a novel deletion of MUC17 at 7q22.1. Cell-of-origin predictions using WGS and epigenetic features suggest hepatocyte-origin of hepatitis-related ICCs. Deleterious germline mutations of cancer-predisposing genes were detected in 11% of patients with BTC. BTCs have distinct genetic features including somatic events and germline predisposition. Copyright © 2018 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

A common red algal origin of the apicomplexan, dinoflagellate, and heterokont plastids.

PubMed

Janouskovec, Jan; Horák, Ales; Oborník, Miroslav; Lukes, Julius; Keeling, Patrick J

2010-06-15

The discovery of a nonphotosynthetic plastid in malaria and other apicomplexan parasites has sparked a contentious debate about its evolutionary origin. Molecular data have led to conflicting conclusions supporting either its green algal origin or red algal origin, perhaps in common with the plastid of related dinoflagellates. This distinction is critical to our understanding of apicomplexan evolution and the evolutionary history of endosymbiosis and photosynthesis; however, the two plastids are nearly impossible to compare due to their nonoverlapping information content. Here we describe the complete plastid genome sequences and plastid-associated data from two independent photosynthetic lineages represented by Chromera velia and an undescribed alga CCMP3155 that we show are closely related to apicomplexans. These plastids contain a suite of features retained in either apicomplexan (four plastid membranes, the ribosomal superoperon, conserved gene order) or dinoflagellate plastids (form II Rubisco acquired by horizontal transfer, transcript polyuridylylation, thylakoids stacked in triplets) and encode a full collective complement of their reduced gene sets. Together with whole plastid genome phylogenies, these characteristics provide multiple lines of evidence that the extant plastids of apicomplexans and dinoflagellates were inherited by linear descent from a common red algal endosymbiont. Our phylogenetic analyses also support their close relationship to plastids of heterokont algae, indicating they all derive from the same endosymbiosis. Altogether, these findings support a relatively simple path of linear descent for the evolution of photosynthesis in a large proportion of algae and emphasize plastid loss in several lineages (e.g., ciliates, Cryptosporidium, and Phytophthora).

A machine learning approach to galaxy-LSS classification - I. Imprints on halo merger trees

NASA Astrophysics Data System (ADS)

Hui, Jianan; Aragon, Miguel; Cui, Xinping; Flegal, James M.

2018-04-01

The cosmic web plays a major role in the formation and evolution of galaxies and defines, to a large extent, their properties. However, the relation between galaxies and environment is still not well understood. Here, we present a machine learning approach to study imprints of environmental effects on the mass assembly of haloes. We present a galaxy-LSS machine learning classifier based on galaxy properties sensitive to the environment. We then use the classifier to assess the relevance of each property. Correlations between galaxy properties and their cosmic environment can be used to predict galaxy membership to void/wall or filament/cluster with an accuracy of 93 per cent. Our study unveils environmental information encoded in properties of haloes not normally considered directly dependent on the cosmic environment such as merger history and complexity. Understanding the physical mechanism by which the cosmic web is imprinted in a halo can lead to significant improvements in galaxy formation models. This is accomplished by extracting features from galaxy properties and merger trees, computing feature scores for each feature and then applying support vector machine (SVM) to different feature sets. To this end, we have discovered that the shape and depth of the merger tree, formation time, and density of the galaxy are strongly associated with the cosmic environment. We describe a significant improvement in the original classification algorithm by performing LU decomposition of the distance matrix computed by the feature vectors and then using the output of the decomposition as input vectors for SVM.

Medusae Fossae

NASA Technical Reports Server (NTRS)

2002-01-01

[figure removed for brevity, see original site] (Released 31 July 2002) This image crosses the equator at about 155 W longitude and shows a sample of the middle member of the Medusae Fossae formation. The layers exposed in the southeast-facing scarp suggest that there is a fairly competent unit underlying the mesa in the center of the image. Dust-avalanches are apparent in the crater depression near the middle of the image. The mesa of Medusae Fossae material has the geomorphic signatures that are typical of the formation elsewhere on Mars, but the surface is probably heavily mantled with fine dust, masking the small-scale character of the unit. The close proximity of the Medusae Fossae unit to the Tharsis region may suggest that it is an ignimbrite or volcanic airfall deposit, but it's eroded character hasn't preserved the primary depositional features that would give away the secrets of formation. One of the most interesting feature in the image is the high-standing knob at the base of the scarp in the lower portion of the image. This knob or butte is high standing because it is composed of material that is not as easily eroded as the rest of the unit. There are a number of possible explanations for this feature, including volcano, inverted crater, or some localized process that caused once friable material to become cemented. Another interesting set of features are the long troughs on the slope in the lower portion of the image. The fact that the features keep the same width for the entire length suggests that these are not simple landslides.

Detail of front entry stairs showing original boot scrape set ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

Detail of front entry stairs showing original boot scrape set in concrete, facing northwest. - Albrook Air Force Station, Company Officer's Quarters, East side of Canfield Avenue, Balboa, Former Panama Canal Zone, CZ

Mapping hydrological environments in central Amazonia: ground validation and surface model based on SRTM DEM data corrected for deforestation

NASA Astrophysics Data System (ADS)

Moulatlet, G. M.; Rennó, C. D.; Costa, F. R. C.; Emilio, T.; Schietti, J.

