Sample records for type-specific sparse labeling

  1. Sparse Multidimensional Patient Modeling using Auxiliary Confidence Labels. (United States)

    Heim, Eric; Hauskrecht, Milos


    In this work, we focus on the problem of learning a classification model that performs inference on patient Electronic Health Records (EHRs). Often, a large amount of costly expert supervision is required to learn such a model. To reduce this cost, we obtain confidence labels that indicate how sure an expert is in the class labels she provides. If meaningful confidence information can be incorporated into a learning method, fewer patient instances may need to be labeled to learn an accurate model. In addition, while accuracy of predictions is important for any inference model, a model of patients must be interpretable so that clinicians can understand how the model is making decisions. To these ends, we develop a novel metric learning method called Confidence bAsed MEtric Learning (CAMEL) that supports inclusion of confidence labels, but also emphasizes interpretability in three ways. First, our method induces sparsity, thus producing simple models that use only a few features from patient EHRs. Second, CAMEL naturally produces confidence scores that can be taken into consideration when clinicians make treatment decisions. Third, the metrics learned by CAMEL induce multidimensional spaces where each dimension represents a different "factor" that clinicians can use to assess patients. In our experimental evaluation, we show on a real-world clinical data set that our CAMEL methods are able to learn models that are as or more accurate as other methods that use the same supervision. Furthermore, we show that when CAMEL uses confidence scores it is able to learn models as or more accurate as others we tested while using only 10% of the training instances. Finally, we perform qualitative assessments on the metrics learned by CAMEL and show that they identify and clearly articulate important factors in how the model performs inference.

  2. Binning sequences using very sparse labels within a metagenome

    Directory of Open Access Journals (Sweden)

    Halgamuge Saman K


    Full Text Available Abstract Background In metagenomic studies, a process called binning is necessary to assign contigs that belong to multiple species to their respective phylogenetic groups. Most of the current methods of binning, such as BLAST, k-mer and PhyloPythia, involve assigning sequence fragments by comparing sequence similarity or sequence composition with already-sequenced genomes that are still far from comprehensive. We propose a semi-supervised seeding method for binning that does not depend on knowledge of completed genomes. Instead, it extracts the flanking sequences of highly conserved 16S rRNA from the metagenome and uses them as seeds (labels to assign other reads based on their compositional similarity. Results The proposed seeding method is implemented on an unsupervised Growing Self-Organising Map (GSOM, and called Seeded GSOM (S-GSOM. We compared it with four well-known semi-supervised learning methods in a preliminary test, separating random-length prokaryotic sequence fragments sampled from the NCBI genome database. We identified the flanking sequences of the highly conserved 16S rRNA as suitable seeds that could be used to group the sequence fragments according to their species. S-GSOM showed superior performance compared to the semi-supervised methods tested. Additionally, S-GSOM may also be used to visually identify some species that do not have seeds. The proposed method was then applied to simulated metagenomic datasets using two different confidence threshold settings and compared with PhyloPythia, k-mer and BLAST. At the reference taxonomic level Order, S-GSOM outperformed all k-mer and BLAST results and showed comparable results with PhyloPythia for each of the corresponding confidence settings, where S-GSOM performed better than PhyloPythia in the ≥ 10 reads datasets and comparable in the ≥ 8 kb benchmark tests. Conclusion In the task of binning using semi-supervised learning methods, results indicate S-GSOM to be the best of

  3. Porosity estimation by semi-supervised learning with sparsely available labeled samples (United States)

    Lima, Luiz Alberto; Görnitz, Nico; Varella, Luiz Eduardo; Vellasco, Marley; Müller, Klaus-Robert; Nakajima, Shinichi


    This paper addresses the porosity estimation problem from seismic impedance volumes and porosity samples located in a small group of exploratory wells. Regression methods, trained on the impedance as inputs and the porosity as output labels, generally suffer from extremely expensive (and hence sparsely available) porosity samples. To optimally make use of the valuable porosity data, a semi-supervised machine learning method was proposed, Transductive Conditional Random Field Regression (TCRFR), showing good performance (Görnitz et al., 2017). TCRFR, however, still requires more labeled data than those usually available, which creates a gap when applying the method to the porosity estimation problem in realistic situations. In this paper, we aim to fill this gap by introducing two graph-based preprocessing techniques, which adapt the original TCRFR for extremely weakly supervised scenarios. Our new method outperforms the previous automatic estimation methods on synthetic data and provides a comparable result to the manual labored, time-consuming geostatistics approach on real data, proving its potential as a practical industrial tool.

  4. Segmentation of MR images via discriminative dictionary learning and sparse coding: application to hippocampus labeling. (United States)

    Tong, Tong; Wolz, Robin; Coupé, Pierrick; Hajnal, Joseph V; Rueckert, Daniel


    We propose a novel method for the automatic segmentation of brain MRI images by using discriminative dictionary learning and sparse coding techniques. In the proposed method, dictionaries and classifiers are learned simultaneously from a set of brain atlases, which can then be used for the reconstruction and segmentation of an unseen target image. The proposed segmentation strategy is based on image reconstruction, which is in contrast to most existing atlas-based labeling approaches that rely on comparing image similarities between atlases and target images. In addition, we propose a Fixed Discriminative Dictionary Learning for Segmentation (F-DDLS) strategy, which can learn dictionaries offline and perform segmentations online, enabling a significant speed-up in the segmentation stage. The proposed method has been evaluated for the hippocampus segmentation of 80 healthy ICBM subjects and 202 ADNI images. The robustness of the proposed method, especially of our F-DDLS strategy, was validated by training and testing on different subject groups in the ADNI database. The influence of different parameters was studied and the performance of the proposed method was also compared with that of the nonlocal patch-based approach. The proposed method achieved a median Dice coefficient of 0.879 on 202 ADNI images and 0.890 on 80 ICBM subjects, which is competitive compared with state-of-the-art methods. Copyright © 2013 Elsevier Inc. All rights reserved.

  5. Automatic prostate MR image segmentation with sparse label propagation and domain-specific manifold regularization. (United States)

    Liao, Shu; Gao, Yaozong; Shi, Yinghuan; Yousuf, Ambereen; Karademir, Ibrahim; Oto, Aytekin; Shen, Dinggang


    Automatic prostate segmentation in MR images plays an important role in prostate cancer diagnosis. However, there are two main challenges: (1) Large inter-subject prostate shape variations; (2) Inhomogeneous prostate appearance. To address these challenges, we propose a new hierarchical prostate MR segmentation method, with the main contributions lying in the following aspects: First, the most salient features are learnt from atlases based on a subclass discriminant analysis (SDA) method, which aims to find a discriminant feature subspace by simultaneously maximizing the inter-class distance and minimizing the intra-class variations. The projected features, instead of only voxel-wise intensity, will be served as anatomical signature of each voxel. Second, based on the projected features, a new multi-atlases sparse label fusion framework is proposed to estimate the prostate likelihood of each voxel in the target image from the coarse level. Third, a domain-specific semi-supervised manifold regularization method is proposed to incorporate the most reliable patient-specific information identified by the prostate likelihood map to refine the segmentation result from the fine level. Our method is evaluated on a T2 weighted prostate MR image dataset consisting of 66 patients and compared with two state-of-the-art segmentation methods. Experimental results show that our method consistently achieves the highest segmentation accuracies than other methods under comparison.

  6. Protein functional properties prediction in sparsely-label PPI networks through regularized non-negative matrix factorization. (United States)

    Wu, Qingyao; Wang, Zhenyu; Li, Chunshan; Ye, Yunming; Li, Yueping; Sun, Ning


    Predicting functional properties of proteins in protein-protein interaction (PPI) networks presents a challenging problem and has important implication in computational biology. Collective classification (CC) that utilizes both attribute features and relational information to jointly classify related proteins in PPI networks has been shown to be a powerful computational method for this problem setting. Enabling CC usually increases accuracy when given a fully-labeled PPI network with a large amount of labeled data. However, such labels can be difficult to obtain in many real-world PPI networks in which there are usually only a limited number of labeled proteins and there are a large amount of unlabeled proteins. In this case, most of the unlabeled proteins may not connected to the labeled ones, the supervision knowledge cannot be obtained effectively from local network connections. As a consequence, learning a CC model in sparsely-labeled PPI networks can lead to poor performance. We investigate a latent graph approach for finding an integration latent graph by exploiting various latent linkages and judiciously integrate the investigated linkages to link (separate) the proteins with similar (different) functions. We develop a regularized non-negative matrix factorization (RNMF) algorithm for CC to make protein functional properties prediction by utilizing various data sources that are available in this problem setting, including attribute features, latent graph, and unlabeled data information. In RNMF, a label matrix factorization term and a network regularization term are incorporated into the non-negative matrix factorization (NMF) objective function to seek a matrix factorization that respects the network structure and label information for classification prediction. Experimental results on KDD Cup tasks predicting the localization and functions of proteins to yeast genes demonstrate the effectiveness of the proposed RNMF method for predicting the protein

  7. Optimization of amino acid type-specific 13C and 15N labeling for the backbone assignment of membrane proteins by solution- and solid-state NMR with the UPLABEL algorithm

    International Nuclear Information System (INIS)

    Hefke, Frederik; Bagaria, Anurag; Reckel, Sina; Ullrich, Sandra Johanna; Dötsch, Volker; Glaubitz, Clemens; Güntert, Peter


    We present a computational method for finding optimal labeling patterns for the backbone assignment of membrane proteins and other large proteins that cannot be assigned by conventional strategies. Following the approach of Kainosho and Tsuji (Biochemistry 21:6273–6279 (1982)), types of amino acids are labeled with 13 C or/and 15 N such that cross peaks between 13 CO(i – 1) and 15 NH(i) result only for pairs of sequentially adjacent amino acids of which the first is labeled with 13 C and the second with 15 N. In this way, unambiguous sequence-specific assignments can be obtained for unique pairs of amino acids that occur exactly once in the sequence of the protein. To be practical, it is crucial to limit the number of differently labeled protein samples that have to be prepared while obtaining an optimal extent of labeled unique amino acid pairs. Our computer algorithm UPLABEL for optimal unique pair labeling, implemented in the program CYANA and in a standalone program, and also available through a web portal, uses combinatorial optimization to find for a given amino acid sequence labeling patterns that maximize the number of unique pair assignments with a minimal number of differently labeled protein samples. Various auxiliary conditions, including labeled amino acid availability and price, previously known partial assignments, and sequence regions of particular interest can be taken into account when determining optimal amino acid type-specific labeling patterns. The method is illustrated for the assignment of the human G-protein coupled receptor bradykinin B2 (B 2 R) and applied as a starting point for the backbone assignment of the membrane protein proteorhodopsin.

  8. NMR characterization of HtpG, the E. coli Hsp90, using sparse labeling with 13C-methyl alanine. (United States)

    Pederson, Kari; Chalmers, Gordon R; Gao, Qi; Elnatan, Daniel; Ramelot, Theresa A; Ma, Li-Chung; Montelione, Gaetano T; Kennedy, Michael A; Agard, David A; Prestegard, James H


    A strategy for acquiring structural information from sparsely isotopically labeled large proteins is illustrated with an application to the E. coli heat-shock protein, HtpG (high temperature protein G), a 145 kDa dimer. It uses 13 C-alanine methyl labeling in a perdeuterated background to take advantage of the sensitivity and resolution of Methyl-TROSY spectra, as well as the backbone-centered structural information from 1 H- 13 C residual dipolar couplings (RDCs) of alanine methyl groups. In all, 40 of the 47 expected crosspeaks were resolved and 36 gave RDC data. Assignments of crosspeaks were partially achieved by transferring assignments from those made on individual domains using triple resonance methods. However, these were incomplete and in many cases the transfer was ambiguous. A genetic algorithm search for consistency between predictions based on domain structures and measurements for chemical shifts and RDCs allowed 60% of the 40 resolved crosspeaks to be assigned with confidence. Chemical shift changes of these crosspeaks on adding an ATP analog to the apo-protein are shown to be consistent with structural changes expected on comparing previous crystal structures for apo- and complex- structures. RDCs collected on the assigned alanine methyl peaks are used to generate a new solution model for the apo-protein structure.

  9. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan


    Sparse coding approximates the data sample as a sparse linear combination of some basic codewords and uses the sparse codes as new presentations. In this paper, we investigate learning discriminative sparse codes by sparse coding in a semi-supervised manner, where only a few training samples are labeled. By using the manifold structure spanned by the data set of both labeled and unlabeled samples and the constraints provided by the labels of the labeled samples, we learn the variable class labels for all the samples. Furthermore, to improve the discriminative ability of the learned sparse codes, we assume that the class labels could be predicted from the sparse codes directly using a linear classifier. By solving the codebook, sparse codes, class labels and classifier parameters simultaneously in a unified objective function, we develop a semi-supervised sparse coding algorithm. Experiments on two real-world pattern recognition problems demonstrate the advantage of the proposed methods over supervised sparse coding methods on partially labeled data sets.

  10. Supervised Transfer Sparse Coding

    KAUST Repository

    Al-Shedivat, Maruan


    A combination of the sparse coding and transfer learn- ing techniques was shown to be accurate and robust in classification tasks where training and testing objects have a shared feature space but are sampled from differ- ent underlying distributions, i.e., belong to different do- mains. The key assumption in such case is that in spite of the domain disparity, samples from different domains share some common hidden factors. Previous methods often assumed that all the objects in the target domain are unlabeled, and thus the training set solely comprised objects from the source domain. However, in real world applications, the target domain often has some labeled objects, or one can always manually label a small num- ber of them. In this paper, we explore such possibil- ity and show how a small number of labeled data in the target domain can significantly leverage classifica- tion accuracy of the state-of-the-art transfer sparse cod- ing methods. We further propose a unified framework named supervised transfer sparse coding (STSC) which simultaneously optimizes sparse representation, domain transfer and classification. Experimental results on three applications demonstrate that a little manual labeling and then learning the model in a supervised fashion can significantly improve classification accuracy.

  11. Cell-Type-Specific Optogenetics in Monkeys. (United States)

    Namboodiri, Vijay Mohan K; Stuber, Garret D


    The recent advent of technologies enabling cell-type-specific recording and manipulation of neuronal activity spurred tremendous progress in neuroscience. However, they have been largely limited to mice, which lack the richness in behavior of primates. Stauffer et al. now present a generalizable method for achieving cell-type specificity in monkeys. Copyright © 2016 Elsevier Inc. All rights reserved.

  12. Discriminative sparse coding on multi-manifolds

    KAUST Repository

    Wang, J.J.-Y.


    Sparse coding has been popularly used as an effective data representation method in various applications, such as computer vision, medical imaging and bioinformatics. However, the conventional sparse coding algorithms and their manifold-regularized variants (graph sparse coding and Laplacian sparse coding), learn codebooks and codes in an unsupervised manner and neglect class information that is available in the training set. To address this problem, we propose a novel discriminative sparse coding method based on multi-manifolds, that learns discriminative class-conditioned codebooks and sparse codes from both data feature spaces and class labels. First, the entire training set is partitioned into multiple manifolds according to the class labels. Then, we formulate the sparse coding as a manifold-manifold matching problem and learn class-conditioned codebooks and codes to maximize the manifold margins of different classes. Lastly, we present a data sample-manifold matching-based strategy to classify the unlabeled data samples. Experimental results on somatic mutations identification and breast tumor classification based on ultrasonic images demonstrate the efficacy of the proposed data representation and classification approach. 2013 The Authors. All rights reserved.

  13. Sparse Exploratory Factor Analysis. (United States)

    Trendafilov, Nickolay T; Fontanella, Sara; Adachi, Kohei


    Sparse principal component analysis is a very active research area in the last decade. It produces component loadings with many zero entries which facilitates their interpretation and helps avoid redundant variables. The classic factor analysis is another popular dimension reduction technique which shares similar interpretation problems and could greatly benefit from sparse solutions. Unfortunately, there are very few works considering sparse versions of the classic factor analysis. Our goal is to contribute further in this direction. We revisit the most popular procedures for exploratory factor analysis, maximum likelihood and least squares. Sparse factor loadings are obtained for them by, first, adopting a special reparameterization and, second, by introducing additional [Formula: see text]-norm penalties into the standard factor analysis problems. As a result, we propose sparse versions of the major factor analysis procedures. We illustrate the developed algorithms on well-known psychometric problems. Our sparse solutions are critically compared to ones obtained by other existing methods.

  14. Structural Sparse Tracking

    KAUST Repository

    Zhang, Tianzhu


    Sparse representation has been applied to visual tracking by finding the best target candidate with minimal reconstruction error by use of target templates. However, most sparse representation based trackers only consider holistic or local representations and do not make full use of the intrinsic structure among and inside target candidates, thereby making the representation less effective when similar objects appear or under occlusion. In this paper, we propose a novel Structural Sparse Tracking (SST) algorithm, which not only exploits the intrinsic relationship among target candidates and their local patches to learn their sparse representations jointly, but also preserves the spatial layout structure among the local patches inside each target candidate. We show that our SST algorithm accommodates most existing sparse trackers with the respective merits. Both qualitative and quantitative evaluations on challenging benchmark image sequences demonstrate that the proposed SST algorithm performs favorably against several state-of-the-art methods.

  15. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan


    Learning ranking scores is critical for the multimedia database retrieval problem. In this paper, we propose a novel ranking score learning algorithm by exploring the sparse structure and using it to regularize ranking scores. To explore the sparse structure, we assume that each multimedia object could be represented as a sparse linear combination of all other objects, and combination coefficients are regarded as a similarity measure between objects and used to regularize their ranking scores. Moreover, we propose to learn the sparse combination coefficients and the ranking scores simultaneously. A unified objective function is constructed with regard to both the combination coefficients and the ranking scores, and is optimized by an iterative algorithm. Experiments on two multimedia database retrieval data sets demonstrate the significant improvements of the propose algorithm over state-of-the-art ranking score learning algorithms.

  16. Sparse approximation with bases

    CERN Document Server


    This book systematically presents recent fundamental results on greedy approximation with respect to bases. Motivated by numerous applications, the last decade has seen great successes in studying nonlinear sparse approximation. Recent findings have established that greedy-type algorithms are suitable methods of nonlinear approximation in both sparse approximation with respect to bases and sparse approximation with respect to redundant systems. These insights, combined with some previous fundamental results, form the basis for constructing the theory of greedy approximation. Taking into account the theoretical and practical demand for this kind of theory, the book systematically elaborates a theoretical framework for greedy approximation and its applications.  The book addresses the needs of researchers working in numerical mathematics, harmonic analysis, and functional analysis. It quickly takes the reader from classical results to the latest frontier, but is written at the level of a graduate course and do...

  17. Efficient convolutional sparse coding

    Energy Technology Data Exchange (ETDEWEB)

    Wohlberg, Brendt


    Computationally efficient algorithms may be applied for fast dictionary learning solving the convolutional sparse coding problem in the Fourier domain. More specifically, efficient convolutional sparse coding may be derived within an alternating direction method of multipliers (ADMM) framework that utilizes fast Fourier transforms (FFT) to solve the main linear system in the frequency domain. Such algorithms may enable a significant reduction in computational cost over conventional approaches by implementing a linear solver for the most critical and computationally expensive component of the conventional iterative algorithm. The theoretical computational cost of the algorithm may be reduced from O(M.sup.3N) to O(MN log N), where N is the dimensionality of the data and M is the number of elements in the dictionary. This significant improvement in efficiency may greatly increase the range of problems that can practically be addressed via convolutional sparse representations.

  18. Multiple Sparse Representations Classification (United States)

    Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik


    Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and

  19. Supervised Convolutional Sparse Coding

    KAUST Repository

    Affara, Lama Ahmed


    Convolutional Sparse Coding (CSC) is a well-established image representation model especially suited for image restoration tasks. In this work, we extend the applicability of this model by proposing a supervised approach to convolutional sparse coding, which aims at learning discriminative dictionaries instead of purely reconstructive ones. We incorporate a supervised regularization term into the traditional unsupervised CSC objective to encourage the final dictionary elements to be discriminative. Experimental results show that using supervised convolutional learning results in two key advantages. First, we learn more semantically relevant filters in the dictionary and second, we achieve improved image reconstruction on unseen data.

  20. Sparse matrix test collections

    Energy Technology Data Exchange (ETDEWEB)

    Duff, I.


    This workshop will discuss plans for coordinating and developing sets of test matrices for the comparison and testing of sparse linear algebra software. We will talk of plans for the next release (Release 2) of the Harwell-Boeing Collection and recent work on improving the accessibility of this Collection and others through the World Wide Web. There will only be three talks of about 15 to 20 minutes followed by a discussion from the floor.

  1. Pansharpening via sparse regression (United States)

    Tang, Songze; Xiao, Liang; Liu, Pengfei; Huang, Lili; Zhou, Nan; Xu, Yang


    Pansharpening is an effective way to enhance the spatial resolution of a multispectral (MS) image by fusing it with a provided panchromatic image. Instead of restricting the coding coefficients of low-resolution (LR) and high-resolution (HR) images to be equal, we propose a pansharpening approach via sparse regression in which the relationship between sparse coefficients of HR and LR MS images is modeled by ridge regression and elastic-net regression simultaneously learning the corresponding dictionaries. The compact dictionaries are learned based on the sampled patch pairs from the high- and low-resolution images, which can greatly characterize the structural information of the LR MS and HR MS images. Later, taking the complex relationship between the coding coefficients of LR MS and HR MS images into account, the ridge regression is used to characterize the relationship of intrapatches. The elastic-net regression is employed to describe the relationship of interpatches. Thus, the HR MS image can be almost identically reconstructed by multiplying the HR dictionary and the calculated sparse coefficient vector with the learned regression relationship. The simulated and real experimental results illustrate that the proposed method outperforms several well-known methods, both quantitatively and perceptually.

  2. Neuronal survival in the brain: neuron type-specific mechanisms

    DEFF Research Database (Denmark)

    Pfisterer, Ulrich Gottfried; Khodosevich, Konstantin


    Neurogenic regions of mammalian brain produce many more neurons that will eventually survive and reach a mature stage. Developmental cell death affects both embryonically produced immature neurons and those immature neurons that are generated in regions of adult neurogenesis. Removal of substantial...... for survival in a certain brain region. This review focuses on how immature neurons survive during normal and impaired brain development, both in the embryonic/neonatal brain and in brain regions associated with adult neurogenesis, and emphasizes neuron type-specific mechanisms that help to survive for various...

  3. Compressed sensing & sparse filtering

    CERN Document Server

    Carmi, Avishy Y; Godsill, Simon J


    This book is aimed at presenting concepts, methods and algorithms ableto cope with undersampled and limited data. One such trend that recently gained popularity and to some extent revolutionised signal processing is compressed sensing. Compressed sensing builds upon the observation that many signals in nature are nearly sparse (or compressible, as they are normally referred to) in some domain, and consequently they can be reconstructed to within high accuracy from far fewer observations than traditionally held to be necessary. Apart from compressed sensing this book contains other related app

  4. Image classification by semisupervised sparse coding with confident unlabeled samples (United States)

    Li, Xiao; Fang, Min; Wu, Jinqiao; He, Liang; Tian, Xian


    Sparse coding has achieved very excellent performance in image classification tasks, especially when the supervision information is incorporated into the dictionary learning process. However, there is a large amount of unlabeled samples that are expensive and boring to annotate. We propose an image classification algorithm by semisupervised sparse coding with confident unlabeled samples. In order to make the learnt sparse coding more discriminative, we select and annotate some confident unlabeled samples. A minimization model is developed in which the reconstruction error of the labeled, the selected unlabeled and the remaining unlabeled data and the classification error are integrated, which enhances the discriminant property of the dictionary and sparse representations. The experimental results on image classification tasks demonstrate that our algorithm can significantly improve the image classification performance.

  5. Exploiting ontology graph for predicting sparsely annotated gene function. (United States)

    Wang, Sheng; Cho, Hyunghoon; Zhai, ChengXiang; Berger, Bonnie; Peng, Jian


    Systematically predicting gene (or protein) function based on molecular interaction networks has become an important tool in refining and enhancing the existing annotation catalogs, such as the Gene Ontology (GO) database. However, functional labels with only a few (algorithm that independently considers each label faces a paucity of information and thus is prone to capture non-generalizable patterns in the data, resulting in poor predictive performance. There exist a variety of algorithms for function prediction, but none properly address this 'overfitting' issue of sparsely annotated functions, or do so in a manner scalable to tens of thousands of functions in the human catalog. We propose a novel function prediction algorithm, clusDCA, which transfers information between similar functional labels to alleviate the overfitting problem for sparsely annotated functions. Our method is scalable to datasets with a large number of annotations. In a cross-validation experiment in yeast, mouse and human, our method greatly outperformed previous state-of-the-art function prediction algorithms in predicting sparsely annotated functions, without sacrificing the performance on labels with sufficient information. Furthermore, we show that our method can accurately predict genes that will be assigned a functional label that has no known annotations, based only on the ontology graph structure and genes associated with other labels, which further suggests that our method effectively utilizes the similarity between gene functions. © The Author 2015. Published by Oxford University Press.

  6. Cell-Type Specific Penetrating Peptides: Therapeutic Promises and Challenges

    Directory of Open Access Journals (Sweden)

    Maliha Zahid


    Full Text Available Cell penetrating peptides (CPP, also known as protein transduction domains (PTD, are small peptides able to carry peptides, proteins, nucleic acid, and nanoparticles, including viral particles, across the cellular membranes into cells, resulting in internalization of the intact cargo. In general, CPPs can be broadly classified into tissue-specific and non-tissue specific peptides, with the latter further sub-divided into three types: (1 cationic peptides of 6–12 amino acids in length comprised predominantly of arginine, lysine and/or ornithine residues; (2 hydrophobic peptides such as leader sequences of secreted growth factors or cytokines; and (3 amphipathic peptides obtained by linking hydrophobic peptides to nuclear localizing signals. Tissue-specific peptides are usually identified by screening of large peptide phage display libraries. These transduction peptides have the potential for a myriad of diagnostic as well as therapeutic applications, ranging from delivery of fluorescent or radioactive compounds for imaging, to delivery of peptides and proteins of therapeutic potential, and improving uptake of DNA, RNA, siRNA and even viral particles. Here we review the potential applications as well as hurdles to the tremendous potential of these CPPs, in particular the cell-type specific peptides.

  7. Cell-Type-Specific Splicing of Piezo2 Regulates Mechanotransduction

    Directory of Open Access Journals (Sweden)

    Marcin Szczot


    Full Text Available Summary: Piezo2 is a mechanically activated ion channel required for touch discrimination, vibration detection, and proprioception. Here, we discovered that Piezo2 is extensively spliced, producing different Piezo2 isoforms with distinct properties. Sensory neurons from both mice and humans express a large repertoire of Piezo2 variants, whereas non-neuronal tissues express predominantly a single isoform. Notably, even within sensory ganglia, we demonstrate the splicing of Piezo2 to be cell type specific. Biophysical characterization revealed substantial differences in ion permeability, sensitivity to calcium modulation, and inactivation kinetics among Piezo2 splice variants. Together, our results describe, at the molecular level, a potential mechanism by which transduction is tuned, permitting the detection of a variety of mechanosensory stimuli. : Szczot et al. find that the mechanoreceptor Piezo2 is extensively alternatively spliced, generating multiple distinct isoforms. Their findings indicate that these splice products have specific tissue and cell type expression patterns and exhibit differences in receptor properties. Keywords: Piezo, touch, sensation, ion-channel, splicing

  8. Parallel Sparse Matrix - Vector Product

    DEFF Research Database (Denmark)

    Alexandersen, Joe; Lazarov, Boyan Stefanov; Dammann, Bernd

    This technical report contains a case study of a sparse matrix-vector product routine, implemented for parallel execution on a compute cluster with both pure MPI and hybrid MPI-OpenMP solutions. C++ classes for sparse data types were developed and the report shows how these class can be used...

  9. Sparse decompositions in 'incoherent' dictionaries

    DEFF Research Database (Denmark)

    Gribonval, R.; Nielsen, Morten


    a unique sparse representation in such a dictionary. In particular, it is proved that the result of Donoho and Huo, concerning the replacement of a combinatorial optimization problem with a linear programming problem when searching for sparse representations, has an analog for dictionaries that may...

  10. Cardiac Glycoside Glucoevatromonoside Induces Cancer Type-Specific Cell Death

    Directory of Open Access Journals (Sweden)

    Naira F. Z. Schneider


    Full Text Available Cardiac glycosides (CGs are natural compounds used traditionally to treat congestive heart diseases. Recent investigations repositioned CGs as potential anticancer agents. To discover novel cytotoxic CG scaffolds, we selected the cardenolide glucoevatromonoside (GEV out of 46 CGs for its low nanomolar anti-lung cancer activity. GEV presented reduced toxicity toward non-cancerous cell types (lung MRC-5 and PBMC and high-affinity binding to the Na+/K+-ATPase α subunit, assessed by computational docking. GEV-induced cell death was caspase-independent, as investigated by a multiparametric approach, and culminates in severe morphological alterations in A549 cells, monitored by transmission electron microscopy, live cell imaging and flow cytometry. This non-canonical cell death was not preceded or accompanied by exacerbation of autophagy. In the presence of GEV, markers of autophagic flux (e.g. LC3I-II conversion were impacted, even in presence of bafilomycin A1. Cell death induction remained unaffected by calpain, cathepsin, parthanatos, or necroptosis inhibitors. Interestingly, GEV triggered caspase-dependent apoptosis in U937 acute myeloid leukemia cells, witnessing cancer-type specific cell death induction. Differential cell cycle modulation by this CG led to a G2/M arrest, cyclin B1 and p53 downregulation in A549, but not in U937 cells. We further extended the anti-cancer potential of GEV to 3D cell culture using clonogenic and spheroid formation assays and validated our findings in vivo by zebrafish xenografts. Altogether, GEV shows an interesting anticancer profile with the ability to exert cytotoxic effects via induction of different cell death modalities.

  11. Airborne LIDAR Points Classification Based on Tensor Sparse Representation (United States)

    Li, N.; Pfeifer, N.; Liu, C.


    The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. This paper proposes a tensor sparse representation classification (SRC) method for airborne LiDAR points. The LiDAR points are represented as tensors to keep attributes in its spatial space. Then only a few of training data is used for dictionary learning, and the sparse tensor is calculated based on tensor OMP algorithm. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on real LiDAR points whose result shows that objects can be distinguished by this algorithm successfully.

  12. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup


    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaickingand 4D light field view synthesis.

  13. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup


    Convolutional sparse coding (CSC) is a promising direction for unsupervised learning in computer vision. In contrast to recent supervised methods, CSC allows for convolutional image representations to be learned that are equally useful for high-level vision tasks and low-level image reconstruction and can be applied to a wide range of tasks without problem-specific retraining. Due to their extreme memory requirements, however, existing CSC solvers have so far been limited to low-dimensional problems and datasets using a handful of low-resolution example images at a time. In this paper, we propose a new approach to solving CSC as a consensus optimization problem, which lifts these limitations. By learning CSC features from large-scale image datasets for the first time, we achieve significant quality improvements in a number of imaging tasks. Moreover, the proposed method enables new applications in high-dimensional feature learning that has been intractable using existing CSC methods. This is demonstrated for a variety of reconstruction problems across diverse problem domains, including 3D multispectral demosaicing and 4D light field view synthesis.

  14. Temperament type specific metabolite profiles of the prefrontal cortex and serum in cattle.

    Directory of Open Access Journals (Sweden)

    Bodo Brand

    Full Text Available In the past decade the number of studies investigating temperament in farm animals has increased greatly because temperament has been shown not only to affect handling but also reproduction, health and economically important production traits. However, molecular pathways underlying temperament and molecular pathways linking temperament to production traits, health and reproduction have yet to be studied in full detail. Here we report the results of metabolite profiling of the prefrontal cortex and serum of cattle with distinct temperament types that were performed to further explore their molecular divergence in the response to the slaughter procedure and to identify new targets for further research of cattle temperament. By performing an untargeted comprehensive metabolite profiling, 627 and 1097 metabolite features comprising 235 and 328 metabolites could be detected in the prefrontal cortex and serum, respectively. In total, 54 prefrontal cortex and 51 serum metabolite features were indicated to have a high relevance in the classification of temperament types by a sparse partial least square discriminant analysis. A clear discrimination between fearful/neophobic-alert, interested-stressed, subdued/uninterested-calm and outgoing/neophilic-alert temperament types could be observed based on the abundance of the identified relevant prefrontal cortex and serum metabolites. Metabolites with high relevance in the classification of temperament types revealed that the main differences between temperament types in the response to the slaughter procedure were related to the abundance of glycerophospholipids, fatty acyls and sterol lipids. Differences in the abundance of metabolites related to C21 steroid metabolism and oxidative stress indicated that the differences in the metabolite profiles of the four extreme temperament types could be the result of a temperament type specific regulation of molecular pathways that are known to be involved in the

  15. In Defense of Sparse Tracking: Circulant Sparse Tracker

    KAUST Repository

    Zhang, Tianzhu


    Sparse representation has been introduced to visual tracking by finding the best target candidate with minimal reconstruction error within the particle filter framework. However, most sparse representation based trackers have high computational cost, less than promising tracking performance, and limited feature representation. To deal with the above issues, we propose a novel circulant sparse tracker (CST), which exploits circulant target templates. Because of the circulant structure property, CST has the following advantages: (1) It can refine and reduce particles using circular shifts of target templates. (2) The optimization can be efficiently solved entirely in the Fourier domain. (3) High dimensional features can be embedded into CST to significantly improve tracking performance without sacrificing much computation time. Both qualitative and quantitative evaluations on challenging benchmark sequences demonstrate that CST performs better than all other sparse trackers and favorably against state-of-the-art methods.

  16. Data analysis in high-dimensional sparse spaces

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder

    The present thesis considers data analysis of problems with many features in relation to the number of observations (large p, small n problems). The theoretical considerations for such problems are outlined including the curses and blessings of dimensionality, and the importance of dimension...... reduction. In this context the trade off between a rich solution which answers the questions at hand and a simple solution which generalizes to unseen data is described. For all of the given data examples labelled output exists and the analyses are therefore limited to supervised settings. Three novel...... classification techniques for high-dimensional problems are presented: Sparse discriminant analysis, sparse mixture discriminant analysis and orthogonality constrained support vector machines. The first two introduces sparseness to the well known linear and mixture discriminant analysis and thereby provide low...

  17. Sparse Linear Identifiable Multivariate Modeling

    DEFF Research Database (Denmark)

    Henao, Ricardo; Winther, Ole


    In this paper we consider sparse and identifiable linear latent variable (factor) and linear Bayesian network models for parsimonious analysis of multivariate data. We propose a computationally efficient method for joint parameter and model inference, and model comparison. It consists of a fully...... Bayesian hierarchy for sparse models using slab and spike priors (two-component δ-function and continuous mixtures), non-Gaussian latent factors and a stochastic search over the ordering of the variables. The framework, which we call SLIM (Sparse Linear Identifiable Multivariate modeling), is validated...... computational complexity. We attribute this mainly to the stochastic search strategy used, and to parsimony (sparsity and identifiability), which is an explicit part of the model. We propose two extensions to the basic i.i.d. linear framework: non-linear dependence on observed variables, called SNIM (Sparse Non-linear...

  18. Sparse representation-based color visualization method for hyperspectral imaging (United States)

    Wang, Li-Guo; Liu, Dan-Feng; Zhao, Liang


    In this paper, we designed a color visualization model for sparse representation of the whole hyperspectral image, in which, not only the spectral information in the sparse representation but also the spatial information of the whole image is retained. After the sparse representation, the color labels of the effective elements of the sparse coding dictionary are selected according to the sparse coefficient and then the mixed images are displayed. The generated images maintain spectral distance preservation and have good separability. For local ground objects, the proposed single-pixel mixed array and improved oriented sliver textures methods are integrated to display the specific composition of each pixel. This avoids the confusion of the color presentation in the mixed-pixel color display and can also be used to reconstruct the original hyperspectral data. Finally, the model effectiveness was proved using real data. This method is promising and can find use in many fields, such as energy exploration, environmental monitoring, disaster warning, and so on.

  19. Bayesian Inference Methods for Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Pedersen, Niels Lovmand


    of Bayesian inference algorithms for sparse channel estimation. Sparse inference methods aim at finding the sparse representation of a signal given in some overcomplete dictionary of basis vectors. Within this context, one of our main contributions to the field of SBL is a hierarchical representation...... and computational complexity. We also analyze the impact of transceiver filters on the sparseness of the channel response, and propose a dictionary design that permits the deployment of sparse inference methods in conditions of low bandwidth....

  20. Sparse Representations of Hyperspectral Images

    KAUST Repository

    Swanson, Robin J.


    Hyperspectral image data has long been an important tool for many areas of sci- ence. The addition of spectral data yields significant improvements in areas such as object and image classification, chemical and mineral composition detection, and astronomy. Traditional capture methods for hyperspectral data often require each wavelength to be captured individually, or by sacrificing spatial resolution. Recently there have been significant improvements in snapshot hyperspectral captures using, in particular, compressed sensing methods. As we move to a compressed sensing image formation model the need for strong image priors to shape our reconstruction, as well as sparse basis become more important. Here we compare several several methods for representing hyperspectral images including learned three dimensional dictionaries, sparse convolutional coding, and decomposable nonlocal tensor dictionaries. Addi- tionally, we further explore their parameter space to identify which parameters provide the most faithful and sparse representations.

  1. Sparse supervised principal component analysis (SSPCA) for dimension reduction and variable selection

    DEFF Research Database (Denmark)

    Sharifzadeh, Sara; Ghodsi, Ali; Clemmensen, Line H.


    Principal component analysis (PCA) is one of the main unsupervised pre-processing methods for dimension reduction. When the training labels are available, it is worth using a supervised PCA strategy. In cases that both dimension reduction and variable selection are required, sparse PCA (SPCA......) methods are preferred. In this paper, a sparse supervised PCA (SSPCA) method is proposed for pre-processing. This method is appropriate especially in problems where, a high dimensional input necessitates the use of a sparse method and a target label is also available to guide the variable selection......) algorithm. We compare the proposed method with PCA, PMD-based SPCA and supervised PCA. In addition, SSPCA is also compared with sparse partial least squares (SPLS), due to the similarity between the two objective functions. Experimental results from the simulated as well as real data sets show that, SSPCA...

  2. Image understanding using sparse representations

    CERN Document Server

    Thiagarajan, Jayaraman J; Turaga, Pavan; Spanias, Andreas


    Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blin

  3. Joint Sparse Recovery With Semisupervised MUSIC (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng


    Discrete multiple signal classification (MUSIC) with its low computational cost and mild condition requirement becomes a significant noniterative algorithm for joint sparse recovery (JSR). However, it fails in rank defective problem caused by coherent or limited amount of multiple measurement vectors (MMVs). In this letter, we provide a novel sight to address this problem by interpreting JSR as a binary classification problem with respect to atoms. Meanwhile, MUSIC essentially constructs a supervised classifier based on the labeled MMVs so that its performance will heavily depend on the quality and quantity of these training samples. From this viewpoint, we develop a semisupervised MUSIC (SS-MUSIC) in the spirit of machine learning, which declares that the insufficient supervised information in the training samples can be compensated from those unlabeled atoms. Instead of constructing a classifier in a fully supervised manner, we iteratively refine a semisupervised classifier by exploiting the labeled MMVs and some reliable unlabeled atoms simultaneously. Through this way, the required conditions and iterations can be greatly relaxed and reduced. Numerical experimental results demonstrate that SS-MUSIC can achieve much better recovery performances than other MUSIC extended algorithms as well as some typical greedy algorithms for JSR in terms of iterations and recovery probability.

  4. Biclustering Sparse Binary Genomic Data

    NARCIS (Netherlands)

    Van Uitert, M.; Meuleman, W.; Wessels, L.F.A.


    Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two

  5. Word mining in a sparsely-labeled handwritten collection

    NARCIS (Netherlands)

    Schomaker, L. R. B.; Yanikoglu, BA; Berkner, K


    Word-spotting techniques are usually based on detailed modeling of target words, followed by search for the locations of such a target word in images of handwriting. In this study, the focus is on deciding for the presence of target words in lines of text, regardless and disregarding their

  6. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin


    © 2015, © American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America. Recent years have seen active developments of various penalized regression methods, such as LASSO and elastic net, to analyze high-dimensional data. In these approaches, the direction and length of the regression coefficients are determined simultaneously. Due to the introduction of penalties, the length of the estimates can be far from being optimal for accurate predictions. We introduce a new framework, regression by projection, and its sparse version to analyze high-dimensional data. The unique nature of this framework is that the directions of the regression coefficients are inferred first, and the lengths and the tuning parameters are determined by a cross-validation procedure to achieve the largest prediction accuracy. We provide a theoretical result for simultaneous model selection consistency and parameter estimation consistency of our method in high dimension. This new framework is then generalized such that it can be applied to principal components analysis, partial least squares, and canonical correlation analysis. We also adapt this framework for discriminant analysis. Compared with the existing methods, where there is relatively little control of the dependency among the sparse components, our method can control the relationships among the components. We present efficient algorithms and related theory for solving the sparse regression by projection problem. Based on extensive simulations and real data analysis, we demonstrate that our method achieves good predictive performance and variable selection in the regression setting, and the ability to control relationships between the sparse components leads to more accurate classification. In supplementary materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.

  7. Programming for Sparse Minimax Optimization

    DEFF Research Database (Denmark)

    Jonasson, K.; Madsen, Kaj


    We present an algorithm for nonlinear minimax optimization which is well suited for large and sparse problems. The method is based on trust regions and sequential linear programming. On each iteration, a linear minimax problem is solved for a basic step. If necessary, this is followed by the dete......We present an algorithm for nonlinear minimax optimization which is well suited for large and sparse problems. The method is based on trust regions and sequential linear programming. On each iteration, a linear minimax problem is solved for a basic step. If necessary, this is followed...... by the determination of a minimum norm corrective step based on a first-order Taylor approximation. No Hessian information needs to be stored. Global convergence is proved. This new method has been extensively tested and compared with other methods, including two well known codes for nonlinear programming...

  8. Shearlets and Optimally Sparse Approximations

    DEFF Research Database (Denmark)

    Kutyniok, Gitta; Lemvig, Jakob; Lim, Wang-Q


    of such functions. Recently, cartoon-like images were introduced in 2D and 3D as a suitable model class, and approximation properties were measured by considering the decay rate of the $L^2$ error of the best $N$-term approximation. Shearlet systems are to date the only representation system, which provide...... optimally sparse approximations of this model class in 2D as well as 3D. Even more, in contrast to all other directional representation systems, a theory for compactly supported shearlet frames was derived which moreover also satisfy this optimality benchmark. This chapter shall serve as an introduction...... to and a survey about sparse approximations of cartoon-like images by band-limited and also compactly supported shearlet frames as well as a reference for the state-of-the-art of this research field....

  9. Language Recognition via Sparse Coding (United States)


    exploiting variation of the nonzero locations and magnitude, we can build a discrimi- native pipeline for language recognition. Figure 1 describes a...classify each language as a target within six predefined language clusters. The language clusters are Ara- bic, Chinese, English , French, Slavic, and... Language Recognition via Sparse Coding† Youngjune L. Gwon1, William M. Campbell1, Douglas Sturim1, H. T. Kung2 1MIT Lincoln Laboratory 2Harvard

  10. Multiple Descriptions Using Sparse Decompositions

    DEFF Research Database (Denmark)

    Jensen, Tobias Lindstrøm; Østergaard, Jan; Dahl, Joachim


    In this paper, we consider the design of multiple descriptions (MDs) using sparse decompositions. In a description erasure channel only a subset of the transmitted descriptions is received. The MD problem concerns the design of the descriptions such that they individually approximate the source...... first-order method to the proposed convex problem such that we can solve large-scale instances for image sequences....

  11. Biclustering sparse binary genomic data. (United States)

    van Uitert, Miranda; Meuleman, Wouter; Wessels, Lodewyk


    Genomic datasets often consist of large, binary, sparse data matrices. In such a dataset, one is often interested in finding contiguous blocks that (mostly) contain ones. This is a biclustering problem, and while many algorithms have been proposed to deal with gene expression data, only two algorithms have been proposed that specifically deal with binary matrices. None of the gene expression biclustering algorithms can handle the large number of zeros in sparse binary matrices. The two proposed binary algorithms failed to produce meaningful results. In this article, we present a new algorithm that is able to extract biclusters from sparse, binary datasets. A powerful feature is that biclusters with different numbers of rows and columns can be detected, varying from many rows to few columns and few rows to many columns. It allows the user to guide the search towards biclusters of specific dimensions. When applying our algorithm to an input matrix derived from TRANSFAC, we find transcription factors with distinctly dissimilar binding motifs, but a clear set of common targets that are significantly enriched for GO categories.


    Directory of Open Access Journals (Sweden)

    N. Li


    Full Text Available The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. This paper proposes a tensor sparse representation classification (SRC method for airborne LiDAR points. The LiDAR points are represented as tensors to keep attributes in its spatial space. Then only a few of training data is used for dictionary learning, and the sparse tensor is calculated based on tensor OMP algorithm. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on real LiDAR points whose result shows that objects can be distinguished by this algorithm successfully.

  13. Pairwise Constraint-Guided Sparse Learning for Feature Selection. (United States)

    Liu, Mingxia; Zhang, Daoqiang


    Feature selection aims to identify the most informative features for a compact and accurate data representation. As typical supervised feature selection methods, Lasso and its variants using L1-norm-based regularization terms have received much attention in recent studies, most of which use class labels as supervised information. Besides class labels, there are other types of supervised information, e.g., pairwise constraints that specify whether a pair of data samples belong to the same class (must-link constraint) or different classes (cannot-link constraint). However, most of existing L1-norm-based sparse learning methods do not take advantage of the pairwise constraints that provide us weak and more general supervised information. For addressing that problem, we propose a pairwise constraint-guided sparse (CGS) learning method for feature selection, where the must-link and the cannot-link constraints are used as discriminative regularization terms that directly concentrate on the local discriminative structure of data. Furthermore, we develop two variants of CGS, including: 1) semi-supervised CGS that utilizes labeled data, pairwise constraints, and unlabeled data and 2) ensemble CGS that uses the ensemble of pairwise constraint sets. We conduct a series of experiments on a number of data sets from University of California-Irvine machine learning repository, a gene expression data set, two real-world neuroimaging-based classification tasks, and two large-scale attribute classification tasks. Experimental results demonstrate the efficacy of our proposed methods, compared with several established feature selection methods.

  14. Combining sparse coding and time-domain features for heart sound classification. (United States)

    Whitaker, Bradley M; Suresha, Pradyumna B; Liu, Chengyu; Clifford, Gari D; Anderson, David V


    This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which used sparse coding as a feature extraction tool on audio PCG data for heart sound classification. In sparse coding, preprocessed data is decomposed into a dictionary matrix and a sparse coefficient matrix. The dictionary matrix represents statistically important features of the audio segments. The sparse coefficient matrix is a mapping that represents which features are used by each segment. Working in the sparse domain, we train support vector machines (SVMs) for each audio segment (S1, systole, S2, diastole) and the full cardiac cycle. We train a sixth SVM to combine the results from the preliminary SVMs into a single binary label for the entire PCG recording. In addition to classifying heart sounds using sparse coding, this paper presents two novel modifications. The first uses a matrix norm in the dictionary update step of sparse coding to encourage the dictionary to learn discriminating features from the abnormal heart recordings. The second combines the sparse coding features with time-domain features in the final SVM stage. The original algorithm submitted to the challenge achieved a cross-validated mean accuracy (MAcc) score of 0.8652 (Se  =  0.8669 and Sp  =  0.8634). After incorporating the modifications new to this paper, we report an improved cross-validated MAcc of 0.8926 (Se  =  0.9007 and Sp  =  0.8845). Our results show that sparse coding is an effective way to define spectral features of the cardiac cycle and its sub-cycles for the purpose of classification. In addition, we demonstrate that sparse coding can be combined with additional feature extraction methods to improve classification accuracy.

  15. Sparse matrix decompositions for clustering


    Blumensath, Thomas


    Clustering can be understood as a matrix decomposition problem, where a feature vector matrix is represented as a product of two matrices, a matrix of cluster centres and a matrix with sparse columns, where each column assigns individual features to one of the cluster centres. This matrix factorisation is the basis of classical clustering methods, such as those based on non-negative matrix factorisation but can also be derived for other methods, such as k-means clustering. In this paper we de...

  16. Dynamic Representations of Sparse Graphs

    DEFF Research Database (Denmark)

    Brodal, Gerth Stølting; Fagerberg, Rolf


    We present a linear space data structure for maintaining graphs with bounded arboricity—a large class of sparse graphs containing e.g. planar graphs and graphs of bounded treewidth—under edge insertions, edge deletions, and adjacency queries. The data structure supports adjacency queries in worst...... case O(c) time, and edge insertions and edge deletions in amortized O(1) and O(c+log n) time, respectively, where n is the number of nodes in the graph, and c is the bound on the arboricity....

  17. Sparse Matrices in Frame Theory

    DEFF Research Database (Denmark)

    Lemvig, Jakob; Krahmer, Felix; Kutyniok, Gitta


    Frame theory is closely intertwined with signal processing through a canon of methodologies for the analysis of signals using (redundant) linear measurements. The canonical dual frame associated with a frame provides a means for reconstruction by a least squares approach, but other dual frames...... yield alternative reconstruction procedures. The novel paradigm of sparsity has recently entered the area of frame theory in various ways. Of those different sparsity perspectives, we will focus on the situations where frames and (not necessarily canonical) dual frames can be written as sparse matrices...

  18. Multi-label Learning with Missing Labels Using Mixed Dependency Graphs

    KAUST Repository

    Wu, Baoyuan


    This work focuses on the problem of multi-label learning with missing labels (MLML), which aims to label each test instance with multiple class labels given training instances that have an incomplete/partial set of these labels (i.e., some of their labels are missing). The key point to handle missing labels is propagating the label information from the provided labels to missing labels, through a dependency graph that each label of each instance is treated as a node. We build this graph by utilizing different types of label dependencies. Specifically, the instance-level similarity is served as undirected edges to connect the label nodes across different instances and the semantic label hierarchy is used as directed edges to connect different classes. This base graph is referred to as the mixed dependency graph, as it includes both undirected and directed edges. Furthermore, we present another two types of label dependencies to connect the label nodes across different classes. One is the class co-occurrence, which is also encoded as undirected edges. Combining with the above base graph, we obtain a new mixed graph, called mixed graph with co-occurrence (MG-CO). The other is the sparse and low rank decomposition of the whole label matrix, to embed high-order dependencies over all labels. Combining with the base graph, the new mixed graph is called as MG-SL (mixed graph with sparse and low rank decomposition). Based on MG-CO and MG-SL, we further propose two convex transductive formulations of the MLML problem, denoted as MLMG-CO and MLMG-SL respectively. In both formulations, the instance-level similarity is embedded through a quadratic smoothness term, while the semantic label hierarchy is used as a linear constraint. In MLMG-CO, the class co-occurrence is also formulated as a quadratic smoothness term, while the sparse and low rank decomposition is incorporated into MLMG-SL, through two additional matrices (one is assumed as sparse, and the other is assumed as low

  19. Learning a Nonnegative Sparse Graph for Linear Regression. (United States)

    Fang, Xiaozhao; Xu, Yong; Li, Xuelong; Lai, Zhihui; Wong, Wai Keung


    Previous graph-based semisupervised learning (G-SSL) methods have the following drawbacks: 1) they usually predefine the graph structure and then use it to perform label prediction, which cannot guarantee an overall optimum and 2) they only focus on the label prediction or the graph structure construction but are not competent in handling new samples. To this end, a novel nonnegative sparse graph (NNSG) learning method was first proposed. Then, both the label prediction and projection learning were integrated into linear regression. Finally, the linear regression and graph structure learning were unified within the same framework to overcome these two drawbacks. Therefore, a novel method, named learning a NNSG for linear regression was presented, in which the linear regression and graph learning were simultaneously performed to guarantee an overall optimum. In the learning process, the label information can be accurately propagated via the graph structure so that the linear regression can learn a discriminative projection to better fit sample labels and accurately classify new samples. An effective algorithm was designed to solve the corresponding optimization problem with fast convergence. Furthermore, NNSG provides a unified perceptiveness for a number of graph-based learning methods and linear regression methods. The experimental results showed that NNSG can obtain very high classification accuracy and greatly outperforms conventional G-SSL methods, especially some conventional graph construction methods.

  20. Sparse High Dimensional Models in Economics. (United States)

    Fan, Jianqing; Lv, Jinchi; Qi, Lei


    This paper reviews the literature on sparse high dimensional models and discusses some applications in economics and finance. Recent developments of theory, methods, and implementations in penalized least squares and penalized likelihood methods are highlighted. These variable selection methods are proved to be effective in high dimensional sparse modeling. The limits of dimensionality that regularization methods can handle, the role of penalty functions, and their statistical properties are detailed. Some recent advances in ultra-high dimensional sparse modeling are also briefly discussed.

  1. Robust visual tracking via multiscale deep sparse networks (United States)

    Wang, Xin; Hou, Zhiqiang; Yu, Wangsheng; Xue, Yang; Jin, Zefenfen; Dai, Bo


    In visual tracking, deep learning with offline pretraining can extract more intrinsic and robust features. It has significant success solving the tracking drift in a complicated environment. However, offline pretraining requires numerous auxiliary training datasets and is considerably time-consuming for tracking tasks. To solve these problems, a multiscale sparse networks-based tracker (MSNT) under the particle filter framework is proposed. Based on the stacked sparse autoencoders and rectifier linear unit, the tracker has a flexible and adjustable architecture without the offline pretraining process and exploits the robust and powerful features effectively only through online training of limited labeled data. Meanwhile, the tracker builds four deep sparse networks of different scales, according to the target's profile type. During tracking, the tracker selects the matched tracking network adaptively in accordance with the initial target's profile type. It preserves the inherent structural information more efficiently than the single-scale networks. Additionally, a corresponding update strategy is proposed to improve the robustness of the tracker. Extensive experimental results on a large scale benchmark dataset show that the proposed method performs favorably against state-of-the-art methods in challenging environments.

  2. Image fusion using sparse overcomplete feature dictionaries

    Energy Technology Data Exchange (ETDEWEB)

    Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt


    Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.

