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Sample records for sparse support recovery

  1. Sparse Signal Recovery via ECME Thresholding Pursuits

    Directory of Open Access Journals (Sweden)

    Heping Song

    2012-01-01

    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.

  2. Behavior of greedy sparse representation algorithms on nested supports

    DEFF Research Database (Denmark)

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

    2013-01-01

    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...

  3. Narrowband interference parameterization for sparse Bayesian recovery

    KAUST Repository

    Ali, Anum

    2015-09-11

    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.

  4. Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery

    Science.gov (United States)

    Kim, Daeun; Haldar, Justin P.

    2016-01-01

    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

  5. Joint Sparse Recovery With Semisupervised MUSIC

    Science.gov (United States)

    Wen, Zaidao; Hou, Biao; Jiao, Licheng

    2017-05-01

    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.

  6. An Overview on Sparse Recovery-based STAP

    Directory of Open Access Journals (Sweden)

    Ma Ze-qiang

    2014-04-01

    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.

  7. 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....

  8. Support agnostic Bayesian matching pursuit for block sparse signals

    KAUST Repository

    Masood, Mudassir

    2013-05-01

    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.

  9. Distributed coding of multiview sparse sources with joint recovery

    DEFF Research Database (Denmark)

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

    2016-01-01

    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....

  10. A sharp recovery condition for block sparse signals by block orthogonal multi-matching pursuit

    OpenAIRE

    Chen, Wengu; Ge, Huanmin

    2016-01-01

    We consider the block orthogonal multi-matching pursuit (BOMMP) algorithm for the recovery of block sparse signals. A sharp bound is obtained for the exact reconstruction of block $K$-sparse signals via the BOMMP algorithm in the noiseless case, based on the block restricted isometry constant (block-RIC). Moreover, we show that the sharp bound combining with an extra condition on the minimum $\\ell_2$ norm of nonzero blocks of block $K-$sparse signals is sufficient to recover the true support ...

  11. Dynamical Sparse Recovery with Finite-time Convergence

    OpenAIRE

    Yu, Lei; Zheng, Gang; Barbot, Jean-Pierre

    2017-01-01

    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,...

  12. Efficient coordinated recovery of sparse channels in massive MIMO

    KAUST Repository

    Masood, Mudassir

    2015-01-01

    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.

  13. Precise RFID localization in impaired environment through sparse signal recovery

    Science.gov (United States)

    Subedi, Saurav; Zhang, Yimin D.; Amin, Moeness G.

    2013-05-01

    Radio frequency identification (RFID) is a rapidly developing wireless communication technology for electronically identifying, locating, and tracking products, assets, and personnel. RFID has become one of the most important means to construct real-time locating systems (RTLS) that track and identify the location of objects in real time using simple, inexpensive tags and readers. The applicability and usefulness of RTLS techniques depend on their achievable accuracy. In particular, when multilateration-based localization techniques are exploited, the achievable accuracy primarily relies on the precision of the range estimates between a reader and the tags. Such range information can be obtained by using the received signal strength indicator (RSSI) and/or the phase difference of arrival (PDOA). In both cases, however, the accuracy is significantly compromised when the operation environment is impaired. In particular, multipath propagation significantly affects the measurement accuracy of both RSSI and phase information. In addition, because RFID systems are typically operated in short distances, RSSI and phase measurements are also coupled with the reader and tag antenna patterns, making accurate RFID localization very complicated and challenging. In this paper, we develop new methods to localize RFID tags or readers by exploiting sparse signal recovery techniques. The proposed method allows the channel environment and antenna patterns to be taken into account and be properly compensated at a low computational cost. As such, the proposed technique yields superior performance in challenging operation environments with the above-mentioned impairments.

  14. Artifact detection in electrodermal activity using sparse recovery

    Science.gov (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.

    2017-05-01

    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.

  15. Sampling of finite elements for sparse recovery in large scale 3D electrical impedance tomography

    International Nuclear Information System (INIS)

    Javaherian, Ashkan; Moeller, Knut; Soleimani, Manuchehr

    2015-01-01

    This study proposes a method to improve performance of sparse recovery inverse solvers in 3D electrical impedance tomography (3D EIT), especially when the volume under study contains small-sized inclusions, e.g. 3D imaging of breast tumours. Initially, a quadratic regularized inverse solver is applied in a fast manner with a stopping threshold much greater than the optimum. Based on assuming a fixed level of sparsity for the conductivity field, finite elements are then sampled via applying a compressive sensing (CS) algorithm to the rough blurred estimation previously made by the quadratic solver. Finally, a sparse inverse solver is applied solely to the sampled finite elements, with the solution to the CS as its initial guess. The results show the great potential of the proposed CS-based sparse recovery in improving accuracy of sparse solution to the large-size 3D EIT. (paper)

  16. Sampling of finite elements for sparse recovery in large scale 3D electrical impedance tomography.

    Science.gov (United States)

    Javaherian, Ashkan; Soleimani, Manuchehr; Moeller, Knut

    2015-01-01

    This study proposes a method to improve performance of sparse recovery inverse solvers in 3D electrical impedance tomography (3D EIT), especially when the volume under study contains small-sized inclusions, e.g. 3D imaging of breast tumours. Initially, a quadratic regularized inverse solver is applied in a fast manner with a stopping threshold much greater than the optimum. Based on assuming a fixed level of sparsity for the conductivity field, finite elements are then sampled via applying a compressive sensing (CS) algorithm to the rough blurred estimation previously made by the quadratic solver. Finally, a sparse inverse solver is applied solely to the sampled finite elements, with the solution to the CS as its initial guess. The results show the great potential of the proposed CS-based sparse recovery in improving accuracy of sparse solution to the large-size 3D EIT.

  17. Single image super-resolution based on compressive sensing and improved TV minimization sparse recovery

    Science.gov (United States)

    Vishnukumar, S.; Wilscy, M.

    2017-12-01

    In this paper, we propose a single image Super-Resolution (SR) method based on Compressive Sensing (CS) and Improved Total Variation (TV) Minimization Sparse Recovery. In the CS framework, low-resolution (LR) image is treated as the compressed version of high-resolution (HR) image. Dictionary Training and Sparse Recovery are the two phases of the method. K-Singular Value Decomposition (K-SVD) method is used for dictionary training and the dictionary represents HR image patches in a sparse manner. Here, only the interpolated version of the LR image is used for training purpose and thereby the structural self similarity inherent in the LR image is exploited. In the sparse recovery phase the sparse representation coefficients with respect to the trained dictionary for LR image patches are derived using Improved TV Minimization method. HR image can be reconstructed by the linear combination of the dictionary and the sparse coefficients. The experimental results show that the proposed method gives better results quantitatively as well as qualitatively on both natural and remote sensing images. The reconstructed images have better visual quality since edges and other sharp details are preserved.

  18. Pruning-Based Sparse Recovery for Electrocardiogram Reconstruction from Compressed Measurements

    Directory of Open Access Journals (Sweden)

    Jaeseok Lee

    2017-01-01

    Full Text Available Due to the necessity of the low-power implementation of newly-developed electrocardiogram (ECG sensors, exact ECG data reconstruction from the compressed measurements has received much attention in recent years. Our interest lies in improving the compression ratio (CR, as well as the ECG reconstruction performance of the sparse signal recovery. To this end, we propose a sparse signal reconstruction method by pruning-based tree search, which attempts to choose the globally-optimal solution by minimizing the cost function. In order to achieve low complexity for the real-time implementation, we employ a novel pruning strategy to avoid exhaustive tree search. Through the restricted isometry property (RIP-based analysis, we show that the exact recovery condition of our approach is more relaxed than any of the existing methods. Through the simulations, we demonstrate that the proposed approach outperforms the existing sparse recovery methods for ECG reconstruction.

  19. Efficient Techniques of Sparse Signal Analysis for Enhanced Recovery of Information in Biomedical Engineering and Geosciences

    KAUST Repository

    Sana, Furrukh

    2016-11-01

    Sparse signals are abundant among both natural and man-made signals. Sparsity implies that the signal essentially resides in a small dimensional subspace. The sparsity of the signal can be exploited to improve its recovery from limited and noisy observations. Traditional estimation algorithms generally lack the ability to take advantage of signal sparsity. This dissertation considers several problems in the areas of biomedical engineering and geosciences with the aim of enhancing the recovery of information by exploiting the underlying sparsity in the problem. The objective is to overcome the fundamental bottlenecks, both in terms of estimation accuracies and required computational resources. In the first part of dissertation, we present a high precision technique for the monitoring of human respiratory movements by exploiting the sparsity of wireless ultra-wideband signals. The proposed technique provides a novel methodology of overcoming the Nyquist sampling constraint and enables robust performance in the presence of noise and interferences. We also present a comprehensive framework for the important problem of extracting the fetal electrocardiogram (ECG) signals from abdominal ECG recordings of pregnant women. The multiple measurement vectors approach utilized for this purpose provides an efficient mechanism of exploiting the common structure of ECG signals, when represented in sparse transform domains, and allows leveraging information from multiple ECG electrodes under a joint estimation formulation. In the second part of dissertation, we adopt sparse signal processing principles for improved information recovery in large-scale subsurface reservoir characterization problems. We propose multiple new algorithms for sparse representation of the subsurface geological structures, incorporation of useful prior information in the estimation process, and for reducing computational complexities of the problem. The techniques presented here enable significantly

  20. Linear Program Relaxation of Sparse Nonnegative Recovery in Compressive Sensing Microarrays

    Directory of Open Access Journals (Sweden)

    Linxia Qin

    2012-01-01

    Full Text Available Compressive sensing microarrays (CSM are DNA-based sensors that operate using group testing and compressive sensing principles. Mathematically, one can cast the CSM as sparse nonnegative recovery (SNR which is to find the sparsest solutions subjected to an underdetermined system of linear equations and nonnegative restriction. In this paper, we discuss the l1 relaxation of the SNR. By defining nonnegative restricted isometry/orthogonality constants, we give a nonnegative restricted property condition which guarantees that the SNR and the l1 relaxation share the common unique solution. Besides, we show that any solution to the SNR must be one of the extreme points of the underlying feasible set.

  1. Exact recovery of sparse multiple measurement vectors by [Formula: see text]-minimization.

    Science.gov (United States)

    Wang, Changlong; Peng, Jigen

    2018-01-01

    The joint sparse recovery problem is a generalization of the single measurement vector problem widely studied in compressed sensing. It aims to recover a set of jointly sparse vectors, i.e., those that have nonzero entries concentrated at a common location. Meanwhile [Formula: see text]-minimization subject to matrixes is widely used in a large number of algorithms designed for this problem, i.e., [Formula: see text]-minimization [Formula: see text] Therefore the main contribution in this paper is two theoretical results about this technique. The first one is proving that in every multiple system of linear equations there exists a constant [Formula: see text] such that the original unique sparse solution also can be recovered from a minimization in [Formula: see text] quasi-norm subject to matrixes whenever [Formula: see text]. The other one is showing an analytic expression of such [Formula: see text]. Finally, we display the results of one example to confirm the validity of our conclusions, and we use some numerical experiments to show that we increase the efficiency of these algorithms designed for [Formula: see text]-minimization by using our results.

  2. Spectrum recovery method based on sparse representation for segmented multi-Gaussian model

    Science.gov (United States)

    Teng, Yidan; Zhang, Ye; Ti, Chunli; Su, Nan

    2016-09-01

    Hyperspectral images can realize crackajack features discriminability for supplying diagnostic characteristics with high spectral resolution. However, various degradations may generate negative influence on the spectral information, including water absorption, bands-continuous noise. On the other hand, the huge data volume and strong redundancy among spectrums produced intense demand on compressing HSIs in spectral dimension, which also leads to the loss of spectral information. The reconstruction of spectral diagnostic characteristics has irreplaceable significance for the subsequent application of HSIs. This paper introduces a spectrum restoration method for HSIs making use of segmented multi-Gaussian model (SMGM) and sparse representation. A SMGM is established to indicating the unsymmetrical spectral absorption and reflection characteristics, meanwhile, its rationality and sparse property are discussed. With the application of compressed sensing (CS) theory, we implement sparse representation to the SMGM. Then, the degraded and compressed HSIs can be reconstructed utilizing the uninjured or key bands. Finally, we take low rank matrix recovery (LRMR) algorithm for post processing to restore the spatial details. The proposed method was tested on the spectral data captured on the ground with artificial water absorption condition and an AVIRIS-HSI data set. The experimental results in terms of qualitative and quantitative assessments demonstrate that the effectiveness on recovering the spectral information from both degradations and loss compression. The spectral diagnostic characteristics and the spatial geometry feature are well preserved.

  3. Orthogonal Matching Pursuit for Enhanced Recovery of Sparse Geological Structures With the Ensemble Kalman Filter

    KAUST Repository

    Sana, Furrukh

    2016-02-23

    Estimating the locations and the structures of subsurface channels holds significant importance for forecasting the subsurface flow and reservoir productivity. These channels exhibit high permeability and are easily contrasted from the low-permeability rock formations in their surroundings. This enables formulating the flow channels estimation problem as a sparse field recovery problem. The ensemble Kalman filter (EnKF) is a widely used technique for the estimation and calibration of subsurface reservoir model parameters, such as permeability. However, the conventional EnKF framework does not provide an efficient mechanism to incorporate prior information on the wide varieties of subsurface geological structures, and often fails to recover and preserve flow channel structures. Recent works in the area of compressed sensing (CS) have shown that estimating in a sparse domain, using algorithms such as the orthogonal matching pursuit (OMP), may significantly improve the estimation quality when dealing with such problems. We propose two new, and computationally efficient, algorithms combining OMP with the EnKF to improve the estimation and recovery of the subsurface geological channels. Numerical experiments suggest that the proposed algorithms provide efficient mechanisms to incorporate and preserve structural information in the EnKF and result in significant improvements in recovering flow channel structures.

  4. Sparse recovery of undersampled intensity patterns for coherent diffraction imaging at high X-ray energies.

    Science.gov (United States)

    Maddali, S; Calvo-Almazan, I; Almer, J; Kenesei, P; Park, J-S; Harder, R; Nashed, Y; Hruszkewycz, S O

    2018-03-21

    Coherent X-ray photons with energies higher than 50 keV offer new possibilities for imaging nanoscale lattice distortions in bulk crystalline materials using Bragg peak phase retrieval methods. However, the compression of reciprocal space at high energies typically results in poorly resolved fringes on an area detector, rendering the diffraction data unsuitable for the three-dimensional reconstruction of compact crystals. To address this problem, we propose a method by which to recover fine fringe detail in the scattered intensity. This recovery is achieved in two steps: multiple undersampled measurements are made by in-plane sub-pixel motion of the area detector, then this data set is passed to a sparsity-based numerical solver that recovers fringe detail suitable for standard Bragg coherent diffraction imaging (BCDI) reconstruction methods of compact single crystals. The key insight of this paper is that sparsity in a BCDI data set can be enforced by recognising that the signal in the detector, though poorly resolved, is band-limited. This requires fewer in-plane detector translations for complete signal recovery, while adhering to information theory limits. We use simulated BCDI data sets to demonstrate the approach, outline our sparse recovery strategy, and comment on future opportunities.

  5. Sparsely corrupted stimulated scattering signals recovery by iterative reweighted continuous basis pursuit

    International Nuclear Information System (INIS)

    Wang, Kunpeng; Chai, Yi; Su, Chunxiao

    2013-01-01

    In this paper, we consider the problem of extracting the desired signals from noisy measurements. This is a classical problem of signal recovery which is of paramount importance in inertial confinement fusion. To accomplish this task, we develop a tractable algorithm based on continuous basis pursuit and reweighted ℓ 1 -minimization. By modeling the observed signals as superposition of scale time-shifted copies of theoretical waveform, structured noise, and unstructured noise on a finite time interval, a sparse optimization problem is obtained. We propose to solve this problem through an iterative procedure that alternates between convex optimization to estimate the amplitude, and local optimization to estimate the dictionary. The performance of the method was evaluated both numerically and experimentally. Numerically, we recovered theoretical signals embedded in increasing amounts of unstructured noise and compared the results with those obtained through popular denoising methods. We also applied the proposed method to a set of actual experimental data acquired from the Shenguang-II laser whose energy was below the detector noise-equivalent energy. Both simulation and experiments show that the proposed method improves the signal recovery performance and extends the dynamic detection range of detectors

  6. Narrowband Interference Mitigation in SC-FDMA Using Bayesian Sparse Recovery

    KAUST Repository

    Ali, Anum

    2016-09-29

    This paper presents a novel narrowband interference (NBI) mitigation scheme for single carrier-frequency division multiple access systems. The proposed NBI cancellation scheme exploits the frequency-domain sparsity of the unknown signal and adopts a low complexity Bayesian sparse recovery procedure. At the transmitter, a few randomly chosen data locations are kept data free to sense the NBI signal at the receiver. Furthermore, it is noted that in practice, the sparsity of the NBI signal is destroyed by a grid mismatch between the NBI sources and the system under consideration. Toward this end, first, an accurate grid mismatch model is presented that is capable of assuming independent offsets for multiple NBI sources, and second, the sparsity of the unknown signal is restored prior to reconstruction using a sparsifying transform. To improve the spectral efficiency of the proposed scheme, a data-aided NBI recovery procedure is outlined that relies on adaptively selecting a subset of data-points and using them as additional measurements. Numerical results demonstrate the effectiveness of the proposed scheme for NBI mitigation.

  7. Reconstruct the Support Vectors to Improve LSSVM Sparseness for Mill Load Prediction

    Directory of Open Access Journals (Sweden)

    Gangquan Si

    2017-01-01

    Full Text Available The sparse strategy plays a significant role in the application of the least square support vector machine (LSSVM, to alleviate the condition that the solution of LSSVM is lacking sparseness and robustness. In this paper, a sparse method using reconstructed support vectors is proposed, which has also been successfully applied to mill load prediction. Different from other sparse algorithms, it no longer selects the support vectors from training data set according to the ranked contributions for optimization of LSSVM. Instead, the reconstructed data is obtained first based on the initial model with all training data. Then, select support vectors from reconstructed data set according to the location information of density clustering in training data set, and the process of selecting is terminated after traversing the total training data set. Finally, the training model could be built based on the optimal reconstructed support vectors and the hyperparameter tuned subsequently. What is more, the paper puts forward a supplemental algorithm to subtract the redundancy support vectors of previous model. Lots of experiments on synthetic data sets, benchmark data sets, and mill load data sets are carried out, and the results illustrate the effectiveness of the proposed sparse method for LSSVM.

  8. Single Image Super-Resolution by Non-Linear Sparse Representation and Support Vector Regression

    Directory of Open Access Journals (Sweden)

    Yungang Zhang

    2017-02-01

    Full Text Available Sparse representations are widely used tools in image super-resolution (SR tasks. In the sparsity-based SR methods, linear sparse representations are often used for image description. However, the non-linear data distributions in images might not be well represented by linear sparse models. Moreover, many sparsity-based SR methods require the image patch self-similarity assumption; however, the assumption may not always hold. In this paper, we propose a novel method for single image super-resolution (SISR. Unlike most prior sparsity-based SR methods, the proposed method uses non-linear sparse representation to enhance the description of the non-linear information in images, and the proposed framework does not need to assume the self-similarity of image patches. Based on the minimum reconstruction errors, support vector regression (SVR is applied for predicting the SR image. The proposed method was evaluated on various benchmark images, and promising results were obtained.

  9. A Sparse Bayesian Imaging Technique for Efficient Recovery of Reservoir Channels With Time-Lapse Seismic Measurements

    KAUST Repository

    Sana, Furrukh

    2016-06-01

    Subsurface reservoir flow channels are characterized by high-permeability values and serve as preferred pathways for fluid propagation. Accurate estimation of their geophysical structures is thus of great importance for the oil industry. The ensemble Kalman filter (EnKF) is a widely used statistical technique for estimating subsurface reservoir model parameters. However, accurate reconstruction of the subsurface geological features with the EnKF is challenging because of the limited measurements available from the wells and the smoothing effects imposed by the \\\\ell _{2} -norm nature of its update step. A new EnKF scheme based on sparse domain representation was introduced by Sana et al. (2015) to incorporate useful prior structural information in the estimation process for efficient recovery of subsurface channels. In this paper, we extend this work in two ways: 1) investigate the effects of incorporating time-lapse seismic data on the channel reconstruction; and 2) explore a Bayesian sparse reconstruction algorithm with the potential ability to reduce the computational requirements. Numerical results suggest that the performance of the new sparse Bayesian based EnKF scheme is enhanced with the availability of seismic measurements, leading to further improvement in the recovery of flow channels structures. The sparse Bayesian approach further provides a computationally efficient framework for enforcing a sparse solution, especially with the possibility of using high sparsity rates through the inclusion of seismic data.

  10. Life Support Systems: Oxygen Generation and Recovery

    Data.gov (United States)

    National Aeronautics and Space Administration — The Advanced Exploration Systems (AES) Life Support Systems project Oxygen Generation and Recovery technology development area encompasses several sub-tasks in an...

  11. Recovery Supports for Young People: What Do Existing Supports Reveal about the Recovery Environment?

    Science.gov (United States)

    Fisher, Emily A.

    2014-01-01

    This article seeks to address how our understanding of the recovery process and resulting supports can be made more comprehensive: How can links from treatment to home to school to communities be made so that there are fewer and fewer recovery gaps for adolescents? Using the ecology of recovery model developed by White (2009) as the impetus for…

  12. Harnessing data structure for recovery of randomly missing structural vibration responses time history: Sparse representation versus low-rank structure

    Science.gov (United States)

    Yang, Yongchao; Nagarajaiah, Satish

    2016-06-01

    Randomly missing data of structural vibration responses time history often occurs in structural dynamics and health monitoring. For example, structural vibration responses are often corrupted by outliers or erroneous measurements due to sensor malfunction; in wireless sensing platforms, data loss during wireless communication is a common issue. Besides, to alleviate the wireless data sampling or communication burden, certain accounts of data are often discarded during sampling or before transmission. In these and other applications, recovery of the randomly missing structural vibration responses from the available, incomplete data, is essential for system identification and structural health monitoring; it is an ill-posed inverse problem, however. This paper explicitly harnesses the data structure itself-of the structural vibration responses-to address this (inverse) problem. What is relevant is an empirical, but often practically true, observation, that is, typically there are only few modes active in the structural vibration responses; hence a sparse representation (in frequency domain) of the single-channel data vector, or, a low-rank structure (by singular value decomposition) of the multi-channel data matrix. Exploiting such prior knowledge of data structure (intra-channel sparse or inter-channel low-rank), the new theories of ℓ1-minimization sparse recovery and nuclear-norm-minimization low-rank matrix completion enable recovery of the randomly missing or corrupted structural vibration response data. The performance of these two alternatives, in terms of recovery accuracy and computational time under different data missing rates, is investigated on a few structural vibration response data sets-the seismic responses of the super high-rise Canton Tower and the structural health monitoring accelerations of a real large-scale cable-stayed bridge. Encouraging results are obtained and the applicability and limitation of the presented methods are discussed.

  13. Chemical inhibition of cell recovery after irradiation with sparsely and densely ionizing radiation

    Energy Technology Data Exchange (ETDEWEB)

    Evastratova, Ekaterina S.; Petin, Vladislav [A. Tsyb Medical Radiological Research Centre-branch of the National Medical Research Radiological Centre of the Ministry of Health of the Russian Federation, Obninsk (Russian Federation); Kim, Jin Hong; Kim, Jin Kyu [Korea Atomic Energy Research Institute, Advanced Radiation Technology Institute (ARTI), Jeongeup (Korea, Republic of); Lim, Youg Khi [Dept. of Radiological Science, Gachon University, Incheon (Korea, Republic of)

    2017-02-15

    The dependence of cell survival on exposure dose and the duration of the liquid holding recovery (LHR) was obtained for diploid yeast cells irradiated with ionizing radiation of different linear energy transfer (LET) and recovering from radiation damage without and with various concentrations of cisplatin - the most widely used anticancer drug. The ability of yeast cells to recover from radiation damage was less effective after cell exposure to high-LET radiation, when cells were irradiated without drug. The increase in cisplatin concentration resulted in the disappearance of this difference whereas the fraction of irreversible damage was permanently enlarged independently of radiation quality. The probability of cell recovery was shown to be constant for various conditions of irradiation and recovery. A new mechanism of cisplatin action was suggested according with which the inhibition of cell recovery after exposure to ionizing radiations was completely explained by the production of irreversible damage.

