Cultivating Research Pedagogies with Adolescents: Created Spaces, Engaged Participation, and Embodied Inquiry

ERIC Educational Resources Information Center

Wissman, Kelly K.; Staples, Jeanine M.; Vasudevan, Lalitha; Nichols, Rachel E.

2015-01-01

This paper conceptualizes an approach to adolescent literacies research we call "research pedagogies." This approach recognizes the pedagogical features of the research process and includes three dimensions: created spaces, engaged participation, and embodied inquiry. By drawing upon and sometimes recasting foundational anthropological…

Creating Body Shapes From Verbal Descriptions by Linking Similarity Spaces.

PubMed

Hill, Matthew Q; Streuber, Stephan; Hahn, Carina A; Black, Michael J; O'Toole, Alice J

2016-11-01

Brief verbal descriptions of people's bodies (e.g., "curvy," "long-legged") can elicit vivid mental images. The ease with which these mental images are created belies the complexity of three-dimensional body shapes. We explored the relationship between body shapes and body descriptions and showed that a small number of words can be used to generate categorically accurate representations of three-dimensional bodies. The dimensions of body-shape variation that emerged in a language-based similarity space were related to major dimensions of variation computed directly from three-dimensional laser scans of 2,094 bodies. This relationship allowed us to generate three-dimensional models of people in the shape space using only their coordinates on analogous dimensions in the language-based description space. Human descriptions of photographed bodies and their corresponding models matched closely. The natural mapping between the spaces illustrates the role of language as a concise code for body shape that captures perceptually salient global and local body features. © The Author(s) 2016.

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

Functional magnetic resonance imaging activation detection: fuzzy cluster analysis in wavelet and multiwavelet domains.

PubMed

Jahanian, Hesamoddin; Soltanian-Zadeh, Hamid; Hossein-Zadeh, Gholam-Ali

2005-09-01

To present novel feature spaces, based on multiscale decompositions obtained by scalar wavelet and multiwavelet transforms, to remedy problems associated with high dimension of functional magnetic resonance imaging (fMRI) time series (when they are used directly in clustering algorithms) and their poor signal-to-noise ratio (SNR) that limits accurate classification of fMRI time series according to their activation contents. Using randomization, the proposed method finds wavelet/multiwavelet coefficients that represent the activation content of fMRI time series and combines them to define new feature spaces. Using simulated and experimental fMRI data sets, the proposed feature spaces are compared to the cross-correlation (CC) feature space and their performances are evaluated. In these studies, the false positive detection rate is controlled using randomization. To compare different methods, several points of the receiver operating characteristics (ROC) curves, using simulated data, are estimated and compared. The proposed features suppress the effects of confounding signals and improve activation detection sensitivity. Experimental results show improved sensitivity and robustness of the proposed method compared to the conventional CC analysis. More accurate and sensitive activation detection can be achieved using the proposed feature spaces compared to CC feature space. Multiwavelet features show superior detection sensitivity compared to the scalar wavelet features. (c) 2005 Wiley-Liss, Inc.

Reverse engineering the face space: Discovering the critical features for face identification.

PubMed

Abudarham, Naphtali; Yovel, Galit

2016-01-01

How do we identify people? What are the critical facial features that define an identity and determine whether two faces belong to the same person or different people? To answer these questions, we applied the face space framework, according to which faces are represented as points in a multidimensional feature space, such that face space distances are correlated with perceptual similarities between faces. In particular, we developed a novel method that allowed us to reveal the critical dimensions (i.e., critical features) of the face space. To that end, we constructed a concrete face space, which included 20 facial features of natural face images, and asked human observers to evaluate feature values (e.g., how thick are the lips). Next, we systematically and quantitatively changed facial features, and measured the perceptual effects of these manipulations. We found that critical features were those for which participants have high perceptual sensitivity (PS) for detecting differences across identities (e.g., which of two faces has thicker lips). Furthermore, these high PS features vary minimally across different views of the same identity, suggesting high PS features support face recognition across different images of the same face. The methods described here set an infrastructure for discovering the critical features of other face categories not studied here (e.g., Asians, familiar) as well as other aspects of face processing, such as attractiveness or trait inferences.

Nonlinear Prediction As A Tool For Determining Parameters For Phase Space Reconstruction In Meteorology

NASA Astrophysics Data System (ADS)

Miksovsky, J.; Raidl, A.

Time delays phase space reconstruction represents one of useful tools of nonlinear time series analysis, enabling number of applications. Its utilization requires the value of time delay to be known, as well as the value of embedding dimension. There are sev- eral methods how to estimate both these parameters. Typically, time delay is computed first, followed by embedding dimension. Our presented approach is slightly different - we reconstructed phase space for various combinations of mentioned parameters and used it for prediction by means of the nearest neighbours in the phase space. Then some measure of prediction's success was computed (correlation or RMSE, e.g.). The position of its global maximum (minimum) should indicate the suitable combination of time delay and embedding dimension. Several meteorological (particularly clima- tological) time series were used for the computations. We have also created a MS- Windows based program in order to implement this approach - its basic features will be presented as well.

Structural features of biomass in a hybrid MBBR reactor.

PubMed

Xiao, G Y; Ganczarczyk, J

2006-03-01

The structural features of biomass present in the hybrid MBBR (Moving Bed Biofilm Reactor) aeration tank were studied in two subsequent periods, which differed in hydraulic and substrate loads. The physical characteristics of attached-growth biomass, such as, biofilm thickness, density, porosity, inner and surface fractal dimensions, and those of suspended-growth biomass, such as, floc size distribution, density, porosity, inner and surface fractal dimensions, were investigated in each study period and then compared. The results indicated that biofilm always had a higher density, geometric porosity, and a larger boundary fractal dimension than flocs. Both types of biomass were found to exhibit at least two distinct Sierpinski fractal dimensions, indicating two major different pore space populations. With the increasing wastewater flow, both types of biomass were found to shift their structural properties to larger values, except porosity and surface roughness, which decreased. Floc density and biomass Sierpinski fractals were not affected much by the system loadings.

The organization of conspecific face space in nonhuman primates

PubMed Central

Parr, Lisa A.; Taubert, Jessica; Little, Anthony C.; Hancock, Peter J. B.

2013-01-01

Humans and chimpanzees demonstrate numerous cognitive specializations for processing faces, but comparative studies with monkeys suggest that these may be the result of recent evolutionary adaptations. The present study utilized the novel approach of face space, a powerful theoretical framework used to understand the representation of face identity in humans, to further explore species differences in face processing. According to the theory, faces are represented by vectors in a multidimensional space, the centre of which is defined by an average face. Each dimension codes features important for describing a face’s identity, and vector length codes the feature’s distinctiveness. Chimpanzees and rhesus monkeys discriminated male and female conspecifics’ faces, rated by humans for their distinctiveness, using a computerized task. Multidimensional scaling analyses showed that the organization of face space was similar between humans and chimpanzees. Distinctive faces had the longest vectors and were the easiest for chimpanzees to discriminate. In contrast, distinctiveness did not correlate with the performance of rhesus monkeys. The feature dimensions for each species’ face space were visualized and described using morphing techniques. These results confirm species differences in the perceptual representation of conspecific faces, which are discussed within an evolutionary framework. PMID:22670823

On equivalent parameter learning in simplified feature space based on Bayesian asymptotic analysis.

PubMed

Yamazaki, Keisuke

2012-07-01

Parametric models for sequential data, such as hidden Markov models, stochastic context-free grammars, and linear dynamical systems, are widely used in time-series analysis and structural data analysis. Computation of the likelihood function is one of primary considerations in many learning methods. Iterative calculation of the likelihood such as the model selection is still time-consuming though there are effective algorithms based on dynamic programming. The present paper studies parameter learning in a simplified feature space to reduce the computational cost. Simplifying data is a common technique seen in feature selection and dimension reduction though an oversimplified space causes adverse learning results. Therefore, we mathematically investigate a condition of the feature map to have an asymptotically equivalent convergence point of estimated parameters, referred to as the vicarious map. As a demonstration to find vicarious maps, we consider the feature space, which limits the length of data, and derive a necessary length for parameter learning in hidden Markov models. Copyright © 2012 Elsevier Ltd. All rights reserved.

Binary classification of items of interest in a repeatable process

DOEpatents

Abell, Jeffrey A; Spicer, John Patrick; Wincek, Michael Anthony; Wang, Hui; Chakraborty, Debejyo

2015-01-06

A system includes host and learning machines. Each machine has a processor in electrical communication with at least one sensor. Instructions for predicting a binary quality status of an item of interest during a repeatable process are recorded in memory. The binary quality status includes passing and failing binary classes. The learning machine receives signals from the at least one sensor and identifies candidate features. Features are extracted from the candidate features, each more predictive of the binary quality status. The extracted features are mapped to a dimensional space having a number of dimensions proportional to the number of extracted features. The dimensional space includes most of the passing class and excludes at least 90 percent of the failing class. Received signals are compared to the boundaries of the recorded dimensional space to predict, in real time, the binary quality status of a subsequent item of interest.

Innovations in individual feature history management - The significance of feature-based temporal model

USGS Publications Warehouse

Choi, J.; Seong, J.C.; Kim, B.; Usery, E.L.

2008-01-01

A feature relies on three dimensions (space, theme, and time) for its representation. Even though spatiotemporal models have been proposed, they have principally focused on the spatial changes of a feature. In this paper, a feature-based temporal model is proposed to represent the changes of both space and theme independently. The proposed model modifies the ISO's temporal schema and adds new explicit temporal relationship structure that stores temporal topological relationship with the ISO's temporal primitives of a feature in order to keep track feature history. The explicit temporal relationship can enhance query performance on feature history by removing topological comparison during query process. Further, a prototype system has been developed to test a proposed feature-based temporal model by querying land parcel history in Athens, Georgia. The result of temporal query on individual feature history shows the efficiency of the explicit temporal relationship structure. ?? Springer Science+Business Media, LLC 2007.

Analysis and classification of commercial ham slice images using directional fractal dimension features.

PubMed

Mendoza, Fernando; Valous, Nektarios A; Allen, Paul; Kenny, Tony A; Ward, Paddy; Sun, Da-Wen

2009-02-01

This paper presents a novel and non-destructive approach to the appearance characterization and classification of commercial pork, turkey and chicken ham slices. Ham slice images were modelled using directional fractal (DF(0°;45°;90°;135°)) dimensions and a minimum distance classifier was adopted to perform the classification task. Also, the role of different colour spaces and the resolution level of the images on DF analysis were investigated. This approach was applied to 480 wafer thin ham slices from four types of hams (120 slices per type): i.e., pork (cooked and smoked), turkey (smoked) and chicken (roasted). DF features were extracted from digitalized intensity images in greyscale, and R, G, B, L(∗), a(∗), b(∗), H, S, and V colour components for three image resolution levels (100%, 50%, and 25%). Simulation results show that in spite of the complexity and high variability in colour and texture appearance, the modelling of ham slice images with DF dimensions allows the capture of differentiating textural features between the four commercial ham types. Independent DF features entail better discrimination than that using the average of four directions. However, DF dimensions reveal a high sensitivity to colour channel, orientation and image resolution for the fractal analysis. The classification accuracy using six DF dimension features (a(90°)(∗),a(135°)(∗),H(0°),H(45°),S(0°),H(90°)) was 93.9% for training data and 82.2% for testing data.

Machinery running state identification based on discriminant semi-supervised local tangent space alignment for feature fusion and extraction

NASA Astrophysics Data System (ADS)

Su, Zuqiang; Xiao, Hong; Zhang, Yi; Tang, Baoping; Jiang, Yonghua

2017-04-01

Extraction of sensitive features is a challenging but key task in data-driven machinery running state identification. Aimed at solving this problem, a method for machinery running state identification that applies discriminant semi-supervised local tangent space alignment (DSS-LTSA) for feature fusion and extraction is proposed. Firstly, in order to extract more distinct features, the vibration signals are decomposed by wavelet packet decomposition WPD, and a mixed-domain feature set consisted of statistical features, autoregressive (AR) model coefficients, instantaneous amplitude Shannon entropy and WPD energy spectrum is extracted to comprehensively characterize the properties of machinery running state(s). Then, the mixed-dimension feature set is inputted into DSS-LTSA for feature fusion and extraction to eliminate redundant information and interference noise. The proposed DSS-LTSA can extract intrinsic structure information of both labeled and unlabeled state samples, and as a result the over-fitting problem of supervised manifold learning and blindness problem of unsupervised manifold learning are overcome. Simultaneously, class discrimination information is integrated within the dimension reduction process in a semi-supervised manner to improve sensitivity of the extracted fusion features. Lastly, the extracted fusion features are inputted into a pattern recognition algorithm to achieve the running state identification. The effectiveness of the proposed method is verified by a running state identification case in a gearbox, and the results confirm the improved accuracy of the running state identification.

Vehicle logo recognition using multi-level fusion model

NASA Astrophysics Data System (ADS)

Ming, Wei; Xiao, Jianli

2018-04-01

Vehicle logo recognition plays an important role in manufacturer identification and vehicle recognition. This paper proposes a new vehicle logo recognition algorithm. It has a hierarchical framework, which consists of two fusion levels. At the first level, a feature fusion model is employed to map the original features to a higher dimension feature space. In this space, the vehicle logos become more recognizable. At the second level, a weighted voting strategy is proposed to promote the accuracy and the robustness of the recognition results. To evaluate the performance of the proposed algorithm, extensive experiments are performed, which demonstrate that the proposed algorithm can achieve high recognition accuracy and work robustly.

Spatiotemporal accessible solitons in fractional dimensions.

PubMed

Zhong, Wei-Ping; Belić, Milivoj R; Malomed, Boris A; Zhang, Yiqi; Huang, Tingwen

2016-07-01

We report solutions for solitons of the "accessible" type in globally nonlocal nonlinear media of fractional dimension (FD), viz., for self-trapped modes in the space of effective dimension 2

The formation method of the feature space for the identification of fatigued bills

NASA Astrophysics Data System (ADS)

Kang, Dongshik; Oshiro, Ayumu; Ozawa, Kenji; Mitsui, Ikugo

2014-10-01

Fatigued bills make a trouble such as the paper jam in a bill handling machine. In the discrimination of fatigued bills using an acoustic signal, the variation of an observed bill sound is considered to be one of causes in misclassification. Therefore a technique has demanded in order to make the classification of fatigued bills more efficient. In this paper, we proposed the algorithm that extracted feature quantity of bill sound from acoustic signal using the frequency difference, and carried out discrimination experiment of fatigued bill money by Support Vector Machine(SVM). The feature quantity of frequency difference can represent the frequency components of an acoustic signal is varied by the fatigued degree of bill money. The generalization performance of SVM does not depend on the size of dimensions of the feature space, even in a high dimensional feature space such as bill-acoustic signals. Furthermore, SVM can induce an optimal classifier which considers the combination of features by the virtue of polynomial kernel functions.

Detection of relationships among multi-modal brain imaging meta-features via information flow.

PubMed

Miller, Robyn L; Vergara, Victor M; Calhoun, Vince D

2018-01-15

Neuroscientists and clinical researchers are awash in data from an ever-growing number of imaging and other bio-behavioral modalities. This flow of brain imaging data, taken under resting and various task conditions, combines with available cognitive measures, behavioral information, genetic data plus other potentially salient biomedical and environmental information to create a rich but diffuse data landscape. The conditions being studied with brain imaging data are often extremely complex and it is common for researchers to employ more than one imaging, behavioral or biological data modality (e.g., genetics) in their investigations. While the field has advanced significantly in its approach to multimodal data, the vast majority of studies still ignore joint information among two or more features or modalities. We propose an intuitive framework based on conditional probabilities for understanding information exchange between features in what we are calling a feature meta-space; that is, a space consisting of many individual featurae spaces. Features can have any dimension and can be drawn from any data source or modality. No a priori assumptions are made about the functional form (e.g., linear, polynomial, exponential) of captured inter-feature relationships. We demonstrate the framework's ability to identify relationships between disparate features of varying dimensionality by applying it to a large multi-site, multi-modal clinical dataset, balance between schizophrenia patients and controls. In our application it exposes both expected (previously observed) relationships, and novel relationships rarely considered investigated by clinical researchers. To the best of our knowledge there is not presently a comparably efficient way to capture relationships of indeterminate functional form between features of arbitrary dimension and type. We are introducing this method as an initial foray into a space that remains relatively underpopulated. The framework we propose is powerful, intuitive and very efficiently provides a high-level overview of a massive data space. In our application it exposes both expected relationships and relationships very rarely considered worth investigating by clinical researchers. Copyright © 2017 Elsevier B.V. All rights reserved.

Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics

NASA Astrophysics Data System (ADS)

Wehmeyer, Christoph; Noé, Frank

2018-06-01

Inspired by the success of deep learning techniques in the physical and chemical sciences, we apply a modification of an autoencoder type deep neural network to the task of dimension reduction of molecular dynamics data. We can show that our time-lagged autoencoder reliably finds low-dimensional embeddings for high-dimensional feature spaces which capture the slow dynamics of the underlying stochastic processes—beyond the capabilities of linear dimension reduction techniques.

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.

Multiview Locally Linear Embedding for Effective Medical Image Retrieval

PubMed Central

Shen, Hualei; Tao, Dacheng; Ma, Dianfu

2013-01-01

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the “curse of dimensionality”. Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods. PMID:24349277

Exploring Tactile Perceptual Dimensions Using Materials Associated with Sensory Vocabulary.

PubMed

Sakamoto, Maki; Watanabe, Junji

2017-01-01

Considering tactile sensation when designing products is important because the decision to purchase often depends on how products feel. Numerous psychophysical studies have attempted to identify important factors that describe tactile perceptions. However, the numbers and types of major tactile dimensions reported in previous studies have varied because of differences in materials used across experiments. To obtain a more complete picture of perceptual space with regard to touch, our study focuses on using vocabulary that expresses tactile sensations as a guiding principle for collecting material samples because these types of words are expected to cover all the basic categories within tactile perceptual space. We collected 120 materials based on a variety of Japanese sound-symbolic words for tactile sensations, and used the materials to examine tactile perceptual dimensions and their associations with affective evaluations. Analysis revealed six major dimensions: "Affective evaluation and Friction," "Compliance," "Surface," "Volume," "Temperature," and "Naturalness." These dimensions include four factors that previous studies have regarded as fundamental, as well as two new factors: "Volume" and "Naturalness." Additionally, we showed that "Affective evaluation" is more closely related to the "Friction" component (slipperiness and dryness) than to other tactile perceptual features. Our study demonstrates that using vocabulary could be an effective method for selecting material samples to explore tactile perceptual space.

A Novel Method Using Abstract Convex Underestimation in Ab-Initio Protein Structure Prediction for Guiding Search in Conformational Feature Space.

PubMed

Hao, Xiao-Hu; Zhang, Gui-Jun; Zhou, Xiao-Gen; Yu, Xu-Feng

2016-01-01

To address the searching problem of protein conformational space in ab-initio protein structure prediction, a novel method using abstract convex underestimation (ACUE) based on the framework of evolutionary algorithm was proposed. Computing such conformations, essential to associate structural and functional information with gene sequences, is challenging due to the high-dimensionality and rugged energy surface of the protein conformational space. As a consequence, the dimension of protein conformational space should be reduced to a proper level. In this paper, the high-dimensionality original conformational space was converted into feature space whose dimension is considerably reduced by feature extraction technique. And, the underestimate space could be constructed according to abstract convex theory. Thus, the entropy effect caused by searching in the high-dimensionality conformational space could be avoided through such conversion. The tight lower bound estimate information was obtained to guide the searching direction, and the invalid searching area in which the global optimal solution is not located could be eliminated in advance. Moreover, instead of expensively calculating the energy of conformations in the original conformational space, the estimate value is employed to judge if the conformation is worth exploring to reduce the evaluation time, thereby making computational cost lower and the searching process more efficient. Additionally, fragment assembly and the Monte Carlo method are combined to generate a series of metastable conformations by sampling in the conformational space. The proposed method provides a novel technique to solve the searching problem of protein conformational space. Twenty small-to-medium structurally diverse proteins were tested, and the proposed ACUE method was compared with It Fix, HEA, Rosetta and the developed method LEDE without underestimate information. Test results show that the ACUE method can more rapidly and more efficiently obtain the near-native protein structure.

Concept design and alternate arrangements of orbiter mid-deck habitability features

NASA Technical Reports Server (NTRS)

Church, R. A.; Ciciora, J. A.; Porter, K. L.; Stevenson, G. E.

1976-01-01

The evaluations and recommendations for habitability features in the space shuttle orbiter mid-deck are summarized. The orbiter mission plans, the mid-deck dimensions and baseline arrangements along with crew compliments and typical activities were defined. Female and male anthropometric data based on zero-g operations were also defined. Evaluations of baseline and alternate feasible concepts provided several recommendations which are discussed.

String theory and aspects of higher dimensional gravity

NASA Astrophysics Data System (ADS)

Copsey, Keith

2007-05-01

String theory generically requires that there are more than the four dimensions easily observable. It has become clear in recent years that gravity in more than four dimensions presents qualitative new features and this thesis is dedicated to exploring some of these phenomena. I discuss the thermodynamics of new types of black holes with new types of charges and study aspects of the AdS-CFT correspondence dual to gravitational phenomena unique to higher dimensions. I further describe the construction of a broad new class of solutions in more than four dimensions containing dynamical minimal spheres ("bubbles of nothing") in asymptotically flat and AdS space without any asymptotic Kaluza-Klein direction.

Axially Tapered And Bilayer Microchannels For Evaporative Cooling Devices

DOEpatents

Nilson, Robert; Griffiths, Stewart

2005-10-04

The invention consists of an evaporative cooling device comprising one or more microchannels whose cross section is axially reduced to control the maximum capillary pressure differential between liquid and vapor phases. In one embodiment, the evaporation channels have a rectangular cross section that is reduced in width along a flow path. In another embodiment, channels of fixed width are patterned with an array of microfabricated post-like features such that the feature size and spacing are gradually reduced along the flow path. Other embodiments incorporate bilayer channels consisting of an upper cover plate having a pattern of slots or holes of axially decreasing size and a lower fluid flow layer having channel widths substantially greater than the characteristic microscale dimensions of the patterned cover plate. The small dimensions of the cover plate holes afford large capillary pressure differentials while the larger dimensions of the lower region reduce viscous flow resistance.

Tip Characterization Method using Multi-feature Characterizer for CD-AFM

PubMed Central

Orji, Ndubuisi G.; Itoh, Hiroshi; Wang, Chumei; Dixson, Ronald G.; Walecki, Peter S.; Schmidt, Sebastian W.; Irmer, Bernd

2016-01-01

In atomic force microscopy (AFM) metrology, the tip is a key source of uncertainty. Images taken with an AFM show a change in feature width and shape that depends on tip geometry. This geometric dilation is more pronounced when measuring features with high aspect ratios, and makes it difficult to obtain absolute dimensions. In order to accurately measure nanoscale features using an AFM, the tip dimensions should be known with a high degree of precision. We evaluate a new AFM tip characterizer, and apply it to critical dimension AFM (CD-AFM) tips used for high aspect ratio features. The characterizer is made up of comb-shaped lines and spaces, and includes a series of gratings that could be used as an integrated nanoscale length reference. We also demonstrate a simulation method that could be used to specify what range of tip sizes and shapes the characterizer can measure. Our experiments show that for non re-entrant features, the results obtained with this characterizer are consistent to 1 nm with the results obtained by using widely accepted but slower methods that are common practice in CD-AFM metrology. A validation of the integrated length standard using displacement interferometry indicates a uniformity of better than 0.75%, suggesting that the sample could be used as highly accurate and SI traceable lateral scale for the whole evaluation process. PMID:26720439

Shape component analysis: structure-preserving dimension reduction on biological shape spaces.

PubMed

Lee, Hao-Chih; Liao, Tao; Zhang, Yongjie Jessica; Yang, Ge

2016-03-01

Quantitative shape analysis is required by a wide range of biological studies across diverse scales, ranging from molecules to cells and organisms. In particular, high-throughput and systems-level studies of biological structures and functions have started to produce large volumes of complex high-dimensional shape data. Analysis and understanding of high-dimensional biological shape data require dimension-reduction techniques. We have developed a technique for non-linear dimension reduction of 2D and 3D biological shape representations on their Riemannian spaces. A key feature of this technique is that it preserves distances between different shapes in an embedded low-dimensional shape space. We demonstrate an application of this technique by combining it with non-linear mean-shift clustering on the Riemannian spaces for unsupervised clustering of shapes of cellular organelles and proteins. Source code and data for reproducing results of this article are freely available at https://github.com/ccdlcmu/shape_component_analysis_Matlab The implementation was made in MATLAB and supported on MS Windows, Linux and Mac OS. geyang@andrew.cmu.edu. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Persistent topological features of dynamical systems

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

Maletić, Slobodan, E-mail: slobodan@hitsz.edu.cn; Institute of Nuclear Sciences Vinča, University of Belgrade, Belgrade; Zhao, Yi, E-mail: zhao.yi@hitsz.edu.cn

Inspired by an early work of Muldoon et al., Physica D 65, 1–16 (1993), we present a general method for constructing simplicial complex from observed time series of dynamical systems based on the delay coordinate reconstruction procedure. The obtained simplicial complex preserves all pertinent topological features of the reconstructed phase space, and it may be analyzed from topological, combinatorial, and algebraic aspects. In focus of this study is the computation of homology of the invariant set of some well known dynamical systems that display chaotic behavior. Persistent homology of simplicial complex and its relationship with the embedding dimensions are examinedmore » by studying the lifetime of topological features and topological noise. The consistency of topological properties for different dynamic regimes and embedding dimensions is examined. The obtained results shed new light on the topological properties of the reconstructed phase space and open up new possibilities for application of advanced topological methods. The method presented here may be used as a generic method for constructing simplicial complex from a scalar time series that has a number of advantages compared to the mapping of the same time series to a complex network.« less

Using learning automata to determine proper subset size in high-dimensional spaces

NASA Astrophysics Data System (ADS)

Seyyedi, Seyyed Hossein; Minaei-Bidgoli, Behrouz

2017-03-01

In this paper, we offer a new method called FSLA (Finding the best candidate Subset using Learning Automata), which combines the filter and wrapper approaches for feature selection in high-dimensional spaces. Considering the difficulties of dimension reduction in high-dimensional spaces, FSLA's multi-objective functionality is to determine, in an efficient manner, a feature subset that leads to an appropriate tradeoff between the learning algorithm's accuracy and efficiency. First, using an existing weighting function, the feature list is sorted and selected subsets of the list of different sizes are considered. Then, a learning automaton verifies the performance of each subset when it is used as the input space of the learning algorithm and estimates its fitness upon the algorithm's accuracy and the subset size, which determines the algorithm's efficiency. Finally, FSLA introduces the fittest subset as the best choice. We tested FSLA in the framework of text classification. The results confirm its promising performance of attaining the identified goal.

What's what in auditory cortices?

PubMed

Retsa, Chrysa; Matusz, Pawel J; Schnupp, Jan W H; Murray, Micah M

2018-08-01

Distinct anatomical and functional pathways are postulated for analysing a sound's object-related ('what') and space-related ('where') information. It remains unresolved to which extent distinct or overlapping neural resources subserve specific object-related dimensions (i.e. who is speaking and what is being said can both be derived from the same acoustic input). To address this issue, we recorded high-density auditory evoked potentials (AEPs) while participants selectively attended and discriminated sounds according to their pitch, speaker identity, uttered syllable ('what' dimensions) or their location ('where'). Sound acoustics were held constant across blocks; the only manipulation involved the sound dimension that participants had to attend to. The task-relevant dimension was varied across blocks. AEPs from healthy participants were analysed within an electrical neuroimaging framework to differentiate modulations in response strength from modulations in response topography; the latter of which forcibly follow from changes in the configuration of underlying sources. There were no behavioural differences in discrimination of sounds across the 4 feature dimensions. As early as 90ms post-stimulus onset, AEP topographies differed across 'what' conditions, supporting a functional sub-segregation within the auditory 'what' pathway. This study characterises the spatio-temporal dynamics of segregated, yet parallel, processing of multiple sound object-related feature dimensions when selective attention is directed to them. Copyright © 2018 Elsevier Inc. All rights reserved.

Multiband tangent space mapping and feature selection for classification of EEG during motor imagery.

PubMed

Islam, Md Rabiul; Tanaka, Toshihisa; Molla, Md Khademul Islam

2018-05-08

When designing multiclass motor imagery-based brain-computer interface (MI-BCI), a so-called tangent space mapping (TSM) method utilizing the geometric structure of covariance matrices is an effective technique. This paper aims to introduce a method using TSM for finding accurate operational frequency bands related brain activities associated with MI tasks. A multichannel electroencephalogram (EEG) signal is decomposed into multiple subbands, and tangent features are then estimated on each subband. A mutual information analysis-based effective algorithm is implemented to select subbands containing features capable of improving motor imagery classification accuracy. Thus obtained features of selected subbands are combined to get feature space. A principal component analysis-based approach is employed to reduce the features dimension and then the classification is accomplished by a support vector machine (SVM). Offline analysis demonstrates the proposed multiband tangent space mapping with subband selection (MTSMS) approach outperforms state-of-the-art methods. It acheives the highest average classification accuracy for all datasets (BCI competition dataset 2a, IIIa, IIIb, and dataset JK-HH1). The increased classification accuracy of MI tasks with the proposed MTSMS approach can yield effective implementation of BCI. The mutual information-based subband selection method is implemented to tune operation frequency bands to represent actual motor imagery tasks.

Fractal dimension of interfaces in Edwards-Anderson spin glasses for up to six space dimensions.

PubMed

Wang, Wenlong; Moore, M A; Katzgraber, Helmut G

2018-03-01

The fractal dimension of domain walls produced by changing the boundary conditions from periodic to antiperiodic in one spatial direction is studied using both the strong-disorder renormalization group algorithm and the greedy algorithm for the Edwards-Anderson Ising spin-glass model for up to six space dimensions. We find that for five or fewer space dimensions, the fractal dimension is lower than the space dimension. This means that interfaces are not space filling, thus implying that replica symmetry breaking is absent in space dimensions fewer than six. However, the fractal dimension approaches the space dimension in six dimensions, indicating that replica symmetry breaking occurs above six dimensions. In two space dimensions, the strong-disorder renormalization group results for the fractal dimension are in good agreement with essentially exact numerical results, but the small difference is significant. We discuss the origin of this close agreement. For the greedy algorithm there is analytical expectation that the fractal dimension is equal to the space dimension in six dimensions and our numerical results are consistent with this expectation.

Glass processing in a microgravity environment

NASA Technical Reports Server (NTRS)

Uhlmann, D. R.

1982-01-01

The basic techniques used in the processing of glasses and crystalline ceramics under terrestrial conditions are briefly reviewed, and the features of the space environment relevant to the processing of glasses are examined. These include reduced gravitational forces, a vacuum of essentially unlimited pumping capacity, unique radiation conditions, and the unlimited dimensions of space. Of these factors, particular attention is given to reduced gravitational forces, and the advantages of containerless processing are discussed. Finally, current programs concerned with glass processing in space are reviewed along with additional areas which merit investigation.

Hybrid Discrete-Continuous Markov Decision Processes

NASA Technical Reports Server (NTRS)

Feng, Zhengzhu; Dearden, Richard; Meuleau, Nicholas; Washington, Rich

2003-01-01

This paper proposes a Markov decision process (MDP) model that features both discrete and continuous state variables. We extend previous work by Boyan and Littman on the mono-dimensional time-dependent MDP to multiple dimensions. We present the principle of lazy discretization, and piecewise constant and linear approximations of the model. Having to deal with several continuous dimensions raises several new problems that require new solutions. In the (piecewise) linear case, we use techniques from partially- observable MDPs (POMDPS) to represent value functions as sets of linear functions attached to different partitions of the state space.

Generalized Born-Oppenheimer treatment of Jahn-Teller systems in Hilbert spaces of arbitrary dimension: theory and application to a three-state model potential.

PubMed

Varandas, A J C; Sarkar, B

2011-05-14

Generalized Born-Oppenheimer equations including the geometrical phase effect are derived for three- and four-fold electronic manifolds in Jahn-Teller systems near the degeneracy seam. The method is readily extendable to N-fold systems of arbitrary dimension. An application is reported for a model threefold system, and the results are compared with Born-Oppenheimer (geometrical phase ignored), extended Born-Oppenheimer, and coupled three-state calculations. The theory shows unprecedented simplicity while depicting all features of more elaborated ones.

Functional Principal Component Analysis and Randomized Sparse Clustering Algorithm for Medical Image Analysis

PubMed Central

Lin, Nan; Jiang, Junhai; Guo, Shicheng; Xiong, Momiao

2015-01-01

Due to the advancement in sensor technology, the growing large medical image data have the ability to visualize the anatomical changes in biological tissues. As a consequence, the medical images have the potential to enhance the diagnosis of disease, the prediction of clinical outcomes and the characterization of disease progression. But in the meantime, the growing data dimensions pose great methodological and computational challenges for the representation and selection of features in image cluster analysis. To address these challenges, we first extend the functional principal component analysis (FPCA) from one dimension to two dimensions to fully capture the space variation of image the signals. The image signals contain a large number of redundant features which provide no additional information for clustering analysis. The widely used methods for removing the irrelevant features are sparse clustering algorithms using a lasso-type penalty to select the features. However, the accuracy of clustering using a lasso-type penalty depends on the selection of the penalty parameters and the threshold value. In practice, they are difficult to determine. Recently, randomized algorithms have received a great deal of attentions in big data analysis. This paper presents a randomized algorithm for accurate feature selection in image clustering analysis. The proposed method is applied to both the liver and kidney cancer histology image data from the TCGA database. The results demonstrate that the randomized feature selection method coupled with functional principal component analysis substantially outperforms the current sparse clustering algorithms in image cluster analysis. PMID:26196383

Holography in Rindler Space

NASA Astrophysics Data System (ADS)

Samantray, Prasant

This thesis addresses certain quantum aspects of the event horizon using the AdS/CFT correspondence. This correspondence is profound since it describes a quantum theory of gravity in d + 1 dimensions from the perspective of a dual quantum field theory living in d dimensions. We begin by considering Rindler space which is the part of Minkowski space seen by an observer with a constant proper acceleration. Because it has an event horizon, Rindler space has been studied in great detail within the context of quantum field theory. However, a quantum gravitational treatment of Rindler space is handicapped by the fact that quantum gravity in flat space is poorly understood. By contrast, quantum gravity in anti-de Sitter space (AdS), is relatively well understood via the AdS/CFT correspondence. Taking this cue, we construct Rindler coordinates for AdS (Rindler-AdS space) in d + 1 spacetime dimensions. In three spacetime dimensions, we find novel one-parameter families of stationary vacua labeled by a rotation parameter β. The interesting thing about these rotating Rindler-AdS spaces is that they possess an observer-dependent ergoregion in addition to having an event horizon. Turning next to the application of AdS/CFT correspondence to Rindler-AdS space, we posit that the two Rindler wedges in AdSd +1 are dual to an entangled conformal field theory (CFT) that lives on two boundaries with geometry R × Hd-1. Specializing to three spacetime dimensions, we derive the thermodynamics of Rindler-AdS space using the boundary CFT. We then probe the causal structure of the spacetime by sending in a time-like source and observe that the CFT “knows” when the source has fallen past the Rindler horizon. We conclude by proposing an alternate foliation of Rindler-AdS which is dual to a CFT living in de Sitter space. Towards the end, we consider the concept of weak measurements in quantum mechanics, wherein the measuring instrument is weakly coupled to the system being measured. We consider such measurements in the context of two examples, viz. the decay of an excited atom, and the tunneling of a particle trapped in a well, and discuss the salient features of such measurements.

High dimensional feature reduction via projection pursuit

NASA Technical Reports Server (NTRS)

Jimenez, Luis; Landgrebe, David

1994-01-01

The recent development of more sophisticated remote sensing systems enables the measurement of radiation in many more spectral intervals than previously possible. An example of that technology is the AVIRIS system, which collects image data in 220 bands. As a result of this, new algorithms must be developed in order to analyze the more complex data effectively. Data in a high dimensional space presents a substantial challenge, since intuitive concepts valid in a 2-3 dimensional space to not necessarily apply in higher dimensional spaces. For example, high dimensional space is mostly empty. This results from the concentration of data in the corners of hypercubes. Other examples may be cited. Such observations suggest the need to project data to a subspace of a much lower dimension on a problem specific basis in such a manner that information is not lost. Projection Pursuit is a technique that will accomplish such a goal. Since it processes data in lower dimensions, it should avoid many of the difficulties of high dimensional spaces. In this paper, we begin the investigation of some of the properties of Projection Pursuit for this purpose.

Pattern classification using an olfactory model with PCA feature selection in electronic noses: study and application.

PubMed

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6~8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3~5 pattern classes considering the trade-off between time consumption and classification rate.

Detecting multiple moving objects in crowded environments with coherent motion regions

DOEpatents

Cheriyadat, Anil M.; Radke, Richard J.

2013-06-11

Coherent motion regions extend in time as well as space, enforcing consistency in detected objects over long time periods and making the algorithm robust to noisy or short point tracks. As a result of enforcing the constraint that selected coherent motion regions contain disjoint sets of tracks defined in a three-dimensional space including a time dimension. An algorithm operates directly on raw, unconditioned low-level feature point tracks, and minimizes a global measure of the coherent motion regions. At least one discrete moving object is identified in a time series of video images based on the trajectory similarity factors, which is a measure of a maximum distance between a pair of feature point tracks.

Multisensor Analysis of Spectral Dimensionality and Soil Diversity in the Great Central Valley of California.

PubMed

Sousa, Daniel; Small, Christopher

2018-02-14

Planned hyperspectral satellite missions and the decreased revisit time of multispectral imaging offer the potential for data fusion to leverage both the spectral resolution of hyperspectral sensors and the temporal resolution of multispectral constellations. Hyperspectral imagery can also be used to better understand fundamental properties of multispectral data. In this analysis, we use five flight lines from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) archive with coincident Landsat 8 acquisitions over a spectrally diverse region of California to address the following questions: (1) How much of the spectral dimensionality of hyperspectral data is captured in multispectral data?; (2) Is the characteristic pyramidal structure of the multispectral feature space also present in the low order dimensions of the hyperspectral feature space at comparable spatial scales?; (3) How much variability in rock and soil substrate endmembers (EMs) present in hyperspectral data is captured by multispectral sensors? We find nearly identical partitions of variance, low-order feature space topologies, and EM spectra for hyperspectral and multispectral image composites. The resulting feature spaces and EMs are also very similar to those from previous global multispectral analyses, implying that the fundamental structure of the global feature space is present in our relatively small spatial subset of California. Finally, we find that the multispectral dataset well represents the substrate EM variability present in the study area - despite its inability to resolve narrow band absorptions. We observe a tentative but consistent physical relationship between the gradation of substrate reflectance in the feature space and the gradation of sand versus clay content in the soil classification system.

Multisensor Analysis of Spectral Dimensionality and Soil Diversity in the Great Central Valley of California

PubMed Central

Small, Christopher

2018-01-01

Planned hyperspectral satellite missions and the decreased revisit time of multispectral imaging offer the potential for data fusion to leverage both the spectral resolution of hyperspectral sensors and the temporal resolution of multispectral constellations. Hyperspectral imagery can also be used to better understand fundamental properties of multispectral data. In this analysis, we use five flight lines from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) archive with coincident Landsat 8 acquisitions over a spectrally diverse region of California to address the following questions: (1) How much of the spectral dimensionality of hyperspectral data is captured in multispectral data?; (2) Is the characteristic pyramidal structure of the multispectral feature space also present in the low order dimensions of the hyperspectral feature space at comparable spatial scales?; (3) How much variability in rock and soil substrate endmembers (EMs) present in hyperspectral data is captured by multispectral sensors? We find nearly identical partitions of variance, low-order feature space topologies, and EM spectra for hyperspectral and multispectral image composites. The resulting feature spaces and EMs are also very similar to those from previous global multispectral analyses, implying that the fundamental structure of the global feature space is present in our relatively small spatial subset of California. Finally, we find that the multispectral dataset well represents the substrate EM variability present in the study area – despite its inability to resolve narrow band absorptions. We observe a tentative but consistent physical relationship between the gradation of substrate reflectance in the feature space and the gradation of sand versus clay content in the soil classification system. PMID:29443900

Guiding exploration in conformational feature space with Lipschitz underestimation for ab-initio protein structure prediction.

PubMed

Hao, Xiaohu; Zhang, Guijun; Zhou, Xiaogen

2018-04-01

Computing conformations which are essential to associate structural and functional information with gene sequences, is challenging due to the high dimensionality and rugged energy surface of the protein conformational space. Consequently, the dimension of the protein conformational space should be reduced to a proper level, and an effective exploring algorithm should be proposed. In this paper, a plug-in method for guiding exploration in conformational feature space with Lipschitz underestimation (LUE) for ab-initio protein structure prediction is proposed. The conformational space is converted into ultrafast shape recognition (USR) feature space firstly. Based on the USR feature space, the conformational space can be further converted into Underestimation space according to Lipschitz estimation theory for guiding exploration. As a consequence of the use of underestimation model, the tight lower bound estimate information can be used for exploration guidance, the invalid sampling areas can be eliminated in advance, and the number of energy function evaluations can be reduced. The proposed method provides a novel technique to solve the exploring problem of protein conformational space. LUE is applied to differential evolution (DE) algorithm, and metropolis Monte Carlo(MMC) algorithm which is available in the Rosetta; When LUE is applied to DE and MMC, it will be screened by the underestimation method prior to energy calculation and selection. Further, LUE is compared with DE and MMC by testing on 15 small-to-medium structurally diverse proteins. Test results show that near-native protein structures with higher accuracy can be obtained more rapidly and efficiently with the use of LUE. Copyright © 2018 Elsevier Ltd. All rights reserved.

A real negative selection algorithm with evolutionary preference for anomaly detection

NASA Astrophysics Data System (ADS)

Yang, Tao; Chen, Wen; Li, Tao

2017-04-01

Traditional real negative selection algorithms (RNSAs) adopt the estimated coverage (c0) as the algorithm termination threshold, and generate detectors randomly. With increasing dimensions, the data samples could reside in the low-dimensional subspace, so that the traditional detectors cannot effectively distinguish these samples. Furthermore, in high-dimensional feature space, c0 cannot exactly reflect the detectors set coverage rate for the nonself space, and it could lead the algorithm to be terminated unexpectedly when the number of detectors is insufficient. These shortcomings make the traditional RNSAs to perform poorly in high-dimensional feature space. Based upon "evolutionary preference" theory in immunology, this paper presents a real negative selection algorithm with evolutionary preference (RNSAP). RNSAP utilizes the "unknown nonself space", "low-dimensional target subspace" and "known nonself feature" as the evolutionary preference to guide the generation of detectors, thus ensuring the detectors can cover the nonself space more effectively. Besides, RNSAP uses redundancy to replace c0 as the termination threshold, in this way RNSAP can generate adequate detectors under a proper convergence rate. The theoretical analysis and experimental result demonstrate that, compared to the classical RNSA (V-detector), RNSAP can achieve a higher detection rate, but with less detectors and computing cost.

Quad-polarized synthetic aperture radar and multispectral data classification using classification and regression tree and support vector machine-based data fusion system

NASA Astrophysics Data System (ADS)

Bigdeli, Behnaz; Pahlavani, Parham

2017-01-01

Interpretation of synthetic aperture radar (SAR) data processing is difficult because the geometry and spectral range of SAR are different from optical imagery. Consequently, SAR imaging can be a complementary data to multispectral (MS) optical remote sensing techniques because it does not depend on solar illumination and weather conditions. This study presents a multisensor fusion of SAR and MS data based on the use of classification and regression tree (CART) and support vector machine (SVM) through a decision fusion system. First, different feature extraction strategies were applied on SAR and MS data to produce more spectral and textural information. To overcome the redundancy and correlation between features, an intrinsic dimension estimation method based on noise-whitened Harsanyi, Farrand, and Chang determines the proper dimension of the features. Then, principal component analysis and independent component analysis were utilized on stacked feature space of two data. Afterward, SVM and CART classified each reduced feature space. Finally, a fusion strategy was utilized to fuse the classification results. To show the effectiveness of the proposed methodology, single classification on each data was compared to the obtained results. A coregistered Radarsat-2 and WorldView-2 data set from San Francisco, USA, was available to examine the effectiveness of the proposed method. The results show that combinations of SAR data with optical sensor based on the proposed methodology improve the classification results for most of the classes. The proposed fusion method provided approximately 93.24% and 95.44% for two different areas of the data.

Attentional Selection Can Be Predicted by Reinforcement Learning of Task-relevant Stimulus Features Weighted by Value-independent Stickiness.

PubMed

Balcarras, Matthew; Ardid, Salva; Kaping, Daniel; Everling, Stefan; Womelsdorf, Thilo

2016-02-01

Attention includes processes that evaluate stimuli relevance, select the most relevant stimulus against less relevant stimuli, and bias choice behavior toward the selected information. It is not clear how these processes interact. Here, we captured these processes in a reinforcement learning framework applied to a feature-based attention task that required macaques to learn and update the value of stimulus features while ignoring nonrelevant sensory features, locations, and action plans. We found that value-based reinforcement learning mechanisms could account for feature-based attentional selection and choice behavior but required a value-independent stickiness selection process to explain selection errors while at asymptotic behavior. By comparing different reinforcement learning schemes, we found that trial-by-trial selections were best predicted by a model that only represents expected values for the task-relevant feature dimension, with nonrelevant stimulus features and action plans having only a marginal influence on covert selections. These findings show that attentional control subprocesses can be described by (1) the reinforcement learning of feature values within a restricted feature space that excludes irrelevant feature dimensions, (2) a stochastic selection process on feature-specific value representations, and (3) value-independent stickiness toward previous feature selections akin to perseveration in the motor domain. We speculate that these three mechanisms are implemented by distinct but interacting brain circuits and that the proposed formal account of feature-based stimulus selection will be important to understand how attentional subprocesses are implemented in primate brain networks.

Navigation, behaviors, and control modes in an autonomous vehicle

NASA Astrophysics Data System (ADS)

Byler, Eric A.

1995-01-01

An Intelligent Mobile Sensing System (IMSS) has been developed for the automated inspection of radioactive and hazardous waste storage containers in warehouse facilities at Department of Energy sites. A 2D space of control modes was used that provides a combined view of reactive and planning approaches wherein a 2D situation space is defined by dimensions representing the predictability of the agent's task environment and the constraint imposed by its goals. In this sense selection of appropriate systems for planning, navigation, and control depends on the problem at hand. The IMSS vehicle navigation system is based on a combination of feature based motion, landmark sightings, and an a priori logical map of the mockup storage facility. Motion for the inspection activities are composed of different interactions of several available control modes, several obstacle avoidance modes, and several feature identification modes. Features used to drive these behaviors are both visual and acoustic.

Functional feature embedded space mapping of fMRI data.

PubMed

Hu, Jin; Tian, Jie; Yang, Lei

2006-01-01

We have proposed a new method for fMRI data analysis which is called Functional Feature Embedded Space Mapping (FFESM). Our work mainly focuses on the experimental design with periodic stimuli which can be described by a number of Fourier coefficients in the frequency domain. A nonlinear dimension reduction technique Isomap is applied to the high dimensional features obtained from frequency domain of the fMRI data for the first time. Finally, the presence of activated time series is identified by the clustering method in which the information theoretic criterion of minimum description length (MDL) is used to estimate the number of clusters. The feasibility of our algorithm is demonstrated by real human experiments. Although we focus on analyzing periodic fMRI data, the approach can be extended to analyze non-periodic fMRI data (event-related fMRI) by replacing the Fourier analysis with a wavelet analysis.

Big-data-based edge biomarkers: study on dynamical drug sensitivity and resistance in individuals.

PubMed

Zeng, Tao; Zhang, Wanwei; Yu, Xiangtian; Liu, Xiaoping; Li, Meiyi; Chen, Luonan

2016-07-01

Big-data-based edge biomarker is a new concept to characterize disease features based on biomedical big data in a dynamical and network manner, which also provides alternative strategies to indicate disease status in single samples. This article gives a comprehensive review on big-data-based edge biomarkers for complex diseases in an individual patient, which are defined as biomarkers based on network information and high-dimensional data. Specifically, we firstly introduce the sources and structures of biomedical big data accessible in public for edge biomarker and disease study. We show that biomedical big data are typically 'small-sample size in high-dimension space', i.e. small samples but with high dimensions on features (e.g. omics data) for each individual, in contrast to traditional big data in many other fields characterized as 'large-sample size in low-dimension space', i.e. big samples but with low dimensions on features. Then, we demonstrate the concept, model and algorithm for edge biomarkers and further big-data-based edge biomarkers. Dissimilar to conventional biomarkers, edge biomarkers, e.g. module biomarkers in module network rewiring-analysis, are able to predict the disease state by learning differential associations between molecules rather than differential expressions of molecules during disease progression or treatment in individual patients. In particular, in contrast to using the information of the common molecules or edges (i.e.molecule-pairs) across a population in traditional biomarkers including network and edge biomarkers, big-data-based edge biomarkers are specific for each individual and thus can accurately evaluate the disease state by considering the individual heterogeneity. Therefore, the measurement of big data in a high-dimensional space is required not only in the learning process but also in the diagnosing or predicting process of the tested individual. Finally, we provide a case study on analyzing the temporal expression data from a malaria vaccine trial by big-data-based edge biomarkers from module network rewiring-analysis. The illustrative results show that the identified module biomarkers can accurately distinguish vaccines with or without protection and outperformed previous reported gene signatures in terms of effectiveness and efficiency. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

Pattern Classification Using an Olfactory Model with PCA Feature Selection in Electronic Noses: Study and Application

PubMed Central

Fu, Jun; Huang, Canqin; Xing, Jianguo; Zheng, Junbao

2012-01-01

Biologically-inspired models and algorithms are considered as promising sensor array signal processing methods for electronic noses. Feature selection is one of the most important issues for developing robust pattern recognition models in machine learning. This paper describes an investigation into the classification performance of a bionic olfactory model with the increase of the dimensions of input feature vector (outer factor) as well as its parallel channels (inner factor). The principal component analysis technique was applied for feature selection and dimension reduction. Two data sets of three classes of wine derived from different cultivars and five classes of green tea derived from five different provinces of China were used for experiments. In the former case the results showed that the average correct classification rate increased as more principal components were put in to feature vector. In the latter case the results showed that sufficient parallel channels should be reserved in the model to avoid pattern space crowding. We concluded that 6∼8 channels of the model with principal component feature vector values of at least 90% cumulative variance is adequate for a classification task of 3∼5 pattern classes considering the trade-off between time consumption and classification rate. PMID:22736979

Online Distributed Learning Over Networks in RKH Spaces Using Random Fourier Features

NASA Astrophysics Data System (ADS)

Bouboulis, Pantelis; Chouvardas, Symeon; Theodoridis, Sergios

2018-04-01

We present a novel diffusion scheme for online kernel-based learning over networks. So far, a major drawback of any online learning algorithm, operating in a reproducing kernel Hilbert space (RKHS), is the need for updating a growing number of parameters as time iterations evolve. Besides complexity, this leads to an increased need of communication resources, in a distributed setting. In contrast, the proposed method approximates the solution as a fixed-size vector (of larger dimension than the input space) using Random Fourier Features. This paves the way to use standard linear combine-then-adapt techniques. To the best of our knowledge, this is the first time that a complete protocol for distributed online learning in RKHS is presented. Conditions for asymptotic convergence and boundness of the networkwise regret are also provided. The simulated tests illustrate the performance of the proposed scheme.

Utilizing the Structure and Content Information for XML Document Clustering

NASA Astrophysics Data System (ADS)

Tran, Tien; Kutty, Sangeetha; Nayak, Richi

This paper reports on the experiments and results of a clustering approach used in the INEX 2008 document mining challenge. The clustering approach utilizes both the structure and content information of the Wikipedia XML document collection. A latent semantic kernel (LSK) is used to measure the semantic similarity between XML documents based on their content features. The construction of a latent semantic kernel involves the computing of singular vector decomposition (SVD). On a large feature space matrix, the computation of SVD is very expensive in terms of time and memory requirements. Thus in this clustering approach, the dimension of the document space of a term-document matrix is reduced before performing SVD. The document space reduction is based on the common structural information of the Wikipedia XML document collection. The proposed clustering approach has shown to be effective on the Wikipedia collection in the INEX 2008 document mining challenge.

On the Reality of Illusory Conjunctions.

PubMed

Botella, Juan; Suero, Manuel; Durán, Juan I

2017-01-01

The reality of illusory conjunctions in perception has been sometimes questioned, arguing that they can be explained by other mechanisms. Most relevant experiments are based on migrations along the space dimension. But the low rate of illusory conjunctions along space can easily hide them among other types of errors. As migrations over time are a more frequent phenomenon, illusory conjunctions can be disentangled from other errors. We report an experiment in which series of colored letters were presented in several spatial locations, allowing for migrations over both space and time. The distribution of frequencies were fit by several multinomial tree models based on alternative hypothesis about illusory conjunctions and the potential sources of free-floating features. The best-fit model acknowledges that most illusory conjunctions are migrations in the time domain. Migrations in space are probably present, but the rate is very low. Other conjunction errors, as those produced by guessing or miscategorizations of the to-be-reported feature, are also present in the experiment. The main conclusion is that illusory conjunctions do exist.

Gene masking - a technique to improve accuracy for cancer classification with high dimensionality in microarray data.

PubMed

Saini, Harsh; Lal, Sunil Pranit; Naidu, Vimal Vikash; Pickering, Vincel Wince; Singh, Gurmeet; Tsunoda, Tatsuhiko; Sharma, Alok

2016-12-05

High dimensional feature space generally degrades classification in several applications. In this paper, we propose a strategy called gene masking, in which non-contributing dimensions are heuristically removed from the data to improve classification accuracy. Gene masking is implemented via a binary encoded genetic algorithm that can be integrated seamlessly with classifiers during the training phase of classification to perform feature selection. It can also be used to discriminate between features that contribute most to the classification, thereby, allowing researchers to isolate features that may have special significance. This technique was applied on publicly available datasets whereby it substantially reduced the number of features used for classification while maintaining high accuracies. The proposed technique can be extremely useful in feature selection as it heuristically removes non-contributing features to improve the performance of classifiers.

On the Essence of Space

NASA Astrophysics Data System (ADS)

Kalanov, Temur Z.

2003-04-01

A new theory of space is suggested. It represents the new point of view which has arisen from the critical analysis of the foundations of physics (in particular the theory of relativity and quantum mechanics), mathematics, cosmology and philosophy. The main idea following from the analysis is that the concept of movement represents a key to understanding of the essence of space. The starting-point of the theory is represented by the following philosophical (dialectical materialistic) principles. (a) The principle of the materiality (of the objective reality) of the Nature: the Nature (the Universe) is a system (a set) of material objects (particles, bodies, fields); each object has properties, features, and the properties, the features are inseparable characteristics of material object and belong only to material object. (b) The principle of the existence of material object: an object exists as the objective reality, and movement is a form of existence of object. (c) The principle (definition) of movement of object: the movement is change (i.e. transition of some states into others) in general; the movement determines a direction, and direction characterizes the movement. (d) The principle of existence of time: the time exists as the parameter of the system of reference. These principles lead to the following statements expressing the essence of space. (1) There is no space in general, and there exist space only as a form of existence of the properties and features of the object. It means that the space is a set of the measures of the object (the measure is the philosophical category meaning unity of the qualitative and quantitative determinacy of the object). In other words, the space of the object is a set of the states of the object. (2) The states of the object are manifested only in a system of reference. The main informational property of the unitary system researched physical object + system of reference is that the system of reference determines (measures, calculates) the parameters of the subsystem researched physical object (for example, the coordinates of the object M); the parameters characterize the system of reference (for example, the system of coordinates S). (3) Each parameter of the object is its measure. Total number of the mutually independent parameters of the object is called dimension of the space of the object. (4) The set of numerical values (i.e. the range, the spectrum) of each parameter is the subspace of the object. (The coordinate space, the momentum space and the energy space are examples of the subspaces of the object). (5) The set of the parameters of the object is divided into two non intersecting (opposite) classes: the class of the internal parameters and the class of the non internal (i.e. external) parameters. The class of the external parameters is divided into two non intersecting (opposite) subclasses: the subclass of the absolute parameters (characterizing the form, the sizes of the object) and the subclass of the non absolute (relative) parameters (characterizing the position, the coordinates of the object). (6) Set of the external parameters forms the external space of object. It is called geometrical space of object. (7) Since a macroscopic object has three mutually independent sizes, the dimension of its external absolute space is equal to three. Consequently, the dimension of its external relative space is also equal to three. Thus, the total dimension of the external space of the macroscopic object is equal to six. (8) In general case, the external absolute space (i.e. the form, the sizes) and the external relative space (i.e. the position, the coordinates) of any object are mutually dependent because of influence of a medium. The geometrical space of such object is called non Euclidean space. If the external absolute space and the external relative space of some object are mutually independent, then the external relative space of such object is the homogeneous and isotropic geometrical space. It is called Euclidean space of the object. Consequences: (i) the question of true geometry of the Universe is incorrect; (ii) the theory of relativity has no physical meaning.

An Eye-Tracking Study of Multiple Feature Value Category Structure Learning: The Role of Unique Features

PubMed Central

Liu, Zhiya; Song, Xiaohong; Seger, Carol A.

2015-01-01

We examined whether the degree to which a feature is uniquely characteristic of a category can affect categorization above and beyond the typicality of the feature. We developed a multiple feature value category structure with different dimensions within which feature uniqueness and typicality could be manipulated independently. Using eye tracking, we found that the highest attentional weighting (operationalized as number of fixations, mean fixation time, and the first fixation of the trial) was given to a dimension that included a feature that was both unique and highly typical of the category. Dimensions that included features that were highly typical but not unique, or were unique but not highly typical, received less attention. A dimension with neither a unique nor a highly typical feature received least attention. On the basis of these results we hypothesized that subjects categorized via a rule learning procedure in which they performed an ordered evaluation of dimensions, beginning with unique and strongly typical dimensions, and in which earlier dimensions received higher weighting in the decision. This hypothesis accounted for performance on transfer stimuli better than simple implementations of two other common theories of category learning, exemplar models and prototype models, in which all dimensions were evaluated in parallel and received equal weighting. PMID:26274332

An Eye-Tracking Study of Multiple Feature Value Category Structure Learning: The Role of Unique Features.

PubMed

Liu, Zhiya; Song, Xiaohong; Seger, Carol A

2015-01-01

We examined whether the degree to which a feature is uniquely characteristic of a category can affect categorization above and beyond the typicality of the feature. We developed a multiple feature value category structure with different dimensions within which feature uniqueness and typicality could be manipulated independently. Using eye tracking, we found that the highest attentional weighting (operationalized as number of fixations, mean fixation time, and the first fixation of the trial) was given to a dimension that included a feature that was both unique and highly typical of the category. Dimensions that included features that were highly typical but not unique, or were unique but not highly typical, received less attention. A dimension with neither a unique nor a highly typical feature received least attention. On the basis of these results we hypothesized that subjects categorized via a rule learning procedure in which they performed an ordered evaluation of dimensions, beginning with unique and strongly typical dimensions, and in which earlier dimensions received higher weighting in the decision. This hypothesis accounted for performance on transfer stimuli better than simple implementations of two other common theories of category learning, exemplar models and prototype models, in which all dimensions were evaluated in parallel and received equal weighting.

A novel and efficient technique for identification and classification of GPCRs.

PubMed

Gupta, Ravi; Mittal, Ankush; Singh, Kuldip

2008-07-01

G-protein coupled receptors (GPCRs) play a vital role in different biological processes, such as regulation of growth, death, and metabolism of cells. GPCRs are the focus of significant amount of current pharmaceutical research since they interact with more than 50% of prescription drugs. The dipeptide-based support vector machine (SVM) approach is the most accurate technique to identify and classify the GPCRs. However, this approach has two major disadvantages. First, the dimension of dipeptide-based feature vector is equal to 400. The large dimension makes the classification task computationally and memory wise inefficient. Second, it does not consider the biological properties of protein sequence for identification and classification of GPCRs. In this paper, we present a novel-feature-based SVM classification technique. The novel features are derived by applying wavelet-based time series analysis approach on protein sequences. The proposed feature space summarizes the variance information of seven important biological properties of amino acids in a protein sequence. In addition, the dimension of the feature vector for proposed technique is equal to 35. Experiments were performed on GPCRs protein sequences available at GPCRs Database. Our approach achieves an accuracy of 99.9%, 98.06%, 97.78%, and 94.08% for GPCR superfamily, families, subfamilies, and subsubfamilies (amine group), respectively, when evaluated using fivefold cross-validation. Further, an accuracy of 99.8%, 97.26%, and 97.84% was obtained when evaluated on unseen or recall datasets of GPCR superfamily, families, and subfamilies, respectively. Comparison with dipeptide-based SVM technique shows the effectiveness of our approach.

Reducing the dimensions of acoustic devices using anti-acoustic-null media

NASA Astrophysics Data System (ADS)

Li, Borui; Sun, Fei; He, Sailing

2018-02-01

An anti-acoustic-null medium (anti-ANM), a special homogeneous medium with anisotropic mass density, is designed by transformation acoustics (TA). Anti-ANM can greatly compress acoustic space along the direction of its main axis, where the size compression ratio is extremely large. This special feature can be utilized to reduce the geometric dimensions of classic acoustic devices. For example, the height of a parabolic acoustic reflector can be greatly reduced. We also design a brass-air structure on the basis of the effective medium theory to materialize the anti-ANM in a broadband frequency range. Numerical simulations verify the performance of the proposed anti-ANM.