  3. MOBE-ChIP: Probing Cell Type-Specific Binding Through Large-Scale Chromatin Immunoprecipitation. (United States)

    Wang, Shenqi; Lau, On Sun


    In multicellular organisms, the initiation and maintenance of specific cell types often require the activity of cell type-specific transcriptional regulators. Understanding their roles in gene regulation is crucial but probing their DNA targets in vivo, especially in a genome-wide manner, remains a technical challenge with their limited expression. To improve the sensitivity of chromatin immunoprecipitation (ChIP) for detecting the cell type-specific signals, we have developed the Maximized Objects for Better Enrichment (MOBE)-ChIP, where ChIP is performed at a substantially larger experimental scale and under low background conditions. Here, we describe the procedure in the study of transcription factors in the model plant Arabidopsis. However, with some modifications, the technique should also be implemented in other systems. Besides cell type-specific studies, MOBE-ChIP can also be used as a general strategy to improve ChIP signals.

  4. Diffusion Indexes with Sparse Loadings

    DEFF Research Database (Denmark)

    Kristensen, Johannes Tang

    to the problem by using the LASSO as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model which is better suited for forecasting compared......The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs...... to the traditional principal components (PC) approach.We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on US macroeconomic data. Overall we find that compared to PC we obtain improvements in forecasting accuracy and thus find...

  5. Diffusion Indexes With Sparse Loadings

    DEFF Research Database (Denmark)

    Kristensen, Johannes Tang


    The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs...... to the problem by using the least absolute shrinkage and selection operator (LASSO) as a variable selection method to choose between the possible variables and thus obtain sparse loadings from which factors or diffusion indexes can be formed. This allows us to build a more parsimonious factor model...... that is better suited for forecasting compared to the traditional principal components (PC) approach. We provide an asymptotic analysis of the estimator and illustrate its merits empirically in a forecasting experiment based on U.S. macroeconomic data. Overall we find that compared to PC we obtain improvements...


    International Nuclear Information System (INIS)

    Andrecut, M.; Stil, J. M.; Taylor, A. R.


    Faraday rotation measure synthesis is a method for analyzing multichannel polarized radio emissions, and it has emerged as an important tool in the study of Galactic and extragalactic magnetic fields. The method requires the recovery of the Faraday dispersion function from measurements restricted to limited wavelength ranges, which is an ill-conditioned deconvolution problem. Here, we discuss a recovery method that assumes a sparse approximation of the Faraday dispersion function in an overcomplete dictionary of functions. We discuss the general case when both thin and thick components are included in the model, and we present the implementation of a greedy deconvolution algorithm. We illustrate the method with several numerical simulations that emphasize the effect of the covered range and sampling resolution in the Faraday depth space, and the effect of noise on the observed data.

  7. Continuous speech recognition with sparse coding

    CSIR Research Space (South Africa)

    Smit, WJ


    Full Text Available , we show how sparse codes can be used to do continuous speech recognition. We use the TIDIGITS dataset to illustrate the process. First a waveform is transformed into a spectrogram, and a sparse code for the spectrogram is found by means of a linear...

  8. Numerical solution of large sparse linear systems

    International Nuclear Information System (INIS)

    Meurant, Gerard; Golub, Gene.


    This note is based on one of the lectures given at the 1980 CEA-EDF-INRIA Numerical Analysis Summer School whose aim is the study of large sparse linear systems. The main topics are solving least squares problems by orthogonal transformation, fast Poisson solvers and solution of sparse linear system by iterative methods with a special emphasis on preconditioned conjuguate gradient method [fr

  9. Sparse seismic imaging using variable projection

    NARCIS (Netherlands)

    Aravkin, Aleksandr Y.; Tu, Ning; van Leeuwen, Tristan


    We consider an important class of signal processing problems where the signal of interest is known to be sparse, and can be recovered from data given auxiliary information about how the data was generated. For example, a sparse Green's function may be recovered from seismic experimental data using

  10. Approximate Orthogonal Sparse Embedding for Dimensionality Reduction. (United States)

    Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Yang, Jian; Zhang, David


    Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1-norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.

  11. Dimensionality reduction of hyperspectral images based on sparse discriminant manifold embedding (United States)

    Huang, Hong; Luo, Fulin; Liu, Jiamin; Yang, Yaqiong


    Sparse manifold clustering and embedding (SMCE) adaptively selects neighbor points from the same manifold and approximately spans a low-dimensional affine subspace, but it does not explicitly give a projection matrix and encounters the out-of-sample problem. To overcome this drawback, we propose a new dimensionality reduction method, called sparse manifold embedding (SME), based on graph embedding and sparse representation for hyperspectral image (HSI). It utilizes the sparse coefficients of affine subspace to construct a similarity graph and preserves this sparse similarity in embedding space. Furthermore, we try to make full use of the prior label information to design a novel supervised learning method termed sparse discriminant manifold embedding (SDME). SDME not only inherits the merits of the sparsity property of affine subspace but also boosts the compactness of intra-manifold, which achieves discriminating features and further improves the classification performance of HSI. Experiments on two real hyperspectral data sets (Indian Pines and PaviaU) show the benefits of the proposed SME and SDME methods.

  12. Morphological changes in different populations of bladder afferent neurons detected by herpes simplex virus (HSV) vectors with cell-type-specific promoters in mice with spinal cord injury. (United States)

    Shimizu, Nobutaka; Doyal, Mark F; Goins, William F; Kadekawa, Katsumi; Wada, Naoki; Kanai, Anthony J; de Groat, William C; Hirayama, Akihide; Uemura, Hirotsugu; Glorioso, Joseph C; Yoshimura, Naoki


    Functional and morphological changes in C-fiber bladder afferent pathways are reportedly involved in neurogenic detrusor overactivity (NDO) after spinal cord injury (SCI). This study examined the morphological changes in different populations of bladder afferent neurons after SCI using replication-defective herpes simplex virus (HSV) vectors encoding the mCherry reporter driven by neuronal cell-type-specific promoters. Spinal intact (SI) and SCI mice were injected into the bladder wall with HSV mCherry vectors driven by the cytomegalovirus (CMV) promoter, CGRP promoter, TRPV1 promoter or neurofilament 200 (NF200) promoter. Two weeks after vector inoculation into the bladder wall, L1 and L6 dorsal root ganglia (DRG) were removed bilaterally for immunofluorescent staining using anti-mCherry antibody. The number of CMV promoter vector-labeled neurons was not altered after SCI. The number of CGRP and TRPV1 promoter vector-labeled neurons was significantly increased whereas the number of NF200 vector-labeled neurons was decreased in L6 DRG after SCI. The median size of CGRP promoter-labeled C-fiber neurons was increased from 247.0 in SI mice to 271.3μm 2 in SCI mice whereas the median cell size of TRPV1 promoter vector-labeled neurons was decreased from 245.2 in SI mice to 216.5μm 2 in SCI mice. CGRP and TRPV1 mRNA levels of laser-captured bladder afferent neurons labeled with Fast Blue were significantly increased in SCI mice compared to SI mice. Thus, using a novel HSV vector-mediated neuronal labeling technique, we found that SCI induces expansion of the CGRP- and TRPV1-expressing C-fiber cell population, which could contribute to C-fiber afferent hyperexcitability and NDO after SCI. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

  13. A quantitative comparison of cell-type-specific microarray gene expression profiling methods in the mouse brain.

    Directory of Open Access Journals (Sweden)

    Benjamin W Okaty

    Full Text Available Expression profiling of restricted neural populations using microarrays can facilitate neuronal classification and provide insight into the molecular bases of cellular phenotypes. Due to the formidable heterogeneity of intermixed cell types that make up the brain, isolating cell types prior to microarray processing poses steep technical challenges that have been met in various ways. These methodological differences have the potential to distort cell-type-specific gene expression profiles insofar as they may insufficiently filter out contaminating mRNAs or induce aberrant cellular responses not normally present in vivo. Thus we have compared the repeatability, susceptibility to contamination from off-target cell-types, and evidence for stress-responsive gene expression of five different purification methods--Laser Capture Microdissection (LCM, Translating Ribosome Affinity Purification (TRAP, Immunopanning (PAN, Fluorescence Activated Cell Sorting (FACS, and manual sorting of fluorescently labeled cells (Manual. We found that all methods obtained comparably high levels of repeatability, however, data from LCM and TRAP showed significantly higher levels of contamination than the other methods. While PAN samples showed higher activation of apoptosis-related, stress-related and immediate early genes, samples from FACS and Manual studies, which also require dissociated cells, did not. Given that TRAP targets actively translated mRNAs, whereas other methods target all transcribed mRNAs, observed differences may also reflect translational regulation.

  14. Food Labels (United States)

    ... on their food labels. When a food says "light" ("lite") or "low fat" on the label, it ... on this topic for: Teens Nutrition & Fitness Center Smart Supermarket Shopping Figuring Out Fat and Calories How ...

  15. Regression with Sparse Approximations of Data

    DEFF Research Database (Denmark)

    Noorzad, Pardis; Sturm, Bob L.


    We propose sparse approximation weighted regression (SPARROW), a method for local estimation of the regression function that uses sparse approximation with a dictionary of measurements. SPARROW estimates the regression function at a point with a linear combination of a few regressands selected...... by a sparse approximation of the point in terms of the regressors. We show SPARROW can be considered a variant of \\(k\\)-nearest neighbors regression (\\(k\\)-NNR), and more generally, local polynomial kernel regression. Unlike \\(k\\)-NNR, however, SPARROW can adapt the number of regressors to use based...

  16. Sparse modeling theory, algorithms, and applications

    CERN Document Server

    Rish, Irina


    ""A comprehensive, clear, and well-articulated book on sparse modeling. This book will stand as a prime reference to the research community for many years to come.""-Ricardo Vilalta, Department of Computer Science, University of Houston""This book provides a modern introduction to sparse methods for machine learning and signal processing, with a comprehensive treatment of both theory and algorithms. Sparse Modeling is an ideal book for a first-year graduate course.""-Francis Bach, INRIA - École Normale Supřieure, Paris

  17. Sparse adaptive filters for echo cancellation

    CERN Document Server

    Paleologu, Constantin


    Adaptive filters with a large number of coefficients are usually involved in both network and acoustic echo cancellation. Consequently, it is important to improve the convergence rate and tracking of the conventional algorithms used for these applications. This can be achieved by exploiting the sparseness character of the echo paths. Identification of sparse impulse responses was addressed mainly in the last decade with the development of the so-called ``proportionate''-type algorithms. The goal of this book is to present the most important sparse adaptive filters developed for echo cancellati

  18. Sparse Image Reconstruction in Computed Tomography

    DEFF Research Database (Denmark)

    Jørgensen, Jakob Sauer

    In recent years, increased focus on the potentially harmful effects of x-ray computed tomography (CT) scans, such as radiation-induced cancer, has motivated research on new low-dose imaging techniques. Sparse image reconstruction methods, as studied for instance in the field of compressed sensing...... and limitations of sparse reconstruction methods in CT, in particular in a quantitative sense. For example, relations between image properties such as contrast, structure and sparsity, tolerable noise levels, suficient sampling levels, the choice of sparse reconstruction formulation and the achievable image...

  19. Massive Asynchronous Parallelization of Sparse Matrix Factorizations

    Energy Technology Data Exchange (ETDEWEB)

    Chow, Edmond [Georgia Inst. of Technology, Atlanta, GA (United States)


    Solving sparse problems is at the core of many DOE computational science applications. We focus on the challenge of developing sparse algorithms that can fully exploit the parallelism in extreme-scale computing systems, in particular systems with massive numbers of cores per node. Our approach is to express a sparse matrix factorization as a large number of bilinear constraint equations, and then solving these equations via an asynchronous iterative method. The unknowns in these equations are the matrix entries of the factorization that is desired.

  20. Technique detection software for Sparse Matrices

    Directory of Open Access Journals (Sweden)

    KHAN Muhammad Taimoor


    Full Text Available Sparse storage formats are techniques for storing and processing the sparse matrix data efficiently. The performance of these storage formats depend upon the distribution of non-zeros, within the matrix in different dimensions. In order to have better results we need a technique that suits best the organization of data in a particular matrix. So the decision of selecting a better technique is the main step towards improving the system's results otherwise the efficiency can be decreased. The purpose of this research is to help identify the best storage format in case of reduced storage size and high processing efficiency for a sparse matrix.

  1. The association of current hormonal contraceptive use with type-specific HPV detection. (United States)

    Ghanem, Khalil G; Datta, S Deblina; Unger, Elizabeth R; Hagensee, Michael; Shlay, Judith C; Kerndt, Peter; Hsu, Katherine; Koutsky, Laura A


    Increased duration of hormonal contraceptive (HC) use may be positively associated with the risk of invasive cervical cancer. This is a secondary analysis from the HPV Sentinel Surveillance Study. The authors examined the association between type-specific human papillomavirus (HPV) detection and current HC use among 7718 women attending 26 sexually transmitted disease, family planning and primary care clinics in the USA. There was an association between HC use and HPV-16 detection (adjusted prevalence rate ratio 1.34 (95% CI 1.05 to 1.71) for oral contraceptive users and 1.41 (1.01 to 2.04) for depot-medroxyprogesterone acetate users); there was no association between HC use and detection of other HPV types or any HPV overall. Longitudinal studies are needed to better define this type-specific association and its clinical significance.

  2. Bridging the Gap: Towards a Cell-Type Specific Understanding of Neural Circuits Underlying Fear Behaviors (United States)

    McCullough, KM; Morrison, FG; Ressler, KJ


    Fear and anxiety-related disorders are remarkably common and debilitating, and are often characterized by dysregulated fear responses. Rodent models of fear learning and memory have taken great strides towards elucidating the specific neuronal circuitries underlying the learning of fear responses. The present review addresses recent research utilizing optogenetic approaches to parse circuitries underlying fear behaviors. It also highlights the powerful advances made when optogenetic techniques are utilized in a genetically defined, cell-type specific, manner. The application of next-generation genetic and sequencing approaches in a cell-type specific context will be essential for a mechanistic understanding of the neural circuitry underlying fear behavior and for the rational design of targeted, circuit specific, pharmacologic interventions for the treatment and prevention of fear-related disorders. PMID:27470092

  3. Second Workshop on Sparse Grids and Applications

    CERN Document Server

    Pflüger, Dirk


    Sparse grids have gained increasing interest in recent years for the numerical treatment of high-dimensional problems. Whereas classical numerical discretization schemes fail in more than three or four dimensions, sparse grids make it possible to overcome the “curse” of dimensionality to some degree, extending the number of dimensions that can be dealt with. This volume of LNCSE collects the papers from the proceedings of the second workshop on sparse grids and applications, demonstrating once again the importance of this numerical discretization scheme. The selected articles present recent advances on the numerical analysis of sparse grids as well as efficient data structures, and the range of applications extends to uncertainty quantification settings and clustering, to name but a few examples.

  4. Parallel transposition of sparse data structures

    DEFF Research Database (Denmark)

    Wang, Hao; Liu, Weifeng; Hou, Kaixi


    transposition for sparse matrices and graphs, have not received the attention they deserve. In this paper, we first identify that the transposition operation can be a bottleneck of some fundamental sparse matrix and graph algorithms. Then, we revisit the performance and scalability of parallel transposition...... approaches on x86-based multi-core and many-core processors. Based on the insights obtained, we propose two new parallel transposition algorithms: ScanTrans and MergeTrans. The experimental results show that our ScanTrans method achieves an average of 2.8-fold (up to 6.2-fold) speedup over the parallel......Many applications in computational sciences and social sciences exploit sparsity and connectivity of acquired data. Even though many parallel sparse primitives such as sparse matrix-vector (SpMV) multiplication have been extensively studied, some other important building blocks, e.g., parallel...

  5. Biclustering via Sparse Singular Value Decomposition

    KAUST Repository

    Lee, Mihee


    Sparse singular value decomposition (SSVD) is proposed as a new exploratory analysis tool for biclustering or identifying interpretable row-column associations within high-dimensional data matrices. SSVD seeks a low-rank, checkerboard structured matrix approximation to data matrices. The desired checkerboard structure is achieved by forcing both the left- and right-singular vectors to be sparse, that is, having many zero entries. By interpreting singular vectors as regression coefficient vectors for certain linear regressions, sparsity-inducing regularization penalties are imposed to the least squares regression to produce sparse singular vectors. An efficient iterative algorithm is proposed for computing the sparse singular vectors, along with some discussion of penalty parameter selection. A lung cancer microarray dataset and a food nutrition dataset are used to illustrate SSVD as a biclustering method. SSVD is also compared with some existing biclustering methods using simulated datasets. © 2010, The International Biometric Society.

  6. Finding Nonoverlapping Substructures of a Sparse Matrix

    Energy Technology Data Exchange (ETDEWEB)

    Pinar, Ali; Vassilevska, Virginia


    Many applications of scientific computing rely on computations on sparse matrices. The design of efficient implementations of sparse matrix kernels is crucial for the overall efficiency of these applications. Due to the high compute-to-memory ratio and irregular memory access patterns, the performance of sparse matrix kernels is often far away from the peak performance on a modern processor. Alternative data structures have been proposed, which split the original matrix A into A{sub d} and A{sub s}, so that A{sub d} contains all dense blocks of a specified size in the matrix, and A{sub s} contains the remaining entries. This enables the use of dense matrix kernels on the entries of A{sub d} producing better memory performance. In this work, we study the problem of finding a maximum number of nonoverlapping dense blocks in a sparse matrix, which is previously not studied in the sparse matrix community. We show that the maximum nonoverlapping dense blocks problem is NP-complete by using a reduction from the maximum independent set problem on cubic planar graphs. We also propose a 2/3-approximation algorithm that runs in linear time in the number of nonzeros in the matrix. This extended abstract focuses on our results for 2x2 dense blocks. However we show that our results can be generalized to arbitrary sized dense blocks, and many other oriented substructures, which can be exploited to improve the memory performance of sparse matrix operations.

  7. Synthesizing spatiotemporally sparse smartphone sensor data for bridge modal identification (United States)

    Ozer, Ekin; Feng, Maria Q.


    Smartphones as vibration measurement instruments form a large-scale, citizen-induced, and mobile wireless sensor network (WSN) for system identification and structural health monitoring (SHM) applications. Crowdsourcing-based SHM is possible with a decentralized system granting citizens with operational responsibility and control. Yet, citizen initiatives introduce device mobility, drastically changing SHM results due to uncertainties in the time and the space domains. This paper proposes a modal identification strategy that fuses spatiotemporally sparse SHM data collected by smartphone-based WSNs. Multichannel data sampled with the time and the space independence is used to compose the modal identification parameters such as frequencies and mode shapes. Structural response time history can be gathered by smartphone accelerometers and converted into Fourier spectra by the processor units. Timestamp, data length, energy to power conversion address temporal variation, whereas spatial uncertainties are reduced by geolocation services or determining node identity via QR code labels. Then, parameters collected from each distributed network component can be extended to global behavior to deduce modal parameters without the need of a centralized and synchronous data acquisition system. The proposed method is tested on a pedestrian bridge and compared with a conventional reference monitoring system. The results show that the spatiotemporally sparse mobile WSN data can be used to infer modal parameters despite non-overlapping sensor operation schedule.

  8. Sparse Representation Based Binary Hypothesis Model for Hyperspectral Image Classification

    Directory of Open Access Journals (Sweden)

    Yidong Tang


    Full Text Available The sparse representation based classifier (SRC and its kernel version (KSRC have been employed for hyperspectral image (HSI classification. However, the state-of-the-art SRC often aims at extended surface objects with linear mixture in smooth scene and assumes that the number of classes is given. Considering the small target with complex background, a sparse representation based binary hypothesis (SRBBH model is established in this paper. In this model, a query pixel is represented in two ways, which are, respectively, by background dictionary and by union dictionary. The background dictionary is composed of samples selected from the local dual concentric window centered at the query pixel. Thus, for each pixel the classification issue becomes an adaptive multiclass classification problem, where only the number of desired classes is required. Furthermore, the kernel method is employed to improve the interclass separability. In kernel space, the coding vector is obtained by using kernel-based orthogonal matching pursuit (KOMP algorithm. Then the query pixel can be labeled by the characteristics of the coding vectors. Instead of directly using the reconstruction residuals, the different impacts the background dictionary and union dictionary have on reconstruction are used for validation and classification. It enhances the discrimination and hence improves the performance.

  9. A Modified Sparse Representation Method for Facial Expression Recognition

    Directory of Open Access Journals (Sweden)

    Wei Wang


    Full Text Available In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit method is used to speed up the convergence of OMP (orthogonal matching pursuit. Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan’s JAFFE and CMU’s CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result.

  10. Long-term in vitro, cell-type-specific genome-wide reprogramming of gene expression

    International Nuclear Information System (INIS)

    Hakelien, Anne-Mari; Gaustad, Kristine G.; Taranger, Christel K.; Skalhegg, Bjorn S.; Kuentziger, Thomas; Collas, Philippe


    We demonstrate a cell extract-based, genome-wide and heritable reprogramming of gene expression in vitro. Kidney epithelial 293T cells have previously been shown to take on T cell properties following a brief treatment with an extract of Jurkat T cells. We show here that 293T cells exposed for 1 h to a Jurkat cell extract undergo genome-wide, target cell-type-specific and long-lasting transcriptional changes. Microarray analyses indicate that on any given week after extract treatment, ∼2500 genes are upregulated >3-fold, of which ∼900 are also expressed in Jurkat cells. Concomitantly, ∼1500 genes are downregulated or repressed, of which ∼500 are also downregulated in Jurkat cells. Gene expression changes persist for over 30 passages (∼80 population doublings) in culture. Target cell-type specificity of these changes is shown by the lack of activation or repression of Jurkat-specific genes by extracts of 293T cells or carcinoma cells. Quantitative RT-PCR analysis confirms the long-term transcriptional activation of genes involved in key T cell functions. Additionally, growth of cells in suspended aggregates, expression of CD3 and CD28 T cell surface markers, and interleukin-2 secretion by 293T cells treated with extract of adult peripheral blood T cells illustrate a functional nuclear reprogramming. Therefore, target cell-type-specific and heritable changes in gene expression, and alterations in cell function, can be promoted by extracts derived from transformed cells as well as from adult primary cells

  11. Partitioning Heritability of Regulatory and Cell-Type-Specific Variants across 11 Common Diseases

    DEFF Research Database (Denmark)

    Gusev, Alexander; Lee, S Hong; Trynka, Gosia


    partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1× enrichment......; p = 3.7 × 10(-17)) and 38% (SE = 4%) of hg(2) from genotyped SNPs (1.6× enrichment, p = 1.0 × 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained

  12. Deep ensemble learning of sparse regression models for brain disease diagnosis. (United States)

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


    Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer's disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call 'Deep Ensemble Sparse Regression Network.' To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. Copyright © 2017 Elsevier B.V. All rights reserved.

  13. Nutrition Labeling

    DEFF Research Database (Denmark)

    Grunert, Klaus G


    because consumers will avoid products that the label shows to be nutritionally deficient, but also because food producers will try to avoid marketing products that appear, according to the label, as nutritionally problematic, for example, because of a high content of saturated fat or salt. Nutrition......Nutrition labeling refers to the provision of information on a food product’s nutritional content on the package label. It can serve both public health and commercial purposes. From a public health perspective, the aim of nutrition labeling is to provide information that can enable consumers...... to make healthier choices when choosing food products. Nutrition labeling is thus closely linked to the notion of the informed consumer, that chooses products according to their aims, on the basis of the information at their disposal. Because many consumers are assumed to be interested in making healthy...

  14. Image super-resolution via sparse representation. (United States)

    Yang, Jianchao; Wright, John; Huang, Thomas S; Ma, Yi


    This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low resolution and high resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low resolution image patch can be applied with the high resolution image patch dictionary to generate a high resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs, reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle super-resolution with noisy inputs in a more unified framework.

  15. Spicule formation in calcareous sponges: Coordinated expression of biomineralization genes and spicule-type specific genes. (United States)

    Voigt, Oliver; Adamska, Maja; Adamski, Marcin; Kittelmann, André; Wencker, Lukardis; Wörheide, Gert


    The ability to form mineral structures under biological control is widespread among animals. In several species, specific proteins have been shown to be involved in biomineralization, but it is uncertain how they influence the shape of the growing biomineral and the resulting skeleton. Calcareous sponges are the only sponges that form calcitic spicules, which, based on the number of rays (actines) are distinguished in diactines, triactines and tetractines. Each actine is formed by only two cells, called sclerocytes. Little is known about biomineralization proteins in calcareous sponges, other than that specific carbonic anhydrases (CAs) have been identified, and that uncharacterized Asx-rich proteins have been isolated from calcitic spicules. By RNA-Seq and RNA in situ hybridization (ISH), we identified five additional biomineralization genes in Sycon ciliatum: two bicarbonate transporters (BCTs) and three Asx-rich extracellular matrix proteins (ARPs). We show that these biomineralization genes are expressed in a coordinated pattern during spicule formation. Furthermore, two of the ARPs are spicule-type specific for triactines and tetractines (ARP1 or SciTriactinin) or diactines (ARP2 or SciDiactinin). Our results suggest that spicule formation is controlled by defined temporal and spatial expression of spicule-type specific sets of biomineralization genes.

  16. Innate immune response to pulmonary contusion: identification of cell type-specific inflammatory responses. (United States)

    Hoth, J Jason; Wells, Jonathan D; Yoza, Barbara K; McCall, Charles E


    Lung injury from pulmonary contusion is a common traumatic injury, predominantly seen after blunt chest trauma, such as in vehicular accidents. The local and systemic inflammatory response to injury includes activation of innate immune receptors, elaboration of a variety of inflammatory mediators, and recruitment of inflammatory cells to the injured lung. Using a mouse model of pulmonary contusion, we had previously shown that innate immune Toll-like receptors 2 and 4 (TLR2 and TLR4) mediate the inflammatory response to lung injury. In this study, we used chimeric mice generated by adoptive bone marrow transfer between TLR2 or TLR4 and wild-type mice. We found that, in the lung, both bone marrow-derived and nonmyeloid cells contribute to TLR-dependent inflammatory responses after injury in a cell type-specific manner. We also show a novel TLR2-dependent injury mechanism that is associated with enhanced airway epithelial cell apoptosis and increased pulmonary FasL and Fas expression in the lungs from injured mice. Thus, in addition to cardiopulmonary physiological dysfunction, cell type-specific TLR and their differential response to injury may provide novel specific targets for management of patients with pulmonary contusion.

  17. Cell-type-specific gene delivery into neuronal cells in vitro and in vivo

    International Nuclear Information System (INIS)

    Parveen, Zahida; Mukhtar, Muhammad; Rafi, Mohammed; Wenger, David A.; Siddiqui, Khwaja M.; Siler, Catherine A.; Dietzschold, Bernhard; Pomerantz, Roger J.; Schnell, Matthias J.; Dornburg, Ralph


    The avian retroviruses reticuloendotheliosis virus strain A (REV-A) and spleen necrosis virus (SNV) are not naturally infectious in human cells. However, REV-A-derived viral vectors efficiently infect human cells when they are pseudotyped with envelope proteins displaying targeting ligands specific for human cell-surface receptors. Here we report that vectors containing the gag region of REV-A and pol of SNV can be pseudotyped with the envelope protein of vesicular stomatitis virus (VSV) and the glycoproteins of different rabies virus (RV) strains. Vectors pseudotyped with the envelope protein of the highly neurotropic RV strain CVS-N2c facilitated cell type-specific gene delivery into mouse and human neurons, but did not infect other human cell types. Moreover, when such vector particles were injected into the brain of newborn mice, only neuronal cells were infected in vivo. Cell-type-specific gene delivery into neurons may present quite specific gene therapy approaches for many degenerative diseases of the brain

  18. Cell-type-specific, Aptamer-functionalized Agents for Targeted Disease Therapy

    Directory of Open Access Journals (Sweden)

    Jiehua Zhou


    Full Text Available One hundred years ago, Dr. Paul Ehrlich popularized the “magic bullet” concept for cancer therapy in which an ideal therapeutic agent would only kill the specific tumor cells it targeted. Since then, “targeted therapy” that specifically targets the molecular defects responsible for a patient's condition has become a long-standing goal for treating human disease. However, safe and efficient drug delivery during the treatment of cancer and infectious disease remains a major challenge for clinical translation and the development of new therapies. The advent of SELEX technology has inspired many groundbreaking studies that successfully adapted cell-specific aptamers for targeted delivery of active drug substances in both in vitro and in vivo models. By covalently linking or physically functionalizing the cell-specific aptamers with therapeutic agents, such as siRNA, microRNA, chemotherapeutics or toxins, or delivery vehicles, such as organic or inorganic nanocarriers, the targeted cells and tissues can be specifically recognized and the therapeutic compounds internalized, thereby improving the local concentration of the drug and its therapeutic efficacy. Currently, many cell-type-specific aptamers have been developed that can target distinct diseases or tissues in a cell-type-specific manner. In this review, we discuss recent advances in the use of cell-specific aptamers for targeted disease therapy, as well as conjugation strategies and challenges.

  19. Layer- and Cell Type-Specific Modulation of Excitatory Neuronal Activity in the Neocortex

    Directory of Open Access Journals (Sweden)

    Gabriele Radnikow


    Full Text Available From an anatomical point of view the neocortex is subdivided into up to six layers depending on the cortical area. This subdivision has been described already by Meynert and Brodmann in the late 19/early 20. century and is mainly based on cytoarchitectonic features such as the size and location of the pyramidal cell bodies. Hence, cortical lamination is originally an anatomical concept based on the distribution of excitatory neuron. However, it has become apparent in recent years that apart from the layer-specific differences in morphological features, many functional properties of neurons are also dependent on cortical layer or cell type. Such functional differences include changes in neuronal excitability and synaptic activity by neuromodulatory transmitters. Many of these neuromodulators are released from axonal afferents from subcortical brain regions while others are released intrinsically. In this review we aim to describe layer- and cell-type specific differences in the effects of neuromodulator receptors in excitatory neurons in layers 2–6 of different cortical areas. We will focus on the neuromodulator systems using adenosine, acetylcholine, dopamine, and orexin/hypocretin as examples because these neuromodulator systems show important differences in receptor type and distribution, mode of release and functional mechanisms and effects. We try to summarize how layer- and cell type-specific neuromodulation may affect synaptic signaling in cortical microcircuits.

  20. DALSA: Domain Adaptation for Supervised Learning From Sparsely Annotated MR Images. (United States)

    Goetz, Michael; Weber, Christian; Binczyk, Franciszek; Polanska, Joanna; Tarnawski, Rafal; Bobek-Billewicz, Barbara; Koethe, Ullrich; Kleesiek, Jens; Stieltjes, Bram; Maier-Hein, Klaus H


    We propose a new method that employs transfer learning techniques to effectively correct sampling selection errors introduced by sparse annotations during supervised learning for automated tumor segmentation. The practicality of current learning-based automated tissue classification approaches is severely impeded by their dependency on manually segmented training databases that need to be recreated for each scenario of application, site, or acquisition setup. The comprehensive annotation of reference datasets can be highly labor-intensive, complex, and error-prone. The proposed method derives high-quality classifiers for the different tissue classes from sparse and unambiguous annotations and employs domain adaptation techniques for effectively correcting sampling selection errors introduced by the sparse sampling. The new approach is validated on labeled, multi-modal MR images of 19 patients with malignant gliomas and by comparative analysis on the BraTS 2013 challenge data sets. Compared to training on fully labeled data, we reduced the time for labeling and training by a factor greater than 70 and 180 respectively without sacrificing accuracy. This dramatically eases the establishment and constant extension of large annotated databases in various scenarios and imaging setups and thus represents an important step towards practical applicability of learning-based approaches in tissue classification.

  1. Highly sparse representations from dictionaries are unique and independent of the sparseness measure

    DEFF Research Database (Denmark)

    Gribonval, Rémi; Nielsen, Morten


    The purpose of this paper is to study sparse representations of signals from a general dictionary in a Banach space. For so-called localized frames in Hilbert spaces, the canonical frame coefficients are shown to provide a near sparsest expansion for several sparseness measures. However, for frames...

  2. When sparse coding meets ranking: a joint framework for learning sparse codes and ranking scores

    KAUST Repository

    Wang, Jim Jing-Yan


    Sparse coding, which represents a data point as a sparse reconstruction code with regard to a dictionary, has been a popular data representation method. Meanwhile, in database retrieval problems, learning the ranking scores from data points plays an important role. Up to now, these two problems have always been considered separately, assuming that data coding and ranking are two independent and irrelevant problems. However, is there any internal relationship between sparse coding and ranking score learning? If yes, how to explore and make use of this internal relationship? In this paper, we try to answer these questions by developing the first joint sparse coding and ranking score learning algorithm. To explore the local distribution in the sparse code space, and also to bridge coding and ranking problems, we assume that in the neighborhood of each data point, the ranking scores can be approximated from the corresponding sparse codes by a local linear function. By considering the local approximation error of ranking scores, the reconstruction error and sparsity of sparse coding, and the query information provided by the user, we construct a unified objective function for learning of sparse codes, the dictionary and ranking scores. We further develop an iterative algorithm to solve this optimization problem.

  3. Sparse Signal Recovery via ECME Thresholding Pursuits

    Directory of Open Access Journals (Sweden)

    Heping Song


    Full Text Available The emerging theory of compressive sensing (CS provides a new sparse signal processing paradigm for reconstructing sparse signals from the undersampled linear measurements. Recently, numerous algorithms have been developed to solve convex optimization problems for CS sparse signal recovery. However, in some certain circumstances, greedy algorithms exhibit superior performance than convex methods. This paper is a followup to the recent paper of Wang and Yin (2010, who refine BP reconstructions via iterative support detection (ISD. The heuristic idea of ISD was applied to greedy algorithms. We developed two approaches for accelerating the ECME iteration. The described algorithms, named ECME thresholding pursuits (EMTP, introduced two greedy strategies that each iteration detects a support set I by thresholding the result of the ECME iteration and estimates the reconstructed signal by solving a truncated least-squares problem on the support set I. Two effective support detection strategies are devised for the sparse signals with components having a fast decaying distribution of nonzero components. The experimental studies are presented to demonstrate that EMTP offers an appealing alternative to state-of-the-art algorithms for sparse signal recovery.

  4. Fast wavelet based sparse approximate inverse preconditioner

    Energy Technology Data Exchange (ETDEWEB)

    Wan, W.L. [Univ. of California, Los Angeles, CA (United States)


    Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.

  5. Tunable Sparse Network Coding for Multicast Networks

    DEFF Research Database (Denmark)

    Feizi, Soheil; Roetter, Daniel Enrique Lucani; Sørensen, Chres Wiant


    This paper shows the potential and key enabling mechanisms for tunable sparse network coding, a scheme in which the density of network coded packets varies during a transmission session. At the beginning of a transmission session, sparsely coded packets are transmitted, which benefits decoding...... complexity. At the end of a transmission, when receivers have accumulated degrees of freedom, coding density is increased. We propose a family of tunable sparse network codes (TSNCs) for multicast erasure networks with a controllable trade-off between completion time performance to decoding complexity....... Coding density tuning can be performed by designing time-dependent coding matrices. In multicast networks, this tuning can be performed within the network by designing time-dependent pre- coding and network coding matrices with mild conditions on the network structure for specific densities. We present...

  6. Incomplete Sparse Approximate Inverses for Parallel Preconditioning

    International Nuclear Information System (INIS)

    Anzt, Hartwig; University of Tennessee, Knoxville, TN; Huckle, Thomas K.; Bräckle, Jürgen; Dongarra, Jack


    In this study, we propose a new preconditioning method that can be seen as a generalization of block-Jacobi methods, or as a simplification of the sparse approximate inverse (SAI) preconditioners. The “Incomplete Sparse Approximate Inverses” (ISAI) is in particular efficient in the solution of sparse triangular linear systems of equations. Those arise, for example, in the context of incomplete factorization preconditioning. ISAI preconditioners can be generated via an algorithm providing fine-grained parallelism, which makes them attractive for hardware with a high concurrency level. Finally, in a study covering a large number of matrices, we identify the ISAI preconditioner as an attractive alternative to exact triangular solves in the context of incomplete factorization preconditioning.


    KAUST Repository

    Desmal, Abdulla


    A scheme for efficiently solving the nonlinear electromagnetic inverse scattering problem on sparse investigation domains is described. The proposed scheme reconstructs the (complex) dielectric permittivity of an investigation domain from fields measured away from the domain itself. Least-squares data misfit between the computed scattered fields, which are expressed as a nonlinear function of the permittivity, and the measured fields is constrained by the L0/L1-norm of the solution. The resulting minimization problem is solved using nonlinear Landweber iterations, where at each iteration a thresholding function is applied to enforce the sparseness-promoting L0/L1-norm constraint. The thresholded nonlinear Landweber iterations are applied to several two-dimensional problems, where the ``measured\\'\\' fields are synthetically generated or obtained from actual experiments. These numerical experiments demonstrate the accuracy, efficiency, and applicability of the proposed scheme in reconstructing sparse profiles with high permittivity values.

  8. Human papillomavirus type-specific prevalence in the cervical cancer screening population of Czech women.

    Directory of Open Access Journals (Sweden)

    Ruth Tachezy

    Full Text Available BACKGROUND: Infection with high-risk human papillomavirus (HPVtypes has been recognized as a causal factor for the development of cervical cancer and a number of other malignancies. Today, vaccines against HPV, highly effective in the prevention of persistent infection and precancerous lesions, are available for the routine clinical practice. OBJECTIVES: The data on the prevalence and type-specific HPV distribution in the population of each country are crucial for the surveillance of HPV type-specific prevalence at the onset of vaccination against HPV. METHODS: Women attending a preventive gynecological examination who had no history of abnormal cytological finding and/or surgery for cervical lesions were enrolled. All samples were tested for the presence of HPV by High-Risk Hybrid Capture 2 (HR HC2 and by a modified PCR-reverse line blot assay with broad spectrum primers (BS-RLB. RESULTS: Cervical smears of 1393 women were analyzed. In 6.5% of women, atypical cytological findings were detected. Altogether, 28.3% (394/1393 of women were positive for any HPV type by BS-RLB, 18.2% (254/1393 by HR HC2, and 22.3% (310/1393 by BS-RLB for HR HPV types. In women with atypical findings the prevalence for HR and any HPV types were significantly higher than in women with normal cytological findings. Overall, 36 different HPV types were detected, with HPV 16 being the most prevalent (4.8%. HPV positivity decreased with age; the highest prevalence was 31.5% in the age group 21-25 years. CONCLUSIONS: Our study subjects represent the real screening population. HPV prevalence in this population in the Czech Republic is higher than in other countries of Eastern Europe. Also the spectrum of the most prevalent HPV types differs from those reported by others but HPV 16 is, concordantly, the most prevalent type. Country-specific HPV type-specific prevalences provide baseline information which will enable to measure the impact of HPV vaccination in the future.

  9. Sparse regularization for force identification using dictionaries (United States)

    Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng


    The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.

  10. Fluctuations in percolation of sparse complex networks (United States)

    Bianconi, Ginestra


    We study the role of fluctuations in percolation of sparse complex networks. To this end we consider two random correlated realizations of the initial damage of the nodes and we evaluate the fraction of nodes that are expected to remain in the giant component of the network in both cases or just in one case. Our framework includes a message-passing algorithm able to predict the fluctuations in a single network, and an analytic prediction of the expected fluctuations in ensembles of sparse networks. This approach is applied to real ecological and infrastructure networks and it is shown to characterize the expected fluctuations in their response to external damage.

  11. Multisnapshot Sparse Bayesian Learning for DOA

    DEFF Research Database (Denmark)

    Gerstoft, Peter; Mecklenbrauker, Christoph F.; Xenaki, Angeliki


    The directions of arrival (DOA) of plane waves are estimated from multisnapshot sensor array data using sparse Bayesian learning (SBL). The prior for the source amplitudes is assumed independent zero-mean complex Gaussian distributed with hyperparameters, the unknown variances (i.e., the source...... powers). For a complex Gaussian likelihood with hyperparameter, the unknown noise variance, the corresponding Gaussian posterior distribution is derived. The hyperparameters are automatically selected by maximizing the evidence and promoting sparse DOA estimates. The SBL scheme for DOA estimation...

  12. A characterization of sparse nonstationary Gabor expansions

    DEFF Research Database (Denmark)

    Ottosen, Emil Solsbæk; Nielsen, Morten

    and prove that the nonstationary Gabor frame forms a Banach frame for the decomposition space. Furthermore, we show that the decomposition space norm can be completely characterized by a sparseness condition on the frame coefficients and we prove an upper bound on the approximation error that occurs when......We investigate the problem of constructing sparse time-frequency representations with flexible frequency resolution, studying the theory of nonstationary Gabor frames in the framework of decomposition spaces. Given a painless nonstationary Gabor frame, we construct a compatible decomposition space...... thresholding the frame coefficients for signals belonging to the decomposition space....

  13. Structure-based bayesian sparse reconstruction

    KAUST Repository

    Quadeer, Ahmed Abdul


    Sparse signal reconstruction algorithms have attracted research attention due to their wide applications in various fields. In this paper, we present a simple Bayesian approach that utilizes the sparsity constraint and a priori statistical information (Gaussian or otherwise) to obtain near optimal estimates. In addition, we make use of the rich structure of the sensing matrix encountered in many signal processing applications to develop a fast sparse recovery algorithm. The computational complexity of the proposed algorithm is very low compared with the widely used convex relaxation methods as well as greedy matching pursuit techniques, especially at high sparsity. © 1991-2012 IEEE.

  14. Scalable group level probabilistic sparse factor analysis

    DEFF Research Database (Denmark)

    Hinrich, Jesper Løve; Nielsen, Søren Føns Vind; Riis, Nicolai Andre Brogaard


    Many data-driven approaches exist to extract neural representations of functional magnetic resonance imaging (fMRI) data, but most of them lack a proper probabilistic formulation. We propose a scalable group level probabilistic sparse factor analysis (psFA) allowing spatially sparse maps, component...... pruning using automatic relevance determination (ARD) and subject specific heteroscedastic spatial noise modeling. For task-based and resting state fMRI, we show that the sparsity constraint gives rise to components similar to those obtained by group independent component analysis. The noise modeling...

  15. Binary Sparse Phase Retrieval via Simulated Annealing

    Directory of Open Access Journals (Sweden)

    Wei Peng


    Full Text Available This paper presents the Simulated Annealing Sparse PhAse Recovery (SASPAR algorithm for reconstructing sparse binary signals from their phaseless magnitudes of the Fourier transform. The greedy strategy version is also proposed for a comparison, which is a parameter-free algorithm. Sufficient numeric simulations indicate that our method is quite effective and suggest the binary model is robust. The SASPAR algorithm seems competitive to the existing methods for its efficiency and high recovery rate even with fewer Fourier measurements.

  16. Single Muscle Fiber Proteomics Reveals Fiber-Type-Specific Features of Human Muscle Aging

    Directory of Open Access Journals (Sweden)

    Marta Murgia


    Full Text Available Skeletal muscle is a key tissue in human aging, which affects different muscle fiber types unequally. We developed a highly sensitive single muscle fiber proteomics workflow to study human aging and show that the senescence of slow and fast muscle fibers is characterized by diverging metabolic and protein quality control adaptations. Whereas mitochondrial content declines with aging in both fiber types, glycolysis and glycogen metabolism are upregulated in slow but downregulated in fast muscle fibers. Aging mitochondria decrease expression of the redox enzyme monoamine oxidase A. Slow fibers upregulate a subset of actin and myosin chaperones, whereas an opposite change happens in fast fibers. These changes in metabolism and sarcomere quality control may be related to the ability of slow, but not fast, muscle fibers to maintain their mass during aging. We conclude that single muscle fiber analysis by proteomics can elucidate pathophysiology in a sub-type-specific manner.

  17. The female gametophyte: an emerging model for cell type-specific systems biology in plant development

    Directory of Open Access Journals (Sweden)

    Marc William Schmid


    Full Text Available Systems biology, a holistic approach describing a system emerging from the interactions of its molecular components, critically depends on accurate qualitative determination and quantitative measurements of these components. Development and improvement of large-scale profiling methods (omics now facilitates comprehensive measurements of many relevant molecules. For multicellular organisms, such as animals, fungi, algae, and plants, the complexity of the system is augmented by the presence of specialized cell types and organs, and a complex interplay within and between them. Cell type-specific analyses are therefore crucial for the understanding of developmental processes and environmental responses. This review first gives an overview of current methods used for large-scale profiling of specific cell types exemplified by recent advances in plant biology. The focus then lies on suitable model systems to study plant development and cell type specification. We introduce the female gametophyte of flowering plants as an ideal model to study fundamental developmental processes. Moreover, the female reproductive lineage is of importance for the emergence of evolutionary novelties such as an unequal parental contribution to the tissue nurturing the embryo or the clonal production of seeds by asexual reproduction (apomixis. Understanding these processes is not only interesting from a developmental or evolutionary perspective, but bears great potential for further crop improvement and the simplification of breeding efforts. We finally highlight novel methods, which are already available or which will likely soon facilitate large-scale profiling of the specific cell types of the female gametophyte in both model and non-model species. We conclude that it may take only few years until an evolutionary systems biology approach toward female gametogenesis may decipher some of its biologically most interesting and economically most valuable processes.

  18. Image Quality Assessment via Quality-aware Group Sparse Coding

    Directory of Open Access Journals (Sweden)

    Minglei Tong


    Full Text Available Image quality assessment has been attracting growing attention at an accelerated pace over the past decade, in the fields of image processing, vision and machine learning. In particular, general purpose blind image quality assessment is technically challenging and lots of state-of-the-art approaches have been developed to solve this problem, most under the supervised learning framework where the human scored samples are needed for training a regression model. In this paper, we propose an unsupervised learning approach that work without the human label. In the off-line stage, our method trains a dictionary covering different levels of image quality patch atoms across the training samples without knowing the human score, where each atom is associated with a quality score induced from the reference image; at the on-line stage, given each image patch, our method performs group sparse coding to encode the sample, such that the sample quality can be estimated from the few labeled atoms whose encoding coefficients are nonzero. Experimental results on the public dataset show the promising performance of our approach and future research direction is also discussed.

  19. Subspace Based Blind Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki


    The paper proposes a subspace based blind sparse channel estimation method using 1–2 optimization by replacing the 2–norm minimization in the conventional subspace based method by the 1–norm minimization problem. Numerical results confirm that the proposed method can significantly improve...

  20. Better Size Estimation for Sparse Matrix Products

    DEFF Research Database (Denmark)

    Amossen, Rasmus Resen; Campagna, Andrea; Pagh, Rasmus


    We consider the problem of doing fast and reliable estimation of the number of non-zero entries in a sparse Boolean matrix product. Let n denote the total number of non-zero entries in the input matrices. We show how to compute a 1 ± ε approximation (with small probability of error) in expected t...

  1. Multilevel sparse functional principal component analysis. (United States)

    Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S


    We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions.

  2. Comparison of sparse point distribution models

    DEFF Research Database (Denmark)

    Erbou, Søren Gylling Hemmingsen; Vester-Christensen, Martin; Larsen, Rasmus


    This paper compares several methods for obtaining sparse and compact point distribution models suited for data sets containing many variables. These are evaluated on a database consisting of 3D surfaces of a section of the pelvic bone obtained from CT scans of 33 porcine carcasses. The superior m...

  3. SAR Image Despeckling Via Structural Sparse Representation (United States)

    Lu, Ting; Li, Shutao; Fang, Leyuan; Benediktsson, Jón Atli


    A novel synthetic aperture radar (SAR) image despeckling method based on structural sparse representation is introduced. The proposed method utilizes the fact that different regions in SAR images correspond to varying terrain reflectivity. Therefore, SAR images can be split into a heterogeneous class (with a varied terrain reflectivity) and a homogeneous class (with a constant terrain reflectivity). In the proposed method, different sparse representation based despeckling schemes are designed by combining the different region characteristics in SAR images. For heterogeneous regions with rich structure and texture information, structural dictionaries are learned to appropriately represent varied structural characteristics. Specifically, each patch in these regions is sparsely coded with the best fitted structural dictionary, thus good structure preservation can be obtained. For homogenous regions without rich structure and texture information, the highly redundant photometric self-similarity is exploited to suppress speckle noise without introducing artifacts. That is achieved by firstly learning the sub-dictionary, then simultaneously sparsely coding for each group of photometrically similar image patches. Visual and objective experimental results demonstrate the superiority of the proposed method over the-state-of-the-art methods.

  4. Sparse DOA estimation with polynomial rooting

    DEFF Research Database (Denmark)

    Xenaki, Angeliki; Gerstoft, Peter; Fernandez Grande, Efren


    Direction-of-arrival (DOA) estimation involves the localization of a few sources from a limited number of observations on an array of sensors. Thus, DOA estimation can be formulated as a sparse signal reconstruction problem and solved efficiently with compressive sensing (CS) to achieve...

  5. Sparse acoustic imaging with a spherical array

    DEFF Research Database (Denmark)

    Fernandez Grande, Efren; Xenaki, Angeliki


    proposes a plane wave expansion method based on measurements with a spherical microphone array, and solved in the framework provided by Compressed Sensing. The proposed methodology results in a sparse solution, i.e. few non-zero coefficients, and it is suitable for both source localization and sound field...

  6. Sparse Text Indexing in Small Space

    DEFF Research Database (Denmark)

    Bille, Philip; Fischer, Johannes; Gørtz, Inge Li


    other applications in which space usage is a concern. Our first solution is Monte Carlo, and outputs the correct tree with high probability. We then give a Las Vegas algorithm, which also uses O(b) space and runs in the same time bounds with high probability when b = O(&sqrt; n). Additional trade......In this work, we present efficient algorithms for constructing sparse suffix trees, sparse suffix arrays, and sparse position heaps for b arbitrary positions of a text T of length n while using only O(b) words of space during the construction. Attempts at breaking the naïve bound of Ω(nb) time...... contribution is to show that the sparse suffix tree (and array) can be constructed in O(nlog 2b) time. To achieve this, we develop a technique that allows one to efficiently answer b longest common prefix queries on suffixes of T, using only O(b) space. We expect that this technique will prove useful in many...

  7. Rotational image deblurring with sparse matrices

    DEFF Research Database (Denmark)

    Hansen, Per Christian; Nagy, James G.; Tigkos, Konstantinos


    We describe iterative deblurring algorithms that can handle blur caused by a rotation along an arbitrary axis (including the common case of pure rotation). Our algorithms use a sparse-matrix representation of the blurring operation, which allows us to easily handle several different boundary...

  8. Quantifying Registration Uncertainty With Sparse Bayesian Modelling. (United States)

    Le Folgoc, Loic; Delingette, Herve; Criminisi, Antonio; Ayache, Nicholas


    We investigate uncertainty quantification under a sparse Bayesian model of medical image registration. Bayesian modelling has proven powerful to automate the tuning of registration hyperparameters, such as the trade-off between the data and regularization functionals. Sparsity-inducing priors have recently been used to render the parametrization itself adaptive and data-driven. The sparse prior on transformation parameters effectively favors the use of coarse basis functions to capture the global trends in the visible motion while finer, highly localized bases are introduced only in the presence of coherent image information and motion. In earlier work, approximate inference under the sparse Bayesian model was tackled in an efficient Variational Bayes (VB) framework. In this paper we are interested in the theoretical and empirical quality of uncertainty estimates derived under this approximate scheme vs. under the exact model. We implement an (asymptotically) exact inference scheme based on reversible jump Markov Chain Monte Carlo (MCMC) sampling to characterize the posterior distribution of the transformation and compare the predictions of the VB and MCMC based methods. The true posterior distribution under the sparse Bayesian model is found to be meaningful: orders of magnitude for the estimated uncertainty are quantitatively reasonable, the uncertainty is higher in textureless regions and lower in the direction of strong intensity gradients.

  9. A sparse-grid isogeometric solver

    KAUST Repository

    Beck, Joakim


    Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90’s in the context of the approximation of high-dimensional PDEs.The tests that we report show that, in accordance to the literature, a sparse-grid construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

  10. A sparse version of IGA solvers

    KAUST Repository

    Beck, Joakim


    Isogeometric Analysis (IGA) typically adopts tensor-product splines and NURBS as a basis for the approximation of the solution of PDEs. In this work, we investigate to which extent IGA solvers can benefit from the so-called sparse-grids construction in its combination technique form, which was first introduced in the early 90s in the context of the approximation of high-dimensional PDEs. The tests that we report show that, in accordance to the literature, a sparse grids construction can indeed be useful if the solution of the PDE at hand is sufficiently smooth. Sparse grids can also be useful in the case of non-smooth solutions when some a-priori knowledge on the location of the singularities of the solution can be exploited to devise suitable non-equispaced meshes. Finally, we remark that sparse grids can be seen as a simple way to parallelize pre-existing serial IGA solvers in a straightforward fashion, which can be beneficial in many practical situations.

  11. Second SIAM conference on sparse matrices: Abstracts. Final technical report

    Energy Technology Data Exchange (ETDEWEB)



    This report contains abstracts on the following topics: invited and long presentations (IP1 & LP1); sparse matrix reordering & graph theory I; sparse matrix tools & environments I; eigenvalue computations I; iterative methods & acceleration techniques I; applications I; parallel algorithms I; sparse matrix reordering & graphy theory II; sparse matrix tool & environments II; least squares & optimization I; iterative methods & acceleration techniques II; applications II; eigenvalue computations II; least squares & optimization II; parallel algorithms II; sparse direct methods; iterative methods & acceleration techniques III; eigenvalue computations III; and sparse matrix reordering & graph theory III.

  12. Pesticide Labels (United States)

    Pesticide labels translate results of our extensive evaluations of pesticide products into conditions, directions and precautions that define parameters for use of a pesticide with the goal of ensuring protection of human health and the environment.

  13. Cell type-specific transcriptional regulation of the gene encoding importin-α1

    International Nuclear Information System (INIS)

    Kamikawa, Yasunao; Yasuhara, Noriko; Yoneda, Yoshihiro


    Importin-α1 belongs to a receptor family that recognizes classical nuclear localization signals. Encoded by Kpna2, this receptor subtype is highly expressed in mouse embryonic stem (ES) cells. In this study, we identified a critical promoter region in Kpna2 and showed that the expression of this gene is differentially regulated in ES cells and NIH3T3 cells. Conserved CCAAT boxes are required for Kpna2 promoter activity in both ES and NIH3T3 cells. Interestingly, deletion of the region from nucleotide position - 251 to - 179 bp resulted in a drastic reduction in Kpna2 transcriptional activity only in ES cells. This region contains Krueppel-like factor (Klf) binding sequences and is responsible for transactivation of the gene by Klf2 and Klf4. Accordingly, endogenous Kpna2 mRNA levels decreased in response to depletion of Klf2 and Klf4 in ES cells. Our results suggest that Klf2 and Klf4 function redundantly to drive high level of Kpna2 expression in ES cells. -- Research Highlights: → We showed the cell type-specific transcriptional regulation of Kpna2 encoding importin-al. → NF-Y binds the CCAAT boxes to activate Kpna2 transcription in NIH3T3 cells. → Klf2 and Klf4 redundantly activate the expression of Kpna2 in ES cells.