2015-03-01

One of the most important freely available digital elevation models (DEMs) for Amazonia is the one obtained by the Shuttle Radar Topography Mission (SRTM). However, since SRTM tends to represent the vegetation surface instead of the ground surface, the broad use of SRTM DEM as a framework for terrain description in Amazonia is hampered by the presence of deforested areas. We present here two data sets: (1) a deforestation-corrected SRTM DEM for the interfluve between the Purus and Madeira rivers, in central Amazonia, which passed through a careful identification of different environments and has deforestation features corrected by a new method of increasing pixel values of the DEM (Rennó, 2009); and (2) a set of 18 hydrological-topographic descriptors based on the corrected SRTM DEM. Deforestation features are related with the opening of an 800 km road in the central part of the interfluve and occupancy of its vicinity. We used topographic profiles from the pristine forest to the deforested feature to evaluate the recovery of the original canopy coverage by minimizing canopy height variation (corrections ranged from 1 to 38 m). The hydrological-topographic description was obtained by the Height Above the Nearest Drainage (HAND) algorithm, which normalizes the terrain elevation (above sea level) by the elevation of the nearest hydrologically connected drainage. The validation of the HAND data set was done by in situ hydrological description of 110 km of walking trails also available in this data set. The new SRTM DEM expands the applicability of SRTM data for landscape modelling; the data sets of hydrological features based on topographic modelling are undoubtedly appropriate for ecological modelling and an important contribution to environmental mapping of Amazonia. The deforestation-corrected SRTM DEM is available at http://ppbio.inpa.gov.br/knb/metacat/naman.318.3/ppbio; the polygons selected for deforestation correction are available at http://ppbio.inpa.gov.br/knb/metacat/naman.317.3/ppbio; the set of hydrological-topographic descriptors is available at http://ppbio.inpa.gov.br/knb/metacat/naman.544.2/ppbio; the environmental description of access trails is available at http://ppbio.inpa.gov.br/knb/metacat/naman.541.2/ppbio; and the limits of deforestation corrections and drainage validation are available at http://ppbio.inpa.gov.br/knb/metacat/liliandias.38.1/ppbio.

Testing Product Generation in Software Product Lines Using Pairwise for Features Coverage

NASA Astrophysics Data System (ADS)

Pérez Lamancha, Beatriz; Polo Usaola, Macario

A Software Product Lines (SPL) is "a set of software-intensive systems sharing a common, managed set of features that satisfy the specific needs of a particular market segment or mission and that are developed from a common set of core assets in a prescribed way". Variability is a central concept that permits the generation of different products of the family by reusing core assets. It is captured through features which, for a SPL, define its scope. Features are represented in a feature model, which is later used to generate the products from the line. From the testing point of view, testing all the possible combinations in feature models is not practical because: (1) the number of possible combinations (i.e., combinations of features for composing products) may be untreatable, and (2) some combinations may contain incompatible features. Thus, this paper resolves the problem by the implementation of combinatorial testing techniques adapted to the SPL context.

High-level intuitive features (HLIFs) for intuitive skin lesion description.

PubMed

Amelard, Robert; Glaister, Jeffrey; Wong, Alexander; Clausi, David A

2015-03-01

A set of high-level intuitive features (HLIFs) is proposed to quantitatively describe melanoma in standard camera images. Melanoma is the deadliest form of skin cancer. With rising incidence rates and subjectivity in current clinical detection methods, there is a need for melanoma decision support systems. Feature extraction is a critical step in melanoma decision support systems. Existing feature sets for analyzing standard camera images are comprised of low-level features, which exist in high-dimensional feature spaces and limit the system's ability to convey intuitive diagnostic rationale. The proposed HLIFs were designed to model the ABCD criteria commonly used by dermatologists such that each HLIF represents a human-observable characteristic. As such, intuitive diagnostic rationale can be conveyed to the user. Experimental results show that concatenating the proposed HLIFs with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low-level features with statistical significance. An example of a graphical interface for providing intuitive rationale is given.

‘Metabolically healthy obesity’: Origins and implications

PubMed Central

Denis, Gerald V.; Obin, Martin S.

2013-01-01

When humans eat more and exercise less, they tend to become obese and unhealthy. The molecular pathways that link obesity to serious diseases like Type 2 diabetes and cardiovascular disease have become a subject of intensive scientific investigation because the exploding prevalence of obesity worldwide represents a grave new threat to the health of hundreds of millions of people. However, obesity is not always destiny. Two important clinical populations have been valuable to understand the mechanisms behind this conundrum: individuals who exhibit metabolic dysfunction, diabetes and elevated cardiovascular disease risk despite a lean body type, and individuals who are relatively protected from these dangers despite significant obesity. Study of this second group of ‘metabolically healthy obese’ people in particular has been revealing because such individuals exhibit specific, identifiable, anatomic, cellular and molecular features that set them apart from the rest of us who suffer declining health with increasing weight. Here, we examine some of these features, including some mouse models that are informative of mechanism, and suggest hypotheses for further study, including the possibility that genes and pathways of the immune system might offer new diagnostic or therapeutic targets. PMID:23068072

Supraglacial channel inception: Modeling and processes

NASA Astrophysics Data System (ADS)

Mantelli, E.; Camporeale, C.; Ridolfi, L.

2015-09-01

Supraglacial drainage systems play a key role in glacial hydrology. Nevertheless, physical processes leading to spatial organization in supraglacial networks are still an open issue. In the present work we thus address from a quantitative point of view the question of what is the physics leading to widely observed patterns made up of evenly spaced channels. To this aim, we set up a novel mathematical model describing a condition antecedent channel formation, i.e., the down-glacier flow of a distributed meltwater film. We then perform a linear stability analysis to assess whether the ice-water interface undergoes a morphological instability compatible with observed patterns. The instability is detected, its features depending on glacier surface slope, ice friction factor, and water as well as ice thermal conditions. By contrast, in our model channel spacing is solely hydrodynamically driven and relies on the interplay between pressure perturbations, flow depth response, and Reynolds stresses. Geometrical features of the predicted pattern are quantitatively consistent with available field data. The hydrodynamic origin of supraglacial channel morphogenesis suggests that alluvial patterns might share the same physical controls.