Photoacoustic effect generated by moving optical sources: Motion in one dimension

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

Bai, Wenyu; Diebold, Gerald J.

2016-03-28

Although the photoacoustic effect is typically generated by pulsed or amplitude modulated optical beams, it is clear from examination of the wave equation for pressure that motion of an optical source in space will result in the production of sound as well. Here, the properties of the photoacoustic effect generated by moving sources in one dimension are investigated. The cases of a moving Gaussian beam, an oscillating delta function source, and an accelerating Gaussian optical sources are reported. The salient feature of one-dimensional sources in the linear acoustic limit is that the amplitude of the beam increases in time withoutmore » bound.« less

Asymptotically locally Euclidean/Kaluza-Klein stationary vacuum black holes in five dimensions

NASA Astrophysics Data System (ADS)

Khuri, Marcus; Weinstein, Gilbert; Yamada, Sumio

2018-05-01

We produce new examples, both explicit and analytical, of bi-axisymmetric stationary vacuum black holes in five dimensions. A novel feature of these solutions is that they are asymptotically locally Euclidean, in which spatial cross-sections at infinity have lens space L(p,q) topology, or asymptotically Kaluza-Klein so that spatial cross-sections at infinity are topologically S^1× S^2. These are nondegenerate black holes of cohomogeneity 2, with any number of horizon components, where the horizon cross-section topology is any one of the three admissible types: S^3, S^1× S^2, or L(p,q). Uniqueness of these solutions is also established. Our method is to solve the relevant harmonic map problem with prescribed singularities, having target symmetric space SL(3,{R})/SO(3). In addition, we analyze the possibility of conical singularities and find a large family for which geometric regularity is guaranteed.

Feature-based attentional weighting and spreading in visual working memory

PubMed Central

Niklaus, Marcel; Nobre, Anna C.; van Ede, Freek

2017-01-01

Attention can be directed at features and feature dimensions to facilitate perception. Here, we investigated whether feature-based-attention (FBA) can also dynamically weight feature-specific representations within multi-feature objects held in visual working memory (VWM). Across three experiments, participants retained coloured arrows in working memory and, during the delay, were cued to either the colour or the orientation dimension. We show that directing attention towards a feature dimension (1) improves the performance in the cued feature dimension at the expense of the uncued dimension, (2) is more efficient if directed to the same rather than to different dimensions for different objects, and (3) at least for colour, automatically spreads to the colour representation of non-attended objects in VWM. We conclude that FBA also continues to operate on VWM representations (with similar principles that govern FBA in the perceptual domain) and challenge the classical view that VWM representations are stored solely as integrated objects. PMID:28233830

Line-frequency doubling of directed self-assembly patterns for single-digit bit pattern media lithography

NASA Astrophysics Data System (ADS)

Patel, K. C.; Ruiz, R.; Lille, J.; Wan, L.; Dobiz, E.; Gao, H.; Robertson, N.; Albrecht, T. R.

2012-03-01

Directed self-assembly is emerging as a promising technology to define sub-20nm features. However, a straightforward path to scale block copolymer lithography to single-digit fabrication remains challenging given the diverse material properties found in the wide spectrum of self-assembling materials. A vast amount of block copolymer research for industrial applications has been dedicated to polystyrene-b-methyl methacrylate (PS-b-PMMA), a model system that displays multiple properties making it ideal for lithography, but that is limited by a weak interaction parameter that prevents it from scaling to single-digit lithography. Other block copolymer materials have shown scalability to much smaller dimensions, but at the expense of other material properties that could delay their insertion into industrial lithographic processes. We report on a line doubling process applied to block copolymer patterns to double the frequency of PS-b-PMMA line/space features, demonstrating the potential of this technique to reach single-digit lithography. We demonstrate a line-doubling process that starts with directed self-assembly of PS-b-PMMA to define line/space features. This pattern is transferred into an underlying sacrificial hard-mask layer followed by a growth of self-aligned spacers which subsequently serve as hard-masks for transferring the 2x frequency doubled pattern to the underlying substrate. We applied this process to two different block copolymer materials to demonstrate line-space patterns with a half pitch of 11nm and 7nm underscoring the potential to reach single-digit critical dimensions. A subsequent patterning step with perpendicular lines can be used to cut the fine line patterns into a 2-D array of islands suitable for bit patterned media. Several integration challenges such as line width control and line roughness are addressed.

Cavity detection and delineation research. Part 4: Microgravimetric survey: Manatee Springs Site, Florida

NASA Astrophysics Data System (ADS)

Butler, D. K.; Whitten, C. B.; Smith, F. L.

1983-03-01

Results of a microgravimetric survey at Manatee Springs, Levy County, Fla., are presented. The survey area was 100 by 400 ft, with 20-ft gravity station spacing, and with the long dimension of the area approximately perpendicular to the known trend of the main cavity. The main cavity is about 80 to 100 ft below the surface and has a cross section about 16 to 20 ft in height and 30 to 40 ft in width beneath the survey area. Using a density contrast of -1.3 g/cucm, the gravity anomaly is calculated to be -35 micro Gal with a width at half maximum of 205 ft. The microgravimetric survey results clearly indicate a broad negative anomaly coincident with the location and trend of the cavity system across the survey area. The anomaly magnitude and width are consistent with those calculated from the known depth and dimensions of the main cavity. In addition, a small, closed negative anomaly feature, superimposed on the broad negative feature due to the main cavity, satisfactorily delineated a small secondary cavity feature which was discovered and mapped by cave divers.

Lack of conformity between Indian classroom furniture and student dimensions: proposed future seat/table dimensions.

PubMed

Savanur, C S; Altekar, C R; De, A

2007-10-01

Children spend one-quarter of a day in school. Of this, 60-80% of time is spent in the classroom. Classroom features, such as workspace and personal space play an important role in children's growth and performance as this age marks the period of anatomical, physiological and psychological developments. Since the classroom is an influential part of a student's life the present study focused on classroom furniture in relation to students' workspace and personal space requirements and standards and was conducted in five schools at Mumbai, India. Dimensions of 104 items of furniture (chairs and desks) were measured as were 42 anthropometric dimensions of 225 students from grade six to grade nine (age: 10-14 years). Questionnaire responses of 292 students regarding the perceived adequacy of their classroom furniture were collected. Results indicated that the seat and desk heights (450 mm, 757 mm respectively) were higher than the comparable students' anthropometric dimensions and that of the recommendations of Bureau of Indian Standards (BIS) (340 + 3 mm, 380 + 3 mm seat-heights, 580 + 3 mm 640 + 3 mm desk-heights) as well as Time-Saver Standards (TSS) (381.0 mm seat-height and 660.4 mm desk-height). The depth of the seats and the desks (299 mm, 319 mm, respectively) were less than comparable students' anthropometric dimensions and the recommendations of BIS (IS 4837: 1990). Students reported discomfort in shoulder, wrist, knee and ankle regions. Based on the students' anthropometric data, proposed future designs with fixed table-heights and adjustable seat-heights along with footrests were identified.

Application of Linear Discriminant Analysis in Dimensionality Reduction for Hand Motion Classification

NASA Astrophysics Data System (ADS)

Phinyomark, A.; Hu, H.; Phukpattaranont, P.; Limsakul, C.

2012-01-01

The classification of upper-limb movements based on surface electromyography (EMG) signals is an important issue in the control of assistive devices and rehabilitation systems. Increasing the number of EMG channels and features in order to increase the number of control commands can yield a high dimensional feature vector. To cope with the accuracy and computation problems associated with high dimensionality, it is commonplace to apply a processing step that transforms the data to a space of significantly lower dimensions with only a limited loss of useful information. Linear discriminant analysis (LDA) has been successfully applied as an EMG feature projection method. Recently, a number of extended LDA-based algorithms have been proposed, which are more competitive in terms of both classification accuracy and computational costs/times with classical LDA. This paper presents the findings of a comparative study of classical LDA and five extended LDA methods. From a quantitative comparison based on seven multi-feature sets, three extended LDA-based algorithms, consisting of uncorrelated LDA, orthogonal LDA and orthogonal fuzzy neighborhood discriminant analysis, produce better class separability when compared with a baseline system (without feature projection), principle component analysis (PCA), and classical LDA. Based on a 7-dimension time domain and time-scale feature vectors, these methods achieved respectively 95.2% and 93.2% classification accuracy by using a linear discriminant classifier.

Knowing where is different from knowing what: Distinct response time profiles and accuracy effects for target location, orientation, and color probability.

PubMed

Jabar, Syaheed B; Filipowicz, Alex; Anderson, Britt

2017-11-01

When a location is cued, targets appearing at that location are detected more quickly. When a target feature is cued, targets bearing that feature are detected more quickly. These attentional cueing effects are only superficially similar. More detailed analyses find distinct temporal and accuracy profiles for the two different types of cues. This pattern parallels work with probability manipulations, where both feature and spatial probability are known to affect detection accuracy and reaction times. However, little has been done by way of comparing these effects. Are probability manipulations on space and features distinct? In a series of five experiments, we systematically varied spatial probability and feature probability along two dimensions (orientation or color). In addition, we decomposed response times into initiation and movement components. Targets appearing at the probable location were reported more quickly and more accurately regardless of whether the report was based on orientation or color. On the other hand, when either color probability or orientation probability was manipulated, response time and accuracy improvements were specific for that probable feature dimension. Decomposition of the response time benefits demonstrated that spatial probability only affected initiation times, whereas manipulations of feature probability affected both initiation and movement times. As detection was made more difficult, the two effects further diverged, with spatial probability disproportionally affecting initiation times and feature probability disproportionately affecting accuracy. In conclusion, all manipulations of probability, whether spatial or featural, affect detection. However, only feature probability affects perceptual precision, and precision effects are specific to the probable attribute.

Alternative approximation concepts for space frame synthesis

NASA Technical Reports Server (NTRS)

Lust, R. V.; Schmit, L. A.

1985-01-01

A structural synthesis methodology for the minimum mass design of 3-dimensionall frame-truss structures under multiple static loading conditions and subject to limits on displacements, rotations, stresses, local buckling, and element cross-sectional dimensions is presented. A variety of approximation concept options are employed to yield near optimum designs after no more than 10 structural analyses. Available options include: (A) formulation of the nonlinear mathematcal programming problem in either reciprocal section property (RSP) or cross-sectional dimension (CSD) space; (B) two alternative approximate problem structures in each design space; and (C) three distinct assumptions about element end-force variations. Fixed element, design element linking, and temporary constraint deletion features are also included. The solution of each approximate problem, in either its primal or dual form, is obtained using CONMIN, a feasible directions program. The frame-truss synthesis methodology is implemented in the COMPASS computer program and is used to solve a variety of problems. These problems were chosen so that, in addition to exercising the various approximation concepts options, the results could be compared with previously published work.

Robotic Assembly of Truss Structures for Space Systems and Future Research Plans

NASA Technical Reports Server (NTRS)

Doggett, William

2002-01-01

Many initiatives under study by both the space science and earth science communities require large space systems, i.e. with apertures greater than 15 m or dimensions greater than 20 m. This paper reviews the effort in NASA Langley Research Center's Automated Structural Assembly Laboratory which laid the foundations for robotic construction of these systems. In the Automated Structural Assembly Laboratory reliable autonomous assembly and disassembly of an 8 meter planar structure composed of 102 truss elements covered by 12 panels was demonstrated. The paper reviews the hardware and software design philosophy which led to reliable operation during weeks of near continuous testing. Special attention is given to highlight the features enhancing assembly reliability.

Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis

PubMed Central

Gajic, Dragoljub; Djurovic, Zeljko; Gligorijevic, Jovan; Di Gennaro, Stefano; Savic-Gajic, Ivana

2015-01-01

We present a new technique for detection of epileptiform activity in EEG signals. After preprocessing of EEG signals we extract representative features in time, frequency and time-frequency domain as well as using non-linear analysis. The features are extracted in a few frequency sub-bands of clinical interest since these sub-bands showed much better discriminatory characteristics compared with the whole frequency band. Then we optimally reduce the dimension of feature space to two using scatter matrices. A decision about the presence of epileptiform activity in EEG signals is made by quadratic classifiers designed in the reduced two-dimensional feature space. The accuracy of the technique was tested on three sets of electroencephalographic (EEG) signals recorded at the University Hospital Bonn: surface EEG signals from healthy volunteers, intracranial EEG signals from the epilepsy patients during the seizure free interval from within the seizure focus and intracranial EEG signals of epileptic seizures also from within the seizure focus. An overall detection accuracy of 98.7% was achieved. PMID:25852534

EM in high-dimensional spaces.

PubMed

Draper, Bruce A; Elliott, Daniel L; Hayes, Jeremy; Baek, Kyungim

2005-06-01

This paper considers fitting a mixture of Gaussians model to high-dimensional data in scenarios where there are fewer data samples than feature dimensions. Issues that arise when using principal component analysis (PCA) to represent Gaussian distributions inside Expectation-Maximization (EM) are addressed, and a practical algorithm results. Unlike other algorithms that have been proposed, this algorithm does not try to compress the data to fit low-dimensional models. Instead, it models Gaussian distributions in the (N - 1)-dimensional space spanned by the N data samples. We are able to show that this algorithm converges on data sets where low-dimensional techniques do not.

Radiation effects in advanced microelectronics technologies

NASA Astrophysics Data System (ADS)

Johnston, A. H.

1998-06-01

The pace of device scaling has increased rapidly in recent years. Experimental CMOS devices have been produced with feature sizes below 0.1 /spl mu/m, demonstrating that devices with feature sizes between 0.1 and 0.25 /spl mu/m will likely be available in mainstream technologies after the year 2000. This paper discusses how the anticipated changes in device dimensions and design are likely to affect their radiation response in space environments. Traditional problems, such as total dose effects, SEU and latchup are discussed, along with new phenomena. The latter include hard errors from heavy ions (microdose and gate-rupture errors), and complex failure modes related to advanced circuit architecture. The main focus of the paper is on commercial devices, which are displacing hardened device technologies in many space applications. However, the impact of device scaling on hardened devices is also discussed.

From fluid dynamics to microscopic transport approach

NASA Astrophysics Data System (ADS)

Saini, Abhilasha; Bhardwaj, Sudhir; Keswani, Bright

2018-05-01

Here we are exploring the widespread features or the characteristics of the microscopic transport modeling and also the speculations made for the approach to fit it to the dynamics of high energy heavy ion collisions, when we see its expansion in space-time dimensions. The explanation of initial stages of the hot and high dense region, the hydrodynamics is instigated and further moderate stages of reaction are complemented to microscopic transport.

Realization of State-Space Models for Wave Propagation Simulations

DTIC Science & Technology

2012-01-01

reduction techniques can be applied to reduce the dimension of the model further if warranted. INFRASONIC PROPAGATION MODEL Infrasound is sound below 20...capable of scatter- ing and blocking the propagation. This is because the infrasound wavelengths are near the scales of topographic features. These...and Development Center (ERDC) Big Black Test Site (BBTS) and an infrasound -sensing array at the ERDC Waterways Experiment Station (WES). Both are

Thermal Non-Equilibrium Flows in Three Space Dimensions

NASA Astrophysics Data System (ADS)

Zeng, Yanni

2016-01-01

We study the equations describing the motion of a thermal non-equilibrium gas in three space dimensions. It is a hyperbolic system of six equations with a relaxation term. The dissipation mechanism induced by the relaxation is weak in the sense that the Shizuta-Kawashima criterion is violated. This implies that a perturbation of a constant equilibrium state consists of two parts: one decays in time while the other stays. In fact, the entropy wave grows weakly along the particle path as the process is irreversible. We study thermal properties related to the well-posedness of the nonlinear system. We also obtain a detailed pointwise estimate on the Green's function for the Cauchy problem when the system is linearized around an equilibrium constant state. The Green's function provides a complete picture of the wave pattern, with an exact and explicit leading term. Comparing with existing results for one dimensional flows, our results reveal a new feature of three dimensional flows: not only does the entropy wave not decay, but the velocity also contains a non-decaying part, strongly coupled with its decaying one. The new feature is supported by the second order approximation via the Chapman-Enskog expansions, which are the Navier-Stokes equations with vanished shear viscosity and heat conductivity.

Information networks in the stock market based on the distance of the multi-attribute dimensions between listed companies

NASA Astrophysics Data System (ADS)

Liu, Qian; Li, Huajiao; Liu, Xueyong; Jiang, Meihui

2018-04-01

In the stock market, there are widespread information connections between economic agents. Listed companies can obtain mutual information about investment decisions from common shareholders, and the extent of sharing information often determines the relationships between listed companies. Because different shareholder compositions and investment shares lead to different formations of the company's governance mechanisms, we map the investment relationships between shareholders to the multi-attribute dimensional spaces of the listed companies (each shareholder investment in a company is a company dimension). Then, we construct the listed company's information network based on co-shareholder relationships. The weights for the edges in the information network are measured with the Euclidean distance between the listed companies in the multi-attribute dimension space. We define two indices to analyze the information network's features. We conduct an empirical study that analyzes Chinese listed companies' information networks. The results from the analysis show that with the diversification and decentralization of shareholder investments, almost all Chinese listed companies exchanged information through common shareholder relationships, and there is a gradual reduction in information sharing capacity between listed companies that have common shareholders. This network analysis has benefits for risk management and portfolio investments.

Testing the Dimension of Hilbert Spaces

NASA Astrophysics Data System (ADS)

Brunner, Nicolas; Pironio, Stefano; Acin, Antonio; Gisin, Nicolas; Méthot, André Allan; Scarani, Valerio

2008-05-01

Given a set of correlations originating from measurements on a quantum state of unknown Hilbert space dimension, what is the minimal dimension d necessary to describe such correlations? We introduce the concept of dimension witness to put lower bounds on d. This work represents a first step in a broader research program aiming to characterize Hilbert space dimension in various contexts related to fundamental questions and quantum information applications.

An efficient sampling algorithm for uncertain abnormal data detection in biomedical image processing and disease prediction.

PubMed

Liu, Fei; Zhang, Xi; Jia, Yan

2015-01-01

In this paper, we propose a computer information processing algorithm that can be used for biomedical image processing and disease prediction. A biomedical image is considered a data object in a multi-dimensional space. Each dimension is a feature that can be used for disease diagnosis. We introduce a new concept of the top (k1,k2) outlier. It can be used to detect abnormal data objects in the multi-dimensional space. This technique focuses on uncertain space, where each data object has several possible instances with distinct probabilities. We design an efficient sampling algorithm for the top (k1,k2) outlier in uncertain space. Some improvement techniques are used for acceleration. Experiments show our methods' high accuracy and high efficiency.

Neural representations of emotion are organized around abstract event features.

PubMed

Skerry, Amy E; Saxe, Rebecca

2015-08-03

Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. Copyright © 2015 Elsevier Ltd. All rights reserved.

Neural Representations of Emotion Are Organized around Abstract Event Features

PubMed Central

Skerry, Amy E.; Saxe, Rebecca

2016-01-01

Summary Research on emotion attribution has tended to focus on the perception of overt expressions of at most five or six basic emotions. However, our ability to identify others' emotional states is not limited to perception of these canonical expressions. Instead, we make fine-grained inferences about what others feel based on the situations they encounter, relying on knowledge of the eliciting conditions for different emotions. In the present research, we provide convergent behavioral and neural evidence concerning the representations underlying these concepts. First, we find that patterns of activity in mentalizing regions contain information about subtle emotional distinctions conveyed through verbal descriptions of eliciting situations. Second, we identify a space of abstract situation features that well captures the emotion discriminations subjects make behaviorally and show that this feature space outperforms competing models in capturing the similarity space of neural patterns in these regions. Together, the data suggest that our knowledge of others' emotions is abstract and high dimensional, that brain regions selective for mental state reasoning support relatively subtle distinctions between emotion concepts, and that the neural representations in these regions are not reducible to more primitive affective dimensions such as valence and arousal. PMID:26212878

Feature-based attentional modulation increases with stimulus separation in divided-attention tasks.

PubMed

Sally, Sharon L; Vidnyánsky, Zoltán; Papathomas, Thomas V

2009-01-01

Attention modifies our visual experience by selecting certain aspects of a scene for further processing. It is therefore important to understand factors that govern the deployment of selective attention over the visual field. Both location and feature-specific mechanisms of attention have been identified and their modulatory effects can interact at a neural level (Treue and Martinez-Trujillo, 1999). The effects of spatial parameters on feature-based attentional modulation were examined for the feature dimensions of orientation, motion and color using three divided-attention tasks. Subjects performed concurrent discriminations of two briefly presented targets (Gabor patches) to the left and right of a central fixation point at eccentricities of +/-2.5 degrees , 5 degrees , 10 degrees and 15 degrees in the horizontal plane. Gabors were size-scaled to maintain consistent single-task performance across eccentricities. For all feature dimensions, the data show a linear increase in the attentional effects with target separation. In a control experiment, Gabors were presented on an isoeccentric viewing arc at 10 degrees and 15 degrees at the closest spatial separation (+/-2.5 degrees ) of the main experiment. Under these conditions, the effects of feature-based attentional effects were largely eliminated. Our results are consistent with the hypothesis that feature-based attention prioritizes the processing of attended features. Feature-based attentional mechanisms may have helped direct the attentional focus to the appropriate target locations at greater separations, whereas similar assistance may not have been necessary at closer target spacings. The results of the present study specify conditions under which dual-task performance benefits from sharing similar target features and may therefore help elucidate the processes by which feature-based attention operates.

Thermodynamic glass transition in a spin glass without time-reversal symmetry

PubMed Central

Baños, Raquel Alvarez; Cruz, Andres; Fernandez, Luis Antonio; Gil-Narvion, Jose Miguel; Gordillo-Guerrero, Antonio; Guidetti, Marco; Iñiguez, David; Maiorano, Andrea; Marinari, Enzo; Martin-Mayor, Victor; Monforte-Garcia, Jorge; Muñoz Sudupe, Antonio; Navarro, Denis; Parisi, Giorgio; Perez-Gaviro, Sergio; Ruiz-Lorenzo, Juan Jesus; Schifano, Sebastiano Fabio; Seoane, Beatriz; Tarancon, Alfonso; Tellez, Pedro; Tripiccione, Raffaele; Yllanes, David

2012-01-01

Spin glasses are a longstanding model for the sluggish dynamics that appear at the glass transition. However, spin glasses differ from structural glasses in a crucial feature: they enjoy a time reversal symmetry. This symmetry can be broken by applying an external magnetic field, but embarrassingly little is known about the critical behavior of a spin glass in a field. In this context, the space dimension is crucial. Simulations are easier to interpret in a large number of dimensions, but one must work below the upper critical dimension (i.e., in d < 6) in order for results to have relevance for experiments. Here we show conclusive evidence for the presence of a phase transition in a four-dimensional spin glass in a field. Two ingredients were crucial for this achievement: massive numerical simulations were carried out on the Janus special-purpose computer, and a new and powerful finite-size scaling method. PMID:22493229

[Effects of fundamental frequency and speech rate on impression formation].

PubMed

Uchida, Teruhisa; Nakaune, Naoko

2004-12-01

This study investigated the systematic relationship between nonverbal features of speech and personality trait ratings of the speaker. In Study 1, fundamental frequency (F0) in original speech was converted into five levels from 64% to 156.25%. Then 132 undergraduates rated each of the converted speeches in terms of personality traits. In Study 2 134 undergraduates similarly rated the speech stimuli, which had five speech rate levels as well as two F0 levels. Results showed that listener ratings along Big Five dimensions were mostly independent. Each dimension had a slightly different change profile over the five levels of F0 and speech rate. A quadratic regression equation provided a good approximation for each rating as a function of F0 or speech rate. The quadratic regression equations put together would provide us with a rough estimate of personality trait impression as a function of prosodic features. The functional relationship among F0, speech rate, and trait ratings was shown as a curved surface in the three-dimensional space.

Compact Representation of High-Dimensional Feature Vectors for Large-Scale Image Recognition and Retrieval.

PubMed

Zhang, Yu; Wu, Jianxin; Cai, Jianfei

2016-05-01

In large-scale visual recognition and image retrieval tasks, feature vectors, such as Fisher vector (FV) or the vector of locally aggregated descriptors (VLAD), have achieved state-of-the-art results. However, the combination of the large numbers of examples and high-dimensional vectors necessitates dimensionality reduction, in order to reduce its storage and CPU costs to a reasonable range. In spite of the popularity of various feature compression methods, this paper shows that the feature (dimension) selection is a better choice for high-dimensional FV/VLAD than the feature (dimension) compression methods, e.g., product quantization. We show that strong correlation among the feature dimensions in the FV and the VLAD may not exist, which renders feature selection a natural choice. We also show that, many dimensions in FV/VLAD are noise. Throwing them away using feature selection is better than compressing them and useful dimensions altogether using feature compression methods. To choose features, we propose an efficient importance sorting algorithm considering both the supervised and unsupervised cases, for visual recognition and image retrieval, respectively. Combining with the 1-bit quantization, feature selection has achieved both higher accuracy and less computational cost than feature compression methods, such as product quantization, on the FV and the VLAD image representations.

Kinetic Description of Ionospheric Outflows Based on the Exact Form of Fokker-Planck Collision Operator: Electrons

NASA Technical Reports Server (NTRS)

Khazanov, George V.; Khabibrakhmanov, Ildar K.; Glocer, Alex

2012-01-01

We present the results of a finite difference implementation of the kinetic Fokker-Planck model with an exact form of the nonlinear collisional operator, The model is time dependent and three-dimensional; one spatial dimension and two in velocity space. The spatial dimension is aligned with the local magnetic field, and the velocity space is defined by the magnitude of the velocity and the cosine of pitch angle. An important new feature of model, the concept of integration along the particle trajectories, is discussed in detail. Integration along the trajectories combined with the operator time splitting technique results in a solution scheme which accurately accounts for both the fast convection of the particles along the magnetic field lines and relatively slow collisional process. We present several tests of the model's performance and also discuss simulation results of the evolution of the plasma distribution for realistic conditions in Earth's plasmasphere under different scenarios.

A Moving Mesh Finite Element Algorithm for Singular Problems in Two and Three Space Dimensions

NASA Astrophysics Data System (ADS)

Li, Ruo; Tang, Tao; Zhang, Pingwen

2002-04-01

A framework for adaptive meshes based on the Hamilton-Schoen-Yau theory was proposed by Dvinsky. In a recent work (2001, J. Comput. Phys.170, 562-588), we extended Dvinsky's method to provide an efficient moving mesh algorithm which compared favorably with the previously proposed schemes in terms of simplicity and reliability. In this work, we will further extend the moving mesh methods based on harmonic maps to deal with mesh adaptation in three space dimensions. In obtaining the variational mesh, we will solve an optimization problem with some appropriate constraints, which is in contrast to the traditional method of solving the Euler-Lagrange equation directly. The key idea of this approach is to update the interior and boundary grids simultaneously, rather than considering them separately. Application of the proposed moving mesh scheme is illustrated with some two- and three-dimensional problems with large solution gradients. The numerical experiments show that our methods can accurately resolve detail features of singular problems in 3D.

Nonlinear Network Description for Many-Body Quantum Systems in Continuous Space

NASA Astrophysics Data System (ADS)

Ruggeri, Michele; Moroni, Saverio; Holzmann, Markus

2018-05-01

We show that the recently introduced iterative backflow wave function can be interpreted as a general neural network in continuum space with nonlinear functions in the hidden units. Using this wave function in variational Monte Carlo simulations of liquid 4He in two and three dimensions, we typically find a tenfold increase in accuracy over currently used wave functions. Furthermore, subsequent stages of the iteration procedure define a set of increasingly good wave functions, each with its own variational energy and variance of the local energy: extrapolation to zero variance gives energies in close agreement with the exact values. For two dimensional 4He, we also show that the iterative backflow wave function can describe both the liquid and the solid phase with the same functional form—a feature shared with the shadow wave function, but now joined by much higher accuracy. We also achieve significant progress for liquid 3He in three dimensions, improving previous variational and fixed-node energies.

A space-fractional Monodomain model for cardiac electrophysiology combining anisotropy and heterogeneity on realistic geometries

NASA Astrophysics Data System (ADS)

Cusimano, N.; Gerardo-Giorda, L.

2018-06-01

Classical models of electrophysiology do not typically account for the effects of high structural heterogeneity in the spatio-temporal description of excitation waves propagation. We consider a modification of the Monodomain model obtained by replacing the diffusive term of the classical formulation with a fractional power of the operator, defined in the spectral sense. The resulting nonlocal model describes different levels of tissue heterogeneity as the fractional exponent is varied. The numerical method for the solution of the fractional Monodomain relies on an integral representation of the nonlocal operator combined with a finite element discretisation in space, allowing to handle in a natural way bounded domains in more than one spatial dimension. Numerical tests in two spatial dimensions illustrate the features of the model. Activation times, action potential duration and its dispersion throughout the domain are studied as a function of the fractional parameter: the expected peculiar behaviour driven by tissue heterogeneities is recovered.

Architecture for the silver generation: exploring the meaning of appropriate space for ageing in a Swedish municipality.

PubMed

Andersson, Jonas E

2011-03-01

This paper focuses on an architecture competition for the silver generation, namely those aged 65 years and older. Twenty-seven Swedish informants were interviewed using an interviewing guide that included a photographic survey. The informants emphasised aesthetic dimensions in architecture for the prolongation of ageing in place and independent living in a residential home. This study highlights the individual adjustment of space, and the integrated location in existing urban settings near nature. Based on the findings, a habitational model for exploring the appropriate space for ageing is formulated. It suggests that architecture through location and spatial features needs to generate positive associations with the users. Copyright © 2010 Elsevier Ltd. All rights reserved.

A face and palmprint recognition approach based on discriminant DCT feature extraction.

PubMed

Jing, Xiao-Yuan; Zhang, David

2004-12-01

In the field of image processing and recognition, discrete cosine transform (DCT) and linear discrimination are two widely used techniques. Based on them, we present a new face and palmprint recognition approach in this paper. It first uses a two-dimensional separability judgment to select the DCT frequency bands with favorable linear separability. Then from the selected bands, it extracts the linear discriminative features by an improved Fisherface method and performs the classification by the nearest neighbor classifier. We detailedly analyze theoretical advantages of our approach in feature extraction. The experiments on face databases and palmprint database demonstrate that compared to the state-of-the-art linear discrimination methods, our approach obtains better classification performance. It can significantly improve the recognition rates for face and palmprint data and effectively reduce the dimension of feature space.

D Object Classification Based on Thermal and Visible Imagery in Urban Area

NASA Astrophysics Data System (ADS)

Hasani, H.; Samadzadegan, F.

2015-12-01

The spatial distribution of land cover in the urban area especially 3D objects (buildings and trees) is a fundamental dataset for urban planning, ecological research, disaster management, etc. According to recent advances in sensor technologies, several types of remotely sensed data are available from the same area. Data fusion has been widely investigated for integrating different source of data in classification of urban area. Thermal infrared imagery (TIR) contains information on emitted radiation and has unique radiometric properties. However, due to coarse spatial resolution of thermal data, its application has been restricted in urban areas. On the other hand, visible image (VIS) has high spatial resolution and information in visible spectrum. Consequently, there is a complementary relation between thermal and visible imagery in classification of urban area. This paper evaluates the potential of aerial thermal hyperspectral and visible imagery fusion in classification of urban area. In the pre-processing step, thermal imagery is resampled to the spatial resolution of visible image. Then feature level fusion is applied to construct hybrid feature space include visible bands, thermal hyperspectral bands, spatial and texture features and moreover Principle Component Analysis (PCA) transformation is applied to extract PCs. Due to high dimensionality of feature space, dimension reduction method is performed. Finally, Support Vector Machines (SVMs) classify the reduced hybrid feature space. The obtained results show using thermal imagery along with visible imagery, improved the classification accuracy up to 8% respect to visible image classification.

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models.

PubMed

Khaligh-Razavi, Seyed-Mahdi; Henriksson, Linda; Kay, Kendrick; Kriegeskorte, Nikolaus

2017-02-01

Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies in the training set did not match their frequencies in natural experience or their behavioural importance. The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas. Our results demonstrate the benefits of testing both the specific representational hypothesis expressed by a model's original feature space and the hypothesis space generated by linear transformations of that feature space.

LDA boost classification: boosting by topics

NASA Astrophysics Data System (ADS)

Lei, La; Qiao, Guo; Qimin, Cao; Qitao, Li

2012-12-01

AdaBoost is an efficacious classification algorithm especially in text categorization (TC) tasks. The methodology of setting up a classifier committee and voting on the documents for classification can achieve high categorization precision. However, traditional Vector Space Model can easily lead to the curse of dimensionality and feature sparsity problems; so it affects classification performance seriously. This article proposed a novel classification algorithm called LDABoost based on boosting ideology which uses Latent Dirichlet Allocation (LDA) to modeling the feature space. Instead of using words or phrase, LDABoost use latent topics as the features. In this way, the feature dimension is significantly reduced. Improved Naïve Bayes (NB) is designed as the weaker classifier which keeps the efficiency advantage of classic NB algorithm and has higher precision. Moreover, a two-stage iterative weighted method called Cute Integration in this article is proposed for improving the accuracy by integrating weak classifiers into strong classifier in a more rational way. Mutual Information is used as metrics of weights allocation. The voting information and the categorization decision made by basis classifiers are fully utilized for generating the strong classifier. Experimental results reveals LDABoost making categorization in a low-dimensional space, it has higher accuracy than traditional AdaBoost algorithms and many other classic classification algorithms. Moreover, its runtime consumption is lower than different versions of AdaBoost, TC algorithms based on support vector machine and Neural Networks.

Cell type classifiers for breast cancer microscopic images based on fractal dimension texture analysis of image color layers.

PubMed

Jitaree, Sirinapa; Phinyomark, Angkoon; Boonyaphiphat, Pleumjit; Phukpattaranont, Pornchai

2015-01-01

Having a classifier of cell types in a breast cancer microscopic image (BCMI), obtained with immunohistochemical staining, is required as part of a computer-aided system that counts the cancer cells in such BCMI. Such quantitation by cell counting is very useful in supporting decisions and planning of the medical treatment of breast cancer. This study proposes and evaluates features based on texture analysis by fractal dimension (FD), for the classification of histological structures in a BCMI into either cancer cells or non-cancer cells. The cancer cells include positive cells (PC) and negative cells (NC), while the normal cells comprise stromal cells (SC) and lymphocyte cells (LC). The FD feature values were calculated with the box-counting method from binarized images, obtained by automatic thresholding with Otsu's method of the grayscale images for various color channels. A total of 12 color channels from four color spaces (RGB, CIE-L*a*b*, HSV, and YCbCr) were investigated, and the FD feature values from them were used with decision tree classifiers. The BCMI data consisted of 1,400, 1,200, and 800 images with pixel resolutions 128 × 128, 192 × 192, and 256 × 256, respectively. The best cross-validated classification accuracy was 93.87%, for distinguishing between cancer and non-cancer cells, obtained using the Cr color channel with window size 256. The results indicate that the proposed algorithm, based on fractal dimension features extracted from a color channel, performs well in the automatic classification of the histology in a BCMI. This might support accurate automatic cell counting in a computer-assisted system for breast cancer diagnosis. © Wiley Periodicals, Inc.

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.

A fast image matching algorithm based on key points

NASA Astrophysics Data System (ADS)

Wang, Huilin; Wang, Ying; An, Ru; Yan, Peng

2014-05-01

Image matching is a very important technique in image processing. It has been widely used for object recognition and tracking, image retrieval, three-dimensional vision, change detection, aircraft position estimation, and multi-image registration. Based on the requirements of matching algorithm for craft navigation, such as speed, accuracy and adaptability, a fast key point image matching method is investigated and developed. The main research tasks includes: (1) Developing an improved celerity key point detection approach using self-adapting threshold of Features from Accelerated Segment Test (FAST). A method of calculating self-adapting threshold was introduced for images with different contrast. Hessian matrix was adopted to eliminate insecure edge points in order to obtain key points with higher stability. This approach in detecting key points has characteristics of small amount of computation, high positioning accuracy and strong anti-noise ability; (2) PCA-SIFT is utilized to describe key point. 128 dimensional vector are formed based on the SIFT method for the key points extracted. A low dimensional feature space was established by eigenvectors of all the key points, and each eigenvector was projected onto the feature space to form a low dimensional eigenvector. These key points were re-described by dimension-reduced eigenvectors. After reducing the dimension by the PCA, the descriptor was reduced to 20 dimensions from the original 128. This method can reduce dimensions of searching approximately near neighbors thereby increasing overall speed; (3) Distance ratio between the nearest neighbour and second nearest neighbour searching is regarded as the measurement criterion for initial matching points from which the original point pairs matched are obtained. Based on the analysis of the common methods (e.g. RANSAC (random sample consensus) and Hough transform cluster) used for elimination false matching point pairs, a heuristic local geometric restriction strategy is adopted to discard false matched point pairs further; and (4) Affine transformation model is introduced to correct coordinate difference between real-time image and reference image. This resulted in the matching of the two images. SPOT5 Remote sensing images captured at different date and airborne images captured with different flight attitude were used to test the performance of the method from matching accuracy, operation time and ability to overcome rotation. Results show the effectiveness of the approach.

Learning binary code via PCA of angle projection for image retrieval

NASA Astrophysics Data System (ADS)

Yang, Fumeng; Ye, Zhiqiang; Wei, Xueqi; Wu, Congzhong

2018-01-01

With benefits of low storage costs and high query speeds, binary code representation methods are widely researched for efficiently retrieving large-scale data. In image hashing method, learning hashing function to embed highdimensions feature to Hamming space is a key step for accuracy retrieval. Principal component analysis (PCA) technical is widely used in compact hashing methods, and most these hashing methods adopt PCA projection functions to project the original data into several dimensions of real values, and then each of these projected dimensions is quantized into one bit by thresholding. The variances of different projected dimensions are different, and with real-valued projection produced more quantization error. To avoid the real-valued projection with large quantization error, in this paper we proposed to use Cosine similarity projection for each dimensions, the angle projection can keep the original structure and more compact with the Cosine-valued. We used our method combined the ITQ hashing algorithm, and the extensive experiments on the public CIFAR-10 and Caltech-256 datasets validate the effectiveness of the proposed method.

Electrokinetic pump

DOEpatents

Hencken, Kenneth R.; Sartor, George B.

2004-08-03

An electrokinetic pump in which the porous dielectric medium of conventional electrokinetic pumps is replaced by a patterned microstructure. The patterned microstructure is fabricated by lithographic patterning and etching of a substrate and is formed by features arranged so as to create an array of microchannels. The microchannels have dimensions on the order of the pore spacing in a conventional porous dielectric medium. Embedded unitary electrodes are vapor deposited on either end of the channel structure to provide the electric field necessary for electroosmotic flow.

Application of an Elongated Kelvin Model to Space Shuttle Foams

NASA Technical Reports Server (NTRS)

Sullivan, Roy M.; Ghosn, Louis J.; Lerch, Bradley A.

2008-01-01

Spray-on foam insulation is applied to the exterior of the Space Shuttle s External Tank to limit propellant boil-off and to prevent ice formation. The Space Shuttle foams are rigid closed-cell polyurethane foams. The two foams used most extensively on the Space Shuttle External Tank are BX-265 and NCFI24-124. Since the catastrophic loss of the Space Shuttle Columbia, numerous studies have been conducted to mitigate the likelihood and the severity of foam shedding during the Shuttle s ascent to space. Due to the foaming and rising process, the foam microstructures are elongated in the rise direction. As a result, these two foams exhibit a non-isotropic mechanical behavior. In this paper, a detailed microstructural characterization of the two foams is presented. The key features of the foam cells are summarized and the average cell dimensions in the two foams are compared. Experimental studies to measure the room temperature mechanical response of the two foams in the two principal material directions (parallel to the rise and perpendicular to the rise) are also reported. The measured elastic modulus, proportional limit stress, ultimate tensile stress and the Poisson s ratios for the two foams are compared. The generalized elongated Kelvin foam model previously developed by the authors is reviewed and the equations which result from this model are presented. The resulting equations show that the ratio of the elastic modulus in the rise direction to that in the perpendicular-to-rise direction as well as the ratio of the strengths in the two material directions is only a function of the microstructural dimensions. Using the measured microstructural dimensions and the measured stiffness ratio, the foam tensile strength ratio and Poisson s ratios are predicted for both foams. The predicted tensile strength ratio is in close agreement with the measured strength ratios for both BX-265 and NCFI24-124. The comparison between the predicted Poisson s ratios and the measured values is not as favorable.

Generalization of the photo process window and its application to OPC test pattern design

NASA Astrophysics Data System (ADS)

Eisenmann, Hans; Peter, Kai; Strojwas, Andrzej J.

2003-07-01

From the early development phase up to the production phase, test pattern play a key role for microlithography. The requirement for test pattern is to represent the design well and to cover the space of all process conditions, e.g. to investigate the full process window and all other process parameters. This paper shows that the current state-of-the-art test pattern do not address these requirements sufficiently and makes suggestions for a better selection of test pattern. We present a new methodology to analyze an existing layout (e.g. logic library, test pattern or full chip) for critical layout situations which does not need precise process data. We call this method "process space decomposition", because it is aimed at decomposing the process impact to a layout feature into a sum of single independent contributions, the dimensions of the process space. This is a generalization of the classical process window, which examines defocus and exposure dependency of given test pattern, e.g. CD value of dense and isolated lines. In our process space we additionally define the dimensions resist effects, etch effects, mask error and misalignment, which describe the deviation of the printed silicon pattern from its target. We further extend it by the pattern space using a product based layout (library, full chip or synthetic test pattern). The criticality of pattern is defined by their deviation due to aerial image, their sensitivity to the respective dimension or several combinations of these. By exploring the process space for a given design, the method allows to find the most critical patterns independent of specific process parameters. The paper provides examples for different applications of the method: (1) selection of design oriented test pattern for lithography development (2) test pattern reduction in process characterization (3) verification/optimization of printability and performance of post processing procedures (like OPC) (4) creation of a sensitive process monitor.

The Equivalence of Information-Theoretic and Likelihood-Based Methods for Neural Dimensionality Reduction

PubMed Central

Williamson, Ross S.; Sahani, Maneesh; Pillow, Jonathan W.

2015-01-01

Stimulus dimensionality-reduction methods in neuroscience seek to identify a low-dimensional space of stimulus features that affect a neuron’s probability of spiking. One popular method, known as maximally informative dimensions (MID), uses an information-theoretic quantity known as “single-spike information” to identify this space. Here we examine MID from a model-based perspective. We show that MID is a maximum-likelihood estimator for the parameters of a linear-nonlinear-Poisson (LNP) model, and that the empirical single-spike information corresponds to the normalized log-likelihood under a Poisson model. This equivalence implies that MID does not necessarily find maximally informative stimulus dimensions when spiking is not well described as Poisson. We provide several examples to illustrate this shortcoming, and derive a lower bound on the information lost when spiking is Bernoulli in discrete time bins. To overcome this limitation, we introduce model-based dimensionality reduction methods for neurons with non-Poisson firing statistics, and show that they can be framed equivalently in likelihood-based or information-theoretic terms. Finally, we show how to overcome practical limitations on the number of stimulus dimensions that MID can estimate by constraining the form of the non-parametric nonlinearity in an LNP model. We illustrate these methods with simulations and data from primate visual cortex. PMID:25831448

Evolution Of The Concept Of Dimension

NASA Astrophysics Data System (ADS)

Journeau, Philippe F.

2007-04-01

Concepts of time elapsing `in' a space measuring the real emerge over the centuries. But Kant refutes absolute time and defines it, with space, as forms reacting to Newtonian mechanics. Einstein and Minkowski open a 20th century where time is a dimension, a substratum of reality `with' space rather than `in' it. Kaluza-Klein and String theories then develop a trend of additional spatial dimensions while de Broglie and Bohm open the possiblity that form, to begin with wave, be a reality together `with' a space-time particle. Other recent theories, such as spin networks, causal sets and twistor theory, even head to the idea of other "systems of dimensions." On the basis of such progresses and recent experiments the paper then considers a background independent fourfold time-form-action-space system of dimensions.

Dust Storm Feature Identification and Tracking from 4D Simulation Data

NASA Astrophysics Data System (ADS)

Yu, M.; Yang, C. P.

2016-12-01

Dust storms cause significant damage to health, property and the environment worldwide every year. To help mitigate the damage, dust forecasting models simulate and predict upcoming dust events, providing valuable information to scientists, decision makers, and the public. Normally, the model simulations are conducted in four-dimensions (i.e., latitude, longitude, elevation and time) and represent three-dimensional (3D), spatial heterogeneous features of the storm and its evolution over space and time. This research investigates and proposes an automatic multi-threshold, region-growing based identification algorithm to identify critical dust storm features, and track the evolution process of dust storm events through space and time. In addition, a spatiotemporal data model is proposed, which can support the characterization and representation of dust storm events and their dynamic patterns. Quantitative and qualitative evaluations for the algorithm are conducted to test the sensitivity, and capability of identify and track dust storm events. This study has the potential to assist a better early warning system for decision-makers and the public, thus making hazard mitigation plans more effective.

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video.

PubMed

Ghosh, Tonmoy; Fattah, Shaikh Anowarul; Wahid, Khan A

2018-01-01

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

Extra Dimensions of Space: Are They Going to be Found Soon?

ScienceCinema

Rubakov, Valery [Institute for Nuclear Research, Moscow, Russia

2017-12-09

Our space may well have more than 3 dimensions. Indeed, theories that pretend to be most fundamental choose to live in higher dimensions: a natural area for superstring/Mtheory is 9- or 10-dimensional space. Extra dimensions have been hidden so far, but they would open up above a certain energy threshold. A fascinating possibility is that this happens within reach of particle colliders. This lecture will address the motivation for such a viewpoint and implications of accessible extra dimensions for our understanding of nature.

Texture segmentation of non-cooperative spacecrafts images based on wavelet and fractal dimension

NASA Astrophysics Data System (ADS)

Wu, Kanzhi; Yue, Xiaokui

2011-06-01

With the increase of on-orbit manipulations and space conflictions, missions such as tracking and capturing the target spacecrafts are aroused. Unlike cooperative spacecrafts, fixing beacons or any other marks on the targets is impossible. Due to the unknown shape and geometry features of non-cooperative spacecraft, in order to localize the target and obtain the latitude, we need to segment the target image and recognize the target from the background. The data and errors during the following procedures such as feature extraction and matching can also be reduced. Multi-resolution analysis of wavelet theory reflects human beings' recognition towards images from low resolution to high resolution. In addition, spacecraft is the only man-made object in the image compared to the natural background and the differences will be certainly observed between the fractal dimensions of target and background. Combined wavelet transform and fractal dimension, in this paper, we proposed a new segmentation algorithm for the images which contains complicated background such as the universe and planet surfaces. At first, Daubechies wavelet basis is applied to decompose the image in both x axis and y axis, thus obtain four sub-images. Then, calculate the fractal dimensions in four sub-images using different methods; after analyzed the results of fractal dimensions in sub-images, we choose Differential Box Counting in low resolution image as the principle to segment the texture which has the greatest divergences between different sub-images. This paper also presents the results of experiments by using the algorithm above. It is demonstrated that an accurate texture segmentation result can be obtained using the proposed technique.

Features Extraction of Flotation Froth Images and BP Neural Network Soft-Sensor Model of Concentrate Grade Optimized by Shuffled Cuckoo Searching Algorithm

PubMed Central

Wang, Jie-sheng; Han, Shuang; Shen, Na-na; Li, Shu-xia

2014-01-01

For meeting the forecasting target of key technology indicators in the flotation process, a BP neural network soft-sensor model based on features extraction of flotation froth images and optimized by shuffled cuckoo search algorithm is proposed. Based on the digital image processing technique, the color features in HSI color space, the visual features based on the gray level cooccurrence matrix, and the shape characteristics based on the geometric theory of flotation froth images are extracted, respectively, as the input variables of the proposed soft-sensor model. Then the isometric mapping method is used to reduce the input dimension, the network size, and learning time of BP neural network. Finally, a shuffled cuckoo search algorithm is adopted to optimize the BP neural network soft-sensor model. Simulation results show that the model has better generalization results and prediction accuracy. PMID:25133210

Hierarchical Kohonenen net for anomaly detection in network security.

PubMed

Sarasamma, Suseela T; Zhu, Qiuming A; Huff, Julie

2005-04-01

A novel multilevel hierarchical Kohonen Net (K-Map) for an intrusion detection system is presented. Each level of the hierarchical map is modeled as a simple winner-take-all K-Map. One significant advantage of this multilevel hierarchical K-Map is its computational efficiency. Unlike other statistical anomaly detection methods such as nearest neighbor approach, K-means clustering or probabilistic analysis that employ distance computation in the feature space to identify the outliers, our approach does not involve costly point-to-point computation in organizing the data into clusters. Another advantage is the reduced network size. We use the classification capability of the K-Map on selected dimensions of data set in detecting anomalies. Randomly selected subsets that contain both attacks and normal records from the KDD Cup 1999 benchmark data are used to train the hierarchical net. We use a confidence measure to label the clusters. Then we use the test set from the same KDD Cup 1999 benchmark to test the hierarchical net. We show that a hierarchical K-Map in which each layer operates on a small subset of the feature space is superior to a single-layer K-Map operating on the whole feature space in detecting a variety of attacks in terms of detection rate as well as false positive rate.

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.

High-Order Central WENO Schemes for Multi-Dimensional Hamilton-Jacobi Equations

NASA Technical Reports Server (NTRS)

Bryson, Steve; Levy, Doron; Biegel, Bryan (Technical Monitor)

2002-01-01

We present new third- and fifth-order Godunov-type central schemes for approximating solutions of the Hamilton-Jacobi (HJ) equation in an arbitrary number of space dimensions. These are the first central schemes for approximating solutions of the HJ equations with an order of accuracy that is greater than two. In two space dimensions we present two versions for the third-order scheme: one scheme that is based on a genuinely two-dimensional Central WENO reconstruction, and another scheme that is based on a simpler dimension-by-dimension reconstruction. The simpler dimension-by-dimension variant is then extended to a multi-dimensional fifth-order scheme. Our numerical examples in one, two and three space dimensions verify the expected order of accuracy of the schemes.

Towards a taxonomy for integrated care: a mixed-methods study

PubMed Central

Valentijn, Pim P.; Boesveld, Inge C.; van der Klauw, Denise M.; Ruwaard, Dirk; Struijs, Jeroen N.; Molema, Johanna J.W.; Bruijnzeels, Marc A.; Vrijhoef, Hubertus JM.

2015-01-01

Introduction Building integrated services in a primary care setting is considered an essential important strategy for establishing a high-quality and affordable health care system. The theoretical foundations of such integrated service models are described by the Rainbow Model of Integrated Care, which distinguishes six integration dimensions (clinical, professional, organisational, system, functional and normative integration). The aim of the present study is to refine the Rainbow Model of Integrated Care by developing a taxonomy that specifies the underlying key features of the six dimensions. Methods First, a literature review was conducted to identify features for achieving integrated service delivery. Second, a thematic analysis method was used to develop a taxonomy of key features organised into the dimensions of the Rainbow Model of Integrated Care. Finally, the appropriateness of the key features was tested in a Delphi study among Dutch experts. Results The taxonomy consists of 59 key features distributed across the six integration dimensions of the Rainbow Model of Integrated Care. Key features associated with the clinical, professional, organisational and normative dimensions were considered appropriate by the experts. Key features linked to the functional and system dimensions were considered less appropriate. Discussion This study contributes to the ongoing debate of defining the concept and typology of integrated care. This taxonomy provides a development agenda for establishing an accepted scientific framework of integrated care from an end-user, professional, managerial and policy perspective. PMID:25759607

Towards a taxonomy for integrated care: a mixed-methods study.

PubMed

Valentijn, Pim P; Boesveld, Inge C; van der Klauw, Denise M; Ruwaard, Dirk; Struijs, Jeroen N; Molema, Johanna J W; Bruijnzeels, Marc A; Vrijhoef, Hubertus Jm

2015-01-01

Building integrated services in a primary care setting is considered an essential important strategy for establishing a high-quality and affordable health care system. The theoretical foundations of such integrated service models are described by the Rainbow Model of Integrated Care, which distinguishes six integration dimensions (clinical, professional, organisational, system, functional and normative integration). The aim of the present study is to refine the Rainbow Model of Integrated Care by developing a taxonomy that specifies the underlying key features of the six dimensions. First, a literature review was conducted to identify features for achieving integrated service delivery. Second, a thematic analysis method was used to develop a taxonomy of key features organised into the dimensions of the Rainbow Model of Integrated Care. Finally, the appropriateness of the key features was tested in a Delphi study among Dutch experts. The taxonomy consists of 59 key features distributed across the six integration dimensions of the Rainbow Model of Integrated Care. Key features associated with the clinical, professional, organisational and normative dimensions were considered appropriate by the experts. Key features linked to the functional and system dimensions were considered less appropriate. This study contributes to the ongoing debate of defining the concept and typology of integrated care. This taxonomy provides a development agenda for establishing an accepted scientific framework of integrated care from an end-user, professional, managerial and policy perspective.

Fortran programs for the time-dependent Gross-Pitaevskii equation in a fully anisotropic trap

NASA Astrophysics Data System (ADS)

Muruganandam, P.; Adhikari, S. K.

2009-10-01

Here we develop simple numerical algorithms for both stationary and non-stationary solutions of the time-dependent Gross-Pitaevskii (GP) equation describing the properties of Bose-Einstein condensates at ultra low temperatures. In particular, we consider algorithms involving real- and imaginary-time propagation based on a split-step Crank-Nicolson method. In a one-space-variable form of the GP equation we consider the one-dimensional, two-dimensional circularly-symmetric, and the three-dimensional spherically-symmetric harmonic-oscillator traps. In the two-space-variable form we consider the GP equation in two-dimensional anisotropic and three-dimensional axially-symmetric traps. The fully-anisotropic three-dimensional GP equation is also considered. Numerical results for the chemical potential and root-mean-square size of stationary states are reported using imaginary-time propagation programs for all the cases and compared with previously obtained results. Also presented are numerical results of non-stationary oscillation for different trap symmetries using real-time propagation programs. A set of convenient working codes developed in Fortran 77 are also provided for all these cases (twelve programs in all). In the case of two or three space variables, Fortran 90/95 versions provide some simplification over the Fortran 77 programs, and these programs are also included (six programs in all). Program summaryProgram title: (i) imagetime1d, (ii) imagetime2d, (iii) imagetime3d, (iv) imagetimecir, (v) imagetimesph, (vi) imagetimeaxial, (vii) realtime1d, (viii) realtime2d, (ix) realtime3d, (x) realtimecir, (xi) realtimesph, (xii) realtimeaxial Catalogue identifier: AEDU_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEDU_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 122 907 No. of bytes in distributed program, including test data, etc.: 609 662 Distribution format: tar.gz Programming language: FORTRAN 77 and Fortran 90/95 Computer: PC Operating system: Linux, Unix RAM: 1 GByte (i, iv, v), 2 GByte (ii, vi, vii, x, xi), 4 GByte (iii, viii, xii), 8 GByte (ix) Classification: 2.9, 4.3, 4.12 Nature of problem: These programs are designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in one-, two- or three-space dimensions with a harmonic, circularly-symmetric, spherically-symmetric, axially-symmetric or anisotropic trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Solution method: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation, in either imaginary or real time, over small time steps. The method yields the solution of stationary and/or non-stationary problems. Additional comments: This package consists of 12 programs, see "Program title", above. FORTRAN77 versions are provided for each of the 12 and, in addition, Fortran 90/95 versions are included for ii, iii, vi, viii, ix, xii. For the particular purpose of each program please see the below. Running time: Minutes on a medium PC (i, iv, v, vii, x, xi), a few hours on a medium PC (ii, vi, viii, xii), days on a medium PC (iii, ix). Program summary (1)Title of program: imagtime1d.F Title of electronic file: imagtime1d.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 1 GByte Programming language used: Fortran 77 Typical running time: Minutes on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in one-space dimension with a harmonic trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in imaginary time over small time steps. The method yields the solution of stationary problems. Program summary (2)Title of program: imagtimecir.F Title of electronic file: imagtimecir.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 1 GByte Programming language used: Fortran 77 Typical running time: Minutes on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in two-space dimensions with a circularly-symmetric trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in imaginary time over small time steps. The method yields the solution of stationary problems. Program summary (3)Title of program: imagtimesph.F Title of electronic file: imagtimesph.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 1 GByte Programming language used: Fortran 77 Typical running time: Minutes on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in three-space dimensions with a spherically-symmetric trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in imaginary time over small time steps. The method yields the solution of stationary problems. Program summary (4)Title of program: realtime1d.F Title of electronic file: realtime1d.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 2 GByte Programming language used: Fortran 77 Typical running time: Minutes on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in one-space dimension with a harmonic trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in real time over small time steps. The method yields the solution of stationary and non-stationary problems. Program summary (5)Title of program: realtimecir.F Title of electronic file: realtimecir.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 2 GByte Programming language used: Fortran 77 Typical running time: Minutes on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in two-space dimensions with a circularly-symmetric trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in real time over small time steps. The method yields the solution of stationary and non-stationary problems. Program summary (6)Title of program: realtimesph.F Title of electronic file: realtimesph.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 2 GByte Programming language used: Fortran 77 Typical running time: Minutes on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in three-space dimensions with a spherically-symmetric trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in real time over small time steps. The method yields the solution of stationary and non-stationary problems. Program summary (7)Title of programs: imagtimeaxial.F and imagtimeaxial.f90 Title of electronic file: imagtimeaxial.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 2 GByte Programming language used: Fortran 77 and Fortran 90 Typical running time: Few hours on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in three-space dimensions with an axially-symmetric trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in imaginary time over small time steps. The method yields the solution of stationary problems. Program summary (8)Title of program: imagtime2d.F and imagtime2d.f90 Title of electronic file: imagtime2d.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 2 GByte Programming language used: Fortran 77 and Fortran 90 Typical running time: Few hours on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in two-space dimensions with an anisotropic trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in imaginary time over small time steps. The method yields the solution of stationary problems. Program summary (9)Title of program: realtimeaxial.F and realtimeaxial.f90 Title of electronic file: realtimeaxial.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 4 GByte Programming language used: Fortran 77 and Fortran 90 Typical running time Hours on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in three-space dimensions with an axially-symmetric trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in real time over small time steps. The method yields the solution of stationary and non-stationary problems. Program summary (10)Title of program: realtime2d.F and realtime2d.f90 Title of electronic file: realtime2d.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 4 GByte Programming language used: Fortran 77 and Fortran 90 Typical running time: Hours on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in two-space dimensions with an anisotropic trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in real time over small time steps. The method yields the solution of stationary and non-stationary problems. Program summary (11)Title of program: imagtime3d.F and imagtime3d.f90 Title of electronic file: imagtime3d.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum RAM memory: 4 GByte Programming language used: Fortran 77 and Fortran 90 Typical running time: Few days on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in three-space dimensions with an anisotropic trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in imaginary time over small time steps. The method yields the solution of stationary problems. Program summary (12)Title of program: realtime3d.F and realtime3d.f90 Title of electronic file: realtime3d.tar.gz Catalogue identifier: Program summary URL: Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Distribution format: tar.gz Computers: PC/Linux, workstation/UNIX Maximum Ram Memory: 8 GByte Programming language used: Fortran 77 and Fortran 90 Typical running time: Days on a medium PC Unusual features: None Nature of physical problem: This program is designed to solve the time-dependent Gross-Pitaevskii nonlinear partial differential equation in three-space dimensions with an anisotropic trap. The Gross-Pitaevskii equation describes the properties of a dilute trapped Bose-Einstein condensate. Method of solution: The time-dependent Gross-Pitaevskii equation is solved by the split-step Crank-Nicolson method by discretizing in space and time. The discretized equation is then solved by propagation in real time over small time steps. The method yields the solution of stationary and non-stationary problems.

Momentum-Space Entanglement and Loschmidt Echo in Luttinger Liquids after a Quantum Quench.

PubMed

Dóra, Balázs; Lundgren, Rex; Selover, Mark; Pollmann, Frank

2016-07-01

Luttinger liquids (LLs) arise by coupling left- and right-moving particles through interactions in one dimension. This most natural partitioning of LLs is investigated by the momentum-space entanglement after a quantum quench using analytical and numerical methods. We show that the momentum-space entanglement spectrum of a LL possesses many universal features both in equilibrium and after a quantum quench. The largest entanglement eigenvalue is identical to the Loschmidt echo, i.e., the overlap of the disentangled and final wave functions of the system. The second largest eigenvalue is the overlap of the first excited state of the disentangled system with zero total momentum and the final wave function. The entanglement gap is universal both in equilibrium and after a quantum quench. The momentum-space entanglement entropy is always extensive and saturates fast to a time independent value after the quench, in sharp contrast to a spatial bipartitioning.

The view from the Shuttle Orbiter - Observing the oceans from manned space flights

NASA Technical Reports Server (NTRS)

Kaltenbach, J. L.; Helfert, M. R.; Wells, G. L.

1984-01-01

Examples of earth-looking hand-held photography and orbital sensor imagery of ocean features and phenomena in the framework of the Space Shuttle Earth Observations Project are presented. These include images of a floating substance in Capricorn Channel off northeastern Queensland, Australia; atolls in the central Maldive Islands; a spiral eddy and probable oil slick in the Caribbean Sea north of Aruba; and spiral eddies recorded in sun glint over the Mozambique Channel. It is concluded that the observation of the world's oceans during Shuttle missions with the trained eyes of the crewmen and documentation with hand-held photography add a significant dimension to the remote sensing of the ocean.

Cryoglobulinemic neuropathy: a pathological study.