  14. On Theorem 10 in "On Polar Polytopes and the Recovery of Sparse Representations"

    DEFF Research Database (Denmark)

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

    2013-01-01

    It is shown that Theorem 10 (Non-Nestedness of ERC) in [Plumbley, IEEE Trans. Info. Theory, vol. 53, pp. 3188, Sep. 2007] neglects the derivations of the exact recovery conditions (ERCs) of constrained \\(\\ell_1\\)-minimization (BP) and orthogonal matching pursuit (OMP), and thus does not reflect...... the recovery properties of these algorithms. Furthermore, an ERC of BP more general than that in [Tropp, IEEE Trans. Info. Theory, vol. 50, pp. 2231, Oct. 2004] is shown....

  15. Image denoising via collaborative support-agnostic recovery

    KAUST Repository

    Behzad, Muzammil

    2017-06-20

    In this paper, we propose a novel patch-based image denoising algorithm using collaborative support-agnostic sparse reconstruction. In the proposed collaborative scheme, similar patches are assumed to share the same support taps. For sparse reconstruction, the likelihood of a tap being active in a patch is computed and refined through a collaboration process with other similar patches in the similarity group. This provides a very good patch support estimation, hence enhancing the quality of image restoration. Performance comparisons with state-of-the-art algorithms, in terms of PSNR and SSIM, demonstrate the superiority of the proposed algorithm.

  16. Practical security and privacy attacks against biometric hashing using sparse recovery

    Science.gov (United States)

    Topcu, Berkay; Karabat, Cagatay; Azadmanesh, Matin; Erdogan, Hakan

    2016-12-01

    Biometric hashing is a cancelable biometric verification method that has received research interest recently. This method can be considered as a two-factor authentication method which combines a personal password (or secret key) with a biometric to obtain a secure binary template which is used for authentication. We present novel practical security and privacy attacks against biometric hashing when the attacker is assumed to know the user's password in order to quantify the additional protection due to biometrics when the password is compromised. We present four methods that can reconstruct a biometric feature and/or the image from a hash and one method which can find the closest biometric data (i.e., face image) from a database. Two of the reconstruction methods are based on 1-bit compressed sensing signal reconstruction for which the data acquisition scenario is very similar to biometric hashing. Previous literature introduced simple attack methods, but we show that we can achieve higher level of security threats using compressed sensing recovery techniques. In addition, we present privacy attacks which reconstruct a biometric image which resembles the original image. We quantify the performance of the attacks using detection error tradeoff curves and equal error rates under advanced attack scenarios. We show that conventional biometric hashing methods suffer from high security and privacy leaks under practical attacks, and we believe more advanced hash generation methods are necessary to avoid these attacks.

  17. Spousal recovery support, recovery experiences, and life satisfaction crossover among dual-earner couples.

    Science.gov (United States)

    Park, YoungAh; Fritz, Charlotte

    2015-03-01

    Research has indicated the importance of recovery from work stress for employee well-being and work engagement. However, very little is known about the specific factors that may support or hinder recovery in the context of dual-earner couples. This study proposes spousal recovery support as a potential resource that dual-earner couples can draw on to enhance their recovery experiences and well-being. It was hypothesized that spousal recovery support would be related to the recipient spouse's life satisfaction via his or her own recovery experiences (i.e., psychological detachment, relaxation, and mastery experiences). The study further investigated the crossover of life satisfaction between working spouses as a potential outcome of recovery processes. Data from 318 full-time employed married couples in South Korea were analyzed using structural equation modeling. Results showed that spousal recovery support was positively related to all 3 recovery experiences of the recipient spouse. Moreover, this recovery support was related to the recipient spouse's life satisfaction via relaxation and mastery experiences. Unexpectedly, psychological detachment was negatively related to life satisfaction, possibly indicating a suppression effect. Life satisfaction crossed over between working spouses. No gender differences were found in the hypothesized paths. Based on these findings, theoretical and practical implications are discussed, and future research directions are presented. PsycINFO Database Record (c) 2015 APA, all rights reserved.

  18. Interference-Aware OFDM Receiver for Channels with Sparse Common Supports

    DEFF Research Database (Denmark)

    Barbu, Oana-Elena; Manchón, Carles Navarro; Badiu, Mihai Alin

    2017-01-01

    We design an algorithm for OFDM receivers operating in co-channel interference conditions, where the serving and interfering transmitters are synchronized in time. The channel estimation problem is formulated as one of sparse signal reconstruction using multiple measurement vectors. The proposed...

  19. Youth Homelessness: The Impact of Supportive Relationships on Recovery.

    Science.gov (United States)

    Gasior, Sara; Forchuk, Cheryl; Regan, Sandra

    2018-03-01

    Background Homeless youth are the fastest growing sub-group within the homeless population. They face impaired access to health services and are often left unsupported. They lack social and family support or relationships with service providers. Unsupported homeless youth often become homeless adults. Purpose To test a model based on Peplau's Theory of Interpersonal Relations, examining the influence of a network of service providers, perceptions of social supports, and family relations on a homeless youth's perceptions of recovery. Methods This study is a secondary analysis and used a sample (n = 187) of data collected as part of the original Youth Matters in London study. A cross-sectional design was used to analyze the relationship between variables. Participants were interviewed at 6-month intervals over a 2.5-year period. Hierarchical multiple regression analysis was used. Results Network of service providers, perceived social supports, and perceived family relations explained 21.8% of the variance in homeless youth perceptions of recovery. Perceived social support and family relations were significantly, positively correlated to perceptions of recovery. Network of service providers was not significantly correlated to perceptions of recovery. Conclusions The findings suggest that stronger social supports and family relations may contribute to increased perceptions of recovery among homeless youth.

  20. The influence of perceived family support on post surgery recovery.

    Science.gov (United States)

    Cardoso-Moreno, M J; Tomás-Aragones, L

    2017-01-01

    The objective of this work was to investigate the possible relationship between perceived family support, levels of cortisol and post surgery recovery. The study sample comprised 42 patients that were due to undergo open cholecystectomy surgery in a Regional Health Authority Reference Centre of the Autonomous Community of Extremadura in Spain. The FACES-II questionnaire was used for the evaluation of perceived family support and to measure the three fundamental dimensions of perceived family behaviour: cohesion, adaptability and family type. The day before surgery, a sample of saliva was taken from each subject in order to determine the level of cortisol. Results showed a clear relationship between family support and recovery. Patients with higher scores on the Cohesion Scale demonstrated better post surgery recovery (F = 8.8; gl = 40; p = .005). A relationship between levels of cortisol, perceived family support and recovery was also revealed. Patients with lower scores on the Cohesion scale and higher cortisol levels demonstrated poorer post surgery recovery (F = 10.96; gl = 40; p = .006). These results are coherent with other studies that have highlighted the beneficial effects of perceived family support on mental and physical health.

  1. Decision support systems for recovery of endangered species

    International Nuclear Information System (INIS)

    Armstrong, C.E.

    1995-01-01

    The listing of a species as endangered under the Endangered Species Act invokes a suite of responses to help improve conditions for the recovery of that species, to include identification of stressors contributing to population loss, decision analysis of the impacts of proposed recovery options, and implementation of optimal recovery measures. The ability of a decision support system to quantify inherent stressor uncertainties and to identify the key stressors that can be controlled or eliminated becomes key to ensuring the recovery of an endangered species. The listing of the Snake River sockeye, spring/summer chinook, and fall chinook salmon species in the Snake River as endangered provides a vivid example of the importance of sophisticated decision support systems. Operational and physical changes under consideration at eight of the hydroelectric dams along the Columbia and Lower Snake River pose significant financial impacts to a variety of stakeholders involved in the salmon population recovery process and carry significant uncertainties of outcome. A decision support system is presented to assist in the identification of optimal recovery actions for this example that includes the following: creation of datamarts of information on environmental, engineering, and ecological values that influence species survival; incorporation of decision analysis tools to determine optimal decision policies; and the use of geographic information systems (GIS) to provide a context for decision analysis and to communicate the impacts of decision policies

  2. Severe Neonatal Legionella Pneumonia: Full Recovery After Extracorporeal Life Support.

    Science.gov (United States)

    Moscatelli, Andrea; Buratti, Silvia; Castagnola, Elio; Mesini, Alessio; Tuo, Pietro

    2015-10-01

    Legionella pneumophila is responsible for hospital or community-acquired pneumonia. Neonatal legionellosis is associated with rapidly severe clinical course and high mortality rates. We describe a case of hospital-acquired Legionella pneumonia in a newborn with undiagnosed tracheoesophageal fistula and acute respiratory failure requiring venovenous extracorporeal membrane oxygenation support before fistula repair. Standardized multiplex polymerase chain reaction assay allowed early diagnosis. Extracorporeal life support associated with appropriate antibiotic therapy, surfactant, and steroid therapy was effective in achieving complete recovery. This is the first report of successful neonatal extracorporeal life support for respiratory failure secondary to L pneumophila. Copyright © 2015 by the American Academy of Pediatrics.

  3. Recovery of cytogenetic damage in plant cells with unequal fractionation of damaging action. 2. Sparsely ionizing radiation effect

    Energy Technology Data Exchange (ETDEWEB)

    Stepanyan, N.S.; Seregina, T.V.; Krupnova, G.F.; Zhestyanikov, V.D. (AN SSSR, Leningrad. Inst. Tsitologii)

    1984-01-01

    In case of unequal fractionation of X-irradiation dose (1, 6 and 3 Gy) in Vicia faba cells the fractionation effect (decrease of chromosomal breaks frequency as compared with their frequency in case of non fractionated effect total in dose) takes place in cells being at the irradiation moment mostly in the S phase, but is not observed in case of irradiation of cells being mostly in the G/sub 2/ phase. Introduction in cells in the interval between fractions of chloramphenical protein synthesis inhibitor increases the chromosomal aberrations frequency in the G/sub 2/ phase cells (in the S phase in irradiated cells the antibiotic blocks up the advance of cells by cycle) only at irradiation in integral dose 1.6 Gy. The strengthening of damaging radiation effect by chloramphenicol is observed in case of its combination with caffeine, DNA reparation inhibitor, however on no account the combination effect exceeds the total sum of both agents effects. In case of non-uniform fractionation of gamma irradiation the treatment of V. faba cells by chloramphenicol in the interval between fractions leads to a slight but certain reparation attenuation of DNA one-strand ruptures. Inhibitor treatment before irradiation in non fractionated total dose sharply suppresses and after irradiation does not change the DNA one-strand breaks reparation. The data testify that in V faba cells in case of sparsely ionizing radiation effect probably the DNA inducible DNA reparation functionates which is responsible for elimination of a small number of DNA one-strand ruptures and a certain part of chromosomal aberrations.

  4. Robust nonhomogeneous training samples detection method for space-time adaptive processing radar using sparse-recovery with knowledge-aided

    Science.gov (United States)

    Li, Zhihui; Liu, Hanwei; Zhang, Yongshun; Guo, Yiduo

    2017-10-01

    The performance of space-time adaptive processing (STAP) may degrade significantly when some of the training samples are contaminated by the signal-like components (outliers) in nonhomogeneous clutter environments. To remove the training samples contaminated by outliers in nonhomogeneous clutter environments, a robust nonhomogeneous training samples detection method using the sparse-recovery (SR) with knowledge-aided (KA) is proposed. First, the reduced-dimension (RD) overcomplete spatial-temporal steering dictionary is designed with the prior knowledge of system parameters and the possible target region. Then, the clutter covariance matrix (CCM) of cell under test is efficiently estimated using a modified focal underdetermined system solver (FOCUSS) algorithm, where a RD overcomplete spatial-temporal steering dictionary is applied. Third, the proposed statistics are formed by combining the estimated CCM with the generalized inner products (GIP) method, and the contaminated training samples can be detected and removed. Finally, several simulation results validate the effectiveness of the proposed KA-SR-GIP method.

  5. Dual-earner couples' weekend recovery support, state of recovery, and work engagement: Work-linked relationship as a moderator.

    Science.gov (United States)

    Park, YoungAh; Haun, Verena C

    2017-10-01

    Despite growing recovery research, little is known about couple-dyadic processes of recovery from work. Given that dual-earner couples experience most of their recovery opportunities during nonwork times when they are together, partners in a couple relationship may substantially affect recovery and work engagement. In this study, we propose a couple-dyadic model in which weekend partner recovery support (reported by the recipient partner) is positively related to the recipient partner's state of recovery after the weekend which, in turn, increases the recipient's work engagement the following week (actor-actor mediation effect). We also test the effect of one's state of recovery on the partner's subsequent work engagement (partner effect). Additionally, work-linked relationship status is tested as a moderator of the partner effect. Actor-partner interdependence mediation modeling is used to analyze the data from 167 dual-earner couples who answered surveys on 4 measurement occasions. The results support the indirect effect of partner recovery support on work engagement through the postweekend state of recovery. Multigroup analysis results reveal that the partner effect of state of recovery on work engagement is significant for work-linked couples only and is absent for non-work-linked couples. Theoretical and practical implications, limitations, and future research directions are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  6. Sparse reconstruction using distribution agnostic bayesian matching pursuit

    KAUST Repository

    Masood, Mudassir

    2013-11-01

    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.

  7. Socioeconomic Factors Affecting Local Support for Black Bear Recovery Strategies(AED)

    Science.gov (United States)

    There is global interest in recovering locally extirpated carnivore species. Successful efforts to recover Louisiana black bear in Louisiana have prompted interest in recovery throughout the species’ historical range. We evaluated support for three potential black bear recovery s...

  8. Positive psychology: an approach to supporting recovery in mental illness.

    Science.gov (United States)

    Schrank, B; Brownell, T; Tylee, A; Slade, M

    2014-09-01

    This paper reviews the literature on positive psychology with a special focus on people with mental illness. It describes the characteristics, critiques, and roots of positive psychology and positive psychotherapy, and summarises the existing evidence on positive psychotherapy. Positive psychology aims to refocus psychological research and practice on the positive aspects of experience, strengths, and resources. Despite a number of conceptual and applied research challenges, the field has rapidly developed since its introduction at the turn of the century. Today positive psychology serves as an umbrella term to accommodate research investigating positive emotions and other positive aspects such as creativity, optimism, resilience, empathy, compassion, humour, and life satisfaction. Positive psychotherapy is a therapeutic intervention that evolved from this research. It shows promising results for reducing depression and increasing well-being in healthy people and those with depression. Positive psychology and positive psychotherapy are increasingly being applied in mental health settings, but research evidence involving people with severe mental illness is still scarce. The focus on strengths and resources in positive psychology and positive psychotherapy may be a promising way to support recovery in people with mental illness, such as depression, substance abuse disorders, and psychosis. More research is needed to adapt and establish these approaches and provide an evidence base for their application.

  9. On-campus programs to support college students in recovery.

    Science.gov (United States)

    Misch, Donald A

    2009-01-01

    The author argues that referral of alcohol-abusing college students to off-campus treatment services, although necessary for some, is not optimal for many. He advocates the implementation of comprehensive on-campus services for students committed to recovery in order to optimize their treatment while allowing them to remain in school and work towards their degree. The author suggests that such on-campus recovery services provide additional benefits to the college or university as well as to other students, and he proposes that on-campus alcohol-abusing students in recovery can serve as important opinion leaders and role models for their peers.

  10. Multiple Sparse Representations Classification

    Science.gov (United States)

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

    2015-01-01

    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

  11. Recovery in the USA: from politics to peer support.

    Science.gov (United States)

    Ostrow, Laysha; Adams, Neal

    2012-02-01

    Efforts to transform the mental health service delivery system to a more consumer-driven and recovery-orientated approach has its roots in a somewhat radical anti-psychiatry and civil-rights movement dating back to the 1970s. This grass-roots effort gained momentum and credibility with Harding's landmark study published in 1988 followed by the work of Anthony et al. from Boston University in beginning to define the term 'recovery'. In 1998 the Office of the US Surgeon General issued its first report on mental health, and this critical view of the shortcomings of the existing service system set the stage for the 2003 President's New Freedom Commission and its recommendations for recovery-orientated systems transformation. The recovery movement has evolved from a more radical view in the early days, to participatory involvement in systems, to returning to alternative models of care that are more independent. Now as more peer specialists work in systems, there is an increased emphasis on non-medical alternatives and the cycle continues. Regardless, recovery, self-determination, choice, etc. are always at the centre. This paper notes the interesting cycles of recovery-orientation and how they spin around the values/tenets of the movement's early roots.

  12. [Should obesity in the elderly be treated? Sparse scientific evidence to support treatment of overweight in this age group].

    Science.gov (United States)

    Rössner, Stephan

    Mean body weight increases with age up to about ago 60 and then levels off. Information about the association between body weight and mortality at old age is sparse, since most studies are cross-sectional. Some studies even suggest a protective effect of overweight at old age. Weight loss treatment may be indicated even at old age to relieve mechanical problems, reduce the need for pharmacotherapy and improve self-esteem. As regards treatment, most clinical drug trials actively exclude older people, and hence little is known about the effects in this age group. However, physical activity, even at old age, has documented beneficial effects, in spite of modest effects on weight. Bariatric surgery, although obviously debatable in this age group, has been safely performed. With an increased segment of the population of higher age under way, strategies to manage obesity, also in this age group, need to be developed.

  13. Structural social support: impact on adult substance use and recovery attempts.

    Science.gov (United States)

    Kim, Kerri L; Davis, Margaret I; Jason, Leonard A; Ferrari, Joseph R

    2006-01-01

    This study examined the structural social support of 132 men residing in a network of self-run, substance abuse recovery homes. The impact of different types of social relationships on individuals' substance use patterns and recovery attempts was investigated. Results suggest that varying relationship types (i.e., parents, significant other, friends, children, coworkers) have significantly different influences on use and recovery. Additionally, each type of relationship had differential impacts on use versus recovery. Children were the sole relationship type that affected both substance use and recovery attempts in a positive nature, suggesting that children may have a beneficial impact on reducing.

  14. On-Campus Programs to Support College Students in Recovery

    Science.gov (United States)

    Misch, Donald A.

    2009-01-01

    The author argues that referral of alcohol-abusing college students to off-campus treatment services, although necessary for some, is not optimal for many. He advocates the implementation of comprehensive on-campus services for students committed to recovery in order to optimize their treatment while allowing them to remain in school and work…

  15. Youth Recovery Outcomes at 6 and 9 Months Following Participation in a Mobile Texting Recovery Support Aftercare Pilot Study

    Science.gov (United States)

    Gonzales, Rachel; Hernandez, Mayra; Murphy, Debra A.; Ang, Alfonso

    2016-01-01

    Background and Objectives We examined youth recovery outcomes at 6- and 9-months post-participation in an aftercare pilot study called ESQYIR (Educating and Supporting inQuisitive Youth in Recovery) that aimed to investigate the utility of a 12-week mobile texting recovery support intervention. Methods A total of 80 youth [Mage 20.4 (SD = 3.5) were randomized to a mobile texting aftercare intervention or an aftercare-as-usual control group. Both groups received identical data collection protocols with psychosocial and behavioral assessments occurring at baseline, during the trial (month 1 & month 2), at discharge from the trial (month 3), and 3-, 6-, and 9-month post-intervention follow-ups. Results Mixed modeling showed that youth who participated in the mobile texting aftercare intervention were less likely to test positive for their primary drug compared to youth in the aftercare-as-usual condition during 6- and 9- month follow-ups (p extracurricular activities) (p < .05) than those in aftercare-as-usual at the 6 and 9 month follow-ups. Conclusions Results suggest that delivering a structured, behavioral-based wellness aftercare intervention using mobile texting can be an effective for sustaining recovery outcomes in youth over time compared to youth who receive aftercare-as-usual. Scientific Significance This study shows that a mobile-texting aftercare intervention sustained effects at 6- and 9- months post-intervention for young people in substance use recovery. PMID:26689171

  16. Memory management and compiler support for rapid recovery from failures in computer systems

    Science.gov (United States)

    Fuchs, W. K.

    1991-01-01

    This paper describes recent developments in the use of memory management and compiler technology to support rapid recovery from failures in computer systems. The techniques described include cache coherence protocols for user transparent checkpointing in multiprocessor systems, compiler-based checkpoint placement, compiler-based code modification for multiple instruction retry, and forward recovery in distributed systems utilizing optimistic execution.

  17. How early reactions in the support limb contribute to balance recovery after tripping

    NARCIS (Netherlands)

    Pijnappels, M.A.G.M.; Bobbert, M.F.; van Dieen, J.H.

    2005-01-01

    Tripping causes a forward angular momentum that has to be arrested to prevent a fall. The support limb, contralateral to the obstructed swing limb, can contribute to an adequate recovery by providing time and clearance for proper positioning of the recovery limb, and by restraining the angular

  18. Supporting technology for enhanced oil recovery for thermal processes

    Energy Technology Data Exchange (ETDEWEB)

    Reid, T.B.; Bolivar, J.

    1997-12-01

    This report contains the results of efforts under the six tasks of the Ninth Amendment and Extension of Annex IV, Enhanced Oil Recovery Thermal Processes of the Venezuela/USA Agreement. The report is presented in sections (for each of the 6 tasks) and each section contains one or more reports prepared by various individuals or groups describing the results of efforts under each of the tasks. A statement of each task, taken from the agreement, is presented on the first page of each section. The tasks are numbered 62 through 67. The first, second, third, fourth fifth, sixth, seventh, eighth, and ninth reports on Annex IV, [Venezuela MEM/USA-DOE Fossil Energy Report IV-1, IV-2, IV-3, IV-4, IV-5, IV-6, IV-7, and IV-8 (DOE/BETC/SP-83/15, DOE/BC-84/6/SP, DOE/BC-86/2/SP, DOE/BC-87/2/SP, DOE/BC-90/1/SP, DOE/BC-90/1/SP) (DOE/BC-92/1/SP, DOE/BC-93/3/SP, and DOE/BC-95/3/SP)] contain the results from the first 61 tasks. Those reports are dated April 1983, August 1984, March 1986, July 1987, November 1988, October 1991, February 1993, and March 1995 respectively.

  19. Cereal and nonfat milk support muscle recovery following exercise

    Directory of Open Access Journals (Sweden)

    Liao Yi-Hung

    2009-05-01

    Full Text Available Abstract Background This study compared the effects of ingesting cereal and nonfat milk (Cereal and a carbohydrate-electrolyte sports drink (Drink immediately following endurance exercise on muscle glycogen synthesis and the phosphorylation state of proteins controlling protein synthesis: Akt, mTOR, rpS6 and eIF4E. Methods Trained cyclists or triathletes (8 male: 28.0 ± 1.6 yrs, 1.8 ± 0.0 m, 75.4 ± 3.2 kg, 61.0 ± 1.6 ml O2•kg-1•min-1; 4 female: 25.3 ± 1.7 yrs, 1.7 ± 0.0 m, 66.9 ± 4.6 kg, 46.4 ± 1.2 mlO2•kg-1•min-1 completed two randomly-ordered trials serving as their own controls. After 2 hours of cycling at 60–65% VO2MAX, a biopsy from the vastus lateralis was obtained (Post0, then subjects consumed either Drink (78.5 g carbohydrate or Cereal (77 g carbohydrate, 19.5 g protein and 2.7 g fat. Blood was drawn before and at the end of exercise, and at 15, 30 and 60 minutes after treatment. A second biopsy was taken 60 minutes after supplementation (Post60. Differences within and between treatments were tested using repeated measures ANOVA. Results At Post60, blood glucose was similar between treatments (Drink 6.1 ± 0.3, Cereal 5.6 ± 0.2 mmol/L, p Conclusion These results suggest that Cereal is as good as a commercially-available sports drink in initiating post-exercise muscle recovery.

  20. Social Support and Recovery from Sport Injury: Elite Skiers Share Their Experiences.

    Science.gov (United States)

    Bianco, Theresa

    2001-01-01

    Interviewed elite skiers who had recovered from serious injuries about stress associated with injury and the role of social support in recovery. Skiers needed various types of emotional, informational, and tangible support from the occurrence of injury through the return to full activity. Treatment team members, ski team members and home support…

  1. Citizen Support for Northern Ohio Community College Funding Initiatives during an Economic Recession Recovery

    Science.gov (United States)

    Flores, Patricia

    2013-01-01

    The current research, "Citizen Support for Northern Ohio Community College Funding Initiatives during an Economic Recession Recovery", asks the question: Do the citizens of Northern Ohio support community college funding during difficult economic times? Based on the theory of Stakeholder Analysis, the purpose of this concurrent,…

  2. Practical support aids addiction recovery: the positive identity model of change.

    Science.gov (United States)

    Johansen, Ayna B; Brendryen, Håvar; Darnell, Farnad J; Wennesland, Dag K

    2013-07-31

    There is a need for studies that can highlight principles of addiction recovery. Because social relationships are involved in all change processes, understanding how social motivations affect the recovery process is vital to guide support programs. The objective was to develop a model of recovery by examining addicted individuals' social motivations through longitudinal assessment of non-professional support dyads. A qualitative, longitudinal study design was used, combining focus groups and in-depth interviews with addicted individuals and their sponsors. Data were analyzed using the principles of grounded theory: open coding and memos for conceptual labelling, axial coding for category building, and selective coding for theory building. The setting was an addiction recovery social support program in Oslo, Norway. The informants included nine adults affected by addiction, six sponsors, and the program coordinator. The participants were addicted to either alcohol (2), benzodiazepines (1), pain killers (1) or polydrug-use (5). The sponsors were unpaid, and had no history of addiction problems. Support perceived to be ineffective emerged in dyads with no operationalized goal, and high emotional availability with low degree of practical support. Support perceived to be effective was signified by the sponsor attending to power imbalance and the addict coming into position to help others and feel useful. The findings appear best understood as a positive identity-model of recovery, indicated by the pursuit of skill building relevant to a non-drug using identity, and enabled by the on-going availability of instrumental support. This produced situations where role reversals were made possible, leading to increased self-esteem. Social support programs should be based on a positive identity-model of recovery that enable the building of a life-sustainable identity.