Beyond the frontiers of neuronal types

PubMed Central

Battaglia, Demian; Karagiannis, Anastassios; Gallopin, Thierry; Gutch, Harold W.; Cauli, Bruno

2012-01-01

Cortical neurons and, particularly, inhibitory interneurons display a large diversity of morphological, synaptic, electrophysiological, and molecular properties, as well as diverse embryonic origins. Various authors have proposed alternative classification schemes that rely on the concomitant observation of several multimodal features. However, a broad variability is generally observed even among cells that are grouped into a same class. Furthermore, the attribution of specific neurons to a single defined class is often difficult, because individual properties vary in a highly graded fashion, suggestive of continua of features between types. Going beyond the description of representative traits of distinct classes, we focus here on the analysis of atypical cells. We introduce a novel paradigm for neuronal type classification, assuming explicitly the existence of a structured continuum of diversity. Our approach, grounded on the theory of fuzzy sets, identifies a small optimal number of model archetypes. At the same time, it quantifies the degree of similarity between these archetypes and each considered neuron. This allows highlighting archetypal cells, which bear a clear similarity to a single model archetype, and edge cells, which manifest a convergence of traits from multiple archetypes. PMID:23403725

Automatic segmentation of multimodal brain tumor images based on classification of super-voxels.

PubMed

Kadkhodaei, M; Samavi, S; Karimi, N; Mohaghegh, H; Soroushmehr, S M R; Ward, K; All, A; Najarian, K

2016-08-01

Despite the rapid growth in brain tumor segmentation approaches, there are still many challenges in this field. Automatic segmentation of brain images has a critical role in decreasing the burden of manual labeling and increasing robustness of brain tumor diagnosis. We consider segmentation of glioma tumors, which have a wide variation in size, shape and appearance properties. In this paper images are enhanced and normalized to same scale in a preprocessing step. The enhanced images are then segmented based on their intensities using 3D super-voxels. Usually in images a tumor region can be regarded as a salient object. Inspired by this observation, we propose a new feature which uses a saliency detection algorithm. An edge-aware filtering technique is employed to align edges of the original image to the saliency map which enhances the boundaries of the tumor. Then, for classification of tumors in brain images, a set of robust texture features are extracted from super-voxels. Experimental results indicate that our proposed method outperforms a comparable state-of-the-art algorithm in term of dice score.

Volumetric data analysis using Morse-Smale complexes

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

Natarajan, V; Pascucci, V

2005-10-13

The 3D Morse-Smale complex is a fundamental topological construct that partitions the domain of a real-valued function into regions having uniform gradient flow behavior. In this paper, we consider the construction and selective presentation of cells of the Morse-Smale complex and their use in the analysis and visualization of scientific datasets. We take advantage of the fact that cells of different dimension often characterize different types of features present in the data. For example, critical points pinpoint changes in topology by showing where components of the level sets are created, destroyed or modified in genus. Edges of the Morse-Smale complexmore » extract filament-like features that are not explicitly modeled in the original data. Interactive selection and rendering of portions of the Morse-Smale complex introduces fundamental data management challenges due to the unstructured nature of the complex even for structured inputs. We describe a data structure that stores the Morse-Smale complex and allows efficient selective traversal of regions of interest. Finally, we illustrate the practical use of this approach by applying it to cryo-electron microscopy data of protein molecules.« less

Domain Regeneration for Cross-Database Micro-Expression Recognition

NASA Astrophysics Data System (ADS)

Zong, Yuan; Zheng, Wenming; Huang, Xiaohua; Shi, Jingang; Cui, Zhen; Zhao, Guoying

2018-05-01

In this paper, we investigate the cross-database micro-expression recognition problem, where the training and testing samples are from two different micro-expression databases. Under this setting, the training and testing samples would have different feature distributions and hence the performance of most existing micro-expression recognition methods may decrease greatly. To solve this problem, we propose a simple yet effective method called Target Sample Re-Generator (TSRG) in this paper. By using TSRG, we are able to re-generate the samples from target micro-expression database and the re-generated target samples would share same or similar feature distributions with the original source samples. For this reason, we can then use the classifier learned based on the labeled source samples to accurately predict the micro-expression categories of the unlabeled target samples. To evaluate the performance of the proposed TSRG method, extensive cross-database micro-expression recognition experiments designed based on SMIC and CASME II databases are conducted. Compared with recent state-of-the-art cross-database emotion recognition methods, the proposed TSRG achieves more promising results.

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

Kasuga, Toshihiro; Usui, Fumihiko; Hasegawa, Sunao

Primitive, outer-belt asteroids are generally of low albedo, reflecting carbonaceous compositions like those of CI and CM meteorites. However, a few outer-belt asteroids having high albedos are known, suggesting the presence of unusually reflective surface minerals or, conceivably, even exposed water ice. Here, we present near-infrared (1.1-2.5 {mu}m) spectra of four outer-belt C-complex asteroids with albedos {>=}0.1. We find no absorption features characteristic of water ice (near 1.5 and 2.0 {mu}m) in the objects. Intimate mixture models set limits to the water ice by weight {<=}2%. Asteroids (723) Hammonia and (936) Kunigunde are featureless and have (60%-95%) amorphous Mg pyroxenesmore » that might explain the high albedos. Asteroid (1276) Ucclia also shows a featureless reflection spectrum with (50%-60%) amorphous Mg pyroxenes. Asteroid (1576) Fabiola shows a possible weak, broad absorption band (1.5-2.1 {mu}m). The feature can be reproduced by (80%) amorphous Mg pyroxenes or orthopyroxene (crystalline silicate), either of which is likely to cause its high albedo. We discuss the origin of high-albedo components in primitive asteroids.« less

Pedestrian detection in crowded scenes with the histogram of gradients principle

NASA Astrophysics Data System (ADS)

Sidla, O.; Rosner, M.; Lypetskyy, Y.

2006-10-01

This paper describes a close to real-time scale invariant implementation of a pedestrian detector system which is based on the Histogram of Oriented Gradients (HOG) principle. Salient HOG features are first selected from a manually created very large database of samples with an evolutionary optimization procedure that directly trains a polynomial Support Vector Machine (SVM). Real-time operation is achieved by a cascaded 2-step classifier which uses first a very fast linear SVM (with the same features as the polynomial SVM) to reject most of the irrelevant detections and then computes the decision function with a polynomial SVM on the remaining set of candidate detections. Scale invariance is achieved by running the detector of constant size on scaled versions of the original input images and by clustering the results over all resolutions. The pedestrian detection system has been implemented in two versions: i) fully body detection, and ii) upper body only detection. The latter is especially suited for very busy and crowded scenarios. On a state-of-the-art PC it is able to run at a frequency of 8 - 20 frames/sec.