PubMed

Vallat, J M; Desproges-Gotteron, R; Leboutet, M J; Loubet, A; Gualde, N; Treves, R

1980-08-01

A 53-year-old woman developed symmetrical polyneuropathy of the lower limbs a few months after she was found to have myeloma with cryoglobulinemia. In musculocutaneous nerve biopsy material, electron microscopy showed both axonal degeneration and demyelination. The most striking finding was the presence, in the endoneurial space, of numerous masses of closely packed tubular structures. These masses also were found in the walls of all the vasa nervorum and within the lumen of some vessels. The morphological features and dimensions of the deposits within nerve were identical to those of cryoprecipitates extracted from serum and examined with the electron microscope. An example of myeloma neuropathy with cryoglobulin deposits within the endoneurial space has not been reported previously.

Visualization of the Left Extraperitoneal Space and Spatial Relationships to Its Related Spaces by the Visible Human Project

PubMed Central

Xu, Haotong; Li, Xiaoxiao; Zhang, Zhengzhi; Qiu, Mingguo; Mu, Qiwen; Wu, Yi; Tan, Liwen; Zhang, Shaoxiang; Zhang, Xiaoming

2011-01-01

Background The major hindrance to multidetector CT imaging of the left extraperitoneal space (LES), and the detailed spatial relationships to its related spaces, is that there is no obvious density difference between them. Traditional gross anatomy and thick-slice sectional anatomy imagery are also insufficient to show the anatomic features of this narrow space in three-dimensions (3D). To overcome these obstacles, we used a new method to visualize the anatomic features of the LES and its spatial associations with related spaces, in random sections and in 3D. Methods In conjunction with Mimics® and Amira® software, we used thin-slice cross-sectional images of the upper abdomen, retrieved from the Chinese and American Visible Human dataset and the Chinese Virtual Human dataset, to display anatomic features of the LES and spatial relationships of the LES to its related spaces, especially the gastric bare area. The anatomic location of the LES was presented on 3D sections reconstructed from CVH2 images and CT images. Principal Findings What calls for special attention of our results is the LES consists of the left sub-diaphragmatic fat space and gastric bare area. The appearance of the fat pad at the cardiac notch contributes to converting the shape of the anteroexternal surface of the LES from triangular to trapezoidal. Moreover, the LES is adjacent to the lesser omentum and the hepatic bare area in the anterointernal and right rear direction, respectively. Conclusion The LES and its related spaces were imaged in 3D using visualization technique for the first time. This technique is a promising new method for exploring detailed communication relationships among other abdominal spaces, and will promote research on the dynamic extension of abdominal diseases, such as acute pancreatitis and intra-abdominal carcinomatosis. PMID:22087259

The Dimension of the Pore Space in Sponges

ERIC Educational Resources Information Center

Silva, L. H. F.; Yamashita, M. T.

2009-01-01

A simple experiment to reveal the dimension of the pore space in sponges is proposed. This experiment is suitable for the first year of a physics or engineering course. The calculated dimension of the void space in a sponge of density 16 mg cm[superscript -3] was 2.948 [plus or minus] 0.008. (Contains 2 figures.)

Improving Classification of Protein Interaction Articles Using Context Similarity-Based Feature Selection.

PubMed

Chen, Yifei; Sun, Yuxing; Han, Bing-Qing

2015-01-01

Protein interaction article classification is a text classification task in the biological domain to determine which articles describe protein-protein interactions. Since the feature space in text classification is high-dimensional, feature selection is widely used for reducing the dimensionality of features to speed up computation without sacrificing classification performance. Many existing feature selection methods are based on the statistical measure of document frequency and term frequency. One potential drawback of these methods is that they treat features separately. Hence, first we design a similarity measure between the context information to take word cooccurrences and phrase chunks around the features into account. Then we introduce the similarity of context information to the importance measure of the features to substitute the document and term frequency. Hence we propose new context similarity-based feature selection methods. Their performance is evaluated on two protein interaction article collections and compared against the frequency-based methods. The experimental results reveal that the context similarity-based methods perform better in terms of the F1 measure and the dimension reduction rate. Benefiting from the context information surrounding the features, the proposed methods can select distinctive features effectively for protein interaction article classification.

4D in situ visualization of electrode morphology changes during accelerated degradation in fuel cells by X-ray computed tomography

NASA Astrophysics Data System (ADS)

White, Robin T.; Wu, Alex; Najm, Marina; Orfino, Francesco P.; Dutta, Monica; Kjeang, Erik

2017-05-01

A four-dimensional visualization approach, featuring three dimensions in space and one dimension in time, is proposed to study local electrode degradation effects during voltage cycling in fuel cells. Non-invasive in situ micro X-ray computed tomography (XCT) with a custom fuel cell fixture is utilized to track the same cathode catalyst layer domain throughout various degradation times from beginning-of-life (BOL) to end-of-life (EOL). With this unique approach, new information regarding damage features and trends are revealed, including crack propagation and catalyst layer thinning being quantified by means of image processing and analysis methods. Degradation heterogeneities as a result of local environmental variations under land and channel are also explored, with a higher structural degradation rate under channels being observed. Density and compositional changes resulting from carbon corrosion and catalyst layer collapse and thinning are observed by changes in relative X-ray attenuation from BOL to EOL, which also indicate possible vulnerable regions where crack initiation and propagation may occur. Electrochemical diagnostics and morphological features observed by micro-XCT are correlated by additionally collecting effective catalyst surface area, double layer capacitance, and polarization curves prior to imaging at various stages of degradation.

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.

The interaction of feature and space based orienting within the attention set

PubMed Central

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. PMID:24523682

Neuron’s eye view: Inferring features of complex stimuli from neural responses

PubMed Central

Chen, Xin; Beck, Jeffrey M.

2017-01-01

Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world—contrast and luminance for vision, pitch and intensity for sound—and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov) dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set. PMID:28827790

A spectrum fractal feature classification algorithm for agriculture crops with hyper spectrum image

NASA Astrophysics Data System (ADS)

Su, Junying

2011-11-01

A fractal dimension feature analysis method in spectrum domain for hyper spectrum image is proposed for agriculture crops classification. Firstly, a fractal dimension calculation algorithm in spectrum domain is presented together with the fast fractal dimension value calculation algorithm using the step measurement method. Secondly, the hyper spectrum image classification algorithm and flowchart is presented based on fractal dimension feature analysis in spectrum domain. Finally, the experiment result of the agricultural crops classification with FCL1 hyper spectrum image set with the proposed method and SAM (spectral angle mapper). The experiment results show it can obtain better classification result than the traditional SAM feature analysis which can fulfill use the spectrum information of hyper spectrum image to realize precision agricultural crops classification.

[A research in speech endpoint detection based on boxes-coupling generalization dimension].

PubMed

Wang, Zimei; Yang, Cuirong; Wu, Wei; Fan, Yingle

2008-06-01

In this paper, a new calculating method of generalized dimension, based on boxes-coupling principle, is proposed to overcome the edge effects and to improve the capability of the speech endpoint detection which is based on the original calculating method of generalized dimension. This new method has been applied to speech endpoint detection. Firstly, the length of overlapping border was determined, and through calculating the generalized dimension by covering the speech signal with overlapped boxes, three-dimension feature vectors including the box dimension, the information dimension and the correlation dimension were obtained. Secondly, in the light of the relation between feature distance and similarity degree, feature extraction was conducted by use of common distance. Lastly, bi-threshold method was used to classify the speech signals. The results of experiment indicated that, by comparison with the original generalized dimension (OGD) and the spectral entropy (SE) algorithm, the proposed method is more robust and effective for detecting the speech signals which contain different kinds of noise in different signal noise ratio (SNR), especially in low SNR.

2+1 black hole with SU(2) hair (and the theory where it grows)

NASA Astrophysics Data System (ADS)

Zanelli, Jorge

2015-04-01

A black hole solution in three spacetime dimensions, endowed with an SU(2) charge is presented. The construction is based on two main features of three dimensions: i) AdS3 spacetime is locally Lorentz-flat, that is, it can be covered with a congruence of local inertial observers, just like flat Minkowski space; ii) The SO(2,1) and SU(2) groups are isomorphic, so that a flat connection of the first can be mapped to a flat connection of the second. The global nontrivial nature of the solution is a consequence of the topology produced by the identification in the covering space that gives rise to the 2+1 black hole. It can be seen that this solution belongs to the vacuum (matter-free) sector of a supersymmetric theory based on the Chern-Simons action for the su(1, 2|2) superalgebra. The action for this system matches that of graphene in the long wavelength limit near the Dirac point. The SU(2) gauge symmetry is interpreted as the freedom to choose locally the definition of spin quantization axis for the electrons.

Universal dual amplitudes and asymptotic expansions for gg→ H and H→ γ γ in four dimensions

NASA Astrophysics Data System (ADS)

Driencourt-Mangin, Félix; Rodrigo, Germán; Sborlini, Germán F. R.

2018-03-01

Though the one-loop amplitudes of the Higgs boson to massless gauge bosons are finite because there is no direct interaction at tree level in the Standard Model, a well-defined regularization scheme is still required for their correct evaluation. We reanalyze these amplitudes in the framework of the four-dimensional unsubtraction and the loop-tree duality (FDU/LTD), and show how a local renormalization solves potential regularization ambiguities. The Higgs boson interactions are also used to illustrate new additional advantages of this formalism. We show that LTD naturally leads to very compact integrand expressions in four space-time dimensions of the one-loop amplitude with virtual electroweak gauge bosons. They exhibit the same functional form as the amplitudes with top quarks and charged scalars, thus opening further possibilities for simplifications in higher-order computations. Another outstanding application is the straightforward implementation of asymptotic expansions by using dual amplitudes. One of the main benefits of the LTD representation is that it is supported in a Euclidean space. This characteristic feature naturally leads to simpler asymptotic expansions.

Signatures of extra dimensions in gravitational waves from black hole quasinormal modes

NASA Astrophysics Data System (ADS)

Chakraborty, Sumanta; Chakravarti, Kabir; Bose, Sukanta; SenGupta, Soumitra

2018-05-01

In this work, we have derived the evolution equation for gravitational perturbation in four-dimensional spacetime in the presence of a spatial extra dimension. The evolution equation is derived by perturbing the effective gravitational field equations on the four-dimensional spacetime, which inherits nontrivial higher-dimensional effects. Note that this is different from the perturbation of the five-dimensional gravitational field equations that exist in the literature and possess quantitatively new features. The gravitational perturbation has further been decomposed into a purely four-dimensional part and another piece that depends on extra dimensions. The four-dimensional gravitational perturbation now admits massive propagating degrees of freedom, owing to the existence of higher dimensions. We have also studied the influence of these massive propagating modes on the quasinormal mode frequencies, signaling the higher-dimensional nature of the spacetime, and have contrasted these massive modes with the massless modes in general relativity. Surprisingly, it turns out that the massive modes experience damping much smaller than that of the massless modes in general relativity and may even dominate over and above the general relativity contribution if one observes the ringdown phase of a black hole merger event at sufficiently late times. Furthermore, the whole analytical framework has been supplemented by the fully numerical Cauchy evolution problem, as well. In this context, we have shown that, except for minute details, the overall features of the gravitational perturbations are captured both in the Cauchy evolution as well as in the analysis of quasinormal modes. The implications on observations of black holes with LIGO and proposed space missions such as LISA are also discussed.

Intentional attention switching in dichotic listening: exploring the efficiency of nonspatial and spatial selection.

PubMed

Lawo, Vera; Fels, Janina; Oberem, Josefa; Koch, Iring

2014-10-01

Using an auditory variant of task switching, we examined the ability to intentionally switch attention in a dichotic-listening task. In our study, participants responded selectively to one of two simultaneously presented auditory number words (spoken by a female and a male, one for each ear) by categorizing its numerical magnitude. The mapping of gender (female vs. male) and ear (left vs. right) was unpredictable. The to-be-attended feature for gender or ear, respectively, was indicated by a visual selection cue prior to auditory stimulus onset. In Experiment 1, explicitly cued switches of the relevant feature dimension (e.g., from gender to ear) and switches of the relevant feature within a dimension (e.g., from male to female) occurred in an unpredictable manner. We found large performance costs when the relevant feature switched, but switches of the relevant feature dimension incurred only small additional costs. The feature-switch costs were larger in ear-relevant than in gender-relevant trials. In Experiment 2, we replicated these findings using a simplified design (i.e., only within-dimension switches with blocked dimensions). In Experiment 3, we examined preparation effects by manipulating the cueing interval and found a preparation benefit only when ear was cued. Together, our data suggest that the large part of attentional switch costs arises from reconfiguration at the level of relevant auditory features (e.g., left vs. right) rather than feature dimensions (ear vs. gender). Additionally, our findings suggest that ear-based target selection benefits more from preparation time (i.e., time to direct attention to one ear) than gender-based target selection.

An approach to the development of numerical algorithms for first order linear hyperbolic systems in multiple space dimensions: The constant coefficient case

NASA Technical Reports Server (NTRS)

Goodrich, John W.

1995-01-01

Two methods for developing high order single step explicit algorithms on symmetric stencils with data on only one time level are presented. Examples are given for the convection and linearized Euler equations with up to the eighth order accuracy in both space and time in one space dimension, and up to the sixth in two space dimensions. The method of characteristics is generalized to nondiagonalizable hyperbolic systems by using exact local polynominal solutions of the system, and the resulting exact propagator methods automatically incorporate the correct multidimensional wave propagation dynamics. Multivariate Taylor or Cauchy-Kowaleskaya expansions are also used to develop algorithms. Both of these methods can be applied to obtain algorithms of arbitrarily high order for hyperbolic systems in multiple space dimensions. Cross derivatives are included in the local approximations used to develop the algorithms in this paper in order to obtain high order accuracy, and improved isotropy and stability. Efficiency in meeting global error bounds is an important criterion for evaluating algorithms, and the higher order algorithms are shown to be up to several orders of magnitude more efficient even though they are more complex. Stable high order boundary conditions for the linearized Euler equations are developed in one space dimension, and demonstrated in two space dimensions.

Computation of the soft anomalous dimension matrix in coordinate space

NASA Astrophysics Data System (ADS)

Mitov, Alexander; Sterman, George; Sung, Ilmo

2010-08-01

We complete the coordinate space calculation of the three-parton correlation in the two-loop massive soft anomalous dimension matrix. The full answer agrees with the result found previously by a different approach. The coordinate space treatment of renormalized two-loop gluon exchange diagrams exhibits their color symmetries in a transparent fashion. We compare coordinate space calculations of the soft anomalous dimension matrix with massive and massless eikonal lines and examine its nonuniform limit at absolute threshold.

Collaborative Sharing of Multidimensional Space-time Data Using HydroShare

NASA Astrophysics Data System (ADS)

Gan, T.; Tarboton, D. G.; Horsburgh, J. S.; Dash, P. K.; Idaszak, R.; Yi, H.; Blanton, B.

2015-12-01

HydroShare is a collaborative environment being developed for sharing hydrological data and models. It includes capability to upload data in many formats as resources that can be shared. The HydroShare data model for resources uses a specific format for the representation of each type of data and specifies metadata common to all resource types as well as metadata unique to specific resource types. The Network Common Data Form (NetCDF) was chosen as the format for multidimensional space-time data in HydroShare. NetCDF is widely used in hydrological and other geoscience modeling because it contains self-describing metadata and supports the creation of array-oriented datasets that may include three spatial dimensions, a time dimension and other user defined dimensions. For example, NetCDF may be used to represent precipitation or surface air temperature fields that have two dimensions in space and one dimension in time. This presentation will illustrate how NetCDF files are used in HydroShare. When a NetCDF file is loaded into HydroShare, header information is extracted using the "ncdump" utility. Python functions developed for the Django web framework on which HydroShare is based, extract science metadata present in the NetCDF file, saving the user from having to enter it. Where the file follows Climate Forecast (CF) convention and Attribute Convention for Dataset Discovery (ACDD) standards, metadata is thus automatically populated. Users also have the ability to add metadata to the resource that may not have been present in the original NetCDF file. HydroShare's metadata editing functionality then writes this science metadata back into the NetCDF file to maintain consistency between the science metadata in HydroShare and the metadata in the NetCDF file. This further helps researchers easily add metadata information following the CF and ACDD conventions. Additional data inspection and subsetting functions were developed, taking advantage of Python and command line libraries for working with NetCDF files. We describe the design and implementation of these features and illustrate how NetCDF files from a modeling application may be curated in HydroShare and thus enhance reproducibility of the associated research. We also discuss future development planned for multidimensional space-time data in HydroShare.

Feature selection method based on multi-fractal dimension and harmony search algorithm and its application

NASA Astrophysics Data System (ADS)

Zhang, Chen; Ni, Zhiwei; Ni, Liping; Tang, Na

2016-10-01

Feature selection is an important method of data preprocessing in data mining. In this paper, a novel feature selection method based on multi-fractal dimension and harmony search algorithm is proposed. Multi-fractal dimension is adopted as the evaluation criterion of feature subset, which can determine the number of selected features. An improved harmony search algorithm is used as the search strategy to improve the efficiency of feature selection. The performance of the proposed method is compared with that of other feature selection algorithms on UCI data-sets. Besides, the proposed method is also used to predict the daily average concentration of PM2.5 in China. Experimental results show that the proposed method can obtain competitive results in terms of both prediction accuracy and the number of selected features.

Judging Normality and Attractiveness in Faces: Direct Evidence of a More Refined Representation for Own-Race, Young Adult Faces.

PubMed

Zhou, Xiaomei; Short, Lindsey A; Chan, Harmonie S J; Mondloch, Catherine J

2016-09-01

Young and older adults are more sensitive to deviations from normality in young than older adult faces, suggesting that the dimensions of face space are optimized for young adult faces. Here, we extend these findings to own-race faces and provide converging evidence using an attractiveness rating task. In Experiment 1, Caucasian and Chinese adults were shown own- and other-race face pairs; one member was undistorted and the other had compressed or expanded features. Participants indicated which member of each pair was more normal (a task that requires referencing a norm) and which was more expanded (a task that simply requires discrimination). Participants showed an own-race advantage in the normality task but not the discrimination task. In Experiment 2, participants rated the facial attractiveness of own- and other-race faces (Experiment 2a) or young and older adult faces (Experiment 2b). Between-rater variability in ratings of individual faces was higher for other-race and older adult faces; reduced consensus in attractiveness judgments reflects a less refined face space. Collectively, these results provide direct evidence that the dimensions of face space are optimized for own-race and young adult faces, which may underlie face race- and age-based deficits in recognition. © The Author(s) 2016.

Application of an Elongated Kelvin Model to Space Shuttle Foams

NASA Technical Reports Server (NTRS)

Sullivan, Roy M.; Ghosn, Louis J.; Lerch, Bradley A.

2009-01-01

The space shuttle foams are rigid closed-cell polyurethane foams. The two foams used most-extensively oil space shuttle external tank are BX-265 and NCFL4-124. Because of the foaming and rising process, the foam microstructures are elongated in the rise direction. As a result, these two foams exhibit a nonisotropic mechanical behavior. A detailed microstructural characterization of the two foams is presented. Key features of the foam cells are described and the average cell dimensions in the two foams are summarized. Experimental studies are also conducted to measure the room temperature mechanical response of the two foams in the two principal material directions (parallel to the rise and perpendicular to the rise). The measured elastic modulus, proportional limit stress, ultimate tensile strength, and Poisson's ratios are reported. The generalized elongated Kelvin foam model previously developed by the authors is reviewed and the equations which result from this model are summarized. Using the measured microstructural dimensions and the measured stiffness ratio, the foam tensile strength ratio and Poisson's ratios are predicted for both foams and are compared with the experimental data. The predicted tensile strength ratio is in close agreement with the measured strength ratio for both BX-265 and NCFI24-124. The comparison between the predicted Poisson's ratios and the measured values is not as favorable.

16 CFR 1212.15 - Specifications.

Code of Federal Regulations, 2014 CFR

2014-01-01

...-resistant features. (2) A detailed description of all dimensions, force requirements, or other features that... for each such dimension or force requirement. (3) Any further information, including, but not limited...

16 CFR 1212.15 - Specifications.

Code of Federal Regulations, 2012 CFR

2012-01-01

...-resistant features. (2) A detailed description of all dimensions, force requirements, or other features that... for each such dimension or force requirement. (3) Any further information, including, but not limited...

16 CFR 1212.15 - Specifications.

Code of Federal Regulations, 2011 CFR

2011-01-01

...-resistant features. (2) A detailed description of all dimensions, force requirements, or other features that... for each such dimension or force requirement. (3) Any further information, including, but not limited...

A signal detection model predicts the effects of set size on visual search accuracy for feature, conjunction, triple conjunction, and disjunction displays

NASA Technical Reports Server (NTRS)

Eckstein, M. P.; Thomas, J. P.; Palmer, J.; Shimozaki, S. S.

2000-01-01

Recently, quantitative models based on signal detection theory have been successfully applied to the prediction of human accuracy in visual search for a target that differs from distractors along a single attribute (feature search). The present paper extends these models for visual search accuracy to multidimensional search displays in which the target differs from the distractors along more than one feature dimension (conjunction, disjunction, and triple conjunction displays). The model assumes that each element in the display elicits a noisy representation for each of the relevant feature dimensions. The observer combines the representations across feature dimensions to obtain a single decision variable, and the stimulus with the maximum value determines the response. The model accurately predicts human experimental data on visual search accuracy in conjunctions and disjunctions of contrast and orientation. The model accounts for performance degradation without resorting to a limited-capacity spatially localized and temporally serial mechanism by which to bind information across feature dimensions.

A method of vehicle license plate recognition based on PCANet and compressive sensing

NASA Astrophysics Data System (ADS)

Ye, Xianyi; Min, Feng

2018-03-01

The manual feature extraction of the traditional method for vehicle license plates has no good robustness to change in diversity. And the high feature dimension that is extracted with Principal Component Analysis Network (PCANet) leads to low classification efficiency. For solving these problems, a method of vehicle license plate recognition based on PCANet and compressive sensing is proposed. First, PCANet is used to extract the feature from the images of characters. And then, the sparse measurement matrix which is a very sparse matrix and consistent with Restricted Isometry Property (RIP) condition of the compressed sensing is used to reduce the dimensions of extracted features. Finally, the Support Vector Machine (SVM) is used to train and recognize the features whose dimension has been reduced. Experimental results demonstrate that the proposed method has better performance than Convolutional Neural Network (CNN) in the recognition and time. Compared with no compression sensing, the proposed method has lower feature dimension for the increase of efficiency.

3D chromosome rendering from Hi-C data using virtual reality

NASA Astrophysics Data System (ADS)

Zhu, Yixin; Selvaraj, Siddarth; Weber, Philip; Fang, Jennifer; Schulze, Jürgen P.; Ren, Bing

2015-01-01

Most genome browsers display DNA linearly, using single-dimensional depictions that are useful to examine certain epigenetic mechanisms such as DNA methylation. However, these representations are insufficient to visualize intrachromosomal interactions and relationships between distal genome features. Relationships between DNA regions may be difficult to decipher or missed entirely if those regions are distant in one dimension but could be spatially proximal when mapped to three-dimensional space. For example, the visualization of enhancers folding over genes is only fully expressed in three-dimensional space. Thus, to accurately understand DNA behavior during gene expression, a means to model chromosomes is essential. Using coordinates generated from Hi-C interaction frequency data, we have created interactive 3D models of whole chromosome structures and its respective domains. We have also rendered information on genomic features such as genes, CTCF binding sites, and enhancers. The goal of this article is to present the procedure, findings, and conclusions of our models and renderings.

a Comparison Study of Different Kernel Functions for Svm-Based Classification of Multi-Temporal Polarimetry SAR Data

NASA Astrophysics Data System (ADS)

Yekkehkhany, B.; Safari, A.; Homayouni, S.; Hasanlou, M.

2014-10-01

In this paper, a framework is developed based on Support Vector Machines (SVM) for crop classification using polarimetric features extracted from multi-temporal Synthetic Aperture Radar (SAR) imageries. The multi-temporal integration of data not only improves the overall retrieval accuracy but also provides more reliable estimates with respect to single-date data. Several kernel functions are employed and compared in this study for mapping the input space to higher Hilbert dimension space. These kernel functions include linear, polynomials and Radial Based Function (RBF). The method is applied to several UAVSAR L-band SAR images acquired over an agricultural area near Winnipeg, Manitoba, Canada. In this research, the temporal alpha features of H/A/α decomposition method are used in classification. The experimental tests show an SVM classifier with RBF kernel for three dates of data increases the Overall Accuracy (OA) to up to 3% in comparison to using linear kernel function, and up to 1% in comparison to a 3rd degree polynomial kernel function.

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.

Feature integration theory revisited: dissociating feature detection and attentional guidance in visual search.

PubMed

Chan, Louis K H; Hayward, William G

2009-02-01

In feature integration theory (FIT; A. Treisman & S. Sato, 1990), feature detection is driven by independent dimensional modules, and other searches are driven by a master map of locations that integrates dimensional information into salience signals. Although recent theoretical models have largely abandoned this distinction, some observed results are difficult to explain in its absence. The present study measured dimension-specific performance during detection and localization, tasks that require operation of dimensional modules and the master map, respectively. Results showed a dissociation between tasks in terms of both dimension-switching costs and cross-dimension attentional capture, reflecting a dimension-specific nature for detection tasks and a dimension-general nature for localization tasks. In a feature-discrimination task, results precluded an explanation based on response mode. These results are interpreted to support FIT's postulation that different mechanisms are involved in parallel and focal attention searches. This indicates that the FIT architecture should be adopted to explain the current results and that a variety of visual attention findings can be addressed within this framework. Copyright 2009 APA, all rights reserved.

Emergent space-time via a geometric renormalization method

NASA Astrophysics Data System (ADS)

Rastgoo, Saeed; Requardt, Manfred

2016-12-01

We present a purely geometric renormalization scheme for metric spaces (including uncolored graphs), which consists of a coarse graining and a rescaling operation on such spaces. The coarse graining is based on the concept of quasi-isometry, which yields a sequence of discrete coarse grained spaces each having a continuum limit under the rescaling operation. We provide criteria under which such sequences do converge within a superspace of metric spaces, or may constitute the basin of attraction of a common continuum limit, which hopefully may represent our space-time continuum. We discuss some of the properties of these coarse grained spaces as well as their continuum limits, such as scale invariance and metric similarity, and show that different layers of space-time can carry different distance functions while being homeomorphic. Important tools in this analysis are the Gromov-Hausdorff distance functional for general metric spaces and the growth degree of graphs or networks. The whole construction is in the spirit of the Wilsonian renormalization group (RG). Furthermore, we introduce a physically relevant notion of dimension on the spaces of interest in our analysis, which, e.g., for regular lattices reduces to the ordinary lattice dimension. We show that this dimension is stable under the proposed coarse graining procedure as long as the latter is sufficiently local, i.e., quasi-isometric, and discuss the conditions under which this dimension is an integer. We comment on the possibility that the limit space may turn out to be fractal in case the dimension is noninteger. At the end of the paper we briefly mention the possibility that our network carries a translocal far order that leads to the concept of wormhole spaces and a scale dependent dimension if the coarse graining procedure is no longer local.

Is time enough in order to know where you are?

NASA Astrophysics Data System (ADS)

Tartaglia, Angelo

2013-09-01

This talk discusses various aspects of the structure of space-time presenting mechanisms leading to the explanation of the "rigidity" of the manifold and to the emergence of time, i.e. of the Lorentzian signature. The proposed ingredient is the analog, in four dimensions, of the deformation energy associated with the common three-dimensional elasticity theory. The inclusion of this additional term in the Lagrangian of empty space-time accounts for gravity as an emergent feature from the microscopic structure of space-time. Once time has legitimately been introduced a global positioning method based on local measurements of proper times between the arrivals of electromagnetic pulses from independent distant sources is presented. The method considers both pulsars as well as artificial emitters located on celestial bodies of the solar system as pulsating beacons to be used for navigation and positioning.

The relation between attachment, personality, internalizing, and externalizing dimensions in adolescents with borderline personality disorder.

PubMed

Ramos, Vera; Canta, Guilherme; de Castro, Filipa; Leal, Isabel

2016-01-01

The relation between attachment and personality features is an important field to explore in adolescent borderline personality disorder (BPD), and previous research has shown that personality features may be conceptualized within latent internalizing and externalizing dimensions. This cross-sectional study used a structural equation model to examine the association between the BPD participants' perception of attachment and personality features, mediated by the underlying internalizing/externalizing personality dimensions. Data were analyzed for 60 adolescents, ages 15 to 18 years, diagnosed with BPD who completed attachment and personality self-report measures. The authors' results showed a good fit of the model, suggesting a significant association between attachment and the internalizing/externalizing dimensions, which simultaneously congregate and influence personality traits. The perception of attachment anxiety was positively related to the internalizing dimension and at the same time negatively related to the externalizing dimension. However, the perception of attachment avoidance was not related to internalizing or externalizing personality dimensions.

Space Station: Leadership for the Future

NASA Technical Reports Server (NTRS)

Martin, Franklin D.; Finn, Terence T.

1987-01-01

No longer limited to occasional spectaculars, space has become an essential, almost commonplace dimension of national life. Among other things, space is an arena of competition with our allies and adversaries, a place of business, a field of research, and an avenue of cooperation with our allies. The space station will play a critical role in each of these endeavors. Perhaps the most significant feature of the space station, essential to its utility for science, commerce, and technology, is the permanent nature of its crew. The space station will build upon the tradition of employing new capabilities to explore further and question deeper, and by providing a permanent presence, the station should significantly increase the opportunities for conducting research in space. Economic productivity is, in part, a function of technical innovation. A major thrust of the station design effort is devoted to enhancing performance through advanced technology. The space station represents the commitment of the United States to a future in space. Perhaps most importantly, as recovery from the loss of Challenger and its crew continues, the space station symbolizes the national determination to remain undeterred by tragedy and to continue exploring the frontiers of space.

Projective mappings and dimensions of vector spaces of three types of Killing-Yano tensors on pseudo Riemannian manifolds of constant curvature

NASA Astrophysics Data System (ADS)

Mikeš, Josef; Stepanov, Sergey; Hinterleitner, Irena

2012-07-01

In our paper we have determined the dimension of the space of conformal Killing-Yano tensors and the dimensions of its two subspaces of closed conformal Killing-Yano and Killing-Yano tensors on pseudo Riemannian manifolds of constant curvature. This result is a generalization of well known results on sharp upper bounds of the dimensions of the vector spaces of conformal Killing-Yano, Killing-Yano and concircular vector fields on pseudo Riemannian manifolds of constant curvature.

An integrative view of storage of low- and high-level visual dimensions in visual short-term memory.

PubMed

Magen, Hagit

2017-03-01

Efficient performance in an environment filled with complex objects is often achieved through the temporal maintenance of conjunctions of features from multiple dimensions. The most striking finding in the study of binding in visual short-term memory (VSTM) is equal memory performance for single features and for integrated multi-feature objects, a finding that has been central to several theories of VSTM. Nevertheless, research on binding in VSTM focused almost exclusively on low-level features, and little is known about how items from low- and high-level visual dimensions (e.g., colored manmade objects) are maintained simultaneously in VSTM. The present study tested memory for combinations of low-level features and high-level representations. In agreement with previous findings, Experiments 1 and 2 showed decrements in memory performance when non-integrated low- and high-level stimuli were maintained simultaneously compared to maintaining each dimension in isolation. However, contrary to previous findings the results of Experiments 3 and 4 showed decrements in memory performance even when integrated objects of low- and high-level stimuli were maintained in memory, compared to maintaining single-dimension objects. Overall, the results demonstrate that low- and high-level visual dimensions compete for the same limited memory capacity, and offer a more comprehensive view of VSTM.

An Efficient Moving Target Detection Algorithm Based on Sparsity-Aware Spectrum Estimation

PubMed Central

Shen, Mingwei; Wang, Jie; Wu, Di; Zhu, Daiyin

2014-01-01

In this paper, an efficient direct data domain space-time adaptive processing (STAP) algorithm for moving targets detection is proposed, which is achieved based on the distinct spectrum features of clutter and target signals in the angle-Doppler domain. To reduce the computational complexity, the high-resolution angle-Doppler spectrum is obtained by finding the sparsest coefficients in the angle domain using the reduced-dimension data within each Doppler bin. Moreover, we will then present a knowledge-aided block-size detection algorithm that can discriminate between the moving targets and the clutter based on the extracted spectrum features. The feasibility and effectiveness of the proposed method are validated through both numerical simulations and raw data processing results. PMID:25222035

Synthetic space with arbitrary dimensions in a few rings undergoing dynamic modulation

NASA Astrophysics Data System (ADS)

Yuan, Luqi; Xiao, Meng; Lin, Qian; Fan, Shanhui

2018-03-01

We show that a single ring resonator undergoing dynamic modulation can be used to create a synthetic space with an arbitrary dimension. In such a system, the phases of the modulation can be used to create a photonic gauge potential in high dimensions. As an illustration of the implication of this concept, we show that the Haldane model, which exhibits nontrivial topology in two dimensions, can be implemented in the synthetic space using three rings. Our results point to a route toward exploring higher-dimensional topological physics in low-dimensional physical structures. The dynamics of photons in such synthetic spaces also provides a mechanism to control the spectrum of light.

Rotation and scale invariant shape context registration for remote sensing images with background variations

NASA Astrophysics Data System (ADS)

Jiang, Jie; Zhang, Shumei; Cao, Shixiang

2015-01-01

Multitemporal remote sensing images generally suffer from background variations, which significantly disrupt traditional region feature and descriptor abstracts, especially between pre and postdisasters, making registration by local features unreliable. Because shapes hold relatively stable information, a rotation and scale invariant shape context based on multiscale edge features is proposed. A multiscale morphological operator is adapted to detect edges of shapes, and an equivalent difference of Gaussian scale space is built to detect local scale invariant feature points along the detected edges. Then, a rotation invariant shape context with improved distance discrimination serves as a feature descriptor. For a distance shape context, a self-adaptive threshold (SAT) distance division coordinate system is proposed, which improves the discriminative property of the feature descriptor in mid-long pixel distances from the central point while maintaining it in shorter ones. To achieve rotation invariance, the magnitude of Fourier transform in one-dimension is applied to calculate angle shape context. Finally, the residual error is evaluated after obtaining thin-plate spline transformation between reference and sensed images. Experimental results demonstrate the robustness, efficiency, and accuracy of this automatic algorithm.

Optimization method of superpixel analysis for multi-contrast Jones matrix tomography (Conference Presentation)

NASA Astrophysics Data System (ADS)

Miyazawa, Arata; Hong, Young-Joo; Makita, Shuichi; Kasaragod, Deepa K.; Miura, Masahiro; Yasuno, Yoshiaki

2017-02-01

Local statistics are widely utilized for quantification and image processing of OCT. For example, local mean is used to reduce speckle, local variation of polarization state (degree-of-polarization-uniformity (DOPU)) is used to visualize melanin. Conventionally, these statistics are calculated in a rectangle kernel whose size is uniform over the image. However, the fixed size and shape of the kernel result in a tradeoff between image sharpness and statistical accuracy. Superpixel is a cluster of pixels which is generated by grouping image pixels based on the spatial proximity and similarity of signal values. Superpixels have variant size and flexible shapes which preserve the tissue structure. Here we demonstrate a new superpixel method which is tailored for multifunctional Jones matrix OCT (JM-OCT). This new method forms the superpixels by clustering image pixels in a 6-dimensional (6-D) feature space (spatial two dimensions and four dimensions of optical features). All image pixels were clustered based on their spatial proximity and optical feature similarity. The optical features are scattering, OCT-A, birefringence and DOPU. The method is applied to retinal OCT. Generated superpixels preserve the tissue structures such as retinal layers, sclera, vessels, and retinal pigment epithelium. Hence, superpixel can be utilized as a local statistics kernel which would be more suitable than a uniform rectangle kernel. Superpixelized image also can be used for further image processing and analysis. Since it reduces the number of pixels to be analyzed, it reduce the computational cost of such image processing.

Edge length dynamics on graphs with applications to p-adic AdS/CFT

DOE PAGES

Gubser, Steven S.; Heydeman, Matthew; Jepsen, Christian; ...

2017-06-30

We formulate a Euclidean theory of edge length dynamics based on a notion of Ricci curvature on graphs with variable edge lengths. In order to write an explicit form for the discrete analog of the Einstein-Hilbert action, we require that the graph should either be a tree or that all its cycles should be sufficiently long. The infinite regular tree with all edge lengths equal is an example of a graph with constant negative curvature, providing a connection with p-adic AdS/CFT, where such a tree takes the place of anti-de Sitter space. Here, we compute simple correlators of the operatormore » holographically dual to edge length fluctuations. This operator has dimension equal to the dimension of the boundary, and it has some features in common with the stress tensor.« less

Edge length dynamics on graphs with applications to p-adic AdS/CFT

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

Gubser, Steven S.; Heydeman, Matthew; Jepsen, Christian

We formulate a Euclidean theory of edge length dynamics based on a notion of Ricci curvature on graphs with variable edge lengths. In order to write an explicit form for the discrete analog of the Einstein-Hilbert action, we require that the graph should either be a tree or that all its cycles should be sufficiently long. The infinite regular tree with all edge lengths equal is an example of a graph with constant negative curvature, providing a connection with p-adic AdS/CFT, where such a tree takes the place of anti-de Sitter space. Here, we compute simple correlators of the operatormore » holographically dual to edge length fluctuations. This operator has dimension equal to the dimension of the boundary, and it has some features in common with the stress tensor.« less

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.

Fractal analysis of urban catchments and their representation in semi-distributed models: imperviousness and sewer system

NASA Astrophysics Data System (ADS)

Gires, Auguste; Tchiguirinskaia, Ioulia; Schertzer, Daniel; Ochoa-Rodriguez, Susana; Willems, Patrick; Ichiba, Abdellah; Wang, Lipen; Pina, Rui; Van Assel, Johan; Bruni, Guendalina; Murla Tuyls, Damian; ten Veldhuis, Marie-Claire

2017-04-01

Land use distribution and sewer system geometry exhibit complex scale dependent patterns in urban environment. This scale dependency is even more visible in a rasterized representation where only a unique class is affected to each pixel. Such features are well grasped with fractal tools, which are based scale invariance and intrinsically designed to characterise and quantify the space filled by a geometrical set exhibiting complex and tortuous patterns. Fractal tools have been widely used in hydrology but seldom in the specific context of urban hydrology. In this paper, they are used to analyse surface and sewer data from 10 urban or peri-urban catchments located in 5 European countries in the framework of the NWE Interreg RainGain project (www.raingain.eu). The aim was to characterise urban catchment properties accounting for the complexity and inhomogeneity typical of urban water systems. Sewer system density and imperviousness (roads or buildings), represented in rasterized maps of 2 m x 2 m pixels, were analysed to quantify their fractal dimension, characteristic of scaling invariance. It appears that both sewer density and imperviousness exhibit scale invariant features that can be characterized with the help of fractal dimensions ranging from 1.6 to 2, depending on the catchment. In a given area, consistent results were found for the two geometrical features, yielding a robust and innovative way of quantifying the level of urbanization. The representation of imperviousness in operational semi-distributed hydrological models for these catchments was also investigated by computing fractal dimensions of the geometrical sets made up of the sub-catchments with coefficients of imperviousness greater than a range of thresholds. It enables to quantify how well spatial structures of imperviousness are represented in the urban hydrological models.

Incidental learning of probability information is differentially affected by the type of visual working memory representation.

PubMed

van Lamsweerde, Amanda E; Beck, Melissa R

2015-12-01

In this study, we investigated whether the ability to learn probability information is affected by the type of representation held in visual working memory. Across 4 experiments, participants detected changes to displays of coloured shapes. While participants detected changes in 1 dimension (e.g., colour), a feature from a second, nonchanging dimension (e.g., shape) predicted which object was most likely to change. In Experiments 1 and 3, items could be grouped by similarity in the changing dimension across items (e.g., colours and shapes were repeated in the display), while in Experiments 2 and 4 items could not be grouped by similarity (all features were unique). Probability information from the predictive dimension was learned and used to increase performance, but only when all of the features within a display were unique (Experiments 2 and 4). When it was possible to group by feature similarity in the changing dimension (e.g., 2 blue objects appeared within an array), participants were unable to learn probability information and use it to improve performance (Experiments 1 and 3). The results suggest that probability information can be learned in a dimension that is not explicitly task-relevant, but only when the probability information is represented with the changing dimension in visual working memory. (c) 2015 APA, all rights reserved).

Compactified Vacuum in Ten Dimensions.

NASA Astrophysics Data System (ADS)

Wurmser, Daniel

1987-09-01

Since the 1920's, theories which unify gravity with the other fundamental forces have called for more than the four observed dimensions of space-time. According to such a theory, the vacuum consists of flat four-dimensional space-time described by the Minkowski metric M ^4 and a "compactified" space B. The dimensions of B are small, and the space can only be observed at distance scales smaller than the present experimental limit. These theories have had serious difficulties. The equations of gravity severely restrict the possible choices for the space B. The allowed spaces are complicated and difficult to study. The vacuum is furthermore unstable in the sense that a small perturbation causes the compactified dimensions to expand indefinitely. There is an addition a semi-classical argument which implies that the compactified vacuum be annihilated by virtual black holes. It follows that a universe with compactified extra dimensions could not have survived to the present. These results were derived by applying the equations of general relativity to spaces of more than four dimensions. The form of these equations was assumed to be unchanged by an increase in the number of dimensions. Recently, it has been proposed that gravity in more than four dimensions may involve terms of higher order in the curvature as well as the linear terms present in ordinary general relativity. I illustrate the effect of such terms by considering the example B = S^6 where S ^6 is the six-dimensional sphere. Only when the extra terms are included is this choice of the compactified space allowed. I explore the effect of a small perturbation on such a vacuum. The ten-dimensional spherically symmetric potential is examined, and I determine conditions under which the formation of virtual black holes is forbidden. The example M^4 times S^6 is still plagued by the semi -classical instability, but this result does not hold in general. The requirement that virtual black holes be forbidden provides a test for any theory which predicts a compactified vacuum.

Multi-dimension feature fusion for action recognition

NASA Astrophysics Data System (ADS)

Dong, Pei; Li, Jie; Dong, Junyu; Qi, Lin

2018-04-01

Typical human actions last several seconds and exhibit characteristic spatio-temporal structure. The challenge for action recognition is to capture and fuse the multi-dimension information in video data. In order to take into account these characteristics simultaneously, we present a novel method that fuses multiple dimensional features, such as chromatic images, depth and optical flow fields. We built our model based on the multi-stream deep convolutional networks with the help of temporal segment networks and extract discriminative spatial and temporal features by fusing ConvNets towers multi-dimension, in which different feature weights are assigned in order to take full advantage of this multi-dimension information. Our architecture is trained and evaluated on the currently largest and most challenging benchmark NTU RGB-D dataset. The experiments demonstrate that the performance of our method outperforms the state-of-the-art methods.

Fast Marching Tree: a Fast Marching Sampling-Based Method for Optimal Motion Planning in Many Dimensions*

PubMed Central

Janson, Lucas; Schmerling, Edward; Clark, Ashley; Pavone, Marco

2015-01-01

In this paper we present a novel probabilistic sampling-based motion planning algorithm called the Fast Marching Tree algorithm (FMT*). The algorithm is specifically aimed at solving complex motion planning problems in high-dimensional configuration spaces. This algorithm is proven to be asymptotically optimal and is shown to converge to an optimal solution faster than its state-of-the-art counterparts, chiefly PRM* and RRT*. The FMT* algorithm performs a “lazy” dynamic programming recursion on a predetermined number of probabilistically-drawn samples to grow a tree of paths, which moves steadily outward in cost-to-arrive space. As such, this algorithm combines features of both single-query algorithms (chiefly RRT) and multiple-query algorithms (chiefly PRM), and is reminiscent of the Fast Marching Method for the solution of Eikonal equations. As a departure from previous analysis approaches that are based on the notion of almost sure convergence, the FMT* algorithm is analyzed under the notion of convergence in probability: the extra mathematical flexibility of this approach allows for convergence rate bounds—the first in the field of optimal sampling-based motion planning. Specifically, for a certain selection of tuning parameters and configuration spaces, we obtain a convergence rate bound of order O(n−1/d+ρ), where n is the number of sampled points, d is the dimension of the configuration space, and ρ is an arbitrarily small constant. We go on to demonstrate asymptotic optimality for a number of variations on FMT*, namely when the configuration space is sampled non-uniformly, when the cost is not arc length, and when connections are made based on the number of nearest neighbors instead of a fixed connection radius. Numerical experiments over a range of dimensions and obstacle configurations confirm our the-oretical and heuristic arguments by showing that FMT*, for a given execution time, returns substantially better solutions than either PRM* or RRT*, especially in high-dimensional configuration spaces and in scenarios where collision-checking is expensive. PMID:27003958

Use of sEMG in identification of low level muscle activities: features based on ICA and fractal dimension.

PubMed

Naik, Ganesh R; Kumar, Dinesh K; Arjunan, Sridhar

2009-01-01

This paper has experimentally verified and compared features of sEMG (Surface Electromyogram) such as ICA (Independent Component Analysis) and Fractal Dimension (FD) for identification of low level forearm muscle activities. The fractal dimension was used as a feature as reported in the literature. The normalized feature values were used as training and testing vectors for an Artificial neural network (ANN), in order to reduce inter-experimental variations. The identification accuracy using FD of four channels sEMG was 58%, and increased to 96% when the signals are separated to their independent components using ICA.

Evaluation of Skylab earth laser beacon imagery. [spaceborne photography

NASA Technical Reports Server (NTRS)

Piech, K. R.; Schott, J. R.

1975-01-01

During the Skylab 3 and 4 missions the Skylab spacecraft was illuminated by a low power argon ion and dye laser. The earth laser beacon was studied visually by the astronauts. In addition, they collected 35 mm hand-held color photographs of the beacons. Photographs are shown that were obtained on Skylab 3 and Skylab 4. The imagery collected during the Skylab mission was analyzed to evaluate the utility of beacon lasers as terrestial 'artificial stars' for space navigation. The analyses of the imagery revealed two unusual features of the earth laser beacon: (1) The beacon, even though of a low power (approximately 1 watt), is considerably brighter than any other terrain feature and is readily visible on imagery at a distance in excess of 1500 km (900 miles). (2) Another feature of the beacon is its large size. The typical beacon extends over about 5 resolution areas with a characteristic dimension of about 200 m.

Fast traffic sign recognition with a rotation invariant binary pattern based feature.

PubMed

Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

2015-01-19

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed.

Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature

PubMed Central

Yin, Shouyi; Ouyang, Peng; Liu, Leibo; Guo, Yike; Wei, Shaojun

2015-01-01

Robust and fast traffic sign recognition is very important but difficult for safe driving assistance systems. This study addresses fast and robust traffic sign recognition to enhance driving safety. The proposed method includes three stages. First, a typical Hough transformation is adopted to implement coarse-grained location of the candidate regions of traffic signs. Second, a RIBP (Rotation Invariant Binary Pattern) based feature in the affine and Gaussian space is proposed to reduce the time of traffic sign detection and achieve robust traffic sign detection in terms of scale, rotation, and illumination. Third, the techniques of ANN (Artificial Neutral Network) based feature dimension reduction and classification are designed to reduce the traffic sign recognition time. Compared with the current work, the experimental results in the public datasets show that this work achieves robustness in traffic sign recognition with comparable recognition accuracy and faster processing speed, including training speed and recognition speed. PMID:25608217

ADER schemes for scalar non-linear hyperbolic conservation laws with source terms in three-space dimensions

NASA Astrophysics Data System (ADS)

Toro, E. F.; Titarev, V. A.

2005-01-01

In this paper we develop non-linear ADER schemes for time-dependent scalar linear and non-linear conservation laws in one-, two- and three-space dimensions. Numerical results of schemes of up to fifth order of accuracy in both time and space illustrate that the designed order of accuracy is achieved in all space dimensions for a fixed Courant number and essentially non-oscillatory results are obtained for solutions with discontinuities. We also present preliminary results for two-dimensional non-linear systems.

Random bearings and their stability.

PubMed

Mahmoodi Baram, Reza; Herrmann, Hans J

2005-11-25

Self-similar space-filling bearings have been proposed some time ago as models for the motion of tectonic plates and appearance of seismic gaps. These models have two features which, however, seem unrealistic, namely, high symmetry in the arrangement of the particles, and lack of a lower cutoff in the size of the particles. In this work, an algorithm for generating random bearings in both two and three dimensions is presented. Introducing a lower cutoff for the sizes of the particles, the instabilities of the bearing under an external force such as gravity, are studied.

Multispectral image fusion based on fractal features

NASA Astrophysics Data System (ADS)

Tian, Jie; Chen, Jie; Zhang, Chunhua

2004-01-01

Imagery sensors have been one indispensable part of the detection and recognition systems. They are widely used to the field of surveillance, navigation, control and guide, et. However, different imagery sensors depend on diverse imaging mechanisms, and work within diverse range of spectrum. They also perform diverse functions and have diverse circumstance requires. So it is unpractical to accomplish the task of detection or recognition with a single imagery sensor under the conditions of different circumstances, different backgrounds and different targets. Fortunately, the multi-sensor image fusion technique emerged as important route to solve this problem. So image fusion has been one of the main technical routines used to detect and recognize objects from images. While, loss of information is unavoidable during fusion process, so it is always a very important content of image fusion how to preserve the useful information to the utmost. That is to say, it should be taken into account before designing the fusion schemes how to avoid the loss of useful information or how to preserve the features helpful to the detection. In consideration of these issues and the fact that most detection problems are actually to distinguish man-made objects from natural background, a fractal-based multi-spectral fusion algorithm has been proposed in this paper aiming at the recognition of battlefield targets in the complicated backgrounds. According to this algorithm, source images are firstly orthogonally decomposed according to wavelet transform theories, and then fractal-based detection is held to each decomposed image. At this step, natural background and man-made targets are distinguished by use of fractal models that can well imitate natural objects. Special fusion operators are employed during the fusion of area that contains man-made targets so that useful information could be preserved and features of targets could be extruded. The final fused image is reconstructed from the composition of source pyramid images. So this fusion scheme is a multi-resolution analysis. The wavelet decomposition of image can be actually considered as special pyramid decomposition. According to wavelet decomposition theories, the approximation of image (formula available in paper) at resolution 2j+1 equal to its orthogonal projection in space , that is, where Ajf is the low-frequency approximation of image f(x, y) at resolution 2j and , , represent the vertical, horizontal and diagonal wavelet coefficients respectively at resolution 2j. These coefficients describe the high-frequency information of image at direction of vertical, horizontal and diagonal respectively. Ajf, , and are independent and can be considered as images. In this paper J is set to be 1, so the source image is decomposed to produce the son-images Af, D1f, D2f and D3f. To solve the problem of detecting artifacts, the concepts of vertical fractal dimension FD1, horizontal fractal dimension FD2 and diagonal fractal dimension FD3 are proposed in this paper. The vertical fractal dimension FD1 corresponds to the vertical wavelet coefficients image after the wavelet decomposition of source image, the horizontal fractal dimension FD2 corresponds to the horizontal wavelet coefficients and the diagonal fractal dimension FD3 the diagonal one. These definitions enrich the illustration of source images. Therefore they are helpful to classify the targets. Then the detection of artifacts in the decomposed images is a problem of pattern recognition in 4-D space. The combination of FD0, FD1, FD2 and FD3 make a vector of (FD0, FD1, FD2, FD3), which can be considered as a united feature vector of the studied image. All the parts of the images are classified in the 4-D pattern space created by the vector of (FD0, FD1, FD2, FD3) so that the area that contains man-made objects could be detected. This detection can be considered as a coarse recognition, and then the significant areas in each son-images are signed so that they can be dealt with special rules. There has been various fusion rules developed with each one aiming at a special problem. These rules have different performance, so it is very important to select an appropriate rule during the design of an image fusion system. Recent research denotes that the rule should be adjustable so that it is always suitable to extrude the features of targets and to preserve the pixels of useful information. In this paper, owing to the consideration that fractal dimension is one of the main features to distinguish man-made targets from natural objects, the fusion rule was defined that if the studied region of image contains man-made target, the pixels of the source image whose fractal dimension is minimal are saved to be the pixels of the fused image, otherwise, a weighted average operator is adopted to avoid loss of information. The main idea of this rule is to store the pixels with low fractal dimensions, so it can be named Minimal Fractal dimensions (MFD) fusion rule. This fractal-based algorithm is compared with a common weighted average fusion algorithm. An objective assessment is taken to the two fusion results. The criteria of Entropy, Cross-Entropy, Peak Signal-to-Noise Ratio (PSNR) and Standard Gray Scale Difference are defined in this paper. Reversely to the idea of constructing an ideal image as the assessing reference, the source images are selected to be the reference in this paper. It can be deemed that this assessment is to calculate how much the image quality has been enhanced and the quantity of information has been increased when the fused image is compared with the source images. The experimental results imply that the fractal-based multi-spectral fusion algorithm can effectively preserve the information of man-made objects with a high contrast. It is proved that this algorithm could well preserve features of military targets because that battlefield targets are most man-made objects and in common their images differ from fractal models obviously. Furthermore, the fractal features are not sensitive to the imaging conditions and the movement of targets, so this fractal-based algorithm may be very practical.

Features in chemical kinetics. III. Attracting subspaces in a hyper-spherical representation of the reactive system

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

Ceccato, Alessandro; Frezzato, Diego, E-mail: diego.frezzato@unipd.it; Nicolini, Paolo

In this work, we deal with general reactive systems involving N species and M elementary reactions under applicability of the mass-action law. Starting from the dynamic variables introduced in two previous works [P. Nicolini and D. Frezzato, J. Chem. Phys. 138(23), 234101 (2013); 138(23), 234102 (2013)], we turn to a new representation in which the system state is specified in a (N × M){sup 2}-dimensional space by a point whose coordinates have physical dimension of inverse-of-time. By adopting hyper-spherical coordinates (a set of dimensionless “angular” variables and a single “radial” one with physical dimension of inverse-of-time) and by examining themore » properties of their evolution law both formally and numerically on model kinetic schemes, we show that the system evolves towards the equilibrium as being attracted by a sequence of fixed subspaces (one at a time) each associated with a compact domain of the concentration space. Thus, we point out that also for general non-linear kinetics there exist fixed “objects” on the global scale, although they are conceived in such an abstract and extended space. Moreover, we propose a link between the persistence of the belonging of a trajectory to such subspaces and the closeness to the slow manifold which would be perceived by looking at the bundling of the trajectories in the concentration space.« less

CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video

PubMed Central

Ghosh, Tonmoy; Wahid, Khan A.

2018-01-01

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data. PMID:29468094

Multi-Scale Fractal Analysis of Image Texture and Pattern

NASA Technical Reports Server (NTRS)

Emerson, Charles W.

1998-01-01

Fractals embody important ideas of self-similarity, in which the spatial behavior or appearance of a system is largely independent of scale. Self-similarity is defined as a property of curves or surfaces where each part is indistinguishable from the whole, or where the form of the curve or surface is invariant with respect to scale. An ideal fractal (or monofractal) curve or surface has a constant dimension over all scales, although it may not be an integer value. This is in contrast to Euclidean or topological dimensions, where discrete one, two, and three dimensions describe curves, planes, and volumes. Theoretically, if the digital numbers of a remotely sensed image resemble an ideal fractal surface, then due to the self-similarity property, the fractal dimension of the image will not vary with scale and resolution. However, most geographical phenomena are not strictly self-similar at all scales, but they can often be modeled by a stochastic fractal in which the scaling and self-similarity properties of the fractal have inexact patterns that can be described by statistics. Stochastic fractal sets relax the monofractal self-similarity assumption and measure many scales and resolutions in order to represent the varying form of a phenomenon as a function of local variables across space. In image interpretation, pattern is defined as the overall spatial form of related features, and the repetition of certain forms is a characteristic pattern found in many cultural objects and some natural features. Texture is the visual impression of coarseness or smoothness caused by the variability or uniformity of image tone or color. A potential use of fractals concerns the analysis of image texture. In these situations it is commonly observed that the degree of roughness or inexactness in an image or surface is a function of scale and not of experimental technique. The fractal dimension of remote sensing data could yield quantitative insight on the spatial complexity and information content contained within these data. A software package known as the Image Characterization and Modeling System (ICAMS) was used to explore how fractal dimension is related to surface texture and pattern. The ICAMS software was verified using simulated images of ideal fractal surfaces with specified dimensions. The fractal dimension for areas of homogeneous land cover in the vicinity of Huntsville, Alabama was measured to investigate the relationship between texture and resolution for different land covers.

a Probabilistic Embedding Clustering Method for Urban Structure Detection

NASA Astrophysics Data System (ADS)

Lin, X.; Li, H.; Zhang, Y.; Gao, L.; Zhao, L.; Deng, M.

2017-09-01

Urban structure detection is a basic task in urban geography. Clustering is a core technology to detect the patterns of urban spatial structure, urban functional region, and so on. In big data era, diverse urban sensing datasets recording information like human behaviour and human social activity, suffer from complexity in high dimension and high noise. And unfortunately, the state-of-the-art clustering methods does not handle the problem with high dimension and high noise issues concurrently. In this paper, a probabilistic embedding clustering method is proposed. Firstly, we come up with a Probabilistic Embedding Model (PEM) to find latent features from high dimensional urban sensing data by "learning" via probabilistic model. By latent features, we could catch essential features hidden in high dimensional data known as patterns; with the probabilistic model, we can also reduce uncertainty caused by high noise. Secondly, through tuning the parameters, our model could discover two kinds of urban structure, the homophily and structural equivalence, which means communities with intensive interaction or in the same roles in urban structure. We evaluated the performance of our model by conducting experiments on real-world data and experiments with real data in Shanghai (China) proved that our method could discover two kinds of urban structure, the homophily and structural equivalence, which means clustering community with intensive interaction or under the same roles in urban space.

Multimaterial magnetically assisted 3D printing of composite materials.

PubMed

Kokkinis, Dimitri; Schaffner, Manuel; Studart, André R

2015-10-23

3D printing has become commonplace for the manufacturing of objects with unusual geometries. Recent developments that enabled printing of multiple materials indicate that the technology can potentially offer a much wider design space beyond unusual shaping. Here we show that a new dimension in this design space can be exploited through the control of the orientation of anisotropic particles used as building blocks during a direct ink-writing process. Particle orientation control is demonstrated by applying low magnetic fields on deposited inks pre-loaded with magnetized stiff platelets. Multimaterial dispensers and a two-component mixing unit provide additional control over the local composition of the printed material. The five-dimensional design space covered by the proposed multimaterial magnetically assisted 3D printing platform (MM-3D printing) opens the way towards the manufacturing of functional heterogeneous materials with exquisite microstructural features thus far only accessible by biological materials grown in nature.

Potential utilization of the NASA/George C. Marshall Space Flight Center in earthquake engineering research

NASA Technical Reports Server (NTRS)

Scholl, R. E. (Editor)

1979-01-01

Earthquake engineering research capabilities of the National Aeronautics and Space Administration (NASA) facilities at George C. Marshall Space Flight Center (MSFC), Alabama, were evaluated. The results indicate that the NASA/MSFC facilities and supporting capabilities offer unique opportunities for conducting earthquake engineering research. Specific features that are particularly attractive for large scale static and dynamic testing of natural and man-made structures include the following: large physical dimensions of buildings and test bays; high loading capacity; wide range and large number of test equipment and instrumentation devices; multichannel data acquisition and processing systems; technical expertise for conducting large-scale static and dynamic testing; sophisticated techniques for systems dynamics analysis, simulation, and control; and capability for managing large-size and technologically complex programs. Potential uses of the facilities for near and long term test programs to supplement current earthquake research activities are suggested.

Cultural ethology as a new approach of interplanetary crew's behavior

NASA Astrophysics Data System (ADS)

Tafforin, Carole; Giner Abati, Francisco

2017-10-01

From an evolutionary perspective, during short-term and medium-term orbital flights, human beings developed new spatial and motor behaviors to compensate for the lack of terrestrial gravity. Past space ethological studies have shown adaptive strategies to the tri-dimensional environment, with the goal of optimizing relationships between the astronaut and unusual sensorial-motor conditions. During a long-term interplanetary journey, crewmembers will have to develop new individual and social behaviors to adapt, far from earth, to isolation and confinement and as a result to extreme conditions of living and working together. Recent space psychological studies pointed out that heterogeneity is a feature of interplanetary crews, based on personality, gender mixing, internationality and diversity of backgrounds. Intercultural issues could arise between space voyagers. As a new approach we propose to emphasize the behavioral strategies of human groups' adaptation to this new multicultural dimension of the environment.

Multimaterial magnetically assisted 3D printing of composite materials

NASA Astrophysics Data System (ADS)

Kokkinis, Dimitri; Schaffner, Manuel; Studart, André R.

2015-10-01

3D printing has become commonplace for the manufacturing of objects with unusual geometries. Recent developments that enabled printing of multiple materials indicate that the technology can potentially offer a much wider design space beyond unusual shaping. Here we show that a new dimension in this design space can be exploited through the control of the orientation of anisotropic particles used as building blocks during a direct ink-writing process. Particle orientation control is demonstrated by applying low magnetic fields on deposited inks pre-loaded with magnetized stiff platelets. Multimaterial dispensers and a two-component mixing unit provide additional control over the local composition of the printed material. The five-dimensional design space covered by the proposed multimaterial magnetically assisted 3D printing platform (MM-3D printing) opens the way towards the manufacturing of functional heterogeneous materials with exquisite microstructural features thus far only accessible by biological materials grown in nature.