  3. Virtual Reality Cue Refusal Video Game for Alcohol and Cigarette Recovery Support: Summative Study.

    Science.gov (United States)

    Metcalf, Mary; Rossie, Karen; Stokes, Katie; Tallman, Christina; Tanner, Bradley

    2018-04-16

    New technologies such as virtual reality, augmented reality, and video games hold promise to support and enhance individuals in addiction treatment and recovery. Quitting or decreasing cigarette or alcohol use can lead to significant health improvements for individuals, decreasing heart disease risk and cancer risks (for both nicotine and alcohol use), among others. However, remaining in recovery from use is a significant challenge for most individuals. We developed and assessed the Take Control game, a partially immersive Kinect for Windows platform game that allows users to counter substance cues through active movements (hitting, kicking, etc). Formative analysis during phase I and phase II guided development. We conducted a small wait-list control trial using a quasi-random sampling technique (systematic) with 61 participants in recovery from addiction to alcohol or tobacco. Participants used the game 3 times and reported on substance use, cravings, satisfaction with the game experience, self-efficacy related to recovery, and side effects from exposure to a virtual reality intervention and substance cues. Participants found the game engaging and fun and felt playing the game would support recovery efforts. On average, reported substance use decreased for participants during the intervention period. Participants in recovery for alcohol use saw more benefit than those in recovery for tobacco use, with a statistically significant increase in self-efficacy, attitude, and behavior during the intervention. Side effects from the use of a virtual reality intervention were minor and decreased over time; cravings and side effects also decreased during the study. The preliminary results suggest the intervention holds promise as an adjunct to standard treatment for those in recovery, particularly from alcohol use. ©Mary Metcalf, Karen Rossie, Katie Stokes, Christina Tallman, Bradley Tanner. Originally published in JMIR Serious Games (http://games.jmir.org), 16.04.2018.

  4. Enhancing outcomes for persons with co-occurring disorders through skills training and peer recovery support.

    Science.gov (United States)

    O'Connell, Maria J; Flanagan, Elizabeth H; Delphin-Rittmon, Miriam E; Davidson, Larry

    2017-03-10

    "Recovery supports", often provided by persons in recovery themselves, have emerged over the last decade as important components of recovery-oriented systems of care for persons with substance use disorders. This study assesses the benefit of adding peer recovery supports to the care of adults with co-occurring psychosis and substance use. 137 adults with both disorders who had at least one prior admission within the past year were recruited during an index hospitalization into a randomized trial of standard care vs skills training with and without a peer-led social engagement program. Participants were assessed at admission and at three and nine months post-discharge on symptoms, functioning, substance use, and other factors. At three months, skills training was effective in reducing alcohol use and symptoms, with the addition of peer-led support resulting in higher levels of relatedness, self-criticism, and outpatient service use. At nine months, skills training was effective in decreasing symptoms and inpatient readmissions and increasing functioning, with the addition of peer support resulting in reduced alcohol use. Adding peer-led support may increase engagement in care over the short term and reduce substance use over the longer-term for adults with co-occurring disorders.

  5. A qualitative exploration of social support during treatment for severe alcohol use disorder and recovery

    Directory of Open Access Journals (Sweden)

    Alyssa T. Brooks

    2017-12-01

    Full Text Available Introduction: Severe alcohol use disorder (AUD affects multiple aspects of an individual's life as well as their loved ones' lives. Perceived social support has the potential to help or hinder recovery efforts. Methods: In this analysis we seek to understand the changes of social networks among individuals with severe AUD (n=33 throughout their recovery process and the potential relationship between the quality and nature of those networks and sustained sobriety as they transition from an inpatient research facility providing rehabilitation treatment back to the community. Interviews were conducted in 2014 and 2015. We conducted in-depth thematic analysis of themes related to social support using an exploratory approach. Results: The most common types of social support mentioned in both inpatient and outpatient settings were instrumental and emotional. Participants most frequently mentioned Alcoholics Anonymous (AA, an abstinence-based support system, as a source of support and often used the inpatient program as an exemplar when describing their ideal social networks. Conclusion: These data provide insight into the complexity of the issues and barriers that individuals in recovery may be facing across “transition periods.” From an intervention standpoint, it may be beneficial to focus on helping people choose environments and their accompanying social contexts and networks that are most conducive to recovery. Further elucidating the concept of social support and its role in recovery could provide information on unique needs of individuals and guide clinicians in engaging patients to develop new or sustain healthy existing social networks that result in continued sobriety. Keywords: Alcohol use disorder, Substance use, Social support, Social networks, Qualitative research, Rehabilitation treatment

  6. Binary Sparse Phase Retrieval via Simulated Annealing

    Directory of Open Access Journals (Sweden)

    Wei Peng

    2016-01-01

    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.

  7. Contracts for field projects and supporting research on enhanced oil recovery. Progress review No. 89

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-04-01

    Summaries are presented for the DOE contracts related to supported research for thermal recovery of petroleum, geoscience technology, and field demonstrations in high-priority reservoir classes. Data included for each project are: title, contract number, principal investigator, research organization, beginning date, expected completion date, amount of award, objectives of the research, and summary of technical progress.

  8. Interventions to support recovery after domestic and sexual violence in primary care.

    Science.gov (United States)

    Hegarty, Kelsey; Tarzia, Laura; Hooker, Leesa; Taft, Angela

    2016-10-01

    Experiences of domestic and sexual violence are common in patients attending primary care. Most often they are not identified due to barriers to asking by health practitioners and disclosure by patients. Women are more likely than men to experience such violence and present with mental and physical health symptoms to health practitioners. If identified through screening or case finding as experiencing violence they need to be supported to recover from these traumas. This paper draws on systematic reviews published in 2013-2015 and a further literature search undertaken to identify recent intervention studies relevant to recovery from domestic and sexual violence in primary care. There is limited evidence as to what interventions in primary care assist with recovery from domestic violence; however, they can be categorized into the following areas: first line response and referral, psychological treatments, safety planning and advocacy, including through home visitation and peer support programmes, and parenting and mother-child interventions. Sexual violence interventions usually include trauma informed care and models to support recovery. The most promising results have been from nurse home visiting advocacy programmes, mother-child psychotherapeutic interventions, and specific psychological treatments (Cognitive Behaviour Therapy, Trauma informed Cognitive Behaviour Therapy and, for sexual assault, Exposure and Eye Movement Desensitization and Reprocessing Interventions). Holistic healing models have not been formally tested by randomized controlled trials, but show some promise. Further research into what supports women and their children on their trajectory of recovery from domestic and sexual violence is urgently needed.

  9. Moms Supporting Moms: Digital Storytelling With Peer Mentors in Recovery From Substance Use.

    Science.gov (United States)

    Paterno, Mary T; Fiddian-Green, Alice; Gubrium, Aline

    2018-01-01

    Substance use disorder (SUD) is a growing issue nationally, and SUD in pregnancy has significant consequences for mothers and their children. This article describes findings from a pilot project that used digital storytelling as a mechanism for understanding substance use and recovery from the perspective of women in recovery from SUD in pregnancy who worked as peer mentors with pregnant women currently experiencing SUD. Research on peer mentorship has primarily focused on outcomes for mentees but not the experience of the peer mentors themselves. In this qualitative study, a 3-day digital storytelling workshop was conducted with five women in recovery serving as peer mentors in their community. Each mentor also participated in an individual, in-depth interview. The digital storytelling workshop process helped peer mentors make linkages between their past substance use experiences to their present work of recovery, and fostered deep social connections between mentors through the shared experience. The workshop process also elicited a sense of hope among participants, which served as groundwork for developing advocacy-based efforts. Digital storytelling may be therapeutic for women in recovery and has the potential to be integrated into recovery programs to bolster hope and social support among participants.

  10. Positive Peer Support or Negative Peer Influence? the Role of Peers among Adolescents in Recovery High Schools

    Science.gov (United States)

    Karakos, Holly L.

    2014-01-01

    Evidence from previous research suggests that peers at times exert negative influence and at other times exert positive influence on drug and alcohol use among adolescents in recovery. This study explores recovery high school staff members' perceptions of peer support among students in recovery high schools using qualitative interview data. Themes…

  11. Investigating Social Support and Network Relationships in Substance Use Disorder Recovery.

    Science.gov (United States)

    Stevens, Ed; Jason, Leonard A; Ram, Daphna; Light, John

    2015-01-01

    Social support and characteristics of one's social network have been shown to be beneficial for abstinence and substance use disorder recovery. The current study explores how specific sources of social support relate to general feelings of social support and abstinence-specific self-efficacy. Data were collected from 31 of 33 individuals residing in 5 recovery houses. Participants were asked to complete social support and social network measures, along with measures assessing abstinence from substance use, abstinence self-efficacy, and involvement in 12-step groups. A significant positive relationship was found between general social support and abstinence-specific self-efficacy. General social support was also significantly associated with the specific social support measures of sense of community and Alcoholics Anonymous (AA) affiliation. Social network size predicted abstinence-related factors such as AA affiliation and perceived stress. These results provide insight regarding individual feelings of social support and abstinence-specific self-efficacy by showing that one's social network-level characteristics are related to one's perceptions of social support. We also found preliminary evidence that individual Oxford Houses influence one's feelings of social support.

  12. Using mobile phone technology to provide recovery support for women offenders.

    Science.gov (United States)

    Scott, Christy K; Johnson, Kimberly; Dennis, Michael L

    2013-10-01

    Mobile technology holds promise as a recovery tool for people with substance use disorders. However, some populations who may benefit the most may not have access to or experience with mobile phones. Incarcerated women represent a group at high risk for recidivism and relapse to substance abuse. Cost-effective mechanisms must be in place to support their recovery upon release. This study explores using mobile technology as a recovery management tool for women offenders residing in the community following release from jail. This study surveyed 325 minority women offenders with substance use disorders to determine whether or not they use cell phones, their comfort with texting and search features, and the social networks that they access from mobile phones. We found that 83% of survey subjects had cell phones; 30% of those were smartphones. Seventy-seven percent of the women reported access to supportive friends, and 88% had close family members they contacted regularly using mobile technology. Results indicated that most of the women were comfortable using a mobile phone, although the majority of them had prepaid minutes rather than plans, and most did currently use smartphones or have the capability to download applications or access social networks via their phones. Most women reported that they would be comfortable using a mobile phone to text, e-mail, and answer surveys. The high rate of adoption of mobile technology by women offenders makes them a promising target for recovery support delivered via mobile phone.

  13. Sparse Exploratory Factor Analysis.

    Science.gov (United States)

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

    2017-07-13

    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. Life cycle assessment as development and decision support tool for wastewater resource recovery technology.

    Science.gov (United States)

    Fang, Linda L; Valverde-Pérez, Borja; Damgaard, Anders; Plósz, Benedek Gy; Rygaard, Martin

    2016-01-01

    Life cycle assessment (LCA) has been increasingly used in the field of wastewater treatment where the focus has been to identify environmental trade-offs of current technologies. In a novel approach, we use LCA to support early stage research and development of a biochemical system for wastewater resource recovery. The freshwater and nutrient content of wastewater are recognized as potential valuable resources that can be recovered for beneficial reuse. Both recovery and reuse are intended to address existing environmental concerns, for example, water scarcity and use of non-renewable phosphorus. However, the resource recovery may come at the cost of unintended environmental impacts. One promising recovery system, referred to as TRENS, consists of an enhanced biological phosphorus removal and recovery system (EBP2R) connected to a photobioreactor. Based on a simulation of a full-scale nutrient and water recovery system in its potential operating environment, we assess the potential environmental impacts of such a system using the EASETECH model. In the simulation, recovered water and nutrients are used in scenarios of agricultural irrigation-fertilization and aquifer recharge. In these scenarios, TRENS reduces global warming up to 15% and marine eutrophication impacts up to 9% compared to conventional treatment. This is due to the recovery and reuse of nutrient resources, primarily nitrogen. The key environmental concerns obtained through the LCA are linked to increased human toxicity impacts from the chosen end use of wastewater recovery products. The toxicity impacts are from both heavy metals release associated with land application of recovered nutrients and production of AlCl3, which is required for advanced wastewater treatment prior to aquifer recharge. Perturbation analysis of the LCA pinpointed nutrient substitution and heavy metals content of algae biofertilizer as critical areas for further research if the performance of nutrient recovery systems such as

  15. The Recovery Process When Participating in Cancer Support and Rehabilitation Programs in Sweden

    Directory of Open Access Journals (Sweden)

    Christina Melin-Johansson

    2015-07-01

    Full Text Available The aim was to illuminate the meaning of participating in support and rehabilitation programs described by people diagnosed with cancer. Nineteen persons were interviewed in focus groups and face-to-face. Data were analyzed with a qualitative phenomenological hermeneutical method for researching lived experiences. Interpretation proceeded through three phases: naïve reading, structural analysis, and comprehensive understanding. Three themes were disclosed: receiving support for recovery when being most vulnerable, recapturing capabilities through supportive activities, and searching to find stability and well-being in a changed life situation. Participating in the programs was an existential transition from living in an unpredictable situation that was turned into something meaningful. Recovery did not mean the return to a state of normality; rather, it meant a continuing recovery from cancer treatments and symptoms involving recapturing capabilities and searching for a balance in a forever changed life. This study provides new insights about the experiences of participating in cancer support and rehabilitation programs.

  16. Substance Abuse Counselors’ Recovery Status and Self-Schemas: Preliminary Implications for Empirically Supported Treatment Implementation

    Science.gov (United States)

    Nielson, Elizabeth M.

    2016-01-01

    Purpose The purpose of this paper is to better understand the relationship between substance abuse counselors’ personal recovery status, self-schemas, and willingness to use empirically supported treatments for substance use disorders. Methods A phenomenological qualitative study enrolled 12 practicing substance abuse counselors. Results Within this sample, recovering counselors tended to see those who suffer from addiction as qualitatively different from those who do not and hence themselves as similar to their patients, while nonrecovering counselors tended to see patients as experiencing a specific variety of the same basic human struggles everyone experiences, and hence also felt able to relate to their patients’ struggles. Discussion Since empirically supported treatments may fit more or less neatly within one or the other of these viewpoints, this finding suggests that counselors’ recovery status and corresponding self-schemas may be related to counselor willingness to learn and practice specific treatments. PMID:28626597

  17. Substance Abuse Counselors' Recovery Status and Self-Schemas: Preliminary Implications for Empirically Supported Treatment Implementation.

    Science.gov (United States)

    Nielson, Elizabeth M

    2016-01-01

    The purpose of this paper is to better understand the relationship between substance abuse counselors' personal recovery status, self-schemas, and willingness to use empirically supported treatments for substance use disorders. A phenomenological qualitative study enrolled 12 practicing substance abuse counselors. Within this sample, recovering counselors tended to see those who suffer from addiction as qualitatively different from those who do not and hence themselves as similar to their patients, while nonrecovering counselors tended to see patients as experiencing a specific variety of the same basic human struggles everyone experiences, and hence also felt able to relate to their patients' struggles. Since empirically supported treatments may fit more or less neatly within one or the other of these viewpoints, this finding suggests that counselors' recovery status and corresponding self-schemas may be related to counselor willingness to learn and practice specific treatments.

  18. SPARSE FARADAY ROTATION MEASURE SYNTHESIS

    International Nuclear Information System (INIS)

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

    2012-01-01

    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.

  19. The role of extracorporeal life support in acute myocarditis: a bridge to recovery?

    Science.gov (United States)

    Sanders, D Bradford; Sowell, Steven R; Willis, Brigham; Lane, John; Pierce, Christopher; Pophal, Stephen; Arabia, Francisco A; Nigro, John J

    2012-12-01

    Acute myocardial failure associated with myocarditis is highly lethal. Left ventricular assist device support for these patients has been advocated to decompress the left ventricle and facilitate myocardial remodeling and recovery. Concerns exist regarding the ability of venoarterial (VA) extracorporeal life support (ECLS) to decompress the left ventricle and allow effective myocardial recovery. ECLS has several advantages, including availability, rapid deployment, and flexibility, as compared with contemporary ventricular assist devices. The objective of this study was to provide a brief review of acute myocarditis and present our series of patients. After Institutional Review Board approval, we conducted a retrospective data analysis of patients on ECLS experiencing rapidly progressive myocardial failure from a normal baseline. Patients with a history of intrinsic heart disease were excluded. All patients were thought to have myocarditis and had failed medical therapy requiring emergent ECLS support. Five patients demographics are detailed in Table 1. Patients experienced life-threatening intractable dysrhythmias or cardiac arrest and were refractory to medical therapy with severe acidosis and impending multisystem organ failure. All patients were stabilized with VA ECLS, and the left ventricle and atrium were decompressed in four of five patients. A left atrial vent was placed in one patient. Myocardial recovery with successful weaning from ECLS was obtained in four of five patients and to a normal ejection fraction in three of the five. One patient failed ECLS weaning and required biventricular VAD support secondary to severe myocardial necrosis from giant cell myocarditis and was transplanted, one died, all others are alive at follow-up. ECLS is safe and effective to treat acute myocardial failure and may be used to obtain myocardial recovery in certain subsets. We devised a decision algorithm for ECLS deployment in this patient cohort and routinely use ECLS.

  20. Bridge to recovery in two cases of dilated cardiomyopathy after long-term mechanical circulatory support

    OpenAIRE

    Pacholewicz, Jerzy; Zakliczy?ski, Micha?; Kowalik, Violetta; Nadziakiewicz, Pawe?; Kowalski, Oskar; Kalarus, Zbigniew; Zembala, Marian

    2014-01-01

    Ventricular assist devices (VADs) have become an established therapeutic option for patients with end-stage heart failure. Achieving the potential for recovery of native heart function using VADs is an established form of treatment in a selected group of patients with HF. We report two cases of VAD patients with different types of pump used for mechanical circulatory support, a continuous flow pump (Heart-Ware?) and a pulsatile pump (POLVAD MEV?), which allow regeneration of the native heart....

  1. Structural Sparse Tracking

    KAUST Repository

    Zhang, Tianzhu

    2015-06-01

    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.

  2. Sleep Homeostasis During Repeated Sleep Restriction and Recovery: Support from EEG Dynamics

    Science.gov (United States)

    Åkerstedt, Torbjörn; Kecklund, Göran; Ingre, Michael; Lekander, Mats; Axelsson, John

    2009-01-01

    Study Objectives: Sleep reduction normally causes a homeostatic response during subsequent recovery sleep, but this does not seem to be true for repeated partial sleep loss. The aim of the present study was to test the response to repeated partial sleep loss through detailed focus on spectral data and parts of sleep. Design: The experiment involved 4 h of sleep across 5 days in the laboratory (partial sleep deprivation [PSD]), followed by 3 days of recovery sleep. PSD was achieved through a delayed bedtime. Nine individuals participated. To avoid “laboratory monotony,” subjects were permitted to leave the lab for a few hours each day. Measurements and results: All sleep stages and the latencies to sleep and slow wave sleep (SWS) showed a significant reduction during PSD. However, SWS and TST (total sleep time) during the first half of sleep increased gradually across days with PSD. During the first recovery sleep, SWS was significantly increased, while stage 1 and latency to stage 3 were reduced. All were back to baseline on the second night of recovery sleep. Summed spectral power during the first 3.8 h of sleep showed a gradual and robust increase (50% above baseline) in the range 1.25–7.25 Hz across days with PSD up to first recovery sleep and then returned to baseline. Conclusions: SWS and summed power density in a broad low-frequency band respond to repeated partial sleep deprivation in a dose-response fashion during the first 4 h sleep, apparently reflecting a robust and stable homeostatic response to sleep loss. Citation: Åkerstedt T; Kecklund G; Ingre M; Lekander M; Axelsson J. Sleep homeostasis during repeated sleep restriction and recovery: support from EEG dynamics. SLEEP 2009;32(2):217–222. PMID:19238809

  3. Service and infrastructure needs to support recovery programmes for Indigenous community mental health consumers.

    Science.gov (United States)

    Sayers, Jan M; Cleary, Michelle; Hunt, Glenn E; Burmeister, Oliver K

    2017-04-01

    Mental health is a major concern in Indigenous communities, as Indigenous people experience poorer health outcomes generally, and poorer social and emotional well-being throughout their lives, compared to non-Indigenous populations. Interviews were conducted with 20 mental health workers from a housing assistance programme for Indigenous clients with mental illness. Service and infrastructure needs identified to support clients were classified under the following overarching theme 'supports along the road to recovery'. Subthemes were: (i) It is OK to seek help; (ii) linking in to the local community; (iii) trusting the workers; and (iv) help with goal setting and having activities that support their achievement. This paper highlights the importance of targeted housing and accommodation support programmes for Indigenous people to prevent homelessness, and the essential services and infrastructure required to support Indigenous clients' mental health needs. These insights may inform service review, workforce development, and further research. © 2016 Australian College of Mental Health Nurses Inc.

  4. Shearlets and Optimally Sparse Approximations

    DEFF Research Database (Denmark)

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

    2012-01-01

    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....

  5. Dipole localization in Moon rocks from sparse magnetic data

    OpenAIRE

    Chevillard , Sylvain; Leblond , Juliette; Mavreas , Konstantinos

    2017-01-01

    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.

  6. Recovery of Terephthalic Acid by employing magnetic nanoparticles as a solid support

    Directory of Open Access Journals (Sweden)

    Elmira Ghamary

    2018-03-01

    Full Text Available Abstract The aim of this research work is focused on the improvement of Terephthalic acid recovery from PET wastes by using organically modified nano-Fe3O4@Cyanuric Chloride as the solid support. The performance of organically modified nano magnetic was examined in detail and the obtained results were compared with the unsupported reaction data. Required reaction time for complete glycolysis of the wastes, consumption of the solvent as well as catalyst decreases up 99%, 37.5% and 40% respectively. Result showed that nano-Fe 3O4@Cyanuric Chloride delivered good performance as solid support in depolymerizing of PET to the terephthalic acid.

  7. An integrated decision support system for wastewater nutrient recovery and recycling to agriculture

    Science.gov (United States)

    Roy, E. D.; Bomeisl, L.; Cornbrooks, P.; Mo, W.

    2017-12-01

    Nutrient recovery and recycling has become a key research topic within the wastewater engineering and nutrient management communities. Several technologies now exist that can effectively capture nutrients from wastewater, and innovation in this area continues to be an important research pursuit. However, practical nutrient recycling solutions require more than capable nutrient capture technologies. We also need to understand the role that wastewater nutrient recovery and recycling can play within broader nutrient management schemes at the landscape level, including important interactions at the nexus of food, energy, and water. We are developing an integrated decision support system that combines wastewater treatment data, agricultural data, spatial nutrient balance modeling, life cycle assessment, stakeholder knowledge, and multi-criteria decision making. Our goals are to: (1) help guide design decisions related to the implementation of sustainable nutrient recovery technology, (2) support innovations in watershed nutrient management that operate at the interface of the built environment and agriculture, and (3) aid efforts to protect aquatic ecosystems while supporting human welfare in a circular nutrient economy. These goals will be realized partly through the assessment of plausible alternative scenarios for the future. In this presentation, we will describe the tool and focus on nutrient balance results for the New England region. These results illustrate that both centralized and decentralized wastewater nutrient recovery schemes have potential to transform nutrient flows in many New England watersheds, diverting wastewater N and P away from aquatic ecosystems and toward local or regional agricultural soils where they can offset a substantial percentage of imported fertilizer. We will also highlight feasibility criteria and next steps to integrate stakeholder knowledge, economics, and life cycle assessment into the tool.