A Fairy-Tale Landscape

NASA Technical Reports Server (NTRS)

2008-01-01

[figure removed for brevity, see original site] Click on image for animation

Fun, fairy-tale nicknames have been assigned to features in this animated view of the workspace reachable by the robotic arm of NASA's Phoenix Mars Lander. For example, 'Sleepy Hollow' denotes a trench and 'Headless' designates a rock.

A 'National Park,' marked by purple text and a purple arrow, has been set aside for protection until scientists and engineers have tested the operation of the robotic scoop. First touches with the scoop will be to the left of the 'National Park' line.

Scientists use such informal names for easy identification of features of interest during the mission.

In this view, rocks are circled in yellow, other areas of interest in green. The images were taken by the lander's 7-foot mast camera, called the Surface Stereo Imager.

The Phoenix Mission is led by the University of Arizona, Tucson, on behalf of NASA. Project management of the mission is by NASA's Jet Propulsion Laboratory, Pasadena, Calif. Spacecraft development is by Lockheed Martin Space Systems, Denver.

Using cellular automata to generate image representation for biological sequences.

PubMed

Xiao, X; Shao, S; Ding, Y; Huang, Z; Chen, X; Chou, K-C

2005-02-01

A novel approach to visualize biological sequences is developed based on cellular automata (Wolfram, S. Nature 1984, 311, 419-424), a set of discrete dynamical systems in which space and time are discrete. By transforming the symbolic sequence codes into the digital codes, and using some optimal space-time evolvement rules of cellular automata, a biological sequence can be represented by a unique image, the so-called cellular automata image. Many important features, which are originally hidden in a long and complicated biological sequence, can be clearly revealed thru its cellular automata image. With biological sequences entering into databanks rapidly increasing in the post-genomic era, it is anticipated that the cellular automata image will become a very useful vehicle for investigation into their key features, identification of their function, as well as revelation of their "fingerprint". It is anticipated that by using the concept of the pseudo amino acid composition (Chou, K.C. Proteins: Structure, Function, and Genetics, 2001, 43, 246-255), the cellular automata image approach can also be used to improve the quality of predicting protein attributes, such as structural class and subcellular location.

Preliminary Geological Map of the Fortuna Tessera (V-2) Quadrangle, Venus

NASA Technical Reports Server (NTRS)

Ivanov, M. A.; Head, J. W.

2009-01-01

The Fortuna Tessera quadrangle (50-75 N, 0-60 E) is a large region of tessera [1] that includes the major portion of Fortuna and Laima Tesserae [2]. Near the western edge of the map area, Fortuna Tessera is in contact with the highest moun-tain belt on Venus, Maxwell Montes. Deformational belts of Sigrun-Manto Fossae (extensional structures) and Au ra Dorsa (contractional structures) separate the tessera regions. Highly deformed terrains correspond to elevated regions and mildly deformed units are with low-lying areas. The sets of features within the V-2 quadrangle permit us to address the following important questions: (1) the timing and processes of crustal thickening/thinning, (2) the nature and origin of tesserae and deformation belts and their relation to crustal thickening processes, (3) the existence or absence of major evolutionary trends of volcanism and tectonics. The key feature in all of these problems is the regional sequence of events. Here we present description of units that occur in the V-2 quadrangle, their regional correlation chart (Fig. 1), and preliminary geological map of the region (Fig. 2).

Childhood maxillary myxoma: case report and review of management.

PubMed

Tincani, Alfio Jose; Araújo, Priscila P C; DelNegro, André; Altemani, Albina; Martins, Antonio Santos

2007-11-01

Myxomas are benign neoplasms of uncertain origin and etiology. First described by Virchow in 1863, they are derived from primitive mesenchymal structures and feature components of the umbilical cord. More recently, in 1995, Takahashi et al., through extensive research confirmed the fibroblastic and histiocytic origin of the tumor. We present a case in a female infant whose outcome and follow-up are discussed as well as a literature review in order to discuss many features of this rare pathology.

5. GENERAL VIEW SHOWING ORIGINAL SETTING AT WHARFSIDE WITH CONTAINERIZED ...

Library of Congress Historic Buildings Survey, Historic Engineering Record, Historic Landscapes Survey

5. GENERAL VIEW SHOWING ORIGINAL SETTING AT WHARFSIDE WITH CONTAINERIZED FREIGHT LOADING EQUIPMENT AT PORT OF OAKLAND FACILITY - Oakland Army Base, Transit Shed, East of Dunkirk Street & South of Burma Road, Oakland, Alameda County, CA

Mechanical properties of the fiberglass prepreg system used for the National Transonic Facility replacement blade set

NASA Technical Reports Server (NTRS)

Young, Clarence P., Jr.; Wallace, John W.

1991-01-01

The results are presented of mechanical and physical properties characterization testing for the fiber glass prepreg system used to fabricate 15 of the replacement set of 25 fan blades for the National Transonic Facility. The fan blades were fabricated to be identical to the original blade set with the exception that the 7576 style E glass cloth used for the replacement set has a different surface finish than the original 7576 cloth. The 7781 E glass cloth and resin system were unchanged. The data are presented for elevated, room, and cryogenic temperatures. The results are compared with data from the original blade set and evaluated against selected structural design criteria. Test experience is described along with recommendations for future testing of these materials if required.

Probing collective oscillation of d-orbital electrons at the nanoscale

NASA Astrophysics Data System (ADS)

Dhall, Rohan; Vigil-Fowler, Derek; Houston Dycus, J.; Kirste, Ronny; Mita, Seiji; Sitar, Zlatko; Collazo, Ramon; LeBeau, James M.