  14. Size- and type-specific exposure assessment of an asbestos products factory in China. (United States)

    Courtice, Midori N; Berman, D Wayne; Yano, Eiji; Kohyama, Norihiko; Wang, Xiaorong


    This study describes fibre size and type-specific airborne asbestos exposures in an asbestos product factory. Forty-four membrane filter samples were analysed by scanning electron microscopy to determine the size distribution of asbestos fibres, by workshop. Fibre frequencies of bivariate (length by width) categories were calculated and differences between workshops were tested by analysis of variance. Data were recorded for 13,435 chrysotile and 1075 tremolite fibres. The proportions between size metrics traditionally measured and potentially biologically important size metrics were found to vary in this study from proportions reported in other cohort studies. One, common size distribution was generated for each asbestos type over the entire factory because statistically significant differences in frequency between workshops were not detected. This study provides new information on asbestos fibre size and type distributions in an asbestos factory. The extent to which biologically relevant fibre size indices were captured or overlooked between studies can potentially reconcile currently unexplained differences in asbestos-related disease (ARD) risk between cohorts. The fibre distributions presented here, when combined with similar data from other sites, will contribute to the development of quantitative models for predicting risk and our understanding of the effects of fibre characteristics in the development of ARD.

  15. TACO: a general-purpose tool for predicting cell-type-specific transcription factor dimers. (United States)

    Jankowski, Aleksander; Prabhakar, Shyam; Tiuryn, Jerzy


    Cooperative binding of transcription factor (TF) dimers to DNA is increasingly recognized as a major contributor to binding specificity. However, it is likely that the set of known TF dimers is highly incomplete, given that they were discovered using ad hoc approaches, or through computational analyses of limited datasets. Here, we present TACO (Transcription factor Association from Complex Overrepresentation), a general-purpose standalone software tool that takes as input any genome-wide set of regulatory elements and predicts cell-type-specific TF dimers based on enrichment of motif complexes. TACO is the first tool that can accommodate motif complexes composed of overlapping motifs, a characteristic feature of many known TF dimers. Our method comprehensively outperforms existing tools when benchmarked on a reference set of 29 known dimers. We demonstrate the utility and consistency of TACO by applying it to 152 DNase-seq datasets and 94 ChIP-seq datasets. Based on these results, we uncover a general principle governing the structure of TF-TF-DNA ternary complexes, namely that the flexibility of the complex is correlated with, and most likely a consequence of, inter-motif spacing.

  16. Bacteria-type-specific biparental immune priming in the pipefishSyngnathus typhle. (United States)

    Beemelmanns, Anne; Roth, Olivia


    The transfer of acquired and specific immunity against previously encountered bacteria from mothers to offspring boosts the immune response of the next generation and supports the development of a successful pathogen defense. While most studies claim that the transfer of immunity is a maternal trait, in the sex-role-reversed pipefish Syngnathus typhle, fathers nurse the embryos over a placenta-like structure, which opens the door for additional paternal immune priming. We examined the potential and persistence of bacteria-type-specific parental immune priming in the pipefish S. typhle over maturation time using a fully reciprocal design with two different bacteria species ( Vibrio spp. and Tenacibaculum maritimum ). Our results suggest that S. typhle is able to specifically prime the next generation against prevalent local bacteria and to a limited extent even also against newly introduced bacteria species. Long-term protection was thereby maintained only against prevailing Vibrio bacteria. Maternal and paternal transgenerational immune priming can complement each other, as they affect different pathways of the offspring immune system and come with distinct degree of specificity. The differential regulation of DNA-methylation genes upon parental bacteria exposure in premature pipefish offspring indicates that epigenetic regulation processes are involved in transferring immune-related information across generations. The identified trade-offs between immune priming and reproduction determine TGIP as a costly trait, which might constrain the evolution of long-lasting TGIP, if parental and offspring generations do not share the same parasite assembly.

  17. Restricting calcium currents is required for correct fiber type specification in skeletal muscle. (United States)

    Sultana, Nasreen; Dienes, Beatrix; Benedetti, Ariane; Tuluc, Petronel; Szentesi, Peter; Sztretye, Monika; Rainer, Johannes; Hess, Michael W; Schwarzer, Christoph; Obermair, Gerald J; Csernoch, Laszlo; Flucher, Bernhard E


    Skeletal muscle excitation-contraction (EC) coupling is independent of calcium influx. In fact, alternative splicing of the voltage-gated calcium channel CaV1.1 actively suppresses calcium currents in mature muscle. Whether this is necessary for normal development and function of muscle is not known. However, splicing defects that cause aberrant expression of the calcium-conducting developmental CaV1.1e splice variant correlate with muscle weakness in myotonic dystrophy. Here, we deleted CaV1.1 (Cacna1s) exon 29 in mice. These mice displayed normal overall motor performance, although grip force and voluntary running were reduced. Continued expression of the developmental CaV1.1e splice variant in adult mice caused increased calcium influx during EC coupling, altered calcium homeostasis, and spontaneous calcium sparklets in isolated muscle fibers. Contractile force was reduced and endurance enhanced. Key regulators of fiber type specification were dysregulated and the fiber type composition was shifted toward slower fibers. However, oxidative enzyme activity and mitochondrial content declined. These findings indicate that limiting calcium influx during skeletal muscle EC coupling is important for the secondary function of the calcium signal in the activity-dependent regulation of fiber type composition and to prevent muscle disease. © 2016. Published by The Company of Biologists Ltd.

  18. Cell type-specific suppression of mechanosensitive genes by audible sound stimulation. (United States)

    Kumeta, Masahiro; Takahashi, Daiji; Takeyasu, Kunio; Yoshimura, Shige H


    Audible sound is a ubiquitous environmental factor in nature that transmits oscillatory compressional pressure through the substances. To investigate the property of the sound as a mechanical stimulus for cells, an experimental system was set up using 94.0 dB sound which transmits approximately 10 mPa pressure to the cultured cells. Based on research on mechanotransduction and ultrasound effects on cells, gene responses to the audible sound stimulation were analyzed by varying several sound parameters: frequency, wave form, composition, and exposure time. Real-time quantitative PCR analyses revealed a distinct suppressive effect for several mechanosensitive and ultrasound-sensitive genes that were triggered by sounds. The effect was clearly observed in a wave form- and pressure level-specific manner, rather than the frequency, and persisted for several hours. At least two mechanisms are likely to be involved in this sound response: transcriptional control and RNA degradation. ST2 stromal cells and C2C12 myoblasts exhibited a robust response, whereas NIH3T3 cells were partially and NB2a neuroblastoma cells were completely insensitive, suggesting a cell type-specific response to sound. These findings reveal a cell-level systematic response to audible sound and uncover novel relationships between life and sound.

  19. Unique cell-type specific patterns of DNA methylation in the root meristem (United States)

    Kawakatsu, Taiji; Stuart, Tim; Valdes, Manuel; Breakfield, Natalie; Schmitz, Robert J.; Nery, Joseph R.; Urich, Mark A.; Han, Xinwei; Lister, Ryan; Benfey, Philip N.; Ecker, Joseph R.


    DNA methylation is an epigenetic modification that differs between plant organs and tissues, but the extent of variation between cell types is not known. Here, we report single-base resolution whole genome DNA methylomes, mRNA transcriptomes, and small RNA transcriptomes for six cell populations covering the major cell types of the Arabidopsis root meristem. We identify widespread cell type specific patterns of DNA methylation, especially in the CHH sequence context. The genome of the columella root cap is the most highly methylated Arabidopsis cell characterized to date. It is hypermethylated within transposable elements, accompanied by increased abundance of transcripts encoding RNA-directed DNA methylation (RdDM) pathway components and 24 nt small RNAs. Absence of the nucleosome remodeler DECREASED DNA METHYLATION 1, required for maintenance of DNA methylation, and low abundance of histone transcripts involved in heterochromatin formation suggests a loss of heterochromatin may occur in the columella, thus allowing access of RdDM factors to the whole genome, and producing excess 24 nt small RNAs in this tissue. Together, these maps provide new insights into the epigenomic diversity that exists between distinct plant somatic cell types. PMID:27243651

  20. Cervical HPV type-specific pre-vaccination prevalence and age distribution in Croatia. (United States)

    Sabol, Ivan; Milutin Gašperov, Nina; Matovina, Mihaela; Božinović, Ksenija; Grubišić, Goran; Fistonić, Ivan; Belci, Dragan; Alemany, Laia; Džebro, Sonja; Dominis, Mara; Šekerija, Mario; Tous, Sara; de Sanjosé, Silvia; Grce, Magdalena


    The main etiological factor of precancerous lesion and invasive cervical cancer are oncogenic human papillomaviruses types (HPVs). The objective of this study was to establish the distribution of the most common HPVs in different cervical lesions and cancer prior to the implementation of organized population-based cervical screening and HPV vaccination in Croatia. In this study, 4,432 cervical specimens, collected through a 16-year period, were tested for the presence of HPV-DNA by polymerase chain reaction (PCR) with three sets of broad-spectrum primers and type-specific primers for most common low-risk (LR) types (HPV-6, 11) and the most common high-risk (HR) types (HPV-16, 18, 31, 33, 45, 52, 58). Additional 35 archival formalin-fixed, paraffin embedded tissue of cervical cancer specimens were analyzed using LiPA25 assay. The highest age-specific HPV-prevalence was in the group 18-24 years, which decreased continuously with age (Ptypes significantly increased (Ptype found with a prevalence (with or without another HPV-type) of 6.9% in normal cytology, 15.5% in atypical squamous cells of undetermined significance, 14.4% in low-grade squamous intraepithelial lesions, 33.3% in high-grade squamous intraepithelial lesions, and 60.9% in cervical cancer specimens (Ptypes among Croatian women, which will enable to predict and to monitor the impact of HPV-vaccination and to design effective screening strategies in Croatia.

  1. Protein conservation and variation suggest mechanisms of cell type-specific modulation of signaling pathways.

    Directory of Open Access Journals (Sweden)

    Martin H Schaefer


    Full Text Available Many proteins and signaling pathways are present in most cell types and tissues and yet perform specialized functions. To elucidate mechanisms by which these ubiquitous pathways are modulated, we overlaid information about cross-cell line protein abundance and variability, and evolutionary conservation onto functional pathway components and topological layers in the pathway hierarchy. We found that the input (receptors and the output (transcription factors layers evolve more rapidly than proteins in the intermediary transmission layer. In contrast, protein expression variability decreases from the input to the output layer. We observed that the differences in protein variability between the input and transmission layer can be attributed to both the network position and the tendency of variable proteins to physically interact with constitutively expressed proteins. Differences in protein expression variability and conservation are also accompanied by the tendency of conserved and constitutively expressed proteins to acquire somatic mutations, while germline mutations tend to occur in cell type-specific proteins. Thus, conserved core proteins in the transmission layer could perform a fundamental role in most cell types and are therefore less tolerant to germline mutations. In summary, we propose that the core signal transmission machinery is largely modulated by a variable input layer through physical protein interactions. We hypothesize that the bow-tie organization of cellular signaling on the level of protein abundance variability contributes to the specificity of the signal response in different cell types.

  2. Improved Sparse Channel Estimation for Cooperative Communication Systems

    Directory of Open Access Journals (Sweden)

    Guan Gui


    Full Text Available Accurate channel state information (CSI is necessary at receiver for coherent detection in amplify-and-forward (AF cooperative communication systems. To estimate the channel, traditional methods, that is, least squares (LS and least absolute shrinkage and selection operator (LASSO, are based on assumptions of either dense channel or global sparse channel. However, LS-based linear method neglects the inherent sparse structure information while LASSO-based sparse channel method cannot take full advantage of the prior information. Based on the partial sparse assumption of the cooperative channel model, we propose an improved channel estimation method with partial sparse constraint. At first, by using sparse decomposition theory, channel estimation is formulated as a compressive sensing problem. Secondly, the cooperative channel is reconstructed by LASSO with partial sparse constraint. Finally, numerical simulations are carried out to confirm the superiority of proposed methods over global sparse channel estimation methods.

  3. Fingerprint Compression Based on Sparse Representation. (United States)

    Shao, Guangqi; Wu, Yanping; A, Yong; Liu, Xiao; Guo, Tiande


    A new fingerprint compression algorithm based on sparse representation is introduced. Obtaining an overcomplete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combination of dictionary atoms. In the algorithm, we first construct a dictionary for predefined fingerprint image patches. For a new given fingerprint images, represent its patches according to the dictionary by computing l(0)-minimization and then quantize and encode the representation. In this paper, we consider the effect of various factors on compression results. Three groups of fingerprint images are tested. The experiments demonstrate that our algorithm is efficient compared with several competing compression techniques (JPEG, JPEG 2000, and WSQ), especially at high compression ratios. The experiments also illustrate that the proposed algorithm is robust to extract minutiae.

  4. Sparse brain network using penalized linear regression (United States)

    Lee, Hyekyoung; Lee, Dong Soo; Kang, Hyejin; Kim, Boong-Nyun; Chung, Moo K.


    Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.

  5. Sparse model selection via integral terms (United States)

    Schaeffer, Hayden; McCalla, Scott G.


    Model selection and parameter estimation are important for the effective integration of experimental data, scientific theory, and precise simulations. In this work, we develop a learning approach for the selection and identification of a dynamical system directly from noisy data. The learning is performed by extracting a small subset of important features from an overdetermined set of possible features using a nonconvex sparse regression model. The sparse regression model is constructed to fit the noisy data to the trajectory of the dynamical system while using the smallest number of active terms. Computational experiments detail the model's stability, robustness to noise, and recovery accuracy. Examples include nonlinear equations, population dynamics, chaotic systems, and fast-slow systems.

  6. Greedy vs. L1 convex optimization in sparse coding

    DEFF Research Database (Denmark)

    Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor


    Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes. During this procedure, sparse codes which can be achieved...... their performance from various aspects to better understand their applicability, including computation time, reconstruction error, sparsity, detection...

  7. Sparse district-heating in Sweden

    Energy Technology Data Exchange (ETDEWEB)

    Nilsson, Stefan Forsaeus [SP Technical Research Institute of Sweden, Building Technology and Mechanics, P.O. Box 24036, SE-400 22 Goeteborg (Sweden); Reidhav, Charlotte [Chalmers University of Technology, Department of Civil and Environmental Engineering, SE-412 96 Goeteborg (Sweden); Lygnerud, Kristina [Goeteborg University, School of Business, Economics and Law, Department of Business Administration, P.O. Box 610, SE-405 30 Goeteborg (Sweden); Werner, Sven [Chalmers University of Technology, Department of Energy and Environment, SE-412 96 Goeteborg (Sweden)


    This paper presents a review of the sparse district-heating research programme undertaken in Sweden between 2002 and 2006. The goal of the programme was to increase the future competitiveness for district heat in low heat density areas, e.g., suburban single-family houses and small villages. Such areas are unfavourable, since revenues from heat sold are low compared with the investment cost for the local distribution network. In Sweden, district heat has a dominant position in the heat market for residential and service-sector buildings. In order for the business to grow, it is necessary to increase the rate of expansion in the detached-house segment. This is why the programme was initiated. The extent of the programme was set at EUR 3.6 million with equal financing from the Swedish District-Heating Association and the Swedish Energy-Agency. The research was carried out in three phases: a state of the art survey; a development phase focused on productivity gains where new research on both technology and customer interaction was performed; and finally a demonstration phase where new methods were tested in full-scale field operation. The programme has shown that the Swedish district-heating industry needs to adjust in order to reach a higher profitability for sparse district-heating investments. Tradition from large-scale high-density district heating is hard to scale to fit sparse district-heating systems. For example, the construction becomes very labour intensive and the industry is weak when it comes to market-oriented business logic, sales and private customer interaction. Innovation seems to be a way forward and active management of innovations is a way to create increased value of the investments. Other keys to improving the profitability of sparse district-heating investments are more efficient working routines (resulting in higher productivity) and revised ways of customer communications. These seem more important than increasing efficiency in district

  8. Dictionary Learning Algorithms for Sparse Representation


    Kreutz-Delgado, Kenneth; Murray, Joseph F.; Rao, Bhaskar D.; Engan, Kjersti; Lee, Te-Won; Sejnowski, Terrence J.


    Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or wo...

  9. Muscle-Type Specific Autophosphorylation of CaMKII Isoforms after Paced Contractions

    Directory of Open Access Journals (Sweden)

    Wouter Eilers


    Full Text Available We explored to what extent isoforms of the regulator of excitation-contraction and excitation-transcription coupling, calcium/calmodulin protein kinase II (CaMKII contribute to the specificity of myocellular calcium sensing between muscle types and whether concentration transients in its autophosphorylation can be simulated. CaMKII autophosphorylation at Thr287 was assessed in three muscle compartments of the rat after slow or fast motor unit-type stimulation and was compared against a computational model (CaMuZclE coupling myocellular calcium dynamics with CaMKII Thr287 phosphorylation. Qualitative differences existed between fast- (gastrocnemius medialis and slow-type muscle (soleus for the expression pattern of CaMKII isoforms. Phospho-Thr287 content of δA CaMKII, associated with nuclear functions, demonstrated a transient and compartment-specific increase after excitation, which contrasted to the delayed autophosphorylation of the sarcoplasmic reticulum-associated βM CaMKII. In soleus muscle, excitation-induced δA CaMKII autophosphorylation demonstrated frequency dependence (P = 0.02. In the glycolytic compartment of gastrocnemius medialis, CaMKII autophosphorylation after excitation was blunted. In silico assessment emphasized the importance of mitochondrial calcium buffer capacity for excitation-induced CaMKII autophosphorylation but did not predict its isoform specificity. The findings expose that CaMKII autophosphorylation with paced contractions is regulated in an isoform and muscle type-specific fashion and highlight properties emerging for phenotype-specific regulation of CaMKII.

  10. Muscle type-specific responses to NAD+ salvage biosynthesis promote muscle function in Caenorhabditis elegans. (United States)

    Vrablik, Tracy L; Wang, Wenqing; Upadhyay, Awani; Hanna-Rose, Wendy


    Salvage biosynthesis of nicotinamide adenine dinucleotide (NAD(+)) from nicotinamide (NAM) lowers NAM levels and replenishes the critical molecule NAD(+) after it is hydrolyzed. This pathway is emerging as a regulator of multiple biological processes. Here we probe the contribution of the NAM-NAD(+) salvage pathway to muscle development and function using Caenorhabditis elegans. C. elegans males with mutations in the nicotinamidase pnc-1, which catalyzes the first step of this NAD(+) salvage pathway, cannot mate due to a spicule muscle defect. Multiple muscle types are impaired in the hermaphrodites, including body wall muscles, pharyngeal muscles and vulval muscles. An active NAD(+) salvage pathway is required for optimal function of each muscle cell type. However, we found surprising muscle-cell-type specificity in terms of both the timing and relative sensitivity to perturbation of NAD(+) production or NAM levels. Active NAD(+) biosynthesis during development is critical for function of the male spicule protractor muscles during adulthood, but these muscles can surprisingly do without salvage biosynthesis in adulthood under the conditions examined. The body wall muscles require ongoing NAD(+) salvage biosynthesis both during development and adulthood for maximum function. The vulval muscles do not function in the presence of elevated NAM concentrations, but NAM supplementation is only slightly deleterious to body wall muscles during development or upon acute application in adults. Thus, the pathway plays distinct roles in different tissues. As NAM-NAD(+) biosynthesis also impacts muscle differentiation in vertebrates, we propose that similar complexities may be found among vertebrate muscle cell types. Copyright © 2010 Elsevier Inc. All rights reserved.

  11. Differentiation and fiber type-specific activity of a muscle creatine kinase intronic enhancer

    Directory of Open Access Journals (Sweden)

    Tai Phillip WL


    Full Text Available Abstract Background Hundreds of genes, including muscle creatine kinase (MCK, are differentially expressed in fast- and slow-twitch muscle fibers, but the fiber type-specific regulatory mechanisms are not well understood. Results Modulatory region 1 (MR1 is a 1-kb regulatory region within MCK intron 1 that is highly active in terminally differentiating skeletal myocytes in vitro. A MCK small intronic enhancer (MCK-SIE containing a paired E-box/myocyte enhancer factor 2 (MEF2 regulatory motif resides within MR1. The SIE's transcriptional activity equals that of the extensively characterized 206-bp MCK 5'-enhancer, but the MCK-SIE is flanked by regions that can repress its activity via the individual and combined effects of about 15 different but highly conserved 9- to 24-bp sequences. ChIP and ChIP-Seq analyses indicate that the SIE and the MCK 5'-enhancer are occupied by MyoD, myogenin and MEF2. Many other E-boxes located within or immediately adjacent to intron 1 are not occupied by MyoD or myogenin. Transgenic analysis of a 6.5-kb MCK genomic fragment containing the 5'-enhancer and proximal promoter plus the 3.2-kb intron 1, with and without MR1, indicates that MR1 is critical for MCK expression in slow- and intermediate-twitch muscle fibers (types I and IIa, respectively, but is not required for expression in fast-twitch muscle fibers (types IIb and IId. Conclusions In this study, we discovered that MR1 is critical for MCK expression in slow- and intermediate-twitch muscle fibers and that MR1's positive transcriptional activity depends on a paired E-box MEF2 site motif within a SIE. This is the first study to delineate the DNA controls for MCK expression in different skeletal muscle fiber types.

  12. Cell-type specificity of ChIP-predicted transcription factor binding sites

    Directory of Open Access Journals (Sweden)

    Håndstad Tony


    Full Text Available Abstract Background Context-dependent transcription factor (TF binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq identifies genome-wide TF binding sites for one particular context—the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding? Results We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity. Conclusions Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data—ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure—we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.

  13. Cell-type-specific roles for COX-2 in UVB-induced skin cancer (United States)

    Herschman, Harvey


    In human tumors, and in mouse models, cyclooxygenase-2 (COX-2) levels are frequently correlated with tumor development/burden. In addition to intrinsic tumor cell expression, COX-2 is often present in fibroblasts, myofibroblasts and endothelial cells of the tumor microenvironment, and in infiltrating immune cells. Intrinsic cancer cell COX-2 expression is postulated as only one of many sources for prostanoids required for tumor promotion/progression. Although both COX-2 inhibition and global Cox-2 gene deletion ameliorate ultraviolet B (UVB)-induced SKH-1 mouse skin tumorigenesis, neither manipulation can elucidate the cell type(s) in which COX-2 expression is required for tumorigenesis; both eliminate COX-2 activity in all cells. To address this question, we created Cox-2 flox/flox mice, in which the Cox-2 gene can be eliminated in a cell-type-specific fashion by targeted Cre recombinase expression. Cox-2 deletion in skin epithelial cells of SKH-1 Cox-2 flox/flox;K14Cre + mice resulted, following UVB irradiation, in reduced skin hyperplasia and increased apoptosis. Targeted epithelial cell Cox-2 deletion also resulted in reduced tumor incidence, frequency, size and proliferation rate, altered tumor cell differentiation and reduced tumor vascularization. Moreover, Cox-2 flox/flox;K14Cre + papillomas did not progress to squamous cell carcinomas. In contrast, Cox-2 deletion in SKH-1 Cox-2 flox/flox; LysMCre + myeloid cells had no effect on UVB tumor induction. We conclude that (i) intrinsic epithelial COX-2 activity plays a major role in UVB-induced skin cancer, (ii) macrophage/myeloid COX-2 plays no role in UVB-induced skin cancer and (iii) either there may be another COX-2-dependent prostanoid source(s) that drives UVB skin tumor induction or there may exist a COX-2-independent pathway(s) to UVB-induced skin cancer. PMID:24469308

  14. Neurophysiology of space travel: energetic solar particles cause cell type-specific plasticity of neurotransmission. (United States)

    Lee, Sang-Hun; Dudok, Barna; Parihar, Vipan K; Jung, Kwang-Mook; Zöldi, Miklós; Kang, Young-Jin; Maroso, Mattia; Alexander, Allyson L; Nelson, Gregory A; Piomelli, Daniele; Katona, István; Limoli, Charles L; Soltesz, Ivan


    In the not too distant future, humankind will embark on one of its greatest adventures, the travel to distant planets. However, deep space travel is associated with an inevitable exposure to radiation fields. Space-relevant doses of protons elicit persistent disruptions in cognition and neuronal structure. However, whether space-relevant irradiation alters neurotransmission is unknown. Within the hippocampus, a brain region crucial for cognition, perisomatic inhibitory control of pyramidal cells (PCs) is supplied by two distinct cell types, the cannabinoid type 1 receptor (CB 1 )-expressing basket cells (CB 1 BCs) and parvalbumin (PV)-expressing interneurons (PVINs). Mice subjected to low-dose proton irradiation were analyzed using electrophysiological, biochemical and imaging techniques months after exposure. In irradiated mice, GABA release from CB 1 BCs onto PCs was dramatically increased. This effect was abolished by CB 1 blockade, indicating that irradiation decreased CB 1 -dependent tonic inhibition of GABA release. These alterations in GABA release were accompanied by decreased levels of the major CB 1 ligand 2-arachidonoylglycerol. In contrast, GABA release from PVINs was unchanged, and the excitatory connectivity from PCs to the interneurons also underwent cell type-specific alterations. These results demonstrate that energetic charged particles at space-relevant low doses elicit surprisingly selective long-term plasticity of synaptic microcircuits in the hippocampus. The magnitude and persistent nature of these alterations in synaptic function are consistent with the observed perturbations in cognitive performance after irradiation, while the high specificity of these changes indicates that it may be possible to develop targeted therapeutic interventions to decrease the risk of adverse events during interplanetary travel.

  15. A Procedure for Determining Rock-Type Specific Hoek-Brown Brittle Parameter s (United States)

    Suorineni, F. T.; Chinnasane, D. R.; Kaiser, P. K.


    The Hoek-Brown failure criterion constants m and s are equivalent rock friction and cohesion parameters, respectively. On the laboratory scale, m depends on the rock type and texture (grain size), while s = 1 for all rocks. On the field scale, m is a function of rock type, texture, and rock mass quality (geological strength index, GSI), while s is simply a function of rock mass quality. The brittle Hoek-Brown damage initiation criterion ( m-zero criterion) is a modification to the conventional Hoek-Brown failure criterion with m = 0 and s = 0.11. The m-zero damage initiation criterion has been shown to better predict depths of failure in excavations in some moderate to massive (GSI ≥ 75) rock masses, but over predicts depths of failure in other rock types. It is now recognized that the Hoek-Brown brittle parameter ( s) is not the same for all hard, strong, brittle, moderate to massive rock masses, but depends on the rock type. However, there are no guidelines for its determination for specific rock types. This paper presents a semi-empirical procedure for the determination of rock-type specific brittle Hoek-Brown parameter s from the rock texture, mineralogical composition, and microstructure. The paper also differentiates between brittle and tenuous rocks. It is shown that, while the use of the term ‘brittle’ is appropriate for rock mechanical excavation and mode of failure in weak rocks with limited deformability, it is inappropriate for use in explaining the difference in resistance to stress-induced damage in different rock types, and can cause confusion. The terms ‘tenacity/toughness’ are introduced to describe rock resistance to stress-induced damage in excavation performance assessment, and a rock tenacity/toughness rating system is presented.

  16. Cell type specificity of signaling: view from membrane receptors distribution and their downstream transduction networks. (United States)

    He, Ying; Yu, Zhonghao; Ge, Dongya; Wang-Sattler, Rui; Thiesen, Hans-Jürgen; Xie, Lu; Li, Yixue


    Studies on cell signaling pay more attention to spatial dynamics and how such diverse organization can relate to high order of cellular capabilities. To overview the specificity of cell signaling, we integrated human receptome data with proteome spatial expression profiles to systematically investigate the specificity of receptors and receptor-triggered transduction networks across 62 normal cell types and 14 cancer types. Six percent receptors showed cell-type-specific expression, and 4% signaling networks presented enriched cell-specific proteins induced by the receptors. We introduced a concept of "response context" to annotate the cell-type dependent signaling networks. We found that most cells respond similarly to the same stimulus, as the "response contexts" presented high functional similarity. Despite this, the subtle spatial diversity can be observed from the difference in network architectures. The architecture of the signaling networks in nerve cells displayed less completeness than that in glandular cells, which indicated cellular-context dependent signaling patterns are elaborately spatially organized. Likewise, in cancer cells most signaling networks were generally dysfunctional and less complete than that in normal cells. However, glioma emerged hyper-activated transduction mechanism in malignant state. Receptor ATP6AP2 and TNFRSF21 induced rennin-angiotensin and apoptosis signaling were found likely to explain the glioma-specific mechanism. This work represents an effort to decipher context-specific signaling network from spatial dimension. Our results indicated that although a majority of cells engage general signaling response with subtle differences, the spatial dynamics of cell signaling can not only deepen our insights into different signaling mechanisms, but also help understand cell signaling in disease.

  17. Tissue-type-specific transcriptome analysis identifies developing xylem-specific promoters in poplar. (United States)

    Ko, Jae-Heung; Kim, Hyun-Tae; Hwang, Ildoo; Han, Kyung-Hwan


    Plant biotechnology offers a means to create novel phenotypes. However, commercial application of biotechnology in crop improvement programmes is severely hindered by the lack of utility promoters (or freedom to operate the existing ones) that can drive gene expression in a tissue-specific or temporally controlled manner. Woody biomass is gaining popularity as a source of fermentable sugars for liquid fuel production. To improve the quantity and quality of woody biomass, developing xylem (DX)-specific modification of the feedstock is highly desirable. To develop utility promoters that can drive transgene expression in a DX-specific manner, we used the Affymetrix Poplar Genome Arrays to obtain tissue-type-specific transcriptomes from poplar stems. Subsequent bioinformatics analysis identified 37 transcripts that are specifically or strongly expressed in DX cells of poplar. After further confirmation of their DX-specific expression using semi-quantitative PCR, we selected four genes (DX5, DX8, DX11 and DX15) for in vivo confirmation of their tissue-specific expression in transgenic poplars. The promoter regions of the selected DX genes were isolated and fused to a β-glucuronidase (GUS)-reported gene in a binary vector. This construct was used to produce transgenic poplars via Agrobacterium-mediated transformation. The GUS expression patterns of the resulting transgenic plants showed that these promoters were active in the xylem cells at early seedling growth and had strongest expression in the developing xylem cells at later growth stages of poplar. We conclude that these DX promoters can be used as a utility promoter for DX-specific biomass engineering. © 2012 The Authors. Plant Biotechnology Journal © 2012 Society for Experimental Biology, Association of Applied Biologists and Blackwell Publishing Ltd.

  18. Human muscle fiber type-specific insulin signaling: impact of obesity and type 2 diabetes. (United States)

    Albers, Peter H; Pedersen, Andreas J T; Birk, Jesper B; Kristensen, Dorte E; Vind, Birgitte F; Baba, Otto; Nøhr, Jane; Højlund, Kurt; Wojtaszewski, Jørgen F P


    Skeletal muscle is a heterogeneous tissue composed of different fiber types. Studies suggest that insulin-mediated glucose metabolism is different between muscle fiber types. We hypothesized that differences are due to fiber type-specific expression/regulation of insulin signaling elements and/or metabolic enzymes. Pools of type I and II fibers were prepared from biopsies of the vastus lateralis muscles from lean, obese, and type 2 diabetic subjects before and after a hyperinsulinemic-euglycemic clamp. Type I fibers compared with type II fibers have higher protein levels of the insulin receptor, GLUT4, hexokinase II, glycogen synthase (GS), and pyruvate dehydrogenase-E1α (PDH-E1α) and a lower protein content of Akt2, TBC1 domain family member 4 (TBC1D4), and TBC1D1. In type I fibers compared with type II fibers, the phosphorylation response to insulin was similar (TBC1D4, TBC1D1, and GS) or decreased (Akt and PDH-E1α). Phosphorylation responses to insulin adjusted for protein level were not different between fiber types. Independently of fiber type, insulin signaling was similar (TBC1D1, GS, and PDH-E1α) or decreased (Akt and TBC1D4) in muscle from patients with type 2 diabetes compared with lean and obese subjects. We conclude that human type I muscle fibers compared with type II fibers have a higher glucose-handling capacity but a similar sensitivity for phosphoregulation by insulin. © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.

  19. World Health Organization Guidelines for Containment of Poliovirus Following Type-Specific Polio Eradication - Worldwide, 2015. (United States)

    Previsani, Nicoletta; Tangermann, Rudolph H; Tallis, Graham; Jafari, Hamid S


    In 1988, the World Health Assembly of the World Health Organization (WHO) resolved to eradicate polio worldwide. Among the three wild poliovirus (WPV) types (type 1, type 2, and type 3), WPV type 2 (WPV2) has been eliminated in the wild since 1999, and WPV type 3 (WPV3) has not been reported since 2012. In 2015, only Afghanistan and Pakistan have reported WPV transmission. On May 25, 2015, all WHO Member States endorsed World Health Assembly resolution 68.3 on full implementation of the Polio Eradication and Endgame Strategic Plan 2013-2018 (the Endgame Plan), and with it, the third Global Action Plan to minimize poliovirus facility-associated risk (GAPIII). All WHO Member States have committed to implementing appropriate containment of WPV2 in essential laboratory and vaccine production facilities* by the end of 2015 and of type 2 oral poliovirus vaccine (OPV2) within 3 months of global withdrawal of OPV2, which is planned for April 2016. This report summarizes critical steps for essential laboratory and vaccine production facilities that intend to retain materials confirmed to contain or potentially containing type-specific WPV, vaccine-derived poliovirus (VDPV), or OPV/Sabin viruses, and steps for nonessential facilities† that process specimens that contain or might contain polioviruses. National authorities will need to certify that the essential facilities they host meet the containment requirements described in GAPIII. After certification of WPV eradication, the use of all OPV will cease; final containment of all polioviruses after polio eradication and OPV cessation will minimize the risk for reintroduction of poliovirus into a polio-free world.

  20. Epigenetic regulation of normal human mammary cell type-specific miRNAs

    Energy Technology Data Exchange (ETDEWEB)

    Vrba, Lukas [Univ. of Arizona, Tucson, AZ (United States). Arizona Cancer Center; Inst. of Plant Molecular Biology, Ceske Budejovice (Czech Republic). Biology Centre ASCR; Garbe, James C. [Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Center; Stampfer, Martha R. [Univ. of Arizona, Tucson, AZ (United States). Arizona Cancer Center; Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Life Sciences Center; Futscher, Bernard W. [Univ. of Arizona, Tucson, AZ (United States). Arizona Cancer Center and Dept. of Pharmacology & Toxicology


    Epigenetic mechanisms are important regulators of cell type–specific genes, including miRNAs. In order to identify cell type-specific miRNAs regulated by epigenetic mechanisms, we undertook a global analysis of miRNA expression and epigenetic states in three isogenic pairs of human mammary epithelial cells (HMEC) and human mammary fibroblasts (HMF), which represent two differentiated cell types typically present within a given organ, each with a distinct phenotype and a distinct epigenotype. While miRNA expression and epigenetic states showed strong interindividual concordance within a given cell type, almost 10% of the expressed miRNA showed a cell type–specific pattern of expression that was linked to the epigenetic state of their promoter. The tissue-specific miRNA genes were epigenetically repressed in nonexpressing cells by DNA methylation (38%) and H3K27me3 (58%), with only a small set of miRNAs (21%) showing a dual epigenetic repression where both DNA methylation and H3K27me3 were present at their promoters, such as MIR10A and MIR10B. Individual miRNA clusters of closely related miRNA gene families can each display cell type–specific repression by the same or complementary epigenetic mechanisms, such as the MIR200 family, and MIR205, where fibroblasts repress MIR200C/141 by DNA methylation, MIR200A/200B/429 by H3K27me3, and MIR205 by both DNA methylation and H3K27me3. Since deregulation of many of the epigenetically regulated miRNAs that we identified have been linked to disease processes such as cancer, it is predicted that compromise of the epigenetic control mechanisms is important for this process. Overall, these results highlight the importance of epigenetic regulation in the control of normal cell type–specific miRNA expression.

  1. Cell-type specific expression of p11 controls cocaine reward. (United States)

    Arango-Lievano, Margarita; Schwarz, Justin T; Vernov, Mary; Wilkinson, Matthew B; Bradbury, Kathryn; Feliz, Akira; Marongiu, Roberta; Gelfand, Yaroslav; Warner-Schmidt, Jennifer; Nestler, Eric J; Greengard, Paul; Russo, Scott J; Kaplitt, Michael G


    The high rate of comorbidity between depression and cocaine addiction suggests shared molecular mechanisms and anatomical pathways. Limbic structures, such as the nucleus accumbens (NAc), play a crucial role in both disorders, yet how different cell types within these structures contribute to the pathogenesis remains elusive. Downregulation of p11 (S100A10), specifically in the NAc, elicits depressive-like behaviors in mice, but its role in drug addiction is unknown. We combined mouse genetics and viral strategies to determine how the titration of p11 levels within the entire NAc affects the rewarding actions of cocaine on behavior (six to eight mice per group) and molecular correlates (three experiments, five to eight mice per group). Finally, the manipulation of p11 expression in distinct NAc dopaminoceptive neuronal subsets distinguished cell-type specific effects of p11 on cocaine reward (five to eight mice per group). We demonstrated that p11 knockout mice have enhanced cocaine conditioned place preference, which is reproduced by the focal downregulation of p11 in the NAc of wild-type mice. In wild-type mice, cocaine reduced p11 expression in the NAc, while p11 overexpression exclusively in the NAc reduced cocaine conditioned place preference. Finally, we identified dopamine receptor-1 expressing medium spiny neurons as key mediators of the effects of p11 on cocaine reward. Our data provide evidence that disruption of p11 homeostasis in the NAc, particularly in dopamine receptor-1 expressing medium spiny neurons, may underlie pathophysiological mechanisms of cocaine rewarding action. Treatments to counter maladaptation of p11 levels may provide novel therapeutic opportunities for cocaine addiction. Copyright © 2014 Society of Biological Psychiatry. Published by Elsevier Inc. All rights reserved.

  2. SAR Image despeckling via sparse representation (United States)

    Wang, Zhongmei; Yang, Xiaomei; Zheng, Liang


    SAR image despeckling is an active research area in image processing due to its importance in improving the quality of image for object detection and classification.In this paper, a new approach is proposed for multiplicative noise in SAR image removal based on nonlocal sparse representation by dictionary learning and collaborative filtering. First, a image is divided into many patches, and then a cluster is formed by clustering log-similar image patches using Fuzzy C-means (FCM). For each cluster, an over-complete dictionary is computed using the K-SVD method that iteratively updates the dictionary and the sparse coefficients. The patches belonging to the same cluster are then reconstructed by a sparse combination of the corresponding dictionary atoms. The reconstructed patches are finally collaboratively aggregated to build the denoised image. The experimental results show that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both objective evaluation index (PSNR and ENL) and subjective visual perception.

  3. Decentralized Sparse Multitask RLS Over Networks (United States)

    Cao, Xuanyu; Liu, K. J. Ray


    Distributed adaptive signal processing has attracted much attention in the recent decade owing to its effectiveness in many decentralized real-time applications in networked systems. Because many natural signals are highly sparse with most entries equal to zero, several decentralized sparse adaptive algorithms have been proposed recently. Most of them is focused on the single task estimation problems, in which all nodes receive data associated with the same unknown vector and collaborate to estimate it. However, many applications are inherently multitask oriented and each node has its own unknown vector different from others'. The related multitask estimation problem benefits from collaborations among the nodes as neighbor nodes usually share analogous properties and thus similar unknown vectors. In this work, we study the distributed sparse multitask recursive least squares (RLS) problem over networks. We first propose a decentralized online alternating direction method of multipliers (ADMM) algorithm for the formulated RLS problem. The algorithm is simplified for easy implementation with closed-form computations in each iteration and low storage requirements. Moreover, to further reduce the complexity, we present a decentralized online subgradient method with low computational overhead. We theoretically analyze the convergence behavior of the proposed subgradient method and derive an error bound related to the network topology and algorithm parameters. The effectiveness of the proposed algorithms is corroborated by numerical simulations and an accuracy-complexity tradeoff between the proposed two algorithms is highlighted.

  4. Robust Fringe Projection Profilometry via Sparse Representation. (United States)

    Budianto; Lun, Daniel P K


    In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment.

  5. Lim homeobox genes in the Ctenophore Mnemiopsis leidyi: the evolution of neural cell type specification

    Directory of Open Access Journals (Sweden)

    Simmons David K


    Full Text Available Abstract Background Nervous systems are thought to be important to the evolutionary success and diversification of metazoans, yet little is known about the origin of simple nervous systems at the base of the animal tree. Recent data suggest that ctenophores, a group of macroscopic pelagic marine invertebrates, are the most ancient group of animals that possess a definitive nervous system consisting of a distributed nerve net and an apical statocyst. This study reports on details of the evolution of the neural cell type specifying transcription factor family of LIM homeobox containing genes (Lhx, which have highly conserved functions in neural specification in bilaterian animals. Results Using next generation sequencing, the first draft of the genome of the ctenophore Mnemiopsis leidyi has been generated. The Lhx genes in all animals are represented by seven subfamilies (Lhx1/5, Lhx3/4, Lmx, Islet, Lhx2/9, Lhx6/8, and LMO of which four were found to be represented in the ctenophore lineage (Lhx1/5, Lhx3/4, Lmx, and Islet. Interestingly, the ctenophore Lhx gene complement is more similar to the sponge complement (sponges do not possess neurons than to either the cnidarian-bilaterian or placozoan Lhx complements. Using whole mount in situ hybridization, the Lhx gene expression patterns were examined and found to be expressed around the blastopore and in cells that give rise to the apical organ and putative neural sensory cells. Conclusion This research gives us a first look at neural cell type specification in the ctenophore M. leidyi. Within M. leidyi, Lhx genes are expressed in overlapping domains within proposed neural cellular and sensory cell territories. These data suggest that Lhx genes likely played a conserved role in the patterning of sensory cells in the ancestor of sponges and ctenophores, and may provide a link to the expression of Lhx orthologs in sponge larval photoreceptive cells. Lhx genes were later co-opted into patterning more

  6. Discriminative Transfer Subspace Learning via Low-Rank and Sparse Representation. (United States)

    Xu, Yong; Fang, Xiaozhao; Wu, Jian; Li, Xuelong; Zhang, David


    In this paper, we address the problem of unsupervised domain transfer learning in which no labels are available in the target domain. We use a transformation matrix to transfer both the source and target data to a common subspace, where each target sample can be represented by a combination of source samples such that the samples from different domains can be well interlaced. In this way, the discrepancy of the source and target domains is reduced. By imposing joint low-rank and sparse constraints on the reconstruction coefficient matrix, the global and local structures of data can be preserved. To enlarge the margins between different classes as much as possible and provide more freedom to diminish the discrepancy, a flexible linear classifier (projection) is obtained by learning a non-negative label relaxation matrix that allows the strict binary label matrix to relax into a slack variable matrix. Our method can avoid a potentially negative transfer by using a sparse matrix to model the noise and, thus, is more robust to different types of noise. We formulate our problem as a constrained low-rankness and sparsity minimization problem and solve it by the inexact augmented Lagrange multiplier method. Extensive experiments on various visual domain adaptation tasks show the superiority of the proposed method over the state-of-the art methods. The MATLAB code of our method will be publicly available at

  7. Parallel and Scalable Sparse Basic Linear Algebra Subprograms

    DEFF Research Database (Denmark)

    Liu, Weifeng

    proposes new fundamental algorithms and data structures that accelerate Sparse BLAS routines on modern massively parallel processors: (1) a new heap data structure named ad-heap, for faster heap operations on heterogeneous processors, (2) a new sparse matrix representation named CSR5, for faster sparse......Sparse basic linear algebra subprograms (BLAS) are fundamental building blocks for numerous scientific computations and graph applications. Compared with Dense BLAS, parallelization of Sparse BLAS routines entails extra challenges due to the irregularity of sparse data structures. This thesis...... matrix-vector multiplication (SpMV) on homogeneous processors such as CPUs, GPUs and Xeon Phi, (3) a new CSR-based SpMV algorithm for a variety of tightly coupled CPU-GPU heterogeneous processors, and (4) a new framework and associated algorithms for sparse matrix-matrix multiplication (SpGEMM) on GPUs...

  8. Multi-threaded Sparse Matrix Sparse Matrix Multiplication for Many-Core and GPU Architectures.

    Energy Technology Data Exchange (ETDEWEB)

    Deveci, Mehmet [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Trott, Christian Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rajamanickam, Sivasankaran [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)


    Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scientific computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix- matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.

  9. Artifact detection in electrodermal activity using sparse recovery (United States)

    Kelsey, Malia; Palumbo, Richard Vincent; Urbaneja, Alberto; Akcakaya, Murat; Huang, Jeannie; Kleckner, Ian R.; Barrett, Lisa Feldman; Quigley, Karen S.; Sejdic, Ervin; Goodwin, Matthew S.


    Electrodermal Activity (EDA) - a peripheral index of sympathetic nervous system activity - is a primary measure used in psychophysiology. EDA is widely accepted as an indicator of physiological arousal, and it has been shown to reveal when psychologically novel events occur. Traditionally, EDA data is collected in controlled laboratory experiments. However, recent developments in wireless biosensing have led to an increase in out-of-lab studies. This transition to ambulatory data collection has introduced challenges. In particular, artifacts such as wearer motion, changes in temperature, and electrical interference can be misidentified as true EDA responses. The inability to distinguish artifact from signal hinders analyses of ambulatory EDA data. Though manual procedures for identifying and removing EDA artifacts exist, they are time consuming - which is problematic for the types of longitudinal data sets represented in modern ambulatory studies. This manuscript presents a novel technique to automatically identify and remove artifacts in EDA data using curve fitting and sparse recovery methods. Our method was evaluated using labeled data to determine the accuracy of artifact identification. Procedures, results, conclusions, and future directions are presented.

  10. Discovery of cell-type specific DNA motif grammar in cis-regulatory elements using random Forest. (United States)

    Wang, Xin; Lin, Peijie; Ho, Joshua W K


    It has been observed that many transcription factors (TFs) can bind to different genomic loci depending on the cell type in which a TF is expressed in, even though the individual TF usually binds to the same core motif in different cell types. How a TF can bind to the genome in such a highly cell-type specific manner, is a critical research question. One hypothesis is that a TF requires co-binding of different TFs in different cell types. If this is the case, it may be possible to observe different combinations of TF motifs - a motif grammar - located at the TF binding sites in different cell types. In this study, we develop a bioinformatics method to systematically identify DNA motifs in TF binding sites across multiple cell types based on published ChIP-seq data, and address two questions: (1) can we build a machine learning classifier to predict cell-type specificity based on motif combinations alone, and (2) can we extract meaningful cell-type specific motif grammars from this classifier model. We present a Random Forest (RF) based approach to build a multi-class classifier to predict the cell-type specificity of a TF binding site given its motif content. We applied this RF classifier to two published ChIP-seq datasets of TF (TCF7L2 and MAX) across multiple cell types. Using cross-validation, we show that motif combinations alone are indeed predictive of cell types. Furthermore, we present a rule mining approach to extract the most discriminatory rules in the RF classifier, thus allowing us to discover the underlying cell-type specific motif grammar. Our bioinformatics analysis supports the hypothesis that combinatorial TF motif patterns are cell-type specific.

  11. Cell type-specific expression of FoxP2 in the ferret and mouse retina. (United States)

    Sato, Chihiro; Iwai-Takekoshi, Lena; Ichikawa, Yoshie; Kawasaki, Hiroshi


    Although the anatomical and physiological properties of subtypes of retinal ganglion cells (RGCs) have been extensively investigated, their molecular properties are still unclear. Here, we examined the expression patterns of FoxP2 in the retina of ferrets and mice. We found that FoxP2 was expressed in small subsets of neurons in the adult ferret retina. FoxP2-positive neurons in the ganglion cell layer were divided into two groups. Large FoxP2-positive neurons expressed Brn3a and were retrogradely labeled with cholera toxin subunit B injected into the optic nerve, indicating that they are RGCs. The soma size and the projection pattern of FoxP2-positive RGCs were consistent with those of X cells. Because we previously reported that FoxP2 was selectively expressed in X cells in the ferret lateral geniculate nucleus (LGN), our findings indicate that FoxP2 is specifically expressed in the parvocellular pathway from the retina to the LGN. Small FoxP2-positive neurons were positive for GAD65/67, suggesting that they are GABAergic amacrine cells. Most Foxp2-positive cells were RGCs in the adult mouse retina. Dendritic morphological analyses suggested that Foxp2-positive RGCs included direction-selective RGCs in mice. Thus, our findings suggest that FoxP2 is expressed in specific subtypes of RGCs in the retina of ferrets and mice. Copyright © 2016 Elsevier Ireland Ltd and Japan Neuroscience Society. All rights reserved.

  12. Cell Type-Specific Manipulation with GFP-Dependent Cre Recombinase (United States)

    Tang, Jonathan C Y; Rudolph, Stephanie; Dhande, Onkar S; Abraira, Victoria E; Choi, Seungwon; Lapan, Sylvain; Drew, Iain R; Drokhlyansky, Eugene; Huberman, Andrew D; Regehr, Wade G; Cepko, Constance L


    Summary There are many transgenic GFP reporter lines that allow visualization of specific populations of cells. Using such lines for functional studies requires a method that transforms GFP into a molecule that enables genetic manipulation. Here we report the creation of a method that exploits GFP for gene manipulation, Cre Recombinase Dependent on GFP (CRE-DOG), a split component system that uses GFP and its derivatives to directly induce Cre/loxP recombination. Using plasmid electroporation and AAV viral vectors, we delivered CRE-DOG to multiple GFP mouse lines, leading to effective recombination selectively in GFP-labeled cells. Further, CRE-DOG enabled optogenetic control of these neurons. Beyond providing a new set of tools for manipulation of gene expression selectively in GFP+ cells, we demonstrate that GFP can be used to reconstitute the activity of a protein not known to have a modular structure, suggesting that this strategy might be applicable to a wide range of proteins. PMID:26258682

  13. Regularized generalized eigen-decomposition with applications to sparse supervised feature extraction and sparse discriminant analysis

    DEFF Research Database (Denmark)

    Han, Xixuan; Clemmensen, Line Katrine Harder


    , and it is formulated as a generalized eigenvalue problem. Thus RGED can be applied to effectively extract sparse features and calculate sparse discriminant directions for all variants of Fisher discriminant criterion based models. Particularly, RGED can be applied to matrix-based and even tensor-based discriminant...... techniques, for instance, 2D-Linear Discriminant Analysis (2D-LDA). Furthermore, an iterative algorithm based on the alternating direction method of multipliers is developed. The algorithm approximately solves RGED with monotonically decreasing convergence and at an acceptable speed for results of modest...

  14. Stochastic convex sparse principal component analysis. (United States)

    Baytas, Inci M; Lin, Kaixiang; Wang, Fei; Jain, Anil K; Zhou, Jiayu


    Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is important. In applications, where the features have some physical meanings, we lose the ability to interpret the principal components extracted by conventional PCA because each principal component is a linear combination of all the original features. For this reason, sparse PCA has been proposed to improve the interpretability of traditional PCA by introducing sparsity to the loading vectors of principal components. The sparse PCA can be formulated as an ℓ 1 regularized optimization problem, which can be solved by proximal gradient methods. However, these methods do not scale well because computation of the exact gradient is generally required at each iteration. Stochastic gradient framework addresses this challenge by computing an expected gradient at each iteration. Nevertheless, stochastic approaches typically have low convergence rates due to the high variance. In this paper, we propose a convex sparse principal component analysis (Cvx-SPCA), which leverages a proximal variance reduced stochastic scheme to achieve a geometric convergence rate. We further show that the convergence analysis can be significantly simplified by using a weak condition which allows a broader class of objectives to be applied. The efficiency and effectiveness of the proposed method are demonstrated on a large-scale electronic medical record cohort.

  15. Food labels

    DEFF Research Database (Denmark)

    Selsøe Sørensen, Henrik; Clement, Jesper; Gabrielsen, Gorm


    The food industry develops tasty and healthy food but fails to deliver the message to all consumers. The consumers’ background knowledge is essential for how they find and decode relevant elements in the cocktail of signs which fight for attention on food labels. In this exploratory study, we find...... evidence for dividing consumers into two profiles: one relying on general food knowledge and another using knowledge related to signpost labels. In a combined eyetracking and questionnaire survey we analyse the influence of background knowledge and identify different patterns of visual attention...... for the two consumer profiles. This underlines the complexity in choosing and designing the ‘right’ elements for a food package that consumers actually look at and are able to make rational use of. In spite of any regulation of food information provided by authorities, consumers will still be confronted...

  16. A direct parallel sparse matrix solver

    International Nuclear Information System (INIS)

    Tran, T.M.; Gruber, R.; Appert, K.; Wuthrich, S.


    The direct sparse matrix solver is based on a domain decomposition technique to achieve data and work parallelization. Geometries that have long and thin structures are specially efficiently tractable with this solver, provided that they can be decomposed mainly in one direction. Due to the separation of the algorithm into a factorization stage and a solution stage, time-dependent problems with a constant coefficient matrix are particularly well suited for this solver. The parallelization performances obtained on a Cray T3D show that the method scales up to at least 256 processors. (author) 5 figs., 2 tabs., 9 refs

  17. Functional fixedness in a technologically sparse culture. (United States)

    German, Tim P; Barrett, H Clark


    Problem solving can be inefficient when the solution requires subjects to generate an atypical function for an object and the object's typical function has been primed. Subjects become "fixed" on the design function of the object, and problem solving suffers relative to control conditions in which the object's function is not demonstrated. In the current study, such functional fixedness was demonstrated in a sample of adolescents (mean age of 16 years) among the Shuar of Ecuadorian Amazonia, whose technologically sparse culture provides limited access to large numbers of artifacts with highly specialized functions. This result suggests that design function may universally be the core property of artifact concepts in human semantic memory.

  18. Wavelets for Sparse Representation of Music

    DEFF Research Database (Denmark)

    Endelt, Line Ørtoft; Harbo, Anders La-Cour


    We are interested in obtaining a sparse representation of music signals by means of a discrete wavelet transform (DWT). That means we want the energy in the representation to be concentrated in few DWT coefficients. It is well-known that the decay of the DWT coefficients is strongly related...... to the number of vanishing moments of the mother wavelet, and to the smoothness of the signal. In this paper we present the result of applying two classical families of wavelets to a series of musical signals. The purpose is to determine a general relation between the number of vanishing moments of the wavelet...