Quantum gravity as an information network self-organization of a 4D universe

NASA Astrophysics Data System (ADS)

Trugenberger, Carlo A.

2015-10-01

I propose a quantum gravity model in which the fundamental degrees of freedom are information bits for both discrete space-time points and links connecting them. The Hamiltonian is a very simple network model consisting of a ferromagnetic Ising model for space-time vertices and an antiferromagnetic Ising model for the links. As a result of the frustration between these two terms, the ground state self-organizes as a new type of low-clustering graph with finite Hausdorff dimension 4. The spectral dimension is lower than the Hausdorff dimension: it coincides with the Hausdorff dimension 4 at a first quantum phase transition corresponding to an IR fixed point, while at a second quantum phase transition describing small scales space-time dissolves into disordered information bits. The large-scale dimension 4 of the universe is related to the upper critical dimension 4 of the Ising model. At finite temperatures the universe graph emerges without a big bang and without singularities from a ferromagnetic phase transition in which space-time itself forms out of a hot soup of information bits. When the temperature is lowered the universe graph unfolds and expands by lowering its connectivity, a mechanism I have called topological expansion. The model admits topological black hole excitations corresponding to graphs containing holes with no space-time inside and with "Schwarzschild-like" horizons with a lower spectral dimension.

The Burden of Stigma on Health and Well-Being: A Taxonomy of Concealment, Course, Disruptiveness, Aesthetics, Origin, and Peril Across 93 Stigmas.

PubMed

Pachankis, John E; Hatzenbuehler, Mark L; Wang, Katie; Burton, Charles L; Crawford, Forrest W; Phelan, Jo C; Link, Bruce G

2018-04-01

Most individuals are stigmatized at some point. However, research often examines stigmas separately, thus underestimating the overall impact of stigma and precluding comparisons across stigmatized identities and conditions. In their classic text, Social Stigma: The Psychology of Marked Relationships, Edward Jones and colleagues laid the groundwork for unifying the study of different stigmas by considering the shared dimensional features of stigmas: aesthetics, concealability, course, disruptiveness, origin, peril. Despite the prominence of this framework, no study has documented the extent to which stigmas differ along these dimensions, and the implications of this variation for health and well-being. We reinvigorated this framework to spur a comprehensive account of stigma's impact by classifying 93 stigmas along these dimensions. With the input of expert and general public raters, we then located these stigmas in a six-dimensional space and created discrete clusters organized around these dimensions. Next, we linked this taxonomy to health and stigma-related mechanisms. This quantitative taxonomy offers parsimonious insights into the relationship among the numerous qualities of numerous stigmas and health.

Learning Human Actions by Combining Global Dynamics and Local Appearance.

PubMed

Luo, Guan; Yang, Shuang; Tian, Guodong; Yuan, Chunfeng; Hu, Weiming; Maybank, Stephen J

2014-12-01

In this paper, we address the problem of human action recognition through combining global temporal dynamics and local visual spatio-temporal appearance features. For this purpose, in the global temporal dimension, we propose to model the motion dynamics with robust linear dynamical systems (LDSs) and use the model parameters as motion descriptors. Since LDSs live in a non-Euclidean space and the descriptors are in non-vector form, we propose a shift invariant subspace angles based distance to measure the similarity between LDSs. In the local visual dimension, we construct curved spatio-temporal cuboids along the trajectories of densely sampled feature points and describe them using histograms of oriented gradients (HOG). The distance between motion sequences is computed with the Chi-Squared histogram distance in the bag-of-words framework. Finally we perform classification using the maximum margin distance learning method by combining the global dynamic distances and the local visual distances. We evaluate our approach for action recognition on five short clips data sets, namely Weizmann, KTH, UCF sports, Hollywood2 and UCF50, as well as three long continuous data sets, namely VIRAT, ADL and CRIM13. We show competitive results as compared with current state-of-the-art methods.

CD-measurement technique for hole patterns on stencil mask

NASA Astrophysics Data System (ADS)

Ishikawa, Mikio; Yusa, Satoshi; Takikawa, Tadahiko; Fujita, Hiroshi; Sano, Hisatake; Hoga, Morihisa; Hayashi, Naoya

2004-12-01

EB lithography has a potential to successfully form hole patterns as small as 80 nm with a stencil mask. In a previous paper we proposed a technique using a HOLON dual-mode critical dimension (CD) SEM ESPA-75S in the transmission mode for CD measurement of line-and-space patterns on a stencil mask. In this paper we extend our effort of developing a CD measurement technique to contact hole features and determine it in comparison of measured values between features on mask and those printed on wafer. We have evaluated the width method and the area methods using designed 80-500 nm wide contact hole patterns on a large area membrane mask and their resist images on wafer printed by a LEEPL3000. We find that 1) the width method and the area methods show an excellent mask-wafer correlation for holes over 110 nm, and 2) the area methods show a better mask-wafer correlation than the width method does for holes below 110 nm. We conclude that the area calculated from the transmission SEM image is more suitable in defining the hole dimensions than the width for contact holes on a stencil mask.

A nonlinear controlling function of geological features on magmatic–hydrothermal mineralization

PubMed Central

Zuo, Renguang

2016-01-01

This paper reports a nonlinear controlling function of geological features on magmatic–hydrothermal mineralization, and proposes an alternative method to measure the spatial relationships between geological features and mineral deposits using multifractal singularity theory. It was observed that the greater the proximity to geological controlling features, the greater the number of mineral deposits developed, indicating a nonlinear spatial relationship between these features and mineral deposits. This phenomenon can be quantified using the relationship between the numbers of mineral deposits N(ε) of a D-dimensional set and the scale of ε. The density of mineral deposits can be expressed as ρ(ε) = Cε−(De−a), where ε is the buffer width of geological controlling features, De is Euclidean dimension of space (=2 in this case), a is singularity index, and C is a constant. The expression can be rewritten as ρ = Cεa−2. When a < 2, there is a significant spatial correlation between specific geological features and mineral deposits; lower a values indicate a more significant spatial correlation. This nonlinear relationship and the advantages of this method were illustrated using a case study from Fujian Province in China and a case study from Baguio district in Philippines. PMID:27255794

A nonlinear controlling function of geological features on magmatic-hydrothermal mineralization.

PubMed

Zuo, Renguang

2016-06-03

This paper reports a nonlinear controlling function of geological features on magmatic-hydrothermal mineralization, and proposes an alternative method to measure the spatial relationships between geological features and mineral deposits using multifractal singularity theory. It was observed that the greater the proximity to geological controlling features, the greater the number of mineral deposits developed, indicating a nonlinear spatial relationship between these features and mineral deposits. This phenomenon can be quantified using the relationship between the numbers of mineral deposits N(ε) of a D-dimensional set and the scale of ε. The density of mineral deposits can be expressed as ρ(ε) = Cε(-(De-a)), where ε is the buffer width of geological controlling features, De is Euclidean dimension of space (=2 in this case), a is singularity index, and C is a constant. The expression can be rewritten as ρ = Cε(a-2). When a < 2, there is a significant spatial correlation between specific geological features and mineral deposits; lower a values indicate a more significant spatial correlation. This nonlinear relationship and the advantages of this method were illustrated using a case study from Fujian Province in China and a case study from Baguio district in Philippines.

On the use of attractor dimension as a feature in structural health monitoring

USGS Publications Warehouse

Nichols, J.M.; Virgin, L.N.; Todd, M.D.; Nichols, J.D.

2003-01-01

Recent works in the vibration-based structural health monitoring community have emphasised the use of correlation dimension as a discriminating statistic in seperating a damaged from undamaged response. This paper explores the utility of attractor dimension as a 'feature' and offers some comparisons between different metrics reflecting dimension. This focus is on evaluating the performance of two different measures of dimension as damage indicators in a structural health monitoring context. Results indicate that the correlation dimension is probably a poor choice of statistic for the purpose of signal discrimination. Other measures of dimension may be used for the same purposes with a higher degree of statistical reliability. The question of competing methodologies is placed in a hypothesis testing framework and answered with experimental data taken from a cantilivered beam.

Physics in space-time with scale-dependent metrics

NASA Astrophysics Data System (ADS)

Balankin, Alexander S.

2013-10-01

We construct three-dimensional space Rγ3 with the scale-dependent metric and the corresponding Minkowski space-time Mγ,β4 with the scale-dependent fractal (DH) and spectral (DS) dimensions. The local derivatives based on scale-dependent metrics are defined and differential vector calculus in Rγ3 is developed. We state that Mγ,β4 provides a unified phenomenological framework for dimensional flow observed in quite different models of quantum gravity. Nevertheless, the main attention is focused on the special case of flat space-time M1/3,14 with the scale-dependent Cantor-dust-like distribution of admissible states, such that DH increases from DH=2 on the scale ≪ℓ0 to DH=4 in the infrared limit ≫ℓ0, where ℓ0 is the characteristic length (e.g. the Planck length, or characteristic size of multi-fractal features in heterogeneous medium), whereas DS≡4 in all scales. Possible applications of approach based on the scale-dependent metric to systems of different nature are briefly discussed.

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.

Spatio-temporal models of mental processes from fMRI.

PubMed

Janoos, Firdaus; Machiraju, Raghu; Singh, Shantanu; Morocz, Istvan Ákos

2011-07-15

Understanding the highly complex, spatially distributed and temporally organized phenomena entailed by mental processes using functional MRI is an important research problem in cognitive and clinical neuroscience. Conventional analysis methods focus on the spatial dimension of the data discarding the information about brain function contained in the temporal dimension. This paper presents a fully spatio-temporal multivariate analysis method using a state-space model (SSM) for brain function that yields not only spatial maps of activity but also its temporal structure along with spatially varying estimates of the hemodynamic response. Efficient algorithms for estimating the parameters along with quantitative validations are given. A novel low-dimensional feature-space for representing the data, based on a formal definition of functional similarity, is derived. Quantitative validation of the model and the estimation algorithms is provided with a simulation study. Using a real fMRI study for mental arithmetic, the ability of this neurophysiologically inspired model to represent the spatio-temporal information corresponding to mental processes is demonstrated. Moreover, by comparing the models across multiple subjects, natural patterns in mental processes organized according to different mental abilities are revealed. Copyright © 2011 Elsevier Inc. All rights reserved.

Geography From Another Dimension

NASA Technical Reports Server (NTRS)

2002-01-01

The GEODESY software program is intended to promote geographical awareness among students with its remote sensing capabilities to observe the Earth's surface from distant vantage points. Students and teachers using GEODESY learn to interpret and analyze geographical data pertaining to the physical attributes of their community. For example, the program provides a digital environment of physical features, such as mountains and bodies of water, as well as man-made features, such as roads and parks, using aerial photography, satellite imagery, and geographic information systems data in accordance with National Geography Standards. The main goal is to have the students and teachers gain a better understanding of the unique forces that drive their coexistence. GEODESY was developed with technical assistance and financial support from Stennis Space Center's Commercial Remote Sensing Program Office, now known as the Earth Science Applications Directorate.

Nanofabrication of ultra-low reflectivity black silicon surfaces and devices (Presentation Recording)

NASA Astrophysics Data System (ADS)

White, Victor E.; Yee, Karl Y.; Balasubramanian, Kunjithapatham; Echternach, Pierre M.; Muller, Richard E.; Dickie, Matthew R.; Cady, Eric; Ryan, Daniel J.; Eastwood, Michael; van Gorp, Byron; Riggs, A. J. Eldorado; Zimmerman, Niel; Kasdin, N. Jeremy

2015-08-01

Optical devices with features exhibiting ultra low reflectivity on the order of 10-7 specular reflectance in the visible spectrum are required for coronagraph instruments and some spectrometers employed in space research. Nanofabrication technologies have been developed to produce such devices with various shapes and feature dimensions to meet these requirements. Infrared reflection is also suppressed significantly with chosen wafers and processes. Particularly, devices with very high (>0.9) and very low reflectivity (<10-7) on adjacent areas have been fabricated and characterized. Significantly increased surface area due to the long needle like nano structures also provides some unique applications in other technology areas. We present some of the approaches, challenges and achieved results in producing and characterizing such devices currently employed in laboratory testbeds and instruments.

The density-matrix renormalization group: a short introduction.

PubMed

Schollwöck, Ulrich

2011-07-13

The density-matrix renormalization group (DMRG) method has established itself over the last decade as the leading method for the simulation of the statics and dynamics of one-dimensional strongly correlated quantum lattice systems. The DMRG is a method that shares features of a renormalization group procedure (which here generates a flow in the space of reduced density operators) and of a variational method that operates on a highly interesting class of quantum states, so-called matrix product states (MPSs). The DMRG method is presented here entirely in the MPS language. While the DMRG generally fails in larger two-dimensional systems, the MPS picture suggests a straightforward generalization to higher dimensions in the framework of tensor network states. The resulting algorithms, however, suffer from difficulties absent in one dimension, apart from a much more unfavourable efficiency, such that their ultimate success remains far from clear at the moment.

Multiview alignment hashing for efficient image search.

PubMed

Liu, Li; Yu, Mengyang; Shao, Ling

2015-03-01

Hashing is a popular and efficient method for nearest neighbor search in large-scale data spaces by embedding high-dimensional feature descriptors into a similarity preserving Hamming space with a low dimension. For most hashing methods, the performance of retrieval heavily depends on the choice of the high-dimensional feature descriptor. Furthermore, a single type of feature cannot be descriptive enough for different images when it is used for hashing. Thus, how to combine multiple representations for learning effective hashing functions is an imminent task. In this paper, we present a novel unsupervised multiview alignment hashing approach based on regularized kernel nonnegative matrix factorization, which can find a compact representation uncovering the hidden semantics and simultaneously respecting the joint probability distribution of data. In particular, we aim to seek a matrix factorization to effectively fuse the multiple information sources meanwhile discarding the feature redundancy. Since the raised problem is regarded as nonconvex and discrete, our objective function is then optimized via an alternate way with relaxation and converges to a locally optimal solution. After finding the low-dimensional representation, the hashing functions are finally obtained through multivariable logistic regression. The proposed method is systematically evaluated on three data sets: 1) Caltech-256; 2) CIFAR-10; and 3) CIFAR-20, and the results show that our method significantly outperforms the state-of-the-art multiview hashing techniques.

Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes.

PubMed

Cannistraci, Carlo Vittorio; Ravasi, Timothy; Montevecchi, Franco Maria; Ideker, Trey; Alessio, Massimo

2010-09-15

Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space. 'Minimum Curvilinearity' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering. Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin. MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal. https://sites.google.com/site/carlovittoriocannistraci/home.

An Efficient Multiparty Quantum Secret Sharing Protocol Based on Bell States in the High Dimension Hilbert Space

NASA Astrophysics Data System (ADS)

Gao, Gan; Wang, Li-Ping

2010-11-01

We propose a quantum secret sharing protocol, in which Bell states in the high dimension Hilbert space are employed. The biggest advantage of our protocol is the high source capacity. Compared with the previous secret sharing protocol, ours has the higher controlling efficiency. In addition, as decoy states in the high dimension Hilbert space are used, we needn’t destroy quantum entanglement for achieving the goal to check the channel security.

Naked singularities in higher dimensional Vaidya space-times

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

Ghosh, S. G.; Dadhich, Naresh

We investigate the end state of the gravitational collapse of a null fluid in higher-dimensional space-times. Both naked singularities and black holes are shown to be developing as the final outcome of the collapse. The naked singularity spectrum in a collapsing Vaidya region (4D) gets covered with the increase in dimensions and hence higher dimensions favor a black hole in comparison to a naked singularity. The cosmic censorship conjecture will be fully respected for a space of infinite dimension.

Structure-seeking multilinear methods for the analysis of fMRI data.

PubMed

Andersen, Anders H; Rayens, William S

2004-06-01

In comprehensive fMRI studies of brain function, the data structures often contain higher-order ways such as trial, task condition, subject, and group in addition to the intrinsic dimensions of time and space. While multivariate bilinear methods such as principal component analysis (PCA) have been used successfully for extracting information about spatial and temporal features in data from a single fMRI run, the need to unfold higher-order data sets into bilinear arrays has led to decompositions that are nonunique and to the loss of multiway linkages and interactions present in the data. These additional dimensions or ways can be retained in multilinear models to produce structures that are unique and which admit interpretations that are neurophysiologically meaningful. Multiway analysis of fMRI data from multiple runs of a bilateral finger-tapping paradigm was performed using the parallel factor (PARAFAC) model. A trilinear model was fitted to a data cube of dimensions voxels by time by run. Similarly, a quadrilinear model was fitted to a higher-way structure of dimensions voxels by time by trial by run. The spatial and temporal response components were extracted and validated by comparison to results from traditional SVD/PCA analyses based on scenarios of unfolding into lower-order bilinear structures.

Bottom-up attention capture with distractor and target singletons defined in the same (color) dimension is not a matter of feature uncertainty.

PubMed

Weichselbaum, Hanna; Ansorge, Ulrich

2018-05-18

In visual search, attention capture by an irrelevant color-singleton distractor in another feature dimension than the target is dependent on whether or not the distractor changes its feature: Capture is present if the irrelevant color distractor can take on different features across trials, but absent if the distractor takes on only one feature throughout all trials. This influence could be due to down-weighting of the entire color map. Here we tested whether a similar effect could also be brought about by down-weighting of specific color channels within the same maps. We investigated whether a similar dependence of capture on color certainty might hold true if the distractor were defined in the same (color) dimension as the target. At odds with this possibility, in the first and third blocks-in which feature uncertainty was absent-an irrelevant distractor of a certain color captured attention. In addition, in a second block, varying the distractor color created feature uncertainty, but this did not increase capture. Repeating the exact same procedure with the same participants after one week confirmed the stability of the results. The present study showed that a color distractor presented in the same (color) dimension as the target captures attention independent of feature uncertainty. Thus, the down-weighting of single irrelevant color channels within the same feature map used for target search is not a matter of feature uncertainty.

Graph theory approach to the eigenvalue problem of large space structures

NASA Technical Reports Server (NTRS)

Reddy, A. S. S. R.; Bainum, P. M.

1981-01-01

Graph theory is used to obtain numerical solutions to eigenvalue problems of large space structures (LSS) characterized by a state vector of large dimensions. The LSS are considered as large, flexible systems requiring both orientation and surface shape control. Graphic interpretation of the determinant of a matrix is employed to reduce a higher dimensional matrix into combinations of smaller dimensional sub-matrices. The reduction is implemented by means of a Boolean equivalent of the original matrices formulated to obtain smaller dimensional equivalents of the original numerical matrix. Computation time becomes less and more accurate solutions are possible. An example is provided in the form of a free-free square plate. Linearized system equations and numerical values of a stiffness matrix are presented, featuring a state vector with 16 components.

FormTracer. A mathematica tracing package using FORM

NASA Astrophysics Data System (ADS)

Cyrol, Anton K.; Mitter, Mario; Strodthoff, Nils

2017-10-01

We present FormTracer, a high-performance, general purpose, easy-to-use Mathematica tracing package which uses FORM. It supports arbitrary space and spinor dimensions as well as an arbitrary number of simple compact Lie groups. While keeping the usability of the Mathematica interface, it relies on the efficiency of FORM. An additional performance gain is achieved by a decomposition algorithm that avoids redundant traces in the product tensors spaces. FormTracer supports a wide range of syntaxes which endows it with a high flexibility. Mathematica notebooks that automatically install the package and guide the user through performing standard traces in space-time, spinor and gauge-group spaces are provided. Program Files doi:http://dx.doi.org/10.17632/7rd29h4p3m.1 Licensing provisions: GPLv3 Programming language: Mathematica and FORM Nature of problem: Efficiently compute traces of large expressions Solution method: The expression to be traced is decomposed into its subspaces by a recursive Mathematica expansion algorithm. The result is subsequently translated to a FORM script that takes the traces. After FORM is executed, the final result is either imported into Mathematica or exported as optimized C/C++/Fortran code. Unusual features: The outstanding features of FormTracer are the simple interface, the capability to efficiently handle an arbitrary number of Lie groups in addition to Dirac and Lorentz tensors, and a customizable input-syntax.

Web document ranking via active learning and kernel principal component analysis

NASA Astrophysics Data System (ADS)

Cai, Fei; Chen, Honghui; Shu, Zhen

2015-09-01

Web document ranking arises in many information retrieval (IR) applications, such as the search engine, recommendation system and online advertising. A challenging issue is how to select the representative query-document pairs and informative features as well for better learning and exploring new ranking models to produce an acceptable ranking list of candidate documents of each query. In this study, we propose an active sampling (AS) plus kernel principal component analysis (KPCA) based ranking model, viz. AS-KPCA Regression, to study the document ranking for a retrieval system, i.e. how to choose the representative query-document pairs and features for learning. More precisely, we fill those documents gradually into the training set by AS such that each of which will incur the highest expected DCG loss if unselected. Then, the KPCA is performed via projecting the selected query-document pairs onto p-principal components in the feature space to complete the regression. Hence, we can cut down the computational overhead and depress the impact incurred by noise simultaneously. To the best of our knowledge, we are the first to perform the document ranking via dimension reductions in two dimensions, namely, the number of documents and features simultaneously. Our experiments demonstrate that the performance of our approach is better than that of the baseline methods on the public LETOR 4.0 datasets. Our approach brings an improvement against RankBoost as well as other baselines near 20% in terms of MAP metric and less improvements using P@K and NDCG@K, respectively. Moreover, our approach is particularly suitable for document ranking on the noisy dataset in practice.

A new method for calculating differential distributions directly in Mellin space

NASA Astrophysics Data System (ADS)

Mitov, Alexander

2006-12-01

We present a new method for the calculation of differential distributions directly in Mellin space without recourse to the usual momentum-fraction (or z-) space. The method is completely general and can be applied to any process. It is based on solving the integration-by-parts identities when one of the powers of the propagators is an abstract number. The method retains the full dependence on the Mellin variable and can be implemented in any program for solving the IBP identities based on algebraic elimination, like Laporta. General features of the method are: (1) faster reduction, (2) smaller number of master integrals compared to the usual z-space approach and (3) the master integrals satisfy difference instead of differential equations. This approach generalizes previous results related to fully inclusive observables like the recently calculated three-loop space-like anomalous dimensions and coefficient functions in inclusive DIS to more general processes requiring separate treatment of the various physical cuts. Many possible applications of this method exist, the most notable being the direct evaluation of the three-loop time-like splitting functions in QCD.

a Geometrical Chart of Altered Temporality (and Spatiality)

NASA Astrophysics Data System (ADS)

Saniga, Metod

2005-10-01

The paper presents, to our knowledge, a first fairly comprehensive and mathematically well-underpinned classification of the psychopathology of time (and space). After reviewing the most illustrative first-person accounts of "anomalous/peculiar" experiences of time (and, to a lesser degree, space) we introduce and describe in detail their algebraic geometrical model. The model features six qualitatively different types of the internal structure of time dimension and four types of that of space. As for time, the most pronounced are the ordinary "past-present-future," "present-only" ("eternal/everlasting now") and "no-present" (time "standing still") patterns. Concerning space, the most elementary are the ordinary, i.e., "here-and-there," mode and the "here-only" one ("omnipresence"). We then show what the admissible combinations of temporal and spatial psycho-patterns are and give a rigorous algebraic geometrical classification of them. The predictive power of the model is illustrated by the phenomenon of psychological time-reversal and the experiential difference between time and space. The paper ends with a brief account of some epistemological/ontological questions stemming from the approach.

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.

Study of plasma meniscus and beam halo in negative ion sources using three dimension in real space and three dimension in velocity space particle in cell model

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

Nishioka, S., E-mail: nishioka@ppl.appi.keio.ac.jp; Goto, I.; Hatayama, A.

2014-02-15

Our previous study by two dimension in real space and three dimension in velocity space-particle in cell model shows that the curvature of the plasma meniscus causes the beam halo in the negative ion sources. The negative ions extracted from the periphery of the meniscus are over-focused in the extractor due to the electrostatic lens effect, and consequently become the beam halo. The purpose of this study is to verify this mechanism with the full 3D model. It is shown that the above mechanism is essentially unchanged even in the 3D model, while the fraction of the beam halo ismore » significantly reduced to 6%. This value reasonably agrees with the experimental result.« less

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry.

PubMed

Gligorijevic, Jovan; Gajic, Dragoljub; Brkovic, Aleksandar; Savic-Gajic, Ivana; Georgieva, Olga; Di Gennaro, Stefano

2016-03-01

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings.

Online Condition Monitoring of Bearings to Support Total Productive Maintenance in the Packaging Materials Industry

PubMed Central

Gligorijevic, Jovan; Gajic, Dragoljub; Brkovic, Aleksandar; Savic-Gajic, Ivana; Georgieva, Olga; Di Gennaro, Stefano

2016-01-01

The packaging materials industry has already recognized the importance of Total Productive Maintenance as a system of proactive techniques for improving equipment reliability. Bearing faults, which often occur gradually, represent one of the foremost causes of failures in the industry. Therefore, detection of their faults in an early stage is quite important to assure reliable and efficient operation. We present a new automated technique for early fault detection and diagnosis in rolling-element bearings based on vibration signal analysis. Following the wavelet decomposition of vibration signals into a few sub-bands of interest, the standard deviation of obtained wavelet coefficients is extracted as a representative feature. Then, the feature space dimension is optimally reduced to two using scatter matrices. In the reduced two-dimensional feature space the fault detection and diagnosis is carried out by quadratic classifiers. Accuracy of the technique has been tested on four classes of the recorded vibrations signals, i.e., normal, with the fault of inner race, outer race, and ball operation. The overall accuracy of 98.9% has been achieved. The new technique can be used to support maintenance decision-making processes and, thus, to increase reliability and efficiency in the industry by preventing unexpected faulty operation of bearings. PMID:26938541

Limits on the Time Evolution of Space Dimensions from Newton's Constant

NASA Astrophysics Data System (ADS)

Nasseri, Forough

Limits are imposed upon the possible rate of change of extra spatial dimensions in a decrumpling model Universe with time variable spatial dimensions (TVSD) by considering the time variation of (1+3)-dimensional Newton's constant. Previous studies on the time variation of (1+3)-dimensional Newton's constant in TVSD theory had not include the effects of the volume of the extra dimensions and the effects of the surface area of the unit sphere in D-space dimensions. Our main result is that the absolute value of the present rate of change of spatial dimensions to be less than about 10-14 yr-1. Our results would appear to provide a prima facie case for ruling the TVSD model out. We show that based on observational bounds on the present variation of Newton's constant, one would have to conclude that the spatial dimension of the Universe when the Universe was "at the Planck scale" to be less than or equal to 3.09. If the dimension of space when the Universe was "at the Planck scale" is constrained to be fractional and very close to 3, then the whole edifice of TVSD model loses credibility.

The (Im)Materiality of Educational Space: Interactions between Material, Connected and Textual Dimensions of Networked Technology Use in Schools

ERIC Educational Resources Information Center

Burnett, Cathy

2011-01-01

In contributing to understanding about the barriers and opportunities associated with new technologies in educational settings, this article explores dimensions of the educational spaces associated with using networked technologies in contemporary classrooms. After considering how educational spaces may be "produced" (to use Lefebvre's…

Efficient feature selection using a hybrid algorithm for the task of epileptic seizure detection

NASA Astrophysics Data System (ADS)

Lai, Kee Huong; Zainuddin, Zarita; Ong, Pauline

2014-07-01

Feature selection is a very important aspect in the field of machine learning. It entails the search of an optimal subset from a very large data set with high dimensional feature space. Apart from eliminating redundant features and reducing computational cost, a good selection of feature also leads to higher prediction and classification accuracy. In this paper, an efficient feature selection technique is introduced in the task of epileptic seizure detection. The raw data are electroencephalography (EEG) signals. Using discrete wavelet transform, the biomedical signals were decomposed into several sets of wavelet coefficients. To reduce the dimension of these wavelet coefficients, a feature selection method that combines the strength of both filter and wrapper methods is proposed. Principal component analysis (PCA) is used as part of the filter method. As for wrapper method, the evolutionary harmony search (HS) algorithm is employed. This metaheuristic method aims at finding the best discriminating set of features from the original data. The obtained features were then used as input for an automated classifier, namely wavelet neural networks (WNNs). The WNNs model was trained to perform a binary classification task, that is, to determine whether a given EEG signal was normal or epileptic. For comparison purposes, different sets of features were also used as input. Simulation results showed that the WNNs that used the features chosen by the hybrid algorithm achieved the highest overall classification accuracy.

Feature-to-Feature Inference Under Conditions of Cue Restriction and Dimensional Correlation.

PubMed

Lancaster, Matthew E; Homa, Donald

2017-01-01

The present study explored feature-to-feature and label-to-feature inference in a category task for different category structures. In the correlated condition, each of the 4 dimensions comprising the category was positively correlated to each other and to the category label. In the uncorrelated condition, no correlation existed between the 4 dimensions comprising the category, although the dimension to category label correlation matched that of the correlated condition. After learning, participants made inference judgments of a missing feature, given 1, 2, or 3 feature cues; on half the trials, the category label was also included as a cue. The results showed superior inference of features following training on the correlated structure, with accurate inference when only a single feature was presented. In contrast, a single-feature cue resulted in chance levels of inference for the uncorrelated structure. Feature inference systematically improved with number of cues after training on the correlated structure. Surprisingly, a similar outcome was obtained for the uncorrelated structure, an outcome that must have reflected mediation via the category label. A descriptive model is briefly introduced to explain the results, with a suggestion that this paradigm might be profitably extended to hierarchical structures where the levels of feature-to-feature inference might vary with the depth of the hierarchy.

Why Nature has made a choice of one time and three space coordinates?

NASA Astrophysics Data System (ADS)

Mankoc Borstnik, N.; Nielsen, H. B.

2002-12-01

We propose a possible answer to one of the most exciting open questions in physics and cosmology, that is, the question why we seem to experience four-dimensional spacetime with three ordinary and one time dimensions. Making assumptions (such as particles being in first approximation massless) about the equations of motion, we argue for restrictions on the number of space and time dimensions. Accepting our explanation of the spacetime signature and the number of dimensions would be a point supporting (further) the importance of the 'internal space'.

Almost Classical Creation of a Universe

NASA Astrophysics Data System (ADS)

Guendelman, E. I.; Portnoy, J.

We study the problem of a 1+1 cord with a dynamical massless scalar field living in it, which separates a false vacuum and a conical region in a 2+1 space. A stable ``particle-like'' configuration can be found. Also, oscillating solutions exist which can tunnel to an expanding type solution. The most outstanding feature for these oscillating solution is that we do not need a singularity to create an infinite universe from them, and that an arbitrarily small tunneling is needed to achieve this. Possible consequences for similar processes, involving cosmic strings in 3+1 dimensions are discussed.

Entropy of Movement Outcome in Space-Time.

PubMed

Lai, Shih-Chiung; Hsieh, Tsung-Yu; Newell, Karl M

2015-07-01

Information entropy of the joint spatial and temporal (space-time) probability of discrete movement outcome was investigated in two experiments as a function of different movement strategies (space-time, space, and time instructional emphases), task goals (point-aiming and target-aiming) and movement speed-accuracy constraints. The variance of the movement spatial and temporal errors was reduced by instructional emphasis on the respective spatial or temporal dimension, but increased on the other dimension. The space-time entropy was lower in targetaiming task than the point aiming task but did not differ between instructional emphases. However, the joint probabilistic measure of spatial and temporal entropy showed that spatial error is traded for timing error in tasks with space-time criteria and that the pattern of movement error depends on the dimension of the measurement process. The unified entropy measure of movement outcome in space-time reveals a new relation for the speed-accuracy.

Compactification on phase space

NASA Astrophysics Data System (ADS)

Lovelady, Benjamin; Wheeler, James

2016-03-01

A major challenge for string theory is to understand the dimensional reduction required for comparison with the standard model. We propose reducing the dimension of the compactification by interpreting some of the extra dimensions as the energy-momentum portion of a phase-space. Such models naturally arise as generalized quotients of the conformal group called biconformal spaces. By combining the standard Kaluza-Klein approach with such a conformal gauge theory, we may start from the conformal group of an n-dimensional Euclidean space to form a 2n-dimensional quotient manifold with symplectic structure. A pair of involutions leads naturally to two n-dimensional Lorentzian manifolds. For n = 5, this leaves only two extra dimensions, with a countable family of possible compactifications and an SO(5) Yang-Mills field on the fibers. Starting with n=6 leads to 4-dimensional compactification of the phase space. In the latter case, if the two dimensions each from spacetime and momentum space are compactified onto spheres, then there is an SU(2)xSU(2) (left-right symmetric electroweak) field between phase and configuration space and an SO(6) field on the fibers. Such a theory, with minor additional symmetry breaking, could contain all parts of the standard model.

SKL algorithm based fabric image matching and retrieval

NASA Astrophysics Data System (ADS)

Cao, Yichen; Zhang, Xueqin; Ma, Guojian; Sun, Rongqing; Dong, Deping

2017-07-01

Intelligent computer image processing technology provides convenience and possibility for designers to carry out designs. Shape analysis can be achieved by extracting SURF feature. However, high dimension of SURF feature causes to lower matching speed. To solve this problem, this paper proposed a fast fabric image matching algorithm based on SURF K-means and LSH algorithm. By constructing the bag of visual words on K-Means algorithm, and forming feature histogram of each image, the dimension of SURF feature is reduced at the first step. Then with the help of LSH algorithm, the features are encoded and the dimension is further reduced. In addition, the indexes of each image and each class of image are created, and the number of matching images is decreased by LSH hash bucket. Experiments on fabric image database show that this algorithm can speed up the matching and retrieval process, the result can satisfy the requirement of dress designers with accuracy and speed.

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.

Correlation dimension and phase space contraction via extreme value theory

NASA Astrophysics Data System (ADS)

Faranda, Davide; Vaienti, Sandro

2018-04-01

We show how to obtain theoretical and numerical estimates of correlation dimension and phase space contraction by using the extreme value theory. The maxima of suitable observables sampled along the trajectory of a chaotic dynamical system converge asymptotically to classical extreme value laws where: (i) the inverse of the scale parameter gives the correlation dimension and (ii) the extremal index is associated with the rate of phase space contraction for backward iteration, which in dimension 1 and 2, is closely related to the positive Lyapunov exponent and in higher dimensions is related to the metric entropy. We call it the Dynamical Extremal Index. Numerical estimates are straightforward to obtain as they imply just a simple fit to a univariate distribution. Numerical tests range from low dimensional maps, to generalized Henon maps and climate data. The estimates of the indicators are particularly robust even with relatively short time series.

An analysis-by-synthesis approach to the estimation of vocal cord polyp features.

PubMed

Koizumi, T; Taniguchi, S; Itakura, F

1993-09-01

This paper deals with a new noninvasive method of estimating vocal cord polyp features through hoarse-voice analysis. A noteworthy feature of this method is that it enables us not only to discriminate hoarse voices caused by pathological vocal cords with a single golf-ball-like polyp from normal voices, but also to estimate polyp features such as the mass and dimension of polyp through the use of a novel model of pathological vocal cords which has been devised to simulate the subtle movement of the vocal cords. A synthetic hoarse voice produced with a hoarse-voice synthesizer is compared with a natural hoarse voice caused by the vocal cord polyp in terms of a distance measure and the polyp features are estimated by minimizing the distance measure. Some estimates of polyp dimension that have been obtained by applying this procedure to hoarse voices are found to compare favorably with actual polyp dimensions, demonstrating that the procedure is effective for estimating the features of golf-ball-like vocal cord polyps.

Embedding of the brane into six dimensions

NASA Astrophysics Data System (ADS)

Gogberashvili, Merab

2002-10-01

Embedding of the brane metric into Euclidean (2+4)-space is found. Brane geometry can be visualized as the surface of the hypersphere in six dimensions which ``radius'' is governed by the cosmological constant. Minkowski space in this picture is placed on the intersection of this surface with the plane formed by the extra space-like and time-like coordinates.

Fractal dimension of spatially extended systems

NASA Astrophysics Data System (ADS)

Torcini, A.; Politi, A.; Puccioni, G. P.; D'Alessandro, G.

1991-10-01

Properties of the invariant measure are numerically investigated in 1D chains of diffusively coupled maps. The coarse-grained fractal dimension is carefully computed in various embedding spaces, observing an extremely slow convergence towards the asymptotic value. This is in contrast with previous simulations, where the analysis of an insufficient number of points led the authors to underestimate the increase of fractal dimension with increasing the dimension of the embedding space. Orthogonal decomposition is also performed confirming that the slow convergence is intrinsically related to local nonlinear properties of the invariant measure. Finally, the Kaplan-Yorke conjecture is tested for short chains, showing that, despite the noninvertibility of the dynamical system, a good agreement is found between Lyapunov dimension and information dimension.

Gait recognition based on Gabor wavelets and modified gait energy image for human identification

NASA Astrophysics Data System (ADS)

Huang, Deng-Yuan; Lin, Ta-Wei; Hu, Wu-Chih; Cheng, Chih-Hsiang

2013-10-01

This paper proposes a method for recognizing human identity using gait features based on Gabor wavelets and modified gait energy images (GEIs). Identity recognition by gait generally involves gait representation, extraction, and classification. In this work, a modified GEI convolved with an ensemble of Gabor wavelets is proposed as a gait feature. Principal component analysis is then used to project the Gabor-wavelet-based gait features into a lower-dimension feature space for subsequent classification. Finally, support vector machine classifiers based on a radial basis function kernel are trained and utilized to recognize human identity. The major contributions of this paper are as follows: (1) the consideration of the shadow effect to yield a more complete segmentation of gait silhouettes; (2) the utilization of motion estimation to track people when walkers overlap; and (3) the derivation of modified GEIs to extract more useful gait information. Extensive performance evaluation shows a great improvement of recognition accuracy due to the use of shadow removal, motion estimation, and gait representation using the modified GEIs and Gabor wavelets.

Application of the laguerre deconvolution method for time-resolved fluorescence spectroscopy to the characterization of atherosclerotic plaques.

PubMed

Jo, J A; Fang, Q; Papaioannou, T; Qiao, J H; Fishbein, M C; Beseth, B; Dorafshar, A H; Reil, T; Baker, D; Freischlag, J; Marcu, L

2005-01-01

This study investigates the ability of time-resolved laser-induced fluorescence spectroscopy (TR-LIFS) to detect inflammation in atherosclerotic lesion, a key feature of plaque vulnerability. A total of 348 TR-LIFS measurements were taken from carotid plaques of 30 patients, and subsequently analyzed using the Laguerre deconvolution technique. The investigated spots were classified as Early, Fibrotic/Calcified or Inflamed lesions. A stepwise linear discriminant analysis algorithm was developed using spectral and TR features (normalized intensity values and Laguerre expansion coefficients at discrete emission wavelengths, respectively). Features from only three emission wavelengths (390, 450 and 500 nm) were used in the classifier. The Inflamed lesions were discriminated with sensitivity > 80% and specificity > 90 %, when the Laguerre expansion coefficients were included in the feature space. These results indicate that TR-LIFS information derived from the Laguerre expansion coefficients at few selected emission wavelengths can discriminate inflammation in atherosclerotic plaques. We believe that TR-LIFS derived Laguerre expansion coefficients can provide a valuable additional dimension for the detection of vulnerable plaques.

Solvent-Vapor-Mitigation of Electrostatics in 3D Cyclopropenium Diblock Copolyelectrolyte Network

NASA Astrophysics Data System (ADS)

Russell, Sebastian; Kumar, Sanat; Campos, Luis

Photolithography is progressively becoming an obsolete manufacturing technique in the microelectronic industry as block copolymer (BCP) nanoassembles approach sub 10-nm features sizes. Thermodynamically, the morphology and limiting feature size, for BCP, are determined by the relative volume fraction and magnitude of the incompatibility (χN) between each block. Therefore, to achieve smaller dimensions, it is imperative to devise copolymer systems that are strongly segregating (χN >>10) by utilizing high monomer incompatibility, large χ. For synthetic cylinder forming BCPs, achieving sub-10 nm features with a high degree of lateral ordering still remains a challenge. Covalently bound ions could potentially be a route towards enhancing the segmental incompatibility and this presentation will focus on the self-assembly of post-polymerization functionalized cyclopropenium-ion diblock copolyelectrolytes (DBCPE) through solvent vapor annealing. By varying the BCPE's total degree of polymerization and charge fraction we have mapped the kinetic phase-space. This control over morphology has opened the door to sub-10nm features with tunable densities by varying the length of the neutral and polyelectrolyte block, respectively. Chemical Engineering Department.

Structural optimization under overhang constraints imposed by additive manufacturing technologies

NASA Astrophysics Data System (ADS)

Allaire, G.; Dapogny, C.; Estevez, R.; Faure, A.; Michailidis, G.

2017-12-01

This article addresses one of the major constraints imposed by additive manufacturing processes on shape optimization problems - that of overhangs, i.e. large regions hanging over void without sufficient support from the lower structure. After revisiting the 'classical' geometric criteria used in the literature, based on the angle between the structural boundary and the build direction, we propose a new mechanical constraint functional, which mimics the layer by layer construction process featured by additive manufacturing technologies, and thereby appeals to the physical origin of the difficulties caused by overhangs. This constraint, as well as some variants, is precisely defined; their shape derivatives are computed in the sense of Hadamard's method, and numerical strategies are extensively discussed, in two and three space dimensions, to efficiently deal with the appearance of overhang features in the course of shape optimization processes.

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

Curchod, Basile F. E.; Agostini, Federica, E-mail: agostini@mpi-halle.mpg.de; Gross, E. K. U.

Nonadiabatic quantum interferences emerge whenever nuclear wavefunctions in different electronic states meet and interact in a nonadiabatic region. In this work, we analyze how nonadiabatic quantum interferences translate in the context of the exact factorization of the molecular wavefunction. In particular, we focus our attention on the shape of the time-dependent potential energy surface—the exact surface on which the nuclear dynamics takes place. We use a one-dimensional exactly solvable model to reproduce different conditions for quantum interferences, whose characteristic features already appear in one-dimension. The time-dependent potential energy surface develops complex features when strong interferences are present, in clear contrastmore » to the observed behavior in simple nonadiabatic crossing cases. Nevertheless, independent classical trajectories propagated on the exact time-dependent potential energy surface reasonably conserve a distribution in configuration space that mimics one of the exact nuclear probability densities.« less

Exploring Lovelock theory moduli space for Schrödinger solutions

NASA Astrophysics Data System (ADS)

Jatkar, Dileep P.; Kundu, Nilay

2016-09-01

We look for Schrödinger solutions in Lovelock gravity in D > 4. We span the entire parameter space and determine parametric relations under which the Schrödinger solution exists. We find that in arbitrary dimensions pure Lovelock theories have Schrödinger solutions of arbitrary radius, on a co-dimension one locus in the Lovelock parameter space. This co-dimension one locus contains the subspace over which the Lovelock gravity can be written in the Chern-Simons form. Schrödinger solutions do not exist outside this locus and on this locus they exist for arbitrary dynamical exponent z. This freedom in z is due to the degeneracy in the configuration space. We show that this degeneracy survives certain deformation away from the Lovelock moduli space.

High-resolution Self-Organizing Maps for advanced visualization and dimension reduction.

PubMed

Saraswati, Ayu; Nguyen, Van Tuc; Hagenbuchner, Markus; Tsoi, Ah Chung

2018-05-04

Kohonen's Self Organizing feature Map (SOM) provides an effective way to project high dimensional input features onto a low dimensional display space while preserving the topological relationships among the input features. Recent advances in algorithms that take advantages of modern computing hardware introduced the concept of high resolution SOMs (HRSOMs). This paper investigates the capabilities and applicability of the HRSOM as a visualization tool for cluster analysis and its suitabilities to serve as a pre-processor in ensemble learning models. The evaluation is conducted on a number of established benchmarks and real-world learning problems, namely, the policeman benchmark, two web spam detection problems, a network intrusion detection problem, and a malware detection problem. It is found that the visualization resulted from an HRSOM provides new insights concerning these learning problems. It is furthermore shown empirically that broad benefits from the use of HRSOMs in both clustering and classification problems can be expected. Copyright © 2018 Elsevier Ltd. All rights reserved.

Alternative method for variable aspect ratio vias using a vortex mask

NASA Astrophysics Data System (ADS)

Schepis, Anthony R.; Levinson, Zac; Burbine, Andrew; Smith, Bruce W.

2014-03-01

Historically IC (integrated circuit) device scaling has bridged the gap between technology nodes. Device size reduction is enabled by increased pattern density, enhancing functionality and effectively reducing cost per chip. Exemplifying this trend are aggressive reductions in memory cell sizes that have resulted in systems with diminishing area between bit/word lines. This affords an even greater challenge in the patterning of contact level features that are inherently difficult to resolve because of their relatively small area and complex aerial image. To accommodate these trends, semiconductor device design has shifted toward the implementation of elliptical contact features. This empowers designers to maximize the use of free device space, preserving contact area and effectively reducing the via dimension just along a single axis. It is therefore critical to provide methods that enhance the resolving capacity of varying aspect ratio vias for implementation in electronic design systems. Vortex masks, characterized by their helically induced propagation of light and consequent dark core, afford great potential for the patterning of such features when coupled with a high resolution negative tone resist system. This study investigates the integration of a vortex mask in a 193nm immersion (193i) lithography system and qualifies its ability to augment aspect ratio through feature density using aerial image vector simulation. It was found that vortex fabricated vias provide a distinct resolution advantage over traditionally patterned contact features employing a 6% attenuated phase shift mask (APM). 1:1 features were resolvable at 110nm pitch with a 38nm critical dimension (CD) and 110nm depth of focus (DOF) at 10% exposure latitude (EL). Furthermore, iterative source-mask optimization was executed as means to augment aspect ratio. By employing mask asymmetries and directionally biased sources aspect ratios ranging between 1:1 and 2:1 were achievable, however, this range is ultimately dictated by pitch employed.

Feature Selection and Pedestrian Detection Based on Sparse Representation.

PubMed

Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei

2015-01-01

Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony.

Enriching 3D optical surface scans with prior knowledge: tissue thickness computation by exploiting local neighborhoods.

PubMed

Wissel, Tobias; Stüber, Patrick; Wagner, Benjamin; Bruder, Ralf; Schweikard, Achim; Ernst, Floris

2016-04-01

Patient immobilization and X-ray-based imaging provide neither a convenient nor a very accurate way to ensure low repositioning errors or to compensate for motion in cranial radiotherapy. We therefore propose an optical tracking device that exploits subcutaneous structures as landmarks in addition to merely spatial registration. To develop such head tracking algorithms, precise and robust computation of these structures is necessary. Here, we show that the tissue thickness can be predicted with high accuracy and moreover exploit local neighborhood information within the laser spot grid on the forehead to further increase this estimation accuracy. We use statistical learning with Support Vector Regression and Gaussian Processes to learn a relationship between optical backscatter features and an MR tissue thickness ground truth. We compare different kernel functions for the data of five different subjects. The incident angle of the laser on the forehead as well as local neighborhoods is incorporated into the feature space. The latter represent the backscatter features from four neighboring laser spots. We confirm that the incident angle has a positive effect on the estimation error of the tissue thickness. The root-mean-square error falls even below 0.15 mm when adding the complete neighborhood information. This prior knowledge also leads to a smoothing effect on the reconstructed skin patch. Learning between different head poses yields similar results. The partial overlap of the point clouds makes the trade-off between novel information and increased feature space dimension obvious and hence feature selection by e.g., sequential forward selection necessary.

Visual short-term memory for oriented, colored objects.

PubMed

Shin, Hongsup; Ma, Wei Ji

2017-08-01

A central question in the study of visual short-term memory (VSTM) has been whether its basic units are objects or features. Most studies addressing this question have used change detection tasks in which the feature value before the change is highly discriminable from the feature value after the change. This approach assumes that memory noise is negligible, which recent work has shown not to be the case. Here, we investigate VSTM for orientation and color within a noisy-memory framework, using change localization with a variable magnitude of change. A specific consequence of the noise is that it is necessary to model the inference (decision) stage. We find that (a) orientation and color have independent pools of memory resource (consistent with classic results); (b) an irrelevant feature dimension is either encoded but ignored during decision-making, or encoded with low precision and taken into account during decision-making; and (c) total resource available in a given feature dimension is lower in the presence of task-relevant stimuli that are neutral in that feature dimension. We propose a framework in which feature resource comes both in packaged and in targeted form.

Integration trumps selection in object recognition.

PubMed

Saarela, Toni P; Landy, Michael S

2015-03-30

Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several "cues" (color, luminance, texture, etc.), and humans can integrate sensory cues to improve detection and recognition [1-3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue invariance by responding to a given shape independent of the visual cue defining it [5-8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10, 11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11, 12], imaging [13-16], and single-cell and neural population recordings [17, 18]. Besides single features, attention can select whole objects [19-21]. Objects are among the suggested "units" of attention because attention to a single feature of an object causes the selection of all of its features [19-21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. Copyright © 2015 Elsevier Ltd. All rights reserved.

Integration trumps selection in object recognition

PubMed Central

Saarela, Toni P.; Landy, Michael S.

2015-01-01

Summary Finding and recognizing objects is a fundamental task of vision. Objects can be defined by several “cues” (color, luminance, texture etc.), and humans can integrate sensory cues to improve detection and recognition [1–3]. Cortical mechanisms fuse information from multiple cues [4], and shape-selective neural mechanisms can display cue-invariance by responding to a given shape independent of the visual cue defining it [5–8]. Selective attention, in contrast, improves recognition by isolating a subset of the visual information [9]. Humans can select single features (red or vertical) within a perceptual dimension (color or orientation), giving faster and more accurate responses to items having the attended feature [10,11]. Attention elevates neural responses and sharpens neural tuning to the attended feature, as shown by studies in psychophysics and modeling [11,12], imaging [13–16], and single-cell and neural population recordings [17,18]. Besides single features, attention can select whole objects [19–21]. Objects are among the suggested “units” of attention because attention to a single feature of an object causes the selection of all of its features [19–21]. Here, we pit integration against attentional selection in object recognition. We find, first, that humans can integrate information near-optimally from several perceptual dimensions (color, texture, luminance) to improve recognition. They cannot, however, isolate a single dimension even when the other dimensions provide task-irrelevant, potentially conflicting information. For object recognition, it appears that there is mandatory integration of information from multiple dimensions of visual experience. The advantage afforded by this integration, however, comes at the expense of attentional selection. PMID:25802154

Analyzing multicomponent receptive fields from neural responses to natural stimuli

PubMed Central

Rowekamp, Ryan; Sharpee, Tatyana O

2011-01-01

The challenge of building increasingly better models of neural responses to natural stimuli is to accurately estimate the multiple stimulus features that may jointly affect the neural spike probability. The selectivity for combinations of features is thought to be crucial for achieving classical properties of neural responses such as contrast invariance. The joint search for these multiple stimulus features is difficult because estimating spike probability as a multidimensional function of stimulus projections onto candidate relevant dimensions is subject to the curse of dimensionality. An attractive alternative is to search for relevant dimensions sequentially, as in projection pursuit regression. Here we demonstrate using analytic arguments and simulations of model cells that different types of sequential search strategies exhibit systematic biases when used with natural stimuli. Simulations show that joint optimization is feasible for up to three dimensions with current algorithms. When applied to the responses of V1 neurons to natural scenes, models based on three jointly optimized dimensions had better predictive power in a majority of cases compared to dimensions optimized sequentially, with different sequential methods yielding comparable results. Thus, although the curse of dimensionality remains, at least several relevant dimensions can be estimated by joint information maximization. PMID:21780916

Dynamics of entanglement in expanding quantum fields

NASA Astrophysics Data System (ADS)

Berges, Jürgen; Floerchinger, Stefan; Venugopalan, Raju

2018-04-01

We develop a functional real-time approach to computing the entanglement between spatial regions for Gaussian states in quantum field theory. The entanglement entropy is characterized in terms of local correlation functions on space-like Cauchy hypersurfaces. The framework is applied to explore an expanding light cone geometry in the particular case of the Schwinger model for quantum electrodynamics in 1+1 space-time dimensions. We observe that the entanglement entropy becomes extensive in rapidity at early times and that the corresponding local reduced density matrix is a thermal density matrix for excitations around a coherent field with a time dependent temperature. Since the Schwinger model successfully describes many features of multiparticle production in e + e - collisions, our results provide an attractive explanation in this framework for the apparent thermal nature of multiparticle production even in the absence of significant final state scattering.

Clustering, hierarchical organization, and the topography of abstract and concrete nouns.

PubMed

Troche, Joshua; Crutch, Sebastian; Reilly, Jamie

2014-01-01

The empirical study of language has historically relied heavily upon concrete word stimuli. By definition, concrete words evoke salient perceptual associations that fit well within feature-based, sensorimotor models of word meaning. In contrast, many theorists argue that abstract words are "disembodied" in that their meaning is mediated through language. We investigated word meaning as distributed in multidimensional space using hierarchical cluster analysis. Participants (N = 365) rated target words (n = 400 English nouns) across 12 cognitive dimensions (e.g., polarity, ease of teaching, emotional valence). Factor reduction revealed three latent factors, corresponding roughly to perceptual salience, affective association, and magnitude. We plotted the original 400 words for the three latent factors. Abstract and concrete words showed overlap in their topography but also differentiated themselves in semantic space. This topographic approach to word meaning offers a unique perspective to word concreteness.

1+1 dimensional compactifications of string theory.

PubMed

Goheer, Naureen; Kleban, Matthew; Susskind, Leonard

2004-05-14

We argue that stable, maximally symmetric compactifications of string theory to 1+1 dimensions are in conflict with holography. In particular, the finite horizon entropies of the Rindler wedge in 1+1 dimensional Minkowski and anti-de Sitter space, and of the de Sitter horizon in any dimension, are inconsistent with the symmetries of these spaces. The argument parallels one made recently by the same authors, in which we demonstrated the incompatibility of the finiteness of the entropy and the symmetries of de Sitter space in any dimension. If the horizon entropy is either infinite or zero, the conflict is resolved.

AVC: Selecting discriminative features on basis of AUC by maximizing variable complementarity.

PubMed

Sun, Lei; Wang, Jun; Wei, Jinmao

2017-03-14

The Receiver Operator Characteristic (ROC) curve is well-known in evaluating classification performance in biomedical field. Owing to its superiority in dealing with imbalanced and cost-sensitive data, the ROC curve has been exploited as a popular metric to evaluate and find out disease-related genes (features). The existing ROC-based feature selection approaches are simple and effective in evaluating individual features. However, these approaches may fail to find real target feature subset due to their lack of effective means to reduce the redundancy between features, which is essential in machine learning. In this paper, we propose to assess feature complementarity by a trick of measuring the distances between the misclassified instances and their nearest misses on the dimensions of pairwise features. If a misclassified instance and its nearest miss on one feature dimension are far apart on another feature dimension, the two features are regarded as complementary to each other. Subsequently, we propose a novel filter feature selection approach on the basis of the ROC analysis. The new approach employs an efficient heuristic search strategy to select optimal features with highest complementarities. The experimental results on a broad range of microarray data sets validate that the classifiers built on the feature subset selected by our approach can get the minimal balanced error rate with a small amount of significant features. Compared with other ROC-based feature selection approaches, our new approach can select fewer features and effectively improve the classification performance.

An enhanced data visualization method for diesel engine malfunction classification using multi-sensor signals.

PubMed

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-10-21

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine.

An Enhanced Data Visualization Method for Diesel Engine Malfunction Classification Using Multi-Sensor Signals

PubMed Central

Li, Yiqing; Wang, Yu; Zi, Yanyang; Zhang, Mingquan

2015-01-01

The various multi-sensor signal features from a diesel engine constitute a complex high-dimensional dataset. The non-linear dimensionality reduction method, t-distributed stochastic neighbor embedding (t-SNE), provides an effective way to implement data visualization for complex high-dimensional data. However, irrelevant features can deteriorate the performance of data visualization, and thus, should be eliminated a priori. This paper proposes a feature subset score based t-SNE (FSS-t-SNE) data visualization method to deal with the high-dimensional data that are collected from multi-sensor signals. In this method, the optimal feature subset is constructed by a feature subset score criterion. Then the high-dimensional data are visualized in 2-dimension space. According to the UCI dataset test, FSS-t-SNE can effectively improve the classification accuracy. An experiment was performed with a large power marine diesel engine to validate the proposed method for diesel engine malfunction classification. Multi-sensor signals were collected by a cylinder vibration sensor and a cylinder pressure sensor. Compared with other conventional data visualization methods, the proposed method shows good visualization performance and high classification accuracy in multi-malfunction classification of a diesel engine. PMID:26506347

A mixture model-based approach to the clustering of microarray expression data.

PubMed

McLachlan, G J; Bean, R W; Peel, D

2002-03-01

This paper introduces the software EMMIX-GENE that has been developed for the specific purpose of a model-based approach to the clustering of microarray expression data, in particular, of tissue samples on a very large number of genes. The latter is a nonstandard problem in parametric cluster analysis because the dimension of the feature space (the number of genes) is typically much greater than the number of tissues. A feasible approach is provided by first selecting a subset of the genes relevant for the clustering of the tissue samples by fitting mixtures of t distributions to rank the genes in order of increasing size of the likelihood ratio statistic for the test of one versus two components in the mixture model. The imposition of a threshold on the likelihood ratio statistic used in conjunction with a threshold on the size of a cluster allows the selection of a relevant set of genes. However, even this reduced set of genes will usually be too large for a normal mixture model to be fitted directly to the tissues, and so the use of mixtures of factor analyzers is exploited to reduce effectively the dimension of the feature space of genes. The usefulness of the EMMIX-GENE approach for the clustering of tissue samples is demonstrated on two well-known data sets on colon and leukaemia tissues. For both data sets, relevant subsets of the genes are able to be selected that reveal interesting clusterings of the tissues that are either consistent with the external classification of the tissues or with background and biological knowledge of these sets. EMMIX-GENE is available at http://www.maths.uq.edu.au/~gjm/emmix-gene/

Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension

NASA Astrophysics Data System (ADS)

Hamrouni, Sameh; Rougon, Nicolas; Pr"teux, Françoise

2011-03-01

In perfusion MRI (p-MRI) exams, short-axis (SA) image sequences are captured at multiple slice levels along the long-axis of the heart during the transit of a vascular contrast agent (Gd-DTPA) through the cardiac chambers and muscle. Compensating cardio-thoracic motions is a requirement for enabling computer-aided quantitative assessment of myocardial ischaemia from contrast-enhanced p-MRI sequences. The classical paradigm consists of registering each sequence frame on a reference image using some intensity-based matching criterion. In this paper, we introduce a novel unsupervised method for the spatio-temporal groupwise registration of cardiac p-MRI exams based on normalized mutual information (NMI) between high-dimensional feature distributions. Here, local contrast enhancement curves are used as a dense set of spatio-temporal features, and statistically matched through variational optimization to a target feature distribution derived from a registered reference template. The hard issue of probability density estimation in high-dimensional state spaces is bypassed by using consistent geometric entropy estimators, allowing NMI to be computed directly from feature samples. Specifically, a computationally efficient kth-nearest neighbor (kNN) estimation framework is retained, leading to closed-form expressions for the gradient flow of NMI over finite- and infinite-dimensional motion spaces. This approach is applied to the groupwise alignment of cardiac p-MRI exams using a free-form Deformation (FFD) model for cardio-thoracic motions. Experiments on simulated and natural datasets suggest its accuracy and robustness for registering p-MRI exams comprising more than 30 frames.

Exact dimension estimation of interacting qubit systems assisted by a single quantum probe

NASA Astrophysics Data System (ADS)

Sone, Akira; Cappellaro, Paola

2017-12-01

Estimating the dimension of an Hilbert space is an important component of quantum system identification. In quantum technologies, the dimension of a quantum system (or its corresponding accessible Hilbert space) is an important resource, as larger dimensions determine, e.g., the performance of quantum computation protocols or the sensitivity of quantum sensors. Despite being a critical task in quantum system identification, estimating the Hilbert space dimension is experimentally challenging. While there have been proposals for various dimension witnesses capable of putting a lower bound on the dimension from measuring collective observables that encode correlations, in many practical scenarios, especially for multiqubit systems, the experimental control might not be able to engineer the required initialization, dynamics, and observables. Here we propose a more practical strategy that relies not on directly measuring an unknown multiqubit target system, but on the indirect interaction with a local quantum probe under the experimenter's control. Assuming only that the interaction model is given and the evolution correlates all the qubits with the probe, we combine a graph-theoretical approach and realization theory to demonstrate that the system dimension can be exactly estimated from the model order of the system. We further analyze the robustness in the presence of background noise of the proposed estimation method based on realization theory, finding that despite stringent constrains on the allowed noise level, exact dimension estimation can still be achieved.

Shadow-driven 4D haptic visualization.

PubMed

Zhang, Hui; Hanson, Andrew

2007-01-01

Just as we can work with two-dimensional floor plans to communicate 3D architectural design, we can exploit reduced-dimension shadows to manipulate the higher-dimensional objects generating the shadows. In particular, by taking advantage of physically reactive 3D shadow-space controllers, we can transform the task of interacting with 4D objects to a new level of physical reality. We begin with a teaching tool that uses 2D knot diagrams to manipulate the geometry of 3D mathematical knots via their projections; our unique 2D haptic interface allows the user to become familiar with sketching, editing, exploration, and manipulation of 3D knots rendered as projected imageson a 2D shadow space. By combining graphics and collision-sensing haptics, we can enhance the 2D shadow-driven editing protocol to successfully leverage 2D pen-and-paper or blackboard skills. Building on the reduced-dimension 2D editing tool for manipulating 3D shapes, we develop the natural analogy to produce a reduced-dimension 3D tool for manipulating 4D shapes. By physically modeling the correct properties of 4D surfaces, their bending forces, and their collisions in the 3D haptic controller interface, we can support full-featured physical exploration of 4D mathematical objects in a manner that is otherwise far beyond the experience accessible to human beings. As far as we are aware, this paper reports the first interactive system with force-feedback that provides "4D haptic visualization" permitting the user to model and interact with 4D cloth-like objects.