2018-02-01

Here, we demonstrate that high energy electrons can be used to explore the collective oscillation of s, p, and d orbital electrons at the nanometer length scale. Using epitaxial AlGaN/AlN quantum wells as a test system, we observe the emergence of additional features in the loss spectrum with the increasing Ga content. A comparison of the observed spectra with ab-initio theory reveals that the origin of these spectral features lies in excitations of 3d-electrons contributed by Ga. We find that these modes differ in energy from the valence electron plasmons in Al1-xGaxN due to the different polarizabilities of the d electrons. Finally, we study the dependence of observed spectral features on the Ga content, lending insights into the origin of these spectral features, and their coupling with electron-hole excitations.

Paleokarst processes in the Eocene limestones of the Pyramids Plateau, Giza, Egypt

NASA Astrophysics Data System (ADS)

El Aref, M. M.; Refai, E.

The Eocene limestones of the Pyramids plateau are characterized by landforms of stepped terraced escarpment and karst ridges with isolated hills. The carbonate country rocks are also dominated by minor surface, surface to subsurface and subsurface solution features associated with karst products. The systematic field observations eludicate the denudation trend of the minor solution features and suggest the origin of the regional landscapes. The lithologic and structural characters of the limestone country rocks comprise the main factors controlling the surface and subsurface karst evolution. The development of the karst features and the associated sediments in the study area provides information on the paleohydrolic, chemical and climatic environments involved in the origin of the karstification.

Color heterogeneity of the surface of Phobos - Relationships to geologic features and comparison to meteorite analogs

NASA Technical Reports Server (NTRS)

Murchie, Scott L.; Britt, Daniel T.; Head, James W.; Pratt, Stephen F.; Fisher, Paul C.

1991-01-01

Color ratio images created from multispectral observations of Phobos are analyzed in order to characterize the spectral properties of Phobos' surface, to assess their spatial distributions and relationships with geologic features, and to compare Phobos' surface materials with possible meteorite analogs. Data calibration and processing is briefly discussed, and the observed spectral properties of Phobos and their lateral variations are examined. Attention is then given to the color properties of different types of impact craters, the origin of lateral variations in surface color, the relation between the spatial distribution of color properties and independently identifiable geologic features, and the relevance of color variation spatial distribution to the origin of the grooves.

Spin-related origin of the magnetotransport feature at filling factor 7/11

NASA Astrophysics Data System (ADS)

Gamez, Gerardo; Muraki, Koji

2010-03-01

Experiments by Pan et al. disclosed quantum Hall (QH) effect-like features at unconventional filling fractions, such as 4/11 and 7/11, not included in the Jain sequence [1]. These features were considered as evidence for a new class of fractional quantum Hall (FQH) states whose origin, unlike ordinary FQH states, is linked to interactions between composite fermions (CFs). However, the exact origin of these features is not well established yet. Here we focus on 7/11, where a minimum in the longitudinal resistance and a plateau-like structure in the Hall resistance are observed at a much higher field, 11.4 T, in a 30-nm quantum well (QW). Our density-dependent studies show that at this field, the FQH states flanking 7/11, viz. the 2/3 and 3/5 states, are both fully spin polarized. Despite of this fact, tilted-field experiments reveal that the 7/11 feature weakens and then disappears upon tilting. Using a CF model, we show that the spin degree of freedom may not be completely frozen in the region between the 2/3 and 3/5 states even when both states are fully polarized. Systematic studies unveil that the exact location of the 7/11 feature depends on the electron density and the QW width, in accordance with the model. Our model can also account for the reported contrasting behavior upon tilting of 7/11 and its electron-hole counterpart 4/11. [1] Pan et al., Phys. Rev. Lett. 90, 016801 (2003).

Using Gaussian windows to explore a multivariate data set

NASA Technical Reports Server (NTRS)

Jaeckel, Louis A.

1991-01-01

In an earlier paper, I recounted an exploratory analysis, using Gaussian windows, of a data set derived from the Infrared Astronomical Satellite. Here, my goals are to develop strategies for finding structural features in a data set in a many-dimensional space, and to find ways to describe the shape of such a data set. After a brief review of Gaussian windows, I describe the current implementation of the method. I give some ways of describing features that we might find in the data, such as clusters and saddle points, and also extended structures such as a 'bar', which is an essentially one-dimensional concentration of data points. I then define a distance function, which I use to determine which data points are 'associated' with a feature. Data points not associated with any feature are called 'outliers'. I then explore the data set, giving the strategies that I used and quantitative descriptions of the features that I found, including clusters, bars, and a saddle point. I tried to use strategies and procedures that could, in principle, be used in any number of dimensions.

Three-dimensional velocity models of the Mount St. Helens magmatic system using the iMUSH active-source data set

NASA Astrophysics Data System (ADS)

Kiser, E.; Levander, A.; Zelt, C. A.; Palomeras, I.; Creager, K.; Ulberg, C. W.; Schmandt, B.; Hansen, S. M.; Harder, S. H.; Abers, G. A.; Crosbie, K.

2017-12-01

Building upon previously published 2D results, this presentation will show the first 3D velocity models down to the Moho using the iMUSH (imaging Magma Under St. Helens) active-source seismic data set. Direct P and S wave travel times from 23 borehole shots recorded at approximately 6000 seismograph locations are used to model Vp, Vs, and Vp/Vs over an area extending approximately 75 km from the edifice of Mount St. Helens and down to approximately 15 km depth. At shallow depths, results show several high and low velocity anomalies that correspond well with known geological features. These include the Chehalis Basin northwest of Mount St. Helens, and the Silver Star Mountain, Spirit Lake, and Spud Mountain plutons. Starting at 4 km depth, low velocities and high Vp/Vs values are observed near Mount St. Helens, which may be associated with shallow magmatic fluids. High Vp/Vs values are also observed beneath the Indian Heaven Volcanic Field southeast of Mount St. Helens. At the regional scale, high amplitude north/south trending low and high velocity features extend from the western margin of the resolved models to approximately 30 km west of Mount St. Helens. In general these high and low velocity features also correspond to high and low Vp/Vs anomalies, respectively. These results are in agreement with previous studies that conclude that the accreted terrane Siletzia is composed of multiple igneous bodies interspersed with sedimentary units in this region. Another regional feature of interest is a broad low Vp/Vs area between Mount St. Helens, Mount Adams, and Mount Rainier that spatially correlates with the Southern Washington Cascades Conductor, indicating a non-magmatic origin to this body at shallow and mid-crustal depths. In addition to these shallow results, preliminary 3D velocity models of the entire crust will be presented that utilize both direct and reflected seismic phases from the Moho and other mid-crustal discontinuities. These models will constrain the lateral extents of lower-crustal high and low velocity features observed in previous 2D results. This information will be critical for understanding the distribution of cumulate bodies, magma reservoirs, and accreted terranes in the lower crust, and how these features have affected recent volcanic activity in this region.