  8. Recovery strategies implemented by sport support staff of elite rugby players in South Africa

    Directory of Open Access Journals (Sweden)

    D.V. Van Wyk

    2009-01-01

    Full Text Available Objective: The main aim of this study was to determine strategies used toaccelerate recovery of elite rugby players after training and matches, asused by medical support staff of rugby teams in South A frica. A  secondaryaim was to focus on specifics of implementing ice/cold water immersion asrecovery strategy. Design: A  Questionnaire-based cross sectional descriptive survey was used.Setting and Participants: Most (n=58 of the medical support staff ofrugby teams (doctors, physiotherapists, biokineticists and fitness trainerswho attended the inaugural Rugby Medical A ssociation conference linked to the South A frican Sports MedicineA ssociation Conference in Pretoria (14-16th November, 2007 participated in the study. Results: Recovery strategies were utilized mostly after matches. Stretching and ice/cold water immersion were utilized the most (83%. More biokineticists and fitness trainers advocated the usage of stretching than their counter-parts (medical doctors and physiotherapists. Ice/Cold water immersion and A ctive Recovery were the top two ratedstrategies. A  summary of the details around implementation of ice/cold water therapy is shown (mean as utilized bythe subjects: (i The time to immersion after matches was 12±9 min; (ii The total duration of one immersion sessionwas 6±6 min; (iii 3 immersion sessions per average training week was utilized by subjects; (iv The average water temperature was 10±3 ºC.; (v Ice cubes were used most frequently to cool water for immersion sessions, and(vi plastic drums were mostly used as the container for water. Conclusion: In this survey the representative group of support staff provided insight to which strategies are utilizedin South A frican elite rugby teams to accelerate recovery of players after training and/or matches.

  9. Exploring Student Service Members/Veterans Social Support and Campus Climate in the Context of Recovery

    Directory of Open Access Journals (Sweden)

    Susan M. Love

    2015-09-01

    Full Text Available Now that the financial needs of post 9/11 student service members/veterans have begun to be addressed, the attention has shifted to disabilities and recovery strategies of student service members/veterans. Therefore, in a cross sectional design, this study electronically surveyed 189 enrolled student service members/veterans attending a large urban state university about their experiences of returning to school. Specifically, this study described the students’ rates of Post-Traumatic Stress Disorder (PTSD and alcohol abuse, perceived stress, adaptive and non-adaptive coping strategies, social support, participation in campus activities, and perceived campus climate. Moreover, correlates of recovery were examined. Although the majority of the returning students were doing well, 36.1% reported a high level of stress, 15.1% reported a high level of anger, 17.3% reported active symptoms of PTSD, and 27.1% screened positive for alcohol problems. Social networks were found to be the most salient factor in recovery. The study’s limitations are discussed and specific support strategies are presented that can be employed by disability services, counseling services and college administrators.

  10. Life cycle assessment as development and decision support tool for wastewater resource recovery technology

    DEFF Research Database (Denmark)

    Fang, Linda L.; Valverde Perez, Borja; Damgaard, Anders

    2016-01-01

    Life cycle assessment (LCA) has been increasingly used in the field of wastewater treatment where the focus has been to identify environmental trade-offs of current technologies. In a novel approach, we use LCA to support early stage research and development of a biochemical system for wastewater......, TRENS reduces global warming up to 15% and marine eutrophication impacts up to 9% compared to conventional treatment. This is due to the recovery and reuse of nutrient resources, primarily nitrogen. The key environmental concerns obtained through the LCA are linked to increased human toxicity impacts...... of the LCA pinpointed nutrient substitution and heavy metals content of algae biofertilizer as critical areas for further research if the performance of nutrient recovery systems such as TRENS is to be better characterized. Our study provides valuable feedback to the TRENS developers and identifies...

  11. Structure-based bayesian sparse reconstruction

    KAUST Repository

    Quadeer, Ahmed Abdul

    2012-12-01

    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.

  12. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-04-17

    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.

  13. Coma Recovery Scale-Revised: evidentiary support for hierarchical grading of level of consciousness.

    Science.gov (United States)

    Gerrard, Paul; Zafonte, Ross; Giacino, Joseph T

    2014-12-01

    To investigate the neurobehavioral pattern of recovery of consciousness as reflected by performance on the subscales of the Coma Recovery Scale-Revised (CRS-R). Retrospective item response theory (IRT) and factor analysis. Inpatient rehabilitation facilities. Rehabilitation inpatients (N=180) with posttraumatic disturbance in consciousness who participated in a double-blinded, randomized, controlled drug trial. Not applicable. Scores on CRS-R subscales. The CRS-R was found to fit factor analytic models adhering to the assumptions of unidimensionality and monotonicity. In addition, subscales were mutually independent based on residual correlations. Nonparametric IRT reaffirmed the finding of monotonicity. A highly constrained confirmatory factor analysis model, which imposed equal factor loadings on all items, was found to fit the data well and was used to estimate a 1-parameter IRT model. This study provides evidence of the unidimensionality of the CRS-R and supports the hierarchical structure of the CRS-R subscales, suggesting that it is an effective tool for establishing diagnosis and monitoring recovery of consciousness after severe traumatic brain injury. Copyright © 2014 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  14. Recovery of Uranium from Seawater: Preparation and Development of Polymer-Supported Extractants

    Energy Technology Data Exchange (ETDEWEB)

    Spiro, Alexandratos [City Univ. (CUNY), NY (United States). Hunter College

    2013-12-01

    A new series of polymer-supported extractants is proposed for the removal and recovery of uranium from seawater. The objective is to produce polymers with improved stability, loading capacity, and sorption kinetics compared to what is found with amidoximes. The target ligands are diphosphonates and aminomethyldiphosphonates. Small molecule analogues, especially of aminomethyldiphos-phonates, have exceptionally high stability constants for the uranyl ion. The polymeric diphosphonates will have high affinities due to their ability to form six-membered rings with the uranyl ion while the aminomethyldiphosphonates may have yet higher affinities due to possible tridentate coordination and their greater acidity. A representative set of the polymers to be prepared are indicated.

  15. Design of expanded bed supports for the recovery of plasmid DNA by anion exchange adsorption

    DEFF Research Database (Denmark)

    Theodossiou, Irini; Søndergaard, M.; Thomas, Owen R. T.

    2001-01-01

    and blueprints for improved expanded bed adsorbents have been put forward. The characterisation and testing of small (20-40 mum) high density (>3.7 g cm(-3)) pellicular expanded bed materials functionalised with various anion exchange structures is presented. In studies with calf thymus DNA, dynamic binding......In this study we detail the rational design of new chromatographic adsorbents tailored for the capture of plasmid DNA. Features present on current chromatographic supports that can significantly enhance plasmid binding capacity have been identified in packed bed chromatography experiments...... sensitivity to inter-particle bridging by nucleic acid polymers, gave low DNA recoveries (0.8 mg ml(-1)) capture of plasmid...

  16. Distribution Agnostic Structured Sparsity Recovery: Algorithms and Applications

    KAUST Repository

    Masood, Mudassir

    2015-05-01

    Compressed sensing has been a very active area of research and several elegant algorithms have been developed for the recovery of sparse signals in the past few years. However, most of these algorithms are either computationally expensive or make some assumptions that are not suitable for all real world problems. Recently, focus has shifted to Bayesian-based approaches that are able to perform sparse signal recovery at much lower complexity while invoking constraint and/or a priori information about the data. While Bayesian approaches have their advantages, these methods must have access to a priori statistics. Usually, these statistics are unknown and are often difficult or even impossible to predict. An effective workaround is to assume a distribution which is typically considered to be Gaussian, as it makes many signal processing problems mathematically tractable. Seemingly attractive, this assumption necessitates the estimation of the associated parameters; which could be hard if not impossible. In the thesis, we focus on this aspect of Bayesian recovery and present a framework to address the challenges mentioned above. The proposed framework allows Bayesian recovery of sparse signals but at the same time is agnostic to the distribution of the unknown sparse signal components. The algorithms based on this framework are agnostic to signal statistics and utilize 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. In the thesis, we propose several algorithms based on this framework which utilize the structure present in signals for improved recovery. In addition to the algorithm that considers just the sparsity structure of sparse signals, tools that target additional structure of the sparsity recovery problem are proposed. These include several algorithms for a) block-sparse signal estimation, b) joint reconstruction of several common support sparse signals, and c

  17. Supporting recovery by improving patient engagement in a forensic mental health hospital: results from a demonstration project.

    Science.gov (United States)

    Livingston, James D; Nijdam-Jones, Alicia; Lapsley, Sara; Calderwood, Colleen; Brink, Johann

    2013-01-01

    Mental health services are shifting toward approaches that promote patients' choices and acknowledge the value of their lived experiences. To support patients' recovery and improve their experiences of care in a Canadian forensic mental health hospital, an intervention was launched to increase patient engagement by establishing a peer support program, strengthening a patient advisory committee, and creating a patient-led research team. The effect of the intervention on patient- and system-level outcomes was studied using a naturalistic, prospective, longitudinal approach. Quantitative and qualitative data were gathered from inpatients and service providers twice during the 19-month intervention. Despite succeeding in supporting patients' participation, the intervention had minimal impacts on internalized stigma, personal recovery, personal empowerment, service engagement, therapeutic milieu, and the recovery orientation of services. Peer support demonstrated positive effects on internalized stigma and personal recovery. Strengthening patient engagement contributes toward improving experiences of care in a forensic hospital, but it may have limited effects on outcomes.

  18. Contracts for field projects and supporting research on enhanced oil recovery. Progress review No. 71, quarter ending June 30, 1992

    Energy Technology Data Exchange (ETDEWEB)

    1993-06-01

    Progress reports are presented for the following tasks: chemical flooding--supporting research; gas displacement--supporting research; thermal recovery--supporting research; geoscience technology; resource assessment technology; microbial technology; and novel technology. A list of available publication is also provided.

  19. Contracts for field projects and supporting research on enhanced oil recovery: Progress review No. 74, Quarter ending March 31, 1993

    Energy Technology Data Exchange (ETDEWEB)

    1994-03-01

    Accomplishments for the past quarter are presented for the following tasks: chemical flooding--supporting research; gas displacement--supporting research; thermal recovery--supporting research; geoscience technology; resource assessment technology; microbial technology; field demonstrations in high-priority reservoir classes; and novel technology. A list of available publication is also provided.

  20. Advancing the Science of Myocardial Recovery With Mechanical Circulatory Support

    Directory of Open Access Journals (Sweden)

    Stavros G. Drakos, MD, PhD

    2017-06-01

    Full Text Available Summary: The medical burden of heart failure (HF has spurred interest in clinicians and scientists to develop therapies to restore the function of a failing heart. To advance this agenda, the National Heart, Lung, and Blood Institute (NHLBI convened a Working Group of experts from June 2 to 3, 2016, in Bethesda, Maryland, to develop NHLBI recommendations aimed at advancing the science of cardiac recovery in the setting of mechanical circulatory support (MCS. MCS devices effectively reduce volume and pressure overload that drives the cycle of progressive myocardial dysfunction, thereby triggering structural and functional reverse remodeling. Research in this field could be innovative in many ways, and the Working Group specifically discussed opportunities associated with genome-phenome systems biology approaches; genetic epidemiology; bioinformatics and precision medicine at the population level; advanced imaging modalities, including molecular and metabolic imaging; and the development of minimally invasive surgical and percutaneous bioengineering approaches. These new avenues of investigations could lead to new treatments that target phylogenetically conserved pathways involved in cardiac reparative mechanisms. A central point that emerged from the NHLBI Working Group meeting was that the lessons learned from the MCS investigational setting can be extrapolated to the broader HF population. With the precedents set by the significant effect of studies of other well-controlled and tractable subsets on larger populations, such as the genetic work in both cancer and cardiovascular disease, the work to improve our understanding of cardiac recovery and resilience in MCS patients could be transformational for the greater HF population. Key Words: cardiac remodeling, mechanical circulatory support, myocardial recovery, ventricular assist devices

  1. A pilot study of a smartphone application supporting recovery from drug addiction.

    Science.gov (United States)

    Liang, Di; Han, Hui; Du, Jiang; Zhao, Min; Hser, Yih-Ing

    2018-05-01

    Mobile health (mHealth) technologies have the potential to facilitate self-monitoring and self-management for individuals with substance use disorders (SUD). S-Health is a bilingual smartphone application based on cognitive behavioral principles and is designed to support recovery from drug addiction by trigger recognition so as to allow practice in-the-moment coping to prevent relapse. For this pilot randomized controlled study, 75 participants were recruited from methadone maintenance treatment clinics and the social worker consortium in Shanghai, China. Participants in the control group (N=25) received text messages from S-Health (e.g., HIV prevention and other educational materials). Participants in the intervention group (N=50) received both text messages and daily surveys on cravings, affects, triggers, responses to triggers, and social contexts. At the end of the 1-month study trial, 26.2% of the intervention group and 50% of the control group had positive urine test results (p=0.06). Also, the number of days using drug in the past week was significantly lower among participants in the intervention group (Mean=0.71, SD=1.87) relative to the control group (Mean=2.20, SD=3.06) (pbenefits to deliver mobile health intervention among participants with SUD. Further research with larger samples over a longer period of time is needed to test the effectiveness of S-Health as a self-monitoring tool supporting recovery from addiction. Copyright © 2018 Elsevier Inc. All rights reserved.

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

    CERN Document Server

    Cheng, Hong

    2015-01-01

    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

  3. Personal and clinical recovery with the individual placement and support intervention in Denmark

    DEFF Research Database (Denmark)

    Nielsen, Iben Gammelgaard; Stenager, Elsebeth; Eplov, Lene

    on outcomes often referred to as recovery measures i.e. symptoms and self-esteem is ambiguous. One branch of the recovery literature distinguishes between two kinds of recovery. The one, personal recovery is defined by: what helps the individual move beyond the role of being a patient with a mental illness...

  4. Sparse approximation with bases

    CERN Document Server

    2015-01-01

    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...

  5. Efficient convolutional sparse coding

    Energy Technology Data Exchange (ETDEWEB)

    Wohlberg, Brendt

    2017-06-20

    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.

  6. Supervised Convolutional Sparse Coding

    KAUST Repository

    Affara, Lama Ahmed

    2018-04-08

    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.

  7. Dynamic Representations of Sparse Graphs

    DEFF Research Database (Denmark)

    Brodal, Gerth Stølting; Fagerberg, Rolf

    1999-01-01

    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....

  8. Life cycle assessment as decision support tool in early stage development of a new technology for wastewater resource recovery

    DEFF Research Database (Denmark)

    Fang, Linda L.; Valverde Perez, Borja; Damgaard, Anders

    2015-01-01

    Life cycle assessment (LCA) has been increasingly used in the field of wastewater treatment where the focus has been to identify environmental trade-offs of current technologies. In a novel approach, we use LCA to support early stage research and development of a biochemical system for wastewater...... resource recovery. The freshwater and nutrient content of wastewater are to a large extent recognized as potential valuable resources that can be recovered for beneficial reuse. Both recovery and reuse are intended to address existing environmental concerns, for example water scarcity and use of non......-renewable phosphorus. However, the resource recovery may come at the cost of unintended environmental impacts. One promising recovery system, referred to as TRENS, consists of an enhanced biological phosphorus removal and recovery system (EBP2R) connected to a photobioreactor. We present the environmental impact...

  9. Supervised Transfer Sparse Coding

    KAUST Repository

    Al-Shedivat, Maruan

    2014-07-27

    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.

  10. Analysis of Space Shuttle Ground Support System Fault Detection, Isolation, and Recovery Processes and Resources

    Science.gov (United States)

    Gross, Anthony R.; Gerald-Yamasaki, Michael; Trent, Robert P.

    2009-01-01

    As part of the FDIR (Fault Detection, Isolation, and Recovery) Project for the Constellation Program, a task was designed within the context of the Constellation Program FDIR project called the Legacy Benchmarking Task to document as accurately as possible the FDIR processes and resources that were used by the Space Shuttle ground support equipment (GSE) during the Shuttle flight program. These results served as a comparison with results obtained from the new FDIR capability. The task team assessed Shuttle and EELV (Evolved Expendable Launch Vehicle) historical data for GSE-related launch delays to identify expected benefits and impact. This analysis included a study of complex fault isolation situations that required a lengthy troubleshooting process. Specifically, four elements of that system were considered: LH2 (liquid hydrogen), LO2 (liquid oxygen), hydraulic test, and ground special power.

  11. Development of Support Service for Prevention and Recovery from Dementia and Science of Lethe

    Science.gov (United States)

    Otake, Mihoko

    Purpose of this study is to explore service design method through the development of support service for prevention and recovery from dementia towards science of lethe. We designed and implemented conversation support service via coimagination method based on multiscale service design method, both were proposed by the author. Multiscale service model consists of tool, event, human, network, style and rule. Service elements at different scales are developed according to the model. Interactive conversation supported by coimagination method activates cognitive functions so as to prevent progress of dementia. This paper proposes theoretical bases for science of lethe. Firstly, relationship among coimagination method and three cognitive functions including division of attention, planning, episodic memory which decline at mild cognitive imparement. Secondly, thought state transition model during conversation which describes cognitive enhancement via interactive communication. Thirdly, Set Theoretical Measure of Interaction is proposed for evaluating effectiveness of conversation to cognitive enhancement. Simulation result suggests that the ideas which cannot be explored by each speaker are explored during interactive conversation. Finally, coimagination method compared with reminiscence therapy and its possibility for collaboration is discussed.

  12. Contracts for field projects and supporting research on enhanced oil recovery. Progress review number 86, quarter ending March 31, 1996

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-05-01

    Summaries are presented for 37 enhanced oil recovery contracts being supported by the Department of Energy. The projects are grouped into gas displacement methods, thermal recovery methods, geoscience technology, reservoir characterization, and field demonstrations in high-priority reservoir classes. Each summary includes the objectives of the project and a summary of the technical progress, as well as information on contract dates, size of award, principal investigator, and company or facility doing the research.

  13. Development of Support Service for Prevention and Recovery from Dementia and Science of Lethe

    Science.gov (United States)

    Otake, Mihoko

    This paper proposes multiscale service design method through the development of support service for prevention and recovery from dementia towards science of lethe. Proposed multiscale service model consists of tool, event, human, network, style and rule. Service elements at different scales are developed according to the model. Firstly, the author proposes and practices coimagination method as an ``event'', which is expected to prevent the progress of cognitive impairment. Coimagination support system was developed as a ``tool''. Experimental results suggest the effective activation of episodic memory, division of attention, and planning function of participants by the measurement of cognitive activities during the coimagination. Then, Fonobono Research Institute was established as a ''network'' for ``human'' who studies coimagination, which is a multisector research organization including elderly people living around Kashiwa city, companies including instrument and welfare companies, Kashiwa city and Chiba prefecture, researchers of the University of Tokyo. The institute proposes and realizes lifelong research as a novel life ``style'' for elderly people, and discusses life with two rounds as an innovative ``rule'' for social system of aged society.

  14. Improving health professionals' self-efficacy to support cardiac patients' emotional recovery: the 'Cardiac Blues Project'.

    Science.gov (United States)

    Murphy, Barbara M; Higgins, Rosemary O; Shand, Lyndel; Page, Karen; Holloway, Elizabeth; Le Grande, Michael R; Jackson, Alun C

    2017-02-01

    Many patients experience the 'cardiac blues' at the time of an acute cardiac event, and one in five go on to develop severe depression. These emotional responses often go undetected and unacknowledged. We initiated the 'Cardiac Blues Project' in order to help support patients' emotional recovery. As part of the project, we developed online training in order to support health professionals in the identification and management of the cardiac blues and depression. The aim of this study was to assess the acceptability of the training and its impacts on health professionals' self-efficacy. In July 2014, a 'cardiac blues' pack of patient resources, including access to health professional online training, was mailed to 606 centres across Australia. In the first 3 months after distribution, 140 health professionals registered to undertake the online training and participated in the present study. Participants provided information via a six-item pre- and post-training self-efficacy scale and on 10 post-training acceptability items. Health professionals' self-efficacy improved significantly after undertaking the online training across the six domains assessed and for the total score. Acceptability of the training was high across all 10 items assessed. Ratings of usefulness of the training in clinical practice were particularly favourable amongst those who worked directly with cardiac patients. The health professional training significantly improves health professionals' confidence in identifying and managing the 'cardiac blues' and depression. Monitoring of uptake is ongoing and future studies will investigate patient outcomes.

  15. Supporting technology for enhanced oil recovery: Sixth amendment and extension to Annex IV enhanced oil recovery thermal processes

    Energy Technology Data Exchange (ETDEWEB)

    Reid, T.B. (USDOE Bartlesville Project Office, OK (United States)); Rivas, O. (INTEVEP, Filial de Petroleos de Venezuela, SA, Caracas (Venezuela))

    1991-10-01

    This report contains the results of efforts under the six tasks of the Sixth Amendment and Extension of Annex 4, Enhanced Oil Recovery Thermal Processes of the Venezuela/USA Agreement. The report is presented in sections (for each of the 6 tasks) and each section contains one or more reports prepared by various individuals or groups describing the results of efforts under each of the tasks. A statement of each task, taken from the agreement, is presented on the first page of each section. The tasks are numbered 44 through 49. Tasks are: DOE-SUPRI-laboratory research on steam foam, CAT-SCAN, and in-situ combustion; INTEVEP-laboratory research and field projects on steam foam; DOE-NIPER-laboratory research and field projects light oil steam flooding; INTEVEP-laboratory research and field studies on wellbore heat losses; DOE-LLNL-laboratory research and field projects on electromagnetic induction tomography; INTEVEP-laoboratory research on mechanistic studies.

  16. Sparse matrix test collections

    Energy Technology Data Exchange (ETDEWEB)

    Duff, I.

    1996-12-31

    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.

  17. Pansharpening via sparse regression

    Science.gov (United States)

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

    2017-09-01

    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.

  18. Sparse model selection via integral terms

    Science.gov (United States)

    Schaeffer, Hayden; McCalla, Scott G.

    2017-08-01

    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.

  19. Support of enhanced oil recovery to independent producers in Texas. Quarterly technical progress report, July 1, 1995--September 30, 1995

    Energy Technology Data Exchange (ETDEWEB)

    Fotouh, K.H.

    1995-09-30

    The main objective of this project is to support independent oil producers in Texas and to improve the productivity of marginal wells utilizing enhanced oil recovery techniques. The main task carried out this quarter was the generation of an electronic data base.

  20. Nutrition to Support Recovery from Endurance Exercise: Optimal Carbohydrate and Protein Replacement.

    Science.gov (United States)

    Moore, Daniel R

    2015-01-01

    Proper nutrition is vital to optimize recovery after endurance exercise. Dietary carbohydrate and protein provide the requisite substrates to enhance glycogen resynthesis and remodel skeletal muscle proteins, respectively, both of which would be important to rapidly restore muscle function and performance. With short recovery windows (optimal ingestion of both carbohydrate and protein.

  1. Contracts for field projects and supporting research on enhanced oil recovery. Progress review number 87

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-10-01

    Approximately 30 research projects are summarized in this report. Title of the project, contract number, company or university, award amount, principal investigators, objectives, and summary of technical progress are given for each project. Enhanced oil recovery projects include chemical flooding, gas displacement, and thermal recovery. Most of the research projects though are related to geoscience technology and reservoir characterization.

  2. Compressed sensing & sparse filtering

    CERN Document Server

    Carmi, Avishy Y; Godsill, Simon J

    2013-01-01

    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

  3. RECIPROCAL RESPONSIBILITY AND SOCIAL SUPPORT AMONG WOMEN IN SUBSTANCE USE RECOVERY.

    Science.gov (United States)

    Brereton, Kate L; Alvarez, Josefina; Jason, Leonard A; Stevens, Edward B; Dyson, Vida B; McNeilly, Catherine; Ferrari, Joseph R

    2014-01-01

    This study sought to identify individual- and house-level predictors of women's employment, education, and retention in self-run recovery homes. Data from a national study of 292 women in Oxford House, an international organization of recovery homes grounded on self-help/mutual aid and 12-step principles were analyzed. Results indicated that the house's Reciprocal Responsibility predicted number of days of paid work. Individual and house variables did not predict participation in education. The presence of recovery home members in personal social networks was statistically significant in predicting retention in the recovery home. Lastly, results indicated that number of days of paid work were not predictive of likelihood of substance use in the next 12 months. The findings of this study indicate that the ability to develop social networks and Reciprocal Responsibility in recovery homes can contribute to positive outcomes for women.

  4. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan

    2014-07-06

    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.

  5. Evaluation of evidence supporting the effectiveness of desert tortoise recovery actions

    Science.gov (United States)

    Boarman, William I.; Kristan, William B.