  19. Parallel preconditioning techniques for sparse CG solvers

    Energy Technology Data Exchange (ETDEWEB)

    Basermann, A.; Reichel, B.; Schelthoff, C. [Central Institute for Applied Mathematics, Juelich (Germany)


    Conjugate gradient (CG) methods to solve sparse systems of linear equations play an important role in numerical methods for solving discretized partial differential equations. The large size and the condition of many technical or physical applications in this area result in the need for efficient parallelization and preconditioning techniques of the CG method. In particular for very ill-conditioned matrices, sophisticated preconditioner are necessary to obtain both acceptable convergence and accuracy of CG. Here, we investigate variants of polynomial and incomplete Cholesky preconditioners that markedly reduce the iterations of the simply diagonally scaled CG and are shown to be well suited for massively parallel machines.

  20. A view of Kanerva's sparse distributed memory (United States)

    Denning, P. J.


    Pentti Kanerva is working on a new class of computers, which are called pattern computers. Pattern computers may close the gap between capabilities of biological organisms to recognize and act on patterns (visual, auditory, tactile, or olfactory) and capabilities of modern computers. Combinations of numeric, symbolic, and pattern computers may one day be capable of sustaining robots. The overview of the requirements for a pattern computer, a summary of Kanerva's Sparse Distributed Memory (SDM), and examples of tasks this computer can be expected to perform well are given.

  1. Sparse Dataflow Analysis with Pointers and Reachability

    DEFF Research Database (Denmark)

    Madsen, Magnus; Møller, Anders


    Many static analyzers exploit sparseness techniques to reduce the amount of information being propagated and stored during analysis. Although several variations are described in the literature, no existing technique is suitable for analyzing JavaScript code. In this paper, we point out the need...... quadtrees. The framework is presented as a systematic modification of a traditional dataflow analysis algorithm. Our experimental results demonstrate the effectiveness of the technique for a suite of JavaScript programs. By also comparing the performance with an idealized staged approach that computes...

  2. Abnormal Event Detection Using Local Sparse Representation

    DEFF Research Database (Denmark)

    Ren, Huamin; Moeslund, Thomas B.


    We propose to detect abnormal events via a sparse subspace clustering algorithm. Unlike most existing approaches, which search for optimized normal bases and detect abnormality based on least square error or reconstruction error from the learned normal patterns, we propose an abnormality...... measurement based on the difference between the normal space and local space. Specifically, we provide a reasonable normal bases through repeated K spectral clustering. Then for each testing feature we first use temporal neighbors to form a local space. An abnormal event is found if any abnormal feature...

  3. Partitioning sparse rectangular matrices for parallel processing

    Energy Technology Data Exchange (ETDEWEB)

    Kolda, T.G.


    The authors are interested in partitioning sparse rectangular matrices for parallel processing. The partitioning problem has been well-studied in the square symmetric case, but the rectangular problem has received very little attention. They will formalize the rectangular matrix partitioning problem and discuss several methods for solving it. They will extend the spectral partitioning method for symmetric matrices to the rectangular case and compare this method to three new methods -- the alternating partitioning method and two hybrid methods. The hybrid methods will be shown to be best.

  4. Emotionel Labeling


    Andersen, Nanna Sofie Garnov; Pedersen, Mette Kofoed; de Wit, Liv Kantsø; Ørndorf, Siri; Dissing, Celina Kyrn


    This project arises from the ideas of social constructionist theorist Kenneth J. Gergen and his presentation of Emotional Labeling as presented in his work The saturated self: Dilemmas of identity in contemporary life (1991). And on that note we are examining how emotions are being dealt with in a Danish kindergarten. We investigate what might influence the issue of emotions being taught has on children’s emotional development in everyday life. In order to do so we have conducted observations...

  5. Cell Type-Specific Modulation of Cobalamin Uptake by Bovine Serum.

    Directory of Open Access Journals (Sweden)

    Hua Zhao

    Full Text Available Tracking cellular 57Co-labelled cobalamin (57Co-Cbl uptake is a well-established method for studying Cbl homeostasis. Previous studies established that bovine serum is not generally permissive for cellular Cbl uptake when used as a supplement in cell culture medium, whereas supplementation with human serum promotes cellular Cbl uptake. The underlying reasons for these differences are not fully defined. In the current study we address this question. We extend earlier observations by showing that fetal calf serum inhibits cellular 57Co-Cbl uptake by HT1080 cells (a fibrosarcoma-derived fibroblast cell line. Furthermore, we discovered that a simple heat-treatment protocol (95°C for 10 min ameliorates this inhibitory activity for HT1080 cell 57Co-Cbl uptake. We provide evidence that the very high level of haptocorrin in bovine serum (as compared to human serum is responsible for this inhibitory activity. We suggest that bovine haptocorrin competes with cell-derived transcobalamin for Cbl binding, and that cellular Cbl uptake may be minimised in the presence of large amounts of bovine haptocorrin that are present under routine in vitro cell culture conditions. In experiments conducted with AG01518 cells (a neonatal foreskin-derived fibroblast cell line, overall cellular 57Co-Cbl uptake was 86% lower than for HT1080 cells, cellular TC production was below levels detectable by western blotting, and heat treatment of fetal calf serum resulted in only a modest increase in cellular 57Co-Cbl uptake. We recommend a careful assessment of cell culture protocols should be conducted in order to determine the potential benefits that heat-treated bovine serum may provide for in vitro studies of mammalian cell lines.

  6. Sparse adaptive finite elements for radiative transfer

    International Nuclear Information System (INIS)

    Widmer, G.; Hiptmair, R.; Schwab, Ch.


    The linear radiative transfer equation, a partial differential equation for the radiation intensity u(x,s), with independent variables x element of D is contained in R n in the physical domain D of dimension n=2,3, and angular variable s element of S 2 :={y element of R 3 :|y|=1}, is solved in the n+2-dimensional computational domain DxS 2 . We propose an adaptive multilevel Galerkin finite element method (FEM) for its numerical solution. Our approach is based on (a) a stabilized variational formulation of the transport operator, (b) on so-called sparse tensor products of two hierarchic families of finite element spaces in H 1 (D) and in L 2 (S 2 ), respectively, and (c) on wavelet thresholding techniques to adapt the discretization to the underlying problem. An a priori error analysis shows, under strong regularity assumptions on the solution, that the sparse tensor product method is clearly superior to a discrete ordinates method, as it converges with essentially optimal asymptotic rates while its complexity grows essentially only as that for a linear transport problem in R n . Numerical experiments for n=2 on a set of example problems agree with the convergence and complexity analysis of the method and show that introducing adaptivity can improve performance in terms of accuracy vs. number of degrees even further

  7. Determining biosonar images using sparse representations. (United States)

    Fontaine, Bertrand; Peremans, Herbert


    Echolocating bats are thought to be able to create an image of their environment by emitting pulses and analyzing the reflected echoes. In this paper, the theory of sparse representations and its more recent further development into compressed sensing are applied to this biosonar image formation task. Considering the target image representation as sparse allows formulation of this inverse problem as a convex optimization problem for which well defined and efficient solution methods have been established. The resulting technique, referred to as L1-minimization, is applied to simulated data to analyze its performance relative to delay accuracy and delay resolution experiments. This method performs comparably to the coherent receiver for the delay accuracy experiments, is quite robust to noise, and can reconstruct complex target impulse responses as generated by many closely spaced reflectors with different reflection strengths. This same technique, in addition to reconstructing biosonar target images, can be used to simultaneously localize these complex targets by interpreting location cues induced by the bat's head related transfer function. Finally, a tentative explanation is proposed for specific bat behavioral experiments in terms of the properties of target images as reconstructed by the L1-minimization method.

  8. Interferometric interpolation of sparse marine data

    KAUST Repository

    Hanafy, Sherif M.


    We present the theory and numerical results for interferometrically interpolating 2D and 3D marine surface seismic profiles data. For the interpolation of seismic data we use the combination of a recorded Green\\'s function and a model-based Green\\'s function for a water-layer model. Synthetic (2D and 3D) and field (2D) results show that the seismic data with sparse receiver intervals can be accurately interpolated to smaller intervals using multiples in the data. An up- and downgoing separation of both recorded and model-based Green\\'s functions can help in minimizing artefacts in a virtual shot gather. If the up- and downgoing separation is not possible, noticeable artefacts will be generated in the virtual shot gather. As a partial remedy we iteratively use a non-stationary 1D multi-channel matching filter with the interpolated data. Results suggest that a sparse marine seismic survey can yield more information about reflectors if traces are interpolated by interferometry. Comparing our results to those of f-k interpolation shows that the synthetic example gives comparable results while the field example shows better interpolation quality for the interferometric method. © 2013 European Association of Geoscientists & Engineers.

  9. Topological sparse learning of dynamic form patterns. (United States)

    Guthier, T; Willert, V; Eggert, J


    Motion is a crucial source of information for a variety of tasks in social interactions. The process of how humans recognize complex articulated movements such as gestures or face expressions remains largely unclear. There is an ongoing discussion if and how explicit low-level motion information, such as optical flow, is involved in the recognition process. Motivated by this discussion, we introduce a computational model that classifies the spatial configuration of gradient and optical flow patterns. The patterns are learned with an unsupervised learning algorithm based on translation-invariant nonnegative sparse coding called VNMF that extracts prototypical optical flow patterns shaped, for example, as moving heads or limb parts. A key element of the proposed system is a lateral inhibition term that suppresses activations of competing patterns in the learning process, leading to a low number of dominant and topological sparse activations. We analyze the classification performance of the gradient and optical flow patterns on three real-world human action recognition and one face expression recognition data set. The results indicate that the recognition of human actions can be achieved by gradient patterns alone, but adding optical flow patterns increases the classification performance. The combined patterns outperform other biological-inspired models and are competitive with current computer vision approaches.

  10. Atmospheric inverse modeling via sparse reconstruction

    Directory of Open Access Journals (Sweden)

    N. Hase


    Full Text Available Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4 emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.

  11. Balanced and sparse Tamo-Barg codes

    KAUST Repository

    Halbawi, Wael


    We construct balanced and sparse generator matrices for Tamo and Barg\\'s Locally Recoverable Codes (LRCs). More specifically, for a cyclic Tamo-Barg code of length n, dimension k and locality r, we show how to deterministically construct a generator matrix where the number of nonzeros in any two columns differs by at most one, and where the weight of every row is d + r - 1, where d is the minimum distance of the code. Since LRCs are designed mainly for distributed storage systems, the results presented in this work provide a computationally balanced and efficient encoding scheme for these codes. The balanced property ensures that the computational effort exerted by any storage node is essentially the same, whilst the sparse property ensures that this effort is minimal. The work presented in this paper extends a similar result previously established for Reed-Solomon (RS) codes, where it is now known that any cyclic RS code possesses a generator matrix that is balanced as described, but is sparsest, meaning that each row has d nonzeros.

  12. Sparse graphs using exchangeable random measures. (United States)

    Caron, François; Fox, Emily B


    Statistical network modelling has focused on representing the graph as a discrete structure, namely the adjacency matrix. When assuming exchangeability of this array-which can aid in modelling, computations and theoretical analysis-the Aldous-Hoover theorem informs us that the graph is necessarily either dense or empty. We instead consider representing the graph as an exchangeable random measure and appeal to the Kallenberg representation theorem for this object. We explore using completely random measures (CRMs) to define the exchangeable random measure, and we show how our CRM construction enables us to achieve sparse graphs while maintaining the attractive properties of exchangeability. We relate the sparsity of the graph to the Lévy measure defining the CRM. For a specific choice of CRM, our graphs can be tuned from dense to sparse on the basis of a single parameter. We present a scalable Hamiltonian Monte Carlo algorithm for posterior inference, which we use to analyse network properties in a range of real data sets, including networks with hundreds of thousands of nodes and millions of edges.

  13. Atmospheric inverse modeling via sparse reconstruction (United States)

    Hase, Nils; Miller, Scot M.; Maaß, Peter; Notholt, Justus; Palm, Mathias; Warneke, Thorsten


    Many applications in atmospheric science involve ill-posed inverse problems. A crucial component of many inverse problems is the proper formulation of a priori knowledge about the unknown parameters. In most cases, this knowledge is expressed as a Gaussian prior. This formulation often performs well at capturing smoothed, large-scale processes but is often ill equipped to capture localized structures like large point sources or localized hot spots. Over the last decade, scientists from a diverse array of applied mathematics and engineering fields have developed sparse reconstruction techniques to identify localized structures. In this study, we present a new regularization approach for ill-posed inverse problems in atmospheric science. It is based on Tikhonov regularization with sparsity constraint and allows bounds on the parameters. We enforce sparsity using a dictionary representation system. We analyze its performance in an atmospheric inverse modeling scenario by estimating anthropogenic US methane (CH4) emissions from simulated atmospheric measurements. Different measures indicate that our sparse reconstruction approach is better able to capture large point sources or localized hot spots than other methods commonly used in atmospheric inversions. It captures the overall signal equally well but adds details on the grid scale. This feature can be of value for any inverse problem with point or spatially discrete sources. We show an example for source estimation of synthetic methane emissions from the Barnett shale formation.

  14. Manifold Adaptive Label Propagation for Face Clustering. (United States)

    Pei, Xiaobing; Lyu, Zehua; Chen, Changqing; Chen, Chuanbo


    In this paper, a novel label propagation (LP) method is presented, called the manifold adaptive label propagation (MALP) method, which is to extend original LP by integrating sparse representation constraint into regularization framework of LP method. Similar to most LP, first of all, MALP also finds graph edges from given data and gives weights to the graph edges. Our goal is to find graph weights matrix adaptively. The key advantage of our approach is that MALP simultaneously finds graph weights matrix and predicts the label of unlabeled data. This paper also derives efficient algorithm to solve the proposed problem. Extensions of our MALP in kernel space and robust version are presented. The proposed method has been applied to the problem of semi-supervised face clustering using the well-known ORL, Yale, extended YaleB, and PIE datasets. Our experimental evaluations show the effectiveness of our method.

  15. Linearithmic time sparse and convex maximum margin clustering. (United States)

    Zhang, Xiao-Lei; Wu, Ji


    Recently, a new clustering method called maximum margin clustering (MMC) was proposed and has shown promising performances. It was originally formulated as a difficult nonconvex integer problem. To make the MMC problem practical, the researchers either relaxed the original MMC problem to inefficient convex optimization problems or reformulated it to nonconvex optimization problems, which sacrifice the convexity for efficiency. However, no approaches can both hold the convexity and be efficient. In this paper, a new linearithmic time sparse and convex MMC algorithm, called support-vector-regression-based MMC (SVR-MMC), is proposed. Generally, it first uses the SVR as the core of the MMC. Then, it is relaxed as a convex optimization problem, which is iteratively solved by the cutting-plane algorithm. Each cutting-plane subproblem is further decomposed to a serial supervised SVR problem by a new global extended-level method (GELM). Finally, each supervised SVR problem is solved in a linear time complexity by a new sparse-kernel SVR (SKSVR) algorithm. We further extend the SVR-MMC algorithm to the multiple-kernel clustering (MKC) problem and the multiclass MMC (M3C) problem, which are denoted as SVR-MKC and SVR-M3C, respectively. One key point of the algorithms is the utilization of the SVR. It can prevent the MMC and its extensions meeting an integer matrix programming problem. Another key point is the new SKSVR. It provides a linear time interface to the nonlinear kernel scenarios, so that the SVR-MMC and its extensions can keep a linearthmic time complexity in nonlinear kernel scenarios. Our experimental results on various real-world data sets demonstrate the effectiveness and the efficiency of the SVR-MMC and its two extensions. Moreover, the unsupervised application of the SVR-MKC to the voice activity detection (VAD) shows that the SVR-MKC can achieve good performances that are close to its supervised counterpart, meet the real-time demand of the VAD, and need no

  16. Parallel sparse direct solver for integrated circuit simulation

    CERN Document Server

    Chen, Xiaoming; Yang, Huazhong


    This book describes algorithmic methods and parallelization techniques to design a parallel sparse direct solver which is specifically targeted at integrated circuit simulation problems. The authors describe a complete flow and detailed parallel algorithms of the sparse direct solver. They also show how to improve the performance by simple but effective numerical techniques. The sparse direct solver techniques described can be applied to any SPICE-like integrated circuit simulator and have been proven to be high-performance in actual circuit simulation. Readers will benefit from the state-of-the-art parallel integrated circuit simulation techniques described in this book, especially the latest parallel sparse matrix solution techniques. · Introduces complicated algorithms of sparse linear solvers, using concise principles and simple examples, without complex theory or lengthy derivations; · Describes a parallel sparse direct solver that can be adopted to accelerate any SPICE-like integrated circuit simulato...

  17. Person Re-Identification by Iterative Re-Weighted Sparse Ranking. (United States)

    Lisanti, Giuseppe; Masi, Iacopo; Bagdanov, Andrew D; Del Bimbo, Alberto


    In this paper we introduce a method for person re-identification based on discriminative, sparse basis expansions of targets in terms of a labeled gallery of known individuals. We propose an iterative extension to sparse discriminative classifiers capable of ranking many candidate targets. The approach makes use of soft- and hard- re-weighting to redistribute energy among the most relevant contributing elements and to ensure that the best candidates are ranked at each iteration. Our approach also leverages a novel visual descriptor which we show to be discriminative while remaining robust to pose and illumination variations. An extensive comparative evaluation is given demonstrating that our approach achieves state-of-the-art performance on single- and multi-shot person re-identification scenarios on the VIPeR, i-LIDS, ETHZ, and CAVIAR4REID datasets. The combination of our descriptor and iterative sparse basis expansion improves state-of-the-art rank-1 performance by six percentage points on VIPeR and by 20 on CAVIAR4REID compared to other methods with a single gallery image per person. With multiple gallery and probe images per person our approach improves by 17 percentage points the state-of-the-art on i-LIDS and by 72 on CAVIAR4REID at rank-1. The approach is also quite efficient, capable of single-shot person re-identification over galleries containing hundreds of individuals at about 30 re-identifications per second.

  18. Tensor-Based Sparse Representation Classification for Urban Airborne LiDAR Points

    Directory of Open Access Journals (Sweden)

    Nan Li


    Full Text Available The common statistical methods for supervised classification usually require a large amount of training data to achieve reasonable results, which is time consuming and inefficient. In many methods, only the features of each point are used, regardless of their spatial distribution within a certain neighborhood. This paper proposes a tensor-based sparse representation classification (TSRC method for airborne LiDAR (Light Detection and Ranging points. To keep features arranged in their spatial arrangement, each LiDAR point is represented as a 4th-order tensor. Then, TSRC is performed for point classification based on the 4th-order tensors. Firstly, a structured and discriminative dictionary set is learned by using only a few training samples. Subsequently, for classifying a new point, the sparse tensor is calculated based on the tensor OMP (Orthogonal Matching Pursuit algorithm. The test tensor data is approximated by sub-dictionary set and its corresponding subset of sparse tensor for each class. The point label is determined by the minimal reconstruction residuals. Experiments are carried out on eight real LiDAR point clouds whose result shows that objects can be distinguished by TSRC successfully. The overall accuracy of all the datasets is beyond 80% by TSRC. TSRC also shows a good improvement on LiDAR points classification when compared with other common classifiers.

  19. Application of super-resolution reconstruction of sparse representation in mass spectrometry imaging. (United States)

    Tang, Fei; Bi, Ying; He, Jiuming; Li, Tiegang; Abliz, Zeper; Wang, Xiaohao


    Mass Spectrometry Imaging (MSI) is useful for analyzing biological samples directly, as a spatially resolved, label-free technique. Here we present a method for super-resolution reconstruction of sparse representation to improve resolution of MSI data. Air Flow-Assisted Ionization Mass Spectrometry Imaging (AFAI-MSI) was used to acquire MSI data from ink samples, thyroid tumour samples, rat renal biopsies, and rat brain biopsy samples. Super-resolution reconstruction of sparse representation was adopted for the collected MSI data. After comparison of the reconstructed high-resolution image and the original high-resolution image, it is found that super-resolution reconstruction image is closer to the original high-resolution image than the image obtained with the interpolation method, and the highest Peak Signal-to-Noise Ratio (PSNR) difference value is over 1.4dB. Therefore, the application of the super-resolution reconstruction technique, based on sparse representation MSI, is feasible and effective. The method proposed here not only improves the resolution of MSI in post-data processing, but also acquires fewer sampling points at the same resolution, thereby greatly reducing the sampling time, with great application value for large-volume sample MSI, high-resolution MSI, etc. Copyright © 2015 John Wiley & Sons, Ltd.

  20. A Cost-Sensitive Sparse Representation Based Classification for Class-Imbalance Problem

    Directory of Open Access Journals (Sweden)

    Zhenbing Liu


    Full Text Available Sparse representation has been successfully used in pattern recognition and machine learning. However, most existing sparse representation based classification (SRC methods are to achieve the highest classification accuracy, assuming the same losses for different misclassifications. This assumption, however, may not hold in many practical applications as different types of misclassification could lead to different losses. In real-world application, much data sets are imbalanced of the class distribution. To address these problems, we propose a cost-sensitive sparse representation based classification (CSSRC for class-imbalance problem method by using probabilistic modeling. Unlike traditional SRC methods, we predict the class label of test samples by minimizing the misclassification losses, which are obtained via computing the posterior probabilities. Experimental results on the UCI databases validate the efficacy of the proposed approach on average misclassification cost, positive class misclassification rate, and negative class misclassification rate. In addition, we sampled test samples and training samples with different imbalance ratio and use F-measure, G-mean, classification accuracy, and running time to evaluate the performance of the proposed method. The experiments show that our proposed method performs competitively compared to SRC, CSSVM, and CS4VM.

  1. Dose-shaping using targeted sparse optimization. (United States)

    Sayre, George A; Ruan, Dan


    Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method. In designing the energy minimization objective (E tot (sparse)), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot (sparse) improves tradeoff between

  2. Dose-shaping using targeted sparse optimization

    International Nuclear Information System (INIS)

    Sayre, George A.; Ruan, Dan


    Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E tot sparse ), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L 1 norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E tot sparse improves

  3. Cell Type-specific β2-Adrenergic Receptor Clusters Identified Using Photoactivated Localization Microscopy Are Not Lipid Raft Related, but Depend on Actin Cytoskeleton Integrity* (United States)

    Scarselli, Marco; Annibale, Paolo; Radenovic, Aleksandra


    Recent developments in the field of optical super-resolution techniques allow both a 10-fold increase in resolution as well as an increased ability to quantify the number of labeled molecules visualized in the fluorescence measurement. By using photoactivated localization microscopy (PALM) and an experimental approach based on the systematic comparison with a nonclustering peptide as a negative control, we found that the prototypical G protein-coupled receptor β2-adrenergic receptor is partially preassociated in nanoscale-sized clusters only in the cardiomyocytes, such as H9C2 cells, but not in other cell lines, such as HeLa and Chinese hamster ovary (CHO). The addition of the agonist for very short times or the addition of the inverse agonist did not significantly affect the organization of receptor assembly. To investigate the mechanism governing cluster formation, we altered plasma membrane properties with cholesterol removal and actin microfilament disruption. Although cholesterol is an essential component of cell membranes and it is supposed to be enriched in the lipid rafts, its sequestration and removal did not affect receptor clustering, whereas the inhibition of actin polymerization did decrease the number of clusters. Our findings are therefore consistent with a model in which β2 receptor clustering is influenced by the actin cytoskeleton, but it does not rely on lipid raft integrity, thus ruling out the possibility that cell type-specific β2 receptor clustering is associated with the raft. PMID:22442147

  4. Enzyme-Linked Immunosorbent Assay Using a Virus Type-Specific Peptide Based on a Subdomain of Envelope Protein Erns for Serologic Diagnosis of Pestivirus Infections in Swine (United States)

    Langedijk, J. P. M.; Middel, W. G. J.; Meloen, R. H.; Kramps, J. A.; de Smit, J. A.


    Peptides deduced from the C-terminal end (residues 191 to 227) of pestivirus envelope protein Erns were used to develop enzyme-linked immunosorbent assays (ELISAs) to measure specifically antibodies against different types of pestiviruses. The choice of the peptide was based on the modular structure of the Erns protein, and the peptide was selected for its probable independent folding and good exposure, which would make it a good candidate for an antigenic peptide to be used in a diagnostic test. A solid-phase peptide ELISA which was cross-reactive for several types of pestivirus antibodies and which can be used for the general detection of pestivirus antibodies was developed. To identify type-specific pestivirus antibodies, a liquid-phase peptide ELISA, with a labeled, specific classical swine fever virus (CSFV) peptide and an unlabeled bovine viral diarrhea virus peptide to block cross-reactivity, was developed. Specificity and sensitivity of the liquid-phase peptide ELISA for CSFV were 98 and 100%, respectively. Because the peptide is a fragment of the Erns protein, it can be used to differentiate between infected and vaccinated animals when a vaccine based on the E2 protein, which is another pestivirus envelope protein, is used. PMID:11230402

  5. Sparse principal component analysis in hyperspectral change detection

    DEFF Research Database (Denmark)

    Nielsen, Allan Aasbjerg; Larsen, Rasmus; Vestergaard, Jacob Schack


    This contribution deals with change detection by means of sparse principal component analysis (PCA) of simple differences of calibrated, bi-temporal HyMap data. Results show that if we retain only 15 nonzero loadings (out of 126) in the sparse PCA the resulting change scores appear visually very...... similar although the loadings are very different from their usual non-sparse counterparts. The choice of three wavelength regions as being most important for change detection demonstrates the feature selection capability of sparse PCA....

  6. Hand posture recognition via joint feature sparse representation (United States)

    Cao, Chuqing; Sun, Ying; Li, Ruifeng; Chen, Lin


    In this study, we cast hand posture recognition as a sparse representation problem, and propose a novel approach called joint feature sparse representation classifier for efficient and accurate sparse representation based on multiple features. By integrating different features for sparse representation, including gray-level, texture, and shape feature, the proposed method can fuse benefits of each feature and hence is robust to partial occlusion and varying illumination. Additionally, a new database optimization method is introduced to improve computational speed. Experimental results, based on public and self-build databases, show that our method performs well compared to the state-of-the-art methods for hand posture recognition.

  7. Learning from Weak and Noisy Labels for Semantic Segmentation

    KAUST Repository

    Lu, Zhiwu


    A weakly supervised semantic segmentation (WSSS) method aims to learn a segmentation model from weak (image-level) as opposed to strong (pixel-level) labels. By avoiding the tedious pixel-level annotation process, it can exploit the unlimited supply of user-tagged images from media-sharing sites such as Flickr for large scale applications. However, these ‘free’ tags/labels are often noisy and few existing works address the problem of learning with both weak and noisy labels. In this work, we cast the WSSS problem into a label noise reduction problem. Specifically, after segmenting each image into a set of superpixels, the weak and potentially noisy image-level labels are propagated to the superpixel level resulting in highly noisy labels; the key to semantic segmentation is thus to identify and correct the superpixel noisy labels. To this end, a novel L1-optimisation based sparse learning model is formulated to directly and explicitly detect noisy labels. To solve the L1-optimisation problem, we further develop an efficient learning algorithm by introducing an intermediate labelling variable. Extensive experiments on three benchmark datasets show that our method yields state-of-the-art results given noise-free labels, whilst significantly outperforming the existing methods when the weak labels are also noisy.

  8. Eigensolver for a Sparse, Large Hermitian Matrix (United States)

    Tisdale, E. Robert; Oyafuso, Fabiano; Klimeck, Gerhard; Brown, R. Chris


    A parallel-processing computer program finds a few eigenvalues in a sparse Hermitian matrix that contains as many as 100 million diagonal elements. This program finds the eigenvalues faster, using less memory, than do other, comparable eigensolver programs. This program implements a Lanczos algorithm in the American National Standards Institute/ International Organization for Standardization (ANSI/ISO) C computing language, using the Message Passing Interface (MPI) standard to complement an eigensolver in PARPACK. [PARPACK (Parallel Arnoldi Package) is an extension, to parallel-processing computer architectures, of ARPACK (Arnoldi Package), which is a collection of Fortran 77 subroutines that solve large-scale eigenvalue problems.] The eigensolver runs on Beowulf clusters of computers at the Jet Propulsion Laboratory (JPL).

  9. Sparse random matrices: The eigenvalue spectrum revisited

    International Nuclear Information System (INIS)

    Semerjian, Guilhem; Cugliandolo, Leticia F.


    We revisit the derivation of the density of states of sparse random matrices. We derive a recursion relation that allows one to compute the spectrum of the matrix of incidence for finite trees that determines completely the low concentration limit. Using the iterative scheme introduced by Biroli and Monasson [J. Phys. A 32, L255 (1999)] we find an approximate expression for the density of states expected to hold exactly in the opposite limit of large but finite concentration. The combination of the two methods yields a very simple geometric interpretation of the tails of the spectrum. We test the analytic results with numerical simulations and we suggest an indirect numerical method to explore the tails of the spectrum. (author)

  10. Narrowband interference parameterization for sparse Bayesian recovery

    KAUST Repository

    Ali, Anum


    This paper addresses the problem of narrowband interference (NBI) in SC-FDMA systems by using tools from compressed sensing and stochastic geometry. The proposed NBI cancellation scheme exploits the frequency domain sparsity of the unknown signal and adopts a Bayesian sparse recovery procedure. This is done by keeping a few randomly chosen sub-carriers data free to sense the NBI signal at the receiver. As Bayesian recovery requires knowledge of some NBI parameters (i.e., mean, variance and sparsity rate), we use tools from stochastic geometry to obtain analytical expressions for the required parameters. Our simulation results validate the analysis and depict suitability of the proposed recovery method for NBI mitigation. © 2015 IEEE.

  11. Better Size Estimation for Sparse Matrix Products

    DEFF Research Database (Denmark)

    Amossen, Rasmus Resen; Campagna, Andrea; Pagh, Rasmus


    We consider the problem of doing fast and reliable estimation of the number of non-zero entries in a sparse Boolean matrix product. Let n denote the total number of non-zero entries in the input matrices. We show how to compute a 1 ± ε approximation (with small probability of error) in expected...... time O(n) for any ε > 4*(n^(-1/4)). The previously best estimation algorithm, due to Cohen (JCSS 1997), uses time O(n/ε^2). We also present a variant using O(sort(n)) I/Os in expectation in the cache-oblivious model. We also describe how sampling can be used to maintain (independent) sketches...... of matrices that allow estimation to be performed in time o(n) if z is sufficiently large. This gives a simpler alternative to the sketching technique of Ganguly et al. (PODS 2005), and matches a space lower bound shown in that paper....

  12. Sparse suffix tree construction in small space

    DEFF Research Database (Denmark)

    Bille, Philip; Fischer, Johannes; Gørtz, Inge Li


    in 1968. First results were only obtained in 1996, but only for the case where the suffixes were evenly spaced in T. In this paper there is no constraint on the locations of the suffixes. We show that the sparse suffix tree can be constructed in O(nlog2 b) time. To achieve this we develop a technique...... the correct tree with high probability. We then give a Las-Vegas algorithm which also uses O(b) space and runs in the same time bounds with high probability when b = O(√n). Furthermore, additional tradeoffs between the space usage and the construction time for the Monte-Carlo algorithm are given....

  13. Fast Generation of Sparse Random Kernel Graphs. (United States)

    Hagberg, Aric; Lemons, Nathan


    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most (n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity.

  14. Multiplication method for sparse interferometric fringes. (United States)

    Liu, Cong; Zhang, Xingyi; Zhou, Youhe


    Fringe analysis in the interferometry has been of long-standing interest to the academic community. However, the process of sparse fringe is always a headache in the measurement, especially when the specimen is very small. Through theoretical derivation and experimental measurements, our work demonstrates a new method for fringe multiplication. Theoretically, arbitrary integral-multiple fringe multiplication can be acquired by using the interferogram phase as the parameter. We simulate digital images accordingly and find that not only the skeleton lines of the multiplied fringe are very convenient to extract, but also the main frequency of which can be easily separated from the DC component. Meanwhile, the experimental results have a good agreement with the theoretic ones in a validation using the classical photoelasticity.

  15. Bayesian learning of sparse multiscale image representations. (United States)

    Hughes, James Michael; Rockmore, Daniel N; Wang, Yang


    Multiscale representations of images have become a standard tool in image analysis. Such representations offer a number of advantages over fixed-scale methods, including the potential for improved performance in denoising, compression, and the ability to represent distinct but complementary information that exists at various scales. A variety of multiresolution transforms exist, including both orthogonal decompositions such as wavelets as well as nonorthogonal, overcomplete representations. Recently, techniques for finding adaptive, sparse representations have yielded state-of-the-art results when applied to traditional image processing problems. Attempts at developing multiscale versions of these so-called dictionary learning models have yielded modest but encouraging results. However, none of these techniques has sought to combine a rigorous statistical formulation of the multiscale dictionary learning problem and the ability to share atoms across scales. We present a model for multiscale dictionary learning that overcomes some of the drawbacks of previous approaches by first decomposing an input into a pyramid of distinct frequency bands using a recursive filtering scheme, after which we perform dictionary learning and sparse coding on the individual levels of the resulting pyramid. The associated image model allows us to use a single set of adapted dictionary atoms that is shared--and learned--across all scales in the model. The underlying statistical model of our proposed method is fully Bayesian and allows for efficient inference of parameters, including the level of additive noise for denoising applications. We apply the proposed model to several common image processing problems including non-Gaussian and nonstationary denoising of real-world color images.

  16. 5D whole-heart sparse MRI. (United States)

    Feng, Li; Coppo, Simone; Piccini, Davide; Yerly, Jerome; Lim, Ruth P; Masci, Pier Giorgio; Stuber, Matthias; Sodickson, Daniel K; Otazo, Ricardo


    A 5D whole-heart sparse imaging framework is proposed for simultaneous assessment of myocardial function and high-resolution cardiac and respiratory motion-resolved whole-heart anatomy in a single continuous noncontrast MR scan. A non-electrocardiograph (ECG)-triggered 3D golden-angle radial balanced steady-state free precession sequence was used for data acquisition. The acquired 3D k-space data were sorted into a 5D dataset containing separated cardiac and respiratory dimensions using a self-extracted respiratory motion signal and a recorded ECG signal. Images were then reconstructed using XD-GRASP, a multidimensional compressed sensing technique exploiting correlations/sparsity along cardiac and respiratory dimensions. 5D whole-heart imaging was compared with respiratory motion-corrected 3D and 4D whole-heart imaging in nine volunteers for evaluation of the myocardium, great vessels, and coronary arteries. It was also compared with breath-held, ECG-gated 2D cardiac cine imaging for validation of cardiac function quantification. 5D whole-heart images received systematic higher quality scores in the myocardium, great vessels and coronary arteries. Quantitative coronary sharpness and length were always better for the 5D images. Good agreement was obtained for quantification of cardiac function compared with 2D cine imaging. 5D whole-heart sparse imaging represents a robust and promising framework for simplified comprehensive cardiac MRI without the need for breath-hold and motion correction. Magn Reson Med 79:826-838, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

  17. Joint Sparse and Low-Rank Multitask Learning with Laplacian-Like Regularization for Hyperspectral Classification

    Directory of Open Access Journals (Sweden)

    Zhi He


    Full Text Available Multitask learning (MTL has recently provided significant performance improvements in supervised classification of hyperspectral images (HSIs by incorporating shared information across multiple tasks. However, the original MTL cannot effectively exploit both local and global structures of the HSI and the class label information is not fully used. Moreover, although the mathematical morphology (MM has attracted considerable interest in feature extraction of HSI, it remains a challenging issue to sufficiently utilize multiple morphological profiles obtained by various structuring elements (SEs. In this paper, we propose a joint sparse and low-rank MTL method with Laplacian-like regularization (termed as sllMTL for hyperspectral classification by utilizing the three-dimensional morphological profiles (3D-MPs features. The main steps of the proposed method are twofold. First, the 3D-MPs are extracted by the 3D-opening and 3D-closing operators. Different SEs are adopted to result in multiple 3D-MPs. Second, sllMTL is proposed for hyperspectral classification by taking the 3D-MPs as features of different tasks. In the sllMTL, joint sparse and low-rank structures are exploited to capture the task specificity and relatedness, respectively. Laplacian-like regularization is also added to make full use of the label information of training samples. Experiments on three datasets demonstrate the OA of the proposed method is at least about 2% higher than other state-of-the-art methods with very limited training samples.

  18. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching (United States)

    Guo, Yanrong; Gao, Yaozong


    Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods. PMID:26685226

  19. Deformable MR Prostate Segmentation via Deep Feature Learning and Sparse Patch Matching. (United States)

    Guo, Yanrong; Gao, Yaozong; Shen, Dinggang


    Automatic and reliable segmentation of the prostate is an important but difficult task for various clinical applications such as prostate cancer radiotherapy. The main challenges for accurate MR prostate localization lie in two aspects: (1) inhomogeneous and inconsistent appearance around prostate boundary, and (2) the large shape variation across different patients. To tackle these two problems, we propose a new deformable MR prostate segmentation method by unifying deep feature learning with the sparse patch matching. First, instead of directly using handcrafted features, we propose to learn the latent feature representation from prostate MR images by the stacked sparse auto-encoder (SSAE). Since the deep learning algorithm learns the feature hierarchy from the data, the learned features are often more concise and effective than the handcrafted features in describing the underlying data. To improve the discriminability of learned features, we further refine the feature representation in a supervised fashion. Second, based on the learned features, a sparse patch matching method is proposed to infer a prostate likelihood map by transferring the prostate labels from multiple atlases to the new prostate MR image. Finally, a deformable segmentation is used to integrate a sparse shape model with the prostate likelihood map for achieving the final segmentation. The proposed method has been extensively evaluated on the dataset that contains 66 T2-wighted prostate MR images. Experimental results show that the deep-learned features are more effective than the handcrafted features in guiding MR prostate segmentation. Moreover, our method shows superior performance than other state-of-the-art segmentation methods.

  20. MR image super-resolution reconstruction using sparse representation, nonlocal similarity and sparse derivative prior. (United States)

    Zhang, Di; He, Jiazhong; Zhao, Yun; Du, Minghui


    In magnetic resonance (MR) imaging, image spatial resolution is determined by various instrumental limitations and physical considerations. This paper presents a new algorithm for producing a high-resolution version of a low-resolution MR image. The proposed method consists of two consecutive steps: (1) reconstructs a high-resolution MR image from a given low-resolution observation via solving a joint sparse representation and nonlocal similarity L1-norm minimization problem; and (2) applies a sparse derivative prior based post-processing to suppress blurring effects. Extensive experiments on simulated brain MR images and two real clinical MR image datasets validate that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both quantitative measures and visual perception. Copyright © 2015 Elsevier Ltd. All rights reserved.

  1. Comprehensive identification and annotation of cell type-specific and ubiquitous CTCF-binding sites in the human genome.

    Directory of Open Access Journals (Sweden)

    Hebing Chen

    Full Text Available Chromatin insulators are DNA elements that regulate the level of gene expression either by preventing gene silencing through the maintenance of heterochromatin boundaries or by preventing gene activation by blocking interactions between enhancers and promoters. CCCTC-binding factor (CTCF, a ubiquitously expressed 11-zinc-finger DNA-binding protein, is the only protein implicated in the establishment of insulators in vertebrates. While CTCF has been implicated in diverse regulatory functions, CTCF has only been studied in a limited number of cell types across human genome. Thus, it is not clear whether the identified cell type-specific differences in CTCF-binding sites are functionally significant. Here, we identify and characterize cell type-specific and ubiquitous CTCF-binding sites in the human genome across 38 cell types designated by the Encyclopedia of DNA Elements (ENCODE consortium. These cell type-specific and ubiquitous CTCF-binding sites show uniquely versatile transcriptional functions and characteristic chromatin features. In addition, we confirm the insulator barrier function of CTCF-binding and explore the novel function of CTCF in DNA replication. These results represent a critical step toward the comprehensive and systematic understanding of CTCF-dependent insulators and their versatile roles in the human genome.

  2. Classification of multispectral or hyperspectral satellite imagery using clustering of sparse approximations on sparse representations in learned dictionaries obtained using efficient convolutional sparse coding

    Energy Technology Data Exchange (ETDEWEB)

    Moody, Daniela; Wohlberg, Brendt


    An approach for land cover classification, seasonal and yearly change detection and monitoring, and identification of changes in man-made features may use a clustering of sparse approximations (CoSA) on sparse representations in learned dictionaries. The learned dictionaries may be derived using efficient convolutional sparse coding to build multispectral or hyperspectral, multiresolution dictionaries that are adapted to regional satellite image data. Sparse image representations of images over the learned dictionaries may be used to perform unsupervised k-means clustering into land cover categories. The clustering process behaves as a classifier in detecting real variability. This approach may combine spectral and spatial textural characteristics to detect geologic, vegetative, hydrologic, and man-made features, as well as changes in these features over time.

  3. Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection

    Directory of Open Access Journals (Sweden)

    Peilin He


    Full Text Available For an electronic nose (E-nose in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning combined with sparse autoencoder and radial basis function (RBF into the field. Self-taught learning is a kind of transfer learning that can transfer knowledge from other fields to target fields, can solve such problems that labeled data (target fields and unlabeled data (other fields do not share the same class labels, even if they are from entirely different distribution. In our paper, we obtain numerous cheap unlabeled pollutant gas samples (benzene, formaldehyde, acetone and ethylalcohol; however, labeled wound infection samples are hard to gain. Thus, we pose self-taught learning to utilize these gas samples, obtaining a basis vector θ. Then, using the basis vector θ, we reconstruct the new representation of wound infection samples under sparsity constraint, which is the input of classifiers. We compare RBF with partial least squares discriminant analysis (PLSDA, and reach a conclusion that the performance of RBF is superior to others. We also change the dimension of our data set and the quantity of unlabeled data to search the input matrix that produces the highest accuracy.

  4. Local posterior concentration rate for multilevel sparse sequences

    NARCIS (Netherlands)

    Belitser, E.N.; Nurushev, N.


    We consider empirical Bayesian inference in the many normal means model in the situation when the high-dimensional mean vector is multilevel sparse, that is,most of the entries of the parameter vector are some fixed values. For instance, the traditional sparse signal is a particular case (with one

  5. In-Storage Embedded Accelerator for Sparse Pattern Processing (United States)


    acceptable. For example, a facial recognition application would operate on features extracted from facial images. A feature vector corresponds to a...In-Storage Embedded Accelerator for Sparse Pattern Processing Sang-Woo Jun * , Huy T. Nguyen # , Vijay Gadepally #* , and Arvind * # MIT Lincoln...Laboratory, * MIT Computer Science & Artificial Intelligence Laboratory Abstract— We present a novel system architecture for sparse pattern

  6. Joint Group Sparse PCA for Compressed Hyperspectral Imaging. (United States)

    Khan, Zohaib; Shafait, Faisal; Mian, Ajmal


    A sparse principal component analysis (PCA) seeks a sparse linear combination of input features (variables), so that the derived features still explain most of the variations in the data. A group sparse PCA introduces structural constraints on the features in seeking such a linear combination. Collectively, the derived principal components may still require measuring all the input features. We present a joint group sparse PCA (JGSPCA) algorithm, which forces the basic coefficients corresponding to a group of features to be jointly sparse. Joint sparsity ensures that the complete basis involves only a sparse set of input features, whereas the group sparsity ensures that the structural integrity of the features is maximally preserved. We evaluate the JGSPCA algorithm on the problems of compressed hyperspectral imaging and face recognition. Compressed sensing results show that the proposed method consistently outperforms sparse PCA and group sparse PCA in reconstructing the hyperspectral scenes of natural and man-made objects. The efficacy of the proposed compressed sensing method is further demonstrated in band selection for face recognition.

  7. Robust Face Recognition Via Gabor Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    Hao Yu-Juan


    Full Text Available Sparse representation based on compressed sensing theory has been widely used in the field of face recognition, and has achieved good recognition results. but the face feature extraction based on sparse representation is too simple, and the sparse coefficient is not sparse. In this paper, we improve the classification algorithm based on the fusion of sparse representation and Gabor feature, and then improved algorithm for Gabor feature which overcomes the problem of large dimension of the vector dimension, reduces the computation and storage cost, and enhances the robustness of the algorithm to the changes of the environment.The classification efficiency of sparse representation is determined by the collaborative representation,we simplify the sparse constraint based on L1 norm to the least square constraint, which makes the sparse coefficients both positive and reduce the complexity of the algorithm. Experimental results show that the proposed method is robust to illumination, facial expression and pose variations of face recognition, and the recognition rate of the algorithm is improved.

  8. Comparison of Methods for Sparse Representation of Musical Signals

    DEFF Research Database (Denmark)

    Endelt, Line Ørtoft; la Cour-Harbo, Anders


    by a number of sparseness measures and results are shown on the ℓ1 norm of the coefficients, using a dictionary containing a Dirac basis, a Discrete Cosine Transform, and a Wavelet Packet. Evaluated only on the sparseness Matching Pursuit is the best method, and it is also relatively fast....

  9. Convergence results for 3D sparse grid approaches

    NARCIS (Netherlands)

    J. Noordmans; P.W. Hemker (Piet)


    textabstractThe convergence behaviour is investigated of solution algorithms for the anisotropic Poisson problem on partially ordered, sparse families of regular grids in 3D. In order to study multilevel techniques on sparse families of grids, first we consider the convergence of a two-level

  10. Confidence of model based shape reconstruction from sparse data

    DEFF Research Database (Denmark)

    Baka, N.; de Bruijne, Marleen; Reiber, J. H. C.


    Statistical shape models (SSM) are commonly applied for plausible interpolation of missing data in medical imaging. However, when fitting a shape model to sparse information, many solutions may fit the available data. In this paper we derive a constrained SSM to fit noisy sparse input landmarks...

  11. An Overview on Sparse Recovery-based STAP

    Directory of Open Access Journals (Sweden)

    Ma Ze-qiang


    Full Text Available This paper gives a brief review on the Sparse-Recovery (SR-based Space-Time Adaptive Processing (STAP technique. First, the motivation for introducing sparse recovery into STAP is presented. Next, the potential advantages and mathematical explanation of the sparse-recovery-based STAP are discussed. A major part of this paper presents the state-of-art research results in spatio-temporal spectrum-sparsity-based STAP, including the basic frame, off-grid problem, multiple measurement vector problem, and direct domain problem. The sparse-recovery-based STAP on conformal array problem is also introduced. Finally, a summary of sparse-recovery-based STAP is provided, and the problems that need to be solved and some potential research areas are discussed.

  12. Sparse Frequency Waveform Design for Radar-Embedded Communication

    Directory of Open Access Journals (Sweden)

    Chaoyun Mai


    Full Text Available According to the Tag application with function of covert communication, a method for sparse frequency waveform design based on radar-embedded communication is proposed. Firstly, sparse frequency waveforms are designed based on power spectral density fitting and quasi-Newton method. Secondly, the eigenvalue decomposition of the sparse frequency waveform sequence is used to get the dominant space. Finally the communication waveforms are designed through the projection of orthogonal pseudorandom vectors in the vertical subspace. Compared with the linear frequency modulation waveform, the sparse frequency waveform can further improve the bandwidth occupation of communication signals, thus achieving higher communication rate. A certain correlation exists between the reciprocally orthogonal communication signals samples and the sparse frequency waveform, which guarantees the low SER (signal error rate and LPI (low probability of intercept. The simulation results verify the effectiveness of this method.

  13. Figuring Out Food Labels (United States)

    ... beware of. Using Food Labels for a Well-Balanced Diet Here are some guidelines on using food labels ... food label smarts to create a healthy, well-balanced diet. It might seem complicated at first, but it ...

  14. Understanding Food Labels (United States)

    ... Healthy eating for girls Understanding food labels Understanding food labels There is lots of info on food ... need to avoid because of food allergies. Other food label terms top In addition to the Nutrition ...

  15. Transformer fault diagnosis using continuous sparse autoencoder. (United States)

    Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou


    This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis.

  16. Sparse alignment for robust tensor learning. (United States)

    Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming


    Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods.

  17. Link Prediction via Sparse Gaussian Graphical Model

    Directory of Open Access Journals (Sweden)

    Liangliang Zhang


    Full Text Available Link prediction is an important task in complex network analysis. Traditional link prediction methods are limited by network topology and lack of node property information, which makes predicting links challenging. In this study, we address link prediction using a sparse Gaussian graphical model and demonstrate its theoretical and practical effectiveness. In theory, link prediction is executed by estimating the inverse covariance matrix of samples to overcome information limits. The proposed method was evaluated with four small and four large real-world datasets. The experimental results show that the area under the curve (AUC value obtained by the proposed method improved by an average of 3% and 12.5% compared to 13 mainstream similarity methods, respectively. This method outperforms the baseline method, and the prediction accuracy is superior to mainstream methods when using only 80% of the training set. The method also provides significantly higher AUC values when using only 60% in Dolphin and Taro datasets. Furthermore, the error rate of the proposed method demonstrates superior performance with all datasets compared to mainstream methods.

  18. Sparse Superpixel Unmixing for Hyperspectral Image Analysis (United States)

    Castano, Rebecca; Thompson, David R.; Gilmore, Martha


    Software was developed that automatically detects minerals that are present in each pixel of a hyperspectral image. An algorithm based on sparse spectral unmixing with Bayesian Positive Source Separation is used to produce mineral abundance maps from hyperspectral images. A superpixel segmentation strategy enables efficient unmixing in an interactive session. The algorithm computes statistically likely combinations of constituents based on a set of possible constituent minerals whose abundances are uncertain. A library of source spectra from laboratory experiments or previous remote observations is used. A superpixel segmentation strategy improves analysis time by orders of magnitude, permitting incorporation into an interactive user session (see figure). Mineralogical search strategies can be categorized as supervised or unsupervised. Supervised methods use a detection function, developed on previous data by hand or statistical techniques, to identify one or more specific target signals. Purely unsupervised results are not always physically meaningful, and may ignore subtle or localized mineralogy since they aim to minimize reconstruction error over the entire image. This algorithm offers advantages of both methods, providing meaningful physical interpretations and sensitivity to subtle or unexpected minerals.

  19. Seismic classification through sparse filter dictionaries

    Energy Technology Data Exchange (ETDEWEB)

    Hickmann, Kyle Scott [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Srinivasan, Gowri [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)


    We tackle a multi-label classi cation problem involving the relation between acoustic- pro le features and the measured seismogram. To isolate components of the seismo- grams unique to each class of acoustic pro le we build dictionaries of convolutional lters. The convolutional- lter dictionaries for the individual classes are then combined into a large dictionary for the entire seismogram set. A given seismogram is classi ed by computing its representation in the large dictionary and then comparing reconstruction accuracy with this representation using each of the sub-dictionaries. The sub-dictionary with the minimal reconstruction error identi es the seismogram class.

  20. Cell Type-Specific Chromatin Signatures Underline Regulatory DNA Elements in Human Induced Pluripotent Stem Cells and Somatic Cells. (United States)

    Zhao, Ming-Tao; Shao, Ning-Yi; Hu, Shijun; Ma, Ning; Srinivasan, Rajini; Jahanbani, Fereshteh; Lee, Jaecheol; Zhang, Sophia L; Snyder, Michael P; Wu, Joseph C


    Regulatory DNA elements in the human genome play important roles in determining the transcriptional abundance and spatiotemporal gene expression during embryonic heart development and somatic cell reprogramming. It is not well known how chromatin marks in regulatory DNA elements are modulated to establish cell type-specific gene expression in the human heart. We aimed to decipher the cell type-specific epigenetic signatures in regulatory DNA elements and how they modulate heart-specific gene expression. We profiled genome-wide transcriptional activity and a variety of epigenetic marks in the regulatory DNA elements using massive RNA-seq (n=12) and ChIP-seq (chromatin immunoprecipitation combined with high-throughput sequencing; n=84) in human endothelial cells (CD31 + CD144 + ), cardiac progenitor cells (Sca-1 + ), fibroblasts (DDR2 + ), and their respective induced pluripotent stem cells. We uncovered 2 classes of regulatory DNA elements: class I was identified with ubiquitous enhancer (H3K4me1) and promoter (H3K4me3) marks in all cell types, whereas class II was enriched with H3K4me1 and H3K4me3 in a cell type-specific manner. Both class I and class II regulatory elements exhibited stimulatory roles in nearby gene expression in a given cell type. However, class I promoters displayed more dominant regulatory effects on transcriptional abundance regardless of distal enhancers. Transcription factor network analysis indicated that human induced pluripotent stem cells and somatic cells from the heart selected their preferential regulatory elements to maintain cell type-specific gene expression. In addition, we validated the function of these enhancer elements in transgenic mouse embryos and human cells and identified a few enhancers that could possibly regulate the cardiac-specific gene expression. Given that a large number of genetic variants associated with human diseases are located in regulatory DNA elements, our study provides valuable resources for deciphering

  1. Golgi-associated anion exchanger, AE2:identification, cell type specific targeting and structural role in the Golgi complex


    Holappa, K. (Katja)


    Abstract Anion exchanger 2 (AE2) is a member of the anion exchanger gene family, which includes three additional members, AE1, AE3, and AE4. They are also known as Na+-independent Cl-/HCO3- exchangers, and their major function is to regulate intracellular pH and chloride concentration. All four isoforms have several N-terminally truncated variants that are often expressed cell type specifically. Red blood cells express the full-length AE1 isoform that interacts with ankyrin, an adapter pro...

  2. Manifold regularization for sparse unmixing of hyperspectral images. (United States)

    Liu, Junmin; Zhang, Chunxia; Zhang, Jiangshe; Li, Huirong; Gao, Yuelin


    Recently, sparse unmixing has been successfully applied to spectral mixture analysis of remotely sensed hyperspectral images. Based on the assumption that the observed image signatures can be expressed in the form of linear combinations of a number of pure spectral signatures known in advance, unmixing of each mixed pixel in the scene is to find an optimal subset of signatures in a very large spectral library, which is cast into the framework of sparse regression. However, traditional sparse regression models, such as collaborative sparse regression , ignore the intrinsic geometric structure in the hyperspectral data. In this paper, we propose a novel model, called manifold regularized collaborative sparse regression , by introducing a manifold regularization to the collaborative sparse regression model. The manifold regularization utilizes a graph Laplacian to incorporate the locally geometrical structure of the hyperspectral data. An algorithm based on alternating direction method of multipliers has been developed for the manifold regularized collaborative sparse regression model. Experimental results on both the simulated and real hyperspectral data sets have demonstrated the effectiveness of our proposed model.