New features and applications of PRESTO, a computer code for the performance of regenerative, superheated steam turbine cycles

NASA Technical Reports Server (NTRS)

Choo, Y. K.; Staiger, P. J.

1982-01-01

The code was designed to analyze performance at valves-wide-open design flow. The code can model conventional steam cycles as well as cycles that include such special features as process steam extraction and induction and feedwater heating by external heat sources. Convenience features and extensions to the special features were incorporated into the PRESTO code. The features are described, and detailed examples illustrating the use of both the original and the special features are given.

Forest Type and Above Ground Biomass Estimation Based on Sentinel-2A and WorldView-2 Data Evaluation of Predictor nd Data Suitability

NASA Astrophysics Data System (ADS)

Fritz, Andreas; Enßle, Fabian; Zhang, Xiaoli; Koch, Barbara

2016-08-01

The present study analyses the two earth observation sensors regarding their capability of modelling forest above ground biomass and forest density. Our research is carried out at two different demonstration sites. The first is located in south-western Germany (region Karlsruhe) and the second is located in southern China in Jiangle County (Province Fujian). A set of spectral and spatial predictors are computed from both, Sentinel-2A and WorldView-2 data. Window sizes in the range of 3*3 pixels to 21*21 pixels are computed in order to cover the full range of the canopy sizes of mature forest stands. Textural predictors of first and second order (grey-level-co-occurrence matrix) are calculated and are further used within a feature selection procedure. Additionally common spectral predictors from WorldView-2 and Sentinel-2A data such as all relevant spectral bands and NDVI are integrated in the analyses. To examine the most important predictors, a predictor selection algorithm is applied to the data, whereas the entire predictor set of more than 1000 predictors is used to find most important ones. Out of the original set only the most important predictors are then further analysed. Predictor selection is done with the Boruta package in R (Kursa and Rudnicki (2010)), whereas regression is computed with random forest. Prior the classification and regression a tuning of parameters is done by a repetitive model selection (100 runs), based on the .632 bootstrapping. Both are implemented in the caret R pack- age (Kuhn et al. (2016)). To account for the variability in the data set 100 independent runs are performed. Within each run 80 percent of the data is used for training and the 20 percent are used for an independent validation. With the subset of original predictors mapping of above ground biomass is performed.

The Model-Based Study of the Effectiveness of Reporting Lists of Small Feature Sets Using RNA-Seq Data.

PubMed

Kim, Eunji; Ivanov, Ivan; Hua, Jianping; Lampe, Johanna W; Hullar, Meredith Aj; Chapkin, Robert S; Dougherty, Edward R

2017-01-01

Ranking feature sets for phenotype classification based on gene expression is a challenging issue in cancer bioinformatics. When the number of samples is small, all feature selection algorithms are known to be unreliable, producing significant error, and error estimators suffer from different degrees of imprecision. The problem is compounded by the fact that the accuracy of classification depends on the manner in which the phenomena are transformed into data by the measurement technology. Because next-generation sequencing technologies amount to a nonlinear transformation of the actual gene or RNA concentrations, they can potentially produce less discriminative data relative to the actual gene expression levels. In this study, we compare the performance of ranking feature sets derived from a model of RNA-Seq data with that of a multivariate normal model of gene concentrations using 3 measures: (1) ranking power, (2) length of extensions, and (3) Bayes features. This is the model-based study to examine the effectiveness of reporting lists of small feature sets using RNA-Seq data and the effects of different model parameters and error estimators. The results demonstrate that the general trends of the parameter effects on the ranking power of the underlying gene concentrations are preserved in the RNA-Seq data, whereas the power of finding a good feature set becomes weaker when gene concentrations are transformed by the sequencing machine.

Geospatial Analytics in Retail Site Selection and Sales Prediction.

PubMed

Ting, Choo-Yee; Ho, Chiung Ching; Yee, Hui Jia; Matsah, Wan Razali

2018-03-01

Studies have shown that certain features from geography, demography, trade area, and environment can play a vital role in retail site selection, largely due to the impact they asserted on retail performance. Although the relevant features could be elicited by domain experts, determining the optimal feature set can be intractable and labor-intensive exercise. The challenges center around (1) how to determine features that are important to a particular retail business and (2) how to estimate retail sales performance given a new location? The challenges become apparent when the features vary across time. In this light, this study proposed a nonintervening approach by employing feature selection algorithms and subsequently sales prediction through similarity-based methods. The results of prediction were validated by domain experts. In this study, data sets from different sources were transformed and aggregated before an analytics data set that is ready for analysis purpose could be obtained. The data sets included data about feature location, population count, property type, education status, and monthly sales from 96 branches of a telecommunication company in Malaysia. The finding suggested that (1) optimal retail performance can only be achieved through fulfillment of specific location features together with the surrounding trade area characteristics and (2) similarity-based method can provide solution to retail sales prediction.