    2006-01-01

    As a federally threatened species, the desert tortoise's (Gopherus agassizii) recovery is required under the Endangered Species Act (ESA). According to the criteria established by the Desert Tortoise Recovery Plan (U.S. Fish and Wildlife Service 1994) for delisting the tortoise from ESA protection, the species as a whole will be considered recovered when tortoises have exhibited a statistically significant upward trend for at least one tortoise generation (25 years), enough habitat is protected to allow persistence, provisions are in place to maintain discrete population growth rates at or above 1.0, regulatory measures are in place to ensure continued management of tortoise habitat, and there is no longer reason to believe that the species will require ESA protection in the future. Just as species extinction can be thought of as the cumulative extinction of all populations, species recovery can be thought of as recovery of constituent populations; management efforts for recovery generally are implemented and assessed at the population level. A recent review of the Desert Tortoise Recovery Plan, including an exhaustive literature search, has been compiled by the Desert Tortoise Recovery Plan Assessment Committee (Tracy et al. 2004).

  6. Concept definition study for recovery of tumbling satellites. Volume 2: Supporting research and technology report

    Science.gov (United States)

    Cable, D. A.; Derocher, W. L., Jr.; Cathcart, J. A.; Keeley, M. G.; Madayev, L.; Nguyen, T. K.; Preese, J. R.

    1986-01-01

    A number of areas of research and laboratory experiments were identified which could lead to development of a cost efficient remote, disable satellite recovery system. Estimates were planned of disabled satellite motion. A concept is defined as a Tumbling Satellite Recovery kit which includes a modular system, composed of a number of subsystem mechanisms that can be readily integrated into varying combinations. This would enable the user to quickly configure a tailored remote, disabled satellite recovery kit to meet a broad spectrum of potential scenarios. The capability was determined of U.S. Earth based satellite tracking facilities to adequately determine the orientation and motion rates of disabled satellites.

  7. Aggregation Tool to Create Curated Data albums to Support Disaster Recovery and Response

    Science.gov (United States)

    Ramachandran, Rahul; Kulkarni, Ajinkya; Maskey, Manil; Bakare, Rohan; Basyal, Sabin; Li, Xiang; Flynn, Shannon

    2014-01-01

    recovery efforts. The search process for the analyst could be made much more efficient and productive if a tool could go beyond a typical search engine and provide not just links to web sites but actual links to specific data relevant to the natural disaster, parse unstructured reports for useful information nuggets, as well as gather other related reports, summaries, news stories, and images. This presentation will describe a semantic aggregation tool developed to address similar problem for Earth Science researchers. This tool provides automated curation, and creates "Data Albums" to support case studies. The generated "Data Albums" are compiled collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; information about the event contained in news reports, and images or videos to supplement research analysis. An ontology-based relevancy-ranking algorithm drives the curation of relevant data sets for a given event. This tool is now being used to generate a catalog of Hurricane Case Studies at Global Hydrology Resource Center (GHRC), one of NASA's Distribute Active Archive Centers. Another instance of the Data Albums tool is currently being created in collaboration with NASA/MSFC's SPoRT Center, which conducts research on unique NASA products and capabilities that can be transitioned to the operational community to solve forecast problems. This new instance focuses on severe weather to support SPoRT researchers in their model evaluation studies

  8. Aggregation Tool to Create Curated Data albums to Support Disaster Recovery and Response

    Science.gov (United States)

    Ramachandran, R.; Kulkarni, A.; Maskey, M.; Li, X.; Flynn, S.

    2014-12-01

    Economic losses due to natural hazards are estimated to be around 6-10 billion dollars annually for the U.S. and this number keeps increasing every year. This increase has been attributed to population growth and migration to more hazard prone locations. As this trend continues, in concert with shifts in weather patterns caused by climate change, it is anticipated that losses associated with natural disasters will keep growing substantially. One of challenges disaster response and recovery analysts face is to quickly find, access and utilize a vast variety of relevant geospatial data collected by different federal agencies. More often analysts may be familiar with limited, but specific datasets and are often unaware of or unfamiliar with a large quantity of other useful resources. Finding airborne or satellite data useful to a natural disaster event often requires a time consuming search through web pages and data archives. The search process for the analyst could be made much more efficient and productive if a tool could go beyond a typical search engine and provide not just links to web sites but actual links to specific data relevant to the natural disaster, parse unstructured reports for useful information nuggets, as well as gather other related reports, summaries, news stories, and images. This presentation will describe a semantic aggregation tool developed to address similar problem for Earth Science researchers. This tool provides automated curation, and creates "Data Albums" to support case studies. The generated "Data Albums" are compiled collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; information about the event contained in news reports, and images or videos to supplement research analysis. An ontology-based relevancy-ranking algorithm drives the curation of relevant data sets for a given event. This

  9. Increased Modularity of Resting State Networks Supports Improved Narrative Production in Aphasia Recovery.

    Science.gov (United States)

    Duncan, E Susan; Small, Steven L

    2016-09-01

    The networks that emerge in the analysis of resting state functional magnetic resonance imaging (rsfMRI) data are believed to reflect the intrinsic organization of the brain. One key property of such complex biological networks is modularity, a measure of community structure. This topological characteristic changes in neurological disease and recovery. Nineteen subjects with language disorders after stroke (aphasia) underwent neuroimaging and behavioral assessment at multiple time points before (baseline) and after an imitation-based therapy. Language was assessed with a narrative production task. Group independent component analysis was performed on the rsfMRI data to identify resting state networks (RSNs). For each participant and each rsfMRI acquisition, we constructed a graph comprising all RSNs. We assigned nodal community based on a region's RSN membership, calculated the modularity score, and then correlated changes in modularity and therapeutic gains on the narrative task. We repeated this comparison controlling for pretherapy performance and using a community structure not based on RSN membership. Increased RSN modularity was positively correlated with improvement on the narrative task immediately post-therapy. This finding remained significant when controlling for pretherapy performance. There were no significant findings for network modularity and behavior when nodal community was assigned without consideration of RSN membership. We interpret these findings as support for the adaptive role of network segregation in behavioral improvement in aphasia therapy. This has important clinical implications for the targeting of noninvasive brain stimulation in poststroke remediation and suggests potential for further insight into the processes underlying such changes through computational modeling.

  10. Bridge to recovery in two cases of dilated cardiomyopathy after long-term mechanical circulatory support.

    Science.gov (United States)

    Pacholewicz, Jerzy; Zakliczyński, Michał; Kowalik, Violetta; Nadziakiewicz, Paweł; Kowalski, Oskar; Kalarus, Zbigniew; Zembala, Marian

    2014-06-01

    Ventricular assist devices (VADs) have become an established therapeutic option for patients with end-stage heart failure. Achieving the potential for recovery of native heart function using VADs is an established form of treatment in a selected group of patients with HF. We report two cases of VAD patients with different types of pump used for mechanical circulatory support, a continuous flow pump (Heart-Ware(®)) and a pulsatile pump (POLVAD MEV(®)), which allow regeneration of the native heart. Patients were qualified as INTERMACS level 3-4 for elective implantation of an LVAD. Implantations were performed without complications. The postoperative course was uncomplicated. In the HeartWare patient the follow-up was complicated by episodes of epistaxis and recurrent GIB as well as driveline infection. The follow-up of the POLVAD MEV patient was uneventful. Recurrent GIB forced us to withdraw aspirin and warfarin therapy and maintain only clopidogrel in the HeartWare patient.. In mid-February 2013 the patient was admitted due to dysfunction of the centrifugal pump with a continuous low-flow alarm and increase power consumption. Under close monitoring of the patient a decision was made to stop the pump immediately and evaluate cardiac function. The serial echocardiography studies showed significant improvement in LVEF up to 45% and no significant valvular pathology. In February 2013 LVAD explant was performed by left thoracotomy without complications. At six-month follow-up the patient was in a good clinical condition, in NYHA class I/II, and on pharmacological treatment.

  11. Recovery From Comorbidity

    Directory of Open Access Journals (Sweden)

    Mathew Carter

    2013-11-01

    Full Text Available Comorbidity among mood, anxiety, and alcohol disorders is common and burdensome, affecting individuals, families, and public health. A systematic and integrative review of the literature across disciplines and research methodologies was performed. Supradisciplinary approaches were applied to the review and the ensuing critical appraisal. Definitions, measurement, and estimation are controversial and inconstant. Recovery from comorbidity cannot be easily extricated from a sociocultural milieu. Methodological challenges in quantitative and qualitative research and across disciplines are many and are discussed. The evidence supporting current treatments is sparse and short-term, and modalities operating in isolation typically fail. People easily fall into the cracks between mental health and addiction services. Clinicians feel untrained and consumers bear the brunt of this: Judgmental and moralistic interactions persist and comorbidity is unrecognized in high-risk populations. Competing historical paradigms of mental illness and addiction present a barrier to progress and reductionism is an impediment to care and an obstacle to the integration and interpretation of research. What matters to consumers is challenging to quantify but worth considering: Finding employment, safe housing, and meaning are crucial to recovery. Complex social networks and peer support in recovery are important but poorly understood. The focus on modalities of limited evidence or generalizability persists in literature and practice. We need to consider different combinations of comorbidity, transitions as opposed to dichotomies of use or illness, and explore the long-term view and emic perspectives.

  12. A Smartphone Application Supporting Recovery from Heroin Addiction: Perspectives of Patients and Providers in China, Taiwan, and the USA.

    Science.gov (United States)

    Schulte, Marya; Liang, Di; Wu, Fei; Lan, Yu-Ching; Tsay, Wening; Du, Jiang; Zhao, Min; Li, Xu; Hser, Yih-Ing

    2016-09-01

    Smartphone-based interventions are increasingly used to support self-monitoring, self-management, and treatment and medication compliance in order to improve overall functioning and well-being. In attempting to develop a smartphone application (S-Health) that assists heroin-dependent patients in recovery, a series of focus groups (72 patients, 22 providers) were conducted in China, Taiwan, and the USA to obtain their perspectives on its acceptance and potential adoption. Data were analyzed according to the Diffusion of Innovation (DOI) theory of characteristics important to the adoption of innovation. Important to Relative Advantage, USA participants cited S-Health's potential ability to overcome logistical barriers, while those in China and Taiwan valued its potential to supplement currently limited services. In terms of Compatibility, participants across sites reported recovery needs and goals that such an application could be helpful in supporting; however, its utility during strong craving was questioned in China and Taiwan. Important factors relevant to Complexity included concerns about smartphone access and familiarity, individualization of content, and particularly in China and Taiwan, participants wanted assurance of privacy and security. The study results suggest a general acceptance, but also indicate cultural variations in access to therapeutic and other social support systems, legal repercussions of substance use, societal perceptions of addiction, and the role of family and other social support in recovery. Taking these factors into consideration is likely to increase diffusion as well as effectiveness of these smartphone-based interventions.

  13. A Discussion of Oxygen Recovery Definitions and Key Performance Parameters for Closed-Loop Atmosphere Revitalization Life Support Technology Development

    Science.gov (United States)

    Abney, Morgan B.; Perry, Jay L.

    2016-01-01

    Over the last 55 years, NASA has evolved life support for crewed space exploration vehicles from simple resupply during Project Mercury to the complex and highly integrated system of systems aboard the International Space Station. As NASA targets exploration destinations farther from low Earth orbit and mission durations of 500 to 1000 days, life support systems must evolve to meet new requirements. In addition to having more robust, reliable, and maintainable hardware, limiting resupply becomes critical for managing mission logistics and cost. Supplying a crew with the basics of food, water, and oxygen become more challenging as the destination ventures further from Earth. Aboard ISS the Atmosphere Revitalization Subsystem (ARS) supplies the crew's oxygen demand by electrolyzing water. This approach makes water a primary logistics commodity that must be managed carefully. Chemical reduction of metabolic carbon dioxide (CO2) provides a method of recycling oxygen thereby reducing the net ARS water demand and therefore minimizing logistics needs. Multiple methods have been proposed to achieve this recovery and have been reported in the literature. However, depending on the architecture and the technology approach, "oxygen recovery" can be defined in various ways. This discontinuity makes it difficult to compare technologies directly. In an effort to clarify community discussions of Oxygen Recovery, we propose specific definitions and describe the methodology used to arrive at those definitions. Additionally, we discuss key performance parameters for Oxygen Recovery technology development including challenges with comparisons to state-of-the-art.

  14. In Dogs With a European Adder Bite, Does the Use of Antivenom With Supportive Treatment Compared to Supportive Treatment Alone Improve Time to Recovery?

    Directory of Open Access Journals (Sweden)

    Lindsay Hodgson

    2017-11-01

    Full Text Available The current literature does not offer convincing evidence for the positive effect of antivenom on time to recovery in dogs envenomated by the European adder. It appears that the use of antivenom in addition to supportive treatment may positively affect local swelling if given within 24 hours of the bite, but the evidence is low quality and further studies are required before a more definitive answer can be reached.

  15. The Train Driver Recovery Problem - Solution Method and Decision Support System Framework

    DEFF Research Database (Denmark)

    Rezanova, Natalia Jurjevna

    2009-01-01

    -tog’s operations. Furthermore, we present comprehensive reviews of operations research applications within railway crew scheduling, rolling stock re-scheduling, railway crew re-scheduling, and airline crew recovery. In addition, the project has resulted in the three scientific publications listed below. 1...... and methods. Computers and Operations Research, in press, 2009. doi: 10.1016/j.cor.2009.03.027. 3. Rezanova NJ, Ryan DM. The train driver recovery problem–A set partitioning based model and solution method. IMM-Technical Report-2006-24. Informatics and Mathematical Modelling, Technical University of Denmark......In this thesis we consider the train driver recovery problem (TDRP). The problem occurs when the daily train driver schedule becomes infeasible due to irregular operations on the railway network. Unforeseen disruptions such as signalling problems or rolling stock failures prevent the train drivers...

  16. 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...

  17. Sparse decompositions in 'incoherent' dictionaries

    DEFF Research Database (Denmark)

    Gribonval, R.; Nielsen, Morten

    2003-01-01

    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...

  18. Role of spared pathways in locomotor recovery after body-weight-supported treadmill training in contused rats.

    Science.gov (United States)

    Singh, Anita; Balasubramanian, Sriram; Murray, Marion; Lemay, Michel; Houle, John

    2011-12-01

    Body-weight-supported treadmill training (BWSTT)-related locomotor recovery has been shown in spinalized animals. Only a few animal studies have demonstrated locomotor recovery after BWSTT in an incomplete spinal cord injury (SCI) model, such as contusion injury. The contribution of spared descending pathways after BWSTT to behavioral recovery is unclear. Our goal was to evaluate locomotor recovery in contused rats after BWSTT, and to study the role of spared pathways in spinal plasticity after BWSTT. Forty-eight rats received a contusion, a transection, or a contusion followed at 9 weeks by a second transection injury. Half of the animals in the three injury groups were given BWSTT for up to 8 weeks. Kinematics and the Basso-Beattie-Bresnahan (BBB) test assessed behavioral improvements. Changes in Hoffmann-reflex (H-reflex) rate depression property, soleus muscle mass, and sprouting of primary afferent fibers were also evaluated. BWSTT-contused animals showed accelerated locomotor recovery, improved H-reflex properties, reduced muscle atrophy, and decreased sprouting of small caliber afferent fibers. BBB scores were not improved by BWSTT. Untrained contused rats that received a transection exhibited a decrease in kinematic parameters immediately after the transection; in contrast, trained contused rats did not show an immediate decrease in kinematic parameters after transection. This suggests that BWSTT with spared descending pathways leads to neuroplasticity at the lumbar spinal level that is capable of maintaining locomotor activity. Discontinuing training after the transection in the trained contused rats abolished the improved kinematics within 2 weeks and led to a reversal of the improved H-reflex response, increased muscle atrophy, and an increase in primary afferent fiber sprouting. Thus continued training may be required for maintenance of the recovery. Transected animals had no effect of BWSTT, indicating that in the absence of spared pathways this

  19. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-04-11

    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.

  20. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup

    2017-12-01

    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.

  1. In Defense of Sparse Tracking: Circulant Sparse Tracker

    KAUST Repository

    Zhang, Tianzhu

    2016-12-13

    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.

  2. Using smartphones to decrease substance use via self-monitoring and recovery support: study protocol for a randomized control trial.

    Science.gov (United States)

    Scott, Christy K; Dennis, Michael L; Gustafson, David H

    2017-08-10

    Alcohol abuse, other substance use disorders, and risk behaviors associated with the human immunodeficiency virus (HIV) represent three of the top 10 modifiable causes of mortality in the US. Despite evidence that continuing care is effective in sustaining recovery from substance use disorders and associated behaviors, patients rarely receive it. Smartphone applications (apps) have been effective in delivering continuing care to patients almost anywhere and anytime. This study tests the effectiveness of two components of such apps: ongoing self-monitoring through Ecological Momentary Assessments (EMAs) and immediate recovery support through Ecological Momentary Interventions (EMIs). The target population, adults enrolled in substance use disorder treatment (n = 400), are being recruited from treatment centers in Chicago and randomly assigned to one of four conditions upon discharge in a 2 × 2 factorial design. Participants receive (1) EMAs only, (2) EMIs only, (3) combined EMAs + EMIs, or (4) a control condition without EMA or EMI for 6 months. People in the experimental conditions receive smartphones with the apps (EMA and/or EMI) specific to their condition. Phones alert participants in the EMA and EMA + EMI conditions at five random times per day and present participants with questions about people, places, activities, and feelings that they experienced in the past 30 min and whether these factors make them want to use substances, support their recovery, or have no impact. Those in the EMI and EMA + EMI conditions have continual access to a suite of support services. In the EMA + EMI condition, participants are prompted to use the EMI(s) when responses to the EMA(s) indicate risk. All groups have access to recovery support as usual. The primary outcome is days of abstinence from alcohol and other drugs. Secondary outcomes are number of HIV risk behaviors and whether abstinence mediates the effects of EMA, EMI, or EMA + EMI on HIV

  3. Mutual support and recovery in the Russian Alcoholics Anonymous online community

    Directory of Open Access Journals (Sweden)

    Lyytikäinen Laura

    2016-04-01

    Full Text Available AIMS – In Russia the paradigm of alcoholism as a disease is still in contrast to the general perception of alcoholics as weak-willed. This article studies alcoholism and recovery in Russia through the case study of the Russian Alcoholics Anonymous online group. It studies how people who are seeking help for their drinking problems in this online community come to incorporate a new self-understanding of being ill with alcoholism.

  4. Low-rank and sparse modeling for visual analysis

    CERN Document Server

    Fu, Yun

    2014-01-01

    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

  5. Sparse Linear Identifiable Multivariate Modeling

    DEFF Research Database (Denmark)

    Henao, Ricardo; Winther, Ole

    2011-01-01

    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...

  6. Contracts for field projects and supporting research on enhanced oil recovery. Progress review No. 82, quarterly report, January--March 1995

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-06-01

    This document consists of a list of projects supporting work on oil recovery programs. A publications list and index of companies and institutions is provided. The remaining portion of the document provides brief descriptions on projects in chemical flooding, gas displacement, thermal recovery, geoscience, resource assessment, and reservoir class field demonstrations.

  7. Service Based Internship Training to Prepare Workers to Support the Recovery of People with Co-Occurring Substance Abuse and Mental Health Disorders

    Science.gov (United States)

    Crowe, Trevor P.; Kelly, Peter; Pepper, James; McLennan, Ross; Deane, Frank P.; Buckingham, Mark

    2013-01-01

    A repeated measures design was used to evaluate a 12 month on-site counsellor internship programme aimed at training staff to support the recovery needs of people with co-occurring substance use and mental health disorders. Fifty-four interns completed measures of recovery knowledge, attitudes, confidence/competence, as well as identifying…

  8. Efficient collaborative sparse channel estimation in massive MIMO

    KAUST Repository

    Masood, Mudassir

    2015-08-12

    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.

  9. Fast convolutional sparse coding using matrix inversion lemma

    Czech Academy of Sciences Publication Activity Database

    Šorel, Michal; Šroubek, Filip

    2016-01-01

    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 http://library.utia.cas.cz/separaty/2016/ZOI/sorel-0459332.pdf

  10. Enabling choice, recovery and participation: evidence-based early intervention support for psychosocial disability in the National Disability Insurance Scheme.

    Science.gov (United States)

    Hayes, Laura; Brophy, Lisa; Harvey, Carol; Tellez, Juan Jose; Herrman, Helen; Killackey, Eoin

    2018-02-01

    The aim of this study was to identify the most effective interventions for early intervention in psychosocial disability in the National Disability Insurance Scheme (NDIS) through an evidence review. A series of rapid reviews were undertaken to establish possible interventions for psychosocial disability, to develop our understanding of early intervention criteria for the NDIS and to determine which interventions would meet these criteria. Three interventions (social skills training, supported employment and supported housing) have a strong evidence base for effectiveness in early intervention in people with psychosocial disability, with the potential for adoption by the NDIS. They support personal choice and recovery outcomes. Illness self-management, cognitive remediation and cognitive behavioural therapy for psychosis demonstrate outcomes to mitigate impairment. The evidence for family psycho-education is also very strong. This review identified evidence-based, recovery-oriented approaches to early intervention in psychosocial disability. They meet the criteria for early intervention in the NDIS, are relevant to participants and consider their preferences. Early intervention has the potential to save costs by reducing participant reliance on the scheme.

  11. Protocol for a systematic review of evaluation research for adults who have participated in the 'SMART recovery' mutual support programme.

    Science.gov (United States)

    Beck, Alison K; Baker, Amanda; Kelly, Peter J; Deane, Frank P; Shakeshaft, Anthony; Hunt, David; Forbes, Erin; Kelly, John F

    2016-05-23

    Self-Management and Recovery Training (SMART Recovery) offers an alternative to predominant 12-step approaches to mutual aid (eg, alcoholics anonymous). Although the principles (eg, self-efficacy) and therapeutic approaches (eg, motivational interviewing and cognitive behavioural therapy) of SMART Recovery are evidence based, further clarity regarding the direct evidence of its effectiveness as a mutual aid package is needed. Relative to methodologically rigorous reviews supporting the efficacy of 12-step approaches, to date, reviews of SMART Recovery have been descriptive. We aim to address this gap by providing a comprehensive overview of the evidence for SMART Recovery in adults with problematic alcohol, substance and/or behavioural addiction, including a commentary on outcomes assessed, potential mediators, feasibility (including economic outcomes) and a critical evaluation of the methods used. Methods are informed by the Cochrane Guidelines for Systematic Reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement. 6 electronic peer-reviewed and 4 grey literature databases have been identified. Preliminary searches have been conducted for SMART Recovery literature (liberal inclusion criteria, not restricted to randomised controlled trials (RCTs), qualitative-only designs excluded). Eligible 'evaluation' articles will be assessed against standardised criteria and checked by an independent assessor. The searches will be re-run just before final analyses and further studies retrieved for inclusion. A narrative synthesis of the findings will be reported, structured around intervention type and content, population characteristics, and outcomes. Where possible, 'summary of findings' tables will be generated for each comparison. When data are available, we will calculate a risk ratio and its 95% CI (dichotomous outcomes) and/or effect size according to Cohen's formula (continuous outcomes) for the primary outcome of each trial. No

  12. Fast alternating projected gradient descent algorithms for recovering spectrally sparse signals

    KAUST Repository

    Cho, Myung

    2016-06-24

    We propose fast algorithms that speed up or improve the performance of recovering spectrally sparse signals from un-derdetermined measurements. Our algorithms are based on a non-convex approach of using alternating projected gradient descent for structured matrix recovery. We apply this approach to two formulations of structured matrix recovery: Hankel and Toeplitz mosaic structured matrix, and Hankel structured matrix. Our methods provide better recovery performance, and faster signal recovery than existing algorithms, including atomic norm minimization.

  13. Mindfulness-Based Cancer Recovery (MBCR) versus Supportive Expressive Group Therapy (SET) for distressed breast cancer survivors: evaluating mindfulness and social support as mediators.