  3. Object tracking by occlusion detection via structured sparse learning

    KAUST Repository

    Zhang, Tianzhu


    Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object\\'s track. This is the case when significant occlusion occurs. To accommodate for non-sparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Experimental results show that our tracker consistently outperforms the state-of-the-art. © 2013 IEEE.

  4. Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays (United States)

    Kwon, Daniel W.; Miller, David W.; Sedwick, Raymond J.


    Traditional methods of actuating spacecraft in sparse aperture arrays use propellant as a reaction mass. For formation flying systems, propellant becomes a critical consumable which can be quickly exhausted while maintaining relative orientation. Additional problems posed by propellant include optical contamination, plume impingement, thermal emission, and vibration excitation. For these missions where control of relative degrees of freedom is important, we consider using a system of electromagnets, in concert with reaction wheels, to replace the consumables. Electromagnetic Formation Flight sparse apertures, powered by solar energy, are designed differently from traditional propulsion systems, which are based on V. This paper investigates the design of sparse apertures both inside and outside the Earth's gravity field.

  5. Sparse Principal Component Analysis in Medical Shape Modeling

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Stegmann, Mikkel Bille; Larsen, Rasmus


    Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims...... analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of sufficiently small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA...

  6. Accelerating Dynamic Cardiac MR Imaging Using Structured Sparse Representation

    Directory of Open Access Journals (Sweden)

    Nian Cai


    Full Text Available Compressed sensing (CS has produced promising results on dynamic cardiac MR imaging by exploiting the sparsity in image series. In this paper, we propose a new method to improve the CS reconstruction for dynamic cardiac MRI based on the theory of structured sparse representation. The proposed method user the PCA subdictionaries for adaptive sparse representation and suppresses the sparse coding noise to obtain good reconstructions. An accelerated iterative shrinkage algorithm is used to solve the optimization problem and achieve a fast convergence rate. Experimental results demonstrate that the proposed method improves the reconstruction quality of dynamic cardiac cine MRI over the state-of-the-art CS method.

  7. A comprehensive study of sparse codes on abnormality detection

    DEFF Research Database (Denmark)

    Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor


    Sparse representation has been applied successfully in abnor-mal event detection, in which the baseline is to learn a dic-tionary accompanied by sparse codes. While much empha-sis is put on discriminative dictionary construction, there areno comparative studies of sparse codes regarding abnormal-ity...... detection. We comprehensively study two types of sparsecodes solutions - greedy algorithms and convex L1-norm so-lutions - and their impact on abnormality detection perfor-mance. We also propose our framework of combining sparsecodes with different detection methods. Our comparative ex-periments are carried...

  8. CAST-ChIP Maps Cell-Type-Specific Chromatin States in the Drosophila Central Nervous System

    Directory of Open Access Journals (Sweden)

    Tamás Schauer


    Full Text Available Chromatin organization and gene activity are responsive to developmental and environmental cues. Although many genes are transcribed throughout development and across cell types, much of gene regulation is highly cell-type specific. To readily track chromatin features at the resolution of cell types within complex tissues, we developed and validated chromatin affinity purification from specific cell types by chromatin immunoprecipitation (CAST-ChIP, a broadly applicable biochemical procedure. RNA polymerase II (Pol II CAST-ChIP identifies ∼1,500 neuronal and glia-specific genes in differentiated cells within the adult Drosophila brain. In contrast, the histone H2A.Z is distributed similarly across cell types and throughout development, marking cell-type-invariant Pol II-bound regions. Our study identifies H2A.Z as an active chromatin signature that is refractory to changes across cell fates. Thus, CAST-ChIP powerfully identifies cell-type-specific as well as cell-type-invariant chromatin states, enabling the systematic dissection of chromatin structure and gene regulation within complex tissues such as the brain.

  9. Prevalence of type-specific HPV infection by age and grade of cervical cytology: data from the ARTISTIC trial (United States)

    Sargent, A; Bailey, A; Almonte, M; Turner, A; Thomson, C; Peto, J; Desai, M; Mather, J; Moss, S; Roberts, C; Kitchener, H C


    Human papillomavirus (HPV) infection causes cervical cancer and premalignant dysplasia. Type-specific HPV prevalence data provide a basis for assessing the impact of HPV vaccination programmes on cervical cytology. We report high-risk HPV (HR-HPV) type-specific prevalence data in relation to cervical cytology for 24 510 women (age range: 20–64; mean age 40.2 years) recruited into the ARTISTIC trial, which is being conducted within the routine NHS Cervical Screening Programme in Greater Manchester. The most common HR-HPV types were HPV16, 18, 31, 51 and 52, which accounted for 60% of all HR-HPV types detected. There was a marked decline in the prevalence of HR-HPV infection with age, but the proportion due to each HPV type did not vary greatly with age. Multiple infections were common below the age of 30 years but less so between age 30 and 64 years. Catch-up vaccination of this sexually active cohort would be expected to reduce the number of women with moderate or worse cytology by 45%, but the number with borderline or mild cytology would fall by only 7%, giving an overall reduction of 12% in the number of women with abnormal cytology and 27% in the number with any HR-HPV infection. In the absence of broader cross-protection, the large majority of low-grade and many high-grade abnormalities may still occur in sexually active vaccinated women. PMID:18392052

  10. Cell-type specific oxytocin gene expression from AAV delivered promoter deletion constructs into the rat supraoptic nucleus in vivo.

    Directory of Open Access Journals (Sweden)

    Raymond L Fields

    Full Text Available The magnocellular neurons (MCNs in the hypothalamus selectively express either oxytocin (OXT or vasopressin (AVP neuropeptide genes, a property that defines their phenotypes. Here we examine the molecular basis of this selectivity in the OXT MCNs by stereotaxic microinjections of adeno-associated virus (AAV vectors that contain various OXT gene promoter deletion constructs using EGFP as the reporter into the rat supraoptic nucleus (SON. Two weeks following injection of the AAVs, immunohistochemical assays of EGFP expression from these constructs were done to determine whether the EGFP reporter co-localizes with either the OXT- or AVP-immunoreactivity in the MCNs. The results show that the key elements in the OT gene promoter that regulate the cell-type specific expression the SON are located -216 to -100 bp upstream of the transcription start site. We hypothesize that within this 116 bp domain a repressor exists that inhibits expression specifically in AVP MCNs, thereby leading to the cell-type specific expression of the OXT gene only in the OXT MCNs.

  11. Acquisition of serum isotype-specific and G type-specific antirotavirus antibodies among children in day care centers. (United States)

    O'Ryan, M L; Matson, D O; Estes, M K; Pickering, L K


    The acquisition of serum antirotavirus antibodies among children in day care centers was monitored through two rotavirus seasons. Twenty-six children were monitored daily for diarrhea and weekly for stool rotavirus excretion through a rotavirus season of infections with serotype G1 and a successive season of infections with both G1 and G3. Sera were collected before and after each rotavirus season and tested for antirotavirus IgA and IgG and for G type-specific blocking antibody. The prevalence of protective serum IgA and IgG titers increased from 36% and 45% before Season 1 to 77% and 96% after Season 2, respectively (P rotavirus infections experienced by a child increased. The group of children with two proven infections developed protective isotype-specific and G type-specific antibodies. These results indicate that in first exposures to rotavirus G types, children develop predominantly homotypic antibody. However, as the number of rotavirus infections increase, children develop heterotypic antibody to G types at levels that correlate with broad protection against rotavirus infection and illness, despite exposure to a restricted number of G types.

  12. MultiSite Gateway-Compatible Cell Type-Specific Gene-Inducible System for Plants1[OPEN (United States)

    Siligato, Riccardo; Wang, Xin; Yadav, Shri Ram; Lehesranta, Satu; Ma, Guojie; Ursache, Robertas; Sevilem, Iris; Zhang, Jing; Gorte, Maartje; Prasad, Kalika; Heidstra, Renze


    A powerful method to study gene function is expression or overexpression in an inducible, cell type-specific system followed by observation of consequent phenotypic changes and visualization of linked reporters in the target tissue. Multiple inducible gene overexpression systems have been developed for plants, but very few of these combine plant selection markers, control of expression domains, access to multiple promoters and protein fusion reporters, chemical induction, and high-throughput cloning capabilities. Here, we introduce a MultiSite Gateway-compatible inducible system for Arabidopsis (Arabidopsis thaliana) plants that provides the capability to generate such constructs in a single cloning step. The system is based on the tightly controlled, estrogen-inducible XVE system. We demonstrate that the transformants generated with this system exhibit the expected cell type-specific expression, similar to what is observed with constitutively expressed native promoters. With this new system, cloning of inducible constructs is no longer limited to a few special cases but can be used as a standard approach when gene function is studied. In addition, we present a set of entry clones consisting of histochemical and fluorescent reporter variants designed for gene and promoter expression studies. PMID:26644504

  13. Characterization of HPV16 L1 loop domains in the formation of a type-specific, conformational epitope

    Directory of Open Access Journals (Sweden)

    Schlegel Richard


    Full Text Available Abstract Background Virus-like particles (VLPs formed by the human papillomavirus (HPV L1 capsid protein are currently being tested in clinical trials as prophylactic vaccines against genital warts and cervical cancer. The efficacy of these vaccines is critically dependent upon L1 type-specific conformational epitopes. To investigate the molecular determinants of the HPV16 L1 conformational epitope recognized by monoclonal antibody 16A, we utilized a domain-swapping approach to generate a series of L1 proteins composed of a canine oral papillomavirus (COPV L1 backbone containing different regions of HPV16 L1. Results Gross domain swaps, which did not alter the ability of L1 to assemble into VLPs, demonstrated that the L1 N-terminus encodes at least a component of the 16A antigenic determinant. Finer epitope mapping, using GST-L1 fusion proteins, mapped the 16A epitope to the L1 variable regions I and possibly II within the N-terminus. Conclusions These results suggest that non-contiguous loop regions of L1 display critical components of a type-specific, conformational epitope.

  14. Sparse Representations for Pattern Classification using Learned Dictionaries (United States)

    Thiagarajan, Jayaraman J.; Ramamurthy, Karthikeyan N.; Spanias, Andreas

    Sparse representations have been often used for inverse problems in signal and image processing. Furthermore, frameworks for signal classification using sparse and overcomplete representations have been developed. Data-dependent representations using learned dictionaries have been significant in applications such as feature extraction and denoising. In this paper, our goal is to perform pattern classification in a domain referred to as the data representation domain, where data from different classes are sparsely represented using an overcomplete dictionary. We propose a source model to characterize the data in each class and present an algorithm to infer the dictionary from the training data of all the classes. We estimate statistical templates in the data representation domain for each class of data, and perform classification using a likelihood measure. Simulation results show that, in the case of highly sparse signals, the proposed classifier provides a consistently good performance even under noisy conditions.

  15. Sparse reconstruction using distribution agnostic bayesian matching pursuit

    KAUST Repository

    Masood, Mudassir


    A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method performs Bayesian estimates of sparse signals even when the signal prior is non-Gaussian or unknown. It is agnostic on signal statistics and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data if not available. The method utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean-square error (MMSE) estimate of the sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

  16. Sparse Machine Learning Methods for Understanding Large Text Corpora (United States)

    National Aeronautics and Space Administration — Sparse machine learning has recently emerged as powerful tool to obtain models of high-dimensional data with high degree of interpretability, at low computational...

  17. Deep Marginalized Sparse Denoising Auto-Encoder for Image Denoising (United States)

    Ma, Hongqiang; Ma, Shiping; Xu, Yuelei; Zhu, Mingming


    Stacked Sparse Denoising Auto-Encoder (SSDA) has been successfully applied to image denoising. As a deep network, the SSDA network with powerful data feature learning ability is superior to the traditional image denoising algorithms. However, the algorithm has high computational complexity and slow convergence rate in the training. To address this limitation, we present a method of image denoising based on Deep Marginalized Sparse Denoising Auto-Encoder (DMSDA). The loss function of Sparse Denoising Auto-Encoder is marginalized so that it satisfies both sparseness and marginality. The experimental results show that the proposed algorithm can not only outperform SSDA in the convergence speed and training time, but also has better denoising performance than the current excellent denoising algorithms, including both the subjective and objective evaluation of image denoising.

  18. 3rd Workshop on Sparse Grids and Applications

    CERN Document Server

    Pflüger, Dirk


    This volume of LNCSE is a collection of the papers from the proceedings of the third workshop on sparse grids and applications. Sparse grids are a popular approach for the numerical treatment of high-dimensional problems. Where classical numerical discretization schemes fail in more than three or four dimensions, sparse grids, in their different guises, are frequently the method of choice, be it spatially adaptive in the hierarchical basis or via the dimensionally adaptive combination technique. Demonstrating once again the importance of this numerical discretization scheme, the selected articles present recent advances on the numerical analysis of sparse grids as well as efficient data structures. The book also discusses a range of applications, including uncertainty quantification and plasma physics.

  19. Support agnostic Bayesian matching pursuit for block sparse signals

    KAUST Repository

    Masood, Mudassir


    A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and utilizes a greedy approach and order-recursive updates of its metrics to find the most dominant sparse supports to determine the approximate minimum mean square error (MMSE) estimate of the block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator. © 2013 IEEE.

  20. Dipole localization in Moon rocks from sparse magnetic data


    Chevillard , Sylvain; Leblond , Juliette; Mavreas , Konstantinos


    International audience; We consider dipole recovery issues from sparse magnetic data, with the use of best quadratic rational approximation techniques, together with geometrical and algebraic properties of the poles of the approximants.

  1. Greedy vs. L1 Convex Optimization in Sparse Coding

    DEFF Research Database (Denmark)

    Ren, Huamin; Pan, Hong; Olsen, Søren Ingvor

    Sparse representation has been applied successfully in many image analysis applications, including abnormal event detection, in which a baseline is to learn a dictionary from the training data and detect anomalies from its sparse codes. During this procedure, sparse codes which can be achieved...... and action recognition, a comparative study of codes in abnormal event detection is less studied and hence no conclusion is gained on the effect of codes in detecting abnormalities. We constrict our comparison in two types of the above L0-norm solutions: greedy algorithms and convex L1-norm solutions....... Considering the property of abnormal event detection, i.e., only normal videos are used as training data due to practical reasons, effective codes in classification application may not perform well in abnormality detection. Therefore, we compare the sparse codes and comprehensively evaluate their performance...

  2. Detection of Pitting in Gears Using a Deep Sparse Autoencoder

    Directory of Open Access Journals (Sweden)

    Yongzhi Qu


    Full Text Available In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.

  3. Sparse Vector Distributions and Recovery from Compressed Sensing

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    It is well known that the performance of sparse vector recovery algorithms from compressive measurements can depend on the distribution underlying the non-zero elements of a sparse vector. However, the extent of these effects has yet to be explored, and formally presented. In this paper, I...... empirically investigate this dependence for seven distributions and fifteen recovery algorithms. The two morals of this work are: 1) any judgement of the recovery performance of one algorithm over that of another must be prefaced by the conditions for which this is observed to be true, including sparse vector...... distributions, and the criterion for exact recovery; and 2) a recovery algorithm must be selected carefully based on what distribution one expects to underlie the sensed sparse signal....

  4. Beam Combination for Sparse Aperture Telescopes, Phase I (United States)

    National Aeronautics and Space Administration — The Stellar Imager, an ultraviolet, sparse-aperture telescope, was one of the fifteen Vision Missions chosen for a study completed last year. Stellar Imager will...

  5. An Atlas for Schistosoma mansoni Organs and Life-Cycle Stages Using Cell Type-Specific Markers and Confocal Microscopy (United States)

    Cogswell, Alexis; Williams, David L.; Newmark, Phillip A.


    Schistosomiasis (bilharzia) is a tropical disease caused by trematode parasites (Schistosoma) that affects hundreds of millions of people in the developing world. Currently only a single drug (praziquantel) is available to treat this disease, highlighting the importance of developing new techniques to study Schistosoma. While molecular advances, including RNA interference and the availability of complete genome sequences for two Schistosoma species, will help to revolutionize studies of these animals, an array of tools for visualizing the consequences of experimental perturbations on tissue integrity and development needs to be made widely available. To this end, we screened a battery of commercially available stains, antibodies and fluorescently labeled lectins, many of which have not been described previously for analyzing schistosomes, for their ability to label various cell and tissue types in the cercarial stage of S. mansoni. This analysis uncovered more than 20 new markers that label most cercarial tissues, including the tegument, the musculature, the protonephridia, the secretory system and the nervous system. Using these markers we present a high-resolution visual depiction of cercarial anatomy. Examining the effectiveness of a subset of these markers in S. mansoni adults and miracidia, we demonstrate the value of these tools for labeling tissues in a variety of life-cycle stages. The methodologies described here will facilitate functional analyses aimed at understanding fundamental biological processes in these parasites. PMID:21408085

  6. Distributed coding of multiview sparse sources with joint recovery

    DEFF Research Database (Denmark)

    Luong, Huynh Van; Deligiannis, Nikos; Forchhammer, Søren


    coding of the sparse sources with a new joint recovery algorithm that incorporates multiple side information signals, where prior knowledge (low quality) of all the sparse sources is initially sent to exploit their correlations. Experimental evaluation using the histograms of shift-invariant feature...... transform (SIFT) descriptors extracted from multiview images shows that our method leads to bit-rate saving of up to 43% compared to the state-of-the-art distributed compressed sensing method with independent encoding of the sources....

  7. Efficient collaborative sparse channel estimation in massive MIMO

    KAUST Repository

    Masood, Mudassir


    We propose a method for estimation of sparse frequency selective channels within MIMO-OFDM systems. These channels are independently sparse and share a common support. The method estimates the impulse response for each channel observed by the antennas at the receiver. Estimation is performed in a coordinated manner by sharing minimal information among neighboring antennas to achieve results better than many contemporary methods. Simulations demonstrate the superior performance of the proposed method.

  8. A flexible framework for sparse simultaneous component based data integration

    Directory of Open Access Journals (Sweden)

    Van Deun Katrijn


    Full Text Available Abstract 1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such

  9. A flexible framework for sparse simultaneous component based data integration. (United States)

    Van Deun, Katrijn; Wilderjans, Tom F; van den Berg, Robert A; Antoniadis, Anestis; Van Mechelen, Iven


    High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are exclusively tied to one data platform

  10. Sparse encoding of automatic visual association in hippocampal networks

    DEFF Research Database (Denmark)

    Hulme, Oliver J; Skov, Martin; Chadwick, Martin J


    by these stimuli. Using multivariate Bayesian decoding, we show that human hippocampal and temporal neocortical structures host sparse associative representations that are automatically triggered by visual input. Furthermore, as predicted theoretically, there was a significant increase in sparsity in the Cornu...... for the sparse encoding of associative density. In the absence of reportability or attentional confounds, this charts a distribution of visual associative representations within hippocampal populations and their temporal lobe afferent fields, and demonstrates the viability of retrospective associative sampling...

  11. Fast convolutional sparse coding using matrix inversion lemma

    Czech Academy of Sciences Publication Activity Database

    Šorel, Michal; Šroubek, Filip


    Roč. 55, č. 1 (2016), s. 44-51 ISSN 1051-2004 R&D Projects: GA ČR GA13-29225S Institutional support: RVO:67985556 Keywords : Convolutional sparse coding * Feature learning * Deconvolution networks * Shift-invariant sparse coding Subject RIV: JD - Computer Applications, Robotics Impact factor: 2.337, year: 2016

  12. Discussion of CoSA: Clustering of Sparse Approximations

    Energy Technology Data Exchange (ETDEWEB)

    Armstrong, Derek Elswick [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)


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

  13. A flexible framework for sparse simultaneous component based data integration (United States)


    1 Background High throughput data are complex and methods that reveal structure underlying the data are most useful. Principal component analysis, frequently implemented as a singular value decomposition, is a popular technique in this respect. Nowadays often the challenge is to reveal structure in several sources of information (e.g., transcriptomics, proteomics) that are available for the same biological entities under study. Simultaneous component methods are most promising in this respect. However, the interpretation of the principal and simultaneous components is often daunting because contributions of each of the biomolecules (transcripts, proteins) have to be taken into account. 2 Results We propose a sparse simultaneous component method that makes many of the parameters redundant by shrinking them to zero. It includes principal component analysis, sparse principal component analysis, and ordinary simultaneous component analysis as special cases. Several penalties can be tuned that account in different ways for the block structure present in the integrated data. This yields known sparse approaches as the lasso, the ridge penalty, the elastic net, the group lasso, sparse group lasso, and elitist lasso. In addition, the algorithmic results can be easily transposed to the context of regression. Metabolomics data obtained with two measurement platforms for the same set of Escherichia coli samples are used to illustrate the proposed methodology and the properties of different penalties with respect to sparseness across and within data blocks. 3 Conclusion Sparse simultaneous component analysis is a useful method for data integration: First, simultaneous analyses of multiple blocks offer advantages over sequential and separate analyses and second, interpretation of the results is highly facilitated by their sparseness. The approach offered is flexible and allows to take the block structure in different ways into account. As such, structures can be found that are

  14. Detection of Contact Binaries Using Sparse High Phase Angle Lightcurves


    Lacerda, Pedro


    We show that candidate contact binary asteroids can be efficiently identified from sparsely sampled photometry taken at phase angles >60deg. At high phase angle, close/contact binary systems produce distinctive lightcurves that spend most of the time at maximum or minimum (typically >1mag apart) brightness with relatively fast transitions between the two. This means that a few (~5) sparse observations will suffice to measure the large range of variation and identify candidate contact binary s...

  15. Mixed Map Labeling

    Directory of Open Access Journals (Sweden)

    Maarten Löffler


    Full Text Available Point feature map labeling is a geometric visualization problem, in which a set of input points must be labeled with a set of disjoint rectangles (the bounding boxes of the label texts. It is predominantly motivated by label placement in maps but it also has other visualization applications. Typically, labeling models either use internal labels, which must touch their feature point, or external (boundary labels, which are placed outside the input image and which are connected to their feature points by crossing-free leader lines. In this paper we study polynomial-time algorithms for maximizing the number of internal labels in a mixed labeling model that combines internal and external labels. The model requires that all leaders are parallel to a given orientation θ ∈ [0, 2π, the value of which influences the geometric properties and hence the running times of our algorithms.

  16. Sparse Reconstruction Schemes for Nonlinear Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla


    synthetically generated or actually measured scattered fields, show that the images recovered by these sparsity-regularized methods are sharper and more accurate than those produced by existing methods. The methods developed in this work have potential application areas ranging from oil/gas reservoir engineering to biological imaging where sparse domains naturally exist.

  17. Exhaustive Search for Sparse Variable Selection in Linear Regression (United States)

    Igarashi, Yasuhiko; Takenaka, Hikaru; Nakanishi-Ohno, Yoshinori; Uemura, Makoto; Ikeda, Shiro; Okada, Masato


    We propose a K-sparse exhaustive search (ES-K) method and a K-sparse approximate exhaustive search method (AES-K) for selecting variables in linear regression. With these methods, K-sparse combinations of variables are tested exhaustively assuming that the optimal combination of explanatory variables is K-sparse. By collecting the results of exhaustively computing ES-K, various approximate methods for selecting sparse variables can be summarized as density of states. With this density of states, we can compare different methods for selecting sparse variables such as relaxation and sampling. For large problems where the combinatorial explosion of explanatory variables is crucial, the AES-K method enables density of states to be effectively reconstructed by using the replica-exchange Monte Carlo method and the multiple histogram method. Applying the ES-K and AES-K methods to type Ia supernova data, we confirmed the conventional understanding in astronomy when an appropriate K is given beforehand. However, we found the difficulty to determine K from the data. Using virtual measurement and analysis, we argue that this is caused by data shortage.

  18. A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization. (United States)

    Li, Hui; Zhang, Qingfu; Deng, Jingda; Xu, Zong-Ben


    Iterative thresholding is a dominating strategy for sparse optimization problems. The main goal of iterative thresholding methods is to find a so-called k-sparse solution. However, the setting of regularization parameters or the estimation of the true sparsity are nontrivial in iterative thresholding methods. To overcome this shortcoming, we propose a preference-based multiobjective evolutionary approach to solve sparse optimization problems in compressive sensing. Our basic strategy is to search the knee part of weakly Pareto front with preference on the true k-sparse solution. In the noiseless case, it is easy to locate the exact position of the k-sparse solution from the distribution of the solutions found by our proposed method. Therefore, our method has the ability to detect the true sparsity. Moreover, any iterative thresholding methods can be used as a local optimizer in our proposed method, and no prior estimation of sparsity is required. The proposed method can also be extended to solve sparse optimization problems with noise. Extensive experiments have been conducted to study its performance on artificial signals and magnetic resonance imaging signals. Our experimental results have shown that our proposed method is very effective for detecting sparsity and can improve the reconstruction ability of existing iterative thresholding methods.

  19. Saliency Detection Using Sparse and Nonlinear Feature Representation (United States)

    Zhao, Qingjie; Manzoor, Muhammad Farhan; Ishaq Khan, Saqib


    An important aspect of visual saliency detection is how features that form an input image are represented. A popular theory supports sparse feature representation, an image being represented with a basis dictionary having sparse weighting coefficient. Another method uses a nonlinear combination of image features for representation. In our work, we combine the two methods and propose a scheme that takes advantage of both sparse and nonlinear feature representation. To this end, we use independent component analysis (ICA) and covariant matrices, respectively. To compute saliency, we use a biologically plausible center surround difference (CSD) mechanism. Our sparse features are adaptive in nature; the ICA basis function are learnt at every image representation, rather than being fixed. We show that Adaptive Sparse Features when used with a CSD mechanism yield better results compared to fixed sparse representations. We also show that covariant matrices consisting of nonlinear integration of color information alone are sufficient to efficiently estimate saliency from an image. The proposed dual representation scheme is then evaluated against human eye fixation prediction, response to psychological patterns, and salient object detection on well-known datasets. We conclude that having two forms of representation compliments one another and results in better saliency detection. PMID:24895644

  20. Visual tracking based on extreme learning machine and sparse representation. (United States)

    Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen


    The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker.

  1. Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

    Directory of Open Access Journals (Sweden)

    Xiangwei Xing


    Full Text Available As a method of representing the test sample with few training samples from an overcomplete dictionary, sparse representation classification (SRC has attracted much attention in synthetic aperture radar (SAR automatic target recognition (ATR recently. In this paper, we develop a novel SAR vehicle recognition method based on sparse representation classification along with aspect information (SRCA, in which the correlation between the vehicle’s aspect angle and the sparse representation vector is exploited. The detailed procedure presented in this paper can be summarized as follows. Initially, the sparse representation vector of a test sample is solved by sparse representation algorithm with a principle component analysis (PCA feature-based dictionary. Then, the coefficient vector is projected onto a sparser one within a certain range of the vehicle’s aspect angle. Finally, the vehicle is classified into a certain category that minimizes the reconstruction error with the novel sparse representation vector. Extensive experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR dataset and the results demonstrate that the proposed method performs robustly under the variations of depression angle and target configurations, as well as incomplete observation.

  2. Structure-aware Local Sparse Coding for Visual Tracking

    KAUST Repository

    Qi, Yuankai


    Sparse coding has been applied to visual tracking and related vision problems with demonstrated success in recent years. Existing tracking methods based on local sparse coding sample patches from a target candidate and sparsely encode these using a dictionary consisting of patches sampled from target template images. The discriminative strength of existing methods based on local sparse coding is limited as spatial structure constraints among the template patches are not exploited. To address this problem, we propose a structure-aware local sparse coding algorithm which encodes a target candidate using templates with both global and local sparsity constraints. For robust tracking, we show local regions of a candidate region should be encoded only with the corresponding local regions of the target templates that are the most similar from the global view. Thus, a more precise and discriminative sparse representation is obtained to account for appearance changes. To alleviate the issues with tracking drifts, we design an effective template update scheme. Extensive experiments on challenging image sequences demonstrate the effectiveness of the proposed algorithm against numerous stateof- the-art methods.

  3. Occlusion detection via structured sparse learning for robust object tracking

    KAUST Repository

    Zhang, Tianzhu


    Sparse representation based methods have recently drawn much attention in visual tracking due to good performance against illumination variation and occlusion. They assume the errors caused by image variations can be modeled as pixel-wise sparse. However, in many practical scenarios, these errors are not truly pixel-wise sparse but rather sparsely distributed in a structured way. In fact, pixels in error constitute contiguous regions within the object’s track. This is the case when significant occlusion occurs. To accommodate for nonsparse occlusion in a given frame, we assume that occlusion detected in previous frames can be propagated to the current one. This propagated information determines which pixels will contribute to the sparse representation of the current track. In other words, pixels that were detected as part of an occlusion in the previous frame will be removed from the target representation process. As such, this paper proposes a novel tracking algorithm that models and detects occlusion through structured sparse learning. We test our tracker on challenging benchmark sequences, such as sports videos, which involve heavy occlusion, drastic illumination changes, and large pose variations. Extensive experimental results show that our proposed tracker consistently outperforms the state-of-the-art trackers.

  4. Vector sparse representation of color image using quaternion matrix analysis. (United States)

    Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong


    Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.

  5. Industrial Robot Label Applicator


    Kukasch, Kai


    The thesis deals with a project carried out for developing and setting up a robot label applicator system. The requirement was that RFID tracking labels can be applied on flexible positions, without manual effort and rearrangement, via programming. The purpose of the robot label applicator system is to increase the efficiency in production sites, where the RFID label position can change, depending on product or other reasons. New label positions should be programmed easily with a human-m...

  6. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 (United States); Chen, Ken Chung [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Stomatology, National Cheng Kung University Medical College and Hospital, Tainan, Taiwan 70403 (China); Shen, Steve G. F.; Yan, Jin [Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Lee, Philip K. M.; Chow, Ben [Hong Kong Dental Implant and Maxillofacial Centre, Hong Kong, China 999077 (China); Liu, Nancy X. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 and Department of Oral and Maxillofacial Surgery, Peking University School and Hospital of Stomatology, Beijing, China 100050 (China); Xia, James J. [Department of Oral and Maxillofacial Surgery, Houston Methodist Hospital Research Institute, Houston, Texas 77030 (United States); Department of Surgery (Oral and Maxillofacial Surgery), Weill Medical College, Cornell University, New York, New York 10065 (United States); Department of Oral and Craniomaxillofacial Surgery and Science, Shanghai Ninth People' s Hospital, Shanghai Jiao Tong University College of Medicine, Shanghai, China 200011 (China); Shen, Dinggang, E-mail: [Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina 27599 and Department of Brain and Cognitive Engineering, Korea University, Seoul, 136701 (Korea, Republic of)


    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  7. Automated bone segmentation from dental CBCT images using patch-based sparse representation and convex optimization

    International Nuclear Information System (INIS)

    Wang, Li; Gao, Yaozong; Shi, Feng; Liao, Shu; Li, Gang; Chen, Ken Chung; Shen, Steve G. F.; Yan, Jin; Lee, Philip K. M.; Chow, Ben; Liu, Nancy X.; Xia, James J.; Shen, Dinggang


    Purpose: Cone-beam computed tomography (CBCT) is an increasingly utilized imaging modality for the diagnosis and treatment planning of the patients with craniomaxillofacial (CMF) deformities. Accurate segmentation of CBCT image is an essential step to generate three-dimensional (3D) models for the diagnosis and treatment planning of the patients with CMF deformities. However, due to the poor image quality, including very low signal-to-noise ratio and the widespread image artifacts such as noise, beam hardening, and inhomogeneity, it is challenging to segment the CBCT images. In this paper, the authors present a new automatic segmentation method to address these problems. Methods: To segment CBCT images, the authors propose a new method for fully automated CBCT segmentation by using patch-based sparse representation to (1) segment bony structures from the soft tissues and (2) further separate the mandible from the maxilla. Specifically, a region-specific registration strategy is first proposed to warp all the atlases to the current testing subject and then a sparse-based label propagation strategy is employed to estimate a patient-specific atlas from all aligned atlases. Finally, the patient-specific atlas is integrated into amaximum a posteriori probability-based convex segmentation framework for accurate segmentation. Results: The proposed method has been evaluated on a dataset with 15 CBCT images. The effectiveness of the proposed region-specific registration strategy and patient-specific atlas has been validated by comparing with the traditional registration strategy and population-based atlas. The experimental results show that the proposed method achieves the best segmentation accuracy by comparison with other state-of-the-art segmentation methods. Conclusions: The authors have proposed a new CBCT segmentation method by using patch-based sparse representation and convex optimization, which can achieve considerably accurate segmentation results in CBCT

  8. Fiber type specific expression of TNF-alpha, IL-6 and IL-18 in human skeletal muscles

    DEFF Research Database (Denmark)

    Plomgaard, Peter; Penkowa, Milena; Pedersen, Bente K


    Skeletal muscle is now recognized as an endocrine organ with the capacity to produce signal peptides in response to muscle contractions. Here we demonstrate that resting healthy human muscles express cytokines in a fiber type specific manner. Human muscle biopsies from seven healthy young males...... were obtained from m. triceps, m. quadriceps vastus lateralis and m. soleus. Type I fibers contributed (mean +/- SE) 24.0 +/- 2.5% in triceps of total fibers, 51.3 +/- 2.4% in vastus and 84.9 +/- 22% in soleus. As expected, differences in the fiber type composition were accompanied by marked...... differences between the three muscles with regard to MHC I and MHC IIa mRNA expression. Immunohistochemistry demonstrated that tumor necrosis factor (TNF)-alpha and interleukin (IL)-18 were solely expressed by type II fibers, whereas the expression of IL-6 was more prominent in type I compared to type II...

  9. Extensions of the Rosner-Colditz breast cancer prediction model to include older women and type-specific predicted risk. (United States)

    Glynn, Robert J; Colditz, Graham A; Tamimi, Rulla M; Chen, Wendy Y; Hankinson, Susan E; Willett, Walter W; Rosner, Bernard


    A breast cancer risk prediction rule previously developed by Rosner and Colditz has reasonable predictive ability. We developed a re-fitted version of this model, based on more than twice as many cases now including women up to age 85, and further extended it to a model that distinguished risk factor prediction of tumors with different estrogen/progesterone receptor status. We compared the calibration and discriminatory ability of the original, the re-fitted, and the type-specific models. Evaluation used data from the Nurses' Health Study during the period 1980-2008, when 4384 incident invasive breast cancers occurred over 1.5 million person-years. Model development used two-thirds of study subjects and validation used one-third. Predicted risks in the validation sample from the original and re-fitted models were highly correlated (ρ = 0.93), but several parameters, notably those related to use of menopausal hormone therapy and age, had different estimates. The re-fitted model was well-calibrated and had an overall C-statistic of 0.65. The extended, type-specific model identified several risk factors with varying associations with occurrence of tumors of different receptor status. However, this extended model relative to the prediction of any breast cancer did not meaningfully reclassify women who developed breast cancer to higher risk categories, nor women remaining cancer free to lower risk categories. The re-fitted Rosner-Colditz model has applicability to risk prediction in women up to age 85, and its discrimination is not improved by consideration of varying associations across tumor subtypes.

  10. Optogenetic determination of the myocardial requirements for extrasystoles by cell type-specific targeting of ChannelRhodopsin-2. (United States)

    Zaglia, Tania; Pianca, Nicola; Borile, Giulia; Da Broi, Francesca; Richter, Claudia; Campione, Marina; Lehnart, Stephan E; Luther, Stefan; Corrado, Domenico; Miquerol, Lucile; Mongillo, Marco


    Extrasystoles lead to several consequences, ranging from uneventful palpitations to lethal ventricular arrhythmias, in the presence of pathologies, such as myocardial ischemia. The role of working versus conducting cardiomyocytes, as well as the tissue requirements (minimal cell number) for the generation of extrasystoles, and the properties leading ectopies to become arrhythmia triggers (topology), in the normal and diseased heart, have not been determined directly in vivo. Here, we used optogenetics in transgenic mice expressing ChannelRhodopsin-2 selectively in either cardiomyocytes or the conduction system to achieve cell type-specific, noninvasive control of heart activity with high spatial and temporal resolution. By combining measurement of optogenetic tissue activation in vivo and epicardial voltage mapping in Langendorff-perfused hearts, we demonstrated that focal ectopies require, in the normal mouse heart, the simultaneous depolarization of at least 1,300-1,800 working cardiomyocytes or 90-160 Purkinje fibers. The optogenetic assay identified specific areas in the heart that were highly susceptible to forming extrasystolic foci, and such properties were correlated to the local organization of the Purkinje fiber network, which was imaged in three dimensions using optical projection tomography. Interestingly, during the acute phase of myocardial ischemia, focal ectopies arising from this location, and including both Purkinje fibers and the surrounding working cardiomyocytes, have the highest propensity to trigger sustained arrhythmias. In conclusion, we used cell-specific optogenetics to determine with high spatial resolution and cell type specificity the requirements for the generation of extrasystoles and the factors causing ectopies to be arrhythmia triggers during myocardial ischemia.

  11. Cell type-specific functions of period genes revealed by novel adipocyte and hepatocyte circadian clock models.

    Directory of Open Access Journals (Sweden)

    Chidambaram Ramanathan


    Full Text Available In animals, circadian rhythms in physiology and behavior result from coherent rhythmic interactions between clocks in the brain and those throughout the body. Despite the many tissue specific clocks, most understanding of the molecular core clock mechanism comes from studies of the suprachiasmatic nuclei (SCN of the hypothalamus and a few other cell types. Here we report establishment and genetic characterization of three cell-autonomous mouse clock models: 3T3 fibroblasts, 3T3-L1 adipocytes, and MMH-D3 hepatocytes. Each model is genetically tractable and has an integrated luciferase reporter that allows for longitudinal luminescence recording of rhythmic clock gene expression using an inexpensive off-the-shelf microplate reader. To test these cellular models, we generated a library of short hairpin RNAs (shRNAs against a panel of known clock genes and evaluated their impact on circadian rhythms. Knockdown of Bmal1, Clock, Cry1, and Cry2 each resulted in similar phenotypes in all three models, consistent with previous studies. However, we observed cell type-specific knockdown phenotypes for the Period and Rev-Erb families of clock genes. In particular, Per1 and Per2, which have strong behavioral effects in knockout mice, appear to play different roles in regulating period length and amplitude in these peripheral systems. Per3, which has relatively modest behavioral effects in knockout mice, substantially affects period length in the three cellular models and in dissociated SCN neurons. In summary, this study establishes new cell-autonomous clock models that are of particular relevance to metabolism and suitable for screening for clock modifiers, and reveals previously under-appreciated cell type-specific functions of clock genes.

  12. Aspect-Aided Dynamic Non-Negative Sparse Representation-Based Microwave Image Classification

    Directory of Open Access Journals (Sweden)

    Xinzheng Zhang


    Full Text Available Classification of target microwave images is an important application in much areas such as security, surveillance, etc. With respect to the task of microwave image classification, a recognition algorithm based on aspect-aided dynamic non-negative least square (ADNNLS sparse representation is proposed. Firstly, an aspect sector is determined, the center of which is the estimated aspect angle of the testing sample. The training samples in the aspect sector are divided into active atoms and inactive atoms by smooth self-representative learning. Secondly, for each testing sample, the corresponding active atoms are selected dynamically, thereby establishing dynamic dictionary. Thirdly, the testing sample is represented with ℓ 1 -regularized non-negative sparse representation under the corresponding dynamic dictionary. Finally, the class label of the testing sample is identified by use of the minimum reconstruction error. Verification of the proposed algorithm was conducted using the Moving and Stationary Target Acquisition and Recognition (MSTAR database which was acquired by synthetic aperture radar. Experiment results validated that the proposed approach was able to capture the local aspect characteristics of microwave images effectively, thereby improving the classification performance.

  13. Sparse modeling of spatial environmental variables associated with asthma. (United States)

    Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W


    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. Synthesizing labeled compounds

    International Nuclear Information System (INIS)

    London, R.E.; Matwiyoff, N.A.; Unkefer, C.J.; Walker, T.E.


    A metabolic study is presented of the chemical reactions provided by isotopic labeling and NMR spectroscopy. Synthesis of 13 C-labeled D-glucose, a 6-carbon sugar, involves adding a labeled nitrile group to the 5-carbon sugar D-arabinose by reaction with labeled hydrogen cyanide. The product of this reaction is then reduced and hydrolyzed to a mixture of the labeled sugars. The two sugars are separated by absorption chromotography. The synthesis of 13 C-labeled L-tyrosine, an amino acid, is also presented

  15. Supernova: A Versatile Vector System for Single-Cell Labeling and Gene Function Studies in vivo. (United States)

    Luo, Wenshu; Mizuno, Hidenobu; Iwata, Ryohei; Nakazawa, Shingo; Yasuda, Kosuke; Itohara, Shigeyoshi; Iwasato, Takuji


    Here we describe "Supernova" series of vector systems that enable single-cell labeling and labeled cell-specific gene manipulation, when introduced by in utero electroporation (IUE) or adeno-associated virus (AAV)-mediated gene delivery. In Supernova, sparse labeling relies on low TRE leakage. In a small population of cells with over-threshold leakage, initial tTA-independent weak expression is enhanced by tTA/TRE-positive feedback along with a site-specific recombination system (e.g., Cre/loxP, Flpe/FRT). Sparse and bright labeling by Supernova with little background enables the visualization of the morphological details of individual neurons in densely packed brain areas such as the cortex and hippocampus, both during development and in adulthood. Sparseness levels are adjustable. Labeled cell-specific gene knockout was accomplished by introducing Cre/loxP-based Supernova vectors into floxed mice. Furthermore, by combining with RNAi, TALEN, and CRISPR/Cas9 technologies, IUE-based Supernova achieved labeled cell-specific gene knockdown and editing/knockout without requiring genetically altered mice. Thus, Supernova system is highly extensible and widely applicable for single-cell analyses in complex organs, such as the mammalian brain.

  16. Completing sparse and disconnected protein-protein network by deep learning. (United States)

    Huang, Lei; Liao, Li; Wu, Cathy H


    Protein-protein interaction (PPI) prediction remains a central task in systems biology to achieve a better and holistic understanding of cellular and intracellular processes. Recently, an increasing number of computational methods have shifted from pair-wise prediction to network level prediction. Many of the existing network level methods predict PPIs under the assumption that the training network should be connected. However, this assumption greatly affects the prediction power and limits the application area because the current golden standard PPI networks are usually very sparse and disconnected. Therefore, how to effectively predict PPIs based on a training network that is sparse and disconnected remains a challenge. In this work, we developed a novel PPI prediction method based on deep learning neural network and regularized Laplacian kernel. We use a neural network with an autoencoder-like architecture to implicitly simulate the evolutionary processes of a PPI network. Neurons of the output layer correspond to proteins and are labeled with values (1 for interaction and 0 for otherwise) from the adjacency matrix of a sparse disconnected training PPI network. Unlike autoencoder, neurons at the input layer are given all zero input, reflecting an assumption of no a priori knowledge about PPIs, and hidden layers of smaller sizes mimic ancient interactome at different times during evolution. After the training step, an evolved PPI network whose rows are outputs of the neural network can be obtained. We then predict PPIs by applying the regularized Laplacian kernel to the transition matrix that is built upon the evolved PPI network. The results from cross-validation experiments show that the PPI prediction accuracies for yeast data and human data measured as AUC are increased by up to 8.4 and 14.9% respectively, as compared to the baseline. Moreover, the evolved PPI network can also help us leverage complementary information from the disconnected training network

  17. Sparse dictionary for synthetic transmit aperture medical ultrasound imaging. (United States)

    Wang, Ping; Jiang, Jin-Yang; Li, Na; Luo, Han-Wu; Li, Fang; Cui, Shi-Gang


    It is possible to recover a signal below the Nyquist sampling limit using a compressive sensing technique in ultrasound imaging. However, the reconstruction enabled by common sparse transform approaches does not achieve satisfactory results. Considering the ultrasound echo signal's features of attenuation, repetition, and superposition, a sparse dictionary with the emission pulse signal is proposed. Sparse coefficients in the proposed dictionary have high sparsity. Images reconstructed with this dictionary were compared with those obtained with the three other common transforms, namely, discrete Fourier transform, discrete cosine transform, and discrete wavelet transform. The performance of the proposed dictionary was analyzed via a simulation and experimental data. The mean absolute error (MAE) was used to quantify the quality of the reconstructions. Experimental results indicate that the MAE associated with the proposed dictionary was always the smallest, the reconstruction time required was the shortest, and the lateral resolution and contrast of the reconstructed images were also the closest to the original images. The proposed sparse dictionary performed better than the other three sparse transforms. With the same sampling rate, the proposed dictionary achieved excellent reconstruction quality.

  18. Image fusion via nonlocal sparse K-SVD dictionary learning. (United States)

    Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang


    Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation.

  19. Low-count PET image restoration using sparse representation (United States)

    Li, Tao; Jiang, Changhui; Gao, Juan; Yang, Yongfeng; Liang, Dong; Liu, Xin; Zheng, Hairong; Hu, Zhanli


    In the field of positron emission tomography (PET), reconstructed images are often blurry and contain noise. These problems are primarily caused by the low resolution of projection data. Solving this problem by improving hardware is an expensive solution, and therefore, we attempted to develop a solution based on optimizing several related algorithms in both the reconstruction and image post-processing domains. As sparse technology is widely used, sparse prediction is increasingly applied to solve this problem. In this paper, we propose a new sparse method to process low-resolution PET images. Two dictionaries (D1 for low-resolution PET images and D2 for high-resolution PET images) are learned from a group real PET image data sets. Among these two dictionaries, D1 is used to obtain a sparse representation for each patch of the input PET image. Then, a high-resolution PET image is generated from this sparse representation using D2. Experimental results indicate that the proposed method exhibits a stable and superior ability to enhance image resolution and recover image details. Quantitatively, this method achieves better performance than traditional methods. This proposed strategy is a new and efficient approach for improving the quality of PET images.

  20. Sparse approximation problem: how rapid simulated annealing succeeds and fails (United States)

    Obuchi, Tomoyuki; Kabashima, Yoshiyuki


    Information processing techniques based on sparseness have been actively studied in several disciplines. Among them, a mathematical framework to approximately express a given dataset by a combination of a small number of basis vectors of an overcomplete basis is termed the sparse approximation. In this paper, we apply simulated annealing, a metaheuristic algorithm for general optimization problems, to sparse approximation in the situation where the given data have a planted sparse representation and noise is present. The result in the noiseless case shows that our simulated annealing works well in a reasonable parameter region: the planted solution is found fairly rapidly. This is true even in the case where a common relaxation of the sparse approximation problem, the G-relaxation, is ineffective. On the other hand, when the dimensionality of the data is close to the number of non-zero components, another metastable state emerges, and our algorithm fails to find the planted solution. This phenomenon is associated with a first-order phase transition. In the case of very strong noise, it is no longer meaningful to search for the planted solution. In this situation, our algorithm determines a solution with close-to-minimum distortion fairly quickly.

  1. Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking

    Directory of Open Access Journals (Sweden)

    Honghong Yang


    Full Text Available Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.

  2. Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing. (United States)

    She, Huajun; Chen, Rong-Rong; Liang, Dong; DiBella, Edward V R; Ying, Leslie


    To develop a sensitivity-based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information. Parallel magnetic resonance imaging reconstruction problem can be formulated as a multichannel sampling problem where solutions are sought analytically. However, the channel functions given by the coil sensitivities in parallel imaging are not known exactly and the estimation error usually leads to artifacts. In this study, we propose a new reconstruction algorithm, termed Sparse BLind Iterative Parallel, for blind iterative parallel imaging reconstruction using compressed sensing. The proposed algorithm reconstructs both the sensitivity functions and the image simultaneously from undersampled data. It enforces the sparseness constraint in the image as done in compressed sensing, but is different from compressed sensing in that the sensing matrix is unknown and additional constraint is enforced on the sensitivities as well. Both phantom and in vivo imaging experiments were carried out with retrospective undersampling to evaluate the performance of the proposed method. Experiments show improvement in Sparse BLind Iterative Parallel reconstruction when compared with Sparse SENSE, JSENSE, IRGN-TV, and L1-SPIRiT reconstructions with the same number of measurements. The proposed Sparse BLind Iterative Parallel algorithm reduces the reconstruction errors when compared to the state-of-the-art parallel imaging methods. Copyright © 2013 Wiley Periodicals, Inc.

  3. Selectivity and sparseness in randomly connected balanced networks.

    Directory of Open Access Journals (Sweden)

    Cengiz Pehlevan

    Full Text Available Neurons in sensory cortex show stimulus selectivity and sparse population response, even in cases where no strong functionally specific structure in connectivity can be detected. This raises the question whether selectivity and sparseness can be generated and maintained in randomly connected networks. We consider a recurrent network of excitatory and inhibitory spiking neurons with random connectivity, driven by random projections from an input layer of stimulus selective neurons. In this architecture, the stimulus-to-stimulus and neuron-to-neuron modulation of total synaptic input is weak compared to the mean input. Surprisingly, we show that in the balanced state the network can still support high stimulus selectivity and sparse population response. In the balanced state, strong synapses amplify the variation in synaptic input and recurrent inhibition cancels the mean. Functional specificity in connectivity emerges due to the inhomogeneity caused by the generative statistical rule used to build the network. We further elucidate the mechanism behind and evaluate the effects of model parameters on population sparseness and stimulus selectivity. Network response to mixtures of stimuli is investigated. It is shown that a balanced state with unselective inhibition can be achieved with densely connected input to inhibitory population. Balanced networks exhibit the "paradoxical" effect: an increase in excitatory drive to inhibition leads to decreased inhibitory population firing rate. We compare and contrast selectivity and sparseness generated by the balanced network to randomly connected unbalanced networks. Finally, we discuss our results in light of experiments.

  4. Dual-specificity anti-sigma factor reinforces control of cell-type specific gene expression in Bacillus subtilis.

    Directory of Open Access Journals (Sweden)

    Mónica Serrano


    Full Text Available Gene expression during spore development in Bacillus subtilis is controlled by cell type-specific RNA polymerase sigma factors. σFand σE control early stages of development in the forespore and the mother cell, respectively. When, at an intermediate stage in development, the mother cell engulfs the forespore, σF is replaced by σG and σE is replaced by σK. The anti-sigma factor CsfB is produced under the control of σF and binds to and inhibits the auto-regulatory σG, but not σF. A position in region 2.1, occupied by an asparagine in σG and by a glutamate in οF, is sufficient for CsfB discrimination of the two sigmas, and allows it to delay the early to late switch in forespore gene expression. We now show that following engulfment completion, csfB is switched on in the mother cell under the control of σK and that CsfB binds to and inhibits σE but not σK, possibly to facilitate the switch from early to late gene expression. We show that a position in region 2.3 occupied by a conserved asparagine in σE and by a conserved glutamate in σK suffices for discrimination by CsfB. We also show that CsfB prevents activation of σG in the mother cell and the premature σG-dependent activation of σK. Thus, CsfB establishes negative feedback loops that curtail the activity of σE and prevent the ectopic activation of σG in the mother cell. The capacity of CsfB to directly block σE activity may also explain how CsfB plays a role as one of the several mechanisms that prevent σE activation in the forespore. Thus the capacity of CsfB to differentiate between the highly similar σF/σG and σE/σK pairs allows it to rinforce the cell-type specificity of these sigma factors and the transition from early to late development in B. subtilis, and possibly in all sporeformers that encode a CsfB orthologue.

  5. Soil Fumigant Labels - Dazomet (United States)

    Updated labels include new safety requirements for buffer zones and related measures. Find information from the Pesticide Product Labeling System (PPLS) for products such as Basamid G, manufactured by Amvac.

  6. Soil Fumigant Labels - Chloropicrin (United States)

    Search by EPA registration number, product name, or company name, and follow the link to the Pesticide Product Label System (PPLS) for details on each fumigant. Updated labels include new safety requirements for buffer zones and related measures.

  7. Semiotic labelled deductive systems

    Energy Technology Data Exchange (ETDEWEB)

    Nossum, R.T. [Imperial College of Science, Technology and Medicine, London (United Kingdom)


    We review the class of Semiotic Models put forward by Pospelov, as well as the Labelled Deductive Systems developed by Gabbay, and construct an embedding of Semiotic Models into Labelled Deductive Systems.

  8. Pesticide Product Label System (United States)

    U.S. Environmental Protection Agency — The Pesticide Product Label System (PPLS) provides a collection of pesticide product labels (Adobe PDF format) that have been approved by EPA under Section 3 of the...

  9. Mental Labels and Tattoos (United States)

    Hyatt, I. Ralph


    Discusses the ease with which mental labels become imprinted in our system, six basic axioms for maintaining negative mental tattoos, and psychological processes for eliminating mental tattoos and labels. (RK)

  10. Electronic Submission of Labels (United States)

    Pesticide registrants can provide draft and final labels to EPA electronically for our review as part of the pesticide registration process. The electronic submission of labels by registrants is voluntary but strongly encouraged.

  11. Improving sensitivity of linear regression-based cell type-specific differential expression deconvolution with per-gene vs. global significance threshold. (United States)

    Glass, Edmund R; Dozmorov, Mikhail G


    The goal of many human disease-oriented studies is to detect molecular mechanisms different between healthy controls and patients. Yet, commonly used gene expression measurements from blood samples suffer from variability of cell composition. This variability hinders the detection of differentially expressed genes and is often ignored. Combined with cell counts, heterogeneous gene expression may provide deeper insights into the gene expression differences on the cell type-specific level. Published computational methods use linear regression to estimate cell type-specific differential expression, and a global cutoff to judge significance, such as False Discovery Rate (FDR). Yet, they do not consider many artifacts hidden in high-dimensional gene expression data that may negatively affect linear regression. In this paper we quantify the parameter space affecting the performance of linear regression (sensitivity of cell type-specific differential expression detection) on a per-gene basis. We evaluated the effect of sample sizes, cell type-specific proportion variability, and mean squared error on sensitivity of cell type-specific differential expression detection using linear regression. Each parameter affected variability of cell type-specific expression estimates and, subsequently, the sensitivity of differential expression detection. We provide the R package, LRCDE, which performs linear regression-based cell type-specific differential expression (deconvolution) detection on a gene-by-gene basis. Accounting for variability around cell type-specific gene expression estimates, it computes per-gene t-statistics of differential detection, p-values, t-statistic-based sensitivity, group-specific mean squared error, and several gene-specific diagnostic metrics. The sensitivity of linear regression-based cell type-specific differential expression detection differed for each gene as a function of mean squared error, per group sample sizes, and variability of the proportions

  12. A Label to Regulate

    DEFF Research Database (Denmark)

    Tricoire, Aurélie; Boxenbaum, Eva; Laurent, Brice

    This paper examines the role labelling plays in the government of the contemporary economy.1Drawing on a detailed study of BBC-Effinergy, a French label for sustainable construction, we showhow the adoption and evolution of voluntary labels can be seen as emblematic of a governmentthrough experim...... experiment engaging 4 operations: stimulating market anticipations, focussing politicalconsultations, producing collective expertise and containing the regulatory transcription of the label....