Segmentation schema for enhancing land cover identification: A case study using Sentinel 2 data

NASA Astrophysics Data System (ADS)

Mongus, Domen; Žalik, Borut

2018-04-01

Land monitoring is performed increasingly using high and medium resolution optical satellites, such as the Sentinel-2. However, optical data is inevitably subjected to the variable operational conditions under which it was acquired. Overlapping of features caused by shadows, soft transitions between shadowed and non-shadowed regions, and temporal variability of the observed land-cover types require radiometric corrections. This study examines a new approach to enhancing the accuracy of land cover identification that resolves this problem. The proposed method constructs an ensemble-type classification model with weak classifiers tuned to the particular operational conditions under which the data was acquired. Iterative segmentation over the learning set is applied for this purpose, where feature space is partitioned according to the likelihood of misclassifications introduced by the classification model. As these are a consequence of overlapping features, such partitioning avoids the need for radiometric corrections of the data, and divides land cover types implicitly into subclasses. As a result, improved performance of all tested classification approaches were measured during the validation that was conducted on Sentinel-2 data. The highest accuracies in terms of F1-scores were achieved using the Naive Bayes Classifier as the weak classifier, while supplementing original spectral signatures with normalised difference vegetation index and texture analysis features, namely, average intensity, contrast, homogeneity, and dissimilarity. In total, an F1-score of nearly 95% was achieved in this way, with F1-scores of each particular land cover type reaching above 90%.

Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications

PubMed Central

Stone, David B.; Tamburro, Gabriella; Fiedler, Patrique; Haueisen, Jens; Comani, Silvia

2018-01-01

Data contamination due to physiological artifacts such as those generated by eyeblinks, eye movements, and muscle activity continues to be a central concern in the acquisition and analysis of electroencephalographic (EEG) data. This issue is further compounded in EEG sports science applications where the presence of artifacts is notoriously difficult to control because behaviors that generate these interferences are often the behaviors under investigation. Therefore, there is a need to develop effective and efficient methods to identify physiological artifacts in EEG recordings during sports applications so that they can be isolated from cerebral activity related to the activities of interest. We have developed an EEG artifact detection model, the Fingerprint Method, which identifies different spatial, temporal, spectral, and statistical features indicative of physiological artifacts and uses these features to automatically classify artifactual independent components in EEG based on a machine leaning approach. Here, we optimized our method using artifact-rich training data and a procedure to determine which features were best suited to identify eyeblinks, eye movements, and muscle artifacts. We then applied our model to an experimental dataset collected during endurance cycling. Results reveal that unique sets of features are suitable for the detection of distinct types of artifacts and that the Optimized Fingerprint Method was able to correctly identify over 90% of the artifactual components with physiological origin present in the experimental data. These results represent a significant advancement in the search for effective means to address artifact contamination in EEG sports science applications. PMID:29618975

Onboard tagging for real-time quality assessment of photoplethysmograms acquired by a wireless reflectance pulse oximeter.

PubMed

Li, Kejia; Warren, Steve; Natarajan, Balasubramaniam

2012-02-01

Onboard assessment of photoplethysmogram (PPG) quality could reduce unnecessary data transmission on battery-powered wireless pulse oximeters and improve the viability of the electronic patient records to which these data are stored. These algorithms show promise to increase the intelligence level of former "dumb" medical devices: devices that acquire and forward data but leave data interpretation to the clinician or host system. To this end, the authors have developed a unique onboard feature detection algorithm to assess the quality of PPGs acquired with a custom reflectance mode, wireless pulse oximeter. The algorithm uses a Bayesian hypothesis testing method to analyze four features extracted from raw and decimated PPG data in order to determine whether the original data comprise valid PPG waveforms or whether they are corrupted by motion or other environmental influences. Based on these results, the algorithm further calculates heart rate and blood oxygen saturation from a "compact representation" structure. PPG data were collected from 47 subjects to train the feature detection algorithm and to gauge their performance. A MATLAB interface was also developed to visualize the features extracted, the algorithm flow, and the decision results, where all algorithm-related parameters and decisions were ascertained on the wireless unit prior to transmission. For the data sets acquired here, the algorithm was 99% effective in identifying clean, usable PPGs versus nonsaturated data that did not demonstrate meaningful pulsatile waveshapes, PPGs corrupted by motion artifact, and data affected by signal saturation.

Automatic Removal of Physiological Artifacts in EEG: The Optimized Fingerprint Method for Sports Science Applications.

PubMed

Stone, David B; Tamburro, Gabriella; Fiedler, Patrique; Haueisen, Jens; Comani, Silvia

2018-01-01

Data contamination due to physiological artifacts such as those generated by eyeblinks, eye movements, and muscle activity continues to be a central concern in the acquisition and analysis of electroencephalographic (EEG) data. This issue is further compounded in EEG sports science applications where the presence of artifacts is notoriously difficult to control because behaviors that generate these interferences are often the behaviors under investigation. Therefore, there is a need to develop effective and efficient methods to identify physiological artifacts in EEG recordings during sports applications so that they can be isolated from cerebral activity related to the activities of interest. We have developed an EEG artifact detection model, the Fingerprint Method, which identifies different spatial, temporal, spectral, and statistical features indicative of physiological artifacts and uses these features to automatically classify artifactual independent components in EEG based on a machine leaning approach. Here, we optimized our method using artifact-rich training data and a procedure to determine which features were best suited to identify eyeblinks, eye movements, and muscle artifacts. We then applied our model to an experimental dataset collected during endurance cycling. Results reveal that unique sets of features are suitable for the detection of distinct types of artifacts and that the Optimized Fingerprint Method was able to correctly identify over 90% of the artifactual components with physiological origin present in the experimental data. These results represent a significant advancement in the search for effective means to address artifact contamination in EEG sports science applications.

Origin of the Hadži ABC structure: An ab initio study

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

Van Hoozen, Brian L.; Petersen, Poul B.