Visual search of cyclic spatio-temporal events

NASA Astrophysics Data System (ADS)

Gautier, Jacques; Davoine, Paule-Annick; Cunty, Claire

2018-05-01

The analysis of spatio-temporal events, and especially of relationships between their different dimensions (space-time-thematic attributes), can be done with geovisualization interfaces. But few geovisualization tools integrate the cyclic dimension of spatio-temporal event series (natural events or social events). Time Coil and Time Wave diagrams represent both the linear time and the cyclic time. By introducing a cyclic temporal scale, these diagrams may highlight the cyclic characteristics of spatio-temporal events. However, the settable cyclic temporal scales are limited to usual durations like days or months. Because of that, these diagrams cannot be used to visualize cyclic events, which reappear with an unusual period, and don't allow to make a visual search of cyclic events. Also, they don't give the possibility to identify the relationships between the cyclic behavior of the events and their spatial features, and more especially to identify localised cyclic events. The lack of possibilities to represent the cyclic time, outside of the temporal diagram of multi-view geovisualization interfaces, limits the analysis of relationships between the cyclic reappearance of events and their other dimensions. In this paper, we propose a method and a geovisualization tool, based on the extension of Time Coil and Time Wave, to provide a visual search of cyclic events, by allowing to set any possible duration to the diagram's cyclic temporal scale. We also propose a symbology approach to push the representation of the cyclic time into the map, in order to improve the analysis of relationships between space and the cyclic behavior of events.

Craniofacial structures' development in prenatal period: An MRI study.

PubMed

Begnoni, G; Serrao, G; Musto, F; Pellegrini, G; Triulzi, F M; Dellavia, C

2018-05-01

The development of skeletal structures (cranial base, upper and lower) and upper airways spaces (oropharyngeal and nasopharyngeal) of the skull has always been an issue of great interest in orthodontics. Foetal MRI images obtained as screening exam during pregnancy can help to understand the development of these structures using a sample cephalometric analysis. A total of 28 MRI images in sagittal section of foetuses from 20th to 32th weeks of gestation were obtained to dispel doubts about the presence of skeletal malformations. Cephalometric measurements were performed on MRI T2-dependent images acquired with a 1.5 T scanner. The Software Osirix 5 permits to study sagittal and vertical dimensions of the skull analysing linear measurements, angles and areas of the skeletal structures. Vertical and sagittal dimension of cranial base, maxilla and mandible grow significantly (P < .01) between the second and third trimester of gestational period as well as nasopharyngeal and oropharyngeal spaces (P < .05). High correlation between the development of anterior cranial base and functional areas devoted to speech and swallow is demonstrated (r: .97). The development of craniofacial structures during foetal period seems to show a close correlation between skeletal features and functional spaces with a peak between the second and third trimester of gestation. MRI images result helpful for the clinician to detect with a sample cephalometric analysis anomalies of skeletal and functional structures during prenatal period. © 2018 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Data Mining and Machine Learning Models for Predicting Drug Likeness and Their Disease or Organ Category.

PubMed

Yosipof, Abraham; Guedes, Rita C; García-Sosa, Alfonso T

2018-01-01

Data mining approaches can uncover underlying patterns in chemical and pharmacological property space decisive for drug discovery and development. Two of the most common approaches are visualization and machine learning methods. Visualization methods use dimensionality reduction techniques in order to reduce multi-dimension data into 2D or 3D representations with a minimal loss of information. Machine learning attempts to find correlations between specific activities or classifications for a set of compounds and their features by means of recurring mathematical models. Both models take advantage of the different and deep relationships that can exist between features of compounds, and helpfully provide classification of compounds based on such features or in case of visualization methods uncover underlying patterns in the feature space. Drug-likeness has been studied from several viewpoints, but here we provide the first implementation in chemoinformatics of the t-Distributed Stochastic Neighbor Embedding (t-SNE) method for the visualization and the representation of chemical space, and the use of different machine learning methods separately and together to form a new ensemble learning method called AL Boost. The models obtained from AL Boost synergistically combine decision tree, random forests (RF), support vector machine (SVM), artificial neural network (ANN), k nearest neighbors (kNN), and logistic regression models. In this work, we show that together they form a predictive model that not only improves the predictive force but also decreases bias. This resulted in a corrected classification rate of over 0.81, as well as higher sensitivity and specificity rates for the models. In addition, separation and good models were also achieved for disease categories such as antineoplastic compounds and nervous system diseases, among others. Such models can be used to guide decision on the feature landscape of compounds and their likeness to either drugs or other characteristics, such as specific or multiple disease-category(ies) or organ(s) of action of a molecule.

Dry etch challenges for CD shrinkage in memory process

NASA Astrophysics Data System (ADS)

Matsushita, Takaya; Matsumoto, Takanori; Mukai, Hidefumi; Kyoh, Suigen; Hashimoto, Kohji

2015-03-01

Line pattern collapse attracts attention as a new problem of the L&S formation in sub-20nm H.P feature. Line pattern collapse that occurs in a slight non-uniformity of adjacent CD (Critical dimension) space using double patterning process has been studied with focus on micro-loading effect in Si etching. Bias RF pulsing plasma etching process using low duty cycle helped increase of selectivity Si to SiO2. In addition to the effect of Bias RF pulsing process, the thin mask obtained from improvement of selectivity has greatly suppressed micro-loading in Si etching. However it was found that micro-loading effect worsen again in sub-20nm space width. It has been confirmed that by using cycle etch process to remove deposition with CFx based etching micro-loading effect could be suppressed. Finally, Si etching process condition using combination of results above could provide finer line and space without "line pattern collapse" in sub-20nm.

Differentiating induced and natural seismicity using space-time-magnitude statistics applied to the Coso Geothermal field

USGS Publications Warehouse

Schoenball, Martin; Davatzes, Nicholas C.; Glen, Jonathan M. G.

2015-01-01

A remarkable characteristic of earthquakes is their clustering in time and space, displaying their self-similarity. It remains to be tested if natural and induced earthquakes share the same behavior. We study natural and induced earthquakes comparatively in the same tectonic setting at the Coso Geothermal Field. Covering the preproduction and coproduction periods from 1981 to 2013, we analyze interevent times, spatial dimension, and frequency-size distributions for natural and induced earthquakes. Individually, these distributions are statistically indistinguishable. Determining the distribution of nearest neighbor distances in a combined space-time-magnitude metric, lets us identify clear differences between both kinds of seismicity. Compared to natural earthquakes, induced earthquakes feature a larger population of background seismicity and nearest neighbors at large magnitude rescaled times and small magnitude rescaled distances. Local stress perturbations induced by field operations appear to be strong enough to drive local faults through several seismic cycles and reactivate them after time periods on the order of a year.

Modeling the Time Course of Feature Perception and Feature Information Retrieval

ERIC Educational Resources Information Center

Kent, Christopher; Lamberts, Koen

2006-01-01

Three experiments investigated whether retrieval of information about different dimensions of a visual object varies as a function of the perceptual properties of those dimensions. The experiments involved two perception-based matching tasks and two retrieval-based matching tasks. A signal-to-respond methodology was used in all tasks. A stochastic…

Classification of small lesions on dynamic breast MRI: Integrating dimension reduction and out-of-sample extension into CADx methodology

PubMed Central

Nagarajan, Mahesh B.; Huber, Markus B.; Schlossbauer, Thomas; Leinsinger, Gerda; Krol, Andrzej; Wismüller, Axel

2014-01-01

Objective While dimension reduction has been previously explored in computer aided diagnosis (CADx) as an alternative to feature selection, previous implementations of its integration into CADx do not ensure strict separation between training and test data required for the machine learning task. This compromises the integrity of the independent test set, which serves as the basis for evaluating classifier performance. Methods and Materials We propose, implement and evaluate an improved CADx methodology where strict separation is maintained. This is achieved by subjecting the training data alone to dimension reduction; the test data is subsequently processed with out-of-sample extension methods. Our approach is demonstrated in the research context of classifying small diagnostically challenging lesions annotated on dynamic breast magnetic resonance imaging (MRI) studies. The lesions were dynamically characterized through topological feature vectors derived from Minkowski functionals. These feature vectors were then subject to dimension reduction with different linear and non-linear algorithms applied in conjunction with out-of-sample extension techniques. This was followed by classification through supervised learning with support vector regression. Area under the receiver-operating characteristic curve (AUC) was evaluated as the metric of classifier performance. Results Of the feature vectors investigated, the best performance was observed with Minkowski functional ’perimeter’ while comparable performance was observed with ’area’. Of the dimension reduction algorithms tested with ’perimeter’, the best performance was observed with Sammon’s mapping (0.84 ± 0.10) while comparable performance was achieved with exploratory observation machine (0.82 ± 0.09) and principal component analysis (0.80 ± 0.10). Conclusions The results reported in this study with the proposed CADx methodology present a significant improvement over previous results reported with such small lesions on dynamic breast MRI. In particular, non-linear algorithms for dimension reduction exhibited better classification performance than linear approaches, when integrated into our CADx methodology. We also note that while dimension reduction techniques may not necessarily provide an improvement in classification performance over feature selection, they do allow for a higher degree of feature compaction. PMID:24355697

Feature extraction algorithm for space targets based on fractal theory

NASA Astrophysics Data System (ADS)

Tian, Balin; Yuan, Jianping; Yue, Xiaokui; Ning, Xin

2007-11-01

In order to offer a potential for extending the life of satellites and reducing the launch and operating costs, satellite servicing including conducting repairs, upgrading and refueling spacecraft on-orbit become much more frequently. Future space operations can be more economically and reliably executed using machine vision systems, which can meet real time and tracking reliability requirements for image tracking of space surveillance system. Machine vision was applied to the research of relative pose for spacecrafts, the feature extraction algorithm was the basis of relative pose. In this paper fractal geometry based edge extraction algorithm which can be used in determining and tracking the relative pose of an observed satellite during proximity operations in machine vision system was presented. The method gets the gray-level image distributed by fractal dimension used the Differential Box-Counting (DBC) approach of the fractal theory to restrain the noise. After this, we detect the consecutive edge using Mathematical Morphology. The validity of the proposed method is examined by processing and analyzing images of space targets. The edge extraction method not only extracts the outline of the target, but also keeps the inner details. Meanwhile, edge extraction is only processed in moving area to reduce computation greatly. Simulation results compared edge detection using the method which presented by us with other detection methods. The results indicate that the presented algorithm is a valid method to solve the problems of relative pose for spacecrafts.

A Unifying Motif for Spatial and Directional Surround Suppression.

PubMed

Liu, Liu D; Miller, Kenneth D; Pack, Christopher C

2018-01-24

In the visual system, the response to a stimulus in a neuron's receptive field can be modulated by stimulus context, and the strength of these contextual influences vary with stimulus intensity. Recent work has shown how a theoretical model, the stabilized supralinear network (SSN), can account for such modulatory influences, using a small set of computational mechanisms. Although the predictions of the SSN have been confirmed in primary visual cortex (V1), its computational principles apply with equal validity to any cortical structure. We have therefore tested the generality of the SSN by examining modulatory influences in the middle temporal area (MT) of the macaque visual cortex, using electrophysiological recordings and pharmacological manipulations. We developed a novel stimulus that can be adjusted parametrically to be larger or smaller in the space of all possible motion directions. We found, as predicted by the SSN, that MT neurons integrate across motion directions for low-contrast stimuli, but that they exhibit suppression by the same stimuli when they are high in contrast. These results are analogous to those found in visual cortex when stimulus size is varied in the space domain. We further tested the mechanisms of inhibition using pharmacological manipulations of inhibitory efficacy. As predicted by the SSN, local manipulation of inhibitory strength altered firing rates, but did not change the strength of surround suppression. These results are consistent with the idea that the SSN can account for modulatory influences along different stimulus dimensions and in different cortical areas. SIGNIFICANCE STATEMENT Visual neurons are selective for specific stimulus features in a region of visual space known as the receptive field, but can be modulated by stimuli outside of the receptive field. The SSN model has been proposed to account for these and other modulatory influences, and tested in V1. As this model is not specific to any particular stimulus feature or brain region, we wondered whether similar modulatory influences might be observed for other stimulus dimensions and other regions. We tested for specific patterns of modulatory influences in the domain of motion direction, using electrophysiological recordings from MT. Our data confirm the predictions of the SSN in MT, suggesting that the SSN computations might be a generic feature of sensory cortex. Copyright © 2018 the authors 0270-6474/18/380989-11$15.00/0.

Learning about the internal structure of categories through classification and feature inference.

PubMed

Jee, Benjamin D; Wiley, Jennifer

2014-01-01

Previous research on category learning has found that classification tasks produce representations that are skewed toward diagnostic feature dimensions, whereas feature inference tasks lead to richer representations of within-category structure. Yet, prior studies often measure category knowledge through tasks that involve identifying only the typical features of a category. This neglects an important aspect of a category's internal structure: how typical and atypical features are distributed within a category. The present experiments tested the hypothesis that inference learning results in richer knowledge of internal category structure than classification learning. We introduced several new measures to probe learners' representations of within-category structure. Experiment 1 found that participants in the inference condition learned and used a wider range of feature dimensions than classification learners. Classification learners, however, were more sensitive to the presence of atypical features within categories. Experiment 2 provided converging evidence that classification learners were more likely to incorporate atypical features into their representations. Inference learners were less likely to encode atypical category features, even in a "partial inference" condition that focused learners' attention on the feature dimensions relevant to classification. Overall, these results are contrary to the hypothesis that inference learning produces superior knowledge of within-category structure. Although inference learning promoted representations that included a broad range of category-typical features, classification learning promoted greater sensitivity to the distribution of typical and atypical features within categories.

Design features that affect the maneuverability of wheelchairs and scooters.

PubMed

Koontz, Alicia M; Brindle, Eric D; Kankipati, Padmaja; Feathers, David; Cooper, Rory A

2010-05-01

To determine the minimum space required for wheeled mobility device users to perform 4 maneuverability tasks and to investigate the impact of selected design attributes on space. Case series. University laboratory, Veterans Affairs research facility, vocational training center, and a national wheelchair sport event. The sample of convenience included manual wheelchair (MWC; n=109), power wheelchair (PWC; n=100), and scooter users (n=14). A mock environment was constructed to create passageways to form an L-turn, 360 degrees -turn in place, and a U-turn with and without a barrier. Passageway openings were increased in 5-cm increments until the user could successfully perform each task without hitting the walls. Structural dimensions of the device and user were collected using an electromechanical probe. Mobility devices were grouped into categories based on design features and compared using 1-way analysis of variance and post hoc pairwise Bonferroni-corrected tests. Minimum passageway widths for the 4 maneuverability tasks. Ultralight MWCs with rear axles posterior to the shoulder had the shortest lengths and required the least amount of space compared with all other types of MWCs (P<.05). Mid-wheel-drive PWCs required the least space for the 360 degrees -turn in place compared with front-wheel-drive and rear-wheel-drive PWCs (P<.01) but performed equally as well as front-wheel-drive models on all other turning tasks. PWCs with seat functions required more space to perform the tasks. Between 10% and 100% of users would not be able to maneuver in spaces that meet current Accessibility Guidelines for Buildings and Facilities specifications. This study provides data that can be used to support wheelchair prescription and home modifications and to update standards to improve the accessibility of public areas.

Effective dimension reduction for sparse functional data

PubMed Central

YAO, F.; LEI, E.; WU, Y.

2015-01-01

Summary We propose a method of effective dimension reduction for functional data, emphasizing the sparse design where one observes only a few noisy and irregular measurements for some or all of the subjects. The proposed method borrows strength across the entire sample and provides a way to characterize the effective dimension reduction space, via functional cumulative slicing. Our theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures of the predictor process and the effective dimension reduction space. A simulation study and an application illustrate the superior finite-sample performance of the method. PMID:26566293

Upper limits to submillimetre-range forces from extra space-time dimensions.

PubMed

Long, Joshua C; Chan, Hilton W; Churnside, Allison B; Gulbis, Eric A; Varney, Michael C M; Price, John C

2003-02-27

String theory is the most promising approach to the long-sought unified description of the four forces of nature and the elementary particles, but direct evidence supporting it is lacking. The theory requires six extra spatial dimensions beyond the three that we observe; it is usually supposed that these extra dimensions are curled up into small spaces. This 'compactification' induces 'moduli' fields, which describe the size and shape of the compact dimensions at each point in space-time. These moduli fields generate forces with strengths comparable to gravity, which according to some recent predictions might be detected on length scales of about 100 microm. Here we report a search for gravitational-strength forces using planar oscillators separated by a gap of 108 micro m. No new forces are observed, ruling out a substantial portion of the previously allowed parameter space for the strange and gluon moduli forces, and setting a new upper limit on the range of the string dilaton and radion forces.

The Flow Dimension and Aquifer Heterogeneity: Field evidence and Numerical Analyses

NASA Astrophysics Data System (ADS)

Walker, D. D.; Cello, P. A.; Valocchi, A. J.; Roberts, R. M.; Loftis, B.

2008-12-01

The Generalized Radial Flow approach to hydraulic test interpretation infers the flow dimension to describe the geometry of the flow field during a hydraulic test. Noninteger values of the flow dimension often are inferred for tests in highly heterogeneous aquifers, yet subsequent modeling studies typically ignore the flow dimension. Monte Carlo analyses of detailed numerical models of aquifer tests examine the flow dimension for several stochastic models of heterogeneous transmissivity, T(x). These include multivariate lognormal, fractional Brownian motion, a site percolation network, and discrete linear features with lengths distributed as power-law. The behavior of the simulated flow dimensions are compared to the flow dimensions observed for multiple aquifer tests in a fractured dolomite aquifer in the Great Lakes region of North America. The combination of multiple hydraulic tests, observed fracture patterns, and the Monte Carlo results are used to screen models of heterogeneity and their parameters for subsequent groundwater flow modeling. The comparison shows that discrete linear features with lengths distributed as a power-law appear to be the most consistent with observations of the flow dimension in fractured dolomite aquifers.

Statistical mechanics and thermodynamic limit of self-gravitating fermions in D dimensions.

PubMed

Chavanis, Pierre-Henri

2004-06-01

We discuss the statistical mechanics of a system of self-gravitating fermions in a space of dimension D. We plot the caloric curves of the self-gravitating Fermi gas giving the temperature as a function of energy and investigate the nature of phase transitions as a function of the dimension of space. We consider stable states (global entropy maxima) as well as metastable states (local entropy maxima). We show that for D> or =4, there exists a critical temperature (for sufficiently large systems) and a critical energy below which the system cannot be found in statistical equilibrium. Therefore, for D> or =4, quantum mechanics cannot stabilize matter against gravitational collapse. This is similar to a result found by Ehrenfest (1917) at the atomic level for Coulomb forces. This makes the dimension D=3 of our Universe very particular with possible implications regarding the anthropic principle. Our study joins a long tradition of scientific and philosophical papers that examined how the dimension of space affects the laws of physics.

Anticipatory Attentional Suppression of Visual Features Indexed by Oscillatory Alpha-Band Power Increases: A High-Density Electrical Mapping Study

PubMed Central

Snyder, Adam C.; Foxe, John J.

2010-01-01

Retinotopically specific increases in alpha-band (~10 Hz) oscillatory power have been strongly implicated in the suppression of processing for irrelevant parts of the visual field during the deployment of visuospatial attention. Here, we asked whether this alpha suppression mechanism also plays a role in the nonspatial anticipatory biasing of feature-based attention. Visual word cues informed subjects what the task-relevant feature of an upcoming visual stimulus (S2) was, while high-density electroencephalographic recordings were acquired. We examined anticipatory oscillatory activity in the Cue-to-S2 interval (~2 s). Subjects were cued on a trial-by-trial basis to attend to either the color or direction of motion of an upcoming dot field array, and to respond when they detected that a subset of the dots differed from the majority along the target feature dimension. We used the features of color and motion, expressly because they have well known, spatially separated cortical processing areas, to distinguish shifts in alpha power over areas processing each feature. Alpha power from dorsal regions increased when motion was the irrelevant feature (i.e., color was cued), and alpha power from ventral regions increased when color was irrelevant. Thus, alpha-suppression mechanisms appear to operate during feature-based selection in much the same manner as has been shown for space-based attention. PMID:20237273

Spectral-Spatial Scale Invariant Feature Transform for Hyperspectral Images.

PubMed

Al-Khafaji, Suhad Lateef; Jun Zhou; Zia, Ali; Liew, Alan Wee-Chung

2018-02-01

Spectral-spatial feature extraction is an important task in hyperspectral image processing. In this paper we propose a novel method to extract distinctive invariant features from hyperspectral images for registration of hyperspectral images with different spectral conditions. Spectral condition means images are captured with different incident lights, viewing angles, or using different hyperspectral cameras. In addition, spectral condition includes images of objects with the same shape but different materials. This method, which is named spectral-spatial scale invariant feature transform (SS-SIFT), explores both spectral and spatial dimensions simultaneously to extract spectral and geometric transformation invariant features. Similar to the classic SIFT algorithm, SS-SIFT consists of keypoint detection and descriptor construction steps. Keypoints are extracted from spectral-spatial scale space and are detected from extrema after 3D difference of Gaussian is applied to the data cube. Two descriptors are proposed for each keypoint by exploring the distribution of spectral-spatial gradient magnitude in its local 3D neighborhood. The effectiveness of the SS-SIFT approach is validated on images collected in different light conditions, different geometric projections, and using two hyperspectral cameras with different spectral wavelength ranges and resolutions. The experimental results show that our method generates robust invariant features for spectral-spatial image matching.

Temperament and Sensory Features of Children with Autism

PubMed Central

Brock, Matthew E.; Freuler, Ashley; Baranek, Grace T.; Watson, Linda R.; Poe, Michele D.; Sabatino, Antoinette

2012-01-01

Purpose This study sought to characterize temperament traits in a sample of children with autism spectrum disorder (ASD), ages 3–7 years old, and to determine the potential association between temperament and sensory features in ASD. Individual differences in sensory processing may form the basis for aspects of temperament and personality, and aberrations in sensory processing may inform why some temperamental traits are characteristic of specific clinical populations. Methods Nine dimensions of temperament from the Behavioral Style Questionnaire (McDevitt & Carey, 1996) were compared among groups of children with ASD (n = 54), developmentally delayed (DD; n = 33), and the original normative sample of typically developing children (Carey & McDevitt, 1978; n = 350) using an ANOVA to determine the extent to which groups differed in their temperament profiles. The hypothesized overlap between three dimensional constructs of sensory features (hyperresponsiveness, hyporesponsivness, and seeking) and the nine dimensions of temperament was analyzed in children with ASD using regression analyses. Results The ASD group displayed temperament scores distinct from norms for typically developing children on most dimensions of temperament (activity, rhythmicity, adaptability, approach, distractibility, intensity, persistence, and threshold) but differed from the DD group on only two dimensions (approach and distractibility). Analyses of associations between sensory constructs and temperament dimensions found that sensory hyporesponsiveness was associated with slowness to adapt, low reactivity, and low distractibility; a combination of increased sensory features (across all three patterns) was associated with increased withdrawal and more negative mood. Conclusions Although most dimensions of temperament distinguished children with ASD as a group, not all dimensions appear equally associated with sensory response patterns. Shared mechanisms underlying sensory responsiveness, temperament, and social withdrawal may be fruitful to explore in future studies. PMID:22366913

Galilei group with multiple central extension, vorticity, and entropy generation: Exotic fluid in 3 +1 dimensions

NASA Astrophysics Data System (ADS)

Das, Praloy; Ghosh, Subir

2017-12-01

A noncommutative extension of an ideal (Hamiltonian) fluid model in 3 +1 dimensions is proposed. The model enjoys several interesting features: it allows a multiparameter central extension in Galilean boost algebra (which is significant being contrary to the existing belief that a similar feature can appear only in 2 +1 -dimensions); noncommutativity generates vorticity in a canonically irrotational fluid; it induces a nonbarotropic pressure leading to a nonisentropic system. (Barotropic fluids are entropy preserving as the pressure depends only on the matter density.) Our fluid model is termed "exotic" since it has a close resemblance with the extensively studied planar (2 +1 dimensions) exotic models and exotic (noncommutative) field theories.

Anderson localization in sigma models

NASA Astrophysics Data System (ADS)

Bruckmann, Falk; Wellnhofer, Jacob

2018-03-01

In QCD above the chiral restoration temperature there exists an Anderson transition in the fermion spectrum from localized to delocalized modes. We investigate whether the same holds for nonlinear sigma models which share properties like dynamical mass generation and asymptotic freedom with QCD. In particular we study the spectra of fermions coupled to (quenched) CP(N-1) configurations at high temperatures. We compare results in two and three space-time dimensions: in two dimensions the Anderson transition is absent, since all fermion modes are localized, while in three dimensions it is present. Our measurements include a more recent observable characterizing level spacings: the distribution of ratios of consecutive level spacings.

Robust continuous clustering

PubMed Central

Shah, Sohil Atul

2017-01-01

Clustering is a fundamental procedure in the analysis of scientific data. It is used ubiquitously across the sciences. Despite decades of research, existing clustering algorithms have limited effectiveness in high dimensions and often require tuning parameters for different domains and datasets. We present a clustering algorithm that achieves high accuracy across multiple domains and scales efficiently to high dimensions and large datasets. The presented algorithm optimizes a smooth continuous objective, which is based on robust statistics and allows heavily mixed clusters to be untangled. The continuous nature of the objective also allows clustering to be integrated as a module in end-to-end feature learning pipelines. We demonstrate this by extending the algorithm to perform joint clustering and dimensionality reduction by efficiently optimizing a continuous global objective. The presented approach is evaluated on large datasets of faces, hand-written digits, objects, newswire articles, sensor readings from the Space Shuttle, and protein expression levels. Our method achieves high accuracy across all datasets, outperforming the best prior algorithm by a factor of 3 in average rank. PMID:28851838

Texture, microstructure, and fractal features of the low-cycle fatigue failure of the metal in pipeline welded joints

NASA Astrophysics Data System (ADS)

Usov, V. V.; Gopkalo, E. E.; Shkatulyak, N. M.; Gopkalo, A. P.; Cherneva, T. S.

2015-09-01

Crystallographic texture and fracture features are studied after low-cycle fatigue tests of laboratory specimens cut from the base metal and the characteristic zones of a welded joint in a pipeline after its longterm operation. The fractal dimensions of fracture surfaces are determined. The fractal dimension is shown to increase during the transition from ductile to quasi-brittle fracture, and a relation between the fractal dimension of a fracture surface and the fatigue life of the specimen is found.

Assessment of Cabin Dimensions to Accommodate Infantry Soldiers for the Future Vertical Lift/Joint Multi-Role Medium-Class Aircraft

DTIC Science & Technology

2014-07-01

an official Department of the Army position unless so designated by other authorized documents. Citation of manufacturer’s or trade names does...The dimensions assessed included seat space widths, cabin ceiling heights, aisle widths, seating configurations, and cabin door widths. Emergency... seat spacing, 66-in. cabin ceiling height, 72-in. floor width, and 32-in. door width. These dimensions will help ensure that Soldiers have adequate

How the Human Brain Represents Perceived Dangerousness or “Predacity” of Animals

PubMed Central

Sha, Long; Guntupalli, J. Swaroop; Oosterhof, Nikolaas; Halchenko, Yaroslav O.; Nastase, Samuel A.; di Oleggio Castello, Matteo Visconti; Abdi, Hervé; Jobst, Barbara C.; Gobbini, M. Ida; Haxby, James V.

2016-01-01

Common or folk knowledge about animals is dominated by three dimensions: (1) level of cognitive complexity or “animacy;” (2) dangerousness or “predacity;” and (3) size. We investigated the neural basis of the perceived dangerousness or aggressiveness of animals, which we refer to more generally as “perception of threat.” Using functional magnetic resonance imaging (fMRI), we analyzed neural activity evoked by viewing images of animal categories that spanned the dissociable semantic dimensions of threat and taxonomic class. The results reveal a distributed network for perception of threat extending along the right superior temporal sulcus. We compared neural representational spaces with target representational spaces based on behavioral judgments and a computational model of early vision and found a processing pathway in which perceived threat emerges as a dominant dimension: whereas visual features predominate in early visual cortex and taxonomy in lateral occipital and ventral temporal cortices, these dimensions fall away progressively from posterior to anterior temporal cortices, leaving threat as the dominant explanatory variable. Our results suggest that the perception of threat in the human brain is associated with neural structures that underlie perception and cognition of social actions and intentions, suggesting a broader role for these regions than has been thought previously, one that includes the perception of potential threat from agents independent of their biological class. SIGNIFICANCE STATEMENT For centuries, philosophers have wondered how the human mind organizes the world into meaningful categories and concepts. Today this question is at the core of cognitive science, but our focus has shifted to understanding how knowledge manifests in dynamic activity of neural systems in the human brain. This study advances the young field of empirical neuroepistemology by characterizing the neural systems engaged by an important dimension in our cognitive representation of the animal kingdom ontological subdomain: how the brain represents the perceived threat, dangerousness, or “predacity” of animals. Our findings reveal how activity for domain-specific knowledge of animals overlaps the social perception networks of the brain, suggesting domain-general mechanisms underlying the representation of conspecifics and other animals. PMID:27170133

Radiation beam collimation system and method

DOEpatents

Schmidt, Oliver A.; Ramanathan, Mohan

2015-08-18

The invention provides a method for collimating a radiation beam, the method comprising subjecting the beam to a collimator that yaws and pitches, either separately or simultaneously relative to the incident angle of the beam. Also provided is a system for collimating radiation beams, the system comprising a collimator body, and a stage for pitching and yawing the body. A feature of the invention is that a single, compact mask body defines one or a plurality of collimators having no moving surfaces relative to each other, whereby the entire mask body is moved about a point in space to provide various collimator opening dimensions to oncoming radiation beams.

Continuously reinforced concrete pavement inventory

NASA Astrophysics Data System (ADS)

Halverson, A. D.; Hagen, M. G.

1982-09-01

A typical concrete pavement has expansion and contraction joints across and along the pavement surface. The joints allow the pavement to change in dimension with changes in temperature. A continuously reinforced concrete pavement (CRCP) does not have expansion or contraction joints. Random, closely spaced cracks are expected to develop naturally and allow for expansion and contraction due to temperature changes. The many random cracks eliminate expensive joint maintenance. This maintenance-free service life feature has not occurred in Minnesota. This CRCP inventory is a physical evaluation of the extent of corrosion on random sections of pavement. It is related to concurrent efforts which will evaluate CRCP rehabilitation techniques.

Invariant resolutions for several Fueter operators

NASA Astrophysics Data System (ADS)

Colombo, Fabrizio; Souček, Vladimir; Struppa, Daniele C.

2006-07-01

A proper generalization of complex function theory to higher dimension is Clifford analysis and an analogue of holomorphic functions of several complex variables were recently described as the space of solutions of several Dirac equations. The four-dimensional case has special features and is closely connected to functions of quaternionic variables. In this paper we present an approach to the Dolbeault sequence for several quaternionic variables based on symmetries and representation theory. In particular we prove that the resolution of the Cauchy-Fueter system obtained algebraically, via Gröbner bases techniques, is equivalent to the one obtained by R.J. Baston (J. Geom. Phys. 1992).

Roughness and uniformity improvements on self-aligned quadruple patterning technique for 10nm node and beyond by wafer stress engineering

NASA Astrophysics Data System (ADS)

Liu, Eric; Ko, Akiteru; O'Meara, David; Mohanty, Nihar; Franke, Elliott; Pillai, Karthik; Biolsi, Peter

2017-05-01

Dimension shrinkage has been a major driving force in the development of integrated circuit processing over a number of decades. The Self-Aligned Quadruple Patterning (SAQP) technique is widely adapted for sub-10nm node in order to achieve the desired feature dimensions. This technique provides theoretical feasibility of multiple pitch-halving from 193nm immersion lithography by using various pattern transferring steps. The major concept of this approach is to a create spacer defined self-aligned pattern by using single lithography print. By repeating the process steps, double, quadruple, or octuple are possible to be achieved theoretically. In these small architectures, line roughness control becomes extremely important since it may contribute to a significant portion of process and device performance variations. In addition, the complexity of SAQP in terms of processing flow makes the roughness improvement indirective and ineffective. It is necessary to discover a new approach in order to improve the roughness in the current SAQP technique. In this presentation, we demonstrate a novel method to improve line roughness performances on 30nm pitch SAQP flow. We discover that the line roughness performance is strongly related to stress management. By selecting different stress level of film to be deposited onto the substrate, we can manipulate the roughness performance in line and space patterns. In addition, the impact of curvature change by applied film stress to SAQP line roughness performance is also studied. No significant correlation is found between wafer curvature and line roughness performance. We will discuss in details the step-by-step physical performances for each processing step in terms of critical dimension (CD)/ critical dimension uniformity (CDU)/line width roughness (LWR)/line edge roughness (LER). Finally, we summarize the process needed to reach the full wafer performance targets of LWR/LER in 1.07nm/1.13nm on 30nm pitch line and space pattern.

Electronic Transport and Possible Superconductivity at Van Hove Singularities in Carbon Nanotubes.

PubMed

Yang, Y; Fedorov, G; Shafranjuk, S E; Klapwijk, T M; Cooper, B K; Lewis, R M; Lobb, C J; Barbara, P

2015-12-09

Van Hove singularities (VHSs) are a hallmark of reduced dimensionality, leading to a divergent density of states in one and two dimensions and predictions of new electronic properties when the Fermi energy is close to these divergences. In carbon nanotubes, VHSs mark the onset of new subbands. They are elusive in standard electronic transport characterization measurements because they do not typically appear as notable features and therefore their effect on the nanotube conductance is largely unexplored. Here we report conductance measurements of carbon nanotubes where VHSs are clearly revealed by interference patterns of the electronic wave functions, showing both a sharp increase of quantum capacitance, and a sharp reduction of energy level spacing, consistent with an upsurge of density of states. At VHSs, we also measure an anomalous increase of conductance below a temperature of about 30 K. We argue that this transport feature is consistent with the formation of Cooper pairs in the nanotube.

Coil geometry effects on scanning single-coil magnetic induction tomography

NASA Astrophysics Data System (ADS)

Feldkamp, Joe R.; Quirk, Stephen

2017-09-01

Alternative coil designs for single coil magnetic induction tomography are considered in this work, with the intention of improving upon the standard design used previously. In particular, we note that the blind spot associated with this coil type, a portion of space along its axis where eddy current generation can be very weak, has an important effect on performance. The seven designs tested here vary considerably in the size of their blind spot. To provide the most discerning test possible, we use laboratory phantoms containing feature dimensions similar to blind spot size. Furthermore, conductivity contrasts are set higher than what would occur naturally in biological systems, which has the effect of weakening eddy current generation at coil locations that straddle the border between high and low conductivity features. Image reconstruction results for the various coils show that coils with smaller blind spots give markedly better performance, though improvements in signal-to-noise ratio could alter that conclusion.

Fukunaga-Koontz transform based dimensionality reduction for hyperspectral imagery

NASA Astrophysics Data System (ADS)

Ochilov, S.; Alam, M. S.; Bal, A.

2006-05-01

Fukunaga-Koontz Transform based technique offers some attractive properties for desired class oriented dimensionality reduction in hyperspectral imagery. In FKT, feature selection is performed by transforming into a new space where feature classes have complimentary eigenvectors. Dimensionality reduction technique based on these complimentary eigenvector analysis can be described under two classes, desired class and background clutter, such that each basis function best represent one class while carrying the least amount of information from the second class. By selecting a few eigenvectors which are most relevant to desired class, one can reduce the dimension of hyperspectral cube. Since the FKT based technique reduces data size, it provides significant advantages for near real time detection applications in hyperspectral imagery. Furthermore, the eigenvector selection approach significantly reduces computation burden via the dimensionality reduction processes. The performance of the proposed dimensionality reduction algorithm has been tested using real-world hyperspectral dataset.

Streaks, multiplets, and holes: High-resolution spatio-temporal behavior of Parkfield seismicity

USGS Publications Warehouse

Waldhauser, F.; Ellsworth, W.L.; Schaff, D.P.; Cole, A.

2004-01-01

Double-difference locations of ???8000 earthquakes from 1969-2002 on the Parkfield section of the San Andreas Fault reveal detailed fault structures and seismicity that is, although complex, highly organized in both space and time. Distinctive features of the seismicity include: 1) multiple recurrence of earthquakes of the same size at precisely the same location on the fault (multiplets), implying frictional or geometric controls on their location and size; 2) sub-horizontal alignments of hypocenters along the fault plane (streaks), suggestive of rheological transitions within the fault zone and/or stress concentrations between locked and creeping areas; 3) regions devoid of microearthquakes with typical dimensions of 1-5 km (holes), one of which contains the M6 1966 Parkfield earthquake hypocenter. These features represent long lived structures that persist through many cycles of individual event. Copyright 2004 by the American Geophysical Union.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI.

PubMed

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-10-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e. , internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature.

A Hybrid of Deep Network and Hidden Markov Model for MCI Identification with Resting-State fMRI

PubMed Central

Suk, Heung-Il; Lee, Seong-Whan; Shen, Dinggang

2015-01-01

In this paper, we propose a novel method for modelling functional dynamics in resting-state fMRI (rs-fMRI) for Mild Cognitive Impairment (MCI) identification. Specifically, we devise a hybrid architecture by combining Deep Auto-Encoder (DAE) and Hidden Markov Model (HMM). The roles of DAE and HMM are, respectively, to discover hierarchical non-linear relations among features, by which we transform the original features into a lower dimension space, and to model dynamic characteristics inherent in rs-fMRI, i.e., internal state changes. By building a generative model with HMMs for each class individually, we estimate the data likelihood of a test subject as MCI or normal healthy control, based on which we identify the clinical label. In our experiments, we achieved the maximal accuracy of 81.08% with the proposed method, outperforming state-of-the-art methods in the literature. PMID:27054199

High-order local maximum principle preserving (MPP) discontinuous Galerkin finite element method for the transport equation

NASA Astrophysics Data System (ADS)

Anderson, R.; Dobrev, V.; Kolev, Tz.; Kuzmin, D.; Quezada de Luna, M.; Rieben, R.; Tomov, V.

2017-04-01

In this work we present a FCT-like Maximum-Principle Preserving (MPP) method to solve the transport equation. We use high-order polynomial spaces; in particular, we consider up to 5th order spaces in two and three dimensions and 23rd order spaces in one dimension. The method combines the concepts of positive basis functions for discontinuous Galerkin finite element spatial discretization, locally defined solution bounds, element-based flux correction, and non-linear local mass redistribution. We consider a simple 1D problem with non-smooth initial data to explain and understand the behavior of different parts of the method. Convergence tests in space indicate that high-order accuracy is achieved. Numerical results from several benchmarks in two and three dimensions are also reported.

Substrate Topography Induces a Crossover from 2D to 3D Behavior in Fibroblast Migration

PubMed Central

Ghibaudo, Marion; Trichet, Léa; Le Digabel, Jimmy; Richert, Alain; Hersen, Pascal; Ladoux, Benoît

2009-01-01

Abstract In a three-dimensional environment, cells migrate through complex topographical features. Using microstructured substrates, we investigate the role of substrate topography in cell adhesion and migration. To do so, fibroblasts are plated on chemically identical substrates composed of microfabricated pillars. When the dimensions of the pillars (i.e., the diameter, length, and spacing) are varied, migrating cells encounter alternating flat and rough surfaces that depend on the spacing between the pillars. Consequently, we show that substrate topography affects cell shape and migration by modifying cell-to-substrate interactions. Cells on micropillar substrates exhibit more elongated and branched shapes with fewer actin stress fibers compared with cells on flat surfaces. By analyzing the migration paths in various environments, we observe different mechanisms of cell migration, including a persistent type of migration, that depend on the organization of the topographical features. These responses can be attributed to a spatial reorganization of the actin cytoskeleton due to physical constraints and a preferential formation of focal adhesions on the micropillars, with an increased lifetime compared to that observed on flat surfaces. By changing myosin II activity, we show that actomyosin contractility is essential in the cellular response to micron-scale topographic signals. Finally, the analysis of cell movements at the frontier between flat and micropillar substrates shows that cell transmigration through the micropillar substrates depends on the spacing between the pillars. PMID:19580774

Convergence of an hp-Adaptive Finite Element Strategy in Two and Three Space-Dimensions

NASA Astrophysics Data System (ADS)

Bürg, Markus; Dörfler, Willy

2010-09-01

We show convergence of an automatic hp-adaptive refinement strategy for the finite element method on the elliptic boundary value problem. The strategy is a generalization of a refinement strategy proposed for one-dimensional situations to problems in two and three space-dimensions.

The isentropic quantum drift-diffusion model in two or three space dimensions

NASA Astrophysics Data System (ADS)

Chen, Xiuqing

2009-05-01

We investigate the isentropic quantum drift-diffusion model, a fourth order parabolic system, in space dimensions d = 2, 3. First, we establish the global weak solutions with large initial value and periodic boundary conditions. Then we show the semiclassical limit by delicate interpolation estimates and compactness argument.

Advancing X-ray scattering metrology using inverse genetic algorithms.

PubMed

Hannon, Adam F; Sunday, Daniel F; Windover, Donald; Kline, R Joseph

2016-01-01

We compare the speed and effectiveness of two genetic optimization algorithms to the results of statistical sampling via a Markov chain Monte Carlo algorithm to find which is the most robust method for determining real space structure in periodic gratings measured using critical dimension small angle X-ray scattering. Both a covariance matrix adaptation evolutionary strategy and differential evolution algorithm are implemented and compared using various objective functions. The algorithms and objective functions are used to minimize differences between diffraction simulations and measured diffraction data. These simulations are parameterized with an electron density model known to roughly correspond to the real space structure of our nanogratings. The study shows that for X-ray scattering data, the covariance matrix adaptation coupled with a mean-absolute error log objective function is the most efficient combination of algorithm and goodness of fit criterion for finding structures with little foreknowledge about the underlying fine scale structure features of the nanograting.

Mesial temporal lobe epilepsy lateralization using SPHARM-based features of hippocampus and SVM

NASA Astrophysics Data System (ADS)

Esmaeilzadeh, Mohammad; Soltanian-Zadeh, Hamid; Jafari-Khouzani, Kourosh

2012-02-01

This paper improves the Lateralization (identification of the epileptogenic hippocampus) accuracy in Mesial Temporal Lobe Epilepsy (mTLE). In patients with this kind of epilepsy, usually one of the brain's hippocampi is the focus of the epileptic seizures, and resection of the seizure focus is the ultimate treatment to control or reduce the seizures. Moreover, the epileptogenic hippocampus is prone to shrinkage and deformation; therefore, shape analysis of the hippocampus is advantageous in the preoperative assessment for the Lateralization. The method utilized for shape analysis is the Spherical Harmonics (SPHARM). In this method, the shape of interest is decomposed using a set of bases functions and the obtained coefficients of expansion are the features describing the shape. To perform shape comparison and analysis, some pre- and post-processing steps such as "alignment of different subjects' hippocampi" and the "reduction of feature-space dimension" are required. To this end, first order ellipsoid is used for alignment. For dimension reduction, we propose to keep only the SPHARM coefficients with maximum conformity to the hippocampus shape. Then, using these coefficients of normal and epileptic subjects along with 3D invariants, specific lateralization indices are proposed. Consequently, the 1536 SPHARM coefficients of each subject are summarized into 3 indices, where for each index the negative (positive) value shows that the left (right) hippocampus is deformed (diseased). Employing these indices, the best achieved lateralization accuracy for clustering and classification algorithms are 85% and 92%, respectively. This is a significant improvement compared to the conventional volumetric method.

Sequential Modulations in a Combined Horizontal and Vertical Simon Task: Is There ERP Evidence for Feature Integration Effects?

PubMed Central

Hoppe, Katharina; Küper, Kristina; Wascher, Edmund

2017-01-01

In the Simon task, participants respond faster when the task-irrelevant stimulus position and the response position are corresponding, for example on the same side, compared to when they have a non-corresponding relation. Interestingly, this Simon effect is reduced after non-corresponding trials. Such sequential effects can be explained in terms of a more focused processing of the relevant stimulus dimension due to increased cognitive control, which transfers from the previous non-corresponding trial (conflict adaptation effects). Alternatively, sequential modulations of the Simon effect can also be due to the degree of trial-to-trial repetitions and alternations of task features, which is confounded with the correspondence sequence (feature integration effects). In the present study, we used a spatially two-dimensional Simon task with vertical response keys to examine the contribution of adaptive cognitive control and feature integration processes to the sequential modulation of the Simon effect. The two-dimensional Simon task creates correspondences in the vertical as well as in the horizontal dimension. A trial-by-trial alternation of the spatial dimension, for example from a vertical to a horizontal stimulus presentation, generates a subset containing no complete repetitions of task features, but only complete alternations and partial repetitions, which are equally distributed over all correspondence sequences. In line with the assumed feature integration effects, we found sequential modulations of the Simon effect only when the spatial dimension repeated. At least for the horizontal dimension, this pattern was confirmed by the parietal P3b, an event-related potential that is assumed to reflect stimulus–response link processes. Contrary to conflict adaptation effects, cognitive control, measured by the fronto-central N2 component of the EEG, was not sequentially modulated. Overall, our data provide behavioral as well as electrophysiological evidence for feature integration effects contributing to sequential modulations of the Simon effect. PMID:28713305

Sequential Modulations in a Combined Horizontal and Vertical Simon Task: Is There ERP Evidence for Feature Integration Effects?

PubMed

Hoppe, Katharina; Küper, Kristina; Wascher, Edmund

2017-01-01

In the Simon task, participants respond faster when the task-irrelevant stimulus position and the response position are corresponding, for example on the same side, compared to when they have a non-corresponding relation. Interestingly, this Simon effect is reduced after non-corresponding trials. Such sequential effects can be explained in terms of a more focused processing of the relevant stimulus dimension due to increased cognitive control, which transfers from the previous non-corresponding trial (conflict adaptation effects). Alternatively, sequential modulations of the Simon effect can also be due to the degree of trial-to-trial repetitions and alternations of task features, which is confounded with the correspondence sequence (feature integration effects). In the present study, we used a spatially two-dimensional Simon task with vertical response keys to examine the contribution of adaptive cognitive control and feature integration processes to the sequential modulation of the Simon effect. The two-dimensional Simon task creates correspondences in the vertical as well as in the horizontal dimension. A trial-by-trial alternation of the spatial dimension, for example from a vertical to a horizontal stimulus presentation, generates a subset containing no complete repetitions of task features, but only complete alternations and partial repetitions, which are equally distributed over all correspondence sequences. In line with the assumed feature integration effects, we found sequential modulations of the Simon effect only when the spatial dimension repeated. At least for the horizontal dimension, this pattern was confirmed by the parietal P3b, an event-related potential that is assumed to reflect stimulus-response link processes. Contrary to conflict adaptation effects, cognitive control, measured by the fronto-central N2 component of the EEG, was not sequentially modulated. Overall, our data provide behavioral as well as electrophysiological evidence for feature integration effects contributing to sequential modulations of the Simon effect.

Discriminability and dimensionality effects in visual search for featural conjunctions: a functional pop-out.

PubMed

Dehaene, S

1989-07-01

Treisman and Gelade's (1980) feature-integration theory of attention states that a scene must be serially scanned before the objects in it can be accurately perceived. Is serial scanning compatible with the speed observed in the perception of real-world scenes? Most real scenes consist of many more dimensions (color, size, shape, depth, etc.) than those generally found in search paradigms. Furthermore, real objects differ from each other along many of these dimensions. The present experiment assessed the influence of the total number of dimensions and target/distractor discriminability (the number of dimensions that suffice to separate a target from distractors) on search times for a conjunction of features. Search was always found to be serial. However, for the most discriminable targets, search rate was so fast that search times were in the same range as pop-out detection times. Apparently, greater discriminability enables subjects to direct attention at a faster rate and at only a fraction of the items in a scene.

Predict subcellular locations of singleplex and multiplex proteins by semi-supervised learning and dimension-reducing general mode of Chou's PseAAC.

PubMed

Pacharawongsakda, Eakasit; Theeramunkong, Thanaruk

2013-12-01

Predicting protein subcellular location is one of major challenges in Bioinformatics area since such knowledge helps us understand protein functions and enables us to select the targeted proteins during drug discovery process. While many computational techniques have been proposed to improve predictive performance for protein subcellular location, they have several shortcomings. In this work, we propose a method to solve three main issues in such techniques; i) manipulation of multiplex proteins which may exist or move between multiple cellular compartments, ii) handling of high dimensionality in input and output spaces and iii) requirement of sufficient labeled data for model training. Towards these issues, this work presents a new computational method for predicting proteins which have either single or multiple locations. The proposed technique, namely iFLAST-CORE, incorporates the dimensionality reduction in the feature and label spaces with co-training paradigm for semi-supervised multi-label classification. For this purpose, the Singular Value Decomposition (SVD) is applied to transform the high-dimensional feature space and label space into the lower-dimensional spaces. After that, due to limitation of labeled data, the co-training regression makes use of unlabeled data by predicting the target values in the lower-dimensional spaces of unlabeled data. In the last step, the component of SVD is used to project labels in the lower-dimensional space back to those in the original space and an adaptive threshold is used to map a numeric value to a binary value for label determination. A set of experiments on viral proteins and gram-negative bacterial proteins evidence that our proposed method improve the classification performance in terms of various evaluation metrics such as Aiming (or Precision), Coverage (or Recall) and macro F-measure, compared to the traditional method that uses only labeled data.

Evaluation of carbon nanotube probes in critical dimension atomic force microscopes.

PubMed

Choi, Jinho; Park, Byong Chon; Ahn, Sang Jung; Kim, Dal-Hyun; Lyou, Joon; Dixson, Ronald G; Orji, Ndubuisi G; Fu, Joseph; Vorburger, Theodore V

2016-07-01

The decreasing size of semiconductor features and the increasing structural complexity of advanced devices have placed continuously greater demands on manufacturing metrology, arising both from the measurement challenges of smaller feature sizes and the growing requirement to characterize structures in more than just a single critical dimension. For scanning electron microscopy, this has resulted in increasing sophistication of imaging models. For critical dimension atomic force microscopes (CD-AFMs), this has resulted in the need for smaller and more complex tips. Carbon nanotube (CNT) tips have thus been the focus of much interest and effort by a number of researchers. However, there have been significant issues surrounding both the manufacture and use of CNT tips. Specifically, the growth or attachment of CNTs to AFM cantilevers has been a challenge to the fabrication of CNT tips, and the flexibility and resultant bending artifacts have presented challenges to using CNT tips. The Korea Research Institute for Standards and Science (KRISS) has invested considerable effort in the controlled fabrication of CNT tips and is collaborating with the National Institute of Standards and Technology on the application of CNT tips for CD-AFM. Progress by KRISS on the precise control of CNT orientation, length, and end modification, using manipulation and focused ion beam processes, has allowed us to implement ball-capped CNT tips and bent CNT tips for CD-AFM. Using two different generations of CD-AFM instruments, we have evaluated these tip types by imaging a line/space grating and a programmed line edge roughness specimen. We concluded that these CNTs are capable of scanning the profiles of these structures, including re-entrant sidewalls, but there remain important challenges to address. These challenges include tighter control of tip geometry and careful optimization of scan parameters and algorithms for using CNT tips.

An Efficient Algorithm for Server Thermal Fault Diagnosis Based on Infrared Image

NASA Astrophysics Data System (ADS)

Liu, Hang; Xie, Ting; Ran, Jian; Gao, Shan

2017-10-01

It is essential for a data center to maintain server security and stability. Long-time overload operation or high room temperature may cause service disruption even a server crash, which would result in great economic loss for business. Currently, the methods to avoid server outages are monitoring and forecasting. Thermal camera can provide fine texture information for monitoring and intelligent thermal management in large data center. This paper presents an efficient method for server thermal fault monitoring and diagnosis based on infrared image. Initially thermal distribution of server is standardized and the interest regions of the image are segmented manually. Then the texture feature, Hu moments feature as well as modified entropy feature are extracted from the segmented regions. These characteristics are applied to analyze and classify thermal faults, and then make efficient energy-saving thermal management decisions such as job migration. For the larger feature space, the principal component analysis is employed to reduce the feature dimensions, and guarantee high processing speed without losing the fault feature information. Finally, different feature vectors are taken as input for SVM training, and do the thermal fault diagnosis after getting the optimized SVM classifier. This method supports suggestions for optimizing data center management, it can improve air conditioning efficiency and reduce the energy consumption of the data center. The experimental results show that the maximum detection accuracy is 81.5%.

The Effect of Furnishing on Perceived Spatial Dimensions and Spaciousness of Interior Space

PubMed Central

von Castell, Christoph; Oberfeld, Daniel; Hecht, Heiko

2014-01-01

Despite the ubiquity of interior space design, there is virtually no scientific research on the influence of furnishing on the perception of interior space. We conducted two experiments in which observers were asked to estimate the spatial dimensions (size of the room dimensions in meters and centimeters) and to judge subjective spaciousness of various rooms. Experiment 1 used true-to-scale model rooms with a square surface area. Furnishing affected both the perceived height and the spaciousness judgments. The furnished room was perceived as higher but less spacious. In Experiment 2, rooms with different square surface areas and constant physical height were presented in virtual reality. Furnishing affected neither the perceived spatial dimensions nor the perceived spaciousness. Possible reasons for this discrepancy, such as the influence of the presentation medium, are discussed. Moreover, our results suggest a compression of perceived height and depth with decreasing surface area of the room. PMID:25409456

The effect of furnishing on perceived spatial dimensions and spaciousness of interior space.

PubMed

von Castell, Christoph; Oberfeld, Daniel; Hecht, Heiko

2014-01-01

Despite the ubiquity of interior space design, there is virtually no scientific research on the influence of furnishing on the perception of interior space. We conducted two experiments in which observers were asked to estimate the spatial dimensions (size of the room dimensions in meters and centimeters) and to judge subjective spaciousness of various rooms. Experiment 1 used true-to-scale model rooms with a square surface area. Furnishing affected both the perceived height and the spaciousness judgments. The furnished room was perceived as higher but less spacious. In Experiment 2, rooms with different square surface areas and constant physical height were presented in virtual reality. Furnishing affected neither the perceived spatial dimensions nor the perceived spaciousness. Possible reasons for this discrepancy, such as the influence of the presentation medium, are discussed. Moreover, our results suggest a compression of perceived height and depth with decreasing surface area of the room.

Asymptotic behavior for systems of nonlinear wave equations with multiple propagation speeds in three space dimensions

NASA Astrophysics Data System (ADS)

Katayama, Soichiro

We consider the Cauchy problem for systems of nonlinear wave equations with multiple propagation speeds in three space dimensions. Under the null condition for such systems, the global existence of small amplitude solutions is known. In this paper, we will show that the global solution is asymptotically free in the energy sense, by obtaining the asymptotic pointwise behavior of the derivatives of the solution. Nonetheless we can also show that the pointwise behavior of the solution itself may be quite different from that of the free solution. In connection with the above results, a theorem is also developed to characterize asymptotically free solutions for wave equations in arbitrary space dimensions.

Modified Saez–Ballester scalar–tensor theory from 5D space-time

NASA Astrophysics Data System (ADS)

Rasouli, S. M. M.; Vargas Moniz, Paulo

2018-01-01

In this paper, we bring together the five-dimensional Saez–Ballester (SB) scalar–tensor theory (Saez and Ballester 1986 Phys. Lett. 113A 9) and the induced-matter-theory (IMT) setting (Wesson and Ponce de Leon 1992  J. Math. Phys. 33 3883), to obtain a modified SB theory (MSBT) in four dimensions. Specifically, by using an intrinsic dimensional reduction procedure into the SB field equations in five-dimensions, a MSBT is obtained onto a hypersurface orthogonal to the extra dimension. This four-dimensional MSBT is shown to bear distinctive new features in contrast to the usual corresponding SB theory as well as to IMT and the modified Brans–Dicke theory (MBDT) (Rasouli et al 2014 Class. Quantum Grav. 31 115002). In more detail, besides the usual induced matter terms retrieved through the IMT, the MSBT scalar field is provided with additional physically distinct (namely, SB induced) terms as well as an intrinsic self-interacting potential (interpreted as a consequence of the IMT process and the concrete geometry associated with the extra dimension). Moreover, our MSBT has four sets of field equations, with two sets having no analog in the standard SB scalar–tensor theory. It should be emphasized that the herein appealing solutions can emerge solely from the geometrical reductional process, from the presence also of extra dimension(s) and not from any ad-hoc matter either in the bulk or on the hypersurface. Subsequently, we apply the herein MSBT to cosmology and consider an extended spatially flat FLRW geometry in a five-dimensional vacuum space-time. After obtaining the exact solutions in the bulk, we proceed to construct, by means of the MSBT setting, the corresponding dynamic, on the four-dimensional hypersurface. More precisely, we obtain the (SB) components of the induced matter, including the induced scalar potential terms. We retrieve two different classes of solutions. Concerning the first class, we show that the MSBT yields a barotropic equation of state for the induced perfect fluid. We then investigate vacuum, dust, radiation, stiff fluid and false vacuum cosmologies for this scenario and contrast the results with those obtained in the standard SB theory, IMT and BD theory. Regarding the second class solutions, we show that the scale factor behaves in a similar way to a de Sitter (DeS) model. However, in our MSBT setting, this behavior is assisted by non-vanishing induced matter instead, without any a priori cosmological constant. Moreover, for all these solutions, we show that the extra dimension contracts with the cosmic time.

Crane cabins' interior space multivariate anthropometric modeling.

PubMed

Essdai, Ahmed; Spasojević Brkić, Vesna K; Golubović, Tamara; Brkić, Aleksandar; Popović, Vladimir

2018-01-01

Previous research has shown that today's crane cabins fail to meet the needs of a large proportion of operators. Performance and financial losses and effects on safety should not be overlooked as well. The first aim of this survey is to model the crane cabin interior space using up-to-date crane operator anthropometric data and to compare the multivariate and univariate method anthropometric models. The second aim of the paper is to define the crane cabin interior space dimensions that enable anthropometric convenience. To facilitate the cabin design, the anthropometric dimensions of 64 crane operators in the first sample and 19 more in the second sample were collected in Serbia. The multivariate anthropometric models, spanning 95% of the population on the basis of a set of 8 anthropometric dimensions, have been developed. The percentile method was also used on the same set of data. The dimensions of the interior space, necessary for the accommodation of the crane operator, are 1174×1080×1865 mm. The percentiles results for the 5th and 95th model are within the obtained dimensions. The results of this study may prove useful to crane cabin designers in eliminating anthropometric inconsistencies and improving the health of operators, but can also aid in improving the safety, performance and financial results of the companies where crane cabins operate.

Algorithms for Brownian first-passage-time estimation

NASA Astrophysics Data System (ADS)

Adib, Artur B.

2009-09-01

A class of algorithms in discrete space and continuous time for Brownian first-passage-time estimation is considered. A simple algorithm is derived that yields exact mean first-passage times (MFPTs) for linear potentials in one dimension, regardless of the lattice spacing. When applied to nonlinear potentials and/or higher spatial dimensions, numerical evidence suggests that this algorithm yields MFPT estimates that either outperform or rival Langevin-based (discrete time and continuous space) estimates.

Standpoints: Researching and Teaching English in the Digital Dimension

ERIC Educational Resources Information Center

Kirkland, David E.

2009-01-01

David E. Kirkland argues that our understanding of literate practice in relation to space needs to be radically reworked to account for new digital dimensions that are dispersed, discontinuous, and yet deeply woven into everyday and institutional worlds. His account highlights the way these digital spaces pepper the official landscape of…

Dissociations and Interactions between Time, Numerosity and Space Processing

ERIC Educational Resources Information Center

Cappelletti, Marinella; Freeman, Elliot D.; Cipolotti, Lisa

2009-01-01

This study investigated time, numerosity and space processing in a patient (CB) with a right hemisphere lesion. We tested whether these magnitude dimensions share a common magnitude system or whether they are processed by dimension-specific magnitude systems. Five experimental tasks were used: Tasks 1-3 assessed time and numerosity independently…

Diagonalizing the Hamiltonian of λϕ4 theory in 2 space-time dimensions

NASA Astrophysics Data System (ADS)

Christensen, Neil

2018-01-01

We propose a new non-perturbative technique for calculating the scattering amplitudes of field-theory directly from the eigenstates of the Hamiltonian. Our method involves a discretized momentum space and a momentum cutoff, thereby truncating the Hilbert space and making numerical diagonalization of the Hamiltonian achievable. We show how to do this in the context of a simplified λϕ4 theory in two space-time dimensions. We present the results of our diagonalization, its dependence on time, its dependence on the parameters of the theory and its renormalization.

Unbounded Violations of Bipartite Bell Inequalities via Operator Space Theory

NASA Astrophysics Data System (ADS)

Junge, M.; Palazuelos, C.; Pérez-García, D.; Villanueva, I.; Wolf, M. M.

2010-12-01

In this work we show that bipartite quantum states with local Hilbert space dimension n can violate a Bell inequality by a factor of order {Ω left(sqrt{n}/log^2n right)} when observables with n possible outcomes are used. A central tool in the analysis is a close relation between this problem and operator space theory and, in particular, the very recent noncommutative L p embedding theory. As a consequence of this result, we obtain better Hilbert space dimension witnesses and quantum violations of Bell inequalities with better resistance to noise.