    Science.gov (United States)

    Schellekens, Melanie P J; Tamagawa, Rie; Labelle, Laura E; Speca, Michael; Stephen, Joanne; Drysdale, Elaine; Sample, Sarah; Pickering, Barbara; Dirkse, Dale; Savage, Linette Lawlor; Carlson, Linda E

    2017-06-01

    Despite growing evidence in support of mindfulness as an underlying mechanism of mindfulness-based interventions (MBIs), it has been suggested that nonspecific therapeutic factors, such as the experience of social support, may contribute to the positive effects of MBIs. In the present study, we examined whether change in mindfulness and/or social support mediated the effect of Mindfulness-Based Cancer Recovery (MBCR) compared to another active intervention (i.e. Supportive Expressive Group Therapy (SET)), on change in mood disturbance, stress symptoms and quality of life. A secondary analysis was conducted of a multi-site randomized clinical trial investigating the impacts of MBCR and SET on distressed breast cancer survivors (MINDSET). We applied the causal steps approach with bootstrapping to test mediation, using pre- and post-intervention questionnaire data of the participants who were randomised to MBCR (n = 69) or SET (n = 70). MBCR participants improved significantly more on mood disturbance, stress symptoms and social support, but not on quality of life or mindfulness, compared to SET participants. Increased social support partially mediated the impact of MBCR versus SET on mood disturbance and stress symptoms. Because no group differences on mindfulness and quality of life were observed, no mediation analyses were performed on these variables. Findings showed that increased social support was related to more improvement in mood and stress after MBCR compared to support groups, whereas changes in mindfulness were not. This suggests a more important role for social support in enhancing outcomes in MBCR than previously thought.

  14. Water recovery and management test support modeling for Space Station Freedom

    Science.gov (United States)

    Mohamadinejad, Habib; Bacskay, Allen S.

    1990-01-01

    The water-recovery and management (WRM) subsystem proposed for the Space Station Freedom program is outlined, and its computerized modeling and simulation based on a Computer Aided System Engineering and Analysis (CASE/A) program are discussed. A WRM test model consisting of a pretreated urine processing (TIMES), hygiene water processing (RO), RO brine processing using TIMES, and hygiene water storage is presented. Attention is drawn to such end-user equipment characteristics as the shower, dishwasher, clotheswasher, urine-collection facility, and handwash. The transient behavior of pretreated-urine, RO waste-hygiene, and RO brine tanks is assessed, as well as the total input/output to or from the system. The model is considered to be beneficial for pretest analytical predictions as a program cost-saving feature.

  15. Contracts for field projects and supporting research on enhanced oil recovery, October--December 1992. Progress review No. 73, quarter ending December 31, 1992

    Energy Technology Data Exchange (ETDEWEB)

    1993-12-01

    Accomplishments for this quarter ending December 31, 1992 are presented for the following tasks: chemical flooding--supporting research; gas displacement--supporting research; thermal recovery--supporting research; geoscience technology; resource assessment technology; microbial technology; reservoir classes; and novel technology.

  16. Contracts for field projects and supporting research on enhanced oil recovery, July--September 1992. Progress review No. 72, quarter ending September 30, 1992

    Energy Technology Data Exchange (ETDEWEB)

    1993-09-01

    Accomplishments for the past quarter are presented for the following tasks: Chemical flooding--supporting research; gas displacement--supporting research; thermal recovery--supporting research; geoscience technology; resource assessment technology; microbial technology; and novel technology. A list of available publication is also provided.

  17. S100A1 in human heart failure: lack of recovery following left ventricular assist device support.

    Science.gov (United States)

    Bennett, Mosi K; Sweet, Wendy E; Baicker-McKee, Sara; Looney, Elizabeth; Karohl, Kristen; Mountis, Maria; Tang, W H Wilson; Starling, Randall C; Moravec, Christine S

    2014-07-01

    We hypothesized that S100A1 is regulated during human hypertrophy and heart failure and that it may be implicated in remodeling after left ventricular assist device. S100A1 is decreased in animal and human heart failure, and restoration produces functional recovery in animal models and in failing human myocytes. With the potential for gene therapy, it is important to carefully explore human cardiac S100A1 regulation and its role in remodeling. We measured S100A1, the sarcoplasmic endoplasmic reticulum Ca(2+)ATPase, phospholamban, and ryanodine receptor proteins, as well as β-adrenergic receptor density in nonfailing, hypertrophied (left ventricular hypertrophy), failing, and failing left ventricular assist device-supported hearts. We determined functional consequences of protein alterations in isolated contracting muscles from the same hearts. S100A1, sarcoplasmic endoplasmic reticulum Ca(2+)ATPase and phospholamban were normal in left ventricular hypertrophy, but decreased in failing hearts, while ryanodine receptor was unchanged in either group. Baseline muscle contraction was not altered in left ventricular hypertrophy or failing hearts. β-Adrenergic receptor and inotropic response were decreased in failing hearts. In failing left ventricular assist device-supported hearts, S100A1 and sarcoplasmic endoplasmic reticulum Ca(2+)ATPase showed no recovery, while phospholamban, β-adrenergic receptor, and the inotropic response fully recovered. S100A1 and sarcoplasmic endoplasmic reticulum Ca(2+)ATPase, both key Ca(2+)-regulatory proteins, are decreased in human heart failure, and these changes are not reversed after left ventricular assist device. The clinical significance of these findings for cardiac recovery remains to be addressed. © 2014 American Heart Association, Inc.

  18. Changing practice to support self-management and recovery in mental illness: application of an implementation model.

    Science.gov (United States)

    Harris, Melanie; Jones, Phil; Heartfield, Marie; Allstrom, Mary; Hancock, Janette; Lawn, Sharon; Battersby, Malcolm

    2015-01-01

    Health services introducing practice changes need effective implementation methods. Within the setting of a community mental health service offering recovery-oriented psychosocial support for people with mental illness, we aimed to: (i) identify a well-founded implementation model; and (ii) assess its practical usefulness in introducing a new programme for recovery-oriented self-management support. We reviewed the literature to identify implementation models applicable to community mental health organisations, and that also had corresponding measurement tools. We used one of these models to inform organisational change strategies. The literature review showed few models with corresponding tools. The Promoting Action on Research Implementation in Health Services (PARIHS) model and the related Organisational Readiness to Change Assessment (ORCA) tool were used. The PARIHS proposes prerequisites for health service change and the ORCA measures the extent to which these prerequisites are present. Application of the ORCA at two time points during implementation of the new programme showed strategy-related gains for some prerequisites but not for others, reflecting observed implementation progress. Additional strategies to address target prerequisites could be drawn from the PARIHS model. The PARIHS model and ORCA tool have potential in designing and monitoring practice change strategies in community mental health organisations. Further practical use and testing of implementation models appears justified in overcoming barriers to change.

  19. Applying Strengths Model principles to build a rural community-based mental health support service and achieve recovery outcomes.

    Science.gov (United States)

    Dunstan, Debra; Anderson, Donnah

    2018-02-01

    The Personal Helpers and Mentors (PHaMs) service is a non-clinical, community-based Australian Government initiative aimed at increasing opportunities for recovery for people whose lives are severely affected by mental illness. Using a strengths-based recovery model, PHaMs caseworkers support and mentor people 'at risk of falling through the gaps' between state funded clinical treatment services and federally funded social services (such as supported housing, education and employment). While there is evidence that PHaMs realises its aim in metropolitan areas, little is known about how services are developed and function in low resource rural settings and what outcomes are achieved. These questions were addressed in a case study of a PHaMs service in a rural town in the state of New South Wales, Australia. Data were collected from two sources: local service documents prepared for staff orientation and operational purposes, and records and reports of service participants\\' performance and achievements. Participants\\' gains in wellbeing, recovery goals, and the target outcome areas of increased access to services, increased personal capacity and self-reliance, and increased community participation, were gathered from self-reports. The Role Functioning Scale was used as a measure of caseworker ratings of participants\\' adaptive functioning. The qualitative data were examined for semantic content and underlying themes. The quantitative analyses involved repeated measures and between-groups comparisons of uncontrolled pre-test–post-test and retrospective pre-test data. From commencement of the service in October 2009 to June 2014, an estimated 31% of the people living with severe mental illness in the local government area had accessed the PHaMs service (N=126; mean age 31.9 years; 42% male, 27% Aboriginal). The document analysis revealed that despite a lack of detail on how a PHaMs service should be developed or delivered, by focusing on the goal of client recovery

  20. Bayesian Inference Methods for Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Pedersen, Niels Lovmand

    2013-01-01

    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....

  1. Supporting Technology for Enhanced Oil Recovery-EOR Thermal Processes Report IV-12

    Energy Technology Data Exchange (ETDEWEB)

    Izequeido, Alexandor

    2001-04-01

    This report contains the results of efforts under the six tasks of the Ninth Amendment and Extension of Annex IV, Enhanced Oil Recovery Thermal Processes of the Venezuela/USA Agreement. The report is presented in sections (for each of the 6 tasks) and each section contains one or more reports prepared by various individuals or groups describing the results of efforts under each of the tasks. A statement of each task, taken from the agreement, is presented on the first page of each section. The tasks are numbered 62 through 67. The first, second, third, fourth, fifth, sixth, seventh, eight, and ninth reports on Annex IV, [Venezuela MEM/USA-DOE Fossil Energy Report IV-1, IV-2, IV-3, IV-4, IV-5, IV-6, IV-7, and IV-8 (DOE/BETC/SP-83/15, DOE/BC-84/6/SP, DOE/BC-86/2/SP, DOE/BC-87/2/SP, DOE/BC-89/1/SP, DOE/BC-90/1/SP) DOE/BC-92/1/SP, DOE/BC-93/3/SP, and DOE/BC-95/3/SP] contain the results from the first 61 tasks. Those reports are dated April 1983, August 1984, March 1986, July 1! 987, November 1988, December 1989, October 1991, February 1993, and March 1995 respectively.

  2. Sparse Representations of Hyperspectral Images

    KAUST Repository

    Swanson, Robin J.

    2015-11-23

    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.

  3. Image understanding using sparse representations

    CERN Document Server

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

    2014-01-01

    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

  4. Activities of the Oil Implementation Task Force, reporting period March--August 1991; Contracts for field projects and supporting research on enhanced oil recovery, reporting period October--December 1990

    Energy Technology Data Exchange (ETDEWEB)

    1991-10-01

    Activities of DOE's Oil Implementation Task Force for the period March--August 1991 are reviewed. Contracts for fields projects and supporting research on enhanced oil recovery are discussed, with a list of related publications given. Enhanced recovery processes covered include chemical flooding, gas displacement, thermal recovery, and microbial recovery.

  5. Implementing a complex intervention to support personal recovery: a qualitative study nested within a cluster randomised controlled trial.

    Science.gov (United States)

    Leamy, Mary; Clarke, Eleanor; Le Boutillier, Clair; Bird, Victoria; Janosik, Monika; Sabas, Kai; Riley, Genevieve; Williams, Julie; Slade, Mike

    2014-01-01

    To investigate staff and trainer perspectives on the barriers and facilitators to implementing a complex intervention to help staff support the recovery of service users with a primary diagnosis of psychosis in community mental health teams. Process evaluation nested within a cluster randomised controlled trial (RCT). 28 interviews with mental health care staff, 3 interviews with trainers, 4 focus groups with intervention teams and 28 written trainer reports. 14 community-based mental health teams in two UK sites (one urban, one semi-rural) who received the intervention. The factors influencing the implementation of the intervention can be organised under two over-arching themes: Organisational readiness for change and Training effectiveness. Organisational readiness for change comprised three sub-themes: NHS Trust readiness; Team readiness; and Practitioner readiness. Training effectiveness comprised three sub-themes: Engagement strategies; Delivery style and Modelling recovery principles. Three findings can inform future implementation and evaluation of complex interventions. First, the underlying intervention model predicted that three areas would be important for changing practice: staff skill development; intention to implement; and actual implementation behaviour. This study highlighted the importance of targeting the transition from practitioners' intent to implement to actual implementation behaviour, using experiential learning and target setting. Second, practitioners make inferences about organisational commitment by observing the allocation of resources, Knowledge Performance Indicators and service evaluation outcome measures. These need to be aligned with recovery values, principles and practice. Finally, we recommend the use of organisational readiness tools as an inclusion criteria for selecting both organisations and teams in cluster RCTs. We believe this would maximise the likelihood of adequate implementation and hence reduce waste in research

  6. Copper recovery in a bench-scale carrier facilitated tubular supported liquid membrane system

    Directory of Open Access Journals (Sweden)

    Makaka S.

    2010-01-01

    Full Text Available The extraction of copper ions in a tubular supported liquid membrane using LIX 984NC as a mobile carrier was studied, evaluating the effect of the feed characteristics (flowrate, density, viscosity on the feedside laminar layer of the membrane. A vertical countercurrent, double pipe perspex benchscale reactor consisting of a single hydrophobic PVDF tubular membrane mounted inside was used in all test work. The membrane was impregnated with LIX 984NC and became the support for this organic transport medium. Dilute Copper solution passed through the centre pipe and sulphuric acid as strippant passed through the shell side. Copper was successfully transported from the feedside to the stripside and from the data obtained, a relationship between Schmidt, Reynolds and Sherwood number was achieved of.

  7. Exploring Self-Care and Preferred Supports for Adult Parents in Recovery from Substance Use Disorders: Qualitative Findings from a Feasibility Study.

    Science.gov (United States)

    Raynor, Phyllis A; Pope, Charlene; York, Janet; Smith, Gigi; Mueller, Martina

    2017-11-01

    Very little is known about the self-care behaviors (SCB) that adult parents employ and the preferred supports they utilize to maintain their recovery from substance use disorders (SUD) while also parenting their children. This study used a qualitative descriptive approach to explore perceptions of self-care and parenting to inform future self-care interventions for parents in early recovery. Nineteen mothers and fathers of at least one child between the ages of 6-18 were interviewed by telephone about parental self-care practices while in recovery from SUD, recovery management, and preferred supports in the community. Participants described the experience of parenting as challenging, with variations in the level of support and resources. Self-care included meaningful connection with recovery support and children, taking care of physical health, maintaining spirituality, healthy eating, exercise, journaling, continuing education, staying busy, sponsorship, establishing boundaries, self-monitoring, abstinence, and dealing with destructive emotions. Participants reported SCB as being a critical component of their ongoing recovery and their parenting practices, though differences in SCB by gender and for minorities require further exploration. Parental gains were perceived as benefits of SCB that minimized the negative impact of prior parental drug use on their children.

  8. Saliency Detection Using Sparse and Nonlinear Feature Representation

    Science.gov (United States)

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

    2014-01-01

    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

  9. Biclustering Sparse Binary Genomic Data

    NARCIS (Netherlands)

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

    2008-01-01

    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

  10. Contracts for field projects and supporting research on enhanced oil recovery and improved drilling technology. Progress review No. 34, quarter ending March 31, 1983

    Energy Technology Data Exchange (ETDEWEB)

    Linville, B. (ed.)

    1983-07-01

    Progress achieved for the quarter ending March 1983 are presented for field projects and supporting research for the following: chemical flooding; carbon dioxide injection; and thermal/heavy oil. In addition, progress reports are presented for: resource assessment technology; extraction technology; environmental and safety; microbial enhanced oil recovery; oil recovered by gravity mining; improved drilling technology; and general supporting research. (ATT)

  11. Moral recovery. Couples should be more responsible in bringing up the number of children that they can support and educate.

    Science.gov (United States)

    Hata, K

    1993-03-01

    The remarks of Filipino Senator Leticia Ramos Shahani are summarized from her speech delivered in the Philippine Senate chambers on August 19, 1992. She also responds to 17 questions on family planning issues. In addition to her high-level international background with the UN, she also was Deputy Minister for Foreign Affairs in 1986 and has been an active proponent of family planning. A number of bills have been introduced by her for an AIDS center, protecting spouses from sexually transmitted diseases, provision of benefits for single parents and their children, prohibiting teenagers from appearing in advertisements for liquor and cigarettes, and other bills which enhance women's status. Over the next 6 years, she plans to continue to go to selected provinces to ensure the cooperation and commitment of local executives to family planning (FP). The country will be helped because there is a President and a Health Secretary who are strong supporters of FP. Although her background is in policy making, she was aware that deficiencies exist in training FP workers, assuring adequate supplies of contraceptives, and providing sufficient information and education. FP should be directed to spacing and limiting births and helping with infertility in a friendly atmosphere. Postponing marriage to an older age should be encouraged. Working before having children, to ensure economic support, needs to be encouraged. FP must be voluntary and without coercion. People have a basic right to information on FP and human sexuality. Support for FP does not mean support for abortion, which is illegal and prohibited by the Catholic Church. Hopefully, knowledge about FP method will prevent the high number of illegal abortions. There is more support now in the legislature for FP. The Senator continues to promote her "moral recovery program." The program emphasizes that people must have the right values concerning discipline, hard work, love of country, and sense of common good. FP is a moral

  12. DOE Hanford Network Upgrades and Disaster Recovery Exercise Support the Cleanup Mission Now and into the Future

    International Nuclear Information System (INIS)

    Eckman, Todd J.; Hertzel, Ali K.; Lane, James J.

    2013-01-01

    In 2013, the U.S. Department of Energy's (DOE) Hanford Site, located in Washington State, funded an update to the critical network infrastructure supporting the Hanford Federal Cloud (HFC). The project, called ET-50, was the final step in a plan that was initiated five years ago called 'Hanford's IT Vision, 2015 and Beyond.' The ET-50 project upgraded Hanford's core data center switches and routers along with a majority of the distribution layer switches. The upgrades allowed HFC the network intelligence to provide Hanford with a more reliable and resilient network architecture. The culmination of the five year plan improved network intelligence and high performance computing as well as helped to provide 10 Gbps capable links between core backbone devices (10 times the previous bandwidth). These improvements allow Hanford the ability to further support bandwidth intense applications, such as video teleconferencing. The ET-50 switch upgrade, along with other upgrades implemented from the five year plan, have prepared Hanford's network for the next evolution of technology in voice, video, and data. Hand-in-hand with ET-50's major data center outage, Mission Support Alliance's (MSA) Information Management (IM) organization executed a disaster recovery (DR) exercise to perform a true integration test and capability study. The DR scope was planned within the constraints of ET-50's 14 hour datacenter outage window. This DR exercise tested Hanford's Continuity of Operations (COOP) capability and failover plans for safety and business critical Hanford Federal Cloud applications. The planned suite of services to be tested was identified prior to the outage and plans were prepared to test the services ability to failover from the primary Hanford data center to the backup data center. The services tested were: Core Network (backbone, firewall, load balancers); Voicemail; Voice over IP (VoIP); Emergency Notification; Virtual desktops; and, Select set of production applications

  13. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin

    2015-04-03

    © 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.

  14. Incineration for resource recovery in a closed ecological life support system

    Science.gov (United States)

    Upadhye, R. S.; Wignarajah, K.; Wydeven, T.

    1993-01-01

    A functional schematic, including mass and energy balance, of a solid waste processing system for a controlled ecological life support system (CELSS) was developed using Aspen Plus, a commercial computer simulation program. The primary processor in this system is an incinerator for oxidizing organic wastes. The major products derived from the incinerator are carbon dioxide and water, which can be recycled to a crop growth chamber (CGC) for food production. The majority of soluble inorganics are extracted or leached from the inedible biomass before they reach the incinerator, so that they can be returned directly to the CGC and reused as nutrients. The heat derived from combustion of organic compounds in the incinerator was used for phase-change water purification. The waste streams treated by the incinerator system conceptualized in this work are inedible biomass from a CGC, human urine (including urinal flush water) and feces, humidity condensate, shower water, and trash. It is estimated that the theoretical minimum surface area required for the radiator to reject the unusable heat output from this system would be 0.72 sq m/person at 298 K.

  15. Fermentation as a first step in carbon and nutrient recovery in regenerative life support systems

    Science.gov (United States)

    Luther, Amanda; Lasseur, Christophe; Rebeyre, Pierre; Clauwaert, Peter; Rabaey, Korneel; Ronsse, Frederik; Zhang, Dong Dong; López Barreiro, Diego; Prins, Wolter

    2016-07-01

    Long term manned space missions, such as the establishment of a base on Mars, will require a regenerative means of supplying the basic resources (i.e., food, water, oxygen) necessary to support human life. The MELiSSA-loop is a closed loop compartmentalized artificial aquatic ecosystem designed to recover water, carbon, and nutrients from solid organic wastes (e.g., inedible food waste and feces) for the regeneration of food and oxygen for humans. The first step in this loop is a strictly anaerobic fermentation unit operated as a membrane bioreactor. In this step the aim is to maximize the hydrolysis of complex organic compounds into simple molecules (CO2, ammonia, volatile fatty acids, …) which can be consumed by plants and bacteria downstream to produce food again. Optimal steady state fermentation of a standardized homogeneous mixture of beets, lettuce, wheat straw, toilet paper, feces, and water was demonstrated to recover approximately 50% of the influent carbon as soluble organics in the effluent through anaerobic fermentation. Approximately 10% of the influent COD was converted to CO2, with the remaining ~40% retained as a mixture of undigested solids and biomass. Approximately 50% of the influent nitrogen was recovered in the effluent, 97% of which was in the form of ammonia. Similar results have been obtained at both lab and pilot scale. With only 10% of the carbon driven to CO2 through this fermentation, a major challenge at this moment for the MELiSSA-loop is closing the carbon cycle, by completely oxidizing the carbon in the organic waste and non-edible parts of the plant into CO2 for higher plants and algae to fix again for food production. To further improve the overall degradation we are investigating the integration of a high temperature and pressure, sub- or near critical water conditions to improve the degradation of fibrous material with the addition of an oxidant (hydrogen peroxide, H2O2) under sub- or near critical conditions to further

  16. Water Recovery Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The AES Water Recovery Project (WRP) is advancing environmental control and life support systems water recovery technologies to support human exploration beyond low...

  17. Programming for Sparse Minimax Optimization

    DEFF Research Database (Denmark)

    Jonasson, K.; Madsen, Kaj

    1994-01-01

    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...

  18. Language Recognition via Sparse Coding

    Science.gov (United States)

    2016-09-08

    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

  19. Multiple Descriptions Using Sparse Decompositions

    DEFF Research Database (Denmark)

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

    2010-01-01

    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....

  20. Biclustering sparse binary genomic data.

    Science.gov (United States)

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

    2008-12-01

    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.

  1. Image Super-Resolution via Adaptive Regularization and Sparse Representation.

    Science.gov (United States)

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

    2016-07-01

    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.

  2. Role of self-efficacy and social support in short-term recovery after total hip replacement: a prospective cohort study.

    Science.gov (United States)

    Brembo, Espen Andreas; Kapstad, Heidi; Van Dulmen, Sandra; Eide, Hilde

    2017-04-11

    Despite the overall success of total hip replacement (THR) in patients with symptomatic osteoarthritis (OA), up to one-quarter of patients report suboptimal recovery. The aim of this study was to determine whether social support and general self-efficacy predict variability in short-term recovery in a Norwegian cohort. We performed secondary analysis of a prospective multicenter study of 223 patients who underwent THR for OA in 2003-2004. The total score of the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) at 3 months after surgery was used as the recovery variable. We measured self-efficacy using the General Self-Efficacy Scale (GSES) and social support with the Social Provisions Scale (SPS). Preoperative and postoperative scores were compared using Wilcoxon tests. The Mann-Whitney U test compared scores between groups that differed in gender and age. Spearman's rho correlation coefficients were used to evaluate associations between selected predictor variables and the recovery variable. We performed univariate and multiple linear regression analyses to identify independent variables and their ability to predict short-term recovery after THR. The median preoperative WOMAC score was 58.3 before and 23.9 after surgery. The mean absolute change was 31.9 (standard deviation [SD] 17.0) and the mean relative change was 54.8% (SD 26.6). Older age, female gender, higher educational level, number of comorbidities, baseline WOMAC score, self-efficacy, and three of six individual provisions correlated significantly with short-term recovery after THR and predicted the variability in recovery in the univariate regression model. In multiple regression models, baseline WOMAC was the most consistent predictor of short-term recovery: a higher preoperative WOMAC score predicted worse short-term recovery (β = 0.44 [0.29, 0.59]). Higher self-efficacy predicted better recovery (β = -0.44 [-0.87, -0.02]). Reliable alliance was a significant predictor

  3. Distribution agnostic structured sparsity recovery algorithms

    KAUST Repository

    Al-Naffouri, Tareq Y.

    2013-05-01

    We present an algorithm and its variants for sparse signal recovery from a small number of its measurements in a distribution agnostic manner. The proposed algorithm finds Bayesian estimate of a sparse signal to be recovered and at the same time is indifferent to the actual distribution of its non-zero elements. Termed Support Agnostic Bayesian Matching Pursuit (SABMP), the algorithm also has the capability of refining the estimates of signal and required parameters in the absence of the exact parameter values. The inherent feature of the algorithm of being agnostic to the distribution of the data grants it the flexibility to adapt itself to several related problems. Specifically, we present two important extensions to this algorithm. One extension handles the problem of recovering sparse signals having block structures while the other handles multiple measurement vectors to jointly estimate the related unknown signals. We conduct extensive experiments to show that SABMP and its variants have superior performance to most of the state-of-the-art algorithms and that too at low-computational expense. © 2013 IEEE.