2015-11-14

Medium and strong hydrogen bonds are well known to give rise to broad features in the vibrational spectrum often spanning several hundred wavenumbers. In some cases, these features can span over 1000 cm{sup −1} and even contain multiple broad peaks. One class of strongly hydrogen-bonded dimers that includes many different phosphinic, phosphoric, sulfinic, and selenic acid homodimers exhibits a three-peaked structure over 1500 cm{sup −1} broad. This unusual feature is often referred to as the Hadži ABC structure. The origin of this feature has been debated since its discovery in the 1950s. Only a couple of theoretical studies have attemptedmore » to interpret the origin of this feature; however, no previous study has been able to reproduce this feature from first principles. Here, we present the first ab initio calculation of the Hadži ABC structure. Using a reduced dimensionality calculation that includes four vibrational modes, we are able to reproduce the three-peak structure and much of the broadness of the feature. Our results indicate that Fermi resonances of the in-plane bend, out-of-plane bend, and combination of these bends play significant roles in explaining this feature. Much of the broadness of the feature and the ability of the OH stretch mode to couple with many overtone bending modes are captured by including an adiabatically separated dimer stretch mode in the model. This mode modulates the distance between the monomer units and accordingly the strength of the hydrogen-bonds causing the OH stretch frequency to shift from 2000 to 3000 cm{sup −1}. Using this model, we were also able to reproduce the vibrational spectrum of the deuterated isotopologue which consists of a single 500 cm{sup −1} broad feature. Whereas previous empirical studies have asserted that Fermi resonances contribute very little to this feature, our study indicates that while not appearing as a separate peak, a Fermi resonance of the in-plane bend contributes substantially to the feature.« less

Feature selection for wearable smartphone-based human activity recognition with able bodied, elderly, and stroke patients.

PubMed

Capela, Nicole A; Lemaire, Edward D; Baddour, Natalie

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations.

Feature Selection for Wearable Smartphone-Based Human Activity Recognition with Able bodied, Elderly, and Stroke Patients

PubMed Central

2015-01-01

Human activity recognition (HAR), using wearable sensors, is a growing area with the potential to provide valuable information on patient mobility to rehabilitation specialists. Smartphones with accelerometer and gyroscope sensors are a convenient, minimally invasive, and low cost approach for mobility monitoring. HAR systems typically pre-process raw signals, segment the signals, and then extract features to be used in a classifier. Feature selection is a crucial step in the process to reduce potentially large data dimensionality and provide viable parameters to enable activity classification. Most HAR systems are customized to an individual research group, including a unique data set, classes, algorithms, and signal features. These data sets are obtained predominantly from able-bodied participants. In this paper, smartphone accelerometer and gyroscope sensor data were collected from populations that can benefit from human activity recognition: able-bodied, elderly, and stroke patients. Data from a consecutive sequence of 41 mobility tasks (18 different tasks) were collected for a total of 44 participants. Seventy-six signal features were calculated and subsets of these features were selected using three filter-based, classifier-independent, feature selection methods (Relief-F, Correlation-based Feature Selection, Fast Correlation Based Filter). The feature subsets were then evaluated using three generic classifiers (Naïve Bayes, Support Vector Machine, j48 Decision Tree). Common features were identified for all three populations, although the stroke population subset had some differences from both able-bodied and elderly sets. Evaluation with the three classifiers showed that the feature subsets produced similar or better accuracies than classification with the entire feature set. Therefore, since these feature subsets are classifier-independent, they should be useful for developing and improving HAR systems across and within populations. PMID:25885272

Assessing postural stability via the correlation patterns of vertical ground reaction force components.

PubMed

Hong, Chih-Yuan; Guo, Lan-Yuen; Song, Rong; Nagurka, Mark L; Sung, Jia-Li; Yen, Chen-Wen

2016-08-02

Many methods have been proposed to assess the stability of human postural balance by using a force plate. While most of these approaches characterize postural stability by extracting features from the trajectory of the center of pressure (COP), this work develops stability measures derived from components of the ground reaction force (GRF). In comparison with previous GRF-based approaches that extract stability features from the GRF resultant force, this study proposes three feature sets derived from the correlation patterns among the vertical GRF (VGRF) components. The first and second feature sets quantitatively assess the strength and changing speed of the correlation patterns, respectively. The third feature set is used to quantify the stabilizing effect of the GRF coordination patterns on the COP. In addition to experimentally demonstrating the reliability of the proposed features, the efficacy of the proposed features has also been tested by using them to classify two age groups (18-24 and 65-73 years) in quiet standing. The experimental results show that the proposed features are considerably more sensitive to aging than one of the most effective conventional COP features and two recently proposed COM features. By extracting information from the correlation patterns of the VGRF components, this study proposes three sets of features to assess human postural stability during quiet standing. As demonstrated by the experimental results, the proposed features are not only robust to inter-trial variability but also more accurate than the tested COP and COM features in classifying the older and younger age groups. An additional advantage of the proposed approach is that it reduces the force sensing requirement from 3D to 1D, substantially reducing the cost of the force plate measurement system.

Comparison of Feature Selection Techniques in Machine Learning for Anatomical Brain MRI in Dementia.

PubMed

Tohka, Jussi; Moradi, Elaheh; Huttunen, Heikki

2016-07-01

We present a comparative split-half resampling analysis of various data driven feature selection and classification methods for the whole brain voxel-based classification analysis of anatomical magnetic resonance images. We compared support vector machines (SVMs), with or without filter based feature selection, several embedded feature selection methods and stability selection. While comparisons of the accuracy of various classification methods have been reported previously, the variability of the out-of-training sample classification accuracy and the set of selected features due to independent training and test sets have not been previously addressed in a brain imaging context. We studied two classification problems: 1) Alzheimer's disease (AD) vs. normal control (NC) and 2) mild cognitive impairment (MCI) vs. NC classification. In AD vs. NC classification, the variability in the test accuracy due to the subject sample did not vary between different methods and exceeded the variability due to different classifiers. In MCI vs. NC classification, particularly with a large training set, embedded feature selection methods outperformed SVM-based ones with the difference in the test accuracy exceeding the test accuracy variability due to the subject sample. The filter and embedded methods produced divergent feature patterns for MCI vs. NC classification that suggests the utility of the embedded feature selection for this problem when linked with the good generalization performance. The stability of the feature sets was strongly correlated with the number of features selected, weakly correlated with the stability of classification accuracy, and uncorrelated with the average classification accuracy.