Earth Observations taken by Expedition 38 crewmember

NASA Image and Video Library

2013-11-16

ISS038-E-005515 (16 Nov. 2013) --- Activity at Kliuchevskoi Volcano on Kamchatka Peninsula in the Russian Federation is featured in this image photographed by an Expedition 38 crew member on the International Space Station. When viewing conditions are favorable, crew members onboard the space station can take unusual and striking images of Earth. This photograph provides a view of an eruption plume emanating from Kliuchevskoi Volcano, one of the many active volcanoes on the Kamchatka Peninsula. Nadir views – looking “straight down”—that are typical of orbital satellite imagery tend to flatten the appearance of the landscape by reducing the sense of three dimensions of the topography. In contrast, this image was taken from the ISS with a very oblique viewing angle that gives a strong sense of three dimensions, which is accentuated by the shadows cast by the volcanic peaks. This resulted in a view similar to what a person might see from a low-altitude airplane. The image was taken when the space station was located over a ground position more than 1,500 kilometers to the southwest. The plume – likely a combination of steam, volcanic gases, and ash – is extended to the east-southeast by prevailing winds; the dark region to the north-northwest of the plume is likely a product of both shadow and ash settling out. Several other volcanoes are visible in the image, including Ushkovsky, Tolbachik, Zimina, and Udina. To the south-southwest of Kliuchevskoi lies Bezymianny Volcano which appears to be emitting a small steam plume (visible at center).

16 CFR 1210.15 - Specifications.

Code of Federal Regulations, 2012 CFR

2012-01-01

... of all dimensions, force requirements, or other features that could affect the child-resistance of the lighter, including the manufacturer's tolerances for each such dimension or force requirement. (3...

16 CFR 1210.15 - Specifications.

Code of Federal Regulations, 2014 CFR

2014-01-01

... of all dimensions, force requirements, or other features that could affect the child-resistance of the lighter, including the manufacturer's tolerances for each such dimension or force requirement. (3...

Fractal analysis of urban environment: land use and sewer system

NASA Astrophysics Data System (ADS)

Gires, A.; Ochoa Rodriguez, S.; Van Assel, J.; Bruni, G.; Murla Tulys, D.; Wang, L.; Pina, R.; Richard, J.; Ichiba, A.; Willems, P.; Tchiguirinskaia, I.; ten Veldhuis, M. C.; Schertzer, D. J. M.

2014-12-01

Land use distribution are usually obtained by automatic processing of satellite and airborne pictures. The complexity of the obtained patterns which are furthermore scale dependent is enhanced in urban environment. This scale dependency is even more visible in a rasterized representation where only a unique class is affected to each pixel. A parameter commonly analysed in urban hydrology is the coefficient of imperviousness, which reflects the proportion of rainfall that will be immediately active in the catchment response. This coefficient is strongly scale dependent with a rasterized representation. This complex behaviour is well grasped with the help of the scale invariant notion of fractal dimension which enables to quantify the space occupied by a geometrical set (here the impervious areas) not only at a single scale but across all scales. This fractal dimension is also compared to the ones computed on the representation of the catchments with the help of operational semi-distributed models. Fractal dimensions of the corresponding sewer systems are also computed and compared with values found in the literature for natural river networks. This methodology is tested on 7 pilot sites of the European NWE Interreg IV RainGain project located in France, Belgium, Netherlands, United-Kingdom and Portugal. Results are compared between all the case study which exhibit different physical features (slope, level of urbanisation, population density...).

The Influence of Urbanism and Information Consumption on Political Dimensions of Social Capital: Exploratory Study of the Localities Adjacent to the Core City from Brașov Metropolitan Area, Romania.

PubMed

Rezeanu, Cătălina-Ionela; Briciu, Arabela; Briciu, Victor; Repanovici, Angela; Coman, Claudiu

2016-01-01

The last two decades have seen a growing trend towards the research of voting behavior in post-communist countries. Urban sociology theorists state that not only space structures influence political participation, but also space structures are changing under the influence of global, local, and individual factors. The growing role played by information in the globalised world has accelerated the paradigm shift in urban sociology: from central place model (based on urban-rural distinction and on monocentric metropolitan areas) to network society (based on space of flows and polycentric metropolitan areas). However, recent studies have mainly focused on countries with solid democracies, rather than on former communist countries. The present study aims to analyze the extent to which a new emerging spatial structure can be envisaged within a metropolitan area of Romania and its consequences for the political dimensions of social capital. The Transilvania University Ethics Commission approved this study (S1 Aprouval). The research is based upon individual and aggregate empirical data, collected from the areas adjacent to the core city in Brașov metropolitan area. Individual data has been collected during October 2012, using the oral survey technique (S1 Survey), based on a standardized questionnaire (stratified simple random sample, N = 600). The National Institute of Statistics and the Electoral Register provided the aggregate data per locality. Unvaried and multivariate analyses (hierarchical regression method) were conducted based on these data. Some dimensions of urbanism, identified as predictors of the political dimensions of social capital, suggest that the area under analysis has a predominantly monocentric character, where the rural-urban distinction continues to remain relevant. There are also arguments favoring the dissolution of the rural-urban distinction and the emergence of polycentric spatial structures. The presence of some influences related to the information consumption on all six indicators of the political dimensions of social capital under analysis suggests the occurrence of emerging forms of a space of flows. The identified effects of social problems associated with transport infrastructure and of migration experience on the political dimensions of social capital, also support the emergence of space of flows. We recommend that, in the urban studies in former communist countries, conceptualization of urbanism as predictor of the political dimensions of social capital should consider both the material dimensions of space, as well as the dimensions of information consumption and migration experience.

The Influence of Urbanism and Information Consumption on Political Dimensions of Social Capital: Exploratory Study of the Localities Adjacent to the Core City from Brașov Metropolitan Area, Romania

PubMed Central

Rezeanu, Cătălina-Ionela; Briciu, Arabela; Briciu, Victor; Repanovici, Angela; Coman, Claudiu

2016-01-01

Background The last two decades have seen a growing trend towards the research of voting behavior in post-communist countries. Urban sociology theorists state that not only space structures influence political participation, but also space structures are changing under the influence of global, local, and individual factors. The growing role played by information in the globalised world has accelerated the paradigm shift in urban sociology: from central place model (based on urban-rural distinction and on monocentric metropolitan areas) to network society (based on space of flows and polycentric metropolitan areas). However, recent studies have mainly focused on countries with solid democracies, rather than on former communist countries. The present study aims to analyze the extent to which a new emerging spatial structure can be envisaged within a metropolitan area of Romania and its consequences for the political dimensions of social capital. Methods The Transilvania University Ethics Commission approved this study (S1 Aprouval). The research is based upon individual and aggregate empirical data, collected from the areas adjacent to the core city in Brașov metropolitan area. Individual data has been collected during October 2012, using the oral survey technique (S1 Survey), based on a standardized questionnaire (stratified simple random sample, N = 600). The National Institute of Statistics and the Electoral Register provided the aggregate data per locality. Unvaried and multivariate analyses (hierarchical regression method) were conducted based on these data. Results Some dimensions of urbanism, identified as predictors of the political dimensions of social capital, suggest that the area under analysis has a predominantly monocentric character, where the rural-urban distinction continues to remain relevant. There are also arguments favoring the dissolution of the rural-urban distinction and the emergence of polycentric spatial structures. The presence of some influences related to the information consumption on all six indicators of the political dimensions of social capital under analysis suggests the occurrence of emerging forms of a space of flows. The identified effects of social problems associated with transport infrastructure and of migration experience on the political dimensions of social capital, also support the emergence of space of flows. Conclusions We recommend that, in the urban studies in former communist countries, conceptualization of urbanism as predictor of the political dimensions of social capital should consider both the material dimensions of space, as well as the dimensions of information consumption and migration experience. PMID:26807882

GridMass: a fast two-dimensional feature detection method for LC/MS.

PubMed

Treviño, Victor; Yañez-Garza, Irma-Luz; Rodriguez-López, Carlos E; Urrea-López, Rafael; Garza-Rodriguez, Maria-Lourdes; Barrera-Saldaña, Hugo-Alberto; Tamez-Peña, José G; Winkler, Robert; Díaz de-la-Garza, Rocío-Isabel

2015-01-01

One of the initial and critical procedures for the analysis of metabolomics data using liquid chromatography and mass spectrometry is feature detection. Feature detection is the process to detect boundaries of the mass surface from raw data. It consists of detected abundances arranged in a two-dimensional (2D) matrix of mass/charge and elution time. MZmine 2 is one of the leading software environments that provide a full analysis pipeline for these data. However, the feature detection algorithms provided in MZmine 2 are based mainly on the analysis of one-dimension at a time. We propose GridMass, an efficient algorithm for 2D feature detection. The algorithm is based on landing probes across the chromatographic space that are moved to find local maxima providing accurate boundary estimations. We tested GridMass on a controlled marker experiment, on plasma samples, on plant fruits, and in a proteome sample. Compared with other algorithms, GridMass is faster and may achieve comparable or better sensitivity and specificity. As a proof of concept, GridMass has been implemented in Java under the MZmine 2 environment and is available at http://www.bioinformatica.mty.itesm.mx/GridMass and MASSyPup. It has also been submitted to the MZmine 2 developing community. Copyright © 2015 John Wiley & Sons, Ltd.

Health sector decentralization and local decision-making: Decision space, institutional capacities and accountability in Pakistan.

PubMed

Bossert, Thomas John; Mitchell, Andrew David

2011-01-01

Health sector decentralization has been widely adopted to improve delivery of health services. While many argue that institutional capacities and mechanisms of accountability required to transform decentralized decision-making into improvements in local health systems are lacking, few empirical studies exist which measure or relate together these concepts. Based on research instruments administered to a sample of 91 health sector decision-makers in 17 districts of Pakistan, this study analyzes relationships between three dimensions of decentralization: decentralized authority (referred to as "decision space"), institutional capacities, and accountability to local officials. Composite quantitative indicators of these three dimensions were constructed within four broad health functions (strategic and operational planning, budgeting, human resources management, and service organization/delivery) and on an overall/cross-function basis. Three main findings emerged. First, district-level respondents report varying degrees of each dimension despite being under a single decentralization regime and facing similar rules across provinces. Second, within dimensions of decentralization-particularly decision space and capacities-synergies exist between levels reported by respondents in one function and those reported in other functions (statistically significant coefficients of correlation ranging from ρ=0.22 to ρ=0.43). Third, synergies exist across dimensions of decentralization, particularly in terms of an overall indicator of institutional capacities (significantly correlated with both overall decision space (ρ=0.39) and accountability (ρ=0.23)). This study demonstrates that decentralization is a varied experience-with some district-level officials making greater use of decision space than others and that those who do so also tend to have more capacity to make decisions and are held more accountable to elected local officials for such choices. These findings suggest that Pakistan's decentralization policy should focus on synergies among dimensions of decentralization to encouraging more use of de jure decision space, work toward more uniform institutional capacity, and encourage greater accountability to local elected officials. Copyright © 2010 Elsevier Ltd. All rights reserved.

Higgs production and decay in models of a warped extra dimension with a bulk Higgs

DOE PAGES

Archer, Paul R.; Carena, Marcela; Carmona, Adrian; ...

2015-01-13

Warped extra-dimension models in which the Higgs boson is allowed to propagate in the bulk of a compact AdS 5 space are conjectured to be dual to models featuring a partially composite Higgs boson. They offer a framework with which to investigate the implications of changing the scaling dimension of the Higgs operator, which can be used to reduce the constraints from electroweak precision data. In the context of such models, we calculate the cross section for Higgs production in gluon fusion and the H → γγ decay rate and show that they are finite (at one-loop order) as amore » consequence of gauge invariance. The extended scalar sector comprising the Kaluza-Klein excitations of the Standard Model scalars is constructed in detail. The largest effects are due to virtual KK fermions, whose contributions to the cross section and decay rate introduce a quadratic sensitivity to the maximum allowed value y * of the random complex entries of the 5D anarchic Yukawa matrices. We find an enhancement of the gluon-fusion cross section and a reduction of the H → γγ rate as well as of the tree-level Higgs couplings to fermions and electroweak gauge bosons. As a result, we perform a detailed study of the correlated signal strengths for different production mechanisms and decay channels as functions of y *, the mass scale of Kaluza-Klein resonances and the scaling dimension of the composite Higgs operator.« less

Local box-counting dimensions of discrete quantum eigenvalue spectra: Analytical connection to quantum spectral statistics

NASA Astrophysics Data System (ADS)

Sakhr, Jamal; Nieminen, John M.

2018-03-01

Two decades ago, Wang and Ong, [Phys. Rev. A 55, 1522 (1997)], 10.1103/PhysRevA.55.1522 hypothesized that the local box-counting dimension of a discrete quantum spectrum should depend exclusively on the nearest-neighbor spacing distribution (NNSD) of the spectrum. In this Rapid Communication, we validate their hypothesis by deriving an explicit formula for the local box-counting dimension of a countably-infinite discrete quantum spectrum. This formula expresses the local box-counting dimension of a spectrum in terms of single and double integrals of the NNSD of the spectrum. As applications, we derive an analytical formula for Poisson spectra and closed-form approximations to the local box-counting dimension for spectra having Gaussian orthogonal ensemble (GOE), Gaussian unitary ensemble (GUE), and Gaussian symplectic ensemble (GSE) spacing statistics. In the Poisson and GOE cases, we compare our theoretical formulas with the published numerical data of Wang and Ong and observe excellent agreement between their data and our theory. We also study numerically the local box-counting dimensions of the Riemann zeta function zeros and the alternate levels of GOE spectra, which are often used as numerical models of spectra possessing GUE and GSE spacing statistics, respectively. In each case, the corresponding theoretical formula is found to accurately describe the numerically computed local box-counting dimension.

Sublimation-Condensation of Multiscale Tellurium Structures

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

Riley, Brian J.; Johnson, Bradley R.; Schaef, Herbert T.

This paper presents a simple technique for making tellurium (Te) nano and microtubes of widely varying dimensions with Multi-Scale Processing (MSP). In this process, the Te metal is placed in a reaction vessel (e.g., borosilicate or fused quartz), the vessel is evacuated, and then sealed under vacuum with a torch. The vessel is heat-treated in a temperature gradient where a portion of the tube that can also contain an additional substrate, is under a decreasing temperature gradient. Scanning and transmission electron microscopies have shown that multifaceted crystalline tubes have been formed extending from nano- up to micron-scale with diameters rangingmore » from 51.2 ± 5.9 to 1042 ± 134 nm between temperatures of 157 and 224 °C, respectively. One-dimensional tubular features are seen at lower temperatures, while three-dimensional features, at the higher temperatures. These features have been characterized with X-ray diffraction and found to be trigonal Te with space group P3121. Our results show that the MSP can adequately be described using a simple Arrhenius equation.« less

Intelligent Control of a Sensor-Actuator System via Kernelized Least-Squares Policy Iteration

PubMed Central

Liu, Bo; Chen, Sanfeng; Li, Shuai; Liang, Yongsheng

2012-01-01

In this paper a new framework, called Compressive Kernelized Reinforcement Learning (CKRL), for computing near-optimal policies in sequential decision making with uncertainty is proposed via incorporating the non-adaptive data-independent Random Projections and nonparametric Kernelized Least-squares Policy Iteration (KLSPI). Random Projections are a fast, non-adaptive dimensionality reduction framework in which high-dimensionality data is projected onto a random lower-dimension subspace via spherically random rotation and coordination sampling. KLSPI introduce kernel trick into the LSPI framework for Reinforcement Learning, often achieving faster convergence and providing automatic feature selection via various kernel sparsification approaches. In this approach, policies are computed in a low-dimensional subspace generated by projecting the high-dimensional features onto a set of random basis. We first show how Random Projections constitute an efficient sparsification technique and how our method often converges faster than regular LSPI, while at lower computational costs. Theoretical foundation underlying this approach is a fast approximation of Singular Value Decomposition (SVD). Finally, simulation results are exhibited on benchmark MDP domains, which confirm gains both in computation time and in performance in large feature spaces. PMID:22736969

PCA-HOG symmetrical feature based diseased cell detection

NASA Astrophysics Data System (ADS)

Wan, Min-jie

2016-04-01

A histogram of oriented gradient (HOG) feature is applied to the field of diseased cell detection, which can detect diseased cells in high resolution tissue images rapidly, accurately and efficiently. Firstly, motivated by symmetrical cellular forms, a new HOG symmetrical feature based on the traditional HOG feature is proposed to meet the condition of cell detection. Secondly, considering the high feature dimension of traditional HOG feature leads to plenty of memory resources and long runtime in practical applications, a classical dimension reduction method called principal component analysis (PCA) is used to reduce the dimension of high-dimensional HOG descriptor. Because of that, computational speed is increased greatly, and the accuracy of detection can be controlled in a proper range at the same time. Thirdly, support vector machine (SVM) classifier is trained with PCA-HOG symmetrical features proposed above. At last, practical tissue images is detected and analyzed by SVM classifier. In order to verify the effectiveness of this new algorithm, it is practically applied to conduct diseased cell detection which takes 200 pieces of H&E (hematoxylin & eosin) high resolution staining histopathological images collected from 20 breast cancer patients as a sample. The experiment shows that the average processing rate can be 25 frames per second and the detection accuracy can be 92.1%.

Bell - Kochen - Specker theorem for any finite dimension ?

NASA Astrophysics Data System (ADS)

Cabello, Adán; García-Alcaine, Guillermo

1996-03-01

The Bell - Kochen - Specker theorem against non-contextual hidden variables can be proved by constructing a finite set of `totally non-colourable' directions, as Kochen and Specker did in a Hilbert space of dimension n = 3. We generalize Kochen and Specker's set to Hilbert spaces of any finite dimension 0305-4470/29/5/016/img2, in a three-step process that shows the relationship between different kinds of proofs (`continuum', `probabilistic', `state-specific' and `state-independent') of the Bell - Kochen - Specker theorem. At the same time, this construction of a totally non-colourable set of directions in any dimension explicitly solves the question raised by Zimba and Penrose about the existence of such a set for n = 5.

Changing the personality of a face: Perceived Big Two and Big Five personality factors modeled in real photographs.

PubMed

Walker, Mirella; Vetter, Thomas

2016-04-01

General, spontaneous evaluations of strangers based on their faces have been shown to reflect judgments of these persons' intention and ability to harm. These evaluations can be mapped onto a 2D space defined by the dimensions trustworthiness (intention) and dominance (ability). Here we go beyond general evaluations and focus on more specific personality judgments derived from the Big Two and Big Five personality concepts. In particular, we investigate whether Big Two/Big Five personality judgments can be mapped onto the 2D space defined by the dimensions trustworthiness and dominance. Results indicate that judgments of the Big Two personality dimensions almost perfectly map onto the 2D space. In contrast, at least 3 of the Big Five dimensions (i.e., neuroticism, extraversion, and conscientiousness) go beyond the 2D space, indicating that additional dimensions are necessary to describe more specific face-based personality judgments accurately. Building on this evidence, we model the Big Two/Big Five personality dimensions in real facial photographs. Results from 2 validation studies show that the Big Two/Big Five are perceived reliably across different samples of faces and participants. Moreover, results reveal that participants differentiate reliably between the different Big Two/Big Five dimensions. Importantly, this high level of agreement and differentiation in personality judgments from faces likely creates a subjective reality which may have serious consequences for those being perceived-notably, these consequences ensue because the subjective reality is socially shared, irrespective of the judgments' validity. The methodological approach introduced here might prove useful in various psychological disciplines. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Size and shape of Brain may be such as to take advantage of two Dimensions of Time

NASA Astrophysics Data System (ADS)

Kriske, Richard

2014-03-01

This author had previously Theorized that there are two non-commuting Dimensions of time. One is Clock Time and the other is Information Time (which we generally refer to as Information, like Spin Up or Spin Down). When time does not commute with another Dimension of Time, one takes the Clock Time at one point in space and the Information time is not known; that is different than if one takes the Information time at that point and the Clock time is not known--This is not explicitly about time but rather space. An example of this non-commutation is that if one knows the Spin at one point and the Time at one point of space then simultaneosly, one knows the Spin at another point of Space and the Time there (It is the same time), it is a restatement of the EPR paradox. As a matter of fact two Dimensions of Time would prove the EPR paradox. It is obvious from that argument that if one needed to take advantage of Information, then a fairly large space needs to be used, a large amount of Energy needs to be Generated and a symmetry needs to be established in Space-like the lobes of a Brain in order to detect the fact that the Tclock and Tinfo are not Commuting. This Non-Commuting deposits a large amount of Information simultaneously in that space, and synchronizes the time there.

How category learning affects object representations: Not all morphspaces stretch alike

PubMed Central

Folstein, Jonathan R.; Gauthier, Isabel; Palmeri, Thomas J.

2012-01-01

How does learning to categorize objects affect how we visually perceive them? Behavioral, neurophysiological, and neuroimaging studies have tested the degree to which category learning influences object representations, with conflicting results. Some studies find that objects become more visually discriminable along dimensions relevant to previously learned categories, while others find no such effect. One critical factor we explore here lies in the structure of the morphspaces used in different studies. Studies finding no increase in discriminability often use “blended” morphspaces, with morphparents lying at corners of the space. By contrast, studies finding increases in discriminability use “factorial” morphspaces, defined by separate morphlines forming axes of the space. Using the same four morphparents, we created both factorial and blended morphspaces matched in pairwise discriminability. Category learning caused a selective increase in discriminability along the relevant dimension of the factorial space, but not in the blended space, and led to the creation of functional dimensions in the factorial space, but not in the blended space. These findings demonstrate that not all morphspaces stretch alike: Only some morphspaces support enhanced discriminability to relevant object dimensions following category learning. Our results have important implications for interpreting neuroimaging studies reporting little or no effect of category learning on object representations in the visual system: Those studies may have been limited by their use of blended morphspaces. PMID:22746950

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.

Abstract feature codes: The building blocks of the implicit learning system.

PubMed

Eberhardt, Katharina; Esser, Sarah; Haider, Hilde

2017-07-01

According to the Theory of Event Coding (TEC; Hommel, Müsseler, Aschersleben, & Prinz, 2001), action and perception are represented in a shared format in the cognitive system by means of feature codes. In implicit sequence learning research, it is still common to make a conceptual difference between independent motor and perceptual sequences. This supposedly independent learning takes place in encapsulated modules (Keele, Ivry, Mayr, Hazeltine, & Heuer 2003) that process information along single dimensions. These dimensions have remained underspecified so far. It is especially not clear whether stimulus and response characteristics are processed in separate modules. Here, we suggest that feature dimensions as they are described in the TEC should be viewed as the basic content of modules of implicit learning. This means that the modules process all stimulus and response information related to certain feature dimensions of the perceptual environment. In 3 experiments, we investigated by means of a serial reaction time task the nature of the basic units of implicit learning. As a test case, we used stimulus location sequence learning. The results show that a stimulus location sequence and a response location sequence cannot be learned without interference (Experiment 2) unless one of the sequences can be coded via an alternative, nonspatial dimension (Experiment 3). These results support the notion that spatial location is one module of the implicit learning system and, consequently, that there are no separate processing units for stimulus versus response locations. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Measurement contextuality is implied by macroscopic realism

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

Chen Zeqian; Montina, A.; Perimeter Institute for Theoretical Physics, 31 Caroline Street North, Waterloo, Ontario, N2L 2Y5

2011-04-15

Ontological theories of quantum mechanics provide a realistic description of single systems by means of well-defined quantities conditioning the measurement outcomes. In order to be complete, they should also fulfill the minimal condition of macroscopic realism. Under the assumption of outcome determinism and for Hilbert space dimension greater than 2, they were all proved to be contextual for projective measurements. In recent years a generalized concept of noncontextuality was introduced that applies also to the case of outcome indeterminism and unsharp measurements. It was pointed out that the Beltrametti-Bugajski model is an example of measurement noncontextual indeterminist theory. Here wemore » provide a simple proof that this model is the only one with such a feature for projective measurements and Hilbert space dimension greater than 2. In other words, there is no extension of quantum theory providing more accurate predictions of outcomes and simultaneously preserving the minimal labeling of events through projective operators. As a corollary, noncontextuality for projective measurements implies noncontextuality for unsharp measurements. By noting that the condition of macroscopic realism requires an extension of quantum theory, unless a breaking of unitarity is invoked, we arrive at the conclusion that the only way to solve the measurement problem in the framework of an ontological theory is by relaxing the hypothesis of measurement noncontextuality in its generalized sense.« less

Event-related brain potentials and cognitive processes related to perceptual-motor information transmission.

PubMed

Kopp, Bruno; Wessel, Karl

2010-05-01

In the present study, event-related potentials (ERPs) were recorded to investigate cognitive processes related to the partial transmission of information from stimulus recognition to response preparation. Participants classified two-dimensional visual stimuli with dimensions size and form. One feature combination was designated as the go-target, whereas the other three feature combinations served as no-go distractors. Size discriminability was manipulated across three experimental conditions. N2c and P3a amplitudes were enhanced in response to those distractors that shared the feature from the faster dimension with the target. Moreover, N2c and P3a amplitudes showed a crossover effect: Size distractors evoked more pronounced ERPs under high size discriminability, but form distractors elicited enhanced ERPs under low size discriminability. These results suggest that partial perceptual-motor transmission of information is accompanied by acts of cognitive control and by shifts of attention between the sources of conflicting information. Selection negativity findings imply adaptive allocation of visual feature-based attention across the two stimulus dimensions.

Extra Dimensions of Space

ERIC Educational Resources Information Center

Lincoln, Don

2013-01-01

They say that there is no such thing as a stupid question. In a pedagogically pure sense, that's probably true. But some questions do seem to flirt dangerously close to being really quite ridiculous. One such question might well be, "How many dimensions of space are there?" I mean, it's pretty obvious that there are three:…

Exploring the Structure of Spatial Representations

PubMed Central

Madl, Tamas; Franklin, Stan; Chen, Ke; Trappl, Robert; Montaldi, Daniela

2016-01-01

It has been suggested that the map-like representations that support human spatial memory are fragmented into sub-maps with local reference frames, rather than being unitary and global. However, the principles underlying the structure of these ‘cognitive maps’ are not well understood. We propose that the structure of the representations of navigation space arises from clustering within individual psychological spaces, i.e. from a process that groups together objects that are close in these spaces. Building on the ideas of representational geometry and similarity-based representations in cognitive science, we formulate methods for learning dissimilarity functions (metrics) characterizing participants’ psychological spaces. We show that these learned metrics, together with a probabilistic model of clustering based on the Bayesian cognition paradigm, allow prediction of participants’ cognitive map structures in advance. Apart from insights into spatial representation learning in human cognition, these methods could facilitate novel computational tools capable of using human-like spatial concepts. We also compare several features influencing spatial memory structure, including spatial distance, visual similarity and functional similarity, and report strong correlations between these dimensions and the grouping probability in participants’ spatial representations, providing further support for clustering in spatial memory. PMID:27347681

Simulations of molecular diffusion in lattices of cells: insights for NMR of red blood cells.

PubMed Central

Regan, David G; Kuchel, Philip W

2002-01-01

The pulsed field-gradient spin-echo (PGSE) nuclear magnetic resonance (NMR) experiment, conducted on a suspension of red blood cells (RBC) in a strong magnetic field yields a q-space plot consisting of a series of maxima and minima. This is mathematically analogous to a classical optical diffraction pattern. The method provides a noninvasive and novel means of characterizing cell suspensions that is sensitive to changes in cell shape and packing density. The positions of the features in a q-space plot characterize the rate of exchange across the membrane, cell dimensions, and packing density. A diffusion tensor, containing information regarding the diffusion anisotropy of the system, can also be derived from the PGSE NMR data. In this study, we carried out Monte Carlo simulations of diffusion in suspensions of "virtual" cells that had either biconcave disc (as in RBC) or oblate spheroid geometry. The simulations were performed in a PGSE NMR context thus enabling predictions of q-space and diffusion tensor data. The simulated data were compared with those from real PGSE NMR diffusion experiments on RBC suspensions that had a range of hematocrit values. Methods that facilitate the processing of q-space data were also developed. PMID:12080109

Simulations of molecular diffusion in lattices of cells: insights for NMR of red blood cells.

PubMed

Regan, David G; Kuchel, Philip W

2002-07-01

The pulsed field-gradient spin-echo (PGSE) nuclear magnetic resonance (NMR) experiment, conducted on a suspension of red blood cells (RBC) in a strong magnetic field yields a q-space plot consisting of a series of maxima and minima. This is mathematically analogous to a classical optical diffraction pattern. The method provides a noninvasive and novel means of characterizing cell suspensions that is sensitive to changes in cell shape and packing density. The positions of the features in a q-space plot characterize the rate of exchange across the membrane, cell dimensions, and packing density. A diffusion tensor, containing information regarding the diffusion anisotropy of the system, can also be derived from the PGSE NMR data. In this study, we carried out Monte Carlo simulations of diffusion in suspensions of "virtual" cells that had either biconcave disc (as in RBC) or oblate spheroid geometry. The simulations were performed in a PGSE NMR context thus enabling predictions of q-space and diffusion tensor data. The simulated data were compared with those from real PGSE NMR diffusion experiments on RBC suspensions that had a range of hematocrit values. Methods that facilitate the processing of q-space data were also developed.

An Overview of Integration and Test of the James Webb Space Telescope Integrated Science Instrument Module

NASA Technical Reports Server (NTRS)

Drury, Michael; Becker, Neil; Bos, Brent; Davila, Pamela; Frey, Bradley; Hylan, Jason; Marsh, James; McGuffey, Douglas; Novak, Maria; Ohl, Raymond;

High-dimensional structured light coding/decoding for free-space optical communications free of obstructions.

PubMed

Du, Jing; Wang, Jian

2015-11-01

Bessel beams carrying orbital angular momentum (OAM) with helical phase fronts exp(ilφ)(l=0;±1;±2;…), where φ is the azimuthal angle and l corresponds to the topological number, are orthogonal with each other. This feature of Bessel beams provides a new dimension to code/decode data information on the OAM state of light, and the theoretical infinity of topological number enables possible high-dimensional structured light coding/decoding for free-space optical communications. Moreover, Bessel beams are nondiffracting beams having the ability to recover by themselves in the face of obstructions, which is important for free-space optical communications relying on line-of-sight operation. By utilizing the OAM and nondiffracting characteristics of Bessel beams, we experimentally demonstrate 12 m distance obstruction-free optical m-ary coding/decoding using visible Bessel beams in a free-space optical communication system. We also study the bit error rate (BER) performance of hexadecimal and 32-ary coding/decoding based on Bessel beams with different topological numbers. After receiving 500 symbols at the receiver side, a zero BER of hexadecimal coding/decoding is observed when the obstruction is placed along the propagation path of light.

Identification and Simulation of Subsurface Soil patterns using hidden Markov random fields and remote sensing and geophysical EMI data sets

NASA Astrophysics Data System (ADS)

Wang, Hui; Wellmann, Florian; Verweij, Elizabeth; von Hebel, Christian; van der Kruk, Jan

2017-04-01

Lateral and vertical spatial heterogeneity of subsurface properties such as soil texture and structure influences the available water and resource supply for crop growth. High-resolution mapping of subsurface structures using non-invasive geo-referenced geophysical measurements, like electromagnetic induction (EMI), enables a characterization of 3D soil structures, which have shown correlations to remote sensing information of the crop states. The benefit of EMI is that it can return 3D subsurface information, however the spatial dimensions are limited due to the labor intensive measurement procedure. Although active and passive sensors mounted on air- or space-borne platforms return 2D images, they have much larger spatial dimensions. Combining both approaches provides us with a potential pathway to extend the detailed 3D geophysical information to a larger area by using remote sensing information. In this study, we aim at extracting and providing insights into the spatial and statistical correlation of the geophysical and remote sensing observations of the soil/vegetation continuum system. To this end, two key points need to be addressed: 1) how to detect and recognize the geometric patterns (i.e., spatial heterogeneity) from multiple data sets, and 2) how to quantitatively describe the statistical correlation between remote sensing information and geophysical measurements. In the current study, the spatial domain is restricted to shallow depths up to 3 meters, and the geostatistical database contains normalized difference vegetation index (NDVI) derived from RapidEye satellite images and apparent electrical conductivities (ECa) measured from multi-receiver EMI sensors for nine depths of exploration ranging from 0-2.7 m. The integrated data sets are mapped into both the physical space (i.e. the spatial domain) and feature space (i.e. a two-dimensional space framed by the NDVI and the ECa data). Hidden Markov Random Fields (HMRF) are employed to model the underlying heterogeneities in spatial domain and finite Gaussian mixture models are adopted to quantitatively describe the statistical patterns in terms of center vectors and covariance matrices in feature space. A recently developed parallel stochastic clustering algorithm is adopted to implement the HMRF models and the Markov chain Monte Carlo based Bayesian inference. Certain spatial patterns such as buried paleo-river channels covered by shallow sediments are investigated as typical examples. The results indicate that the geometric patterns of the subsurface heterogeneity can be represented and quantitatively characterized by HMRF. Furthermore, the statistical patterns of the NDVI and the EMI data from the soil/vegetation-continuum system can be inferred and analyzed in a quantitative manner.

Annihilation cross section of Kaluza Klien dark matter

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

Sharma, Rakesh, E-mail: rakesh-sharma-ujn@yahoo.co.in; Upadhyaya, G. K., E-mail: gopalujjain@yahoo.co.in; Sharma, S.

2015-07-31

The question as to how this universe came into being and as to how it has evolved to its present stage, is an old question. The answer to this question unfolds many secrets regarding fundamental particles and forces between them. Theodor Kaluza proposed the concept that the universe is composed of more than four space-time dimensions. In his work, electromagnetism is united with gravity. Various extra dimension formulations have been proposed to solve a variety of problems. Recently, the idea of more than four space time dimensions is applied to the search for particle identity of dark matter (DM). Signaturemore » of dark matter can be revealed by analysis of very high energy electrons which are coming from outer space. We investigate recent advancement in the field of dark matter search with reference to very high energy electrons from outer space [1-8].« less

Target oriented dimensionality reduction of hyperspectral data by Kernel Fukunaga-Koontz Transform

NASA Astrophysics Data System (ADS)

Binol, Hamidullah; Ochilov, Shuhrat; Alam, Mohammad S.; Bal, Abdullah

2017-02-01

Principal component analysis (PCA) is a popular technique in remote sensing for dimensionality reduction. While PCA is suitable for data compression, it is not necessarily an optimal technique for feature extraction, particularly when the features are exploited in supervised learning applications (Cheriyadat and Bruce, 2003) [1]. Preserving features belonging to the target is very crucial to the performance of target detection/recognition techniques. Fukunaga-Koontz Transform (FKT) based supervised band reduction technique can be used to provide this requirement. FKT achieves feature selection by transforming into a new space in where feature classes have complimentary eigenvectors. Analysis of these eigenvectors under two classes, target and background clutter, can be utilized for target oriented band reduction since each basis functions best represent target class while carrying least information of the background class. By selecting few eigenvectors which are the most relevant to the target class, dimension of hyperspectral data can be reduced and thus, it presents significant advantages for near real time target detection applications. The nonlinear properties of the data can be extracted by kernel approach which provides better target features. Thus, we propose constructing kernel FKT (KFKT) to present target oriented band reduction. The performance of the proposed KFKT based target oriented dimensionality reduction algorithm has been tested employing two real-world hyperspectral data and results have been reported consequently.

Real-Time X-Ray Microscopy of Al-Cu Eutectic Solidification

NASA Technical Reports Server (NTRS)

Kaukler, William F.; Curreri, Peter A.; Sen, Subhayu

1998-01-01

Recent improvements in the resolution of the X-ray Transmission Microscope (XTM) for Solidification Studies provide microstructure feature detectability down to 5 micrometers during solidification. This presentation will show the recent results from observations made in real-time of the solid-liquid interfacial morphologies of the Al-CuAI2 eutectic alloy. Lamellar dimensions and spacings, transitions of morphology caused by growth rate changes, and eutectic grain structures are open to measurements. A unique vantage point viewing the face of the interface isotherm is possible for the first time with the XTM due to its infinite depth of field. A video of the solid-liquid interfaces seen in-situ and in real-time will be shown.

A new method for mapping multidimensional data to lower dimensions

NASA Technical Reports Server (NTRS)

Gowda, K. C.

1983-01-01

A multispectral mapping method is proposed which is based on the new concept of BEND (Bidimensional Effective Normalised Difference). The method, which involves taking one sample point at a time and finding the interrelationships between its features, is found very economical from the point of view of storage and processing time. It has good dimensionality reduction and clustering properties, and is highly suitable for computer analysis of large amounts of data. The transformed values obtained by this procedure are suitable for either a planar 2-space mapping of geological sample points or for making grayscale and color images of geo-terrains. A few examples are given to justify the efficacy of the proposed procedure.

Oliver E. Buckley Condensed Matter Prize: Emergent gravity from interacting Majorana modes

NASA Astrophysics Data System (ADS)

Kitaev, Alexei

I will describe a concrete many-body Hamiltonian that exhibits some features of a quantum black hole. The Sachdev-Ye-Kitaev model is a system of N >> 1 Majorana modes that are all coupled by random 4-th order terms. The problem admits an approximate dynamic mean field solution. At low temperatures, there is a fluctuating collective mode that corresponds to reparametrization of time. The effective action for this mode is equivalent to dilaton gravity in two space-time dimensions. Some important questions are how to quantize the reparametrization mode in Lorentzian time, include dissipative effects, and understand this system from the quantum information perspective. Supported by the Simons Foundation, Award Number 376205.

Preparation, applications, and digital simulation of carbon interdigitated array electrodes.

PubMed

Liu, Fei; Kolesov, Grigory; Parkinson, B A

2014-08-05

Carbon interdigitated array (IDA) electrodes with features sizes down to 1.2 μm were fabricated by controlled pyrolysis of patterned photoresist. Cyclic voltammetry of reversible redox species produced the expected steady-state currents. The collection efficiency depends on the IDA electrode spacing, which ranged from around 2.7 to 16.5 μm, with the smaller dimensions achieving higher collection efficiencies of up to 98%. The signal amplification because of redox cycling makes it possible to detect species at relatively low concentrations (10(-5) molar) and the small spacing allows detection of transient electrogenerated species with much shorter lifetimes (submillisecond). Digital simulation software that accounts for both the width and height of electrode elements as well as the electrode spacing was developed to model the IDA electrode response. The simulations are in quantitative agreement with experimental data for both a simple fast one electron redox reaction and an electron transfer with a following chemical reaction at the IDAs with larger gaps whereas currents measured for the smallest IDA electrodes, that were larger than the simulated currents, are attributed to convection from induced charge electrokinetic flow.

A new dimension in space experimentation

NASA Technical Reports Server (NTRS)

1983-01-01

Space experimentation, cosmic origins, the long-term effects of the space environment on living things, the long-term effects of space environment on materials and hardware, seeds in space, power generation in space, experimentation with crystals, and thermal control are discussed.

Dimension-based attention in visual short-term memory.

PubMed

Pilling, Michael; Barrett, Doug J K

2016-07-01

We investigated how dimension-based attention influences visual short-term memory (VSTM). This was done through examining the effects of cueing a feature dimension in two perceptual comparison tasks (change detection and sameness detection). In both tasks, a memory array and a test array consisting of a number of colored shapes were presented successively, interleaved by a blank interstimulus interval (ISI). In Experiment 1 (change detection), the critical event was a feature change in one item across the memory and test arrays. In Experiment 2 (sameness detection), the critical event was the absence of a feature change in one item across the two arrays. Auditory cues indicated the feature dimension (color or shape) of the critical event with 80 % validity; the cues were presented either prior to the memory array, during the ISI, or simultaneously with the test array. In Experiment 1, the cue validity influenced sensitivity only when the cue was given at the earliest position; in Experiment 2, the cue validity influenced sensitivity at all three cue positions. We attributed the greater effectiveness of top-down guidance by cues in the sameness detection task to the more active nature of the comparison process required to detect sameness events (Hyun, Woodman, Vogel, Hollingworth, & Luck, Journal of Experimental Psychology: Human Perception and Performance, 35; 1140-1160, 2009).

Principal semantic components of language and the measurement of meaning.

PubMed

Samsonovich, Alexei V; Samsonovic, Alexei V; Ascoli, Giorgio A

2010-06-11

Metric systems for semantics, or semantic cognitive maps, are allocations of words or other representations in a metric space based on their meaning. Existing methods for semantic mapping, such as Latent Semantic Analysis and Latent Dirichlet Allocation, are based on paradigms involving dissimilarity metrics. They typically do not take into account relations of antonymy and yield a large number of domain-specific semantic dimensions. Here, using a novel self-organization approach, we construct a low-dimensional, context-independent semantic map of natural language that represents simultaneously synonymy and antonymy. Emergent semantics of the map principal components are clearly identifiable: the first three correspond to the meanings of "good/bad" (valence), "calm/excited" (arousal), and "open/closed" (freedom), respectively. The semantic map is sufficiently robust to allow the automated extraction of synonyms and antonyms not originally in the dictionaries used to construct the map and to predict connotation from their coordinates. The map geometric characteristics include a limited number ( approximately 4) of statistically significant dimensions, a bimodal distribution of the first component, increasing kurtosis of subsequent (unimodal) components, and a U-shaped maximum-spread planar projection. Both the semantic content and the main geometric features of the map are consistent between dictionaries (Microsoft Word and Princeton's WordNet), among Western languages (English, French, German, and Spanish), and with previously established psychometric measures. By defining the semantics of its dimensions, the constructed map provides a foundational metric system for the quantitative analysis of word meaning. Language can be viewed as a cumulative product of human experiences. Therefore, the extracted principal semantic dimensions may be useful to characterize the general semantic dimensions of the content of mental states. This is a fundamental step toward a universal metric system for semantics of human experiences, which is necessary for developing a rigorous science of the mind.

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.

Maintenance of the catalog of artificial objects in space.

NASA Astrophysics Data System (ADS)

Khutorovskij, Z. N.

1994-01-01

The catalog of artificial objects in space (AOS) is useful for estimating the safety of space flights, for constructing temporal and spatial models of the flux of AOS, for determining when and where dangerous AOS will break up, for tracking inoperative instruments and space stations, for eliminating false alarms that are triggered by observations of AOS in the Ballistic Missile Early Warning System and in the Anti-Missile system, etc. At present, the Space Surveillance System (located in the former USSR) automatically maintains a catalog consisting of more than 5000 AOS with dimensions of at least 10 cm. The orbital parameters are continuously updated from radar tracking data. The author describes the software which is used to process the information. He presents some of the features of the system itself, including the number of objects in various stages of the tracking process, the orbital parameters of AOS which break up, and how the fragments are detected, the accuracy of tracking and predicting the orbits of the AOS, and the accuracy with which we can estimate when and where an AOS will break up. As an example, the author presents the results of determination of the time when the orbiting complex Salyut-7 - Kosmos-1686 will break up, and where it will impact.

Relativistic bound states in three space-time dimensions in Minkowski space

NASA Astrophysics Data System (ADS)

Gutierrez, C.; Gigante, V.; Frederico, T.; Tomio, Lauro

2016-01-01

With the aim to derive a workable framework for bound states in Minkowski space, we have investigated the Nakanishi perturbative integral representation of the Bethe-Salpeter (BS) amplitude in two-dimensions (2D) in space and time (2+1). The homogeneous BS amplitude, projected onto the light-front plane, is used to derive an equation for the Nakanishi weight function. The formal development is illustrated in detail and applied to the bound system composed by two scalar particles interacting through the exchange of a massive scalar. The explicit forms of the integral equations are obtained in ladder approximation.

Fractal dimension to classify the heart sound recordings with KNN and fuzzy c-mean clustering methods

NASA Astrophysics Data System (ADS)

Juniati, D.; Khotimah, C.; Wardani, D. E. K.; Budayasa, K.

2018-01-01

The heart abnormalities can be detected from heart sound. A heart sound can be heard directly with a stethoscope or indirectly by a phonocardiograph, a machine of the heart sound recording. This paper presents the implementation of fractal dimension theory to make a classification of phonocardiograms into a normal heart sound, a murmur, or an extrasystole. The main algorithm used to calculate the fractal dimension was Higuchi’s Algorithm. There were two steps to make a classification of phonocardiograms, feature extraction, and classification. For feature extraction, we used Discrete Wavelet Transform to decompose the signal of heart sound into several sub-bands depending on the selected level. After the decomposition process, the signal was processed using Fast Fourier Transform (FFT) to determine the spectral frequency. The fractal dimension of the FFT output was calculated using Higuchi Algorithm. The classification of fractal dimension of all phonocardiograms was done with KNN and Fuzzy c-mean clustering methods. Based on the research results, the best accuracy obtained was 86.17%, the feature extraction by DWT decomposition level 3 with the value of kmax 50, using 5-fold cross validation and the number of neighbors was 5 at K-NN algorithm. Meanwhile, for fuzzy c-mean clustering, the accuracy was 78.56%.

Sequential Analysis of the Numerical Stroop Effect Reveals Response Suppression

ERIC Educational Resources Information Center

Kadosh, Roi Cohen; Gevers, Wim; Notebaert, Wim

2011-01-01

Automatic processing of irrelevant stimulus dimensions has been demonstrated in a variety of tasks. Previous studies have shown that conflict between relevant and irrelevant dimensions can be reduced when a feature of the irrelevant dimension is repeated. The specific level at which the automatic process is suppressed (e.g., perceptual repetition,…

Photonic integrated circuits: new challenges for lithography

NASA Astrophysics Data System (ADS)

Bolten, Jens; Wahlbrink, Thorsten; Prinzen, Andreas; Porschatis, Caroline; Lerch, Holger; Giesecke, Anna Lena

2016-10-01

In this work routes towards the fabrication of photonic integrated circuits (PICs) and the challenges their fabrication poses on lithography, such as large differences in feature dimension of adjacent device features, non-Manhattan-type features, high aspect ratios and significant topographic steps as well as tight lithographic requirements with respect to critical dimension control, line edge roughness and other key figures of merit not only for very small but also for relatively large features, are highlighted. Several ways those challenges are faced in today's low-volume fabrication of PICs, including the concept multi project wafer runs and mix and match approaches, are presented and possible paths towards a real market uptake of PICs are discussed.

Perceptual dimensions differentiate emotions.

PubMed

Cavanaugh, Lisa A; MacInnis, Deborah J; Weiss, Allen M

2015-08-26

Individuals often describe objects in their world in terms of perceptual dimensions that span a variety of modalities; the visual (e.g., brightness: dark-bright), the auditory (e.g., loudness: quiet-loud), the gustatory (e.g., taste: sour-sweet), the tactile (e.g., hardness: soft vs. hard) and the kinaesthetic (e.g., speed: slow-fast). We ask whether individuals use perceptual dimensions to differentiate emotions from one another. Participants in two studies (one where respondents reported on abstract emotion concepts and a second where they reported on specific emotion episodes) rated the extent to which features anchoring 29 perceptual dimensions (e.g., temperature, texture and taste) are associated with 8 emotions (anger, fear, sadness, guilt, contentment, gratitude, pride and excitement). Results revealed that in both studies perceptual dimensions differentiate positive from negative emotions and high arousal from low arousal emotions. They also differentiate among emotions that are similar in arousal and valence (e.g., high arousal negative emotions such as anger and fear). Specific features that anchor particular perceptual dimensions (e.g., hot vs. cold) are also differentially associated with emotions.

Editorial: Focus on Extra Space Dimensions

NASA Astrophysics Data System (ADS)

Agashe, Kaustubh; Pomarol, Alex

2010-07-01

Experiments at the Large Hadron Collider (LHC) have just started. In addition to verifying the Standard Model (SM) of particle physics, these experiments will probe a new energy frontier and test extensions of the SM. The existence of extra dimensions is one of the most attractive possibilities for physics beyond the SM. This focus issue contains a collection of articles addressing both theoretical and phenomenological aspects of extra-dimensional models. Focus on Extra Space Dimensions Contents Minimal universal extra dimensions in CalcHEP/CompHEP AseshKrishna Datta, Kyoungchul Kong and Konstantin T Matchev Disordered extra dimensions Karim Benakli Codimension-2 brane-bulk matching: examples from six and ten dimensions Allan Bayntun, C P Burgess and Leo van Nierop Gauge threshold corrections in warped geometry Kiwoon Choi, Ian-Woo Kim and Chang Sub Shin Holographic methods and gauge-Higgs unification in flat extra dimensions Marco Serone Soft-wall stabilization Joan A Cabrer, Gero von Gersdorff and Mariano Quirós Warped five-dimensional models: phenomenological status and experimental prospects Hooman Davoudiasl, Shrihari Gopalakrishna, Eduardo Pontón and José Santiago

Revisiting special relativity: a natural algebraic alternative to Minkowski spacetime.

PubMed

Chappell, James M; Iqbal, Azhar; Iannella, Nicolangelo; Abbott, Derek

2012-01-01

Minkowski famously introduced the concept of a space-time continuum in 1908, merging the three dimensions of space with an imaginary time dimension [Formula: see text], with the unit imaginary producing the correct spacetime distance [Formula: see text], and the results of Einstein's then recently developed theory of special relativity, thus providing an explanation for Einstein's theory in terms of the structure of space and time. As an alternative to a planar Minkowski space-time of two space dimensions and one time dimension, we replace the unit imaginary [Formula: see text], with the Clifford bivector [Formula: see text] for the plane that also squares to minus one, but which can be included without the addition of an extra dimension, as it is an integral part of the real Cartesian plane with the orthonormal basis [Formula: see text] and [Formula: see text]. We find that with this model of planar spacetime, using a two-dimensional Clifford multivector, the spacetime metric and the Lorentz transformations follow immediately as properties of the algebra. This also leads to momentum and energy being represented as components of a multivector and we give a new efficient derivation of Compton's scattering formula, and a simple formulation of Dirac's and Maxwell's equations. Based on the mathematical structure of the multivector, we produce a semi-classical model of massive particles, which can then be viewed as the origin of the Minkowski spacetime structure and thus a deeper explanation for relativistic effects. We also find a new perspective on the nature of time, which is now given a precise mathematical definition as the bivector of the plane.

Revisiting Special Relativity: A Natural Algebraic Alternative to Minkowski Spacetime

PubMed Central

Chappell, James M.; Iqbal, Azhar; Iannella, Nicolangelo; Abbott, Derek

2012-01-01

Minkowski famously introduced the concept of a space-time continuum in 1908, merging the three dimensions of space with an imaginary time dimension , with the unit imaginary producing the correct spacetime distance , and the results of Einstein’s then recently developed theory of special relativity, thus providing an explanation for Einstein’s theory in terms of the structure of space and time. As an alternative to a planar Minkowski space-time of two space dimensions and one time dimension, we replace the unit imaginary , with the Clifford bivector for the plane that also squares to minus one, but which can be included without the addition of an extra dimension, as it is an integral part of the real Cartesian plane with the orthonormal basis and . We find that with this model of planar spacetime, using a two-dimensional Clifford multivector, the spacetime metric and the Lorentz transformations follow immediately as properties of the algebra. This also leads to momentum and energy being represented as components of a multivector and we give a new efficient derivation of Compton’s scattering formula, and a simple formulation of Dirac’s and Maxwell’s equations. Based on the mathematical structure of the multivector, we produce a semi-classical model of massive particles, which can then be viewed as the origin of the Minkowski spacetime structure and thus a deeper explanation for relativistic effects. We also find a new perspective on the nature of time, which is now given a precise mathematical definition as the bivector of the plane. PMID:23300566

On the complexity and approximability of some Euclidean optimal summing problems

NASA Astrophysics Data System (ADS)

Eremeev, A. V.; Kel'manov, A. V.; Pyatkin, A. V.

2016-10-01

The complexity status of several well-known discrete optimization problems with the direction of optimization switching from maximum to minimum is analyzed. The task is to find a subset of a finite set of Euclidean points (vectors). In these problems, the objective functions depend either only on the norm of the sum of the elements from the subset or on this norm and the cardinality of the subset. It is proved that, if the dimension of the space is a part of the input, then all these problems are strongly NP-hard. Additionally, it is shown that, if the space dimension is fixed, then all the problems are NP-hard even for dimension 2 (on a plane) and there are no approximation algorithms with a guaranteed accuracy bound for them unless P = NP. It is shown that, if the coordinates of the input points are integer, then all the problems can be solved in pseudopolynomial time in the case of a fixed space dimension.

Multidimensional kinetic simulations using dissipative closures and other reduced Vlasov methods for differing particle magnetizations

NASA Astrophysics Data System (ADS)

Newman, David L.

2006-10-01

Kinetic plasma simulations in which the phase-space distribution functions are advanced directly via the coupled Vlasov and Poisson (or Maxwell) equations---better known simply as Vlasov simulations---provide a valuable low-noise complement to the more commonly employed Particle-in-Cell (PIC) simulations. However, in more than one spatial dimension Vlasov simulations become numerically demanding due to the high dimensionality of x--v phase-space. Methods that can reduce this computational demand are therefore highly desirable. Several such methods will be presented, which treat the phase-space dynamics along a dominant dimension (e.g., parallel to a beam or current) with the full Vlasov propagator, while employing a reduced description, such as moment equations, for the evolution perpendicular to the dominant dimension. A key difference between the moment-based (and other reduced) methods considered here and standard fluid methods is that the moments are now functions of a phase-space coordinate (e.g. moments of vy in z--vz--y phase space, where z is the dominant dimension), rather than functions of spatial coordinates alone. Of course, moment-based methods require closure. For effectively unmagnetized species, new dissipative closure methods inspired by those of Hammett and Perkins [PRL, 64, 3019 (1990)] have been developed, which exactly reproduce the linear electrostatic response for a broad class of distributions with power-law tails, as are commonly measured in space plasmas. The nonlinear response, which requires more care, will also be discussed. For weakly magnetized species (i.e., φs<φs) an alternative algorithm has been developed in which the distributions are assumed to gyrate about the magnetic field with a fixed nominal perpendicular ``thermal'' velocity, thereby reducing the required phase-space dimension by one. These reduced algorithms have been incorporated into 2-D codes used to study the evolution of nonlinear structures such as double layers and electron holes in Earth's auroral zone.

Aerial surveillance based on hierarchical object classification for ground target detection

NASA Astrophysics Data System (ADS)

Vázquez-Cervantes, Alberto; García-Huerta, Juan-Manuel; Hernández-Díaz, Teresa; Soto-Cajiga, J. A.; Jiménez-Hernández, Hugo

2015-03-01

Unmanned aerial vehicles have turned important in surveillance application due to the flexibility and ability to inspect and displace in different regions of interest. The instrumentation and autonomy of these vehicles have been increased; i.e. the camera sensor is now integrated. Mounted cameras allow flexibility to monitor several regions of interest, displacing and changing the camera view. A well common task performed by this kind of vehicles correspond to object localization and tracking. This work presents a hierarchical novel algorithm to detect and locate objects. The algorithm is based on a detection-by-example approach; this is, the target evidence is provided at the beginning of the vehicle's route. Afterwards, the vehicle inspects the scenario, detecting all similar objects through UTM-GPS coordinate references. Detection process consists on a sampling information process of the target object. Sampling process encode in a hierarchical tree with different sampling's densities. Coding space correspond to a huge binary space dimension. Properties such as independence and associative operators are defined in this space to construct a relation between the target object and a set of selected features. Different densities of sampling are used to discriminate from general to particular features that correspond to the target. The hierarchy is used as a way to adapt the complexity of the algorithm due to optimized battery duty cycle of the aerial device. Finally, this approach is tested in several outdoors scenarios, proving that the hierarchical algorithm works efficiently under several conditions.

Organization aesthetics in nursing homes.

PubMed

Hujala, Anneli; Rissanen, Sari

2011-05-01

The aim of this study was to make visible the material dimensions of nursing management.   Management theories have mainly ignored the material dimensions, namely the physical spaces in which management actually takes place as well as the physical bodies of organization members. The perspective of organization aesthetics enhances our understanding of the role of materiality in nursing management. The data were collected in 2009 using observation and interviews in eight nursing homes. Qualitative content analysis with critical interpretations was used. Three main issues of organizational aesthetics related to nursing management were identified: (1) the functionality of working spaces and equipment; (2) the relevance of 'organizational' space; and (3) the emotional-aesthetic dimension of daily work. Materiality is closely related to management topics, such as decision-making, values and identity formation of organizational members. Aesthetic dimensions of care are constructed by management practices which, in their turn, influence the nature of management. Implications for nursing management  Nurse managers need to be aware of the unintended and unnoticed consequences of materiality and aesthetics. Space and body issues may have considerable effects, for example, on the identity of care workers and on the attractiveness of the care branch. © 2011 The Authors. Journal compilation © 2011 Blackwell Publishing Ltd.

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients

NASA Astrophysics Data System (ADS)

Sadatnezhad, Khadijeh; Boostani, Reza; Ghanizadeh, Ahmad

2010-12-01

There is extensive overlap of clinical symptoms observed among children with bipolar mood disorder (BMD) and those with attention deficit hyperactivity disorder (ADHD). Thus, diagnosis according to clinical symptoms cannot be very accurate. It is therefore desirable to develop quantitative criteria for automatic discrimination between these disorders. This study is aimed at designing an efficient decision maker to accurately classify ADHD and BMD patients by analyzing their electroencephalogram (EEG) signals. In this study, 22 channels of EEGs have been recorded from 21 subjects with ADHD and 22 individuals with BMD. Several informative features, such as fractal dimension, band power and autoregressive coefficients, were extracted from the recorded signals. Considering the multimodal overlapping distribution of the obtained features, linear discriminant analysis (LDA) was used to reduce the input dimension in a more separable space to make it more appropriate for the proposed classifier. A piecewise linear classifier based on the extended classifier system for function approximation (XCSF) was modified by developing an adaptive mutation rate, which was proportional to the genotypic content of best individuals and their fitness in each generation. The proposed operator controlled the trade-off between exploration and exploitation while maintaining the diversity in the classifier's population to avoid premature convergence. To assess the effectiveness of the proposed scheme, the extracted features were applied to support vector machine, LDA, nearest neighbor and XCSF classifiers. To evaluate the method, a noisy environment was simulated with different noise amplitudes. It is shown that the results of the proposed technique are more robust as compared to conventional classifiers. Statistical tests demonstrate that the proposed classifier is a promising method for discriminating between ADHD and BMD patients.

Heat Shield Employing Cured Thermal Protection Material Blocks Bonded in a Large-Cell Honeycomb Matrix

NASA Technical Reports Server (NTRS)

Zell, Peter

2012-01-01

A document describes a new way to integrate thermal protection materials on external surfaces of vehicles that experience the severe heating environments of atmospheric entry from space. Cured blocks of thermal protection materials are bonded into a compatible, large-cell honeycomb matrix that can be applied on the external surfaces of the vehicles. The honeycomb matrix cell size, and corresponding thermal protection material block size, is envisioned to be between 1 and 4 in. (.2.5 and 10 cm) on a side, with a depth required to protect the vehicle. The cell wall thickness is thin, between 0.01 and 0.10 in. (.0.025 and 0.25 cm). A key feature is that the honeycomb matrix is attached to the vehicle fs unprotected external surface prior to insertion of the thermal protection material blocks. The attachment integrity of the honeycomb can then be confirmed over the full range of temperature and loads that the vehicle will experience. Another key feature of the innovation is the use of uniform-sized thermal protection material blocks. This feature allows for the mass production of these blocks at a size that is convenient for quality control inspection. The honeycomb that receives the blocks must have cells with a compatible set of internal dimensions. The innovation involves the use of a faceted subsurface under the honeycomb. This provides a predictable surface with perpendicular cell walls for the majority of the blocks. Some cells will have positive tapers to accommodate mitered joints between honeycomb panels on each facet of the subsurface. These tapered cells have dimensions that may fall within the boundaries of the uniform-sized blocks.

Proposing an adaptive mutation to improve XCSF performance to classify ADHD and BMD patients.

PubMed

Sadatnezhad, Khadijeh; Boostani, Reza; Ghanizadeh, Ahmad

2010-12-01

There is extensive overlap of clinical symptoms observed among children with bipolar mood disorder (BMD) and those with attention deficit hyperactivity disorder (ADHD). Thus, diagnosis according to clinical symptoms cannot be very accurate. It is therefore desirable to develop quantitative criteria for automatic discrimination between these disorders. This study is aimed at designing an efficient decision maker to accurately classify ADHD and BMD patients by analyzing their electroencephalogram (EEG) signals. In this study, 22 channels of EEGs have been recorded from 21 subjects with ADHD and 22 individuals with BMD. Several informative features, such as fractal dimension, band power and autoregressive coefficients, were extracted from the recorded signals. Considering the multimodal overlapping distribution of the obtained features, linear discriminant analysis (LDA) was used to reduce the input dimension in a more separable space to make it more appropriate for the proposed classifier. A piecewise linear classifier based on the extended classifier system for function approximation (XCSF) was modified by developing an adaptive mutation rate, which was proportional to the genotypic content of best individuals and their fitness in each generation. The proposed operator controlled the trade-off between exploration and exploitation while maintaining the diversity in the classifier's population to avoid premature convergence. To assess the effectiveness of the proposed scheme, the extracted features were applied to support vector machine, LDA, nearest neighbor and XCSF classifiers. To evaluate the method, a noisy environment was simulated with different noise amplitudes. It is shown that the results of the proposed technique are more robust as compared to conventional classifiers. Statistical tests demonstrate that the proposed classifier is a promising method for discriminating between ADHD and BMD patients.

Category inference as a function of correlational structure, category discriminability, and number of available cues.