  13. Exercise in the fasted state facilitates fibre type-specific intramyocellular lipid breakdown and stimulates glycogen resynthesis in humans

    DEFF Research Database (Denmark)

    De Bock, K.; Richter, Erik; Russell, A.P.


    sessions with an interval of 3 weeks. In each session subjects performed 2 h of constant-load bicycle exercise (~75% VO2,max), followed by 4 h of controlled recovery. On one occasion they exercised after an overnight fast (F), and on the other (CHO) they received carbohydrates before (~150 g) and during (1...... g (kg bw)-1 h-1) exercise. In both conditions, subjects ingested 5 g carbohydrates per kg body weight during recovery. Fibre type-specific relative IMTG content was determined by Oil red O staining in needle biopsies from m. vastus lateralis before, immediately after and 4 h after exercise. During F...... in any experimental condition. Exercise-induced net glycogen breakdown was similar in F and CHO. However, compared with CHO (11.0 ± 7.8 mmol kg-1 h-1), mean rate of postexercise muscle glycogen resynthesis was 3-fold greater in F (32.9 ± 2.7 mmol kg-1 h-1, P = 0.01). Furthermore, oral glucose loading...

  14. Dynamics of bacterial communities in two unpolluted soils after spiking with phenanthrene: soil type specific and common responders

    Directory of Open Access Journals (Sweden)

    Guo-Chun eDing


    Full Text Available Considering their key role for ecosystem processes, it is important to understand the response of microbial communities in unpolluted soils to pollution with polycyclic aromatic hydrocarbons (PAH. Phenanthrene, a model compound for PAH, was spiked to a Cambisol and a Luvisol soil. Total community DNA from phenanthrene-spiked and control soils collected on days 0, 21 and 63 were analyzed based on PCR-amplified 16S rRNA genefragments. Denaturing gradient gel electrophoresis (DGGE fingerprints of bacterial communities increasingly deviated with time between spiked and control soils. In taxon specific DGGE, significant responses of Alphaproteobacteria and Actinobacteria became only detectable after 63 days, while significant effects on Betaproteobacteria were detectable in both soils after 21 days. Comparison of the taxonomic distribution of bacteria in spiked and control soils on day 63 as revealed by pyrosequencing indicated soil type specific negative effects of phenanthrene on several taxa, many of them belonging to the Gamma-, Beta- or Deltaproteobacteria. Bacterial richness and evenness decreased in spiked soils. Despite the significant differences in the bacterial community structure between both soils on day 0, similar genera increased in relative abundance after PAH spiking, especially Sphingomonas and Polaromonas. However, this did not result in an increased overall similarity of the bacterial communities in both soils.

  15. Comprehensive analysis of ultrasonic vocalizations in a mouse model of fragile X syndrome reveals limited, call type specific deficits.

    Directory of Open Access Journals (Sweden)

    Snigdha Roy

    Full Text Available Fragile X syndrome (FXS is a well-recognized form of inherited mental retardation, caused by a mutation in the fragile X mental retardation 1 (Fmr1 gene. The gene is located on the long arm of the X chromosome and encodes fragile X mental retardation protein (FMRP. Absence of FMRP in fragile X patients as well as in Fmr1 knockout (KO mice results, among other changes, in abnormal dendritic spine formation and altered synaptic plasticity in the neocortex and hippocampus. Clinical features of FXS include cognitive impairment, anxiety, abnormal social interaction, mental retardation, motor coordination and speech articulation deficits. Mouse pups generate ultrasonic vocalizations (USVs when isolated from their mothers. Whether those social ultrasonic vocalizations are deficient in mouse models of FXS is unknown. Here we compared isolation-induced USVs generated by pups of Fmr1-KO mice with those of their wild type (WT littermates. Though the total number of calls was not significantly different between genotypes, a detailed analysis of 10 different categories of calls revealed that loss of Fmr1 expression in mice causes limited and call-type specific deficits in ultrasonic vocalization: the carrier frequency of flat calls was higher, the percentage of downward calls was lower and that the frequency range of complex calls was wider in Fmr1-KO mice compared to their WT littermates.

  16. Clustered Regularly Interspaced Short Palindromic Repeats Are emm Type-Specific in Highly Prevalent Group A Streptococci. (United States)

    Zheng, Po-Xing; Chan, Yuen-Chi; Chiou, Chien-Shun; Chiang-Ni, Chuan; Wang, Shu-Ying; Tsai, Pei-Jane; Chuang, Woei-Jer; Lin, Yee-Shin; Liu, Ching-Chuan; Wu, Jiunn-Jong


    Clustered regularly interspaced short palindromic repeats (CRISPR) are the bacterial adaptive immune system against foreign nucleic acids. Given the variable nature of CRISPR, it could be a good marker for molecular epidemiology. Group A streptococcus is one of the major human pathogens. It has two CRISPR loci, including CRISPR01 and CRISPR02. The aim of this study was to analyze the distribution of CRISPR-associated gene cassettes (cas) and CRISPR arrays in highly prevalent emm types. The cas cassette and CRISPR array in two CRISPR loci were analyzed in a total of 332 strains, including emm1, emm3, emm4, emm12, and emm28 strains. The CRISPR type was defined by the spacer content of each CRISPR array. All strains had at least one cas cassette or CRISPR array. More than 90% of the spacers were found in one emm type, specifically. Comparing the consistency between emm and CRISPR types by Simpson's index of diversity and the adjusted Wallace coefficient, CRISPR01 type was concordant to emm type, and CRISPR02 showed unidirectional congruence to emm type, suggesting that at least for the majority of isolates causing infection in high income countries, the emm type can be inferred from CRISPR analysis, which can further discriminate isolates sharing the same emm type.

  17. Implementing the LIM code: the structural basis for cell type-specific assembly of LIM-homeodomain complexes

    Energy Technology Data Exchange (ETDEWEB)

    Bhati, Mugdha; Lee, Christopher; Nancarrow, Amy L.; Lee, Mihwa; Craig, Vanessa J.; Bach, Ingolf; Guss, J. Mitchell; Mackay, Joel P.; Matthews, Jacqueline M. (UMASS, MED); (Sydney)


    LIM-homeodomain (LIM-HD) transcription factors form a combinatorial 'LIM code' that contributes to the specification of cell types. In the ventral spinal cord, the binary LIM homeobox protein 3 (Lhx3)/LIM domain-binding protein 1 (Ldb1) complex specifies the formation of V2 interneurons. The additional expression of islet-1 (Isl1) in adjacent cells instead specifies the formation of motor neurons through assembly of a ternary complex in which Isl1 contacts both Lhx3 and Ldb1, displacing Lhx3 as the binding partner of Ldb1. However, little is known about how this molecular switch occurs. Here, we have identified the 30-residue Lhx3-binding domain on Isl1 (Isl1{sub LBD}). Although the LIM interaction domain of Ldb1 (Ldb1{sub LID}) and Isl1{sub LBD} share low levels of sequence homology, X-ray and NMR structures reveal that they bind Lhx3 in an identical manner, that is, Isl1{sub LBD} mimics Ldb1{sub LID}. These data provide a structural basis for the formation of cell type-specific protein-protein interactions in which unstructured linear motifs with diverse sequences compete to bind protein partners. The resulting alternate protein complexes can target different genes to regulate key biological events.

  18. Analysis of proteins of mouse sarcoma pseudotype viruses: type-specific radioimmunoassays for ecotropic virus p30's

    International Nuclear Information System (INIS)

    Kennel, S.J.; Tennant, R.W.


    Murine sarcoma virus pseudotypes were prepared by infection of nonproducer cells (A1-2), which were transformed by the Gazdar strain of mouse sarcoma virus, with Gross (N-tropic), WN1802B (B-tropic), or Moloney (NB-tropic) viruses. The respective host range pseudotype sarcoma viruses were defined by the tritration characteristics on cells with the appropriate Fv-1 genotype. Proteins from virus progeny were analyzed by sodium dodecyl sulfate--polyacrylamide gel electrophoresis. Bands present in both the 65,000- and the 10,000- to 20,000-molecular-weight regions of the gel distinguished the pseudotype viruses from their respective helpers. Furthermore, two protein bands were noted in the p30 region of murine sarcoma virus (Gross), one corresponding to Gross virus p30, and another of slightly slower mobility. However, since the mobility of the putative sarcoma p30 is nearly indentical to that of WN1802B, its presence could not be established by sodium dodecyl sulfate--polyacrylamide gel electrophoresis. Type-specific radioimmunossays for Gross virus p30 and for WN1802B p30 were applied for analysis of pseudotype preparations, and among several ecotropic viruses tested, only the homologous virus scored in the respective assay. By use of these assays, pseudotype viruses were found to contain only 8 to 48% helper-specific p30's; the remainder is presumably derived from the sarcoma virus

  19. Npas4 regulates excitatory-inhibitory balance within neural circuits through cell-type-specific gene programs. (United States)

    Spiegel, Ivo; Mardinly, Alan R; Gabel, Harrison W; Bazinet, Jeremy E; Couch, Cameron H; Tzeng, Christopher P; Harmin, David A; Greenberg, Michael E


    The nervous system adapts to experience by inducing a transcriptional program that controls important aspects of synaptic plasticity. Although the molecular mechanisms of experience-dependent plasticity are well characterized in excitatory neurons, the mechanisms that regulate this process in inhibitory neurons are only poorly understood. Here, we describe a transcriptional program that is induced by neuronal activity in inhibitory neurons. We find that, while neuronal activity induces expression of early-response transcription factors such as Npas4 in both excitatory and inhibitory neurons, Npas4 activates distinct programs of late-response genes in inhibitory and excitatory neurons. These late-response genes differentially regulate synaptic input to these two types of neurons, promoting inhibition onto excitatory neurons while inducing excitation onto inhibitory neurons. These findings suggest that the functional outcomes of activity-induced transcriptional responses are adapted in a cell-type-specific manner to achieve a circuit-wide homeostatic response. Copyright © 2014 Elsevier Inc. All rights reserved.

  20. Evaluation of IRES-mediated, cell-type-specific cytotoxicity of poliovirus using a colorimetric cell proliferation assay. (United States)

    Yang, Xiaoyi; Chen, Eying; Jiang, Hengguang; Muszynski, Karen; Harris, Raymond D; Giardina, Steven L; Gromeier, Matthias; Mitra, Gautam; Soman, Gopalan


    PVS-RIPO is a recombinant oncolytic poliovirus designed for clinical application to target CD155 expressing malignant gliomas and other malignant diseases. PVS-RIPO does not replicate in healthy neurons and is therefore non-pathogenic in rodent and non-human primate models of poliomyelitis. A tetrazolium salt dye-based cellular assay was developed and qualified to define the cytotoxicity of virus preparations on susceptible cells and to explore the target cell specificity of PVS-RIPO. In this assay, PVS-RIPO inhibited proliferation of U87-MG astrocytoma cells in a dose-dependent manner. However, HEK293 cells were much less susceptible to cell killing by PVS-RIPO. In contrast, the Sabin type 1 live attenuated poliovirus vaccine strain (PV(1)S) was cytotoxic to both HEK293 and U87-MG cells. The correlation between expression of CD155 and cytotoxicity was also explored using six different cell lines. There was little or no expression of CD155 and PVS-RIPO-induced cytotoxicity in Jurkat and Daudi cells. HEK293 was the only cell line tested that showed CD155 expression and resistance to PVS-RIPO cytotoxicity. The results indicate that differential cytotoxicity measured by the colorimetric assay can be used to evaluate the cytotoxicity and cell-type specificity of recombinant strains of poliovirus and to demonstrate lot to lot consistency during the manufacture of viruses intended for clinical use.

  1. Combinatorial Modulation of Signaling Pathways Reveals Cell-Type-Specific Requirements for Highly Efficient and Synchronous iPSC Reprogramming

    Directory of Open Access Journals (Sweden)

    Simon E. Vidal


    Full Text Available The differentiated state of somatic cells provides barriers for the derivation of induced pluripotent stem cells (iPSCs. To address why some cell types reprogram more readily than others, we studied the effect of combined modulation of cellular signaling pathways. Surprisingly, inhibition of transforming growth factor β (TGF-β together with activation of Wnt signaling in the presence of ascorbic acid allows >80% of murine fibroblasts to acquire pluripotency after 1 week of reprogramming factor expression. In contrast, hepatic and blood progenitors predominantly required only TGF-β inhibition or canonical Wnt activation, respectively, to reprogram at efficiencies approaching 100%. Strikingly, blood progenitors reactivated endogenous pluripotency loci in a highly synchronous manner, and we demonstrate that expression of specific chromatin-modifying enzymes and reduced TGF-β/mitogen-activated protein (MAP kinase activity are intrinsic properties associated with the unique reprogramming response of these cells. Our observations define cell-type-specific requirements for the rapid and synchronous reprogramming of somatic cells.

  2. Spatial separation of photosynthesis and ethanol production by cell type-specific metabolic engineering of filamentous cyanobacteria. (United States)

    Ehira, Shigeki; Takeuchi, Takuto; Higo, Akiyoshi


    Cyanobacteria, which perform oxygenic photosynthesis, have drawn attention as hosts for the direct production of biofuels and commodity chemicals from CO 2 and H 2 O using light energy. Although cyanobacteria capable of producing diverse chemicals have been generated by metabolic engineering, anaerobic non-photosynthetic culture conditions are often necessary for their production. In this study, we conducted cell type-specific metabolic engineering of the filamentous cyanobacterium Anabaena sp. PCC 7120, which forms a terminally differentiated cell called a heterocyst with a semi-regular spacing of 10-15 cells. Because heterocysts are specialized cells for nitrogen fixation, the intracellular oxygen level of heterocysts is maintained very low even when adjacent cells perform oxygenic photosynthesis. Pyruvate decarboxylase of Zymomonas mobilis and alcohol dehydrogenase of Synechocystis sp. PCC 6803 were exclusively expressed in heterocysts. Ethanol production was concomitant with nitrogen fixation in genetically engineered Anabaena sp. PCC 7120. Engineering of carbon metabolism in heterocysts improved ethanol production, and strain ET14, with an extra copy of the invB gene expressed from a heterocyst-specific promoter, produced 130.9 mg L -1 of ethanol after 9 days. ET14 produced 1681.9 mg L -1 of ethanol by increasing the CO 2 supply. Ethanol production per heterocyst cell was approximately threefold higher than that per cell of unicellular cyanobacterium. This study demonstrates the potential of heterocysts for anaerobic production of biofuels and commodity chemicals under oxygenic photosynthetic conditions.

  3. Rhizoctonia solani and Bacterial Inoculants Stimulate Root Exudation of Antifungal Compounds in Lettuce in a Soil-Type Specific Manner

    Directory of Open Access Journals (Sweden)

    Saskia Windisch


    Full Text Available Previous studies conducted on a unique field site comprising three contrasting soils (diluvial sand DS, alluvial loam AL, loess loam LL under identical cropping history, demonstrated soil type-dependent differences in biocontrol efficiency against Rhizoctonia solani-induced bottom rot disease in lettuce by two bacterial inoculants (Pseudomonas jessenii RU47 and Serratia plymuthica 3Re-4-18. Disease severity declined in the order DS > AL > LL. These differences were confirmed under controlled conditions, using the same soils in minirhizotron experiments. Gas chromatography-mass spectrometry (GC-MS profiling of rhizosphere soil solutions revealed benzoic and lauric acids as antifungal compounds; previously identified in root exudates of lettuce. Pathogen inoculation and pre-inoculation with bacterial inoculants significantly increased the release of antifungal root exudates in a soil type-specific manner; with the highest absolute levels detected on the least-affected LL soil. Soil type-dependent differences were also recorded for the biocontrol effects of the two bacterial inoculants; showing the highest efficiency after double-inoculation on the AL soil. However, this was associated with a reduction of shoot growth and root hair development and a limited micronutrient status of the host plants. Obviously, disease severity and the expression of biocontrol effects are influenced by soil properties with potential impact on reproducibility of practical applications.

  4. Study of equid herpesviruses 2 and 5 in Iceland with a type-specific polymerase chain reaction. (United States)

    Torfason, Einar G; Thorsteinsdóttir, Lilja; Torsteinsdóttir, Sigurbjörg; Svansson, Vilhjálmur


    The horse population in Iceland is a special breed, isolated from other horses for at least 1000 years. This provides an exceptional opportunity to investigate old and new pathogens in an inbred herd with few infectious diseases. We have developed a high sensitivity semi-nested PCR to study equid gammaherpesviruses 2 and 5 (EHV-2 and 5) in Iceland. The first PCR is group specific, the second type-specific, targeting a 113bp sequence in the glyB gene. DNA isolated from white blood cells and 18 different organs was tested for the presence of EHV-2 and 5. This was done in adult horses and foals, healthy and with various enteric infections. Both virus types were easily detected in all types of organs tested or EHV-2 in 79% cases and EHV-5 in 63%. In DNA from PBMC or buffy-coat EHV-2 was found in 20% cases and EHV-5 in 10%, all except one positive were foals. Co-culture of PBMC on fetal horse kidney cells was efficient for detecting EHV-2 but not for EHV-5. We verify here for the first time infections with EHV-2 and 5 in horses in Iceland and show that both viruses are common.

  5. Clustered Regularly Interspaced Short Palindromic Repeats Are emm Type-Specific in Highly Prevalent Group A Streptococci.

    Directory of Open Access Journals (Sweden)

    Po-Xing Zheng

    Full Text Available Clustered regularly interspaced short palindromic repeats (CRISPR are the bacterial adaptive immune system against foreign nucleic acids. Given the variable nature of CRISPR, it could be a good marker for molecular epidemiology. Group A streptococcus is one of the major human pathogens. It has two CRISPR loci, including CRISPR01 and CRISPR02. The aim of this study was to analyze the distribution of CRISPR-associated gene cassettes (cas and CRISPR arrays in highly prevalent emm types. The cas cassette and CRISPR array in two CRISPR loci were analyzed in a total of 332 strains, including emm1, emm3, emm4, emm12, and emm28 strains. The CRISPR type was defined by the spacer content of each CRISPR array. All strains had at least one cas cassette or CRISPR array. More than 90% of the spacers were found in one emm type, specifically. Comparing the consistency between emm and CRISPR types by Simpson's index of diversity and the adjusted Wallace coefficient, CRISPR01 type was concordant to emm type, and CRISPR02 showed unidirectional congruence to emm type, suggesting that at least for the majority of isolates causing infection in high income countries, the emm type can be inferred from CRISPR analysis, which can further discriminate isolates sharing the same emm type.

  6. Semi-supervised change detection approach combining sparse fusion and constrained k means for multi-temporal remote sensing images

    Directory of Open Access Journals (Sweden)

    Anisha M. Lal


    Full Text Available Change detection is the measure of the thematic change information that can guide to more tangible insights into an underlying process involving land cover, land usage and environmental changes. This paper deals with a semi-supervised change detection approach combining sparse fusion and constrained k means clustering on multi-temporal remote sensing images taken at different timings T1 and T2. Initially a remote sensing fusion method with sparse representation over learned dictionaries is applied to the difference images. The dictionaries are learned from the difference images adaptively. The fused image is calculated by combining the sparse coefficients and the dictionary. Finally the fused image is subjected to constrained k means (CKM clustering combining few known labelled patterns and unlabelled patterns which have been collected from experts. The enhanced (CKM approach (ECKM is compared with k means, adaptive k means (AKM and fuzzy c means (FCM. Experimental results were carried out on multi-temporal remote sensing images. Results obtained using PCC and F1 measure confirms the effectiveness of the proposed approach. It is also noticed that the ECKM provides better results with less misclassification of errors as compared to k means, adaptive k means and fuzzy c means.

  7. Behavior of greedy sparse representation algorithms on nested supports

    DEFF Research Database (Denmark)

    Mailhé, Boris; Sturm, Bob L.; Plumbley, Mark


    We study the links between recovery properties of Orthogonal Matching Pursuit (OMP) and the whole General MP class for sparse signals with nested supports, i.e., supports that share an inclusion relationship. In particular, we show that the support recovery optimality of those algorithms is not l......We study the links between recovery properties of Orthogonal Matching Pursuit (OMP) and the whole General MP class for sparse signals with nested supports, i.e., supports that share an inclusion relationship. In particular, we show that the support recovery optimality of those algorithms...... is not locally nested: there is a dictionary and supports Γ ⊃ Γ′ such that OMP can recover all signals with support Γ, but not all signals with support Γ′. We also show that the support recovery optimality of OMP is globally nested: if OMP can recover all s-sparse signals, then it can recover all s...

  8. Algorithms for Sparse Non-negative Tucker Decompositions

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai


    for tensors are the Tucker model and the more restricted PARAFAC model. Both models can be viewed as generalizations of the regular factor analysis to data of more than two modalities. Non-negative matrix factorization (NMF) in conjunction with sparse coding has lately been given much attention due to its...... part based and easy interpretable representation. While NMF has been extended to the PARAFAC model no such attempt has been done to extend NMF to the Tucker model. However, if the tensor data analyzed is non-negative it may well be relevant to consider purely additive (i.e., non-negative Tucker...... decompositions). To reduce ambiguities of this type of decomposition we develop updates that can impose sparseness in any combination of modalities, hence, proposed algorithms for sparse non-negative Tucker decompositions (SN-TUCKER). We demonstrate how the proposed algorithms are superior to existing algorithms...

  9. Sparse-view Reconstruction of Dynamic Processes by Neutron Tomography (United States)

    Wang, Hu; Kaestner, Anders; Zou, Yubin; Lu, Yuanrong; Guo, Zhiyu

    As for neutron tomography, hundreds of projections over the range of 0-180 degrees are required to reconstruct the attenuation matrix with the traditional filtered back projection (FBP) algorithm, and the total acquisition time can reach several hours. This poor temporal resolution constrains that neutron tomography is only feasible to investigate static or quasi-static process. Reducing the number of projections is a possible way to improve the temporal resolution, which however highly relies on sparse-view reconstruction algorithms. To assess the feasibility of sparse-view reconstruction for neutron tomography, both simulation and an experiment of water uptake from a piece of wood composite were studied, and the results indicated that temporal resolution of neutron tomography can be improved when combining the Golden Ratio scan strategy with a prior image-constrained sparse-view reconstruction algorithm-PICCS.

  10. Massively parallel sparse matrix function calculations with NTPoly (United States)

    Dawson, William; Nakajima, Takahito


    We present NTPoly, a massively parallel library for computing the functions of sparse, symmetric matrices. The theory of matrix functions is a well developed framework with a wide range of applications including differential equations, graph theory, and electronic structure calculations. One particularly important application area is diagonalization free methods in quantum chemistry. When the input and output of the matrix function are sparse, methods based on polynomial expansions can be used to compute matrix functions in linear time. We present a library based on these methods that can compute a variety of matrix functions. Distributed memory parallelization is based on a communication avoiding sparse matrix multiplication algorithm. OpenMP task parallellization is utilized to implement hybrid parallelization. We describe NTPoly's interface and show how it can be integrated with programs written in many different programming languages. We demonstrate the merits of NTPoly by performing large scale calculations on the K computer.

  11. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla


    Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

  12. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla


    Newton-type algorithms have been extensively studied in nonlinear microwave imaging due to their quadratic convergence rate and ability to recover images with high contrast values. In the past, Newton methods have been implemented in conjunction with smoothness promoting optimization/regularization schemes. However, this type of regularization schemes are known to perform poorly when applied in imagining domains with sparse content or sharp variations. In this work, an inexact Newton algorithm is formulated and implemented in conjunction with a linear sparse optimization scheme. A novel preconditioning technique is proposed to increase the convergence rate of the optimization problem. Numerical results demonstrate that the proposed framework produces sharper and more accurate images when applied in sparse/sparsified domains.

  13. Identification of MIMO systems with sparse transfer function coefficients (United States)

    Qiu, Wanzhi; Saleem, Syed Khusro; Skafidas, Efstratios


    We study the problem of estimating transfer functions of multivariable (multiple-input multiple-output--MIMO) systems with sparse coefficients. We note that subspace identification methods are powerful and convenient tools in dealing with MIMO systems since they neither require nonlinear optimization nor impose any canonical form on the systems. However, subspace-based methods are inefficient for systems with sparse transfer function coefficients since they work on state space models. We propose a two-step algorithm where the first step identifies the system order using the subspace principle in a state space format, while the second step estimates coefficients of the transfer functions via L1-norm convex optimization. The proposed algorithm retains good features of subspace methods with improved noise-robustness for sparse systems.

  14. A General Sparse Tensor Framework for Electronic Structure Theory. (United States)

    Manzer, Samuel; Epifanovsky, Evgeny; Krylov, Anna I; Head-Gordon, Martin


    Linear-scaling algorithms must be developed in order to extend the domain of applicability of electronic structure theory to molecules of any desired size. However, the increasing complexity of modern linear-scaling methods makes code development and maintenance a significant challenge. A major contributor to this difficulty is the lack of robust software abstractions for handling block-sparse tensor operations. We therefore report the development of a highly efficient symbolic block-sparse tensor library in order to provide access to high-level software constructs to treat such problems. Our implementation supports arbitrary multi-dimensional sparsity in all input and output tensors. We avoid cumbersome machine-generated code by implementing all functionality as a high-level symbolic C++ language library and demonstrate that our implementation attains very high performance for linear-scaling sparse tensor contractions.

  15. P-SPARSLIB: A parallel sparse iterative solution package

    Energy Technology Data Exchange (ETDEWEB)

    Saad, Y. [Univ. of Minnesota, Minneapolis, MN (United States)


    Iterative methods are gaining popularity in engineering and sciences at a time where the computational environment is changing rapidly. P-SPARSLIB is a project to build a software library for sparse matrix computations on parallel computers. The emphasis is on iterative methods and the use of distributed sparse matrices, an extension of the domain decomposition approach to general sparse matrices. One of the goals of this project is to develop a software package geared towards specific applications. For example, the author will test the performance and usefulness of P-SPARSLIB modules on linear systems arising from CFD applications. Equally important is the goal of portability. In the long run, the author wishes to ensure that this package is portable on a variety of platforms, including SIMD environments and shared memory environments.


    Directory of Open Access Journals (Sweden)

    Rami Zewail


    Full Text Available Machine learning and statistical modeling techniques has drawn much interest within the medical imaging research community. However, clinically-relevant modeling of anatomical structures continues to be a challenging task. This paper presents a novel method for multiscale sparse appearance modeling in medical images with application to simulation of pathological deformations in X-ray images of human spine. The proposed appearance model benefits from the non-linear approximation power of Contourlets and its ability to capture higher order singularities to achieve a sparse representation while preserving the accuracy of the statistical model. Independent Component Analysis is used to extract statistical independent modes of variations from the sparse Contourlet-based domain. The new model is then used to simulate clinically-relevant pathological deformations in radiographic images.

  17. Data-driven initialization of SParSE (United States)

    Roh, Min K.; Proctor, Joshua L.


    Despite the ever-increasing affordability and availability of high performance computing platforms, computational analysis of stochastic biochemical systems remains an open problem. A recently developed event-based parameter estimation method, the stochastic parameter search for events (SParSE), is able to efficiently sample reaction rate parameter values that confer a user-specified target event with a given probability and error tolerance. Despite the substantial computational savings, the efficiency of SParSE can be further improved by intelligently generating new initial parameter sets based on previously computed trajectories. In this article, we propose a principled method which combines the efficiencies of SParSE with these geometric machine-learning methods to generate new initial parameters based on the previously collected data.

  18. BigSparse: High-performance external graph analytics


    Jun, Sang-Woo; Wright, Andy; Zhang, Sizhuo; Xu, Shuotao; Arvind


    We present BigSparse, a fully external graph analytics system that picks up where semi-external systems like FlashGraph and X-Stream, which only store vertex data in memory, left off. BigSparse stores both edge and vertex data in an array of SSDs and avoids random updates to the vertex data, by first logging the vertex updates and then sorting the log to sequentialize accesses to the SSDs. This newly introduced sorting overhead is reduced significantly by interleaving sorting with vertex redu...

  19. Sparse Source EEG Imaging with the Variational Garrote

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Stahlhut, Carsten; Hansen, Lars Kai


    EEG imaging, the estimation of the cortical source distribution from scalp electrode measurements, poses an extremely ill-posed inverse problem. Recent work by Delorme et al. (2012) supports the hypothesis that distributed source solutions are sparse. We show that direct search for sparse solutio...... as implemented by the Variational Garrote (Kappen, 2011) provides excellent estimates compared with other widely used schemes, is computationally attractive, and by its separation of ’where’ and ’what’ degrees of freedom paves the road for the introduction of genuine prior information....

  20. Adaptive identification of acoustic multichannel systems using sparse representations

    CERN Document Server

    Helwani, Karim


    This book treats the topic of extending the adaptive filtering theory in the context of massive multichannel systems by taking into account a priori knowledge of the underlying system or signal. The starting point is exploiting the sparseness in acoustic multichannel system in order to solve the non-uniqueness problem with an efficient algorithm for adaptive filtering that does not require any modification of the loudspeaker signals.The book discusses in detail the?derivation of general sparse representations of acoustic MIMO systems?in signal or system dependent transform domains.?Efficient a

  1. A Projected Conjugate Gradient Method for Sparse Minimax Problems

    DEFF Research Database (Denmark)

    Madsen, Kaj; Jonasson, Kristjan


    as it only requires that software for sparse linear programming and a sparse symmetric positive definite equation solver are available. On each iteration a special linear/quadratic model of the function is minimized, but contrary to the usual practice in trust region methods the quadratic model is only...... defined on a one dimensional path from the current iterate to the boundary of the trust region. Conjugate gradients are used to define this path. One iteration involves one LP subproblem and requires three function evaluations and one gradient evaluation. Promising numerical results obtained...

  2. Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

    KAUST Repository

    Desmal, Abdulla


    A sparse nonlinear electromagnetic imaging scheme is proposed for reconstructing dielectric contrast of investigation domains from measured fields. The proposed approach constructs the optimization problem by introducing the sparsity constraint to the data misfit between the scattered fields expressed as a nonlinear function of the contrast and the measured fields and solves it using the nonlinear iterative shrinkage thresholding algorithm. The thresholding is applied to the result of every nonlinear Landweber iteration to enforce the sparsity constraint. Numerical results demonstrate the accuracy and efficiency of the proposed method in reconstructing sparse dielectric profiles.

  3. Low-rank and sparse modeling for visual analysis

    CERN Document Server

    Fu, Yun


    This book provides a view of low-rank and sparse computing, especially approximation, recovery, representation, scaling, coding, embedding and learning among unconstrained visual data. The book includes chapters covering multiple emerging topics in this new field. It links multiple popular research fields in Human-Centered Computing, Social Media, Image Classification, Pattern Recognition, Computer Vision, Big Data, and Human-Computer Interaction. Contains an overview of the low-rank and sparse modeling techniques for visual analysis by examining both theoretical analysis and real-world applic

  4. Sparse Matrix for ECG Identification with Two-Lead Features

    Directory of Open Access Journals (Sweden)

    Kuo-Kun Tseng


    Full Text Available Electrocardiograph (ECG human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods.

  5. Land cover classification in multispectral satellite imagery using sparse approximations on learned dictionaries (United States)

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


    Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a modified Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using CoSA: unsupervised Clustering of Sparse Approximations. We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska (USA). Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties (e.g., soil moisture and inundation), and topographic/geomorphic characteristics. In this paper, we explore learning from both raw multispectral imagery, as well as normalized band difference indexes. We explore a quantitative metric to evaluate the spectral properties of the clusters, in order to potentially aid in assigning land cover categories to the cluster labels.

  6. Unsupervised Transfer Learning via Multi-Scale Convolutional Sparse Coding for Biomedical Applications. (United States)

    Chang, Hang; Han, Ju; Zhong, Cheng; Snijders, Antoine M; Mao, Jian-Hua


    The capabilities of (I) learning transferable knowledge across domains; and (II) fine-tuning the pre-learned base knowledge towards tasks with considerably smaller data scale are extremely important. Many of the existing transfer learning techniques are supervised approaches, among which deep learning has the demonstrated power of learning domain transferrable knowledge with large scale network trained on massive amounts of labeled data. However, in many biomedical tasks, both the data and the corresponding label can be very limited, where the unsupervised transfer learning capability is urgently needed. In this paper, we proposed a novel multi-scale convolutional sparse coding (MSCSC) method, that (I) automatically learns filter banks at different scales in a joint fashion with enforced scale-specificity of learned patterns; and (II) provides an unsupervised solution for learning transferable base knowledge and fine-tuning it towards target tasks. Extensive experimental evaluation of MSCSC demonstrates the effectiveness of the proposed MSCSC in both regular and transfer learning tasks in various biomedical domains.

  7. Change detection in Arctic satellite imagery using clustering of sparse approximations (CoSA) over learned feature dictionaries (United States)

    Moody, Daniela I.; Wilson, Cathy J.; Rowland, Joel C.; Altmann, Garrett L.


    Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.

  8. Cell-Type Specific Channelopathies in the Prefrontal Cortex of the fmr1-/y Mouse Model of Fragile X Syndrome. (United States)

    Kalmbach, Brian E; Johnston, Daniel; Brager, Darrin H


    Fragile X syndrome (FXS) is caused by transcriptional silencing of the fmr1 gene resulting in the loss of fragile X mental retardation protein (FMRP) expression. FXS patients display several behavioral phenotypes associated with prefrontal cortex (PFC) dysfunction. Voltage-gated ion channels, some of which are regulated by FMRP, heavily influence PFC neuron function. Although there is evidence for brain region-specific alterations to the function a single type of ion channel in FXS, it is unclear whether subtypes of principal neurons within a brain region are affected uniformly. We tested for alterations to ion channels critical in regulating neural excitability in two subtypes of prefrontal L5 pyramidal neurons. Using somatic and dendritic patch-clamp recordings, we provide evidence that the functional expression of h-channels (Ih) is down-regulated, whereas A-type K(+) channel function is up-regulated in pyramidal tract-projecting (PT) neurons in the fmr1-/y mouse PFC. This is the opposite pattern of results from published findings from hippocampus where Ih is up-regulated and A-type K(+) channel function is down-regulated. Additionally, we find that somatic Kv1-mediated current is down-regulated, resulting in increased excitability of fmr1-/y PT neurons. Importantly, these h- and K(+) channel differences do not extend to neighboring intratelencephalic-projecting neurons. Thus, the absence of FMRP has divergent effects on the function of individual types of ion channels not only between brain regions, but also variable effects across cell types within the same brain region. Given the importance of ion channels in regulating neural circuits, these results suggest cell-type-specific phenotypes for the disease.

  9. Layer- and cell-type-specific subthreshold and suprathreshold effects of long-term monocular deprivation in rat visual cortex. (United States)

    Medini, Paolo


    Connectivity and dendritic properties are determinants of plasticity that are layer and cell-type specific in the neocortex. However, the impact of experience-dependent plasticity at the level of synaptic inputs and spike outputs remains unclear along vertical cortical microcircuits. Here I compared subthreshold and suprathreshold sensitivity to prolonged monocular deprivation (MD) in rat binocular visual cortex in layer 4 and layer 2/3 pyramids (4Ps and 2/3Ps) and in thick-tufted and nontufted layer 5 pyramids (5TPs and 5NPs), which innervate different extracortical targets. In normal rats, 5TPs and 2/3Ps are the most binocular in terms of synaptic inputs, and 5NPs are the least. Spike responses of all 5TPs were highly binocular, whereas those of 2/3Ps were dominated by either the contralateral or ipsilateral eye. MD dramatically shifted the ocular preference of 2/3Ps and 4Ps, mostly by depressing deprived-eye inputs. Plasticity was profoundly different in layer 5. The subthreshold ocular preference shift was sevenfold smaller in 5TPs because of smaller depression of deprived inputs combined with a generalized loss of responsiveness, and was undetectable in 5NPs. Despite their modest ocular dominance change, spike responses of 5TPs consistently lost their typically high binocularity during MD. The comparison of MD effects on 2/3Ps and 5TPs, the main affected output cells of vertical microcircuits, indicated that subthreshold plasticity is not uniquely determined by the initial degree of input binocularity. The data raise the question of whether 5TPs are driven solely by 2/3Ps during MD. The different suprathreshold plasticity of the two cell populations could underlie distinct functional deficits in amblyopia.

  10. Type-specific HPV and Pap test results among low-income, underserved women: providing insights into management strategies. (United States)

    Saraiya, Mona; Benard, Vicki B; Greek, April A; Steinau, Martin; Patel, Sonya; Massad, L Stewart; Sawaya, George F; Unger, Elizabeth R


    The primary cervical cancer screening strategy for women over age 30 is high-risk human papillomavirus (HPV) testing combined with Papanicolaou (Pap) testing (cotesting) every 5 years. This combination strategy is a preventive service that is required by the Affordable Care Act to be covered with no cost-sharing by most health insurance plans. The cotesting recommendation was made based entirely on prospective data from an insured population that may have a lower proportion of women with HPV positive and Pap negative results (ie, discordant results). The discordant group represents a very difficult group to manage. If the frequency of discordant results among underserved women is higher, health care providers may perceive the cotesting strategy to be a less favorable screening strategy than traditional Pap testing every 3 years. The Centers for Disease Control and Prevention's Cervical Cancer Study was conducted at 15 clinics in 6 federally qualified health centers across Illinois. Providers at these clinics were given the option of cotesting for routine cervical cancer screening. Type-specific HPV detection was performed on residual extracts using linear array. Pap test results were abnormal in 6.0% and HPV was positive in 7.2% of the underserved women screened in this study (mean age, 45.1 years). HPV prevalence decreased with age, from 10.3% among 30- to 39-year-olds to 4.5% among 50- to 60-year-olds. About 5% of the women had a combination of a positive HPV test and normal Pap test results; HPV 16/18 was identified in 14% of discordant women. The rate of discordant results among underserved women was similar to those reported throughout the US in a variety of populations. Typing for HPV 16/18 appears to assist in the management in a small proportion of women with discordant results. Published by Elsevier Inc.

  11. Efficient coordinated recovery of sparse channels in massive MIMO

    KAUST Repository

    Masood, Mudassir


    This paper addresses the problem of estimating sparse channels in massive MIMO-OFDM systems. Most wireless channels are sparse in nature with large delay spread. In addition, these channels as observed by multiple antennas in a neighborhood have approximately common support. The sparsity and common support properties are attractive when it comes to the efficient estimation of large number of channels in massive MIMO systems. Moreover, to avoid pilot contamination and to achieve better spectral efficiency, it is important to use a small number of pilots. We present a novel channel estimation approach which utilizes the sparsity and common support properties to estimate sparse channels and requires a small number of pilots. Two algorithms based on this approach have been developed that perform Bayesian estimates of sparse channels even when the prior is non-Gaussian or unknown. Neighboring antennas share among each other their beliefs about the locations of active channel taps to perform estimation. The coordinated approach improves channel estimates and also reduces the required number of pilots. Further improvement is achieved by the data-aided version of the algorithm. Extensive simulation results are provided to demonstrate the performance of the proposed algorithms.

  12. Sparse Decomposition and Modeling of Anatomical Shape Variation

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Rostrup, Egill; Ryberg, Charlotte


    Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counter...

  13. Sparse decomposition and modeling of anatomical shape variation

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Rostrup, Egill; Ryberg, Charlotte


    Recent advances in statistics have spawned powerful methods for regression and data decomposition that promote sparsity, a property that facilitates interpretation of the results. Sparse models use a small subset of the available variables and may perform as well or better than their full counter...

  14. Sparse principal component analysis in medical shape modeling (United States)

    Sjöstrand, Karl; Stegmann, Mikkel B.; Larsen, Rasmus


    Principal component analysis (PCA) is a widely used tool in medical image analysis for data reduction, model building, and data understanding and exploration. While PCA is a holistic approach where each new variable is a linear combination of all original variables, sparse PCA (SPCA) aims at producing easily interpreted models through sparse loadings, i.e. each new variable is a linear combination of a subset of the original variables. One of the aims of using SPCA is the possible separation of the results into isolated and easily identifiable effects. This article introduces SPCA for shape analysis in medicine. Results for three different data sets are given in relation to standard PCA and sparse PCA by simple thresholding of small loadings. Focus is on a recent algorithm for computing sparse principal components, but a review of other approaches is supplied as well. The SPCA algorithm has been implemented using Matlab and is available for download. The general behavior of the algorithm is investigated, and strengths and weaknesses are discussed. The original report on the SPCA algorithm argues that the ordering of modes is not an issue. We disagree on this point and propose several approaches to establish sensible orderings. A method that orders modes by decreasing variance and maximizes the sum of variances for all modes is presented and investigated in detail.

  15. A Practical View on Tunable Sparse Network Coding

    DEFF Research Database (Denmark)

    Sørensen, Chres Wiant; Shahbaz Badr, Arash; Cabrera Guerrero, Juan Alberto


    Tunable sparse network coding (TSNC) constitutes a promising concept for trading off computational complexity and delay performance. This paper advocates for the use of judicious feedback as a key not only to make TSNC practical, but also to deliver a highly consistent and controlled delay......) can result in a radical improvement of the complexity-delay trade-off....

  16. Image Super-Resolution via Adaptive Regularization and Sparse Representation. (United States)

    Cao, Feilong; Cai, Miaomiao; Tan, Yuanpeng; Zhao, Jianwei


    Previous studies have shown that image patches can be well represented as a sparse linear combination of elements from an appropriately selected over-complete dictionary. Recently, single-image super-resolution (SISR) via sparse representation using blurred and downsampled low-resolution images has attracted increasing interest, where the aim is to obtain the coefficients for sparse representation by solving an l0 or l1 norm optimization problem. The l0 optimization is a nonconvex and NP-hard problem, while the l1 optimization usually requires many more measurements and presents new challenges even when the image is the usual size, so we propose a new approach for SISR recovery based on regularization nonconvex optimization. The proposed approach is potentially a powerful method for recovering SISR via sparse representations, and it can yield a sparser solution than the l1 regularization method. We also consider the best choice for lp regularization with all p in (0, 1), where we propose a scheme that adaptively selects the norm value for each image patch. In addition, we provide a method for estimating the best value of the regularization parameter λ adaptively, and we discuss an alternate iteration method for selecting p and λ . We perform experiments, which demonstrates that the proposed regularization nonconvex optimization method can outperform the convex optimization method and generate higher quality images.

  17. Medical Image Fusion Based on Feature Extraction and Sparse Representation

    Directory of Open Access Journals (Sweden)

    Yin Fei


    Full Text Available As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM and energy information map (EM as well as structure and energy map (SEM to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods.

  18. Discriminative object tracking via sparse representation and online dictionary learning. (United States)

    Xie, Yuan; Zhang, Wensheng; Li, Cuihua; Lin, Shuyang; Qu, Yanyun; Zhang, Yinghua


    We propose a robust tracking algorithm based on local sparse coding with discriminative dictionary learning and new keypoint matching schema. This algorithm consists of two parts: the local sparse coding with online updated discriminative dictionary for tracking (SOD part), and the keypoint matching refinement for enhancing the tracking performance (KP part). In the SOD part, the local image patches of the target object and background are represented by their sparse codes using an over-complete discriminative dictionary. Such discriminative dictionary, which encodes the information of both the foreground and the background, may provide more discriminative power. Furthermore, in order to adapt the dictionary to the variation of the foreground and background during the tracking, an online learning method is employed to update the dictionary. The KP part utilizes refined keypoint matching schema to improve the performance of the SOD. With the help of sparse representation and online updated discriminative dictionary, the KP part are more robust than the traditional method to reject the incorrect matches and eliminate the outliers. The proposed method is embedded into a Bayesian inference framework for visual tracking. Experimental results on several challenging video sequences demonstrate the effectiveness and robustness of our approach.

  19. The equivalent source method as a sparse signal reconstruction

    DEFF Research Database (Denmark)

    Fernandez Grande, Efren; Xenaki, Angeliki


    This study proposes an acoustic holography method for sound field reconstruction based on a point source model, which uses the Compressed Sensing (CS) framework to provide a sparse solution. Sparsity implies that the sound field can be represented by a minimal number of non-zero terms, point...


    Directory of Open Access Journals (Sweden)



    Full Text Available Neuronal ensemble activity codes working memory. In this work, we developed a neuronal ensemble sparse coding method, which can effectively reduce the dimension of the neuronal activity and express neural coding. Multichannel spike trains were recorded in rat prefrontal cortex during a work memory task in Y-maze. As discrete signals, spikes were transferred into continuous signals by estimating entropy. Then the normalized continuous signals were decomposed via non-negative sparse method. The non-negative components were extracted to reconstruct a low-dimensional ensemble, while none of the feature components were missed. The results showed that, for well-trained rats, neuronal ensemble activities in the prefrontal cortex changed dynamically during the working memory task. And the neuronal ensemble is more explicit via using non-negative sparse coding. Our results indicate that the neuronal ensemble sparse coding method can effectively reduce the dimension of neuronal activity and it is a useful tool to express neural coding.

  1. Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit

    DEFF Research Database (Denmark)

    Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan


    We study packetized predictive control, known to be robust against packet dropouts in networked systems. To obtain sparse packets for rate-limited networks, we design control packets via an ℓ0 optimization, which can be eectively solved by orthogonal matching pursuit. Our formulation ensures asym...

  2. Robust visual tracking of infrared object via sparse representation model (United States)

    Ma, Junkai; Liu, Haibo; Chang, Zheng; Hui, Bin


    In this paper, we propose a robust tracking method for infrared object. We introduce the appearance model and the sparse representation in the framework of particle filter to achieve this goal. Representing every candidate image patch as a linear combination of bases in the subspace which is spanned by the target templates is the mechanism behind this method. The natural property, that if the candidate image patch is the target so the coefficient vector must be sparse, can ensure our algorithm successfully. Firstly, the target must be indicated manually in the first frame of the video, then construct the dictionary using the appearance model of the target templates. Secondly, the candidate image patches are selected in following frames and the sparse coefficient vectors of them are calculated via l1-norm minimization algorithm. According to the sparse coefficient vectors the right candidates is determined as the target. Finally, the target templates update dynamically to cope with appearance change in the tracking process. This paper also addresses the problem of scale changing and the rotation of the target occurring in tracking. Theoretic analysis and experimental results show that the proposed algorithm is effective and robust.

  3. Proportionate Minimum Error Entropy Algorithm for Sparse System Identification

    Directory of Open Access Journals (Sweden)

    Zongze Wu


    Full Text Available Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not hold in most real-world environments. In this work, we use the minimum error entropy (MEE criterion, an alternative to the conventional MSE criterion, to develop the proportionate minimum error entropy (PMEE algorithm for sparse system identification, which may achieve much better performance than the MSE based methods especially in heavy-tailed non-Gaussian situations. Moreover, we analyze the convergence of the proposed algorithm and derive a sufficient condition that ensures the mean square convergence. Simulation results confirm the excellent performance of the new algorithm.

  4. Robust Visual Tracking Via Consistent Low-Rank Sparse Learning

    KAUST Repository

    Zhang, Tianzhu


    Object tracking is the process of determining the states of a target in consecutive video frames based on properties of motion and appearance consistency. In this paper, we propose a consistent low-rank sparse tracker (CLRST) that builds upon the particle filter framework for tracking. By exploiting temporal consistency, the proposed CLRST algorithm adaptively prunes and selects candidate particles. By using linear sparse combinations of dictionary templates, the proposed method learns the sparse representations of image regions corresponding to candidate particles jointly by exploiting the underlying low-rank constraints. In addition, the proposed CLRST algorithm is computationally attractive since temporal consistency property helps prune particles and the low-rank minimization problem for learning joint sparse representations can be efficiently solved by a sequence of closed form update operations. We evaluate the proposed CLRST algorithm against 14 state-of-the-art tracking methods on a set of 25 challenging image sequences. Experimental results show that the CLRST algorithm performs favorably against state-of-the-art tracking methods in terms of accuracy and execution time.

  5. Multiple kernel sparse representations for supervised and unsupervised learning. (United States)

    Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas


    In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.

  6. Graph Regularized Nonnegative Matrix Factorization with Sparse Coding

    Directory of Open Access Journals (Sweden)

    Chuang Lin


    Full Text Available In this paper, we propose a sparseness constraint NMF method, named graph regularized matrix factorization with sparse coding (GRNMF_SC. By combining manifold learning and sparse coding techniques together, GRNMF_SC can efficiently extract the basic vectors from the data space, which preserves the intrinsic manifold structure and also the local features of original data. The target function of our method is easy to propose, while the solving procedures are really nontrivial; in the paper we gave the detailed derivation of solving the target function and also a strict proof of its convergence, which is a key contribution of the paper. Compared with sparseness constrained NMF and GNMF algorithms, GRNMF_SC can learn much sparser representation of the data and can also preserve the geometrical structure of the data, which endow it with powerful discriminating ability. Furthermore, the GRNMF_SC is generalized as supervised and unsupervised models to meet different demands. Experimental results demonstrate encouraging results of GRNMF_SC on image recognition and clustering when comparing with the other state-of-the-art NMF methods.

  7. Sparse time series chain graphical models for reconstructing genetic networks

    NARCIS (Netherlands)

    Abegaz, Fentaw; Wit, Ernst

    We propose a sparse high-dimensional time series chain graphical model for reconstructing genetic networks from gene expression data parametrized by a precision matrix and autoregressive coefficient matrix. We consider the time steps as blocks or chains. The proposed approach explores patterns of

  8. New Algorithms and Sparse Regularization for Synthetic Aperture Radar Imaging (United States)


    statistical analysis of one such method, the so-called MUSIC algorithm (multiple signal classification). We have a publication that mathematically justifies...called MUSIC algorithm (multiple signal classification). We have a publication that mathematically justifies the scaling of the phase transition...Demanet Department of Mathematics Massachusetts Institute of Technology. • Grant title: New Algorithms and Sparse Regularization for Synthetic Aperture

  9. Low-rank sparse learning for robust visual tracking

    KAUST Repository

    Zhang, Tianzhu


    In this paper, we propose a new particle-filter based tracking algorithm that exploits the relationship between particles (candidate targets). By representing particles as sparse linear combinations of dictionary templates, this algorithm capitalizes on the inherent low-rank structure of particle representations that are learned jointly. As such, it casts the tracking problem as a low-rank matrix learning problem. This low-rank sparse tracker (LRST) has a number of attractive properties. (1) Since LRST adaptively updates dictionary templates, it can handle significant changes in appearance due to variations in illumination, pose, scale, etc. (2) The linear representation in LRST explicitly incorporates background templates in the dictionary and a sparse error term, which enables LRST to address the tracking drift problem and to be robust against occlusion respectively. (3) LRST is computationally attractive, since the low-rank learning problem can be efficiently solved as a sequence of closed form update operations, which yield a time complexity that is linear in the number of particles and the template size. We evaluate the performance of LRST by applying it to a set of challenging video sequences and comparing it to 6 popular tracking methods. Our experiments show that by representing particles jointly, LRST not only outperforms the state-of-the-art in tracking accuracy but also significantly improves the time complexity of methods that use a similar sparse linear representation model for particles [1]. © 2012 Springer-Verlag.

  10. A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation

    DEFF Research Database (Denmark)

    Hansen, Thomas Lundgaard; Badiu, Mihai Alin; Fleury, Bernard Henri


    This paper concerns sparse decomposition of a noisy signal into atoms which are specified by unknown continuous-valued parameters. An example could be estimation of the model order, frequencies and amplitudes of a superposition of complex sinusoids. The common approach is to reduce the continuous...

  11. Fast Estimation of Optimal Sparseness of Music Signals

    DEFF Research Database (Denmark)

    la Cour-Harbo, Anders


    We want to use a variety of sparseness measured applied to ‘the minimal L1 norm representation' of a music signal in an overcomplete dictionary as features for automatic classification of music. Unfortunately, the process of computing the optimal L1 norm representation is rather slow, and we...

  12. Non-Cartesian MRI scan time reduction through sparse sampling

    NARCIS (Netherlands)

    Wajer, F.T.A.W.


    Non-Cartesian MRI Scan-Time Reduction through Sparse Sampling Magnetic resonance imaging (MRI) signals are measured in the Fourier domain, also called k-space. Samples of the MRI signal can not be taken at will, but lie along k-space trajectories determined by the magnetic field gradients. MRI

  13. Interpolation on sparse Gauss-Chebyshev grids in higher dimensions

    NARCIS (Netherlands)

    F. Sprengel


    textabstractIn this paper, we give a unified approach to error estimates for interpolation on sparse Gauss--Chebyshev grids for multivariate functions from Besov--type spaces with dominating mixed smoothness properties. The error bounds obtained for this method are almost optimal for the considered

  14. Sobol indices for dimension adaptivity in sparse grids

    NARCIS (Netherlands)

    Dwight, R.P.; Desmedt, S.G.L.; Shoeibi Omrani, P.


    Propagation of random variables through computer codes of many inputs is primarily limited by computational expense. The use of sparse grids mitigates these costs somewhat; here we show how Sobol indices can be used to perform dimension adaptivity to mitigate them further. The method is compared to

  15. Superpixel sparse representation for target detection in hyperspectral imagery (United States)

    Dong, Chunhua; Naghedolfeizi, Masoud; Aberra, Dawit; Qiu, Hao; Zeng, Xiangyan


    Sparse Representation (SR) is an effective classification method. Given a set of data vectors, SR aims at finding the sparsest representation of each data vector among the linear combinations of the bases in a given dictionary. In order to further improve the classification performance, the joint SR that incorporates interpixel correlation information of neighborhoods has been proposed for image pixel classification. However, SR and joint SR demand significant amount of computational time and memory, especially when classifying a large number of pixels. To address this issue, we propose a superpixel sparse representation (SSR) algorithm for target detection in hyperspectral imagery. We firstly cluster hyperspectral pixels into nearly uniform hyperspectral superpixels using our proposed patch-based SLIC approach based on their spectral and spatial information. The sparse representations of these superpixels are then obtained by simultaneously decomposing superpixels over a given dictionary consisting of both target and background pixels. The class of a hyperspectral pixel is determined by a competition between its projections on target and background subdictionaries. One key advantage of the proposed superpixel representation algorithm with respect to pixelwise and joint sparse representation algorithms is that it reduces computational cost while still maintaining competitive classification performance. We demonstrate the effectiveness of the proposed SSR algorithm through experiments on target detection in the in-door and out-door scene data under daylight illumination as well as the remote sensing data. Experimental results show that SSR generally outperforms state of the art algorithms both quantitatively and qualitatively.