  4. Sparse Source EEG Imaging with the Variational Garrote

    DEFF Research Database (Denmark)

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

    2013-01-01

    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....

  5. Image fusion via nonlocal sparse K-SVD dictionary learning.

    Science.gov (United States)

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

    2016-03-01

    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.

  6. 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.

  7. A General Sparse Tensor Framework for Electronic Structure Theory.

    Science.gov (United States)

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

    2017-03-14

    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.

  8. 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...

  9. Game Changing Development Program - Next Generation Life Support Project: Oxygen Recovery From Carbon Dioxide Using Ion Exchange Membrane Electrolysis Technology

    Science.gov (United States)

    Burke, Kenneth A.; Jiao, Feng

    2016-01-01

    This report summarizes the Phase I research and development work performed during the March 13, 2015 to July 13, 2016 period. The proposal for this work was submitted in response to NASA Research Announcement NNH14ZOA001N, "Space Technology Research, Development, Demonstration, and Infusion 2014 (SpaceTech-REDDI-2014)," Appendix 14GCD-C2 "Game Changing Development Program, Advanced Oxygen Recovery for Spacecraft Life Support Systems Appendix" The Task Agreement for this Phase I work is Document Control Number: GCDP-02-TA-15015. The objective of the Phase I project was to demonstrate in laboratories two Engineering Development Units (EDU) that perform critical functions of the low temperature carbon dioxide electrolysis and the catalytic conversion of carbon monoxide into carbon and carbon dioxide. The low temperature carbon dioxide electrolysis EDU was built by the University of Delaware with Dr. Feng Jiao as the principal investigator in charge of this EDU development (under NASA Contract NNC15CA04C). The carbon monoxide catalytic conversion EDU was built by the NASA Glenn Research Center with Kenneth Burke as the principal investigator and overall project leader for the development of both EDUs. Both EDUs were successfully developed and demonstrated the critical functions for each process. The carbon dioxide electrolysis EDU was delivered to the NASA Johnson Space Center and the carbon monoxide catalytic conversion EDU was delivered to the NASA Marshall Spaceflight Center.

  10. A predictive model of chemical flooding for enhanced oil recovery purposes: Application of least square support vector machine

    Directory of Open Access Journals (Sweden)

    Mohammad Ali Ahmadi

    2016-06-01

    Full Text Available Applying chemical flooding in petroleum reservoirs turns into interesting subject of the recent researches. Developing strategies of the aforementioned method are more robust and precise when they consider both economical point of views (net present value (NPV and technical point of views (recovery factor (RF. In the present study huge attempts are made to propose predictive model for specifying efficiency of chemical flooding in oil reservoirs. To gain this goal, the new type of support vector machine method which evolved by Suykens and Vandewalle was employed. Also, high precise chemical flooding data banks reported in previous works were employed to test and validate the proposed vector machine model. According to the mean square error (MSE, correlation coefficient and average absolute relative deviation, the suggested LSSVM model has acceptable reliability; integrity and robustness. Thus, the proposed intelligent based model can be considered as an alternative model to monitor the efficiency of chemical flooding in oil reservoir when the required experimental data are not available or accessible.

  11. Left heart bypass support with the Rotaflow Centrifugal Pump® as a bridge to decision and recovery in an adult.

    Science.gov (United States)

    Kashiwa, Koichi; Nishimura, Takashi; Saito, Aya; Kubo, Hitoshi; Fukaya, Aoi; Tamai, Hisayoshi; Yambe, Tomoyuki; Kyo, Shunei; Ono, Minoru

    2012-06-01

    Since left heart bypass or biventricular circulatory assist with an extracorporeal centrifugal pump as a bridge to decision or recovery sometimes requires long-time support, the long-term durability of extracorporeal centrifugal pumps is crucial. The Rotaflow Centrifugal Pump(®) (MAQUET Cardiopulmonary AG, Hirrlingen, Germany) is one of the centrifugal pumps available for long-term use in Japan. However, there have been few reports of left heart bypass or biventricular circulatory support over the mid-term. This is a case report of left heart bypass support with the Rotaflow Centrifugal Pump(®) as a bridge to decision and recovery for an adult patient who could not be weaned from cardiopulmonary bypass and percutaneous cardiopulmonary support after cardiac surgery. We could confirm that the patient's consciousness level was normal; however, the patient could not be weaned from the left heart bypass support lasting 1 month. Therefore, the circulatory assist device was switched to the extracorporeal Nipro ventricular assist device (VAD). This time, left heart bypass support could be maintained for 30 days using a single Rotaflow Centrifugal Pump(®). There were no signs of hemolysis during left heart bypass support. The Rotaflow Centrifugal Pump(®) itself may be used as a device for a bridge to decision or recovery before using a VAD in cardiogenic shock patients.

  12. DOE Hanford Network Upgrades and Disaster Recovery Exercise Support the Cleanup Mission Now and into the Future

    Energy Technology Data Exchange (ETDEWEB)

    Eckman, Todd J.; Hertzel, Ali K.; Lane, James J.

    2013-11-07

    In 2013, the U.S. Department of Energy's (DOE) Hanford Site, located in Washington State, funded an update to the critical network infrastructure supporting the Hanford Federal Cloud (HFC). The project, called ET-50, was the final step in a plan that was initiated five years ago called "Hanford's IT Vision, 2015 and Beyond." The ET-50 project upgraded Hanford's core data center switches and routers along with a majority of the distribution layer switches. The upgrades allowed HFC the network intelligence to provide Hanford with a more reliable and resilient network architecture. The culmination of the five year plan improved network intelligence and high performance computing as well as helped to provide 10 Gbps capable links between core backbone devices (10 times the previous bandwidth). These improvements allow Hanford the ability to further support bandwidth intense applications, such as video teleconferencing. The ET-50 switch upgrade, along with other upgrades implemented from the five year plan, have prepared Hanford's network for the next evolution of technology in voice, video, and data. Hand-in-hand with ET-50's major data center outage, Mission Support Alliance's (MSA) Information Management (IM) organization executed a disaster recovery (DR) exercise to perform a true integration test and capability study. The DR scope was planned within the constraints of ET-50's 14 hour datacenter outage window. This DR exercise tested Hanford's Continuity of Operations (COOP) capability and failover plans for safety and business critical Hanford Federal Cloud applications. The planned suite of services to be tested was identified prior to the outage and plans were prepared to test the services ability to failover from the primary Hanford data center to the backup data center. The services tested were: Core Network (backbone, firewall, load balancers); Voicemail; Voice over IP (VoIP); Emergency Notification; Virtual desktops

  13. Quasi Gradient Projection Algorithm for Sparse Reconstruction in Compressed Sensing

    Directory of Open Access Journals (Sweden)

    Xin Meng

    2014-02-01

    Full Text Available Compressed sensing is a novel signal sampling theory under the condition that the signal is sparse or compressible. The existing recovery algorithms based on the gradient projection can either need prior knowledge or recovery the signal poorly. In this paper, a new algorithm based on gradient projection is proposed, which is referred as Quasi Gradient Projection. The algorithm presented quasi gradient direction and two step sizes schemes along this direction. The algorithm doesn’t need any prior knowledge of the original signal. Simulation results demonstrate that the presented algorithm cans recovery the signal more correctly than GPSR which also don’t need prior knowledge. Meanwhile, the algorithm has a lower computation complexity.

  14. Sample size reduction in groundwater surveys via sparse data assimilation

    KAUST Repository

    Hussain, Z.

    2013-04-01

    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.

  15. Role of self-efficacy and social support in short-term recovery after total hip replacement: a prospective cohort study

    NARCIS (Netherlands)

    Brembo, E.A.; Kapstad, H.; Dulmen, S. van; Eide, H.

    2017-01-01

    BACKGROUND: Despite the overall success of total hip replacement (THR) in patients with symptomatic osteoarthritis (OA), up to one-quarter of patients report suboptimal recovery. The aim of this study was to determine whether social support and general self-efficacy predict variability in short-term

  16. Role of self-efficacy and social support in short-term recovery after total hip replacement: a prospective cohort study.

    NARCIS (Netherlands)

    Brembo, E.A.; Kapstad, H.; Dulmen, S. van; Eide, H.

    2017-01-01

    Background: Despite the overall success of total hip replacement (THR) in patients with symptomatic osteoarthritis (OA), up to one-quarter of patients report suboptimal recovery. The aim of this study was to determine whether social support and general self-efficacy predict variability in short-term

  17. Recovery of Clustered Sparse Signals from Compressive Measurements

    Science.gov (United States)

    2009-12-21

    images in the primary visual cortex (V1) or under- standing the statistical behavior of groups of neurons in the retina [15]. In this section, we...each iteration i: ‖x− x̂i‖2 ≤ 2 −i‖x‖2 + 35 ( ‖n‖2 + SM Ks (1 + ln⌈N/K⌉) ) , (8) when Φ has the M4 (K,C)-RIP with δM4(K,C) ≤ 0.1 and the (ǫK , r)-RAmP

  18. Sparse matrix decompositions for clustering

    OpenAIRE

    Blumensath, Thomas

    2014-01-01

    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...

  19. Sparse Matrices in Frame Theory

    DEFF Research Database (Denmark)

    Lemvig, Jakob; Krahmer, Felix; Kutyniok, Gitta

    2014-01-01

    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...

  20. Un-bundling payments for radioisotopes from radiopharmaceuticals and from diagnostic procedures: A tool to support the implementation of full-cost recovery - NEA discussion document

    International Nuclear Information System (INIS)

    2012-09-01

    The objective of the NEA's HLG-MR policy approach is to ensure a long-term secure supply. The HLG-MR has determined that to attain that objective, a necessary (but not sufficient) requirement is that irradiation services in the 99 Mo/' 99m Tc supply chain must be provided on a full-cost recovery (FCR) basis (OECD-NEA, 2011). The HLG-MR policy approach also recommended that supply chain participants should implement payment reforms that promote full-cost recovery within their reimbursement systems. Reforms might include separate radioisotope pricing or auditing, separate radioisotope payment, differential radioisotope payment for FCR, or other approaches to promote a complete transition to full-cost recovery. This paper is written to provide a basis for further discussion on the use of separate reimbursement to encourage the move to full-cost recovery. Separate reimbursement is one tool that could be used by public and private health insurance to support the move to ensuring sufficient reimbursement rates (or payments) for 99 Mo/' 99m Tc while the industry moves to full-cost recovery for irradiation services, paying for outage reserve capacity and transitioning to using LEU targets. Other tools are available (such as differential payments, separate radioisotope payments, auditing) that could lead to similar outcomes that support the changes necessary in the 99 Mo/' 99m Tc supply chain to ensure a long-term reliable supply of these important medical isotopes

  1. Image Super-Resolution Based on Structure-Modulated Sparse Representation.

    Science.gov (United States)

    Zhang, Yongqin; Liu, Jiaying; Yang, Wenhan; Guo, Zongming

    2015-09-01

    Sparse representation has recently attracted enormous interests in the field of image restoration. The conventional sparsity-based methods enforce sparse coding on small image patches with certain constraints. However, they neglected the characteristics of image structures both within the same scale and across the different scales for the image sparse representation. This drawback limits the modeling capability of sparsity-based super-resolution methods, especially for the recovery of the observed low-resolution images. In this paper, we propose a joint super-resolution framework of structure-modulated sparse representations to improve the performance of sparsity-based image super-resolution. The proposed algorithm formulates the constrained optimization problem for high-resolution image recovery. The multistep magnification scheme with the ridge regression is first used to exploit the multiscale redundancy for the initial estimation of the high-resolution image. Then, the gradient histogram preservation is incorporated as a regularization term in sparse modeling of the image super-resolution problem. Finally, the numerical solution is provided to solve the super-resolution problem of model parameter estimation and sparse representation. Extensive experiments on image super-resolution are carried out to validate the generality, effectiveness, and robustness of the proposed algorithm. Experimental results demonstrate that our proposed algorithm, which can recover more fine structures and details from an input low-resolution image, outperforms the state-of-the-art methods both subjectively and objectively in most cases.

  2. Recovery of avian metapneumovirus subgroup C from cDNA: cross-recognition of avian and human metapneumovirus support proteins.

    Science.gov (United States)

    Govindarajan, Dhanasekaran; Buchholz, Ursula J; Samal, Siba K

    2006-06-01

    Avian metapneumovirus (AMPV) causes an acute respiratory disease in turkeys and is associated with "swollen head syndrome" in chickens, contributing to significant economic losses for the U.S. poultry industry. With a long-term goal of developing a better vaccine for controlling AMPV in the United States, we established a reverse genetics system to produce infectious AMPV of subgroup C entirely from cDNA. A cDNA clone encoding the entire 14,150-nucleotide genome of AMPV subgroup C strain Colorado (AMPV/CO) was generated by assembling five cDNA fragments between the T7 RNA polymerase promoter and the autocatalytic hepatitis delta virus ribozyme of a transcription plasmid, pBR 322. Transfection of this plasmid, along with the expression plasmids encoding the N, P, M2-1, and L proteins of AMPV/CO, into cells stably expressing T7 RNA polymerase resulted in the recovery of infectious AMPV/CO. Characterization of the recombinant AMPV/CO showed that its growth properties in tissue culture were similar to those of the parental virus. The potential of AMPV/CO to serve as a viral vector was also assessed by generating another recombinant virus, rAMPV/CO-GFP, that expressed the enhanced green fluorescent protein (GFP) as a foreign protein. Interestingly, GFP-expressing AMPV and GFP-expressing human metapneumovirus (HMPV) could be recovered using the support plasmids of either virus, denoting that the genome promoters are conserved between the two metapneumoviruses and can be cross-recognized by the polymerase complex proteins of either virus. These results indicate a close functional relationship between AMPV/CO and HMPV.

  3. Contracts for field projects and supporting research on enhanced oil recovery. Progress review No. 78, quarter ending March 31, 1994

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-05-01

    This report presents descriptions of various research projects and field projects concerned with the enhanced recovery of petroleum. Contract numbers, principal investigators, company names, and project management information is included.

  4. Sparse High Dimensional Models in Economics.

    Science.gov (United States)

    Fan, Jianqing; Lv, Jinchi; Qi, Lei

    2011-09-01

    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.

  5. Contracts for field projects and supporting research on enhanced oil recovery. Progress review number 83, quarter ending June 30, 1995

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-08-01

    Summaries of 41 research projects on enhanced recovery are presented under the following sections: (1) chemical flooding; (2) gas displacement; (3) thermal recovery; (4) geoscience technology; (5) resource assessment technology; and (6) reservoir classes. Each presentation gives the title of the project, contract number, research facility, contract date, expected completion data, amount of the award, principal investigator, and DOE program manager, and describes the objectives of the project and a summary of the technical progress.

  6. The application of sparse linear prediction dictionary to compressive sensing in speech signals

    Directory of Open Access Journals (Sweden)

    YOU Hanxu

    2016-04-01

    Full Text Available Appling compressive sensing (CS,which theoretically guarantees that signal sampling and signal compression can be achieved simultaneously,into audio and speech signal processing is one of the most popular research topics in recent years.In this paper,K-SVD algorithm was employed to learn a sparse linear prediction dictionary regarding as the sparse basis of underlying speech signals.Compressed signals was obtained by applying random Gaussian matrix to sample original speech frames.Orthogonal matching pursuit (OMP and compressive sampling matching pursuit (CoSaMP were adopted to recovery original signals from compressed one.Numbers of experiments were carried out to investigate the impact of speech frames length,compression ratios,sparse basis and reconstruction algorithms on CS performance.Results show that sparse linear prediction dictionary can advance the performance of speech signals reconstruction compared with discrete cosine transform (DCT matrix.

  7. Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA for L p -Regularization Using the Multiple Sub-Dictionary Representation

    Directory of Open Access Journals (Sweden)

    Yunyi Li

    2017-12-01

    Full Text Available Both L 1 / 2 and L 2 / 3 are two typical non-convex regularizations of L p ( 0 < p < 1 , which can be employed to obtain a sparser solution than the L 1 regularization. Recently, the multiple-state sparse transformation strategy has been developed to exploit the sparsity in L 1 regularization for sparse signal recovery, which combines the iterative reweighted algorithms. To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases p ∈ { 1 / 2 ,   2 / 3 } based on an iterative L p thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA. Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L 1 algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.

  8. Image fusion using sparse overcomplete feature dictionaries

    Energy Technology Data Exchange (ETDEWEB)

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

    2015-10-06

    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.

  9. 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...

  10. Diffusion Indexes With Sparse Loadings

    DEFF Research Database (Denmark)

    Kristensen, Johannes Tang

    2017-01-01

    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...

  11. Experience of Wellness Recovery Action Planning in Self-Help and Mutual Support Groups for People with Lived Experience of Mental Health Difficulties

    Directory of Open Access Journals (Sweden)

    Rebekah Pratt

    2013-01-01

    Full Text Available The main aim of this research was to assess the relevance and impact of wellness recovery action planning (WRAP as a tool for self-management and wellness planning by individuals with mental health problems from pre-existing and newly formed groups, where the possibilities for continued mutual support in the development of WRAPs could be explored. Interviews and focus groups were conducted and pre-post recovery outcome measures completed (Recovery Assessment Scale and Warwick Edinburgh Mental Well Being Scale. 21 WRAP group participants took part in the research. The WRAP approach, used in groups and delivered by trained facilitators who could also share their lived experience, was very relevant and appeared to have a positive impact on many of the participants. The impact on participants varied from learning more about recovery and developing improved self-awareness to integrating a WRAP approach into daily life. The apparent positive impact of WRAP delivered in the context of mutual support groups indicates that it should be given serious consideration as a unique and worthwhile option for improving mental health. WRAP groups could make a significant contribution to the range of self-management options that are available for improving mental health and well-being.

  12. Supporting technology for enhanced oil recovery: EOR thermal processes. Seventh Amendment and Extension to Annex 4, Enhanced oil recovery thermal processes

    Energy Technology Data Exchange (ETDEWEB)

    Reid, T B [USDOE Bartlesville Project Office, OK (United States); Colonomos, P [INTEVEP, Filial de Petroleos de Venezuela, SA, Caracas (Venezuela)

    1993-02-01

    This report contains the results of efforts under the six tasks of the Seventh Amendment and Extension of Annex IV, Enhanced Oil Recovery Thermal Processes of the Venezuela/USA Agreement. The report is presented in sections (for each of the 6 tasks) and each section contains one or more reports prepared by various individuals or groups describing the results of efforts under each of the tasks. A statement of each task, taken from the agreement, is presented on the first page of each section. The tasks are numbered 50 through 55. The first, second, third, fourth, fifth, sixth and seventh reports on Annex IV, Venezuela MEM/USA-DOE Fossil Energy Report IV-1, IV-2, IV-3, IV-4, IV-5 and IV-6 (DOE/BETC/SP-83/15, DOE/BC-84/6/SP, DOE/BC-86/2/SP, DOE/BC-87/2/SP, DOE/BC-89/l/SP, DOE/BC-90/l/SP, and DOE/BC-92/l/SP) contain the results for the first 49 tasks. Those reports are dated April 1983, August 1984, March 1986, July 1987, November 1988, December 1989, and October 1991, respectively. Each task report has been processed separately for inclusion in the Energy Science and Technology Database.

  13. Decision Support System for Aquifer Recharge (AR) and Aquifer Storage and Recovery (ASR) Planning, Design, and Evaluation Decision Support System for Aquifer Recharge (AR) and Aquifer Storage and Recovery (ASR) Planning, Design, and Evaluation – Principles and Technical Basis

    Science.gov (United States)

    Aquifer recharge (AR) is a technical method being utilized to enhance groundwater resources through man-made replenishment means, such as infiltration basins and injections wells. Aquifer storage and recovery (ASR) furthers the AR techniques by withdrawal of stored groundwater at...

  14. Low order anti-aliasing filters for sparse signals in embedded ...

    Indian Academy of Sciences (India)

    Major emphasis, in compressed sensing (CS) research, has been on the acquisition of sub-Nyquist number of samples of a signal that has a sparse representation on some tight frame or an orthogonal basis, and subsequent reconstruction of the original signal using a plethora of recovery algorithms. In this paper, we ...

  15. Feasibility of a Mobile Phone App to Support Recovery From Addiction in China: Secondary Analysis of a Pilot Study.

    Science.gov (United States)

    Han, Hui; Zhang, Jing Ying; Hser, Yih-Ing; Liang, Di; Li, Xu; Wang, Shan Shan; Du, Jiang; Zhao, Min

    2018-02-27

    Mobile health technologies have been found to improve the self-management of chronic diseases. However, there is limited research regarding their feasibility in supporting recovery from substance use disorders (SUDs) in China. The objective of this study was to examine the feasibility of a mobile phone-based ecological momentary assessment (EMA) app by testing the concordance of drug use assessed by the EMA, urine testing, and a life experience timeline (LET) assessment. A total of 75 participants dependent on heroin or amphetamine-type stimulant (ATS) in Shanghai were recruited to participate in a 4-week pilot study. Of the participants, 50 (67% [50/75]) were randomly assigned to the experimental group and 25 (33% [25/75]) were assigned to the control group. The experimental group used mobile health (mHealth) based EMA technology to assess their daily drug use in natural environments and received 2 short health messages each day, whereas the control group only received 2 short health messages each day from the app. Urine tests and LET assessments were conducted each week and a post-intervention survey was administered to both groups. The correlations among the EMA, the LET assessment, and the urine test were investigated. The mean age of the participants was 41.6 (SD 8.0) years, and 71% (53/75) were male. During the 4 weeks of observation, 690 daily EMA survey data were recorded, with a response rate of 49.29% (690/1400). With respect to drug use, the percent of agreement between the EMA and the LET was 66.7%, 79.2%, 72.4%, and 85.8%, respectively, for each of the 4 weeks, whereas the percent of agreement between the EMA and the urine test was 51.2%, 65.1%, 61.9%, and 71.5%, respectively. The post-intervention survey indicated that 46% (32/70) of the participants preferred face-to-face interviews rather than the mHealth app. This study demonstrated poor agreement between the EMA data and the LET and found that the acceptance of mHealth among individuals with SUDs

  16. Continuous speech recognition with sparse coding

    CSIR Research Space (South Africa)

    Smit, WJ

    2009-04-01

    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...

  17. Numerical solution of large sparse linear systems

    International Nuclear Information System (INIS)

    Meurant, Gerard; Golub, Gene.

    1982-02-01

    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

  18. Sparse seismic imaging using variable projection

    NARCIS (Netherlands)

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

    2013-01-01

    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

  19. Approximate Orthogonal Sparse Embedding for Dimensionality Reduction.

    Science.gov (United States)

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

    2016-04-01

    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.

  20. Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for Lp-Regularization Using the Multiple Sub-Dictionary Representation.

    Science.gov (United States)

    Li, Yunyi; Zhang, Jie; Fan, Shangang; Yang, Jie; Xiong, Jian; Cheng, Xiefeng; Sari, Hikmet; Adachi, Fumiyuki; Gui, Guan

    2017-12-15

    Both L 1/2 and L 2/3 are two typical non-convex regularizations of L p (0dictionary sparse transform strategies for the two typical cases p∈{1/2, 2/3} based on an iterative Lp thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA). Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L₁ algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work.

  1. Sparse optimization for inverse problems in atmospheric modelling

    Czech Academy of Sciences Publication Activity Database

    Adam, Lukáš; Branda, Martin

    2016-01-01

    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 http://library.utia.cas.cz/separaty/2016/MTR/adam-0457037.pdf

  2. Discriminative sparse coding on multi-manifolds

    KAUST Repository

    Wang, J.J.-Y.

    2013-09-26

    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.

  3. Sparse Localization with a Mobile Beacon Based on LU Decomposition in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Chunhui Zhao

    2015-09-01

    Full Text Available Node localization is the core in wireless sensor network. It can be solved by powerful beacons, which are equipped with global positioning system devices to know their location information. In this article, we present a novel sparse localization approach with a mobile beacon based on LU decomposition. Our scheme firstly translates node localization problem into a 1-sparse vector recovery problem by establishing sparse localization model. Then, LU decomposition pre-processing is adopted to solve the problem that measurement matrix does not meet the re¬stricted isometry property. Later, the 1-sparse vector can be exactly recovered by compressive sensing. Finally, as the 1-sparse vector is approximate sparse, weighted Cen¬troid scheme is introduced to accurately locate the node. Simulation and analysis show that our scheme has better localization performance and lower requirement for the mobile beacon than MAP+GC, MAP-M, and MAP-MN schemes. In addition, the obstacles and DOI have little effect on the novel scheme, and it has great localization performance under low SNR, thus, the scheme proposed is robust.

  4. Changing spousal roles and their effect on recovery in gamblers anonymous: GamAnon, social support, wives and husbands.