PubMed

Lancaster, Matthew E; Shelhamer, Ryan; Homa, Donald

2013-04-01

Two experiments investigated category inference when categories were composed of correlated or uncorrelated dimensions and the categories overlapped minimally or moderately. When the categories minimally overlapped, the dimensions were strongly correlated with the category label. Following a classification learning phase, subsequent transfer required the selection of either a category label or a feature when one, two, or three features were missing. Experiments 1 and 2 differed primarily in the number of learning blocks prior to transfer. In each experiment, the inference of the category label or category feature was influenced by both dimensional and category correlations, as well as their interaction. The number of cues available at test impacted performance more when the dimensional correlations were zero and category overlap was high. However, a minimal number of cues were sufficient to produce high levels of inference when the dimensions were highly correlated; additional cues had a positive but reduced impact, even when overlap was high. Subjects were generally more accurate in inferring the category label than a category feature regardless of dimensional correlation, category overlap, or number of cues available at test. Whether the category label functioned as a special feature or not was critically dependent upon these embedded correlations, with feature inference driven more strongly by dimensional correlations.

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

Karamatskos, E. T.; Stockhofe, J.; Kevrekidis, P. G.

In this study, we consider a binary repulsive Bose-Einstein condensate in a harmonic trap in one spatial dimension and investigate particular solutions consisting of two dark-bright solitons. There are two different stationary solutions characterized by the phase difference in the bright component, in-phase and out-of-phase states. We show that above a critical particle number in the bright component, a symmetry-breaking bifurcation of the pitchfork type occurs that leads to a new asymmetric solution whereas the parental branch, i.e., the out-of-phase state, becomes unstable. These three different states support different small amplitude oscillations, characterized by an almost stationary density of themore » dark component and a tunneling of the bright component between the two dark solitons. Within a suitable effective double-well picture, these can be understood as the characteristic features of a bosonic Josephson junction (BJJ), and we show within a two-mode approach that all characteristic features of the BJJ phase space are recovered. For larger deviations from the stationary states, the simplifying double-well description breaks down due to the feedback of the bright component onto the dark one, causing the solitons to move. In this regime we observe intricate anharmonic and aperiodic dynamics, exhibiting remnants of the BJJ phase space.« less

Stability and tunneling dynamics of a dark-bright soliton pair in a harmonic trap

DOE PAGES

Karamatskos, E. T.; Stockhofe, J.; Kevrekidis, P. G.; ...

2015-04-30

In this study, we consider a binary repulsive Bose-Einstein condensate in a harmonic trap in one spatial dimension and investigate particular solutions consisting of two dark-bright solitons. There are two different stationary solutions characterized by the phase difference in the bright component, in-phase and out-of-phase states. We show that above a critical particle number in the bright component, a symmetry-breaking bifurcation of the pitchfork type occurs that leads to a new asymmetric solution whereas the parental branch, i.e., the out-of-phase state, becomes unstable. These three different states support different small amplitude oscillations, characterized by an almost stationary density of themore » dark component and a tunneling of the bright component between the two dark solitons. Within a suitable effective double-well picture, these can be understood as the characteristic features of a bosonic Josephson junction (BJJ), and we show within a two-mode approach that all characteristic features of the BJJ phase space are recovered. For larger deviations from the stationary states, the simplifying double-well description breaks down due to the feedback of the bright component onto the dark one, causing the solitons to move. In this regime we observe intricate anharmonic and aperiodic dynamics, exhibiting remnants of the BJJ phase space.« less

Are English-language pedometer instructions readable?

PubMed

Wallace, Lorraine S; Bielak, Kenneth; Linn, Brian

2010-05-01

We evaluated readability and related features of English-language instructions accompanying pedometers, including reading grade level, layout/formatting characteristics, and emphasis of key points. We identified 15 pedometers currently available for purchase in the US. Reading grade level was calculated using Flesch-Kinkaid (FK) and SMOG formulas. Text point size was measured with a C-Thru Ruler. Page and illustration dimensions were measured to the nearest millimeter (mm) with a standard ruler. Layout features were evaluated using the criteria from the User-Friendliness Tool. FK scores ranged from 8th to 11th grade, while SMOG scores ranged from 8th to 12th grade. Text point size averaged 6.9 +/- 1.9 (range = 4-11). Instructions averaged 8.7 +/- 9.0 (range = 0-36) illustrations, most about the size of a US quarter. While many instructions avoided use of specialty fonts (n = 12; 80.0%), most used a minimal amount of white space. Just 4 (26.7%) sets of instructions highlighted the target goal of 10,000 steps-per-day. Pedometer instructions should be revised to meet the recommended 6th grade reading level. Paper size instructions are printed on should be enlarged, thereby allowing for larger text and illustrations, and additional white space. Recommended number of steps per day and proper pedometer positioning should also be predominantly highlighted.

RBSURFpred: Modeling protein accessible surface area in real and binary space using regularized and optimized regression.

PubMed

Tarafder, Sumit; Toukir Ahmed, Md; Iqbal, Sumaiya; Tamjidul Hoque, Md; Sohel Rahman, M

2018-03-14

Accessible surface area (ASA) of a protein residue is an effective feature for protein structure prediction, binding region identification, fold recognition problems etc. Improving the prediction of ASA by the application of effective feature variables is a challenging but explorable task to consider, specially in the field of machine learning. Among the existing predictors of ASA, REGAd 3 p is a highly accurate ASA predictor which is based on regularized exact regression with polynomial kernel of degree 3. In this work, we present a new predictor RBSURFpred, which extends REGAd 3 p on several dimensions by incorporating 58 physicochemical, evolutionary and structural properties into 9-tuple peptides via Chou's general PseAAC, which allowed us to obtain higher accuracies in predicting both real-valued and binary ASA. We have compared RBSURFpred for both real and binary space predictions with state-of-the-art predictors, such as REGAd 3 p and SPIDER2. We also have carried out a rigorous analysis of the performance of RBSURFpred in terms of different amino acids and their properties, and also with biologically relevant case-studies. The performance of RBSURFpred establishes itself as a useful tool for the community. Copyright © 2018 Elsevier Ltd. All rights reserved.

A comparative analysis of predictors of sense of place dimensions: attachment to, dependence on, and identification with lakeshore properties.

PubMed

Jorgensen, Bradley S; Stedman, Richard C

2006-05-01

Sense of place can be conceived as a multidimensional construct representing beliefs, emotions and behavioural commitments concerning a particular geographic setting. This view, grounded in attitude theory, can better reveal complex relationships between the experience of a place and attributes of that place than approaches that do not differentiate cognitive, affective and conative domains. Shoreline property owners (N=290) in northern Wisconsin were surveyed about their sense of place for their lakeshore properties. A predictive model comprising owners' age, length of ownership, participation in recreational activities, days spent on the property, extent of property development, and perceptions of environmental features, was employed to explain the variation in dimensions of sense of place. In general, the results supported a multidimensional approach to sense of place in a context where there were moderate to high correlations among the three place dimensions. Perceptions of environmental features were the biggest predictors of place dimensions, with owners' perceptions of lake importance varying in explanatory power across place dimensions.

V-ELMpiRNAPred: Identification of human piRNAs by the voting-based extreme learning machine (V-ELM) with a new hybrid feature.

PubMed

Pian, Cong; Chen, Yuan-Yuan; Zhang, Jin; Chen, Zhi; Zhang, Guang-Le; Li, Qiang; Yang, Tao; Zhang, Liang-Yun

2017-02-01

Piwi-interacting RNAs (piRNAs) were recently discovered as endogenous small noncoding RNAs. Some recent research suggests that piRNAs may play an important role in cancer. So the precise identification of human piRNAs is a significant work. In this paper, we introduce a series of new features with 80 dimension called short sequence motifs (SSM). A hybrid feature vector with 1444 dimension can be formed by combining 1364 features of [Formula: see text]-mer strings and 80 features of SSM features. We optimize the 1444 dimension features using the feature score criterion (FSC) and list them in descending order according to the scores. The first 462 are selected as the input feature vector in the classifier. Moreover, eight of 80 SSM features appear in the top 20. This indicates that these eight SSM features play an important part in the identification of piRNAs. Since five of the above eight SSM features are associated with nucleotide A and G ('A*G', 'A**G', 'A***G', 'A****G', 'A*****G'). So, we guess there may exist some biological significance. We also use a neural network algorithm called voting-based extreme learning machine (V-ELM) to identify real piRNAs. The Specificity (Sp) and Sensitivity (Sn) of our method are 95.48% and 94.61%, respectively in human species. This result shows that our method is more effective compared with those of the piRPred, piRNApredictor, Asym-Pibomd, Piano and McRUMs. The web service of V-ELMpiRNAPred is available for free at http://mm20132014.wicp.net:38601/velmprepiRNA/Main.jsp .

Four Essential Dimensions of Workplace Learning

ERIC Educational Resources Information Center

Hopwood, Nick

2014-01-01

Purpose: This conceptual paper aims to argue that times, spaces, bodies and things constitute four essential dimensions of workplace learning. It examines how practices relate or hang together, taking Gherardi's texture of practices or connectedness in action as the foundation for making visible essential but often overlooked dimensions of…

Effect of Stress State on Fracture Features

NASA Astrophysics Data System (ADS)

Das, Arpan

2018-02-01

Present article comprehensively explores the influence of specimen thickness on the quantitative estimates of different ductile fractographic features in two dimensions, correlating tensile properties of a reactor pressure vessel steel tested under ambient temperature where the initial crystallographic texture, inclusion content, and their distribution are kept unaltered. It has been investigated that the changes in tensile fracture morphology of these steels are directly attributable to the resulting stress-state history under tension for given specimen dimensions.

Fractal based modelling and analysis of electromyography (EMG) to identify subtle actions.

PubMed

Arjunan, Sridhar P; Kumar, Dinesh K

2007-01-01

The paper reports the use of fractal theory and fractal dimension to study the non-linear properties of surface electromyogram (sEMG) and to use these properties to classify subtle hand actions. The paper reports identifying a new feature of the fractal dimension, the bias that has been found to be useful in modelling the muscle activity and of sEMG. Experimental results demonstrate that the feature set consisting of bias values and fractal dimension of the recordings is suitable for classification of sEMG against the different hand gestures. The scatter plots demonstrate the presence of simple relationships of these features against the four hand gestures. The results indicate that there is small inter-experimental variation but large inter-subject variation. This may be due to differences in the size and shape of muscles for different subjects. The possible applications of this research include use in developing prosthetic hands, controlling machines and computers.

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata

PubMed Central

Liu, Aiming; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

2017-01-01

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain–computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain–computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain–computer interface systems. PMID:29117100

Feature Selection for Motor Imagery EEG Classification Based on Firefly Algorithm and Learning Automata.

PubMed

Liu, Aiming; Chen, Kun; Liu, Quan; Ai, Qingsong; Xie, Yi; Chen, Anqi

2017-11-08

Motor Imagery (MI) electroencephalography (EEG) is widely studied for its non-invasiveness, easy availability, portability, and high temporal resolution. As for MI EEG signal processing, the high dimensions of features represent a research challenge. It is necessary to eliminate redundant features, which not only create an additional overhead of managing the space complexity, but also might include outliers, thereby reducing classification accuracy. The firefly algorithm (FA) can adaptively select the best subset of features, and improve classification accuracy. However, the FA is easily entrapped in a local optimum. To solve this problem, this paper proposes a method of combining the firefly algorithm and learning automata (LA) to optimize feature selection for motor imagery EEG. We employed a method of combining common spatial pattern (CSP) and local characteristic-scale decomposition (LCD) algorithms to obtain a high dimensional feature set, and classified it by using the spectral regression discriminant analysis (SRDA) classifier. Both the fourth brain-computer interface competition data and real-time data acquired in our designed experiments were used to verify the validation of the proposed method. Compared with genetic and adaptive weight particle swarm optimization algorithms, the experimental results show that our proposed method effectively eliminates redundant features, and improves the classification accuracy of MI EEG signals. In addition, a real-time brain-computer interface system was implemented to verify the feasibility of our proposed methods being applied in practical brain-computer interface systems.

Classification of molecular structure images by using ANN, RF, LBP, HOG, and size reduction methods for early stomach cancer detection

NASA Astrophysics Data System (ADS)

Aytaç Korkmaz, Sevcan; Binol, Hamidullah

2018-03-01

Patients who die from stomach cancer are still present. Early diagnosis is crucial in reducing the mortality rate of cancer patients. Therefore, computer aided methods have been developed for early detection in this article. Stomach cancer images were obtained from Fırat University Medical Faculty Pathology Department. The Local Binary Patterns (LBP) and Histogram of Oriented Gradients (HOG) features of these images are calculated. At the same time, Sammon mapping, Stochastic Neighbor Embedding (SNE), Isomap, Classical multidimensional scaling (MDS), Local Linear Embedding (LLE), Linear Discriminant Analysis (LDA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Laplacian Eigenmaps methods are used for dimensional the reduction of the features. The high dimension of these features has been reduced to lower dimensions using dimensional reduction methods. Artificial neural networks (ANN) and Random Forest (RF) classifiers were used to classify stomach cancer images with these new lower feature sizes. New medical systems have developed to measure the effects of these dimensions by obtaining features in different dimensional with dimensional reduction methods. When all the methods developed are compared, it has been found that the best accuracy results are obtained with LBP_MDS_ANN and LBP_LLE_ANN methods.

Generalized Kustaanheimo-Stiefel transformations

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

Komarov, L.I.; Van Hoang, L.

1994-10-01

A theory is given for the construction of generalized Kustaanheimo-Stiefel (KS) transformations for dimensions q+1 (q=2{sup h}, h=0, 1, 2,...) of the Kepler problem, and the following proposition is proved: A connection between the Kepler problem in a real space of dimension q+1 and the problem of an isotropic harmonic oscillator in a real space dimension N exists and can be established by means of generalized KS transformations in the cases in which N=2q and q=2{sup h} (h=0, 1, 2,...). A simple graphical prescription for constructing generalized KS transformations that realize this connection is proposed.

Mutually unbiased bases and semi-definite programming

NASA Astrophysics Data System (ADS)

Brierley, Stephen; Weigert, Stefan

2010-11-01

A complex Hilbert space of dimension six supports at least three but not more than seven mutually unbiased bases. Two computer-aided analytical methods to tighten these bounds are reviewed, based on a discretization of parameter space and on Gröbner bases. A third algorithmic approach is presented: the non-existence of more than three mutually unbiased bases in composite dimensions can be decided by a global optimization method known as semidefinite programming. The method is used to confirm that the spectral matrix cannot be part of a complete set of seven mutually unbiased bases in dimension six.

Girls in detail, boys in shape: gender differences when drawing cubes in depth.

PubMed

Lange-Küttner, C; Ebersbach, M

2013-08-01

The current study tested gender differences in the developmental transition from drawing cubes in two- versus three dimensions (3D), and investigated the underlying spatial abilities. Six- to nine-year-old children (N = 97) drew two occluding model cubes and solved several other spatial tasks. Girls more often unfolded the various sides of the cubes into a layout, also called diagrammatic cube drawing (object design detail). In girls, the best predictor for drawing the cubes was Mental Rotation Test (MRT) accuracy. In contrast, boys were more likely to preserve the optical appearance of the cube array. Their drawing in 3D was best predicted by MRT reaction time and the Embedded Figures Test (EFT). This confirmed boys' stronger focus on the contours of an object silhouette (object shape). It is discussed whether the two gender-specific approaches to drawing in three dimensions reflect two sides of the appearance-reality distinction in drawing, that is graphic syntax of object design features versus visual perception of projective space. © 2012 The British Psychological Society.

Supporting Teachers to Automatically Build Accessible Pedagogical Resources: The APEINTA Project

NASA Astrophysics Data System (ADS)

Iglesias, Ana; Moreno, Lourdes; Jiménez, Javier

Most of the universities in Europe have started their process of adaptation towards a common educational space according to the European Higher Education Area (EHEA). The social dimension of the Bologna Process is a constituent part of the EHEA and it is a necessary condition for the attractiveness and competitiveness of the EHEA. Two of the main features of the social dimension are the equal access for all the students and the lifelong learning. One of the main problems of the adaptation process to the EHEA is that the teachers have no previous references and models to develop new pedagogical experiences accessible to all the students, nevertheless of their abilities, capabilities or accessibility characteristics. The APEINTA project presented in this paper can be used as a helpful tool for teachers in order to cope with the teaching demands of EHEA, helping the teachers to automatically build accessible pedagogical resources even when the teachers are not accessibility experts. This educational project has been successfully used in 2009 in two different degrees at the Carlos III University of Madrid: Computer Science and Library and Information Science.

Three-dimensional interaction and movements of various dislocations in anisotropic bicrystals with semicoherent interfaces

NASA Astrophysics Data System (ADS)

Vattré, A.; Pan, E.

2018-07-01

Lattice dislocation interactions with semicoherent interfaces are investigated by means of anisotropic field solutions in metallic homo- and hetero-structures. The present framework is based on the mathematically elegant and computationally powerful Stroh formalism, combining further with the Fourier integral and series transforms, which cover different shapes and dimensions of various extrinsic and intrinsic dislocations. Two-dimensional equi-spaced arrays of straight lattice dislocations and finite arrangements of piled-up dislocations as well as any polygonal and elliptical dislocation loops in three dimensions are considered using a superposition scheme. Self, image and Peach-Koehler forces are derived to compute the equilibrium dislocation positions in pile-ups, including the internal structures and energetics of the interfacial dislocation networks. For illustration, the effects due to the elastic and misfit mismatches are discussed in the pure misfit Au/Cu and heterophase Cu/Nb systems, while discrepancies resulting from the approximation of isotropic elasticity are clearly exhibited. These numerical examples not only feature and enhance the existing works in anisotropic bimaterials, but also promote a novel opportunity of analyzing the equilibrium shapes of planar glide dislocation loops at nanoscale.

Impact of feature saliency on visual category learning.

PubMed

Hammer, Rubi

2015-01-01

People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the 'essence' of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies.

Impact of feature saliency on visual category learning

PubMed Central

Hammer, Rubi

2015-01-01

People have to sort numerous objects into a large number of meaningful categories while operating in varying contexts. This requires identifying the visual features that best predict the ‘essence’ of objects (e.g., edibility), rather than categorizing objects based on the most salient features in a given context. To gain this capacity, visual category learning (VCL) relies on multiple cognitive processes. These may include unsupervised statistical learning, that requires observing multiple objects for learning the statistics of their features. Other learning processes enable incorporating different sources of supervisory information, alongside the visual features of the categorized objects, from which the categorical relations between few objects can be deduced. These deductions enable inferring that objects from the same category may differ from one another in some high-saliency feature dimensions, whereas lower-saliency feature dimensions can best differentiate objects from distinct categories. Here I illustrate how feature saliency affects VCL, by also discussing kinds of supervisory information enabling reflective categorization. Arguably, principles debated here are often being ignored in categorization studies. PMID:25954220

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains.

PubMed

Onken, Arno; Liu, Jian K; Karunasekara, P P Chamanthi R; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-11-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding.

Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains

PubMed Central

Onken, Arno; Liu, Jian K.; Karunasekara, P. P. Chamanthi R.; Delis, Ioannis; Gollisch, Tim; Panzeri, Stefano

2016-01-01

Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in their spatial dimension (differences in stimulus tuning across neurons at different locations), in their temporal dimension (temporal neural response variations), or in their combination (temporally coordinated neural population firing). Here we investigate the utility of tensor factorizations of population spike trains along space and time. These factorizations decompose a dataset of single-trial population spike trains into spatial firing patterns (combinations of neurons firing together), temporal firing patterns (temporal activation of these groups of neurons) and trial-dependent activation coefficients (strength of recruitment of such neural patterns on each trial). We validated various factorization methods on simulated data and on populations of ganglion cells simultaneously recorded in the salamander retina. We found that single-trial tensor space-by-time decompositions provided low-dimensional data-robust representations of spike trains that capture efficiently both their spatial and temporal information about sensory stimuli. Tensor decompositions with orthogonality constraints were the most efficient in extracting sensory information, whereas non-negative tensor decompositions worked well even on non-independent and overlapping spike patterns, and retrieved informative firing patterns expressed by the same population in response to novel stimuli. Our method showed that populations of retinal ganglion cells carried information in their spike timing on the ten-milliseconds-scale about spatial details of natural images. This information could not be recovered from the spike counts of these cells. First-spike latencies carried the majority of information provided by the whole spike train about fine-scale image features, and supplied almost as much information about coarse natural image features as firing rates. Together, these results highlight the importance of spike timing, and particularly of first-spike latencies, in retinal coding. PMID:27814363

Person/Situation Selection Research: The Problem of Identifying Salient Situational Dimensions. Research Report No. 13.

ERIC Educational Resources Information Center

Schneider, Benjamin

The study was concerned with the persistent problem in conducting person/situation research--the identification of relevant dimensions or features of the situation. Since the usual strategy for discovering relevant perceptual dimension of organizational life is to ask organizational employees to respond to a set of predetermined questions, this…

Fractal analysis of seafloor textures for target detection in synthetic aperture sonar imagery

NASA Astrophysics Data System (ADS)

Nabelek, T.; Keller, J.; Galusha, A.; Zare, A.

2018-04-01

Fractal analysis of an image is a mathematical approach to generate surface related features from an image or image tile that can be applied to image segmentation and to object recognition. In undersea target countermeasures, the targets of interest can appear as anomalies in a variety of contexts, visually different textures on the seafloor. In this paper, we evaluate the use of fractal dimension as a primary feature and related characteristics as secondary features to be extracted from synthetic aperture sonar (SAS) imagery for the purpose of target detection. We develop three separate methods for computing fractal dimension. Tiles with targets are compared to others from the same background textures without targets. The different fractal dimension feature methods are tested with respect to how well they can be used to detect targets vs. false alarms within the same contexts. These features are evaluated for utility using a set of image tiles extracted from a SAS data set generated by the U.S. Navy in conjunction with the Office of Naval Research. We find that all three methods perform well in the classification task, with a fractional Brownian motion model performing the best among the individual methods. We also find that the secondary features are just as useful, if not more so, in classifying false alarms vs. targets. The best classification accuracy overall, in our experimentation, is found when the features from all three methods are combined into a single feature vector.

Photomask linewidth comparison by PTB and NIST

NASA Astrophysics Data System (ADS)

Bergmann, D.; Bodermann, B.; Bosse, H.; Buhr, E.; Dai, G.; Dixson, R.; Häßler-Grohne, W.; Hahm, K.; Wurm, M.

2015-10-01

We report the initial results of a recent bilateral comparison of linewidth or critical dimension (CD) calibrations on photomask line features between two national metrology institutes (NMIs): the National Institute of Standards and Technology (NIST) in the United States and the Physikalisch-Technische Bundesanstalt (PTB) in Germany. For the comparison, a chrome on glass (CoG) photomask was used which has a layout of line features down to 100 nm nominal size. Different measurement methods were used at both institutes. These included: critical dimension atomic force microscopy (CD-AFM), CD scanning electron microscopy (CD-SEM) and ultraviolet (UV) transmission optical microscopy. The measurands are CD at 50 % height of the features as well as sidewall angle and line width roughness (LWR) of the features. On the isolated opaque features, we found agreement of the CD measurements at the 3 nm to 5 nm level on most features - usually within the combined expanded uncertainties of the measurements.

Identifying Network Structure, Influencers and Social Mood in Digital Spheres: A Sentiment and Content Analysis of Down Syndrome Awareness

ERIC Educational Resources Information Center

Sani-Bozkurt, Sunagul

2018-01-01

Down syndrome is a sensitive subject and one that requires efforts being made to improve conditions for individuals with Down syndrome across multiple dimensions. Social awareness is one of the important dimensions for the inclusion of individuals with Down syndrome. Online spaces, as well as offline spaces, are an important part of our daily…

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

Generalized Weierstrass-Mandelbrot Function Model for Actual Stocks Markets Indexes with Nonlinear Characteristics

NASA Astrophysics Data System (ADS)

Zhang, L.; Yu, C.; Sun, J. Q.

2015-03-01

It is difficult to simulate the dynamical behavior of actual financial markets indexes effectively, especially when they have nonlinear characteristics. So it is significant to propose a mathematical model with these characteristics. In this paper, we investigate a generalized Weierstrass-Mandelbrot function (WMF) model with two nonlinear characteristics: fractal dimension D where 2 > D > 1.5 and Hurst exponent (H) where 1 > H > 0.5 firstly. And then we study the dynamical behavior of H for WMF as D and the spectrum of the time series γ change in three-dimensional space, respectively. Because WMF and the actual stock market indexes have two common features: fractal behavior using fractal dimension and long memory effect by Hurst exponent, we study the relationship between WMF and the actual stock market indexes. We choose a random value of γ and fixed value of D for WMF to simulate the S&P 500 indexes at different time ranges. As shown in the simulation results of three-dimensional space, we find that γ is important in WMF model and different γ may have the same effect for the nonlinearity of WMF. Then we calculate the skewness and kurtosis of actual Daily S&P 500 index in different time ranges which can be used to choose the value of γ. Based on these results, we choose appropriate γ, D and initial value into WMF to simulate Daily S&P 500 indexes. Using the fit line method in two-dimensional space for the simulated values, we find that the generalized WMF model is effective for simulating different actual stock market indexes in different time ranges. It may be useful for understanding the dynamical behavior of many different financial markets.

Inhomogeneous compact extra dimensions

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

Bronnikov, K.A.; Budaev, R.I.; Grobov, A.V.

We show that an inhomogeneous compact extra space possesses two necessary features— their existence does not contradict the observable value of the cosmological constant Λ{sub 4} in pure f ( R ) theory, and the extra dimensions are stable relative to the 'radion mode' of perturbations, the only mode considered. For a two-dimensional extra space, both analytical and numerical solutions for the metric are found, able to provide a zero or arbitrarily small Λ{sub 4}. A no-go theorem has also been proved, that maximally symmetric compact extra spaces are inconsistent with 4D Minkowski space in the framework of pure fmore » ( R ) gravity.« less

The use of multidimensional scaling for facilities layout - An application to the design of the Space Station

NASA Technical Reports Server (NTRS)

Tullis, Thomas S.; Bied Sperling, Barbra; Steinberg, A. L.

1986-01-01

Before an optimum layout of the facilities for the proposed Space Station can be designed, it is necessary to understand the functions that will be performed by the Space Station crew and the relationships among those functions. Five criteria for assessing functional relationships were identified. For each of these criteria, a matrix representing the degree of association of all pairs of functions was developed. The key to making inferences about the layout of the Space Station from these matrices was the use of multidimensional scaling (MDS). Applying MDS to these matrices resulted in spatial configurations of the crew functions in which smaller distances in the MDS configuration reflected closer associations. An MDS analysis of a composite matrix formed by combining the five individual matrices resulted in two dimensions that describe the configuration: a 'private-public' dimension and a 'group-individual' dimension. Seven specific recommendations for Space Station layout were derived from analyses of the MDS configurations. Although these techniques have been applied to the design of the Space Station, they can be applied to the design of any facility where people live or work.

A novel image retrieval algorithm based on PHOG and LSH

NASA Astrophysics Data System (ADS)

Wu, Hongliang; Wu, Weimin; Peng, Jiajin; Zhang, Junyuan

2017-08-01

PHOG can describe the local shape of the image and its relationship between the spaces. The using of PHOG algorithm to extract image features in image recognition and retrieval and other aspects have achieved good results. In recent years, locality sensitive hashing (LSH) algorithm has been superior to large-scale data in solving near-nearest neighbor problems compared with traditional algorithms. This paper presents a novel image retrieval algorithm based on PHOG and LSH. First, we use PHOG to extract the feature vector of the image, then use L different LSH hash table to reduce the dimension of PHOG texture to index values and map to different bucket, and finally extract the corresponding value of the image in the bucket for second image retrieval using Manhattan distance. This algorithm can adapt to the massive image retrieval, which ensures the high accuracy of the image retrieval and reduces the time complexity of the retrieval. This algorithm is of great significance.

The Sequential Implementation of Array Processors when there is Directional Uncertainty

DTIC Science & Technology

1975-08-01

University of Washington kindly supplied office space and ccputing facilities. -The author hat, benefited greatly from discussions with several other...if i Q- inverse of Q I L general observation space R general vector of observation _KR general observation vector of dimension K Exiv] "Tf -- ’ -"-T’T...7" i ’i ’:"’ - ’ ; ’ ’ ’ ’ ’ ’" ’"- Glossary of Symbols (continued) R. ith observation 1 Rm real vector space of dimension m R(T) autocorrelation

Quantile Regression for Analyzing Heterogeneity in Ultra-high Dimension

PubMed Central

Wang, Lan; Wu, Yichao

2012-01-01

Ultra-high dimensional data often display heterogeneity due to either heteroscedastic variance or other forms of non-location-scale covariate effects. To accommodate heterogeneity, we advocate a more general interpretation of sparsity which assumes that only a small number of covariates influence the conditional distribution of the response variable given all candidate covariates; however, the sets of relevant covariates may differ when we consider different segments of the conditional distribution. In this framework, we investigate the methodology and theory of nonconvex penalized quantile regression in ultra-high dimension. The proposed approach has two distinctive features: (1) it enables us to explore the entire conditional distribution of the response variable given the ultra-high dimensional covariates and provides a more realistic picture of the sparsity pattern; (2) it requires substantially weaker conditions compared with alternative methods in the literature; thus, it greatly alleviates the difficulty of model checking in the ultra-high dimension. In theoretic development, it is challenging to deal with both the nonsmooth loss function and the nonconvex penalty function in ultra-high dimensional parameter space. We introduce a novel sufficient optimality condition which relies on a convex differencing representation of the penalized loss function and the subdifferential calculus. Exploring this optimality condition enables us to establish the oracle property for sparse quantile regression in the ultra-high dimension under relaxed conditions. The proposed method greatly enhances existing tools for ultra-high dimensional data analysis. Monte Carlo simulations demonstrate the usefulness of the proposed procedure. The real data example we analyzed demonstrates that the new approach reveals substantially more information compared with alternative methods. PMID:23082036

Scalable non-negative matrix tri-factorization.

PubMed

Čopar, Andrej; Žitnik, Marinka; Zupan, Blaž

2017-01-01

Matrix factorization is a well established pattern discovery tool that has seen numerous applications in biomedical data analytics, such as gene expression co-clustering, patient stratification, and gene-disease association mining. Matrix factorization learns a latent data model that takes a data matrix and transforms it into a latent feature space enabling generalization, noise removal and feature discovery. However, factorization algorithms are numerically intensive, and hence there is a pressing challenge to scale current algorithms to work with large datasets. Our focus in this paper is matrix tri-factorization, a popular method that is not limited by the assumption of standard matrix factorization about data residing in one latent space. Matrix tri-factorization solves this by inferring a separate latent space for each dimension in a data matrix, and a latent mapping of interactions between the inferred spaces, making the approach particularly suitable for biomedical data mining. We developed a block-wise approach for latent factor learning in matrix tri-factorization. The approach partitions a data matrix into disjoint submatrices that are treated independently and fed into a parallel factorization system. An appealing property of the proposed approach is its mathematical equivalence with serial matrix tri-factorization. In a study on large biomedical datasets we show that our approach scales well on multi-processor and multi-GPU architectures. On a four-GPU system we demonstrate that our approach can be more than 100-times faster than its single-processor counterpart. A general approach for scaling non-negative matrix tri-factorization is proposed. The approach is especially useful parallel matrix factorization implemented in a multi-GPU environment. We expect the new approach will be useful in emerging procedures for latent factor analysis, notably for data integration, where many large data matrices need to be collectively factorized.

Optimum ArFi laser bandwidth for 10nm node logic imaging performance

NASA Astrophysics Data System (ADS)

Alagna, Paolo; Zurita, Omar; Timoshkov, Vadim; Wong, Patrick; Rechtsteiner, Gregory; Baselmans, Jan; Mailfert, Julien

2015-03-01

Lithography process window (PW) and CD uniformity (CDU) requirements are being challenged with scaling across all device types. Aggressive PW and yield specifications put tight requirements on scanner performance, especially on focus budgets resulting in complicated systems for focus control. In this study, an imec N10 Logic-type test vehicle was used to investigate the E95 bandwidth impact on six different Metal 1 Logic features. The imaging metrics that track the impact of light source E95 bandwidth on performance of hot spots are: process window (PW), line width roughness (LWR), and local critical dimension uniformity (LCDU). In the first section of this study, the impact of increasing E95 bandwidth was investigated to observe the lithographic process control response of the specified logic features. In the second section, a preliminary assessment of the impact of lower E95 bandwidth was performed. The impact of lower E95 bandwidth on local intensity variability was monitored through the CDU of line end features and the LWR power spectral density (PSD) of line/space patterns. The investigation found that the imec N10 test vehicle (with OPC optimized for standard E95 bandwidth of300fm) features exposed at 200fm showed pattern specific responses, suggesting areas of potential interest for further investigation.

A Relational Encoding of a Conceptual Model with Multiple Temporal Dimensions

NASA Astrophysics Data System (ADS)

Gubiani, Donatella; Montanari, Angelo

The theoretical interest and the practical relevance of a systematic treatment of multiple temporal dimensions is widely recognized in the database and information system communities. Nevertheless, most relational databases have no temporal support at all. A few of them provide a limited support, in terms of temporal data types and predicates, constructors, and functions for the management of time values (borrowed from the SQL standard). One (resp., two) temporal dimensions are supported by historical and transaction-time (resp., bitemporal) databases only. In this paper, we provide a relational encoding of a conceptual model featuring four temporal dimensions, namely, the classical valid and transaction times, plus the event and availability times. We focus our attention on the distinctive technical features of the proposed temporal extension of the relation model. In the last part of the paper, we briefly show how to implement it in a standard DBMS.

Quality Perception of the 2012 World Indoor Athletics Championships

PubMed Central

2016-01-01

Abstract The objective of this study was to compare the views of spectators concerning the quality perception of the World Indoor Athletics Championships. The study group consisted of 568 spectators who watched the events. A measurement scale of event quality in spectator sports (SEQSS) developed by Ko et al. (2011) was used as a data collection tool in the study. In order to determine the views of the spectators concerning the quality of the Indoor Athletics Championships, the dimensions constituting the scale were compared according to the demographic features of the sample group. As a consequence, important differences in most of the dimensions of the scale were revealed with respect to the demographic data of the subjects. The most relevant finding of the study is that the dimension of “physical environment quality”, which is one of the dimensions constituting the event quality, differed significantly in all comparisons that were made according to demographic features. PMID:28031769

Validating continuous digital light processing (cDLP) additive manufacturing accuracy and tissue engineering utility of a dye-initiator package.

PubMed

Wallace, Jonathan; Wang, Martha O; Thompson, Paul; Busso, Mallory; Belle, Vaijayantee; Mammoser, Nicole; Kim, Kyobum; Fisher, John P; Siblani, Ali; Xu, Yueshuo; Welter, Jean F; Lennon, Donald P; Sun, Jiayang; Caplan, Arnold I; Dean, David

2014-03-01

This study tested the accuracy of tissue engineering scaffold rendering via the continuous digital light processing (cDLP) light-based additive manufacturing technology. High accuracy (i.e., <50 µm) allows the designed performance of features relevant to three scale spaces: cell-scaffold, scaffold-tissue, and tissue-organ interactions. The biodegradable polymer poly (propylene fumarate) was used to render highly accurate scaffolds through the use of a dye-initiator package, TiO2 and bis (2,4,6-trimethylbenzoyl)phenylphosphine oxide. This dye-initiator package facilitates high accuracy in the Z dimension. Linear, round, and right-angle features were measured to gauge accuracy. Most features showed accuracies between 5.4-15% of the design. However, one feature, an 800 µm diameter circular pore, exhibited a 35.7% average reduction of patency. Light scattered in the x, y directions by the dye may have reduced this feature's accuracy. Our new fine-grained understanding of accuracy could be used to make further improvements by including corrections in the scaffold design software. Successful cell attachment occurred with both canine and human mesenchymal stem cells (MSCs). Highly accurate cDLP scaffold rendering is critical to the design of scaffolds that both guide bone regeneration and that fully resorb. Scaffold resorption must occur for regenerated bone to be remodeled and, thereby, achieve optimal strength.

Enhancing the Discrimination Ability of a Gas Sensor Array Based on a Novel Feature Selection and Fusion Framework.

PubMed

Deng, Changjian; Lv, Kun; Shi, Debo; Yang, Bo; Yu, Song; He, Zhiyi; Yan, Jia

2018-06-12

In this paper, a novel feature selection and fusion framework is proposed to enhance the discrimination ability of gas sensor arrays for odor identification. Firstly, we put forward an efficient feature selection method based on the separability and the dissimilarity to determine the feature selection order for each type of feature when increasing the dimension of selected feature subsets. Secondly, the K-nearest neighbor (KNN) classifier is applied to determine the dimensions of the optimal feature subsets for different types of features. Finally, in the process of establishing features fusion, we come up with a classification dominance feature fusion strategy which conducts an effective basic feature. Experimental results on two datasets show that the recognition rates of Database I and Database II achieve 97.5% and 80.11%, respectively, when k = 1 for KNN classifier and the distance metric is correlation distance (COR), which demonstrates the superiority of the proposed feature selection and fusion framework in representing signal features. The novel feature selection method proposed in this paper can effectively select feature subsets that are conducive to the classification, while the feature fusion framework can fuse various features which describe the different characteristics of sensor signals, for enhancing the discrimination ability of gas sensors and, to a certain extent, suppressing drift effect.

Propagation characteristics of some novel coplanar waveguide transmission lines on GaAs at MM-wave frequencies

NASA Technical Reports Server (NTRS)

Simons, Rainee N.

1986-01-01

Three new Coplanar Waveguide (CPW) transmission lines, namely, Suspended CPW (SCPW), Stripline-like Suspended CPW (SSCPW) and Inverted CPW (ICPW), are proposed and also analyzed for their propagation characteristics. The substrate thickness, permittivity and dimensions of housing are assumed to be arbitrary. These structures have the following advantages over conventional CPW. Firstly, the ratio of guide wavelength to free space wavelength is closer to unity which results in larger dimensions and hence lower tolerances. Secondly, the effective dielectric constant is lower and hence the electromagnetic field energies are concentrated more in the air regions which should reduce attenuation. Thirdly, for a prescribed impedance level, the above structures have a wider slot width for identical strip width. Thus, low impedance lines can be achieved with reasonable slot dimensions. Fourthly, in an inverted CPW shunt mounting of active devices, such as Gunn and IMPATT diodes, between the strip and the metal trough is possible. This feature further enhances the attractiveness of the above structures. Lastly, an E-plane probe type transition from a rectangular waveguide to suspended CPW can also be easily realized. The computed results for GaAs at Ka-band illustrate the variation of normalized guide wavelength, effective dielectric constant and the characteristic impedance as a function of the: (1) frequency; (2) distance of separation between the trough side walls; (3) normalized strip and slot widths; and (4) normalized air gap.

Non-monotonicity of Trace Distance Under Tensor Products

NASA Astrophysics Data System (ADS)

Maziero, Jonas

2015-10-01

The trace distance (TD) possesses several of the good properties required for a faithful distance measure in the quantum state space. Despite its importance and ubiquitous use in quantum information science, one of its questionable features, its possible non-monotonicity under taking tensor products of its arguments (NMuTP), has been hitherto unexplored. In this article, we advance analytical and numerical investigations of this issue considering different classes of states living in a discrete and finite dimensional Hilbert space. Our results reveal that although this property of TD does not show up for pure states and for some particular classes of mixed states, it is present in a non-negligible fraction of the regarded density operators. Hence, even though the percentage of quartets of states leading to the NMuTP drawback of TD and its strength decrease as the system's dimension grows, this property of TD must be taken into account before using it as a figure of merit for distinguishing mixed quantum states.

OAST Space Theme Workshop. Volume 2: Theme summary. 5: Global service (no. 11). A. Statement. B. 26 April 1976 presentation. C. Summary

NASA Technical Reports Server (NTRS)

1976-01-01

The benefits to be obtained from cost-effective global observation of the earth, its environment, and its natural and man-made features are examined using typical spacecraft and missions which could enhance the benefits of space operations. The technology needs and areas of interest include: (1) a ten-fold increase in the dimensions of deployable and erectable structures to provide booms, antennas, and platforms for global sensor systems; (2) control and stabilization systems capable of pointing accuracies of 1 arc second or less to locate targets of interest and maintain platform or sensor orientation during operations; (3) a factor of five improvements in spacecraft power capacity to support payloads and supporting electronics; (4) auxiliary propulsion systems capable of 5 to 10 years on orbit operation; (5) multipurpose sensors; and (6) end-to-end data management and an information system configured to accept new components or concepts as they develop.

Size and space controlled hexagonal arrays of superparamagnetic iron oxide nanodots: magnetic studies and application

PubMed Central

Ghoshal, Tandra; Maity, Tuhin; Senthamaraikannan, Ramsankar; Shaw, Matthew T.; Carolan, Patrick; Holmes, Justin D.; Roy, Saibal; Morris, Michael A.

2013-01-01

Highly dense hexagonally arranged iron oxide nanodots array were fabricated using PS-b-PEO self-assembled patterns. The copolymer molecular weight, composition and choice of annealing solvent/s allows dimensional and structural control of the nanopatterns at large scale. A mechanism is proposed to create scaffolds through degradation and/or modification of cylindrical domains. A methodology based on selective metal ion inclusion and subsequent processing was used to create iron oxide nanodots array. The nanodots have uniform size and shape and their placement mimics the original self-assembled nanopatterns. For the first time these precisely defined and size selective systems of ordered nanodots allow careful investigation of magnetic properties in dimensions from 50 nm to 10 nm, which delineate the nanodots are superparamagnetic, well-isolated and size monodispersed. This diameter/spacing controlled iron oxide nanodots systems were demonstrated as a resistant mask over silicon to fabricate densely packed, identical ordered, high aspect ratio silicon nanopillars and nanowire features. PMID:24072037

Tasks and premises in quantum state determination

NASA Astrophysics Data System (ADS)

Carmeli, Claudio; Heinosaari, Teiko; Schultz, Jussi; Toigo, Alessandro

2014-02-01

The purpose of quantum tomography is to determine an unknown quantum state from measurement outcome statistics. There are two obvious ways to generalize this setting. First, our task need not be the determination of any possible input state but only some input states, for instance pure states. Second, we may have some prior information, or premise, which guarantees that the input state belongs to some subset of states, for instance the set of states with rank less than half of the dimension of the Hilbert space. We investigate state determination under these two supplemental features, concentrating on the cases where the task and the premise are statements about the rank of the unknown state. We characterize the structure of quantum observables (positive operator valued measures) that are capable of fulfilling these type of determination tasks. After the general treatment we focus on the class of covariant phase space observables, thus providing physically relevant examples of observables both capable and incapable of performing these tasks. In this context, the effect of noise is discussed.

Renormalization group approach to symmetry protected topological phases

NASA Astrophysics Data System (ADS)

van Nieuwenburg, Evert P. L.; Schnyder, Andreas P.; Chen, Wei

2018-04-01

A defining feature of a symmetry protected topological phase (SPT) in one dimension is the degeneracy of the Schmidt values for any given bipartition. For the system to go through a topological phase transition separating two SPTs, the Schmidt values must either split or cross at the critical point in order to change their degeneracies. A renormalization group (RG) approach based on this splitting or crossing is proposed, through which we obtain an RG flow that identifies the topological phase transitions in the parameter space. Our approach can be implemented numerically in an efficient manner, for example, using the matrix product state formalism, since only the largest first few Schmidt values need to be calculated with sufficient accuracy. Using several concrete models, we demonstrate that the critical points and fixed points of the RG flow coincide with the maxima and minima of the entanglement entropy, respectively, and the method can serve as a numerically efficient tool to analyze interacting SPTs in the parameter space.

Predicting DNA binding proteins using support vector machine with hybrid fractal features.

PubMed

Niu, Xiao-Hui; Hu, Xue-Hai; Shi, Feng; Xia, Jing-Bo

2014-02-21

DNA-binding proteins play a vitally important role in many biological processes. Prediction of DNA-binding proteins from amino acid sequence is a significant but not fairly resolved scientific problem. Chaos game representation (CGR) investigates the patterns hidden in protein sequences, and visually reveals previously unknown structure. Fractal dimensions (FD) are good tools to measure sizes of complex, highly irregular geometric objects. In order to extract the intrinsic correlation with DNA-binding property from protein sequences, CGR algorithm, fractal dimension and amino acid composition are applied to formulate the numerical features of protein samples in this paper. Seven groups of features are extracted, which can be computed directly from the primary sequence, and each group is evaluated by the 10-fold cross-validation test and Jackknife test. Comparing the results of numerical experiments, the group of amino acid composition and fractal dimension (21-dimension vector) gets the best result, the average accuracy is 81.82% and average Matthew's correlation coefficient (MCC) is 0.6017. This resulting predictor is also compared with existing method DNA-Prot and shows better performances. © 2013 The Authors. Published by Elsevier Ltd All rights reserved.

Place of Diversity in the Current Ethiopian Education and Training Policy: Analysis of Cardinal Dimensions

ERIC Educational Resources Information Center

Sime, Tariku; Latchanna, Gara

2016-01-01

The purpose of this paper was to investigate to what extent the diversity dimensions are addressed in the current Education and Training Policy. To that end, document analysis was employed. The major diversity dimensions were analyzed based on their cardinal features. The study demonstrated that there is an ambitious need to address issues of…

The Analysis of Dimensionality Reduction Techniques in Cryptographic Object Code Classification

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

Jason L. Wright; Milos Manic

2010-05-01

This paper compares the application of three different dimension reduction techniques to the problem of locating cryptography in compiled object code. A simple classi?er is used to compare dimension reduction via sorted covariance, principal component analysis, and correlation-based feature subset selection. The analysis concentrates on the classi?cation accuracy as the number of dimensions is increased.

Instability of standing waves for Klein-Gordon-Zakharov equations with different propagation speeds in three space dimensions

NASA Astrophysics Data System (ADS)

Gan, Zaihui; Zhang, Jian

2005-07-01

This paper is concerned with the standing wave for Klein-Gordon-Zakharov equations with different propagation speeds in three space dimensions. The existence of standing wave with the ground state is established by applying an intricate variational argument and the instability of the standing wave is shown by applying Pagne and Sattinger's potential well argument and Levine's concavity method.

Stability and chaos in Kustaanheimo-Stiefel space induced by the Hopf fibration

NASA Astrophysics Data System (ADS)

Roa, Javier; Urrutxua, Hodei; Peláez, Jesús

2016-07-01

The need for the extra dimension in Kustaanheimo-Stiefel (KS) regularization is explained by the topology of the Hopf fibration, which defines the geometry and structure of KS space. A trajectory in Cartesian space is represented by a four-dimensional manifold called the fundamental manifold. Based on geometric and topological aspects classical concepts of stability are translated to KS language. The separation between manifolds of solutions generalizes the concept of Lyapunov stability. The dimension-raising nature of the fibration transforms fixed points, limit cycles, attractive sets, and Poincaré sections to higher dimensional subspaces. From these concepts chaotic systems are studied. In strongly perturbed problems, the numerical error can break the topological structure of KS space: points in a fibre are no longer transformed to the same point in Cartesian space. An observer in three dimensions will see orbits departing from the same initial conditions but diverging in time. This apparent randomness of the integration can only be understood in four dimensions. The concept of topological stability results in a simple method for estimating the time-scale in which numerical simulations can be trusted. Ideally, all trajectories departing from the same fibre should be KS transformed to a unique trajectory in three-dimensional space, because the fundamental manifold that they constitute is unique. By monitoring how trajectories departing from one fibre separate from the fundamental manifold a critical time, equivalent to the Lyapunov time, is estimated. These concepts are tested on N-body examples: the Pythagorean problem, and an example of field stars interacting with a binary.

Learning redundant motor tasks with and without overlapping dimensions: facilitation and interference effects.

PubMed

Ranganathan, Rajiv; Wieser, Jon; Mosier, Kristine M; Mussa-Ivaldi, Ferdinando A; Scheidt, Robert A

2014-06-11

Prior learning of a motor skill creates motor memories that can facilitate or interfere with learning of new, but related, motor skills. One hypothesis of motor learning posits that for a sensorimotor task with redundant degrees of freedom, the nervous system learns the geometric structure of the task and improves performance by selectively operating within that task space. We tested this hypothesis by examining if transfer of learning between two tasks depends on shared dimensionality between their respective task spaces. Human participants wore a data glove and learned to manipulate a computer cursor by moving their fingers. Separate groups of participants learned two tasks: a prior task that was unique to each group and a criterion task that was common to all groups. We manipulated the mapping between finger motions and cursor positions in the prior task to define task spaces that either shared or did not share the task space dimensions (x-y axes) of the criterion task. We found that if the prior task shared task dimensions with the criterion task, there was an initial facilitation in criterion task performance. However, if the prior task did not share task dimensions with the criterion task, there was prolonged interference in learning the criterion task due to participants finding inefficient task solutions. These results show that the nervous system learns the task space through practice, and that the degree of shared task space dimensionality influences the extent to which prior experience transfers to subsequent learning of related motor skills. Copyright © 2014 the authors 0270-6474/14/348289-11$15.00/0.

Time Is Not Space: Core Computations and Domain-Specific Networks for Mental Travels.

PubMed

Gauthier, Baptiste; van Wassenhove, Virginie

2016-11-23

Humans can consciously project themselves in the future and imagine themselves at different places. Do mental time travel and mental space navigation abilities share common cognitive and neural mechanisms? To test this, we recorded fMRI while participants mentally projected themselves in time or in space (e.g., 9 years ago, in Paris) and ordered historical events from their mental perspective. Behavioral patterns were comparable for mental time and space and shaped by self-projection and by the distance of historical events to the mental position of the self, suggesting the existence of egocentric mapping in both dimensions. Nonetheless, self-projection in space engaged the medial and lateral parietal cortices, whereas self-projection in time engaged a widespread parietofrontal network. Moreover, while a large distributed network was found for spatial distances, temporal distances specifically engaged the right inferior parietal cortex and the anterior insula. Across these networks, a robust overlap was only found in a small region of the inferior parietal lobe, adding evidence for its role in domain-general egocentric mapping. Our findings suggest that mental travel in time or space capitalizes on egocentric remapping and on distance computation, which are implemented in distinct dimension-specific cortical networks converging in inferior parietal lobe. As humans, we can consciously imagine ourselves at a different time (mental time travel) or at a different place (mental space navigation). Are such abilities domain-general, or are the temporal and spatial dimensions of our conscious experience separable? Here, we tested the hypothesis that mental time travel and mental space navigation required the egocentric remapping of events, including the estimation of their distances to the self. We report that, although both remapping and distance computation are foundational for the processing of the temporal and spatial dimensions of our conscious experience, their neuroanatomical implementations were clearly dissociable and engaged distinct parietal and parietofrontal networks for mental space navigation and mental time travel, respectively. Copyright © 2016 the authors 0270-6474/16/3611891-13$15.00/0.

Edge Singularities and Quasilong-Range Order in Nonequilibrium Steady States.

PubMed

De Nardis, Jacopo; Panfil, Miłosz

2018-05-25

The singularities of the dynamical response function are one of the most remarkable effects in many-body interacting systems. However in one dimension these divergences only exist strictly at zero temperature, making their observation very difficult in most cold atomic experimental settings. Moreover the presence of a finite temperature destroys another feature of one-dimensional quantum liquids: the real space quasilong-range order in which the spatial correlation functions exhibit power-law decay. We consider a nonequilibrium protocol where two interacting Bose gases are prepared either at different temperatures or chemical potentials and then joined. We show that the nonequilibrium steady state emerging at large times around the junction displays edge singularities in the response function and quasilong-range order.

A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal

PubMed Central

Mohapatra, Biswajit

2018-01-01

Electrocardiogram (ECG) signal analysis has received special attention of the researchers in the recent past because of its ability to divulge crucial information about the electrophysiology of the heart and the autonomic nervous system activity in a noninvasive manner. Analysis of the ECG signals has been explored using both linear and nonlinear methods. However, the nonlinear methods of ECG signal analysis are gaining popularity because of their robustness in feature extraction and classification. The current study presents a review of the nonlinear signal analysis methods, namely, reconstructed phase space analysis, Lyapunov exponents, correlation dimension, detrended fluctuation analysis (DFA), recurrence plot, Poincaré plot, approximate entropy, and sample entropy along with their recent applications in the ECG signal analysis. PMID:29854361

Higher order string effects and the properties of the Pomeron

DOE PAGES

Kharzeev, Dmitri; Shuryak, Edward; Zahed, Ismail

2018-01-18

In this paper, we revisit the description of the Pomeron within the effective string theory of QCD. Using a string duality relation, we show how the static potential maps onto the high-energy scattering amplitude that exhibits the Pomeron behavior. Besides the Pomeron intercept and slope, new additional terms stemming from the higher order string corrections are shown to affect both the growth of the nucleon’s size at high energies and its profile in impact parameter space. The stringy description also allows for an odderon that only disappears in critical dimension. Lastlyl, some of the Pomeron’s features that emerge within themore » effective string description can be studied at the future EIC collider.« less

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.

Edge Singularities and Quasilong-Range Order in Nonequilibrium Steady States

NASA Astrophysics Data System (ADS)

De Nardis, Jacopo; Panfil, Miłosz

2018-05-01

The singularities of the dynamical response function are one of the most remarkable effects in many-body interacting systems. However in one dimension these divergences only exist strictly at zero temperature, making their observation very difficult in most cold atomic experimental settings. Moreover the presence of a finite temperature destroys another feature of one-dimensional quantum liquids: the real space quasilong-range order in which the spatial correlation functions exhibit power-law decay. We consider a nonequilibrium protocol where two interacting Bose gases are prepared either at different temperatures or chemical potentials and then joined. We show that the nonequilibrium steady state emerging at large times around the junction displays edge singularities in the response function and quasilong-range order.

A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal.

PubMed

Nayak, Suraj K; Bit, Arindam; Dey, Anilesh; Mohapatra, Biswajit; Pal, Kunal

2018-01-01

Electrocardiogram (ECG) signal analysis has received special attention of the researchers in the recent past because of its ability to divulge crucial information about the electrophysiology of the heart and the autonomic nervous system activity in a noninvasive manner. Analysis of the ECG signals has been explored using both linear and nonlinear methods. However, the nonlinear methods of ECG signal analysis are gaining popularity because of their robustness in feature extraction and classification. The current study presents a review of the nonlinear signal analysis methods, namely, reconstructed phase space analysis, Lyapunov exponents, correlation dimension, detrended fluctuation analysis (DFA), recurrence plot, Poincaré plot, approximate entropy, and sample entropy along with their recent applications in the ECG signal analysis.

Equilibrium and dynamic methods when comparing an English text and its Esperanto translation

NASA Astrophysics Data System (ADS)

Ausloos, M.

2008-11-01

A comparison of two English texts written by Lewis Carroll, one (Alice in Wonderland), also translated into Esperanto, the other (Through the Looking Glass) are discussed in order to observe whether natural and artificial languages significantly differ from each other. One dimensional time series like signals are constructed using only word frequencies (FTS) or word lengths (LTS). The data is studied through (i) a Zipf method for sorting out correlations in the FTS and (ii) a Grassberger-Procaccia (GP) technique based method for finding correlations in LTS. The methods correspond to an equilibrium and a dynamic approach respectively to human texts features. There are quantitative statistical differences between the original English text and its Esperanto translation, but the qualitative differences are very minutes. However different power laws are observed with characteristic exponents for the ranking properties, and the phase space attractor dimensionality. The Zipf exponent can take values much less than unity (∼0.50 or 0.30) depending on how a sentence is defined. This variety in exponents can be conjectured to be an intrinsic measure of the book style or purpose, rather than the language or author vocabulary richness, since a similar exponent is obtained whatever the text. Moreover the attractor dimension r is a simple function of the so called phase space dimension n, i.e., r=nλ, with λ=0.79. Such an exponent could also be conjectured to be a measure of the author style versatility, - here well preserved in the translation.

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.

Jupiter's Northern Hemisphere in a Methane Band (Time Set 2)

NASA Technical Reports Server (NTRS)

1997-01-01

Mosaic of Jupiter's northern hemisphere between 10 and 50 degrees latitude. Jupiter's atmospheric circulation is dominated by alternating eastward and westward jets from equatorial to polar latitudes. The direction and speed of these jets in part determine the color and texture of the clouds seen in this mosaic. Also visible are several other common Jovian cloud features, including large white ovals, bright spots, dark spots, interacting vortices, and turbulent chaotic systems. The north-south dimension of each of the two interacting vortices in the upper half of the mosaic is about 3500 kilometers. Light at 727 nanometers is moderately absorbed by atmospheric methane. This mosaic shows the features of Jupiter's main visible cloud deck and upper-tropospheric haze, with higher features enhanced in brightness over lower features.

North is at the top. The images are projected on a sphere, with features being foreshortened towards the north. The smallest resolved features are tens of kilometers in size. These images were taken on April 3, 1997, at a range of 1.4 million kilometers by the Solid State Imaging system on NASA's Galileo spacecraft.

The Jet Propulsion Laboratory, Pasadena, CA manages the mission for NASA's Office of Space Science, Washington, DC.

This image and other images and data received from Galileo are posted on the World Wide Web, on the Galileo mission home page at URL http://galileo.jpl.nasa.gov. Background information and educational context for the images can be found at URL http://www.jpl.nasa.gov/galileo/sepo

Chaotic particle swarm optimization with mutation for classification.

PubMed

Assarzadeh, Zahra; Naghsh-Nilchi, Ahmad Reza

2015-01-01

In this paper, a chaotic particle swarm optimization with mutation-based classifier particle swarm optimization is proposed to classify patterns of different classes in the feature space. The introduced mutation operators and chaotic sequences allows us to overcome the problem of early convergence into a local minima associated with particle swarm optimization algorithms. That is, the mutation operator sharpens the convergence and it tunes the best possible solution. Furthermore, to remove the irrelevant data and reduce the dimensionality of medical datasets, a feature selection approach using binary version of the proposed particle swarm optimization is introduced. In order to demonstrate the effectiveness of our proposed classifier, mutation-based classifier particle swarm optimization, it is checked out with three sets of data classifications namely, Wisconsin diagnostic breast cancer, Wisconsin breast cancer and heart-statlog, with different feature vector dimensions. The proposed algorithm is compared with different classifier algorithms including k-nearest neighbor, as a conventional classifier, particle swarm-classifier, genetic algorithm, and Imperialist competitive algorithm-classifier, as more sophisticated ones. The performance of each classifier was evaluated by calculating the accuracy, sensitivity, specificity and Matthews's correlation coefficient. The experimental results show that the mutation-based classifier particle swarm optimization unequivocally performs better than all the compared algorithms.

The Analysis of Surface EMG Signals with the Wavelet-Based Correlation Dimension Method

PubMed Central

Zhang, Yanyan; Wang, Jue

2014-01-01

Many attempts have been made to effectively improve a prosthetic system controlled by the classification of surface electromyographic (SEMG) signals. Recently, the development of methodologies to extract the effective features still remains a primary challenge. Previous studies have demonstrated that the SEMG signals have nonlinear characteristics. In this study, by combining the nonlinear time series analysis and the time-frequency domain methods, we proposed the wavelet-based correlation dimension method to extract the effective features of SEMG signals. The SEMG signals were firstly analyzed by the wavelet transform and the correlation dimension was calculated to obtain the features of the SEMG signals. Then, these features were used as the input vectors of a Gustafson-Kessel clustering classifier to discriminate four types of forearm movements. Our results showed that there are four separate clusters corresponding to different forearm movements at the third resolution level and the resulting classification accuracy was 100%, when two channels of SEMG signals were used. This indicates that the proposed approach can provide important insight into the nonlinear characteristics and the time-frequency domain features of SEMG signals and is suitable for classifying different types of forearm movements. By comparing with other existing methods, the proposed method exhibited more robustness and higher classification accuracy. PMID:24868240

Dissociations and interactions between time, numerosity and space processing

PubMed Central

Cappelletti, Marinella; Freeman, Elliot D.; Cipolotti, Lisa

2009-01-01

This study investigated time, numerosity and space processing in a patient (CB) with a right hemisphere lesion. We tested whether these magnitude dimensions share a common magnitude system or whether they are processed by dimension-specific magnitude systems. Five experimental tasks were used: Tasks 1–3 assessed time and numerosity independently and time and numerosity jointly. Tasks 4 and 5 investigated space processing independently and space and numbers jointly. Patient CB was impaired at estimating time and at discriminating between temporal intervals, his errors being underestimations. In contrast, his ability to process numbers and space was normal. A unidirectional interaction between numbers and time was found in both the patient and the control subjects. Strikingly, small numbers were perceived as lasting shorter and large numbers as lasting longer. In contrast, number processing was not affected by time, i.e. short durations did not result in perceiving fewer numbers and long durations in perceiving more numbers. Numbers and space also interacted, with small numbers answered faster when presented on the left side of space, and the reverse for large numbers. Our results demonstrate that time processing can be selectively impaired. This suggests that mechanisms specific for time processing may be partially independent from those involved in processing numbers and space. However, the interaction between numbers and time and between numbers and space also suggests that although independent, there maybe some overlap between time, numbers and space. These data suggest a partly shared mechanism between time, numbers and space which may be involved in magnitude processing or may be recruited to perform cognitive operations on magnitude dimensions. PMID:19501604

Design, fabrication and space suitability tests of wide field of view, ultra-compact, and high resolution telescope for space application.