  16. Sparse group lasso and high dimensional multinomial classification

    DEFF Research Database (Denmark)

    Vincent, Martin; Hansen, N.R.


    The sparse group lasso optimization problem is solved using a coordinate gradient descent algorithm. The algorithm is applicable to a broad class of convex loss functions. Convergence of the algorithm is established, and the algorithm is used to investigate the performance of the multinomial spar...

  17. Monitoring sealed automotive lead-acid batteries by sparse ...

    Indian Academy of Sciences (India)

    A reliable diagnostics of lead-acid batteries would become mandatory with the induction of an improved power net and the increase of electrically assisted features in future automobiles. Sparse-impedance spectroscopic technique described in this paper estimates the internal resistance of sealed automotive lead-acid ...

  18. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun


    When objects undergo large pose change, illumination variation or partial occlusion, most existed visual tracking algorithms tend to drift away from targets and even fail in tracking them. To address this issue, in this study, the authors propose an online algorithm by combining multiple instance learning (MIL) and local sparse representation for tracking an object in a video system. The key idea in our method is to model the appearance of an object by local sparse codes that can be formed as training data for the MIL framework. First, local image patches of a target object are represented as sparse codes with an overcomplete dictionary, where the adaptive representation can be helpful in overcoming partial occlusion in object tracking. Then MIL learns the sparse codes by a classifier to discriminate the target from the background. Finally, results from the trained classifier are input into a particle filter framework to sequentially estimate the target state over time in visual tracking. In addition, to decrease the visual drift because of the accumulative errors when updating the dictionary and classifier, a two-step object tracking method combining a static MIL classifier with a dynamical MIL classifier is proposed. Experiments on some publicly available benchmarks of video sequences show that our proposed tracker is more robust and effective than others. © The Institution of Engineering and Technology 2013.

  19. Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

    KAUST Repository

    Sicat, Ronell Barrera


    This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs.

  20. Monitoring sealed automotive lead-acid batteries by sparse ...

    Indian Academy of Sciences (India)

    Sparse-impedance spectroscopic technique described in this paper estimates the internal resistance of sealed automotive lead-acid batteries in the frequency range 10 Hz-10 kHz, usually produced by the alternators fitted in ... Solid State and Structural Chemistry Unit, Indian Institute of Science, Bangalore 560 012, India ...

  1. Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data (United States)

    Lasko, Thomas A.; Denny, Joshua C.; Levy, Mia A.


    Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don’t think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data – Electronic Medical Records – typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data

  2. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data.

    Directory of Open Access Journals (Sweden)

    Thomas A Lasko

    Full Text Available Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels, and where to look for them (by specifying the input variables. While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don't think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data - Electronic Medical Records - typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such

  3. Computational phenotype discovery using unsupervised feature learning over noisy, sparse, and irregular clinical data. (United States)

    Lasko, Thomas A; Denny, Joshua C; Levy, Mia A


    Inferring precise phenotypic patterns from population-scale clinical data is a core computational task in the development of precision, personalized medicine. The traditional approach uses supervised learning, in which an expert designates which patterns to look for (by specifying the learning task and the class labels), and where to look for them (by specifying the input variables). While appropriate for individual tasks, this approach scales poorly and misses the patterns that we don't think to look for. Unsupervised feature learning overcomes these limitations by identifying patterns (or features) that collectively form a compact and expressive representation of the source data, with no need for expert input or labeled examples. Its rising popularity is driven by new deep learning methods, which have produced high-profile successes on difficult standardized problems of object recognition in images. Here we introduce its use for phenotype discovery in clinical data. This use is challenging because the largest source of clinical data - Electronic Medical Records - typically contains noisy, sparse, and irregularly timed observations, rendering them poor substrates for deep learning methods. Our approach couples dirty clinical data to deep learning architecture via longitudinal probability densities inferred using Gaussian process regression. From episodic, longitudinal sequences of serum uric acid measurements in 4368 individuals we produced continuous phenotypic features that suggest multiple population subtypes, and that accurately distinguished (0.97 AUC) the uric-acid signatures of gout vs. acute leukemia despite not being optimized for the task. The unsupervised features were as accurate as gold-standard features engineered by an expert with complete knowledge of the domain, the classification task, and the class labels. Our findings demonstrate the potential for achieving computational phenotype discovery at population scale. We expect such data

  4. Bonafide, type-specific human papillomavirus persistence among HIV-positive pregnant women: predictive value for cytological abnormalities, a longitudinal cohort study

    Directory of Open Access Journals (Sweden)

    Angela RI Meyrelles


    Full Text Available This study investigated the rate of human papillomavirus (HPV persistence, associated risk factors, and predictors of cytological alteration outcomes in a cohort of human immunodeficiency virus-infected pregnant women over an 18-month period. HPV was typed through L1 gene sequencing in cervical smears collected during gestation and at 12 months after delivery. Outcomes were defined as nonpersistence (clearance of the HPV in the 2nd sample, re-infection (detection of different types of HPV in the 2 samples, and type-specific HPV persistence (the same HPV type found in both samples. An unfavourable cytological outcome was considered when the second exam showed progression to squamous intraepithelial lesion or high squamous intraepithelial lesion. Ninety patients were studied. HPV DNA persistence occurred in 50% of the cases composed of type-specific persistence (30% or re-infection (20%. A low CD4+T-cell count at entry was a risk factor for type-specific, re-infection, or HPV DNA persistence. The odds ratio (OR was almost three times higher in the type-specific group when compared with the re-infection group (OR = 2.8; 95% confidence interval: 0.43-22.79. Our findings show that bonafide (type-specific HPV persistence is a stronger predictor for the development of cytological abnormalities, highlighting the need for HPV typing as opposed to HPV DNA testing in the clinical setting.

  5. Sparse magnetic resonance imaging reconstruction using the bregman iteration (United States)

    Lee, Dong-Hoon; Hong, Cheol-Pyo; Lee, Man-Woo


    Magnetic resonance imaging (MRI) reconstruction needs many samples that are sequentially sampled by using phase encoding gradients in a MRI system. It is directly connected to the scan time for the MRI system and takes a long time. Therefore, many researchers have studied ways to reduce the scan time, especially, compressed sensing (CS), which is used for sparse images and reconstruction for fewer sampling datasets when the k-space is not fully sampled. Recently, an iterative technique based on the bregman method was developed for denoising. The bregman iteration method improves on total variation (TV) regularization by gradually recovering the fine-scale structures that are usually lost in TV regularization. In this study, we studied sparse sampling image reconstruction using the bregman iteration for a low-field MRI system to improve its temporal resolution and to validate its usefulness. The image was obtained with a 0.32 T MRI scanner (Magfinder II, SCIMEDIX, Korea) with a phantom and an in-vivo human brain in a head coil. We applied random k-space sampling, and we determined the sampling ratios by using half the fully sampled k-space. The bregman iteration was used to generate the final images based on the reduced data. We also calculated the root-mean-square-error (RMSE) values from error images that were obtained using various numbers of bregman iterations. Our reconstructed images using the bregman iteration for sparse sampling images showed good results compared with the original images. Moreover, the RMSE values showed that the sparse reconstructed phantom and the human images converged to the original images. We confirmed the feasibility of sparse sampling image reconstruction methods using the bregman iteration with a low-field MRI system and obtained good results. Although our results used half the sampling ratio, this method will be helpful in increasing the temporal resolution at low-field MRI systems.

  6. Deformable segmentation via sparse representation and dictionary learning. (United States)

    Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N


    "Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

  7. Fiber Type-Specific Satellite Cell Content in Cyclists Following Heavy Training With Carbohydrate and Carbohydrate-Protein Supplementation

    Directory of Open Access Journals (Sweden)

    Alec I McKenzie


    Full Text Available The central purpose of this study was to evaluate the fiber type-specific satellite cell and myonuclear responses of endurance-trained cyclists to a block of intensified training, when supplementing with carbohydrate (CHO vs. carbohydrate-protein (PRO. In a crossover design, endurance-trained cyclists (n=8 performed two consecutive training periods, once supplementing with CHO (de facto ‘control’ condition and the other with PRO. Each training period consisted of 10 days of intensified cycle training (ICT – 120% increase in average training duration followed by 10 days of recovery (RVT – reduced volume training; 33% volume reduction vs. normal training. Skeletal muscle biopsies were obtained from the vastus lateralis before and after ICT and again following RVT. Immunofluorescent microscopy was used to quantify SCs (Pax7+, myonuclei (DAPI+, and myosin heavy chain I (MyHC I. Data are expressed as percent change ± 90% confidence limits. The 10-day block of ICTCHO increased MyHC I SC content (35 ± 28% and myonuclear density (16 ± 6%, which remained elevated following RVTCHO (SC = 69 ± 50% vs. PRE; Nuclei = 17 ± 15% vs. PRE. MyHC II SC and myonuclei were not different following ICTCHO, but were higher following RVTCHO (SC = +33 ± 31% vs. PRE; Nuclei = 15 ± 14% vs. PRE, indicating a delayed response compared to MyHC I fibers. The MyHC I SC pool increased following ICTPRO (37 ± 37%, but without a concomitant increase in myonuclei. There were no changes in MyHC II SC or myonuclei following ICTPRO. Collectively, these trained endurance cyclists possessed a relatively large pool of SCs that facilitated rapid (MyHC I and delayed (MyHC II satellite cell proliferation and myonuclear accretion with CHO. The current findings strengthen the growing body of evidence demonstrating alterations in SC number without hypertrophy. SC pool expansion is typically viewed as an advantageous response to exercise. However, when coupled with our previous

  8. ON Bipolar Cells in Macaque Retina: Type-Specific Synaptic Connectivity with Special Reference to OFF Counterparts (United States)

    Tsukamoto, Yoshihiko; Omi, Naoko


    To date, 12 macaque bipolar cell types have been described. This list includes all morphology types first outlined by Polyak (1941) using the Golgi method in the primate retina and subsequently identified by other researchers using electron microscopy (EM) combined with the Golgi method, serial section transmission EM (SSTEM), and immunohistochemical imaging. We used SSTEM for the rod-dense perifoveal area of macaque retina, reconfirmed ON (cone) bipolar cells to be classified as invaginating midget bipolar (IMB), diffuse bipolar (DB)4, DB5, DB6, giant bipolar (GB), and blue bipolar (BB) types, and clarified their type-specific connectivity. DB4 cells made reciprocal synapses with a kind of ON-OFF lateral amacrine cell, similar to OFF DB2 cells. GB cells contacted rods and cones, similar to OFF DB3b cells. Retinal circuits formed by GB and DB3b cells are thought to substantiate the psychophysical finding of fast rod signals in mesopic vision. DB6 cell output synapses were directed to ON midget ganglion (MG) cells at 70% of ribbon contacts, similar to OFF DB1 cells that directed 60% of ribbon contacts to OFF MG cells. IMB cells contacted medium- or long-wavelength sensitive (M/L-) cones but not short-wavelength sensitive (S-) cones, while BB cells contacted S-cones but not M/L-cones. However, IMB and BB dendrites had similar morphological architectures, and a BB cell contacting a single S-cone resembled an IMB cell. Thus, both IMB and BB may be the ON bipolar counterparts of the OFF flat midget bipolar (FMB) type, likewise DB4 of DB2, DB5 of DB3a, DB6 of DB1, and GB of DB3b OFF bipolar type. The ON DB plus GB, and OFF DB cells predominantly contacted M/L-cones and their outputs were directed mainly to parasol ganglion (PG) cells but also moderately to MG cells. BB cells directed S-cone-driven outputs almost exclusively to small bistratified ganglion (SBG) cells. Some FMB cells predominantly contacted S-cones and their outputs were directed to OFF MG cells. Thus, two

  9. A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion. (United States)

    Lee, Kangjoo; Tak, Sungho; Ye, Jong Chul


    We propose a novel statistical analysis method for functional magnetic resonance imaging (fMRI) to overcome the drawbacks of conventional data-driven methods such as the independent component analysis (ICA). Although ICA has been broadly applied to fMRI due to its capacity to separate spatially or temporally independent components, the assumption of independence has been challenged by recent studies showing that ICA does not guarantee independence of simultaneously occurring distinct activity patterns in the brain. Instead, sparsity of the signal has been shown to be more promising. This coincides with biological findings such as sparse coding in V1 simple cells, electrophysiological experiment results in the human medial temporal lobe, etc. The main contribution of this paper is, therefore, a new data driven fMRI analysis that is derived solely based upon the sparsity of the signals. A compressed sensing based data-driven sparse generalized linear model is proposed that enables estimation of spatially adaptive design matrix as well as sparse signal components that represent synchronous, functionally organized and integrated neural hemodynamics. Furthermore, a minimum description length (MDL)-based model order selection rule is shown to be essential in selecting unknown sparsity level for sparse dictionary learning. Using simulation and real fMRI experiments, we show that the proposed method can adapt individual variation better compared to the conventional ICA methods.

  10. Labelling Fashion Markets


    Aspers, P.


    The present article discusses how an ethical and environmental labelling system can be implemented in fashion garment markets. Consumers act in markets that provide them with more information than their limited cognitive capacity allows them to handle. Ethical and environmental labelling in markets characterized by change, such as the fashion garment market, makes decision-making even more complicated. The ethical and environmental labelling system proposed here is designed to alleviate firms...

  11. Deuterium labeled cannabinoids

    International Nuclear Information System (INIS)

    Driessen, R.A.


    Complex reactions involving ring opening, ring closure and rearrangements hamper complete understanding of the fragmentation processes in the mass spectrometric fragmentation patterns of cannabinoids. Specifically labelled compounds are very powerful tools for obtaining more insight into fragmentation mechanisms and ion structures and therefore the synthesis of specifically deuterated cannabinoids was undertaken. For this, it was necessary to investigate the preparation of cannabinoids, appropriately functionalized for specific introduction of deuterium atom labels. The results of mass spectrometry with these labelled cannabinoids are described. (Auth.)

  12. SU-E-J-212: Identifying Bones From MRI: A Dictionary Learnign and Sparse Regression Approach

    International Nuclear Information System (INIS)

    Ruan, D; Yang, Y; Cao, M; Hu, P; Low, D


    Purpose: To develop an efficient and robust scheme to identify bony anatomy based on MRI-only simulation images. Methods: MRI offers important soft tissue contrast and functional information, yet its lack of correlation to electron-density has placed it as an auxiliary modality to CT in radiotherapy simulation and adaptation. An effective scheme to identify bony anatomy is an important first step towards MR-only simulation/treatment paradigm and would satisfy most practical purposes. We utilize a UTE acquisition sequence to achieve visibility of the bone. By contrast to manual + bulk or registration-to identify bones, we propose a novel learning-based approach for improved robustness to MR artefacts and environmental changes. Specifically, local information is encoded with MR image patch, and the corresponding label is extracted (during training) from simulation CT aligned to the UTE. Within each class (bone vs. nonbone), an overcomplete dictionary is learned so that typical patches within the proper class can be represented as a sparse combination of the dictionary entries. For testing, an acquired UTE-MRI is divided to patches using a sliding scheme, where each patch is sparsely regressed against both bone and nonbone dictionaries, and subsequently claimed to be associated with the class with the smaller residual. Results: The proposed method has been applied to the pilot site of brain imaging and it has showed general good performance, with dice similarity coefficient of greater than 0.9 in a crossvalidation study using 4 datasets. Importantly, it is robust towards consistent foreign objects (e.g., headset) and the artefacts relates to Gibbs and field heterogeneity. Conclusion: A learning perspective has been developed for inferring bone structures based on UTE MRI. The imaging setting is subject to minimal motion effects and the post-processing is efficient. The improved efficiency and robustness enables a first translation to MR-only routine. The scheme

  13. Effective sample labeling

    International Nuclear Information System (INIS)

    Rieger, J.T.; Bryce, R.W.


    Ground-water samples collected for hazardous-waste and radiological monitoring have come under strict regulatory and quality assurance requirements as a result of laws such as the Resource Conservation and Recovery Act. To comply with these laws, the labeling system used to identify environmental samples had to be upgraded to ensure proper handling and to protect collection personnel from exposure to sample contaminants and sample preservatives. The sample label now used as the Pacific Northwest Laboratory is a complete sample document. In the event other paperwork on a labeled sample were lost, the necessary information could be found on the label

  14. Bar Code Labels (United States)


    American Bar Codes, Inc. developed special bar code labels for inventory control of space shuttle parts and other space system components. ABC labels are made in a company-developed anodizing aluminum process and consecutively marketed with bar code symbology and human readable numbers. They offer extreme abrasion resistance and indefinite resistance to ultraviolet radiation, capable of withstanding 700 degree temperatures without deterioration and up to 1400 degrees with special designs. They offer high resistance to salt spray, cleaning fluids and mild acids. ABC is now producing these bar code labels commercially or industrial customers who also need labels to resist harsh environments.

  15. Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure

    Directory of Open Access Journals (Sweden)

    Bilmes Jeff


    Full Text Available Abstract Background Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic Bayesian networks (DBNs and support vector machines (SVMs have been shown to provide state-of-the-art performance in secondary structure prediction. As the size of the protein database grows, it becomes feasible to use a richer model in an effort to capture subtle correlations among the amino acids and the predicted labels. In this context, it is beneficial to derive sparse models that discourage over-fitting and provide biological insight. Results In this paper, we first show that we are able to obtain accurate secondary structure predictions. Our per-residue accuracy on a well established and difficult benchmark (CB513 is 80.3%, which is comparable to the state-of-the-art evaluated on this dataset. We then introduce an algorithm for sparsifying the parameters of a DBN. Using this algorithm, we can automatically remove up to 70-95% of the parameters of a DBN while maintaining the same level of predictive accuracy on the SD576 set. At 90% sparsity, we are able to compute predictions three times faster than a fully dense model evaluated on the SD576 set. We also demonstrate, using simulated data, that the algorithm is able to recover true sparse structures with high accuracy, and using real data, that the sparse model identifies known correlation structure (local and non-local related to different classes of secondary structure elements. Conclusions We present a secondary structure prediction method that employs dynamic Bayesian networks and support vector machines. We also introduce an algorithm for sparsifying the parameters of the dynamic Bayesian network. The sparsification approach yields a significant speed-up in generating predictions, and we demonstrate that the amino acid correlations identified by the algorithm correspond to several known features of

  16. Group sparse canonical correlation analysis for genomic data integration. (United States)

    Lin, Dongdong; Zhang, Jigang; Li, Jingyao; Calhoun, Vince D; Deng, Hong-Wen; Wang, Yu-Ping


    The emergence of high-throughput genomic datasets from different sources and platforms (e.g., gene expression, single nucleotide polymorphisms (SNP), and copy number variation (CNV)) has greatly enhanced our understandings of the interplay of these genomic factors as well as their influences on the complex diseases. It is challenging to explore the relationship between these different types of genomic data sets. In this paper, we focus on a multivariate statistical method, canonical correlation analysis (CCA) method for this problem. Conventional CCA method does not work effectively if the number of data samples is significantly less than that of biomarkers, which is a typical case for genomic data (e.g., SNPs). Sparse CCA (sCCA) methods were introduced to overcome such difficulty, mostly using penalizations with l-1 norm (CCA-l1) or the combination of l-1and l-2 norm (CCA-elastic net). However, they overlook the structural or group effect within genomic data in the analysis, which often exist and are important (e.g., SNPs spanning a gene interact and work together as a group). We propose a new group sparse CCA method (CCA-sparse group) along with an effective numerical algorithm to study the mutual relationship between two different types of genomic data (i.e., SNP and gene expression). We then extend the model to a more general formulation that can include the existing sCCA models. We apply the model to feature/variable selection from two data sets and compare our group sparse CCA method with existing sCCA methods on both simulation and two real datasets (human gliomas data and NCI60 data). We use a graphical representation of the samples with a pair of canonical variates to demonstrate the discriminating characteristic of the selected features. Pathway analysis is further performed for biological interpretation of those features. The CCA-sparse group method incorporates group effects of features into the correlation analysis while performs individual feature

  17. Sparse representation, modeling and learning in visual recognition theory, algorithms and applications

    CERN Document Server

    Cheng, Hong


    This unique text/reference presents a comprehensive review of the state of the art in sparse representations, modeling and learning. The book examines both the theoretical foundations and details of algorithm implementation, highlighting the practical application of compressed sensing research in visual recognition and computer vision. Topics and features: provides a thorough introduction to the fundamentals of sparse representation, modeling and learning, and the application of these techniques in visual recognition; describes sparse recovery approaches, robust and efficient sparse represen

  18. A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data (United States)

    Ortega-Martorell, Sandra; Ruiz, Héctor; Vellido, Alfredo; Olier, Iván; Romero, Enrique; Julià-Sapé, Margarida; Martín, José D.; Jarman, Ian H.; Arús, Carles; Lisboa, Paulo J. G.


    tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing. PMID:24376744

  19. Statistical mechanics of semi-supervised clustering in sparse graphs

    International Nuclear Information System (INIS)

    Ver Steeg, Greg; Galstyan, Aram; Allahverdyan, Armen E


    We theoretically study semi-supervised clustering in sparse graphs in the presence of pair-wise constraints on the cluster assignments of nodes. We focus on bi-cluster graphs and study the impact of semi-supervision for varying constraint density and overlap between the clusters. Recent results for unsupervised clustering in sparse graphs indicate that there is a critical ratio of within-cluster and between-cluster connectivities below which clusters cannot be recovered with better than random accuracy. The goal of this paper is to examine the impact of pair-wise constraints on the clustering accuracy. Our results suggest that the addition of constraints does not provide automatic improvement over the unsupervised case. When the density of the constraints is sufficiently small, their only impact is to shift the detection threshold while preserving the criticality. Conversely, if the density of (hard) constraints is above the percolation threshold, the criticality is suppressed and the detection threshold disappears

  20. On-the-fly Overlapping of Sparse Generations

    DEFF Research Database (Denmark)

    Sørensen, Chres Wiant; Roetter, Daniel Enrique Lucani; Fitzek, Frank


    Traditionally, the idea of overlapping generations in network coding research has focused on reducing the complexity of decoding large data files while maintaining the delay performance expected of a system that combines all data packets. However, the effort for encoding and decoding individual...... from the pool of packets to be mixed in the linear combinations. The latter is key to maintain a high impact of the coded packets received during the entire process while maintaining very sparsely coded generations. Interestingly, our proposed approach naturally bridges the idea of overlapping...... generations with that of tunable sparse network coding, thus providing the system with a seamless and adaptive strategy to balance complexity and delay performance. We analyze two families of strategies focused on these ideas. We also compare them to other standard approaches both in terms of delay...

  1. Sparse logistic principal components analysis for binary data

    KAUST Repository

    Lee, Seokho


    We develop a new principal components analysis (PCA) type dimension reduction method for binary data. Different from the standard PCA which is defined on the observed data, the proposed PCA is defined on the logit transform of the success probabilities of the binary observations. Sparsity is introduced to the principal component (PC) loading vectors for enhanced interpretability and more stable extraction of the principal components. Our sparse PCA is formulated as solving an optimization problem with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated by application to a single nucleotide polymorphism data set and a simulation study. © Institute ol Mathematical Statistics, 2010.

  2. Sample size reduction in groundwater surveys via sparse data assimilation

    KAUST Repository

    Hussain, Z.


    In this paper, we focus on sparse signal recovery methods for data assimilation in groundwater models. The objective of this work is to exploit the commonly understood spatial sparsity in hydrodynamic models and thereby reduce the number of measurements to image a dynamic groundwater profile. To achieve this we employ a Bayesian compressive sensing framework that lets us adaptively select the next measurement to reduce the estimation error. An extension to the Bayesian compressive sensing framework is also proposed which incorporates the additional model information to estimate system states from even lesser measurements. Instead of using cumulative imaging-like measurements, such as those used in standard compressive sensing, we use sparse binary matrices. This choice of measurements can be interpreted as randomly sampling only a small subset of dug wells at each time step, instead of sampling the entire grid. Therefore, this framework offers groundwater surveyors a significant reduction in surveying effort without compromising the quality of the survey. © 2013 IEEE.

  3. A Practical View on Tunable Sparse Network Coding

    DEFF Research Database (Denmark)

    Sørensen, Chres Wiant; Shahbaz Badr, Arash; Cabrera Guerrero, Juan Alberto


    Tunable sparse network coding (TSNC) constitutes a promising concept for trading off computational complexity and delay performance. This paper advocates for the use of judicious feedback as a key not only to make TSNC practical, but also to deliver a highly consistent and controlled delay...... performance to end devices. We propose and analyze a TSNC design that can be incorporated into both unicast and multicast data flows. An implementation of our approach is carried out in C++ and compared to random linear network coding (RLNC) and sparse versions of RLNC implemented in the fastest network...... coding library to date. Our measurements show that the processing speed of our TSNC mechanism can be increased by four-fold compared to an optimized RLNC implementation and with a minimal penalty on delay performance. Finally, we show that even a limited number of feedback packets (

  4. High-Order Sparse Linear Predictors for Audio Processing

    DEFF Research Database (Denmark)

    Giacobello, Daniele; van Waterschoot, Toon; Christensen, Mads Græsbøll


    Linear prediction has generally failed to make a breakthrough in audio processing, as it has done in speech processing. This is mostly due to its poor modeling performance, since an audio signal is usually an ensemble of different sources. Nevertheless, linear prediction comes with a whole set...... of interesting features that make the idea of using it in audio processing not far fetched, e.g., the strong ability of modeling the spectral peaks that play a dominant role in perception. In this paper, we provide some preliminary conjectures and experiments on the use of high-order sparse linear predictors...... in audio processing. These predictors, successfully implemented in modeling the short-term and long-term redundancies present in speech signals, will be used to model tonal audio signals, both monophonic and polyphonic. We will show how the sparse predictors are able to model efficiently the different...

  5. Sparse cortical source localization using spatio-temporal atoms. (United States)

    Korats, Gundars; Ranta, Radu; Le Cam, Steven; Louis-Dorr, Valérie


    This paper addresses the problem of sparse localization of cortical sources from scalp EEG recordings. Localization algorithms use propagation model under spatial and/or temporal constraints, but their performance highly depends on the data signal-to-noise ratio (SNR). In this work we propose a dictionary based sparse localization method which uses a data driven spatio-temporal dictionary to reconstruct the measurements using Single Best Replacement (SBR) and Continuation Single Best Replacement (CSBR) algorithms. We tested and compared our methods with the well-known MUSIC and RAP-MUSIC algorithms on simulated realistic data. Tests were carried out for different noise levels. The results show that our method has a strong advantage over MUSIC-type methods in case of synchronized sources.

  6. Sparse canonical correlation analysis: new formulation and algorithm. (United States)

    Chu, Delin; Liao, Li-Zhi; Ng, Michael K; Zhang, Xiaowei


    In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms.

  7. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer


    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images independently. However, learning multidimensional dictionaries and sparse codes for the reconstruction of multi-dimensional data is very important, as it examines correlations among all the data jointly. This provides more capacity for the learned dictionaries to better reconstruct data. In this paper, we propose a generic and novel formulation for the CSC problem that can handle an arbitrary order tensor of data. Backed with experimental results, our proposed formulation can not only tackle applications that are not possible with standard CSC solvers, including colored video reconstruction (5D- tensors), but it also performs favorably in reconstruction with much fewer parameters as compared to naive extensions of standard CSC to multiple features/channels.

  8. Sparse Nonlinear Electromagnetic Imaging Accelerated With Projected Steepest Descent Algorithm

    KAUST Repository

    Desmal, Abdulla


    An efficient electromagnetic inversion scheme for imaging sparse 3-D domains is proposed. The scheme achieves its efficiency and accuracy by integrating two concepts. First, the nonlinear optimization problem is constrained using L₀ or L₁-norm of the solution as the penalty term to alleviate the ill-posedness of the inverse problem. The resulting Tikhonov minimization problem is solved using nonlinear Landweber iterations (NLW). Second, the efficiency of the NLW is significantly increased using a steepest descent algorithm. The algorithm uses a projection operator to enforce the sparsity constraint by thresholding the solution at every iteration. Thresholding level and iteration step are selected carefully to increase the efficiency without sacrificing the convergence of the algorithm. Numerical results demonstrate the efficiency and accuracy of the proposed imaging scheme in reconstructing sparse 3-D dielectric profiles.

  9. A sparse electromagnetic imaging scheme using nonlinear landweber iterations

    KAUST Repository

    Desmal, Abdulla


    Development and use of electromagnetic inverse scattering techniques for imagining sparse domains have been on the rise following the recent advancements in solving sparse optimization problems. Existing techniques rely on iteratively converting the nonlinear forward scattering operator into a sequence of linear ill-posed operations (for example using the Born iterative method) and applying sparsity constraints to the linear minimization problem of each iteration through the use of L0/L1-norm penalty term (A. Desmal and H. Bagci, IEEE Trans. Antennas Propag, 7, 3878–3884, 2014, and IEEE Trans. Geosci. Remote Sens., 3, 532–536, 2015). It has been shown that these techniques produce more accurate and sharper images than their counterparts which solve a minimization problem constrained with smoothness promoting L2-norm penalty term. But these existing techniques are only applicable to investigation domains involving weak scatterers because the linearization process breaks down for high values of dielectric permittivity.

  10. Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery (United States)

    Kim, Daeun; Haldar, Justin P.


    This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set. The proposed algorithms generalize previous approaches that were designed to impose these constraints individually. Similar to previous greedy algorithms for sparse recovery, the proposed algorithms iteratively identify promising support indices. In contrast to previous approaches, the support index selection procedure has been adapted to prioritize indices that are consistent with both the nonnegativity and shared support constraints. Empirical results demonstrate for the first time that the combined use of simultaneous sparsity and nonnegativity constraints can substantially improve recovery performance relative to existing greedy algorithms that impose less signal structure. PMID:26973368

  11. Stable isotopes labelled compounds

    International Nuclear Information System (INIS)


    The catalogue on stable isotopes labelled compounds offers deuterium, nitrogen-15, and multiply labelled compounds. It includes: (1) conditions of sale and delivery, (2) the application of stable isotopes, (3) technical information, (4) product specifications, and (5) the complete delivery programme

  12. Sparse optimization for inverse problems in atmospheric modelling

    Czech Academy of Sciences Publication Activity Database

    Adam, Lukáš; Branda, Martin


    Roč. 79, č. 3 (2016), s. 256-266 ISSN 1364-8152 R&D Projects: GA MŠk(CZ) 7F14287 Institutional support: RVO:67985556 Keywords : Inverse modelling * Sparse optimization * Integer optimization * Least squares * European tracer experiment * Free Matlab codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 4.404, year: 2016

  13. Shape prior modeling using sparse representation and online dictionary learning. (United States)

    Zhang, Shaoting; Zhan, Yiqiang; Zhou, Yan; Uzunbas, Mustafa; Metaxas, Dimitris N


    The recently proposed sparse shape composition (SSC) opens a new avenue for shape prior modeling. Instead of assuming any parametric model of shape statistics, SSC incorporates shape priors on-the-fly by approximating a shape instance (usually derived from appearance cues) by a sparse combination of shapes in a training repository. Theoretically, one can increase the modeling capability of SSC by including as many training shapes in the repository. However, this strategy confronts two limitations in practice. First, since SSC involves an iterative sparse optimization at run-time, the more shape instances contained in the repository, the less run-time efficiency SSC has. Therefore, a compact and informative shape dictionary is preferred to a large shape repository. Second, in medical imaging applications, training shapes seldom come in one batch. It is very time consuming and sometimes infeasible to reconstruct the shape dictionary every time new training shapes appear. In this paper, we propose an online learning method to address these two limitations. Our method starts from constructing an initial shape dictionary using the K-SVD algorithm. When new training shapes come, instead of re-constructing the dictionary from the ground up, we update the existing one using a block-coordinates descent approach. Using the dynamically updated dictionary, sparse shape composition can be gracefully scaled up to model shape priors from a large number of training shapes without sacrificing run-time efficiency. Our method is validated on lung localization in X-Ray and cardiac segmentation in MRI time series. Compared to the original SSC, it shows comparable performance while being significantly more efficient.

  14. Vertex finding by sparse model-based clustering (United States)

    Frühwirth, R.; Eckstein, K.; Frühwirth-Schnatter, S.


    The application of sparse model-based clustering to the problem of primary vertex finding is discussed. The observed z-positions of the charged primary tracks in a bunch crossing are modeled by a Gaussian mixture. The mixture parameters are estimated via Markov Chain Monte Carlo (MCMC). Sparsity is achieved by an appropriate prior on the mixture weights. The results are shown and compared to clustering by the expectation-maximization (EM) algorithm.

  15. Sparse modeling of spatial environmental variables associated with asthma


    Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.


    Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s ...

  16. Representation and processing of structures with binary sparse distributed codes


    Rachkovskij, Dmitri A.


    The schemes for compositional distributed representations include those allowing on-the-fly construction of fixed dimensionality codevectors to encode structures of various complexity. Similarity of such codevectors takes into account both structural and semantic similarity of represented structures. In this paper we provide a comparative description of sparse binary distributed representation developed in the frames of the Associative-Projective Neural Network architecture and more well-know...

  17. Dynamical Sparse Recovery with Finite-time Convergence


    Yu, Lei; Zheng, Gang; Barbot, Jean-Pierre


    International audience; Even though Sparse Recovery (SR) has been successfully applied in a wide range of research communities, there still exists a barrier to real applications because of the inefficiency of the state-of-the-art algorithms. In this paper, we propose a dynamical approach to SR which is highly efficient and with finite-time convergence property. Firstly, instead of solving the ℓ1 regularized optimization programs that requires exhausting iterations, which is computer-oriented,...

  18. Feature-Enhanced, Model-Based Sparse Aperture Imaging (United States)


    information about angle-dependent scattering. Methods employing subaperture analysis and parametric models expect to find contiguous intervals in θ for...transform, which is not a transform in the strict sense, but a method in image analysis for detecting straight lines in binary images [12], uses a ρ-θ...We explore the application of a homotopy continuation-based method for sparse signal representation in overcomplete dictio- naries. Our problem setup

  19. Parallel adaptive sparse approximation methods for analysis of geoacoustic pulses

    Directory of Open Access Journals (Sweden)

    Kim Alina


    Full Text Available The article is devoted to a new approach in the analysis of geoacoustic pulses. The authors proposed a mathematical model based on a sparse representation of the signal. An adaptive matching pursuit method has been developed to identify model parameters. A parallel implementation of this algorithm is proposed on the CUDA platform. This allows real-time processing and modeling of signals.

  20. Iterative algorithms for large sparse linear systems on parallel computers (United States)

    Adams, L. M.


    Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.

  1. Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

    KAUST Repository

    Buse, Gerrit


    In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated grids, both with and without boundary grid points. Similar to the implicit data structures proposed in Feuersänger (Dünngitterverfahren für hochdimensionale elliptische partielle Differntialgleichungen. Diploma Thesis, Institut für Numerische Simulation, Universität Bonn, 2005) and Murarasu et al. (Proceedings of the 16th ACM Symposium on Principles and Practice of Parallel Programming. Cambridge University Press, New York, 2011, pp. 25–34) we also define a bijective mapping from the multi-dimensional space of grid points to a contiguous index, such that the grid data can be stored in a simple array without overhead. Our approach is especially well-suited to exploit all levels of current commodity hardware, including cache-levels and vector extensions. Furthermore, this kind of data structure is extremely attractive for today’s real-time applications, as it gives direct access to the hierarchical structure of the grids, while outperforming other common sparse grid structures (hash maps, etc.) which do not match with modern compute platforms that well. For dimensionality d ≤ 10 we achieve good speedups on a 12 core Intel Westmere-EP NUMA platform compared to the results presented in Murarasu et al. (Proceedings of the International Conference on Computational Science—ICCS 2012. Procedia Computer Science, 2012). As we show, this also holds for the results obtained on Nvidia Fermi GPUs, for which we observe speedups over our own CPU implementation of up to 4.5 when dealing with moderate dimensionality. In high-dimensional settings, in the order of tens to hundreds of dimensions, our sparse grid evaluation kernels on the CPU outperform any other known implementation.

  2. Joint Sparse Representation for Robust Multimodal Biometrics Recognition (United States)


    Richard Baraniuk Vishal M. Patel, Nasser M. Nasrabadi, Rama Chellappa, Sumit Shekhar 611103 c. THIS PAGE The public reporting burden for this collection of...MACHINE INTELLIGENCE, VOL. X, NO. X, MONTH 20XX 1 Joint Sparse Representation for Robust Multimodal Biometrics Recognition Sumit Shekhar, Student Member...difficult for an imposter to Sumit Shekhar, Vishal M. Patel and R. Chellappa are with the Department of Electrical and Computer Engineering and the Center

  3. Color normalization of histology slides using graph regularized sparse NMF (United States)

    Sha, Lingdao; Schonfeld, Dan; Sethi, Amit


    Computer based automatic medical image processing and quantification are becoming popular in digital pathology. However, preparation of histology slides can vary widely due to differences in staining equipment, procedures and reagents, which can reduce the accuracy of algorithms that analyze their color and texture information. To re- duce the unwanted color variations, various supervised and unsupervised color normalization methods have been proposed. Compared with supervised color normalization methods, unsupervised color normalization methods have advantages of time and cost efficient and universal applicability. Most of the unsupervised color normaliza- tion methods for histology are based on stain separation. Based on the fact that stain concentration cannot be negative and different parts of the tissue absorb different stains, nonnegative matrix factorization (NMF), and particular its sparse version (SNMF), are good candidates for stain separation. However, most of the existing unsupervised color normalization method like PCA, ICA, NMF and SNMF fail to consider important information about sparse manifolds that its pixels occupy, which could potentially result in loss of texture information during color normalization. Manifold learning methods like Graph Laplacian have proven to be very effective in interpreting high-dimensional data. In this paper, we propose a novel unsupervised stain separation method called graph regularized sparse nonnegative matrix factorization (GSNMF). By considering the sparse prior of stain concentration together with manifold information from high-dimensional image data, our method shows better performance in stain color deconvolution than existing unsupervised color deconvolution methods, especially in keeping connected texture information. To utilized the texture information, we construct a nearest neighbor graph between pixels within a spatial area of an image based on their distances using heat kernal in lαβ space. The

  4. Radioiodine and its labelled compounds

    International Nuclear Information System (INIS)

    Robles, Ana Maria


    Chemical characteristics and their nuclear characteristics, types of labelled molecules,labelling procedures, direct labelling with various oxidizing agents, indirect labelling with various conjugates attached to protein molecules, purification and quality control. Iodination damage.Safe handling of labelling procedures with iodine radioisotopes.Bibliography

  5. 'Naturemade' -- a new label

    International Nuclear Information System (INIS)

    Niederhaeusern, A.


    This short article discusses the introduction of the 'Naturemade' two-level labelling scheme in the Swiss electricity market, which is to help provide transparency in the market for green power and promote the building of facilities for its production. In the form of an interview with the CEO of Swissolar and the president of Greenpeace Switzerland, the pros and contras of these labels are discussed. In particular, the interview partners' opinions on the possible misuse of the less stringent label and the influence of the labels on the construction of new installations for the generation of electricity from renewable sources are presented. The basic principles of the promotional model behind the labels are listed

  6. Cell-type-specific H+-ATPase activity in root tissues enables K+ retention and mediates acclimation of barley (Hordeum vulgare) to salinity stress

    DEFF Research Database (Denmark)

    Shabala, Lana; Zhang, Jingyi; Pottosin, Igor


    While the importance of cell type specificity in plant adaptive responses is widely accepted, only a limited number of studies have addressed this issue at the functional level. We have combined electrophysiological, imaging, and biochemical techniques to reveal the physiological mechanisms confe...

  7. Efficient MATLAB computations with sparse and factored tensors.

    Energy Technology Data Exchange (ETDEWEB)

    Bader, Brett William; Kolda, Tamara Gibson (Sandia National Lab, Livermore, CA)


    In this paper, the term tensor refers simply to a multidimensional or N-way array, and we consider how specially structured tensors allow for efficient storage and computation. First, we study sparse tensors, which have the property that the vast majority of the elements are zero. We propose storing sparse tensors using coordinate format and describe the computational efficiency of this scheme for various mathematical operations, including those typical to tensor decomposition algorithms. Second, we study factored tensors, which have the property that they can be assembled from more basic components. We consider two specific types: a Tucker tensor can be expressed as the product of a core tensor (which itself may be dense, sparse, or factored) and a matrix along each mode, and a Kruskal tensor can be expressed as the sum of rank-1 tensors. We are interested in the case where the storage of the components is less than the storage of the full tensor, and we demonstrate that many elementary operations can be computed using only the components. All of the efficiencies described in this paper are implemented in the Tensor Toolbox for MATLAB.

  8. Superresolving Black Hole Images with Full-Closure Sparse Modeling (United States)

    Crowley, Chelsea; Akiyama, Kazunori; Fish, Vincent


    It is believed that almost all galaxies have black holes at their centers. Imaging a black hole is a primary objective to answer scientific questions relating to relativistic accretion and jet formation. The Event Horizon Telescope (EHT) is set to capture images of two nearby black holes, Sagittarius A* at the center of the Milky Way galaxy roughly 26,000 light years away and the other M87 which is in Virgo A, a large elliptical galaxy that is 50 million light years away. Sparse imaging techniques have shown great promise for reconstructing high-fidelity superresolved images of black holes from simulated data. Previous work has included the effects of atmospheric phase errors and thermal noise, but not systematic amplitude errors that arise due to miscalibration. We explore a full-closure imaging technique with sparse modeling that uses closure amplitudes and closure phases to improve the imaging process. This new technique can successfully handle data with systematic amplitude errors. Applying our technique to synthetic EHT data of M87, we find that full-closure sparse modeling can reconstruct images better than traditional methods and recover key structural information on the source, such as the shape and size of the predicted photon ring. These results suggest that our new approach will provide superior imaging performance for data from the EHT and other interferometric arrays.

  9. Dentate Gyrus circuitry features improve performance of sparse approximation algorithms.

    Directory of Open Access Journals (Sweden)

    Panagiotis C Petrantonakis

    Full Text Available Memory-related activity in the Dentate Gyrus (DG is characterized by sparsity. Memory representations are seen as activated neuronal populations of granule cells, the main encoding cells in DG, which are estimated to engage 2-4% of the total population. This sparsity is assumed to enhance the ability of DG to perform pattern separation, one of the most valuable contributions of DG during memory formation. In this work, we investigate how features of the DG such as its excitatory and inhibitory connectivity diagram can be used to develop theoretical algorithms performing Sparse Approximation, a widely used strategy in the Signal Processing field. Sparse approximation stands for the algorithmic identification of few components from a dictionary that approximate a certain signal. The ability of DG to achieve pattern separation by sparsifing its representations is exploited here to improve the performance of the state of the art sparse approximation algorithm "Iterative Soft Thresholding" (IST by adding new algorithmic features inspired by the DG circuitry. Lateral inhibition of granule cells, either direct or indirect, via mossy cells, is shown to enhance the performance of the IST. Apart from revealing the potential of DG-inspired theoretical algorithms, this work presents new insights regarding the function of particular cell types in the pattern separation task of the DG.

  10. Low-Rank Sparse Coding for Image Classification

    KAUST Repository

    Zhang, Tianzhu


    In this paper, we propose a low-rank sparse coding (LRSC) method that exploits local structure information among features in an image for the purpose of image-level classification. LRSC represents densely sampled SIFT descriptors, in a spatial neighborhood, collectively as low-rank, sparse linear combinations of code words. As such, it casts the feature coding problem as a low-rank matrix learning problem, which is different from previous methods that encode features independently. This LRSC has a number of attractive properties. (1) It encourages sparsity in feature codes, locality in codebook construction, and low-rankness for spatial consistency. (2) LRSC encodes local features jointly by considering their low-rank structure information, and is computationally attractive. We evaluate the LRSC by comparing its performance on a set of challenging benchmarks with that of 7 popular coding and other state-of-the-art methods. Our experiments show that by representing local features jointly, LRSC not only outperforms the state-of-the-art in classification accuracy but also improves the time complexity of methods that use a similar sparse linear representation model for feature coding.

  11. A comparison of methods for representing sparsely sampled random quantities.

    Energy Technology Data Exchange (ETDEWEB)

    Romero, Vicente Jose; Swiler, Laura Painton; Urbina, Angel; Mullins, Joshua


    This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the desired percentile range of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem interesting and difficult. In this report, five uncertainty representation techniques are characterized for their performance on twenty-one test problems (over thousands of trials for each problem) according to these two opposing objectives and other performance measures. Two of the methods, statistical Tolerance Intervals and a kernel density approach specifically developed for handling sparse data, exhibit significantly better overall performance than the others.

  12. An Improved Information Hiding Method Based on Sparse Representation

    Directory of Open Access Journals (Sweden)

    Minghai Yao


    Full Text Available A novel biometric authentication information hiding method based on the sparse representation is proposed for enhancing the security of biometric information transmitted in the network. In order to make good use of abundant information of the cover image, the sparse representation method is adopted to exploit the correlation between the cover and biometric images. Thus, the biometric image is divided into two parts. The first part is the reconstructed image, and the other part is the residual image. The biometric authentication image cannot be restored by any one part. The residual image and sparse representation coefficients are embedded into the cover image. Then, for the sake of causing much less attention of attackers, the visual attention mechanism is employed to select embedding location and embedding sequence of secret information. Finally, the reversible watermarking algorithm based on histogram is utilized for embedding the secret information. For verifying the validity of the algorithm, the PolyU multispectral palmprint and the CASIA iris databases are used as biometric information. The experimental results show that the proposed method exhibits good security, invisibility, and high capacity.

  13. Sparse EEG Source Localization Using Bernoulli Laplacian Priors. (United States)

    Costa, Facundo; Batatia, Hadj; Chaari, Lotfi; Tourneret, Jean-Yves


    Source localization in electroencephalography has received an increasing amount of interest in the last decade. Solving the underlying ill-posed inverse problem usually requires choosing an appropriate regularization. The usual l2 norm has been considered and provides solutions with low computational complexity. However, in several situations, realistic brain activity is believed to be focused in a few focal areas. In these cases, the l2 norm is known to overestimate the activated spatial areas. One solution to this problem is to promote sparse solutions for instance based on the l1 norm that are easy to handle with optimization techniques. In this paper, we consider the use of an l0 + l1 norm to enforce sparse source activity (by ensuring the solution has few nonzero elements) while regularizing the nonzero amplitudes of the solution. More precisely, the l0 pseudonorm handles the position of the nonzero elements while the l1 norm constrains the values of their amplitudes. We use a Bernoulli-Laplace prior to introduce this combined l0 + l1 norm in a Bayesian framework. The proposed Bayesian model is shown to favor sparsity while jointly estimating the model hyperparameters using a Markov chain Monte Carlo sampling technique. We apply the model to both simulated and real EEG data, showing that the proposed method provides better results than the l2 and l1  norms regularizations in the presence of pointwise sources. A comparison with a recent method based on multiple sparse priors is also conducted.

  14. Cellular adaptation facilitates sparse and reliable coding in sensory pathways. (United States)

    Farkhooi, Farzad; Froese, Anja; Muller, Eilif; Menzel, Randolf; Nawrot, Martin P


    Most neurons in peripheral sensory pathways initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. It is unclear how this phenomenon affects stimulus coding in the later stages of sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in sequential stages of a sensory network with adapting neurons. As a modeling framework we employ a mean-field approach together with an adaptive population density treatment, accompanied by numerical simulations of spiking neural networks. We find that cellular adaptation plays a critical role in the dynamic reduction of the trial-by-trial variability of cortical spike responses by transiently suppressing self-generated fast fluctuations in the cortical balanced network. This provides an explanation for a widespread cortical phenomenon by a simple mechanism. We further show that in the insect olfactory system cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body. Our results reveal a generic, biophysically plausible mechanism that can explain the emergence of a temporally sparse and reliable stimulus representation within a sequential processing architecture.

  15. Review of Sparse Representation-Based Classification Methods on EEG Signal Processing for Epilepsy Detection, Brain-Computer Interface and Cognitive Impairment. (United States)

    Wen, Dong; Jia, Peilei; Lian, Qiusheng; Zhou, Yanhong; Lu, Chengbiao


    At present, the sparse representation-based classification (SRC) has become an important approach in electroencephalograph (EEG) signal analysis, by which the data is sparsely represented on the basis of a fixed dictionary or learned dictionary and classified based on the reconstruction criteria. SRC methods have been used to analyze the EEG signals of epilepsy, cognitive impairment and brain computer interface (BCI), which made rapid progress including the improvement in computational accuracy, efficiency and robustness. However, these methods have deficiencies in real-time performance, generalization ability and the dependence of labeled sample in the analysis of the EEG signals. This mini review described the advantages and disadvantages of the SRC methods in the EEG signal analysis with the expectation that these methods can provide the better tools for analyzing EEG signals.

  16. Estimation of labeling efficiency in pseudocontinuous arterial spin labeling. (United States)

    Aslan, Sina; Xu, Feng; Wang, Peiying L; Uh, Jinsoo; Yezhuvath, Uma S; van Osch, Matthias; Lu, Hanzhang


    Pseudocontinuous arterial spin labeling MRI is a new arterial spin labeling technique that has the potential of combining advantages of continuous arterial spin labeling and pulsed arterial spin labeling. However, unlike continuous arterial spin labeling, the labeling process of pseudocontinuous arterial spin labeling is not strictly an adiabatic inversion and the efficiency of labeling may be subject specific. Here, three experiments were performed to study the labeling efficiency in pseudocontinuous arterial spin labeling MRI. First, the optimal labeling position was determined empirically to be approximately 84 mm below the anterior commissure-posterior commissure line in order to achieve the highest sensitivity. Second, an experimental method was developed to utilize phase-contrast velocity MRI as a normalization factor and to estimate the labeling efficiency in vivo, which was founded to be 0.86 +/- 0.06 (n = 10, mean +/- standard deviation). Third, we compared the labeling efficiency of pseudocontinuous arterial spin labeling MRI under normocapnic and hypercapnic (inhalation of 5% CO(2)) conditions and showed that a higher flow velocity in the feeding arteries resulted in a reduction in the labeling efficiency. In summary, our results suggest that labeling efficiency is a critical parameter in pseudocontinuous arterial spin labeling MRI not only in terms of achieving highest sensitivity but also in quantification of absolute cerebral blood flow in milliliters per minute per 100 g. We propose that the labeling efficiency should be estimated using phase-contrast velocity MRI on a subject-specific basis. (c) 2010 Wiley-Liss, Inc.

  17. Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis. (United States)

    Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul


    Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. Copyright © 2015 Elsevier Inc. All rights reserved.

  18. Food Allergies: Understanding Food Labels (United States)

    ... a few common questions about food label requirements. What foods are labeled? Domestic or imported packaged food is ... allergens found in flavorings, colorings or other additives. What foods aren't labeled? Fresh produce, eggs, fresh meat ...

  19. Soil Fumigant Labels - Methyl Bromide (United States)

    Search soil fumigant pesticide labels by EPA registration number, product name, or company, and follow the link to The Pesticide Product Label System (PPLS) for details. Updated labels include new safety requirements for buffer zones and related measures.

  20. Radioactive labelled orgotein

    International Nuclear Information System (INIS)


    The preparation and use of radioactively labelled orgotein, i.e. water-soluble protein congeners in pure, injectable form, is described. This radiopharmaceutical is useful in scintigraphy, especially for visualization of the kidneys where the orgotein is rapidly concentrated. Details of the processes for labelling bovine orgotein with sup(99m)Tc, 60 Co, 125 I or 131 I are specified. The pharmaceutical preparation of the labelled orgotein for intravenous and parenteral administration is also described. Examples using either sup(99m)TC or 125 I-orgotein in scintiscanning dogs' kidneys are given. (UK)

  1. Storage of sparse files using parallel log-structured file system (United States)

    Bent, John M.; Faibish, Sorin; Grider, Gary; Torres, Aaron


    A sparse file is stored without holes by storing a data portion of the sparse file using a parallel log-structured file system; and generating an index entry for the data portion, the index entry comprising a logical offset, physical offset and length of the data portion. The holes can be restored to the sparse file upon a reading of the sparse file. The data portion can be stored at a logical end of the sparse file. Additional storage efficiency can optionally be achieved by (i) detecting a write pattern for a plurality of the data portions and generating a single patterned index entry for the plurality of the patterned data portions; and/or (ii) storing the patterned index entries for a plurality of the sparse files in a single directory, wherein each entry in the single directory comprises an identifier of a corresponding sparse file.

  2. On Online Labeling with Polynomially Many Labels

    DEFF Research Database (Denmark)

    Babka, Martin; Bulánek, Jan; Cunat, Vladimír


    be necessary to change the labels of some items; such changes may be done at any time at unit cost for each change. The goal is to minimize the total cost. An alternative formulation of this problem is the file maintenance problem, in which the items, instead of being labeled, are maintained in sorted order...... in an array of length m, and we pay unit cost for moving an item. For the case m = cn for constant c > 1, there are known algorithms that use at most O(n log(n)2) relabelings in total [9], and it was shown recently that this is asymptotically optimal [1]. For the case of m = θ(nC) for C > 1, algorithms...

  3. Clinical applications of cells labelling

    International Nuclear Information System (INIS)

    Gonzalez, B.M.


    Blood cells labelled with radionuclides are reviewed and main applications are described. Red blood cell labelling by both random and specific principle. A table with most important clinical uses, 99mTc labelling of RBC are described pre tinning and in vivo reduction of Tc, in vitro labelling and administration of labelled RBC and in vivo modified technique. Labelled leucocytes with several 99mTc-complex radiopharmaceuticals by in vitro technique and specific monoclonal s for white cells(neutrofiles). Labelled platelets for clinical use and research by in vitro technique and in vivo labelling

  4. Integrative modeling of eQTLs and cis-regulatory elements suggests mechanisms underlying cell type specificity of eQTLs.

    Directory of Open Access Journals (Sweden)

    Christopher D Brown

    Full Text Available Genetic variants in cis-regulatory elements or trans-acting regulators frequently influence the quantity and spatiotemporal distribution of gene transcription. Recent interest in expression quantitative trait locus (eQTL mapping has paralleled the adoption of genome-wide association studies (GWAS for the analysis of complex traits and disease in humans. Under the hypothesis that many GWAS associations tag non-coding SNPs with small effects, and that these SNPs exert phenotypic control by modifying gene expression, it has become common to interpret GWAS associations using eQTL data. To fully exploit the mechanistic interpretability of eQTL-GWAS comparisons, an improved understanding of the genetic architecture and causal mechanisms of cell type specificity of eQTLs is required. We address this need by performing an eQTL analysis in three parts: first we identified eQTLs from eleven studies on seven cell types; then we integrated eQTL data with cis-regulatory element (CRE data from the ENCODE project; finally we built a set of classifiers to predict the cell type specificity of eQTLs. The cell type specificity of eQTLs is associated with eQTL SNP overlap with hundreds of cell type specific CRE classes, including enhancer, promoter, and repressive chromatin marks, regions of open chromatin, and many classes of DNA binding proteins. These associations provide insight into the molecular mechanisms generating the cell type specificity of eQTLs and the mode of regulation of corresponding eQTLs. Using a random forest classifier with cell specific CRE-SNP overlap as features, we demonstrate the feasibility of predicting the cell type specificity of eQTLs. We then demonstrate that CREs from a trait-associated cell type can be used to annotate GWAS associations in the absence of eQTL data for that cell type. We anticipate that such integrative, predictive modeling of cell specificity will improve our ability to understand the mechanistic basis of human

  5. Supervised block sparse dictionary learning for simultaneous clustering and classification in computational anatomy. (United States)

    Varol, Erdem; Davatzikos, Christos


    An important prerequisite for computational neuroanatomy is the spatial normalization of the data. Despite its importance for the success of the subsequent statistical analysis, image alignment is dealt with from the perspective of image matching, while its influence on the group analysis is neglected. The choice of the template, the registration algorithm as well as the registration parameters, all confound group differences and impact the outcome of the analysis. In order to limit their influence, we perform multiple registrations by varying these parameters, resulting in multiple instances for each sample. In order to harness the high dimensionality of the data and emphasize the group differences, we propose a supervised dimensionality reduction technique that takes into account the organization of the data. This is achieved by solving a supervised dictionary learning problem for block-sparse signals. Structured sparsity allows the grouping of instances across different independent samples, while label supervision allows for discriminative dictionaries. The block structure of dictionaries allows constructing multiple classifiers that treat each dictionary block as a basis of a subspace that spans a separate band of information. We formulate this problem as a convex optimization problem with a geometric programming (GP) component. Promising results that demonstrate the potential of the proposed approach are shown for an MR image dataset of Autism subjects.

  6. FDA Online Label Repository (United States)

    U.S. Department of Health & Human Services — The drug labels and other drug-specific information on this Web site represent the most recent drug listing information companies have submitted to the Food and Drug...

  7. Semi-supervised Categorization of Wikipedia Collection by Label Expansion (United States)

    Chidlovskii, Boris

    We address the problem of categorizing a large set of linked documents with important content and structure aspects, for example, from Wikipedia collection proposed at the INEX XML Mining track. We cope with the case where there is a small number of labeled pages and a very large number of unlabeled ones. Due to the sparsity of the link based structure of Wikipedia, we apply the spectral and graph-based techniques developed in the semi-supervised machine learning. We use the content and structure views of Wikipedia collection to build a transductive categorizer for the unlabeled pages. We report evaluation results obtained with the label propagation function which ensures a good scalability on sparse graphs.

  8. Pembuatan kulit untuk label

    Directory of Open Access Journals (Sweden)

    Ign. Sunaryo


    Full Text Available Abstract This research is aimed to produce leather for label which is needed by market demand ant to disseminate this technology to industries. There were 10 sides of wet salted cow hides for this research. Those hides were divided into 3 groups, each group consisted of 3 sides that were serially tanned by 3%, 4% and 5% and one side for control. Those hides were then mixed and divided into 3 groups, each group consisted of 3 sides and were then tanned by 6%, 8% and 10% of mimosa. The rest one side was tanned by 6% chrome and 8% mimosa for control. One side of label leather was taken from market used for comparison. Organoleptical, physical and chemical leather testing were carried out in IRDLAI laboratory. The result showed that the quality of the label leather from this research were better than label leather from market. Beside this it could be found out the technology of label manufacture which could produce good quality of label leather that were tanned by 5% chrome and re-tanned by 8% of mimosa

  9. Rapid, sensitive, type specific PCR detection of the E7 region of human papillomavirus type 16 and 18 from paraffin embedded sections of cervical carcinoma

    DEFF Research Database (Denmark)

    Lesnikova, Iana; Lidang, Marianne; Hamilton-Dutoit, Steven


    ABSTRACT: Human papillomavirus (HPV) infection, and in particularly infection with HPVs 16 and 18, is a central carcinogenic factor in the uterine cervix. We established and optimized a PCR assay for the detection and discrimination of HPV types 16 and 18 in archival formaldehyde fixed and paraffin...... embedded (FFPE) sections of cervical cancer.Tissue blocks from 35 cases of in situ or invasive cervical squamous cell carcinoma and surrogate FFPE sections containing the cell lines HeLa and SiHa were tested for HPV 16 and HPV18 by conventional PCR using type specific primers, and for the housekeeping gene...... beta-actin. Using HPV 16 E7 primers, PCR products with the expected length were detected in 18 of 35 of FFPE sections (51%). HPV 18 E7 specific sequences were detected in 3 of 35 FFPE sections (9%).In our experience, the PCR technique is a robust, simple and sensitive way of type specific detection...

  10. Deploying temporary networks for upscaling of sparse network stations (United States)

    Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane


    Soil observations networks at the national scale play an integral role in hydrologic modeling, drought assessment, agricultural decision support, and our ability to understand climate change. Understanding soil moisture variability is necessary to apply these measurements to model calibration, business and consumer applications, or even human health issues. The installation of soil moisture sensors as sparse, national networks is necessitated by limited financial resources. However, this results in the incomplete sampling of the local heterogeneity of soil type, vegetation cover, topography, and the fine spatial distribution of precipitation events. To this end, temporary networks can be installed in the areas surrounding a permanent installation within a sparse network. The temporary networks deployed in this study provide a more representative average at the 3 km and 9 km scales, localized about the permanent gauge. The value of such temporary networks is demonstrated at test sites in Millbrook, New York and Crossville, Tennessee. The capacity of a single U.S. Climate Reference Network (USCRN) sensor set to approximate the average of a temporary network at the 3 km and 9 km scales using a simple linear scaling function is tested. The capacity of a temporary network to provide reliable estimates with diminishing numbers of sensors, the temporal stability of those networks, and ultimately, the relationship of the variability of those networks to soil moisture conditions at the permanent sensor are investigated. In this manner, this work demonstrates the single-season installation of a temporary network as a mechanism to characterize the soil moisture variability at a permanent gauge within a sparse network.

  11. Social biases determine spatiotemporal sparseness of ciliate mating heuristics. (United States)

    Clark, Kevin B


    Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present

  12. Deep Learning on Sparse Manifolds for Faster Object Segmentation. (United States)

    Nascimento, Jacinto C; Carneiro, Gustavo


    We propose a new combination of deep belief networks and sparse manifold learning strategies for the 2D segmentation of non-rigid visual objects. With this novel combination, we aim to reduce the training and inference complexities while maintaining the accuracy of machine learning based non-rigid segmentation methodologies. Typical non-rigid object segmentation methodologies divide the problem into a rigid detection followed by a non-rigid segmentation, where the low dimensionality of the rigid detection allows for a robust training (i.e., a training that does not require a vast amount of annotated images to estimate robust appearance and shape models) and a fast search process during inference. Therefore, it is desirable that the dimensionality of this rigid transformation space is as small as possible in order to enhance the advantages brought by the aforementioned division of the problem. In this paper, we propose the use of sparse manifolds to reduce the dimensionality of the rigid detection space. Furthermore, we propose the use of deep belief networks to allow for a training process that can produce robust appearance models without the need of large annotated training sets. We test our approach in the segmentation of the left ventricle of the heart from ultrasound images and lips from frontal face images. Our experiments show that the use of sparse manifolds and deep belief networks for the rigid detection stage leads to segmentation results that are as accurate as the current state of the art, but with lower search complexity and training processes that require a small amount of annotated training data.

  13. A Root Isolation Algorithm for Sparse Univariate Polynomials


    Alonso, Maria Emilia; Galligo, André


    8 double pages.; International audience; We consider a univariate polynomial f with real coefficients having a high degree $N$ but a rather small number $d+1$ of monomials, with $d\\ll N$. Such a sparse polynomial has a number of real root smaller or equal to $d$. Our target is to find for each real root of $f$ an interval isolating this root from the others. The usual subdivision methods, relying either on Sturm sequences or Moebius transform followed by Descartes's rule of sign, destruct the...

  14. Memorizing binary vector sequences by a sparsely encoded network. (United States)

    Baram, Y


    We present a neural network employing Hebbian storage and sparse internal coding, which is capable of memorizing and correcting sequences of binary vectors by association. A ternary version of the Kanerva memory, folded into a feedback configuration, is shown to perform the basic sequence memorization and regeneration function. The inclusion of lateral connections between the internal cells increases the network capacity considerably and facilitates the correction of individual input patterns and the detection of large errors. The introduction of higher delays in the transmission lines between the external input-output layer and the internal memory layer is shown to further improve the network's error correction capability.

  15. Dictionary-Based Map Compression for Sparse Feature Maps (United States)

    Tanaka, Kanji; Nagasaka, Tomomi

    Obtaining a compact representation of a large-size feature map built by mapper robots is a critical issue in recent mobile robotics. This “map compression” problem is explored from a novel perspective of dictionary-based data compression techniques in the paper. The primary contribution of the paper is the proposal of the dictionary-based map compression approach. A map compression system is presented by employing RANSAC map matching and sparse coding as building blocks. The effectiveness levels of the proposed techniques is investigated in terms of map compression ratio, compression speed, the retrieval performance of compressed/decompressed maps, as well as applications to the Kolmogorov complexity.

  16. Anisotropic Third-Order Regularization for Sparse Digital Elevation Models

    KAUST Repository

    Lellmann, Jan


    We consider the problem of interpolating a surface based on sparse data such as individual points or level lines. We derive interpolators satisfying a list of desirable properties with an emphasis on preserving the geometry and characteristic features of the contours while ensuring smoothness across level lines. We propose an anisotropic third-order model and an efficient method to adaptively estimate both the surface and the anisotropy. Our experiments show that the approach outperforms AMLE and higher-order total variation methods qualitatively and quantitatively on real-world digital elevation data. © 2013 Springer-Verlag.

  17. Sparse inverse covariance estimation with the graphical lasso. (United States)

    Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert


    We consider the problem of estimating sparse graphs by a lasso penalty applied to the inverse covariance matrix. Using a coordinate descent procedure for the lasso, we develop a simple algorithm--the graphical lasso--that is remarkably fast: It solves a 1000-node problem ( approximately 500,000 parameters) in at most a minute and is 30-4000 times faster than competing methods. It also provides a conceptual link between the exact problem and the approximation suggested by Meinshausen and Bühlmann (2006). We illustrate the method on some cell-signaling data from proteomics.

  18. Parallel solution of sparse one-dimensional dynamic programming problems (United States)

    Nicol, David M.


    Parallel computation offers the potential for quickly solving large computational problems. However, it is often a non-trivial task to effectively use parallel computers. Solution methods must sometimes be reformulated to exploit parallelism; the reformulations are often more complex than their slower serial counterparts. We illustrate these points by studying the parallelization of sparse one-dimensional dynamic programming problems, those which do not obviously admit substantial parallelization. We propose a new method for parallelizing such problems, develop analytic models which help us to identify problems which parallelize well, and compare the performance of our algorithm with existing algorithms on a multiprocessor.

  19. Sparse Superpixel Unmixing for Exploratory Analysis of CRISM Hyperspectral Images (United States)

    Thompson, David R.; Castano, Rebecca; Gilmore, Martha S.


    Fast automated analysis of hyperspectral imagery can inform observation planning and tactical decisions during planetary exploration. Products such as mineralogical maps can focus analysts' attention on areas of interest and assist data mining in large hyperspectral catalogs. In this work, sparse spectral unmixing drafts mineral abundance maps with Compact Reconnaissance Imaging Spectrometer (CRISM) images from the Mars Reconnaissance Orbiter. We demonstrate a novel "superpixel" segmentation strategy enabling efficient unmixing in an interactive session. Tests correlate automatic unmixing results based on redundant spectral libraries against hand-tuned summary products currently in use by CRISM researchers.

  20. Sparse Spatio-temporal Inference of Electromagnetic Brain Sources

    DEFF Research Database (Denmark)

    Stahlhut, Carsten; Attias, Hagai Thomas; Wipf, David


    The electromagnetic brain activity measured via MEG (or EEG) can be interpreted as arising from a collection of current dipoles or sources located throughout the cortex. Because the number of candidate locations for these sources is much larger than the number of sensors, source reconstruction......, this paper develops a hierarchical, spatio-temporal Bayesian model that accommodates the principled computation of sparse spatial and smooth temporal M/EEG source reconstructions consistent with neurophysiological assumptions in a variety of event-related imaging paradigms. The underlying methodology relies......-suited for estimation problems that arise from other brain imaging modalities such as functional or diffusion weighted MRI....

  1. Wind Noise Reduction using Non-negative Sparse Coding

    DEFF Research Database (Denmark)

    Schmidt, Mikkel N.; Larsen, Jan; Hsiao, Fu-Tien


    We introduce a new speaker independent method for reducing wind noise in single-channel recordings of noisy speech. The method is based on non-negative sparse coding and relies on a wind noise dictionary which is estimated from an isolated noise recording. We estimate the parameters of the model ...... and discuss their sensitivity. We then compare the algorithm with the classical spectral subtraction method and the Qualcomm-ICSI-OGI noise reduction method. We optimize the sound quality in terms of signal-to-noise ratio and provide results on a noisy speech recognition task....

  2. Human Motion Segmentation via Robust Kernel Sparse Subspace Clustering. (United States)

    Xia, Guiyu; Sun, Huaijiang; Feng, Lei; Zhang, Guoqing; Liu, Yazhou

    Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In this paper, we convert the segmentation of motion capture data into a temporal subspace clustering problem. Under the framework of sparse subspace clustering, we propose to use the geodesic exponential kernel to model the Riemannian manifold structure, use correntropy to measure the reconstruction error, use the triangle constraint to guarantee temporal continuity in each cluster and use multi-view reconstruction to extract the relations between different joints. Therefore, exploiting some special characteristics of motion capture data, we propose a new segmentation method, which is robust to non-Gaussian noise, since correntropy is a localized similarity measure. We also develop an efficient optimization algorithm based on block coordinate descent method to solve the proposed model. Our optimization algorithm has a linear complexity while sparse subspace clustering is originally a quadratic problem. Extensive experiment results both on simulated noisy data set and real noisy data set demonstrate the advantage of the proposed method.Studies on human motion have attracted a lot of attentions. Human motion capture data, which much more precisely records human motion than videos do, has been widely used in many areas. Motion segmentation is an indispensable step for many related applications, but current segmentation methods for motion capture data do not effectively model some important characteristics of motion capture data, such as Riemannian manifold structure and containing non-Gaussian noise. In

  3. Algorithms for sparse, symmetric, definite quadratic lambda-matrix eigenproblems

    International Nuclear Information System (INIS)

    Scott, D.S.; Ward, R.C.


    Methods are presented for computing eigenpairs of the quadratic lambda-matrix, M lambda 2 + C lambda + K, where M, C, and K are large and sparse, and have special symmetry-type properties. These properties are sufficient to insure that all the eigenvalues are real and that theory analogous to the standard symmetric eigenproblem exists. The methods employ some standard techniques such as partial tri-diagonalization via the Lanczos Method and subsequent eigenpair calculation, shift-and- invert strategy and subspace iteration. The methods also employ some new techniques such as Rayleigh-Ritz quadratic roots and the inertia of symmetric, definite, quadratic lambda-matrices

  4. Calculation of the inverse data space via sparse inversion

    KAUST Repository

    Saragiotis, Christos


    The inverse data space provides a natural separation of primaries and surface-related multiples, as the surface multiples map onto the area around the origin while the primaries map elsewhere. However, the calculation of the inverse data is far from trivial as theory requires infinite time and offset recording. Furthermore regularization issues arise during inversion. We perform the inversion by minimizing the least-squares norm of the misfit function by constraining the $ell_1$ norm of the solution, being the inverse data space. In this way a sparse inversion approach is obtained. We show results on field data with an application to surface multiple removal.

  5. Sparse Representation Based Classification with Structure Preserving Dimension Reduction (United States)


    established from the original data Y. A case study of this algorithm is shown in Fig. 1. The algorithm is run over the ‘‘ Libras Movement’’ data set...Residual on different categories (COR) (b) (c) Fig. 1 Sparse-representation- based classifier is applied to the ‘‘ Libras Movement’’ data set with 3...UCI data sets Name Feature number Total size Test size Class Wine 13 178 89 3 Glass 10 214 107 7 Libras Movement 90 360 180 15 Wine Quality 11 4,898

  6. Sparse least-squares reverse time migration using seislets

    KAUST Repository

    Dutta, Gaurav


    We propose sparse least-squares reverse time migration (LSRTM) using seislets as a basis for the reflectivity distribution. This basis is used along with a dip-constrained preconditioner that emphasizes image updates only along prominent dips during the iterations. These dips can be estimated from the standard migration image or from the gradient using plane-wave destruction filters or structural tensors. Numerical tests on synthetic datasets demonstrate the benefits of this method for mitigation of aliasing artifacts and crosstalk noise in multisource least-squares migration.

  7. Fast Convolutional Sparse Coding in the Dual Domain

    KAUST Repository

    Affara, Lama Ahmed


    Convolutional sparse coding (CSC) is an important building block of many computer vision applications ranging from image and video compression to deep learning. We present two contributions to the state of the art in CSC. First, we significantly speed up the computation by proposing a new optimization framework that tackles the problem in the dual domain. Second, we extend the original formulation to higher dimensions in order to process a wider range of inputs, such as color inputs, or HOG features. Our results show a significant speedup compared to the current state of the art in CSC.

  8. Multi-site study of HPV type-specific prevalence in women with cervical cancer, intraepithelial neoplasia and normal cytology, in England


    Howell-Jones, R; Bailey, A; Beddows, S; Sargent, A; de Silva, N; Wilson, G; Anton, J; Nichols, T; Soldan, K; Kitchener, H


    Background: Knowledge of the prevalence of type-specific human papillomavirus (HPV) infections is necessary to predict the expected, and to monitor the actual, impact of HPV immunisation and to design effective screening strategies for vaccinated populations. Methods: Residual specimens of cervical cytology (N=4719), CIN3/CGIN and cervical cancer biopsies (N=1515) were obtained from sites throughout England, anonymised and tested for HPV DNA using the Linear Array typing system (Roche). Resul...

  9. Development and Evaluation of a PCR and Mass Spectroscopy-based (PCR-MS) Method for Quantitative, Type-specific Detection of Human Papillomavirus (United States)

    Patel, Divya A.; Shih, Yang-Jen; Newton, Duane W.; Michael, Claire W.; Oeth, Paul A.; Kane, Michael D.; Opipari, Anthony W.; Ruffin, Mack T.; Kalikin, Linda M.; Kurnit, David M.


    Knowledge of the central role of high-risk human papillomavirus (HPV) in cervical carcinogenesis, coupled with an emerging need to monitor the efficacy of newly introduced HPV vaccines, warrant development and evaluation of type-specific, quantitative HPV detection methods. In the present study, a prototype PCR and mass spectroscopy (PCR-MS)-based method to detect and quantitate 13 high-risk HPV types is compared to the Hybrid Capture 2 High Risk HPV DNA test (HC2; Digene Corp., Gaithersburg, MD) in 199 cervical scraping samples and to DNA sequencing in 77 cervical tumor samples. High-risk HPV types were detected in 76/77 (98.7%) cervical tumor samples by PCR-MS. Degenerate and type-specific sequencing confirmed the types detected by PCR-MS. In 199 cervical scraping samples, all 13 HPV types were detected by PCR-MS. Eighteen (14.5%) of 124 cervical scraping samples that were positive for high-risk HPV by HC2 were negative by PCR-MS. In all these cases, degenerate DNA sequencing failed to detect any of the 13 high-risk HPV types. Nearly half (46.7%) of the 75 cervical scraping samples that were negative for high-risk HPV by the HC2 assay were positive by PCR-MS. Type-specific sequencing in a subset of these samples confirmed the HPV type detected by PCR-MS. Quantitative PCR-MS results demonstrated that 11/75 (14.7%) samples contained as much HPV copies/cell as HC2-positive samples. These findings suggest that this prototype PCR-MS assay performs at least as well as HC2 for HPV detection, while offering the additional, unique advantages of type-specific identification and quantitation. Further validation work is underway to define clinically meaningful HPV detection thresholds and to evaluate the potential clinical application of future generations of the PCR-MS assay. PMID:19410602

  10. Development and evaluation of a PCR and mass spectroscopy (PCR-MS)-based method for quantitative, type-specific detection of human papillomavirus. (United States)

    Patel, Divya A; Shih, Yang-Jen; Newton, Duane W; Michael, Claire W; Oeth, Paul A; Kane, Michael D; Opipari, Anthony W; Ruffin, Mack T; Kalikin, Linda M; Kurnit, David M


    Knowledge of the central role of high-risk human papillomavirus (HPV) in cervical carcinogenesis, coupled with an emerging need to monitor the efficacy of newly introduced HPV vaccines, warrant development and evaluation of type-specific, quantitative HPV detection methods. In the present study, a prototype PCR and mass spectroscopy (PCR-MS)-based method to detect and quantitate 13 high-risk HPV types is compared to the Hybrid Capture 2 High-Risk HPV DNA test (HC2; Digene Corp., Gaithersburg, MD) in 199 cervical scraping samples and to DNA sequencing in 77 cervical tumor samples. High-risk HPV types were detected in 76/77 (98.7%) cervical tumor samples by PCR-MS. Degenerate and type-specific sequencing confirmed the types detected by PCR-MS. In 199 cervical scraping samples, all 13 HPV types were detected by PCR-MS. Eighteen (14.5%) of 124 cervical scraping samples that were positive for high-risk HPV by HC2 were negative by PCR-MS. In all these cases, degenerate DNA sequencing failed to detect any of the 13 high-risk HPV types. Nearly half (46.7%) of the 75 cervical scraping samples that were negative for high-risk HPV by the HC2 assay were positive by PCR-MS. Type-specific sequencing in a subset of these samples confirmed the HPV type detected by PCR-MS. Quantitative PCR-MS results demonstrated that 11/75 (14.7%) samples contained as much HPV copies/cell as HC2-positive samples. These findings suggest that this prototype PCR-MS assay performs at least as well as HC2 for HPV detection, while offering the additional, unique advantages of type-specific identification and quantitation. Further validation work is underway to define clinically meaningful HPV detection thresholds and to evaluate the potential clinical application of future generations of the PCR-MS assay.

  11. Genetic algorithms for map labeling

    NARCIS (Netherlands)

    Dijk, Steven Ferdinand van


    Map labeling is the cartographic problem of placing the names of features (for example cities or rivers) on the map. A good labeling has no intersections between labels. Even basic versions of the problem are NP-hard. In addition, realistic map-labeling problems deal with many cartographic

  12. European consumers and nutrition labelling

    DEFF Research Database (Denmark)

    Wills, Josephine M.; Grunert, Klaus G.; Celemín, Laura Fernández


    Nutrition labelling of food in Europe is not compulsory, unless a nutrition or health claim is made for the product. The European Commission is proposing mandatory nutrition labelling, even front of pack labelling with nutrition information. Yet, how widespread is nutrition labelling in the EU...

  13. Sparse Bayesian extreme learning machine for multi-classification. (United States)

    Luo, Jiahua; Vong, Chi-Man; Wong, Pak-Kin


    Extreme learning machine (ELM) has become a popular topic in machine learning in recent years. ELM is a new kind of single-hidden layer feedforward neural network with an extremely low computational cost. ELM, however, has two evident drawbacks: 1) the output weights solved by Moore-Penrose generalized inverse is a least squares minimization issue, which easily suffers from overfitting and 2) the accuracy of ELM is drastically sensitive to the number of hidden neurons so that a large model is usually generated. This brief presents a sparse Bayesian approach for learning the output weights of ELM in classification. The new model, called Sparse Bayesian ELM (SBELM), can resolve these two drawbacks by estimating the marginal likelihood of network outputs and automatically pruning most of the redundant hidden neurons during learning phase, which results in an accurate and compact model. The proposed SBELM is evaluated on wide types of benchmark classification problems, which verifies that the accuracy of SBELM model is relatively insensitive to the number of hidden neurons; and hence a much more compact model is always produced as compared with other state-of-the-art neural network classifiers.

  14. Sparse signal representation and its applications in ultrasonic NDE. (United States)

    Zhang, Guang-Ming; Zhang, Cheng-Zhong; Harvey, David M


    Many sparse signal representation (SSR) algorithms have been developed in the past decade. The advantages of SSR such as compact representations and super resolution lead to the state of the art performance of SSR for processing ultrasonic non-destructive evaluation (NDE) signals. Choosing a suitable SSR algorithm and designing an appropriate overcomplete dictionary is a key for success. After a brief review of sparse signal representation methods and the design of overcomplete dictionaries, this paper addresses the recent accomplishments of SSR for processing ultrasonic NDE signals. The advantages and limitations of SSR algorithms and various overcomplete dictionaries widely-used in ultrasonic NDE applications are explored in depth. Their performance improvement compared to conventional signal processing methods in many applications such as ultrasonic flaw detection and noise suppression, echo separation and echo estimation, and ultrasonic imaging is investigated. The challenging issues met in practical ultrasonic NDE applications for example the design of a good dictionary are discussed. Representative experimental results are presented for demonstration. Copyright © 2011 Elsevier B.V. All rights reserved.

  15. Ab initio nuclear structure - the large sparse matrix eigenvalue problem

    International Nuclear Information System (INIS)

    Vary, James P; Maris, Pieter; Ng, Esmond; Yang, Chao; Sosonkina, Masha


    The structure and reactions of light nuclei represent fundamental and formidable challenges for microscopic theory based on realistic strong interaction potentials. Several ab initio methods have now emerged that provide nearly exact solutions for some nuclear properties. The ab initio no core shell model (NCSM) and the no core full configuration (NCFC) method, frame this quantum many-particle problem as a large sparse matrix eigenvalue problem where one evaluates the Hamiltonian matrix in a basis space consisting of many-fermion Slater determinants and then solves for a set of the lowest eigenvalues and their associated eigenvectors. The resulting eigenvectors are employed to evaluate a set of experimental quantities to test the underlying potential. For fundamental problems of interest, the matrix dimension often exceeds 10 10 and the number of nonzero matrix elements may saturate available storage on present-day leadership class facilities. We survey recent results and advances in solving this large sparse matrix eigenvalue problem. We also outline the challenges that lie ahead for achieving further breakthroughs in fundamental nuclear theory using these ab initio approaches.

  16. Sparse Detector Imaging Sensor with Two-Class Silhouette Classification

    Directory of Open Access Journals (Sweden)

    David Russomanno


    Full Text Available This paper presents the design and test of a simple active near-infrared sparse detector imaging sensor. The prototype of the sensor is novel in that it can capture remarkable silhouettes or profiles of a wide-variety of moving objects, including humans, animals, and vehicles using a sparse detector array comprised of only sixteen sensing elements deployed in a vertical configuration. The prototype sensor was built to collect silhouettes for a variety of objects and to evaluate several algorithms for classifying the data obtained from the sensor into two classes: human versus non-human. Initial tests show that the classification of individually sensed objects into two classes can be achieved with accuracy greater than ninety-nine percent (99% with a subset of the sixteen detectors using a representative dataset consisting of 512 signatures. The prototype also includes a Webservice interface such that the sensor can be tasked in a network-centric environment. The sensor appears to be a low-cost alternative to traditional, high-resolution focal plane array imaging sensors for some applications. After a power optimization study, appropriate packaging, and testing with more extensive datasets, the sensor may be a good candidate for deployment in vast geographic regions for a myriad of intelligent electronic fence and persistent surveillance applications, including perimeter security scenarios.

  17. Music emotion detection using hierarchical sparse kernel machines. (United States)

    Chin, Yu-Hao; Lin, Chang-Hong; Siahaan, Ernestasia; Wang, Jia-Ching


    For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.

  18. Structure-adaptive sparse denoising for diffusion-tensor MRI. (United States)

    Bao, Lijun; Robini, Marc; Liu, Wanyu; Zhu, Yuemin


    Diffusion tensor magnetic resonance imaging (DT-MRI) is becoming a prospective imaging technique in clinical applications because of its potential for in vivo and non-invasive characterization of tissue organization. However, the acquisition of diffusion-weighted images (DWIs) is often corrupted by noise and artifacts, and the intensity of diffusion-weighted signals is weaker than that of classical magnetic resonance signals. In this paper, we propose a new denoising method for DT-MRI, called structure-adaptive sparse denoising (SASD), which exploits self-similarity in DWIs. We define a similarity measure based on the local mean and on a modified structure-similarity index to find sets of similar patches that are arranged into three-dimensional arrays, and we propose a simple and efficient structure-adaptive window pursuit method to achieve sparse representation of these arrays. The noise component of the resulting structure-adaptive arrays is attenuated by Wiener shrinkage in a transform domain defined by two-dimensional principal component decomposition and Haar transformation. Experiments on both synthetic and real cardiac DT-MRI data show that the proposed SASD algorithm outperforms state-of-the-art methods for denoising images with structural redundancy. Moreover, SASD achieves a good trade-off between image contrast and image smoothness, and our experiments on synthetic data demonstrate that it produces more accurate tensor fields from which biologically relevant metrics can then be computed. Copyright © 2013 Elsevier B.V. All rights reserved.

  19. AMP-Inspired Deep Networks for Sparse Linear Inverse Problems (United States)

    Borgerding, Mark; Schniter, Philip; Rangan, Sundeep


    Deep learning has gained great popularity due to its widespread success on many inference problems. We consider the application of deep learning to the sparse linear inverse problem, where one seeks to recover a sparse signal from a few noisy linear measurements. In this paper, we propose two novel neural-network architectures that decouple prediction errors across layers in the same way that the approximate message passing (AMP) algorithms decouple them across iterations: through Onsager correction. First, we propose a "learned AMP" network that significantly improves upon Gregor and LeCun's "learned ISTA." Second, inspired by the recently proposed "vector AMP" (VAMP) algorithm, we propose a "learned VAMP" network that offers increased robustness to deviations in the measurement matrix from i.i.d. Gaussian. In both cases, we jointly learn the linear transforms and scalar nonlinearities of the network. Interestingly, with i.i.d. signals, the linear transforms and scalar nonlinearities prescribed by the VAMP algorithm coincide with the values learned through back-propagation, leading to an intuitive interpretation of learned VAMP. Finally, we apply our methods to two problems from 5G wireless communications: compressive random access and massive-MIMO channel estimation.

  20. Sparse estimation of model-based diffuse thermal dust emission (United States)

    Irfan, Melis O.; Bobin, Jérôme


    Component separation for the Planck High Frequency Instrument (HFI) data is primarily concerned with the estimation of thermal dust emission, which requires the separation of thermal dust from the cosmic infrared background (CIB). For that purpose, current estimation methods rely on filtering techniques to decouple thermal dust emission from CIB anisotropies, which tend to yield a smooth, low-resolution, estimation of the dust emission. In this paper, we present a new parameter estimation method, premise: Parameter Recovery Exploiting Model Informed Sparse Estimates. This method exploits the sparse nature of thermal dust emission to calculate all-sky maps of thermal dust temperature, spectral index, and optical depth at 353 GHz. premise is evaluated and validated on full-sky simulated data. We find the percentage difference between the premise results and the true values to be 2.8, 5.7, and 7.2 per cent at the 1σ level across the full sky for thermal dust temperature, spectral index, and optical depth at 353 GHz, respectively. A comparison between premise and a GNILC-like method over selected regions of our sky simulation reveals that both methods perform comparably within high signal-to-noise regions. However, outside of the Galactic plane, premise is seen to outperform the GNILC-like method with increasing success as the signal-to-noise ratio worsens.

  1. Sparse discriminant manifold projections for bearing fault diagnosis (United States)

    Chen, Gang; Liu, Fenglin; Huang, Wei


    The monitored vibration signal of bearing is usually nonlinear and nonstationary, and may be corrupted by background noise. Thus, it is very difficult to accurately extract sensitive and reliable characteristics information from the vibration signal to diagnose bearing health conditions. This paper proposes a novel bearing fault diagnosis method based on sparse discriminant manifold projections (SDMP). The SDMP was developed based on sparsity preserving projections, and sparse manifold clustering and embedding. The SDMP can effectively extract the meaningful low-dimensional intrinsic features that hidden in a high-dimensional feature dataset. After dimensionality reduction with the SDMP, the least squares support vector machine (LS-SVM) is utilized to classify the different low-dimensional feature data for fault recognition. The effectiveness and superiorities of the proposed method are demonstrated through several comparative experiments with other three manifold learning methods. The experimental results validate that the SDMP is more effective than the other three manifold learning methods for implementation bearing fault diagnosis, and it is more robust when deal with noise interference signal.

  2. Permuting sparse rectangular matrices into block-diagonal form

    Energy Technology Data Exchange (ETDEWEB)

    Aykanat, Cevdet; Pinar, Ali; Catalyurek, Umit V.


    This work investigates the problem of permuting a sparse rectangular matrix into block diagonal form. Block diagonal form of a matrix grants an inherent parallelism for the solution of the deriving problem, as recently investigated in the context of mathematical programming, LU factorization and QR factorization. We propose graph and hypergraph models to represent the nonzero structure of a matrix, which reduce the permutation problem to those of graph partitioning by vertex separator and hypergraph partitioning, respectively. Besides proposing the models to represent sparse matrices and investigating related combinatorial problems, we provide a detailed survey of relevant literature to bridge the gap between different societies, investigate existing techniques for partitioning and propose new ones, and finally present a thorough empirical study of these techniques. Our experiments on a wide range of matrices, using state-of-the-art graph and hypergraph partitioning tools MeTiS and PaT oH, revealed that the proposed methods yield very effective solutions both in terms of solution quality and run time.

  3. Mapping High Dimensional Sparse Customer Requirements into Product Configurations (United States)

    Jiao, Yao; Yang, Yu; Zhang, Hongshan


    Mapping customer requirements into product configurations is a crucial step for product design, while, customers express their needs ambiguously and locally due to the lack of domain knowledge. Thus the data mining process of customer requirements might result in fragmental information with high dimensional sparsity, leading the mapping procedure risk uncertainty and complexity. The Expert Judgment is widely applied against that background since there is no formal requirements for systematic or structural data. However, there are concerns on the repeatability and bias for Expert Judgment. In this study, an integrated method by adjusted Local Linear Embedding (LLE) and Naïve Bayes (NB) classifier is proposed to map high dimensional sparse customer requirements to product configurations. The integrated method adjusts classical LLE to preprocess high dimensional sparse dataset to satisfy the prerequisite of NB for classifying different customer requirements to corresponding product configurations. Compared with Expert Judgment, the adjusted LLE with NB performs much better in a real-world Tablet PC design case both in accuracy and robustness.

  4. Relationship between speech recognition in noise and sparseness. (United States)

    Li, Guoping; Lutman, Mark E; Wang, Shouyan; Bleeck, Stefan


    Established methods for predicting speech recognition in noise require knowledge of clean speech signals, placing limitations on their application. The study evaluates an alternative approach based on characteristics of noisy speech, specifically its sparseness as represented by the statistic kurtosis. Experiments 1 and 2 involved acoustic analysis of vowel-consonant-vowel (VCV) syllables in babble noise, comparing kurtosis, glimpsing areas, and extended speech intelligibility index (ESII) of noisy speech signals with one another and with pre-existing speech recognition scores. Experiment 3 manipulated kurtosis of VCV syllables and investigated effects on speech recognition scores in normal-hearing listeners. Pre-existing speech recognition data for Experiments 1 and 2; seven normal-hearing participants for Experiment 3. Experiments 1 and 2 demonstrated that kurtosis calculated in the time-domain from noisy speech is highly correlated (r > 0.98) with established prediction models: glimpsing and ESII. All three measures predicted speech recognition scores well. The final experiment showed a clear monotonic relationship between speech recognition scores and kurtosis. Speech recognition performance in noise is closely related to the sparseness (kurtosis) of the noisy speech signal, at least for the types of speech and noise used here and for listeners with normal hearing.

  5. Population Coding in Sparsely Connected Networks of Noisy Neurons

    Directory of Open Access Journals (Sweden)

    Bryan Patrick Tripp


    Full Text Available This study examines the relationship between population coding and spatial connection statistics in networks of noisy neurons. Encoding of sensory information in the neocortex is thought to require coordinated neural populations, because individual cortical neurons respond to a wide range of stimuli, and exhibit highly variable spiking in response to repeated stimuli. Population coding is rooted in network structure, because cortical neurons receive information only from other neurons, and because the information they encode must be decoded by other neurons, if it is to affect behaviour. However, population coding theory has often ignored network structure, or assumed discrete, fully-connected populations (in contrast with the sparsely connected, continuous sheet of the cortex. In this study, we model a sheet of cortical neurons with sparse, primarily local connections, and find that a network with this structure can encode multiple internal state variables with high signal-to-noise ratio. However, in our model, although connection probability varies with the distance between neurons, we find that the connections cannot be instantiated at random according to these probabilities, but must have additional structure if information is to be encoded with high fidelity.

  6. Pedestrian detection from thermal images: A sparse representation based approach (United States)

    Qi, Bin; John, Vijay; Liu, Zheng; Mita, Seiichi


    Pedestrian detection, a key technology in computer vision, plays a paramount role in the applications of advanced driver assistant systems (ADASs) and autonomous vehicles. The objective of pedestrian detection is to identify and locate people in a dynamic environment so that accidents can be avoided. With significant variations introduced by illumination, occlusion, articulated pose, and complex background, pedestrian detection is a challenging task for visual perception. Different from visible images, thermal images are captured and presented with intensity maps based objects' emissivity, and thus have an enhanced spectral range to make human beings perceptible from the cool background. In this study, a sparse representation based approach is proposed for pedestrian detection from thermal images. We first adopted the histogram of sparse code to represent image features and then detect pedestrian with the extracted features in an unimodal and a multimodal framework respectively. In the unimodal framework, two types of dictionaries, i.e. joint dictionary and individual dictionary, are built by learning from prepared training samples. In the multimodal framework, a weighted fusion scheme is proposed to further highlight the contributions from features with higher separability. To validate the proposed approach, experiments were conducted to compare with three widely used features: Haar wavelets (HWs), histogram of oriented gradients (HOG), and histogram of phase congruency (HPC) as well as two classification methods, i.e. AdaBoost and support vector machine (SVM). Experimental results on a publicly available data set demonstrate the superiority of the proposed approach.

  7. Image Classification Based on Convolutional Denoising Sparse Autoencoder

    Directory of Open Access Journals (Sweden)

    Shuangshuang Chen


    Full Text Available Image classification aims to group images into corresponding semantic categories. Due to the difficulties of interclass similarity and intraclass variability, it is a challenging issue in computer vision. In this paper, an unsupervised feature learning approach called convolutional denoising sparse autoencoder (CDSAE is proposed based on the theory of visual attention mechanism and deep learning methods. Firstly, saliency detection method is utilized to get training samples for unsupervised feature learning. Next, these samples are sent to the denoising sparse autoencoder (DSAE, followed by convolutional layer and local contrast normalization layer. Generally, prior in a specific task is helpful for the task solution. Therefore, a new pooling strategy—spatial pyramid pooling (SPP fused with center-bias prior—is introduced into our approach. Experimental results on the common two image datasets (STL-10 and CIFAR-10 demonstrate that our approach is effective in image classification. They also demonstrate that none of these three components: local contrast normalization, SPP fused with center-prior, and l2 vector normalization can be excluded from our proposed approach. They jointly improve image representation and classification performance.

  8. Tissue-specific sparse deconvolution for brain CT perfusion. (United States)

    Fang, Ruogu; Jiang, Haodi; Huang, Junzhou


    Enhancing perfusion maps in low-dose computed tomography perfusion (CTP) for cerebrovascular disease diagnosis is a challenging task, especially for low-contrast tissue categories where infarct core and ischemic penumbra usually occur. Sparse perfusion deconvolution has been recently proposed to effectively improve the image quality and diagnostic accuracy of low-dose perfusion CT by extracting the complementary information from the high-dose perfusion maps to restore the low-dose using a joint spatio-temporal model. However the low-contrast tissue classes where infarct core and ischemic penumbra are likely to occur in cerebral perfusion CT tend to be over-smoothed, leading to loss of essential biomarkers. In this paper, we propose a tissue-specific sparse deconvolution approach to preserve the subtle perfusion information in the low-contrast tissue classes. We first build tissue-specific dictionaries from segmentations of high-dose perfusion maps using online dictionary learning, and then perform deconvolution-based hemodynamic parameters estimation for block-wise tissue segments on the low-dose CTP data. Extensive validation on clinical datasets of patients with cerebrovascular disease demonstrates the superior performance of our proposed method compared to state-of-art, and potentially improve diagnostic accuracy by increasing the differentiation between normal and ischemic tissues in the brain. Copyright © 2015 Elsevier Ltd. All rights reserved.

  9. Integrative sparse principal component analysis of gene expression data. (United States)

    Liu, Mengque; Fan, Xinyan; Fang, Kuangnan; Zhang, Qingzhao; Ma, Shuangge


    In the analysis of gene expression data, dimension reduction techniques have been extensively adopted. The most popular one is perhaps the PCA (principal component analysis). To generate more reliable and more interpretable results, the SPCA (sparse PCA) technique has been developed. With the "small sample size, high dimensionality" characteristic of gene expression data, the analysis results generated from a single dataset are often unsatisfactory. Under contexts other than dimension reduction, integrative analysis techniques, which jointly analyze the raw data of multiple independent datasets, have been developed and shown to outperform "classic" meta-analysis and other multidatasets techniques and single-dataset analysis. In this study, we conduct integrative analysis by developing the iSPCA (integrative SPCA) method. iSPCA achieves the selection and estimation of sparse loadings using a group penalty. To take advantage of the similarity across datasets and generate more accurate results, we further impose contrasted penalties. Different penalties are proposed to accommodate different data conditions. Extensive simulations show that iSPCA outperforms the alternatives under a wide spectrum of settings. The analysis of breast cancer and pancreatic cancer data further shows iSPCA's satisfactory performance. © 2017 WILEY PERIODICALS, INC.

  10. BOES: Building Occupancy Estimation System using sparse ambient vibration monitoring (United States)

    Pan, Shijia; Bonde, Amelie; Jing, Jie; Zhang, Lin; Zhang, Pei; Noh, Hae Young


    In this paper, we present a room-level building occupancy estimation system (BOES) utilizing low-resolution vibration sensors that are sparsely distributed. Many ubiquitous computing and building maintenance systems require fine-grained occupancy knowledge to enable occupant centric services and optimize space and energy utilization. The sensing infrastructure support for current occupancy estimation systems often requires multiple intrusive sensors per room, resulting in systems that are both costly to deploy and difficult to maintain. To address these shortcomings, we developed BOES. BOES utilizes sparse vibration sensors to track occupancy levels and activities. Our system has three major components. 1) It extracts features that distinguish occupant activities from noise prone ambient vibrations and detects human footsteps. 2) Using a sequence of footsteps, the system localizes and tracks individuals by observing changes in the sequences. It uses this tracking information to identify when an occupant leaves or enters a room. 3) The entering and leaving room information are combined with detected individual location information to update the room-level occupancy state of the building. Through validation experiments in two different buildings, our system was able to achieve 99.55% accuracy for event detection, less than three feet average error for localization, and 85% accuracy in occupancy counting.

  11. A linear recurrent kernel online learning algorithm with sparse updates. (United States)

    Fan, Haijin; Song, Qing


    In this paper, we propose a recurrent kernel algorithm with selectively sparse updates for online learning. The algorithm introduces a linear recurrent term in the estimation of the current output. This makes the past information reusable for updating of the algorithm in the form of a recurrent gradient term. To ensure that the reuse of this recurrent gradient indeed accelerates the convergence speed, a novel hybrid recurrent training is proposed to switch on or off learning the recurrent information according to the magnitude of the current training error. Furthermore, the algorithm includes a data-dependent adaptive learning rate which can provide guaranteed system weight convergence at each training iteration. The learning rate is set as zero when the training violates the derived convergence conditions, which makes the algorithm updating process sparse. Theoretical analyses of the weight convergence are presented and experimental results show the good performance of the proposed algorithm in terms of convergence speed and estimation accuracy. Copyright © 2013 Elsevier Ltd. All rights reserved.

  12. A preconditioned inexact newton method for nonlinear sparse electromagnetic imaging

    KAUST Repository

    Desmal, Abdulla


    A nonlinear inversion scheme for the electromagnetic microwave imaging of domains with sparse content is proposed. Scattering equations are constructed using a contrast-source (CS) formulation. The proposed method uses an inexact Newton (IN) scheme to tackle the nonlinearity of these equations. At every IN iteration, a system of equations, which involves the Frechet derivative (FD) matrix of the CS operator, is solved for the IN step. A sparsity constraint is enforced on the solution via thresholded Landweber iterations, and the convergence is significantly increased using a preconditioner that levels the FD matrix\\'s singular values associated with contrast and equivalent currents. To increase the accuracy, the weight of the regularization\\'s penalty term is reduced during the IN iterations consistently with the scheme\\'s quadratic convergence. At the end of each IN iteration, an additional thresholding, which removes small \\'ripples\\' that are produced by the IN step, is applied to maintain the solution\\'s sparsity. Numerical results demonstrate the applicability of the proposed method in recovering sparse and discontinuous dielectric profiles with high contrast values.

  13. High-Density Otologic Camps in Regions of Sparse Expertise. (United States)

    Girma, Bizuayehu; Bitew, Asnake; Kiros, Nega; Redleaf, Miriam


    Aims/Purpose: When 2 models of otologic surgery instruction in Ethiopia are compared, high-density otologic surgery campaigns are more effective for accelerated skills transfer in areas of sparse expertise than the standard outpatient clinic/OR model. A continuously operating otolaryngology/head and neck surgery department in a large public hospital is compared with a nonprofit specialty hospital where outpatients are selected for weeklong surgical campaigns. The number and variety of otologic visits and operations in each setting, presence of expert supervision, and resident-trainees' surgical progress were tallied. The public hospital saw 84 otologic operations in 1 full year. Meanwhile, the ear specialty surgical campaign site saw 185 otologic operations in 6 surgical campaign weeks. All operations at both sites were performed primarily by trainees. Experienced otologists supervised 40% of operations at the public hospital and 100% at the surgical campaign site. At the end of the year, none of the 10 resident-trainees in the public hospital were able to perform a simple underlay tympanoplasty, compared to 6 of 12 resident-trainees in the campaign setting. Where otologic expertise is sparse, otologic surgical campaigns allow the most effective use of resources-patient pathology, medical facilities, trainee attendance, and imported instructors.

  14. Robust sparse image reconstruction of radio interferometric observations with PURIFY (United States)

    Pratley, Luke; McEwen, Jason D.; d'Avezac, Mayeul; Carrillo, Rafael E.; Onose, Alexandru; Wiaux, Yves


    Next-generation radio interferometers, such as the Square Kilometre Array, will revolutionize our understanding of the Universe through their unprecedented sensitivity and resolution. However, to realize these goals significant challenges in image and data processing need to be overcome. The standard methods in radio interferometry for reconstructing images, such as CLEAN, have served the community well over the last few decades and have survived largely because they are pragmatic. However, they produce reconstructed interferometric images that are limited in quality and scalability for big data. In this work, we apply and evaluate alternative interferometric reconstruction methods that make use of state-of-the-art sparse image reconstruction algorithms motivated by compressive sensing, which have been implemented in the PURIFY software package. In particular, we implement and apply the proximal alternating direction method of multipliers algorithm presented in a recent article. First, we assess the impact of the interpolation kernel used to perform gridding and degridding on sparse image reconstruction. We find that the Kaiser-Bessel interpolation kernel performs as well as prolate spheroidal wave functions while providing a computational saving and an analytic form. Secondly, we apply PURIFY to real interferometric observations from the Very Large Array and the Australia Telescope Compact Array and find that images recovered by PURIFY are of higher quality than those recovered by CLEAN. Thirdly, we discuss how PURIFY reconstructions exhibit additional advantages over those recovered by CLEAN. The latest version of PURIFY, with developments presented in this work, is made publicly available.

  15. Threshold partitioning of sparse matrices and applications to Markov chains

    Energy Technology Data Exchange (ETDEWEB)

    Choi, Hwajeong; Szyld, D.B. [Temple Univ., Philadelphia, PA (United States)


    It is well known that the order of the variables and equations of a large, sparse linear system influences the performance of classical iterative methods. In particular if, after a symmetric permutation, the blocks in the diagonal have more nonzeros, classical block methods have a faster asymptotic rate of convergence. In this paper, different ordering and partitioning algorithms for sparse matrices are presented. They are modifications of PABLO. In the new algorithms, in addition to the location of the nonzeros, the values of the entries are taken into account. The matrix resulting after the symmetric permutation has dense blocks along the diagonal, and small entries in the off-diagonal blocks. Parameters can be easily adjusted to obtain, for example, denser blocks, or blocks with elements of larger magnitude. In particular, when the matrices represent Markov chains, the permuted matrices are well suited for block iterative methods that find the corresponding probability distribution. Applications to three types of methods are explored: (1) Classical block methods, such as Block Gauss Seidel. (2) Preconditioned GMRES, where a block diagonal preconditioner is used. (3) Iterative aggregation method (also called aggregation/disaggregation) where the partition obtained from the ordering algorithm with certain parameters is used as an aggregation scheme. In all three cases, experiments are presented which illustrate the performance of the methods with the new orderings. The complexity of the new algorithms is linear in the number of nonzeros and the order of the matrix, and thus adding little computational effort to the overall solution.

  16. Structured sparse error coding for face recognition with occlusion. (United States)

    Li, Xiao-Xin; Dai, Dao-Qing; Zhang, Xiao-Fei; Ren, Chuan-Xian


    Face recognition with occlusion is common in the real world. Inspired by the works of structured sparse representation, we try to explore the structure of the error incurred by occlusion from two aspects: the error morphology and the error distribution. Since human beings recognize the occlusion mainly according to its region shape or profile without knowing accurately what the occlusion is, we argue that the shape of the occlusion is also an important feature. We propose a morphological graph model to describe the morphological structure of the error. Due to the uncertainty of the occlusion, the distribution of the error incurred by occlusion is also uncertain. However, we observe that the unoccluded part and the occluded part of the error measured by the correntropy induced metric follow the exponential distribution, respectively. Incorporating the two aspects of the error structure, we propose the structured sparse error coding for face recognition with occlusion. Our extensive experiments demonstrate that the proposed method is more stable and has higher breakdown point in dealing with the occlusion problems in face recognition as compared to the related state-of-the-art methods, especially for the extreme situation, such as the high level occlusion and the low feature dimension.

  17. Sparse aerosol models beyond the quadrature method of moments (United States)

    McGraw, Robert


    This study examines a class of sparse aerosol models derived from linear programming (LP). The widely used quadrature method of moments (QMOM) is shown to fall into this class. Here it is shown how other sparse aerosol models can be constructed, which are not based on moments of the particle size distribution. The new methods enable one to bound atmospheric aerosol physical and optical properties using arbitrary combinations of model parameters and measurements. Rigorous upper and lower bounds, e.g. on the number of aerosol particles that can activate to form cloud droplets, can be obtained this way from measurement constraints that may include total particle number concentration and size distribution moments. The new LP-based methods allow a much wider range of aerosol properties, such as light backscatter or extinction coefficient, which are not easily connected to particle size moments, to also be assimilated into a list of constraints. Finally, it is shown that many of these more general aerosol properties can be tracked directly in an aerosol dynamics simulation, using SAMs, in much the same way that moments are tracked directly in the QMOM.

  18. Application of alternating decision trees in selecting sparse linear solvers

    KAUST Repository

    Bhowmick, Sanjukta


    The solution of sparse linear systems, a fundamental and resource-intensive task in scientific computing, can be approached through multiple algorithms. Using an algorithm well adapted to characteristics of the task can significantly enhance the performance, such as reducing the time required for the operation, without compromising the quality of the result. However, the best solution method can vary even across linear systems generated in course of the same PDE-based simulation, thereby making solver selection a very challenging problem. In this paper, we use a machine learning technique, Alternating Decision Trees (ADT), to select efficient solvers based on the properties of sparse linear systems and runtime-dependent features, such as the stages of simulation. We demonstrate the effectiveness of this method through empirical results over linear systems drawn from computational fluid dynamics and magnetohydrodynamics applications. The results also demonstrate that using ADT can resolve the problem of over-fitting, which occurs when limited amount of data is available. © 2010 Springer Science+Business Media LLC.

  19. Toward improved pregnancy labelling. (United States)

    Koren, Gideon; Sakaguchi, Sachi; Klieger, Chagit; Kazmin, Alex; Osadchy, Alla; Yazdani-Brojeni, Parvaneh; Matok, Ilan


    Information about the use of a medication in pregnancy is part of overall drug labelling as prepared by the pharmaceutical company and approved by the regulators. It is aimed at assisting clinicians in prescribing, however, very few drugs are labelled for specific indications in pregnancy, since there is rarely information about the use of a drug in this condition. Recently the FDA has drafted new guidelines for the labeling of drugs in pregnancy and breastfeeding, to replace the A,B,C,D,X system that was used for more than 30 years. Here we document the use of the new system through 3 different medications; each representing a different clinical situation in pregnancy--acute infection, chronic pain, and drug use during labor. Advantages and challenges in the new system are being highlighted.

  20. Synthesis of labeled compounds

    International Nuclear Information System (INIS)

    Whaley, T.W.


    Intermediate compounds labeled with 13 C included methane, sodium cyanide, methanol, ethanol, and acetonitrile. A new method for synthesizing 15 N-labeled 4-ethylsulfonyl-1-naphthalene-sulfonamide was developed. Studies were conducted on pathways to oleic-1- 13 C acid and a second pathway investigated was based on carbonation of 8-heptadecynylmagnesium bromide with CO 2 to prepare sterolic acid. Biosynthetic preparations included glucose- 13 C from starch isolated from tobacco leaves following photosynthetic incubation with 13 CO 2 and galactose- 13 C from galactosylglycerol- 13 C from kelp. Research on growth of organisms emphasized photosynthetic growth of algae in which all cellular carbon is labeled. Preliminary experiments were performed to optimize the growth of Escherichia coli on sodium acetate- 13 C