PubMed

Tumarina, M; Ryazanskiy, M; Jeong, S; Hong, G; Vedenkin, N; Park, I H; Milov, A

2018-02-05

We report on the design, manufacture, and testing of an ultra-compact telescope for 16 unit (16U) CubeSats for Earth and space observation. This telescope provides 1 arcsec resolution at a 2.9 degree field of view. Dimensions are optimized to 230 × 230 × 330mm 3 with a mass of less than 6kg including support structure. Our catadioptric 5-element design consists of a full-aperture corrector, a Mangin primary mirror (PM), a secondary mirror (SM), and a 2-lens field corrector. The focal length is 745mm, and squared-circular aperture has an equivalent diameter of 241mm. The designed modulation transfer function (MTF) is 0.275 for the entire unit including baffles at a Nyquist frequency of 161 cycles/mm for the 450-800nm band. As one of the distinguishing features of our state-of-the-art design, all optical surfaces are spherical to simplify adjustment. For the best thermal stability, all optical elements are produced from fused silica. We describe the details of design, adjustment, and laboratory performance tests for space environments in accordance with the requirements for in-orbit operation onboard Earth-observation micro-satellites to be launched in 2018.

Microstructured block copolymer surfaces for control of microbe capture and aggregation

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

Hansen, Ryan R; Shubert, Katherine R; Morrell, Jennifer L.

2014-01-01

The capture and arrangement of surface-associated microbes is influenced by biochemical and physical properties of the substrate. In this report, we develop lectin-functionalized substrates containing patterned, three-dimensional polymeric structures of varied shapes and densities and use these to investigate the effects of topology and spatial confinement on lectin-mediated microbe capture. Films of poly(glycidyl methacrylate)-block-4,4-dimethyl-2-vinylazlactone (PGMA-b-PVDMA) were patterned on silicon surfaces into line or square grid patterns with 5 m wide features and varied edge spacing. The patterned films had three-dimensional geometries with 900 nm film thickness. After surface functionalization with wheat germ agglutinin, the size of Pseudomonas fluorescens aggregates capturedmore » was dependent on the pattern dimensions. Line patterns with edge spacing of 5 m or less led to the capture of individual microbes with minimal formation of aggregates, while grid patterns with the same spacing also captured individual microbes with further reduction in aggregation. Both geometries allowed for increases in aggregate size distribution with increased in edge spacing. These engineered surfaces combine spatial confinement with affinity-based microbe capture based on exopolysaccharide content to control the degree of microbe aggregation, and can also be used as a platform to investigate intercellular interactions and biofilm formation in microbial populations of controlled sizes.« less

Characterization of branch complexity by fractal analyses

USGS Publications Warehouse

Alados, C.L.; Escos, J.; Emlen, J.M.; Freeman, D.C.

1999-01-01

The comparison between complexity in the sense of space occupancy (box-counting fractal dimension D(c) and information dimension D1) and heterogeneity in the sense of space distribution (average evenness index f and evenness variation coefficient J(cv)) were investigated in mathematical fractal objects and natural branch structures. In general, increased fractal dimension was paired with low heterogeneity. Comparisons between branch architecture in Anthyllis cytisoides under different slope exposure and grazing impact revealed that branches were more complex and more homogeneously distributed for plants on northern exposures than southern, while grazing had no impact during a wet year. Developmental instability was also investigated by the statistical noise of the allometric relation between internode length and node order. In conclusion, our study demonstrated that fractal dimension of branch structure can be used to analyze the structural organization of plants, especially if we consider not only fractal dimension but also shoot distribution within the canopy (lacunarity). These indexes together with developmental instability analyses are good indicators of growth responses to the environment.

Characterization of branch complexity by fractal analyses and detect plant functional adaptations

USGS Publications Warehouse

Alados, C.L.; Escos, J.; Emlen, J.M.; Freeman, D.C.

1999-01-01

The comparison between complexity in the sense of space occupancy (box-counting fractal dimension Dc and information dimension DI ) and heterogeneity in the sense of space distribution (average evenness index and evenness variation coefficient JCV) were investigated in mathematical fractal objects and natural branch ¯ J structures. In general, increased fractal dimension was paired with low heterogeneity. Comparisons between branch architecture in Anthyllis cytisoides under different slope exposure and grazing impact revealed that branches were more complex and more homogeneously distributed for plants on northern exposures than southern, while grazing had no impact during a wet year. Developmental instability was also investigated by the statistical noise of the allometric relation between internode length and node order. In conclusion, our study demonstrated that fractal dimension of branch structure can be used to analyze the structural organization of plants, especially if we consider not only fractal dimension but also shoot distribution within the canopy (lacunarity). These indexes together with developmental instability analyses are good indicators of growth responses to the environment.

Robust numerical simulation of porosity evolution in chemical vapor infiltration III: three space dimension

NASA Astrophysics Data System (ADS)

Jin, Shi; Wang, Xuelei

2003-04-01

Chemical vapor infiltration (CVI) process is an important technology to fabricate ceramic matrix composites (CMC's). In this paper, a three-dimension numerical model is presented to describe pore microstructure evolution during the CVI process. We extend the two-dimension model proposed in [S. Jin, X.L. Wang, T.L. Starr, J. Mater. Res. 14 (1999) 3829; S. Jin. X.L. Wang, T.L. Starr, X.F. Chen, J. Comp. Phys. 162 (2000) 467], where the fiber surface is modeled as an evolving interface, to the three space dimension. The 3D method keeps all the virtue of the 2D model: robust numerical capturing of topological changes of the interface such as the merging, and fast detection of the inaccessible pores. For models in the kinetic limit, where the moving speed of the interface is constant, some numerical examples are presented to show that this three-dimension model will effectively track the change of porosity, close-off time, location and shape of all pores.

Two Types of Perseveration in the Dimension Change Card Sort Task

PubMed Central

Hanania, Rima

2010-01-01

Three-year-old children in the Dimension Change Card Sort (DCCS) task can sort cards well by one dimension, but have difficulty switching to sort the same cards by another dimension when asked, i.e., they perseverate on the first relevant information. What is the information that children perseverate on? Using a new version of the DCCS, the experiments in this article reveal that there are two types of perseverators-- those who perseverate at the level of dimensions, and those who perseverate at the level of values (stimulus features). This novel finding has implications for theories of perseveration. PMID:20566206

Emotion computing using Word Mover's Distance features based on Ren_CECps.

PubMed

Ren, Fuji; Liu, Ning

2018-01-01

In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover's Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field.

Dimensions of landscape preferences from pairwise comparisons

Treesearch

F. González Bernaldez; F. Parra

1979-01-01

Analysis of landscape preferences allows the detection of major dimensions as:(1) the opposition between "natural and humanized", (comprising features like vegetation cover, cultivation, pattern of landscape elements, artifacts, excavations, etc.); (2) polarity "precision/ambiguity" (involving opposition between: predominance of straight, vertical...

Transition amplitude for two-time physics

NASA Astrophysics Data System (ADS)

Frederico, João E.; Rivelles, Victor O.

2010-07-01

We present the transition amplitude for a particle moving in a space with two times and D space dimensions having an Sp(2,R) local symmetry and an SO(D,2) rigid symmetry. It was obtained from the BRST-BFV quantization with a unique gauge choice. We show that by constraining the initial and final points of this amplitude to lie on some hypersurface of the D+2 space the resulting amplitude reproduces well-known systems in lower dimensions. This work provides an alternative way to derive the effects of two-time physics where all the results come from a single transition amplitude.

Transition amplitude for two-time physics

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

Frederico, Joao E.; Rivelles, Victor O.; Instituto de Fisica, Universidade de Sao Paulo, Caixa Postal 66318, 05314-970, Sao Paulo, SP

2010-07-15

We present the transition amplitude for a particle moving in a space with two times and D space dimensions having an Sp(2,R) local symmetry and an SO(D,2) rigid symmetry. It was obtained from the BRST-BFV quantization with a unique gauge choice. We show that by constraining the initial and final points of this amplitude to lie on some hypersurface of the D+2 space the resulting amplitude reproduces well-known systems in lower dimensions. This work provides an alternative way to derive the effects of two-time physics where all the results come from a single transition amplitude.

Different underlying mechanisms for face emotion and gender processing during feature-selective attention: Evidence from event-related potential studies.

PubMed

Wang, Hailing; Ip, Chengteng; Fu, Shimin; Sun, Pei

2017-05-01

Face recognition theories suggest that our brains process invariant (e.g., gender) and changeable (e.g., emotion) facial dimensions separately. To investigate whether these two dimensions are processed in different time courses, we analyzed the selection negativity (SN, an event-related potential component reflecting attentional modulation) elicited by face gender and emotion during a feature selective attention task. Participants were instructed to attend to a combination of face emotion and gender attributes in Experiment 1 (bi-dimensional task) and to either face emotion or gender in Experiment 2 (uni-dimensional task). The results revealed that face emotion did not elicit a substantial SN, whereas face gender consistently generated a substantial SN in both experiments. These results suggest that face gender is more sensitive to feature-selective attention and that face emotion is encoded relatively automatically on SN, implying the existence of different underlying processing mechanisms for invariant and changeable facial dimensions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine.

PubMed

Yan, Jian-Jun; Guo, Rui; Wang, Yi-Qin; Liu, Guo-Ping; Yan, Hai-Xia; Xia, Chun-Ming; Shen, Xiaojing

2014-01-01

This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered.

Objective Auscultation of TCM Based on Wavelet Packet Fractal Dimension and Support Vector Machine

PubMed Central

Yan, Jian-Jun; Wang, Yi-Qin; Liu, Guo-Ping; Yan, Hai-Xia; Xia, Chun-Ming; Shen, Xiaojing

2014-01-01

This study was conducted to illustrate that auscultation features based on the fractal dimension combined with wavelet packet transform (WPT) were conducive to the identification the pattern of syndromes of Traditional Chinese Medicine (TCM). The WPT and the fractal dimension were employed to extract features of auscultation signals of 137 patients with lung Qi-deficient pattern, 49 patients with lung Yin-deficient pattern, and 43 healthy subjects. With these features, the classification model was constructed based on multiclass support vector machine (SVM). When all auscultation signals were trained by SVM to decide the patterns of TCM syndromes, the overall recognition rate of model was 79.49%; when male and female auscultation signals were trained, respectively, to decide the patterns, the overall recognition rate of model reached 86.05%. The results showed that the methods proposed in this paper were effective to analyze auscultation signals, and the performance of model can be greatly improved when the distinction of gender was considered. PMID:24883068

The Ethical Dimension of Teacher Practical Knowledge: A Narrative Inquiry into Chinese Teachers' Thinking and Actions in Dilemmatic Spaces

ERIC Educational Resources Information Center

Chen, Xiangming; Wei, Ge; Jiang, Shuling

2017-01-01

Previous research concerning teacher practical knowledge has revealed its epistemological foundations, content structure and research methodology, but little research examines its ethical dimension. Based on a four-year project in China, this study probes the ethical dimension of an experienced teacher's practical knowledge, explicated in a…

Asymptotic Charges at Null Infinity in Any Dimension

NASA Astrophysics Data System (ADS)

Campoleoni, Andrea; Francia, Dario; Heissenberg, Carlo

2018-03-01

We analyse the conservation laws associated with large gauge transformations of massless fields in Minkowski space. Our aim is to highlight the interplay between boundary conditions and finiteness of the asymptotically conserved charges in any space-time dimension, both even and odd, greater than or equal to three. After discussing non-linear Yang-Mills theory and revisiting linearised gravity, our investigation extends to cover the infrared behaviour of bosonic massless quanta of any spin.

Sampling strategies for subsampled segmented EPI PRF thermometry in MR guided high intensity focused ultrasound

PubMed Central

Odéen, Henrik; Todd, Nick; Diakite, Mahamadou; Minalga, Emilee; Payne, Allison; Parker, Dennis L.

2014-01-01

Purpose: To investigate k-space subsampling strategies to achieve fast, large field-of-view (FOV) temperature monitoring using segmented echo planar imaging (EPI) proton resonance frequency shift thermometry for MR guided high intensity focused ultrasound (MRgHIFU) applications. Methods: Five different k-space sampling approaches were investigated, varying sample spacing (equally vs nonequally spaced within the echo train), sampling density (variable sampling density in zero, one, and two dimensions), and utilizing sequential or centric sampling. Three of the schemes utilized sequential sampling with the sampling density varied in zero, one, and two dimensions, to investigate sampling the k-space center more frequently. Two of the schemes utilized centric sampling to acquire the k-space center with a longer echo time for improved phase measurements, and vary the sampling density in zero and two dimensions, respectively. Phantom experiments and a theoretical point spread function analysis were performed to investigate their performance. Variable density sampling in zero and two dimensions was also implemented in a non-EPI GRE pulse sequence for comparison. All subsampled data were reconstructed with a previously described temporally constrained reconstruction (TCR) algorithm. Results: The accuracy of each sampling strategy in measuring the temperature rise in the HIFU focal spot was measured in terms of the root-mean-square-error (RMSE) compared to fully sampled “truth.” For the schemes utilizing sequential sampling, the accuracy was found to improve with the dimensionality of the variable density sampling, giving values of 0.65 °C, 0.49 °C, and 0.35 °C for density variation in zero, one, and two dimensions, respectively. The schemes utilizing centric sampling were found to underestimate the temperature rise, with RMSE values of 1.05 °C and 1.31 °C, for variable density sampling in zero and two dimensions, respectively. Similar subsampling schemes with variable density sampling implemented in zero and two dimensions in a non-EPI GRE pulse sequence both resulted in accurate temperature measurements (RMSE of 0.70 °C and 0.63 °C, respectively). With sequential sampling in the described EPI implementation, temperature monitoring over a 192 × 144 × 135 mm3 FOV with a temporal resolution of 3.6 s was achieved, while keeping the RMSE compared to fully sampled “truth” below 0.35 °C. Conclusions: When segmented EPI readouts are used in conjunction with k-space subsampling for MR thermometry applications, sampling schemes with sequential sampling, with or without variable density sampling, obtain accurate phase and temperature measurements when using a TCR reconstruction algorithm. Improved temperature measurement accuracy can be achieved with variable density sampling. Centric sampling leads to phase bias, resulting in temperature underestimations. PMID:25186406

Method of creating additional parking spaces in the “Tudor Vladimirescu” University Campus

NASA Astrophysics Data System (ADS)

Maftei, A.; Dontu, A. I.; Sachelarie, A.; Budeanu, B.

2016-08-01

The increasing number of vehicles in recent years has yielded a lot of problems regarding road vehicle infrastructure in residential areas, especially in towns. The problem is that roads dimensioning and especially parking spaces are under dimensioned for the current number of vehicles in use. The current paper addresses the problem of the lack of parking spaces in the “Tudor Vladimirescu” University Campus. The Campus infrastructure was build in the early 1970's and has received only a slight upgrade regarding access roads width, the access roads that were enlarged were Prof. Vasile Petrescu Street and Prof. Gheorghe Alexa Street. On the first specified road, parking spaces at 45 degrees were created, but this does not cover the number of needed parking spaces.

Device-Independent Tests of Classical and Quantum Dimensions

NASA Astrophysics Data System (ADS)

Gallego, Rodrigo; Brunner, Nicolas; Hadley, Christopher; Acín, Antonio

2010-12-01

We address the problem of testing the dimensionality of classical and quantum systems in a “black-box” scenario. We develop a general formalism for tackling this problem. This allows us to derive lower bounds on the classical dimension necessary to reproduce given measurement data. Furthermore, we generalize the concept of quantum dimension witnesses to arbitrary quantum systems, allowing one to place a lower bound on the Hilbert space dimension necessary to reproduce certain data. Illustrating these ideas, we provide simple examples of classical and quantum dimension witnesses.

Quantum mechanical derivation of the Wallis formula for π

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

Friedmann, Tamar, E-mail: tfriedma@ur.rochester.edu; Hagen, C. R., E-mail: hagen@pas.rochester.edu

2015-11-15

A famous pre-Newtonian formula for π is obtained directly from the variational approach to the spectrum of the hydrogen atom in spaces of arbitrary dimensions greater than one, including the physical three dimensions.

Self Assembly of Hard, Space-Filling Polytopes

NASA Astrophysics Data System (ADS)

Schultz, Benjamin; Damasceno, Pablo; Engel, Michael; Glotzer, Sharon

2012-02-01

The thermodynamic behavior of systems of hard particles in the limit of infinite pressure is known to yield the densest possible packing [1,2]. Hard polytopes that tile or fill space in two or three spatial dimensions are guaranteed to obtain packing fractions of unity in the infinite pressure limit. Away from this limit, however, other structures may be possible [3]. We present the results of a simulation study of the thermodynamic self-assembly of hard, space-filling particles from disordered initial conditions. We show that for many polytopes, the infinite pressure structure readily assembles at intermediate pressures and packing fractions significantly less than one; in others, assembly of the infinite pressure structure is foiled by mesophases, jamming and phase separation. Common features of these latter systems are identified and strategies for enhancing assembly of the infinite pressure structure at intermediate pressures through building block modification are discussed.[4pt] [1] P. F. Damasceno, M. Engel, S.C. Glotzer arXiv:1109.1323v1 [cond-mat.soft][0pt] [2] A. Haji-Akbari, M. Engel, S.C. Glotzer arXiv:1106.4765v2 [cond-mat.soft][0pt] [3] U. Agarwal, F.A. Escobedo, Nature Materials 10, 230--235 (2011)

Interprofessional learning at work: what spatial theory can tell us about workplace learning in an acute care ward.

PubMed

Gregory, Linda Rosemary; Hopwood, Nick; Boud, David

2014-05-01

It is widely recognized that every workplace potentially provides a rich source of learning. Studies focusing on health care contexts have shown that social interaction within and between professions is crucial in enabling professionals to learn through work, address problems and cope with challenges of clinical practice. While hospital environments are beginning to be understood in spatial terms, the links between space and interprofessional learning at work have not been explored. This paper draws on Lefebvre's tri-partite theoretical framework of perceived, conceived and lived space to enrich understandings of interprofessional learning on an acute care ward in an Australian teaching hospital. Qualitative analysis was undertaken using data from observations of Registered Nurses at work and semi-structured interviews linked to observed events. The paper focuses on a ward round, the medical workroom and the Registrar's room, comparing and contrasting the intended (conceived), practiced (perceived) and pedagogically experienced (lived) spatial dimensions. The paper concludes that spatial theory has much to offer understandings of interprofessional learning in work, and the features of work environments and daily practices that produce spaces that enable or constrain learning.

Space Station Freedom

NASA Technical Reports Server (NTRS)

Keyes, Gilbert

1991-01-01

Information is given in viewgraph form on Space Station Freedom. Topics covered include future evolution, man-tended capability, permanently manned capability, standard payload rack dimensions, the Crystals by Vapor Transport Experiment (CVTE), commercial space projects interfaces, and pricing policy.

Two-dimensional adaptation in the auditory forebrain

PubMed Central

Nagel, Katherine I.; Doupe, Allison J.

2011-01-01

Sensory neurons exhibit two universal properties: sensitivity to multiple stimulus dimensions, and adaptation to stimulus statistics. How adaptation affects encoding along primary dimensions is well characterized for most sensory pathways, but if and how it affects secondary dimensions is less clear. We studied these effects for neurons in the avian equivalent of primary auditory cortex, responding to temporally modulated sounds. We showed that the firing rate of single neurons in field L was affected by at least two components of the time-varying sound log-amplitude. When overall sound amplitude was low, neural responses were based on nonlinear combinations of the mean log-amplitude and its rate of change (first time differential). At high mean sound amplitude, the two relevant stimulus features became the first and second time derivatives of the sound log-amplitude. Thus a strikingly systematic relationship between dimensions was conserved across changes in stimulus intensity, whereby one of the relevant dimensions approximated the time differential of the other dimension. In contrast to stimulus mean, increases in stimulus variance did not change relevant dimensions, but selectively increased the contribution of the second dimension to neural firing, illustrating a new adaptive behavior enabled by multidimensional encoding. Finally, we demonstrated theoretically that inclusion of time differentials as additional stimulus features, as seen so prominently in the single-neuron responses studied here, is a useful strategy for encoding naturalistic stimuli, because it can lower the necessary sampling rate while maintaining the robustness of stimulus reconstruction to correlated noise. PMID:21753019

Changes in airway dimensions following functional appliances in growing patients with skeletal class II malocclusion: A systematic review and meta-analysis.

PubMed

Xiang, MingLi; Hu, Bo; Liu, Yang; Sun, Jicheng; Song, Jinlin

2017-06-01

The purpose of the study was to evaluate the treatment effects of functional appliances (FAs) on upper airway dimensions in growing Class II patients with mandibular retrognathism. Five databases and the references of identified articles were electronically searched for relevant studies that met our eligibility criteria. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. The effects of FAs on airway dimensions were combined by meta-analysis using the RevMan and STATA software. Seven studies (177 treated patients with mean age: 11.48 years and 153 untreated controls with mean age: 11.20 years) were included in this review. Compared to the control group, the oropharyngeal dimensions in the treatment group subjects were significantly increased at the superior pharyngeal space (MD = 1.73 mm/year, 95% CI, 1.13-2.32 mm, P < 0.00001), middle pharyngeal space (MD = 1.68 mm/year, 95% CI, 1.13-2.23 mm, P < 0.00001) and inferior pharyngeal space (MD = 1.21 mm/year, 95% CI, 0.48-1.95 mm, P = 0.001). No significant differences were found in nasopharyngeal and hypopharyngeal dimensions and the position of hyoid bone (P > 0.05). Soft palate length and soft palate inclination were improved significantly in the treatment group (P < 0.05). The results showed that FAs can enlarge the upper airway dimensions, specifically in the oropharyngeal region, in growing subjects with skeletal Class II malocclusion. The early intervention for mandibular retrognathism with FAs may help enlarge the airway dimensions and decrease potential risk of obstructive sleep apnea syndrome for growing patients in the future. Copyright © 2017 Elsevier B.V. All rights reserved.

Origin of Everything and the 21 Dimensions of the Universe

NASA Astrophysics Data System (ADS)

Loev, Mark

2009-03-01

The Dimensions of the Universe correspond with the Dimensions of the human body. The emotion that is a positive for every dimension is Love. The negative emotion that effects each dimension are listed. All seven negative emotions effect Peace, Love and Happiness. 21st Dimension: Happiness Groin & Heart 20th Dimension: Love Groin & Heart 19th Dimension: Peace Groin & heart 18th Dimension: Imagination Wave Eyes Anger 17th Dimension: Z Wave / Closed Birth 16th Dimension: Electromagnetic Wave Ears Anger 15th Dimension: Universal Wave Skin Worry 14th Dimension: Lover Wave Blood Hate 13th Dimension: Disposal Wave Buttocks Fear 12th Dimension: Builder Wave Hands Hate 11th Dimension: Energy Wave Arms Fear 10th Dimension: Time Wave Brain Pessimism 9th Dimension: Gravity Wave Legs Fear 8th Dimension: Sweet Wave Pancreas Fear 7th Dimension: File Wave Left Lung Fear 6th Dimension: Breathing Wave Right Lung Fear 5th Dimension: Digestive Wave Stomach Fear 4th Dimension: Swab Wave Liver Guilt 3rd Dimension: Space Wave Face Sadness 2nd Dimension: Line Wave Mouth Revenge 1st Dimension: Dot Wave Nose Sadness The seven deadly sins correspond: Anger Hate Sadness Fear Worry Pessimism Revenge Note: Guilt is fear

Relationship of the Cricothyroid Space with Vocal Range in Female Singers.

PubMed

Pullon, Beverley

2017-01-01

This study aims to investigate the relationship between the anterior cricothyroid (CT) space at rest with vocal range in female singers. Potential associations with and between voice categories, age, ethnicity, anthropometric indices, neck dimensions, laryngeal dimensions, vocal data along with habitual speaking fundamental frequency were also explored. This is a cohort study. Laryngeal dimensions anterior CT space and heights of the thyroid and cricoid cartilages were measured using ultrasound in 43 healthy, classically trained, female singers during quiet respiration. Voice categories (soprano and mezzo-soprano), age, ethnicity, weight, height, body mass index, neck circumference and length, anterior thyroid and cricoid cartilage heights, practice and performance vocal range, lowest and highest practice and performance notes along with habitual speaking fundamental frequency were collected. The main finding was that mezzo-sopranos have a significantly wider resting CT space than sopranos (11.6 mm versus 10.4 mm; P = 0.007). Mezzo-sopranos also had significantly lower "lowest and highest" performance notes than sopranos. There was no significant correlation between the magnitudes of the anterior CT space with vocal range. The participants with the narrowest and widest anterior CT space had similar vocal ranges. These results suggest that the CT space is not the major determinant of performance vocal range. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

Handy elementary algebraic properties of the geometry of entanglement

NASA Astrophysics Data System (ADS)

Blair, Howard A.; Alsing, Paul M.

2013-05-01

The space of separable states of a quantum system is a hyperbolic surface in a high dimensional linear space, which we call the separation surface, within the exponentially high dimensional linear space containing the quantum states of an n component multipartite quantum system. A vector in the linear space is representable as an n-dimensional hypermatrix with respect to bases of the component linear spaces. A vector will be on the separation surface iff every determinant of every 2-dimensional, 2-by-2 submatrix of the hypermatrix vanishes. This highly rigid constraint can be tested merely in time asymptotically proportional to d, where d is the dimension of the state space of the system due to the extreme interdependence of the 2-by-2 submatrices. The constraint on 2-by-2 determinants entails an elementary closed formformula for a parametric characterization of the entire separation surface with d-1 parameters in the char- acterization. The state of a factor of a partially separable state can be calculated in time asymptotically proportional to the dimension of the state space of the component. If all components of the system have approximately the same dimension, the time complexity of calculating a component state as a function of the parameters is asymptotically pro- portional to the time required to sort the basis. Metric-based entanglement measures of pure states are characterized in terms of the separation hypersurface.

Bak-Tang-Wiesenfeld model in the upper critical dimension: Induced criticality in lower-dimensional subsystems

NASA Astrophysics Data System (ADS)

Dashti-Naserabadi, H.; Najafi, M. N.

2017-10-01

We present extensive numerical simulations of Bak-Tang-Wiesenfeld (BTW) sandpile model on the hypercubic lattice in the upper critical dimension Du=4 . After re-extracting the critical exponents of avalanches, we concentrate on the three- and two-dimensional (2D) cross sections seeking for the induced criticality which are reflected in the geometrical and local exponents. Various features of finite-size scaling (FSS) theory have been tested and confirmed for all dimensions. The hyperscaling relations between the exponents of the distribution functions and the fractal dimensions are shown to be valid for all dimensions. We found that the exponent of the distribution function of avalanche mass is the same for the d -dimensional cross sections and the d -dimensional BTW model for d =2 and 3. The geometrical quantities, however, have completely different behaviors with respect to the same-dimensional BTW model. By analyzing the FSS theory for the geometrical exponents of the two-dimensional cross sections, we propose that the 2D induced models have degrees of similarity with the Gaussian free field (GFF). Although some local exponents are slightly different, this similarity is excellent for the fractal dimensions. The most important one showing this feature is the fractal dimension of loops df, which is found to be 1.50 ±0.02 ≈3/2 =dfGFF .

Bak-Tang-Wiesenfeld model in the upper critical dimension: Induced criticality in lower-dimensional subsystems.

PubMed

Dashti-Naserabadi, H; Najafi, M N

2017-10-01

We present extensive numerical simulations of Bak-Tang-Wiesenfeld (BTW) sandpile model on the hypercubic lattice in the upper critical dimension D_{u}=4. After re-extracting the critical exponents of avalanches, we concentrate on the three- and two-dimensional (2D) cross sections seeking for the induced criticality which are reflected in the geometrical and local exponents. Various features of finite-size scaling (FSS) theory have been tested and confirmed for all dimensions. The hyperscaling relations between the exponents of the distribution functions and the fractal dimensions are shown to be valid for all dimensions. We found that the exponent of the distribution function of avalanche mass is the same for the d-dimensional cross sections and the d-dimensional BTW model for d=2 and 3. The geometrical quantities, however, have completely different behaviors with respect to the same-dimensional BTW model. By analyzing the FSS theory for the geometrical exponents of the two-dimensional cross sections, we propose that the 2D induced models have degrees of similarity with the Gaussian free field (GFF). Although some local exponents are slightly different, this similarity is excellent for the fractal dimensions. The most important one showing this feature is the fractal dimension of loops d_{f}, which is found to be 1.50±0.02≈3/2=d_{f}^{GFF}.

X-Ray Computed Tomography Inspection of the Stardust Heat Shield

NASA Technical Reports Server (NTRS)

McNamara, Karen M.; Schneberk, Daniel J.; Empey, Daniel M.; Koshti, Ajay; Pugel, D. Elizabeth; Cozmuta, Ioana; Stackpoole, Mairead; Ruffino, Norman P.; Pompa, Eddie C.; Oliveras, Ovidio;

The Bargmann-Wigner equations in spherical space

NASA Astrophysics Data System (ADS)

McKeon, D. G. C.; Sherry, T. N.

2006-01-01

The Bargmann-Wigner formalism is adapted to spherical surfaces embedded in three to eleven dimensions. This is demonstrated to generate wave equations in spherical space for a variety of antisymmetric tensor fields. Some of these equations are gauge invariant for particular values of the parameters characterizing them. For spheres embedded in three, four, and five dimensions, this gauge invariance can be generalized so as to become non-Abelian. This non-Abelian gauge invariance is shown to be a property of second-order models for two index antisymmetric tensor fields in any number of dimensions. The O(3) model is quantized and the two-point function is shown to vanish at the one-loop order.

Internal Stresses Lead to Net Forces and Torques on Extended Elastic Bodies

NASA Astrophysics Data System (ADS)

Aharoni, Hillel; Kolinski, John M.; Moshe, Michael; Meirzada, Idan; Sharon, Eran

2016-09-01

A geometrically frustrated elastic body will develop residual stresses arising from the mismatch between the intrinsic geometry of the body and the geometry of the ambient space. We analyze these stresses for an ambient space with gradients in its intrinsic curvature, and show that residual stresses generate effective forces and torques on the center of mass of the body. We analytically calculate these forces in two dimensions, and experimentally demonstrate their action by the migration of a non-Euclidean gel disc in a curved Hele-Shaw cell. An extension of our analysis to higher dimensions shows that these forces are also generated in three dimensions, but are negligible compared to gravity.

Magnetic Field Generation During the Collision of Narrow Plasma Clouds

NASA Astrophysics Data System (ADS)

Sakai, Jun-ichi; Kazimura, Yoshihiro; Haruki, Takayuki

1999-06-01

We investigate the dynamics of the collision of narrow plasma clouds,whose transverse dimension is on the order of the electron skin depth.A 2D3V (two dimensions in space and three dimensions in velocity space)particle-in-cell (PIC) collisionless relativistic code is used toshow the generation of a quasi-staticmagnetic field during the collision of narrow plasma clouds both inelectron-ion and electron-positron (pair) plasmas. The localizedstrong magnetic fluxes result in the generation of the charge separationwith complicated structures, which may be sources of electromagneticas well as Langmuir waves. We also present one applicationof this process, which occurs during coalescence of magnetic islandsin a current sheet of pair plasmas.

Space-Time Crystal and Space-Time Group

NASA Astrophysics Data System (ADS)

Xu, Shenglong; Wu, Congjun

2018-03-01

Crystal structures and the Bloch theorem play a fundamental role in condensed matter physics. We extend the static crystal to the dynamic "space-time" crystal characterized by the general intertwined space-time periodicities in D +1 dimensions, which include both the static crystal and the Floquet crystal as special cases. A new group structure dubbed a "space-time" group is constructed to describe the discrete symmetries of a space-time crystal. Compared to space and magnetic groups, the space-time group is augmented by "time-screw" rotations and "time-glide" reflections involving fractional translations along the time direction. A complete classification of the 13 space-time groups in one-plus-one dimensions (1 +1 D ) is performed. The Kramers-type degeneracy can arise from the glide time-reversal symmetry without the half-integer spinor structure, which constrains the winding number patterns of spectral dispersions. In 2 +1 D , nonsymmorphic space-time symmetries enforce spectral degeneracies, leading to protected Floquet semimetal states. We provide a general framework for further studying topological properties of the (D +1 )-dimensional space-time crystal.

Space-Time Crystal and Space-Time Group.

PubMed

Xu, Shenglong; Wu, Congjun

2018-03-02

Crystal structures and the Bloch theorem play a fundamental role in condensed matter physics. We extend the static crystal to the dynamic "space-time" crystal characterized by the general intertwined space-time periodicities in D+1 dimensions, which include both the static crystal and the Floquet crystal as special cases. A new group structure dubbed a "space-time" group is constructed to describe the discrete symmetries of a space-time crystal. Compared to space and magnetic groups, the space-time group is augmented by "time-screw" rotations and "time-glide" reflections involving fractional translations along the time direction. A complete classification of the 13 space-time groups in one-plus-one dimensions (1+1D) is performed. The Kramers-type degeneracy can arise from the glide time-reversal symmetry without the half-integer spinor structure, which constrains the winding number patterns of spectral dispersions. In 2+1D, nonsymmorphic space-time symmetries enforce spectral degeneracies, leading to protected Floquet semimetal states. We provide a general framework for further studying topological properties of the (D+1)-dimensional space-time crystal.

On a canonical quantization of 3D Anti de Sitter pure gravity

NASA Astrophysics Data System (ADS)

Kim, Jihun; Porrati, Massimo

2015-10-01

We perform a canonical quantization of pure gravity on AdS 3 using as a technical tool its equivalence at the classical level with a Chern-Simons theory with gauge group SL(2,{R})× SL(2,{R}) . We first quantize the theory canonically on an asymptotically AdS space -which is topologically the real line times a Riemann surface with one connected boundary. Using the "constrain first" approach we reduce canonical quantization to quantization of orbits of the Virasoro group and Kähler quantization of Teichmüller space. After explicitly computing the Kähler form for the torus with one boundary component and after extending that result to higher genus, we recover known results, such as that wave functions of SL(2,{R}) Chern-Simons theory are conformal blocks. We find new restrictions on the Hilbert space of pure gravity by imposing invariance under large diffeomorphisms and normalizability of the wave function. The Hilbert space of pure gravity is shown to be the target space of Conformal Field Theories with continuous spectrum and a lower bound on operator dimensions. A projection defined by topology changing amplitudes in Euclidean gravity is proposed. It defines an invariant subspace that allows for a dual interpretation in terms of a Liouville CFT. Problems and features of the CFT dual are assessed and a new definition of the Hilbert space, exempt from those problems, is proposed in the case of highly-curved AdS 3.

A Hybrid Color Space for Skin Detection Using Genetic Algorithm Heuristic Search and Principal Component Analysis Technique

PubMed Central

2015-01-01

Color is one of the most prominent features of an image and used in many skin and face detection applications. Color space transformation is widely used by researchers to improve face and skin detection performance. Despite the substantial research efforts in this area, choosing a proper color space in terms of skin and face classification performance which can address issues like illumination variations, various camera characteristics and diversity in skin color tones has remained an open issue. This research proposes a new three-dimensional hybrid color space termed SKN by employing the Genetic Algorithm heuristic and Principal Component Analysis to find the optimal representation of human skin color in over seventeen existing color spaces. Genetic Algorithm heuristic is used to find the optimal color component combination setup in terms of skin detection accuracy while the Principal Component Analysis projects the optimal Genetic Algorithm solution to a less complex dimension. Pixel wise skin detection was used to evaluate the performance of the proposed color space. We have employed four classifiers including Random Forest, Naïve Bayes, Support Vector Machine and Multilayer Perceptron in order to generate the human skin color predictive model. The proposed color space was compared to some existing color spaces and shows superior results in terms of pixel-wise skin detection accuracy. Experimental results show that by using Random Forest classifier, the proposed SKN color space obtained an average F-score and True Positive Rate of 0.953 and False Positive Rate of 0.0482 which outperformed the existing color spaces in terms of pixel wise skin detection accuracy. The results also indicate that among the classifiers used in this study, Random Forest is the most suitable classifier for pixel wise skin detection applications. PMID:26267377

A qualitative numerical study of high dimensional dynamical systems

NASA Astrophysics Data System (ADS)

Albers, David James

Since Poincare, the father of modern mathematical dynamical systems, much effort has been exerted to achieve a qualitative understanding of the physical world via a qualitative understanding of the functions we use to model the physical world. In this thesis, we construct a numerical framework suitable for a qualitative, statistical study of dynamical systems using the space of artificial neural networks. We analyze the dynamics along intervals in parameter space, separating the set of neural networks into roughly four regions: the fixed point to the first bifurcation; the route to chaos; the chaotic region; and a transition region between chaos and finite-state neural networks. The study is primarily with respect to high-dimensional dynamical systems. We make the following general conclusions as the dimension of the dynamical system is increased: the probability of the first bifurcation being of type Neimark-Sacker is greater than ninety-percent; the most probable route to chaos is via a cascade of bifurcations of high-period periodic orbits, quasi-periodic orbits, and 2-tori; there exists an interval of parameter space such that hyperbolicity is violated on a countable, Lebesgue measure 0, "increasingly dense" subset; chaos is much more likely to persist with respect to parameter perturbation in the chaotic region of parameter space as the dimension is increased; moreover, as the number of positive Lyapunov exponents is increased, the likelihood that any significant portion of these positive exponents can be perturbed away decreases with increasing dimension. The maximum Kaplan-Yorke dimension and the maximum number of positive Lyapunov exponents increases linearly with dimension. The probability of a dynamical system being chaotic increases exponentially with dimension. The results with respect to the first bifurcation and the route to chaos comment on previous results of Newhouse, Ruelle, Takens, Broer, Chenciner, and Iooss. Moreover, results regarding the high-dimensional chaotic region of parameter space is interpreted and related to the closing lemma of Pugh, the windows conjecture of Barreto, the stable ergodicity theorem of Pugh and Shub, and structural stability theorem of Robbin, Robinson, and Mane.

Unitary Operators on the Document Space.

ERIC Educational Resources Information Center

Hoenkamp, Eduard

2003-01-01

Discusses latent semantic indexing (LSI) that would allow search engines to reduce the dimension of the document space by mapping it into a space spanned by conceptual indices. Topics include vector space models; singular value decomposition (SVD); unitary operators; the Haar transform; and new algorithms. (Author/LRW)

The effects of context on multidimensional spatial cognitive models. Ph.D. Thesis - Arizona Univ.

NASA Technical Reports Server (NTRS)

Dupnick, E. G.

1979-01-01

Spatial cognitive models obtained by multidimensional scaling represent cognitive structure by defining alternatives as points in a coordinate space based on relevant dimensions such that interstimulus dissimilarities perceived by the individual correspond to distances between the respective alternatives. The dependence of spatial models on the context of the judgments required of the individual was investigated. Context, which is defined as a perceptual interpretation and cognitive understanding of a judgment situation, was analyzed and classified with respect to five characteristics: physical environment, social environment, task definition, individual perspective, and temporal setting. Four experiments designed to produce changes in the characteristics of context and to test the effects of these changes upon individual cognitive spaces are described with focus on experiment design, objectives, statistical analysis, results, and conclusions. The hypothesis is advanced that an individual can be characterized as having a master cognitive space for a set of alternatives. When the context changes, the individual appears to change the dimension weights to give a new spatial configuration. Factor analysis was used in the interpretation and labeling of cognitive space dimensions.

Systems and Methods for Data Visualization Using Three-Dimensional Displays

NASA Technical Reports Server (NTRS)

Davidoff, Scott (Inventor); Djorgovski, Stanislav G. (Inventor); Estrada, Vicente (Inventor); Donalek, Ciro (Inventor)

2017-01-01

Data visualization systems and methods for generating 3D visualizations of a multidimensional data space are described. In one embodiment a 3D data visualization application directs a processing system to: load a set of multidimensional data points into a visualization table; create representations of a set of 3D objects corresponding to the set of data points; receive mappings of data dimensions to visualization attributes; determine the visualization attributes of the set of 3D objects based upon the selected mappings of data dimensions to 3D object attributes; update a visibility dimension in the visualization table for each of the plurality of 3D object to reflect the visibility of each 3D object based upon the selected mappings of data dimensions to visualization attributes; and interactively render 3D data visualizations of the 3D objects within the virtual space from viewpoints determined based upon received user input.

Metrology test object for dimensional verification in additive manufacturing of metals for biomedical applications.

PubMed

Teeter, Matthew G; Kopacz, Alexander J; Nikolov, Hristo N; Holdsworth, David W

2015-01-01

Additive manufacturing continues to increase in popularity and is being used in applications such as biomaterial ingrowth that requires sub-millimeter dimensional accuracy. The purpose of this study was to design a metrology test object for determining the capabilities of additive manufacturing systems to produce common objects, with a focus on those relevant to medical applications. The test object was designed with a variety of features of varying dimensions, including holes, cylinders, rectangles, gaps, and lattices. The object was built using selective laser melting, and the produced dimensions were compared to the target dimensions. Location of the test objects on the build plate did not affect dimensions. Features with dimensions less than 0.300 mm did not build or were overbuilt to a minimum of 0.300 mm. The mean difference between target and measured dimensions was less than 0.100 mm in all cases. The test object is applicable to multiple systems and materials, tests the effect of location on the build, uses a minimum of material, and can be measured with a variety of efficient metrology tools (including measuring microscopes and micro-CT). Investigators can use this test object to determine the limits of systems and adjust build parameters to achieve maximum accuracy. © IMechE 2014.

Nonuniform dependence on initial data for compressible gas dynamics: The periodic Cauchy problem

NASA Astrophysics Data System (ADS)

Keyfitz, B. L.; Tığlay, F.

2017-11-01

We start with the classic result that the Cauchy problem for ideal compressible gas dynamics is locally well posed in time in the sense of Hadamard; there is a unique solution that depends continuously on initial data in Sobolev space Hs for s > d / 2 + 1 where d is the space dimension. We prove that the data to solution map for periodic data in two dimensions although continuous is not uniformly continuous.

Phases of a stack of membranes in a large number of dimensions of configuration space

NASA Astrophysics Data System (ADS)

Borelli, M. E.; Kleinert, H.

2001-05-01

The phase diagram of a stack of tensionless membranes with nonlinear curvature energy and vertical harmonic interaction is calculated exactly in a large number of dimensions of configuration space. At low temperatures, the system forms a lamellar phase with spontaneously broken translational symmetry in the vertical direction. At a critical temperature, the stack disorders vertically in a meltinglike transition. The critical temperature is determined as a function of the interlayer separation l.

Selective weighting of action-related feature dimensions in visual working memory.

PubMed

Heuer, Anna; Schubö, Anna

2017-08-01

Planning an action primes feature dimensions that are relevant for that particular action, increasing the impact of these dimensions on perceptual processing. Here, we investigated whether action planning also affects the short-term maintenance of visual information. In a combined memory and movement task, participants were to memorize items defined by size or color while preparing either a grasping or a pointing movement. Whereas size is a relevant feature dimension for grasping, color can be used to localize the goal object and guide a pointing movement. The results showed that memory for items defined by size was better during the preparation of a grasping movement than during the preparation of a pointing movement. Conversely, memory for color tended to be better when a pointing movement rather than a grasping movement was being planned. This pattern was not only observed when the memory task was embedded within the preparation period of the movement, but also when the movement to be performed was only indicated during the retention interval of the memory task. These findings reveal that a weighting of information in visual working memory according to action relevance can even be implemented at the representational level during maintenance, demonstrating that our actions continue to influence visual processing beyond the perceptual stage.

Response-specifying cue for action interferes with perception of feature-sharing stimuli.

PubMed

Nishimura, Akio; Yokosawa, Kazuhiko

2010-06-01

Perceiving a visual stimulus is more difficult when a to-be-executed action is compatible with that stimulus, which is known as blindness to response-compatible stimuli. The present study explored how the factors constituting the action event (i.e., response-specifying cue, response intention, and response feature) affect the occurrence of this blindness effect. The response-specifying cue varied along the horizontal and vertical dimensions, while the response buttons were arranged diagonally. Participants responded based on one dimension randomly determined in a trial-by-trial manner. The response intention varied along a single dimension, whereas the response location and the response-specifying cue varied within both vertical and horizontal dimensions simultaneously. Moreover, the compatibility between the visual stimulus and the response location and the compatibility between that stimulus and the response-specifying cue was separately determined. The blindness effect emerged exclusively based on the feature correspondence between the response-specifying cue of the action task and the visual target of the perceptual task. The size of this stimulus-stimulus (S-S) blindness effect did not differ significantly across conditions, showing no effect of response intention and response location. This finding emphasizes the effect of stimulus factors, rather than response factors, of the action event as a source of the blindness to response-compatible stimuli.

Skin cancer texture analysis of OCT images based on Haralick, fractal dimension, Markov random field features, and the complex directional field features

NASA Astrophysics Data System (ADS)

Raupov, Dmitry S.; Myakinin, Oleg O.; Bratchenko, Ivan A.; Zakharov, Valery P.; Khramov, Alexander G.

2016-10-01

In this paper, we propose a report about our examining of the validity of OCT in identifying changes using a skin cancer texture analysis compiled from Haralick texture features, fractal dimension, Markov random field method and the complex directional features from different tissues. Described features have been used to detect specific spatial characteristics, which can differentiate healthy tissue from diverse skin cancers in cross-section OCT images (B- and/or C-scans). In this work, we used an interval type-II fuzzy anisotropic diffusion algorithm for speckle noise reduction in OCT images. The Haralick texture features as contrast, correlation, energy, and homogeneity have been calculated in various directions. A box-counting method is performed to evaluate fractal dimension of skin probes. Markov random field have been used for the quality enhancing of the classifying. Additionally, we used the complex directional field calculated by the local gradient methodology to increase of the assessment quality of the diagnosis method. Our results demonstrate that these texture features may present helpful information to discriminate tumor from healthy tissue. The experimental data set contains 488 OCT-images with normal skin and tumors as Basal Cell Carcinoma (BCC), Malignant Melanoma (MM) and Nevus. All images were acquired from our laboratory SD-OCT setup based on broadband light source, delivering an output power of 20 mW at the central wavelength of 840 nm with a bandwidth of 25 nm. We obtained sensitivity about 97% and specificity about 73% for a task of discrimination between MM and Nevus.

Intelligibility Evaluation of Pathological Speech through Multigranularity Feature Extraction and Optimization.

PubMed

Fang, Chunying; Li, Haifeng; Ma, Lin; Zhang, Mancai

2017-01-01

Pathological speech usually refers to speech distortion resulting from illness or other biological insults. The assessment of pathological speech plays an important role in assisting the experts, while automatic evaluation of speech intelligibility is difficult because it is usually nonstationary and mutational. In this paper, we carry out an independent innovation of feature extraction and reduction, and we describe a multigranularity combined feature scheme which is optimized by the hierarchical visual method. A novel method of generating feature set based on S -transform and chaotic analysis is proposed. There are BAFS (430, basic acoustics feature), local spectral characteristics MSCC (84, Mel S -transform cepstrum coefficients), and chaotic features (12). Finally, radar chart and F -score are proposed to optimize the features by the hierarchical visual fusion. The feature set could be optimized from 526 to 96 dimensions based on NKI-CCRT corpus and 104 dimensions based on SVD corpus. The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78.7% on SVD corpus. The proposed method is thus approved to be effective and reliable for pathological speech intelligibility evaluation.

Twistor Geometry of Null Foliations in Complex Euclidean Space

NASA Astrophysics Data System (ADS)

Taghavi-Chabert, Arman

2017-01-01

We give a detailed account of the geometric correspondence between a smooth complex projective quadric hypersurface Q^n of dimension n ≥ 3, and its twistor space PT, defined to be the space of all linear subspaces of maximal dimension of Q^n. Viewing complex Euclidean space CE^n as a dense open subset of Q^n, we show how local foliations tangent to certain integrable holomorphic totally null distributions of maximal rank on CE^n can be constructed in terms of complex submanifolds of PT. The construction is illustrated by means of two examples, one involving conformal Killing spinors, the other, conformal Killing-Yano 2-forms. We focus on the odd-dimensional case, and we treat the even-dimensional case only tangentially for comparison.

Improved Fabrication of Lithium Films Having Micron Features

NASA Technical Reports Server (NTRS)

Whitacre, Jay

2006-01-01

An improved method has been devised for fabricating micron-dimension Li features. This approach is intended for application in the fabrication of lithium-based microelectrochemical devices -- particularly solid-state thin-film lithium microbatteries.

Egosyntonic and egodystonic: accounting for continuities and discontinuities between personality traits and personality disorders.

PubMed

Paris, Joel

2015-02-01

Egosyntonic and egodystonic features describe differences between trait-based and state-based mental disorders. Many diagnoses, most particularly personality disorder (PD), show both features. These complex forms of psychopathology are an amalgam of traits and symptoms in which both egosyntonicity and egodystonicity can be present but vary in prominence. This distinction might help resolve the long-standing controversy as to whether PDs are best classified using dimensions or categories. Narrative review. PDs in which egosyntonic features tend to predominate can be understood as amplified trait profiles. PDs associated with highly egodystonic clinical symptoms that more closely resemble major mental disorders may be usefully classified using both categories and dimensions. PDs with stronger egosyntonic features are more suitable for dimensional diagnosis. When egodystonic symptoms are more prominent, categorical diagnoses can be useful.

Dimensions of Dialect.

ERIC Educational Resources Information Center

Evertts, Eldonna L., Ed.

This collection of articles discusses social dialects, the problems that dialects cause the disadvantaged, and how these problems can be overcome in curriculum planning and classroom practice. Articles are (1) "English: New Dimensions and New Demands" by Muriel Crosby, (2) "A Checklist of Significant Features for Discriminating Social Dialects" by…

Composing Sound Identity in Taiko Drumming

ERIC Educational Resources Information Center

Powell, Kimberly A.

2012-01-01

Although sociocultural theories emphasize the mutually constitutive nature of persons, activity, and environment, little attention has been paid to environmental features organized across sensory dimensions. I examine sound as a dimension of learning and practice, an organizing presence that connects the sonic with the social. This ethnographic…

Transient behavior of granular materials with symmetric conditions for tumbler shapes and fill fractions

NASA Astrophysics Data System (ADS)

Pohlman, Nicholas; Si, Yun

2014-11-01

The typical granular motion in circular tumblers is considered steady-state since there are no features to disrupt the top surface layer dimension. In polygon tumblers, however, the flowing layer is perpetually changing length, which creates unsteady conditions with corresponding change in the flow behavior. Prior work showed the minimization of free surface energy is independent of tumbler dimension, particle size, and rotation rate. This subsequent research reports on experiments where dimensional symmetry of the free surface in triangular and square tumblers with varying fill fractions do not necessarily produce the symmetric flow behaviors. Results of the quasi-2D tumbler experiment show that other dimensions aligned with gravity and the instantaneous free surface influence the phase when extrema for angle of repose and other flow features occur. The conclusion is that 50% fill fraction may produce geometric symmetry of dimensions, but the symmetry point of flow likely occurs at a lower fill fraction.

CROSS-DISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: The Fractal Dimensions of Complex Networks

NASA Astrophysics Data System (ADS)

Guo, Long; Cai, XU

2009-08-01

It is shown that many real complex networks share distinctive features, such as the small-world effect and the heterogeneous property of connectivity of vertices, which are different from random networks and regular lattices. Although these features capture the important characteristics of complex networks, their applicability depends on the style of networks. To unravel the universal characteristics many complex networks have in common, we study the fractal dimensions of complex networks using the method introduced by Shanker. We find that the average 'density' (ρ(r)) of complex networks follows a better power-law function as a function of distance r with the exponent df, which is defined as the fractal dimension, in some real complex networks. Furthermore, we study the relation between df and the shortcuts Nadd in small-world networks and the size N in regular lattices. Our present work provides a new perspective to understand the dependence of the fractal dimension df on the complex network structure.

Unsupervised Feature Selection Based on the Morisita Index for Hyperspectral Images

NASA Astrophysics Data System (ADS)

Golay, Jean; Kanevski, Mikhail

2017-04-01

Hyperspectral sensors are capable of acquiring images with hundreds of narrow and contiguous spectral bands. Compared with traditional multispectral imagery, the use of hyperspectral images allows better performance in discriminating between land-cover classes, but it also results in large redundancy and high computational data processing. To alleviate such issues, unsupervised feature selection techniques for redundancy minimization can be implemented. Their goal is to select the smallest subset of features (or bands) in such a way that all the information content of a data set is preserved as much as possible. The present research deals with the application to hyperspectral images of a recently introduced technique of unsupervised feature selection: the Morisita-Based filter for Redundancy Minimization (MBRM). MBRM is based on the (multipoint) Morisita index of clustering and on the Morisita estimator of Intrinsic Dimension (ID). The fundamental idea of the technique is to retain only the bands which contribute to increasing the ID of an image. In this way, redundant bands are disregarded, since they have no impact on the ID. Besides, MBRM has several advantages over benchmark techniques: in addition to its ability to deal with large data sets, it can capture highly-nonlinear dependences and its implementation is straightforward in any programming environment. Experimental results on freely available hyperspectral images show the good effectiveness of MBRM in remote sensing data processing. Comparisons with benchmark techniques are carried out and random forests are used to assess the performance of MBRM in reducing the data dimensionality without loss of relevant information. References [1] C. Traina Jr., A.J.M. Traina, L. Wu, C. Faloutsos, Fast feature selection using fractal dimension, in: Proceedings of the XV Brazilian Symposium on Databases, SBBD, pp. 158-171, 2000. [2] J. Golay, M. Kanevski, A new estimator of intrinsic dimension based on the multipoint Morisita index, Pattern Recognition 48(12), pp. 4070-4081, 2015. [3] J. Golay, M. Kanevski, Unsupervised feature selection based on the Morisita estimator of intrinsic dimension, arXiv:1608.05581, 2016.

Anisotropic fractal media by vector calculus in non-integer dimensional space

NASA Astrophysics Data System (ADS)

Tarasov, Vasily E.

2014-08-01

A review of different approaches to describe anisotropic fractal media is proposed. In this paper, differentiation and integration non-integer dimensional and multi-fractional spaces are considered as tools to describe anisotropic fractal materials and media. We suggest a generalization of vector calculus for non-integer dimensional space by using a product measure method. The product of fractional and non-integer dimensional spaces allows us to take into account the anisotropy of the fractal media in the framework of continuum models. The integration over non-integer-dimensional spaces is considered. In this paper differential operators of first and second orders for fractional space and non-integer dimensional space are suggested. The differential operators are defined as inverse operations to integration in spaces with non-integer dimensions. Non-integer dimensional space that is product of spaces with different dimensions allows us to give continuum models for anisotropic type of the media. The Poisson's equation for fractal medium, the Euler-Bernoulli fractal beam, and the Timoshenko beam equations for fractal material are considered as examples of application of suggested generalization of vector calculus for anisotropic fractal materials and media.

Are numbers, size and brightness equally efficient in orienting visual attention? Evidence from an eye-tracking study.

PubMed

Bulf, Hermann; Macchi Cassia, Viola; de Hevia, Maria Dolores

2014-01-01

A number of studies have shown strong relations between numbers and oriented spatial codes. For example, perceiving numbers causes spatial shifts of attention depending upon numbers' magnitude, in a way suggestive of a spatially oriented, mental representation of numbers. Here, we investigated whether this phenomenon extends to non-symbolic numbers, as well as to the processing of the continuous dimensions of size and brightness, exploring whether different quantitative dimensions are equally mapped onto space. After a numerical (symbolic Arabic digits or non-symbolic arrays of dots; Experiment 1) or a non-numerical cue (shapes of different size or brightness level; Experiment 2) was presented, participants' saccadic response to a target that could appear either on the left or the right side of the screen was registered using an automated eye-tracker system. Experiment 1 showed that, both in the case of Arabic digits and dot arrays, right targets were detected faster when preceded by large numbers, and left targets were detected faster when preceded by small numbers. Participants in Experiment 2 were faster at detecting right targets when cued by large-sized shapes and left targets when cued by small-sized shapes, whereas brightness cues did not modulate the detection of peripheral targets. These findings indicate that looking at a symbolic or a non-symbolic number induces attentional shifts to a peripheral region of space that is congruent with the numbers' relative position on a mental number line, and that a similar shift in visual attention is induced by looking at shapes of different size. More specifically, results suggest that, while the dimensions of number and size spontaneously map onto an oriented space, the dimension of brightness seems to be independent at a certain level of magnitude elaboration from the dimensions of spatial extent and number, indicating that not all continuous dimensions are equally mapped onto space.

Visualization of planetary subsurface radar sounder data in three dimensions using stereoscopy

NASA Astrophysics Data System (ADS)

Frigeri, A.; Federico, C.; Pauselli, C.; Ercoli, M.; Coradini, A.; Orosei, R.

2010-12-01

Planetary subsurface sounding radar data extend the knowledge of planetary surfaces to a third dimension: the depth. The interpretation of delays of radar echoes converted into depth often requires the comparative analysis with other data, mainly topography, and radar data from different orbits can be used to investigate the spatial continuity of signals from subsurface geologic features. This scenario requires taking into account spatially referred information in three dimensions. Three dimensional objects are generally easier to understand if represented into a three dimensional space, and this representation can be improved by stereoscopic vision. Since its invention in the first half of 19th century, stereoscopy has been used in a broad range of application, including scientific visualization. The quick improvement of computer graphics and the spread of graphic rendering hardware allow to apply the basic principles of stereoscopy in the digital domain, allowing the stereoscopic projection of complex models. Specialized system for stereoscopic view of scientific data have been available in the industry, and proprietary solutions were affordable only to large research institutions. In the last decade, thanks to the GeoWall Consortium, the basics of stereoscopy have been applied for setting up stereoscopic viewers based on off-the shelf hardware products. Geowalls have been spread and are now used by several geo-science research institutes and universities. We are exploring techniques for visualizing planetary subsurface sounding radar data in three dimensions and we are developing a hardware system for rendering it in a stereoscopic vision system. Several Free Open Source Software tools and libraries are being used, as their level of interoperability is typically high and their licensing system offers the opportunity to implement quickly new functionalities to solve specific needs during the progress of the project. Visualization of planetary radar data in three dimensions represents a challenging task, and the exploration of different strategies will bring to the selection of the most appropriate ones for a meaningful extraction of information from the products of these innovative instruments.

Cellular dynamical mechanisms for encoding the time and place of events along spatiotemporal trajectories in episodic memory.

PubMed

Hasselmo, Michael E; Giocomo, Lisa M; Brandon, Mark P; Yoshida, Motoharu

2010-12-31

Understanding the mechanisms of episodic memory requires linking behavioral data and lesion effects to data on the dynamics of cellular membrane potentials and population interactions within brain regions. Linking behavior to specific membrane channels and neurochemicals has implications for therapeutic applications. Lesions of the hippocampus, entorhinal cortex and subcortical nuclei impair episodic memory function in humans and animals, and unit recording data from these regions in behaving animals indicate episodic memory processes. Intracellular recording in these regions demonstrates specific cellular properties including resonance, membrane potential oscillations and bistable persistent spiking that could underlie the encoding and retrieval of episodic trajectories. A model presented here shows how intrinsic dynamical properties of neurons could mediate the encoding of episodic memories as complex spatiotemporal trajectories. The dynamics of neurons allow encoding and retrieval of unique episodic trajectories in multiple continuous dimensions including temporal intervals, personal location, the spatial coordinates and sensory features of perceived objects and generated actions, and associations between these elements. The model also addresses how cellular dynamics could underlie unit firing data suggesting mechanisms for coding continuous dimensions of space, time, sensation and action. Copyright © 2010 Elsevier B.V. All rights reserved.

Cellular dynamical mechanisms for encoding the time and place of events along spatiotemporal trajectories in episodic memory

PubMed Central

Hasselmo, Michael E.; Giocomo, Lisa M.; Yoshida, Motoharu

2010-01-01

Understanding the mechanisms of episodic memory requires linking behavioural data and lesion effects to data on the dynamics of cellular membrane potentials and population interactions within these brain regions. Linking behavior to specific membrane channels and neurochemicals has implications for therapeutic applications. Lesions of the hippocampus, entorhinal cortex and subcortical nuclei impair episodic memory function in humans and animals, and unit recording data from these regions in behaving animals indicate episodic memory processes. Intracellular recording in these regions demonstrates specific cellular properties including resonance, membrane potential oscillations and bistable persistent spiking that could underlie the encoding and retrieval of episodic trajectories. A model presented here shows how intrinsic dynamical properties of neurons could mediate the encoding of episodic memories as complex spatiotemporal trajectories. The dynamics of neurons allow encoding and retrieval of unique episodic trajectories in multiple continuous dimensions including temporal intervals, personal location, the spatial coordinates and sensory features of perceived objects and generated actions, and associations between these elements. The model also addresses how cellular dynamics could underlie unit firing data suggesting mechanisms for coding continuous dimensions of space, time, sensation and action. PMID:20018213

A nonlinear analysis of the transport Barkhausen-like noise measured in (Bi,Pb)2Sr2Ca2Cu3O10+δ superconductors

NASA Astrophysics Data System (ADS)

García-Fornaris, I.; Millán, H.; Jardim, R. F.; Govea-Alcaide, E.

2013-06-01

We investigated the transport Barkhausen-like noise (TBN) by using nonlinear time series analysis. TBN signals were measured in (Bi,Pb)2Sr2Ca2Cu3O10+δ ceramic samples subjected to different uniaxial compacting pressures (UCP). These samples display similar intragranular properties but different intergranular features. We found positive Lyapunov exponents in all samples, λm≥0.062, indicating the nonlinear dynamics of the experimental TBN signals. It was also observed higher values of the embedding dimension, m >9, and the Kaplan-Yorke dimension, DKY>2.9. Between samples, the behavior of λm and DKY with increasing excitation current is quite different. Such a behavior is explained in terms of changes in the microstructure associated with the UCP. In addition, determinism tests indicated that the TBN masked determinist components, as inferred by |k →| values larger than 0.70 in most of the cases. Evidence on the existence of empirical attractors by reconstructing the phase spaces has been also found. All obtained results are useful indicators of the interplay between the uniaxial compacting pressure, differences in the microstructure of the samples, and the TBN signal dynamics.