    Science.gov (United States)

    Ferentzy, Peter; Skinner, Wayne; Antze, Paul

    2010-09-01

    This paper examines changing spousal roles and their effects upon recovery in Gamblers Anonymous (GA). It is based upon a qualitative study designed to gage uniformity as well as variations in approaches to recovery in GA. Interviews were conducted with 39 GA members (26 men, 13 women; mean age 56.5 years). Though the study was based in the Toronto area, only 13 interviews involved participants from that region. Phone interviews were conducted with GA members from various regions of both Canada and the US. GamAnon, GA's sister fellowship, has been designed for anyone affected seriously by someone's gambling problem. In practice, GamAnon comprises mostly women--spouses of male GA members--who traditionally have taken a keen interest in the ways in which their husbands achieve and maintain abstinence from gambling. Changing spousal roles have led to fewer women joining GamAnon, as many opt instead to part with troubled spouses. As well, more women are attending GA than in the past, typically with husbands who are disinclined to join GamAnon. All of this has drastically altered how GA members pursue recovery. These changes and their implications are discussed.

  5. Regression with Sparse Approximations of Data

    DEFF Research Database (Denmark)

    Noorzad, Pardis; Sturm, Bob L.

    2012-01-01

    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...

  6. Sparse modeling theory, algorithms, and applications

    CERN Document Server

    Rish, Irina

    2014-01-01

    ""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

  7. Sparse adaptive filters for echo cancellation

    CERN Document Server

    Paleologu, Constantin

    2011-01-01

    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

  8. 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...

  9. Massive Asynchronous Parallelization of Sparse Matrix Factorizations

    Energy Technology Data Exchange (ETDEWEB)

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

    2018-01-08

    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.

  10. Technique detection software for Sparse Matrices

    Directory of Open Access Journals (Sweden)

    KHAN Muhammad Taimoor

    2009-12-01

    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.

  11. Implementation of a Substance Use Recovery Support Mobile Phone App in Community Settings: Qualitative Study of Clinician and Staff Perspectives of Facilitators and Barriers.

    Science.gov (United States)

    Lord, Sarah; Moore, Sarah K; Ramsey, Alex; Dinauer, Susan; Johnson, Kimberly

    2016-06-28

    Research supports the effectiveness of technology-based treatment approaches for substance use disorders. These approaches have the potential to broaden the reach of evidence-based care. Yet, there is limited understanding of factors associated with implementation of technology-based care approaches in different service settings. In this study, we explored provider and staff perceptions of facilitators and barriers to implementation of a mobile phone substance use recovery support app with clients in 4 service settings. Interviews were conducted with leadership and provider stakeholders (N=12) from 4 agencies in the first year of an implementation trial of the mobile phone app. We used the Consolidated Framework for Implementation Research as the conceptual foundation for identifying facilitators and barriers to implementation. Implementation process facilitators included careful planning of all aspects of implementation before launch, engaging a dedicated team to implement and foster motivation, working collaboratively with the app development team to address technical barriers and adapt the app to meet client and agency needs, and consistently reviewing app usage data to inform progress. Implementation support strategies included training all staff to promote organization awareness about the recovery support app and emphasize its priority as a clinical care tool, encouraging clients to try the technology before committing to use, scaling rollout to clients, setting clear expectations with clients about use of the app, and using peer coaches and consistent client-centered messaging to promote engagement. Perceived compatibility of the mobile phone app with agency and client needs and readiness to implement emerged as salient agency-level implementation facilitators. Facilitating characteristics of the recovery support app itself included evidence of its impact for recovery support, perceived relative advantage of the app over usual care, the ability to adapt the

  12. Combining sparse coding and time-domain features for heart sound classification.

    Science.gov (United States)

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

    2017-07-31

    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.

  13. PROPAFENONE FOR SINUS RHYTHM RECOVERY AND SUPPORT IN PATIENTS WITH PERSISTENT FORM OF AURICULAR FIBLILLATION. “PROMETHEUS” – OPENED MULTICENTER STUDY IN RUSSIAN FEDERATION

    Directory of Open Access Journals (Sweden)

    I. G. Fomina

    2006-01-01

    Full Text Available Aim. To study efficiency and safety of propafenone internally used for recovery and support of sinus rhythm in patients with the persistent form of auricular fibrillation (AF. Material and methods. 503 patients with the persistent form of AF, aged 31-68 years were included into multicenter study. Patients were randomized into 2 groups. First group included 285 patients, who were prescribed propafenone (Propanorm, PRO.MED.CS Praha a.s., Czech Republic in a single per oral dose of 600 mg for AF paroxysm relief. Second group included 218 patients, who took propafenone for AF paroxysm prevention in daily dose of 450 mg. Efficiency of sustaining antiarrhythmic therapy was assessed in 1, 3 and 9 months after the treatment started by carrying out daily monitoring of EKG. Results. Propafenone in a single per oral dose of 600 mg leaded to sinus rhythm recovery in 230 (81% patients. Average time for sinus rhythm recovery made up 210±50 minutes. Relief, caused by propafenone within 4 hours after taking the drug, was observed in 182 (64% patients. Propafenone in dose of 450 mg daily lets keep sinus rhythm after 1 month of treatment in 161 patients (74%, after 3 months – in 130 patients (60% and after 9 months – in 98 patients (45%. Effect of preventive antiarythmic therapy within first 3 months of treatment with propafenone can be regarded good, and within 9 months – satisfactory. Conclusion. Propafenone in per oral single dose of 600 mg is an efficient method of sinus rhythm recovery in patients with the persistent form of AF, and its long-term usage in dose 450 mg daily is an efficient and safe method of sinus rhythm support.

  14. Second Workshop on Sparse Grids and Applications

    CERN Document Server

    Pflüger, Dirk

    2014-01-01

    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.

  15. Parallel transposition of sparse data structures

    DEFF Research Database (Denmark)

    Wang, Hao; Liu, Weifeng; Hou, Kaixi

    2016-01-01

    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...

  16. Biclustering via Sparse Singular Value Decomposition

    KAUST Repository

    Lee, Mihee

    2010-02-16

    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.

  17. Sparse direct solver for large finite element problems based on the minimum degree algorithm

    Czech Academy of Sciences Publication Activity Database

    Pařík, Petr; Plešek, Jiří

    2017-01-01

    Roč. 113, November (2017), s. 2-6 ISSN 0965-9978 R&D Projects: GA ČR(CZ) GA15-20666S; GA MŠk(CZ) EF15_003/0000493 Institutional support: RVO:61388998 Keywords : sparse direct solution * finite element method * large sparse Linear systems Subject RIV: JR - Other Machinery OBOR OECD: Mechanical engineering Impact factor: 3.000, year: 2016 https://www.sciencedirect.com/science/article/pii/S0965997817302582

  18. On Sparse Multi-Task Gaussian Process Priors for Music Preference Learning

    DEFF Research Database (Denmark)

    Nielsen, Jens Brehm; Jensen, Bjørn Sand; Larsen, Jan

    In this paper we study pairwise preference learning in a music setting with multitask Gaussian processes and examine the effect of sparsity in the input space as well as in the actual judgments. To introduce sparsity in the inputs, we extend a classic pairwise likelihood model to support sparse...... simulation shows the performance on a real-world music preference dataset which motivates and demonstrates the potential of the sparse Gaussian process formulation for pairwise likelihoods....

  19. Finding Nonoverlapping Substructures of a Sparse Matrix

    Energy Technology Data Exchange (ETDEWEB)

    Pinar, Ali; Vassilevska, Virginia

    2005-08-11

    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.

  20. A pilot training program for people in recovery of mental illness as vocational peer support workers in Hong Kong - Job Buddies Training Program (JBTP): A preliminary finding.

    Science.gov (United States)

    Yam, Kevin Kei Nang; Lo, William Tak Lam; Chiu, Rose Lai Ping; Lau, Bien Shuk Yin; Lau, Charles Ka Shing; Wu, Jen Kei Yu; Wan, Siu Man

    2016-10-24

    The present study reviews the delivery of a pilot curriculum-mentorship-based peer vocational support workers training in a Hong Kong public psychiatric hospital. The present paper reports (1) on the development of a peer vocational support workers training - Job Buddies Training Program (JBTP) in Hong Kong; and (2) preliminary findings from both quantitative and qualitative perspectives. The curriculum consists of 15-session coursework, 8-session storytelling workshop and 50-hour practicum to provide Supported Employment Peer Service (SEPS) under the mentorship of occupational therapists. Six trainees were assessed using three psychosocial assessments and qualitative methods. Compared to the baseline, the Job Buddies (JB) trainees showed an increase in awareness of their own recovery progress, occupational competence and problem-solving skills at the end of the training. Their perceived level of self-stigma was also lessened. In post-training evaluation, all Job Buddies trainees said they perceived positive personal growth and discovered their own strengths. They also appreciated the help from their mentors and gained mutual support from other trainees and from exposure with various mini-projects in the training. This pilot study provides an example of incorporating peer support and manualized training into existing work rehabilitation service for our JB trainees. Further studies on the effectiveness of service provided by peer support workers and for development on the potential use of peer support workers in other clinical and rehabilitation settings with larger subjects will be fruitful. Copyright © 2016. Published by Elsevier B.V.

  1. An Adaptive Sparse Grid Algorithm for Elliptic PDEs with Lognormal Diffusion Coefficient

    KAUST Repository

    Nobile, Fabio

    2016-03-18

    In this work we build on the classical adaptive sparse grid algorithm (T. Gerstner and M. Griebel, Dimension-adaptive tensor-product quadrature), obtaining an enhanced version capable of using non-nested collocation points, and supporting quadrature and interpolation on unbounded sets. We also consider several profit indicators that are suitable to drive the adaptation process. We then use such algorithm to solve an important test case in Uncertainty Quantification problem, namely the Darcy equation with lognormal permeability random field, and compare the results with those obtained with the quasi-optimal sparse grids based on profit estimates, which we have proposed in our previous works (cf. e.g. Convergence of quasi-optimal sparse grids approximation of Hilbert-valued functions: application to random elliptic PDEs). To treat the case of rough permeability fields, in which a sparse grid approach may not be suitable, we propose to use the adaptive sparse grid quadrature as a control variate in a Monte Carlo simulation. Numerical results show that the adaptive sparse grids have performances similar to those of the quasi-optimal sparse grids and are very effective in the case of smooth permeability fields. Moreover, their use as control variate in a Monte Carlo simulation allows to tackle efficiently also problems with rough coefficients, significantly improving the performances of a standard Monte Carlo scheme.

  2. Evaluation of the Effects of BioCell Collagen, a Novel Cartilage Extract, on Connective Tissue Support and Functional Recovery From Exercise.

    Science.gov (United States)

    Lopez, Hector L; Ziegenfuss, Tim N; Park, Joosang

    2015-06-01

    Little is known about the effect of nutritional supplementation on metabolic optimization for enhancing adaptation and recovery of the connective tissue elements that support musculoskeletal function. The study aimed to determine the potential effect of supplementation with a novel, hydrolyzed chicken sternal cartilage extract-called BioCell Collagen-on biomarkers and functional indices of recovery from intense exercise. The research team designed a randomized, double-blind, placebo-controlled pilot study. The study was conducted at the Center for Applied Health Sciences in Stow, OH, USA. Participants were 8 healthy, recreationally active individuals, with a mean age of 29.3 y. Participants ingested either 3 g of a novel, hydrolyzed chicken sternal cartilage extract called BioCell Collagen ("supplement") or 3 g of a placebo daily for 6 wk prior to challenge with an upper-body, muscle-damaging resistance exercise (UBC) on day 43 and a rechallenge on day 46 to assess functional recovery. Primary endpoints were levels of 3 blood biomarkers-creatine kinase (CK), lactate dehydrogenase (LDH), and C-reactive protein (CRP)- and scores on a clinical pain scale and a perceived recovery scale (PRS). The extract attenuated the post-UBC increase in serum markers for muscle tissue damage: CK, LDH, and CRP. For the intervention group vs the placebo group, the mean changes were as follows: (1) an increase in CK of 20 U/L vs 4726 U/L, respectively; (2) a decrease in LDH of 3.5 U/L vs an increase of 82.9 U/L, respectively; and (3) an increase in CRP of 0.07 mg/L vs an increase of 0.7 mg/L, respectively. The performance decrement in bench press repetitions to failure was 57.9% on day 43 and 57.8% on day 46 for the intervention group vs 72.2% on day 43 and 65% on day 46 for the placebo group. The overall trend for the performance decrement, together with the results for the PRS, suggested that a more robust muscular recovery and adaptive response occurred with use of the extract. The

  3. Recovery Of Chromium Metal (VI) Using Supported Liquid Membrane (SLM) Method, A study of Influence of NaCl and pH in Receiving Phase on Transport

    Science.gov (United States)

    Cholid Djunaidi, Muhammad; Lusiana, Retno A.; Rahayu, Maya D.

    2017-06-01

    Chromium metal(VI) is a valuable metal but in contrary has high toxicity, so the separation and recovery from waste are very important. One method that can be used for the separation and recovery of chromium (VI) is a Supported Liquid Membrane (SLM). SLM system contains of three main components: a supporting membrane, organic solvents and carrier compounds. The supported Membrane used in this research is Polytetrafluoroethylene (PTFE), organic solvent is kerosene, and the carrier compound used is aliquat 336. The supported liquid membrane is placed between two phases, namely, feed phase as the source of analyte (Cr(VI)) and the receiving phase as the result of separation. Feed phase is the electroplating waste which contains of chromium metal with pH variation about 4, 6 and 9. Whereas the receiving phase are the solution of HCl, NaOH, HCl-NaCl and NaOH-NaCl with pH variation about 1, 3, 5 and 7. The efficiency separation is determined by measurement of chromium in the feed and the receiving phase using AAS (Atomic Absorption Spectrophotometry). The experiment results show that transport of Chrom (VI) by Supported Liquid membrane (SLM) is influenced by pH solution in feed phase and receiving phase as well as NaCl in receiving phase. The highest chromium metal is transported from feed phase about 97,78%, whereas in receiving phase shows about 58,09%. The highest chromium metal transport happens on pH 6 in feed phase, pH 7 in receiving phase with the mixture of NaOH and NaCl using carrier compound aliquat 336.

  4. Image super-resolution via sparse representation.

    Science.gov (United States)

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

    2010-11-01

    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.

  5. Estimation of white matter fiber parameters from compressed multiresolution diffusion MRI using sparse Bayesian learning.

    Science.gov (United States)

    Pisharady, Pramod Kumar; Sotiropoulos, Stamatios N; Duarte-Carvajalino, Julio M; Sapiro, Guillermo; Lenglet, Christophe

    2018-02-15

    We present a sparse Bayesian unmixing algorithm BusineX: Bayesian Unmixing for Sparse Inference-based Estimation of Fiber Crossings (X), for estimation of white matter fiber parameters from compressed (under-sampled) diffusion MRI (dMRI) data. BusineX combines compressive sensing with linear unmixing and introduces sparsity to the previously proposed multiresolution data fusion algorithm RubiX, resulting in a method for improved reconstruction, especially from data with lower number of diffusion gradients. We formulate the estimation of fiber parameters as a sparse signal recovery problem and propose a linear unmixing framework with sparse Bayesian learning for the recovery of sparse signals, the fiber orientations and volume fractions. The data is modeled using a parametric spherical deconvolution approach and represented using a dictionary created with the exponential decay components along different possible diffusion directions. Volume fractions of fibers along these directions define the dictionary weights. The proposed sparse inference, which is based on the dictionary representation, considers the sparsity of fiber populations and exploits the spatial redundancy in data representation, thereby facilitating inference from under-sampled q-space. The algorithm improves parameter estimation from dMRI through data-dependent local learning of hyperparameters, at each voxel and for each possible fiber orientation, that moderate the strength of priors governing the parameter variances. Experimental results on synthetic and in-vivo data show improved accuracy with a lower uncertainty in fiber parameter estimates. BusineX resolves a higher number of second and third fiber crossings. For under-sampled data, the algorithm is also shown to produce more reliable estimates. Copyright © 2017 Elsevier Inc. All rights reserved.

  6. Quantitative study of undersampled recoverability for sparse images in computed tomography

    DEFF Research Database (Denmark)

    Jørgensen, Jakob Heide; Sidky, Emil Y.; Hansen, Per Christian

    2012-01-01

    Image reconstruction methods based on exploiting image sparsity, motivated by compressed sensing (CS), allow reconstruction from a significantly reduced number of projections in X-ray computed tomography (CT). However, CS provides neither theoretical guarantees of accurate CT reconstruction, nor...... any relation between sparsity and a sufficient number of measurements for recovery. In this paper, we demonstrate empirically through computer simulations that minimization of the image 1-norm allows for recovery of sparse images from fewer measurements than unknown pixels, without relying...... on artificial random sampling patterns. We establish quantitatively an average-case relation between image sparsity and sufficient number of measurements for recovery, and we show that the transition from non-recovery to recovery is sharp within well-defined classes of simple and semi-realistic test images...

  7. Sparse estimation of model-based diffuse thermal dust emission

    Science.gov (United States)

    Irfan, Melis O.; Bobin, Jérôme

    2018-03-01

    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.

  8. Facial Expression Recognition via Non-Negative Least-Squares Sparse Coding

    Directory of Open Access Journals (Sweden)

    Ying Chen

    2014-05-01

    Full Text Available Sparse coding is an active research subject in signal processing, computer vision, and pattern recognition. A novel method of facial expression recognition via non-negative least squares (NNLS sparse coding is presented in this paper. The NNLS sparse coding is used to form a facial expression classifier. To testify the performance of the presented method, local binary patterns (LBP and the raw pixels are extracted for facial feature representation. Facial expression recognition experiments are conducted on the Japanese Female Facial Expression (JAFFE database. Compared with other widely used methods such as linear support vector machines (SVM, sparse representation-based classifier (SRC, nearest subspace classifier (NSC, K-nearest neighbor (KNN and radial basis function neural networks (RBFNN, the experiment results indicate that the presented NNLS method performs better than other used methods on facial expression recognition tasks.

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

    DEFF Research Database (Denmark)

    Gribonval, Rémi; Nielsen, Morten

    2007-01-01

    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...

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

    KAUST Repository

    Wang, Jim Jing-Yan

    2017-06-28

    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.

  11. A promoter polymorphism -945C>G in the connective tissue growth factor in heart failure patients with mechanical circulatory support: a new marker for bridge to recovery?

    Science.gov (United States)

    Posch, Maximilian G; Schmidt, Gunther; Steinhoff, Laura; Perrot, Andreas; Drews, Thorsten; Dandel, Michael; Krabatsch, Thomas; Hetzer, Roland; Potapov, Evgenij V

    2015-01-01

    Mechanical circulatory support (MCS) creates improvement of cardiac function in a small portion of patients with idiopathic dilated cardiomyopathy (iDCM). Among other factors, cardiomyocyte hypertrophy seems to represent an important prerequisite for MCS-related cardiac recovery. We have previously shown that connective tissue growth factor (CTGF) leads to adaptive cardiomyocyte hypertrophy associated with a protective cardiac function in transgenic mice. To test whether a functional genetic variant in the CTGF promoter impacts MCS-related cardiac recovery, three groups of iDCM patients with and without cardiac recovery on MCS were genotyped. The CTGF promoter variant (c.-945C>G) was analysed in 314 patients with iDCM receiving medical treatment only (Group I). Forty-nine iDCM patients who were either weaned from MCS for more than 6 months (Group II; n=20) or bridged to cardiac transplantation (Group III: n=29) were also genotyped. Patients on MCS were followed up for at least 12 months. Clinical characteristics and outcome on MCS were correlated with the respective genotypes. The c.-945C>G allele frequencies in 314 iDCM patients (Group I) were similar to controls deposited in the HapMap database or those published in a recent study. There were no differences in allele prevalence between patients with mild to moderate iDCM (Group I) compared with patients with severe iDCM requiring MCS (Groups II and III). Intriguingly, 50% of patients who were weaned from MCS (Group II) were homozygous for the G allele compared with only 17.2% of patients included in Group III, which is a significant difference (P=0.03). Homozygosity of the promoter-activating G allele in the CTGF_c.-945C>G variant is overrepresented in patients with cardiac recovery on MCS when compared with iDCM patients without cardiac recovery. Further studies are needed to evaluate c.-945C>G as a genetic predictor for clinical outcome on MCS. © The Author 2014. Published by Oxford University Press on behalf

  12. Hyperspectral Image Super-Resolution via Non-Negative Structured Sparse Representation.

    Science.gov (United States)

    Dong, Weisheng; Fu, Fazuo; Shi, Guangming; Cao, Xun; Wu, Jinjian; Li, Guangyu; Li, Guangyu

    2016-05-01

    Hyperspectral imaging has many applications from agriculture and astronomy to surveillance and mineralogy. However, it is often challenging to obtain high-resolution (HR) hyperspectral images using existing hyperspectral imaging techniques due to various hardware limitations. In this paper, we propose a new hyperspectral image super-resolution method from a low-resolution (LR) image and a HR reference image of the same scene. The estimation of the HR hyperspectral image is formulated as a joint estimation of the hyperspectral dictionary and the sparse codes based on the prior knowledge of the spatial-spectral sparsity of the hyperspectral image. The hyperspectral dictionary representing prototype reflectance spectra vectors of the scene is first learned from the input LR image. Specifically, an efficient non-negative dictionary learning algorithm using the block-coordinate descent optimization technique is proposed. Then, the sparse codes of the desired HR hyperspectral image with respect to learned hyperspectral basis are estimated from the pair of LR and HR reference images. To improve the accuracy of non-negative sparse coding, a clustering-based structured sparse coding method is proposed to exploit the spatial correlation among the learned sparse codes. The experimental results on both public datasets and real LR hypspectral images suggest that the proposed method substantially outperforms several existing HR hyperspectral image recovery techniques in the literature in terms of both objective quality metrics and computational efficiency.

  13. Venezuela-MEM/USA-DOE Fossil Energy Report XIII-1, Supporting Technology for Enhanced Oil Recovery, Microbial EOR

    Energy Technology Data Exchange (ETDEWEB)

    Ziritt, Jose Luis

    1999-11-03

    The results from Annex XIII of the Cooperative Agreement between the United States Department of Energy (DOE) and the Ministry of Energy and Mines of the Republic of Venezuela (MEMV) have been documented and published with many researchers involved. Integrate comprehensive research programs in the area of Microbial Enhanced Oil Recovery (MEOR) ranged from feasibility laboratory studies to full-scale multi-well field pilots. The objective, to cooperate in a technical exchange of ideas and information was fully met throughout the life of the Annex. Information has been exchanged between the two countries through published reports and technical meetings between experts in both country's research communities. The meetings occurred every two years in locations coincident with the International MEOR conferences & workshops sponsored by DOE (June 1990, University of Oklahoma, September 1992, Brookhaven, September 1995, National Institute of Petroleum and Energy Research). Reports and publications produced during these years are listed in Appendix B. Several Annex managers have guided the exchange through the years. They included Luis Vierma, Jose Luis Zirritt, representing MEMV and E. B. Nuckols, Edith Allison, and Rhonda Lindsey, representing the U.S. DOE. Funding for this area of research remained steady for a few years but decreased in recent years. Because both countries have reduced research programs in this area, future exchanges on this topic will occur through ANNEX XV. Informal networks established between researchers through the years should continue to function between individuals in the two countries.

  14. Venezuela-MEM/USA-DOE Fossil Energy Report IV-11: Supporting technology for enhanced oil recovery - EOR thermal processes

    Energy Technology Data Exchange (ETDEWEB)

    Venezuela

    2000-04-06

    This report contains the results of efforts under the six tasks of the Tenth Amendment anti Extension of Annex IV, Enhanced Oil Recovery Thermal Processes of the Venezuela/USA Energy Agreement. This report is presented in sections (for each of the six Tasks) and each section contains one or more reports that were prepared to describe the results of the effort under each of the Tasks. A statement of each Task, taken from the Agreement Between Project Managers, is presented on the first page of each section. The Tasks are numbered 68 through 73. The first through tenth report on research performed under Annex IV Venezuela MEM/USA-DOE Fossil Energy Report Number IV-1, IV-2, IV-3, IV-4, IV-5, IV-6, IV-7, IV-8, IV-9, IV-10 contain the results of the first 67 Tasks. These reports are dated April 1983, August 1984, March 1986, July 1987, November 1988, December 1989, October 1991, February 1993, March 1995, and December 1997, respectively.

  15. GP-support by means of AGnES-practice assistants and the use of telecare devices in a sparsely populated region in Northern Germany – proof of concept

    Directory of Open Access Journals (Sweden)

    Scriba Sibylle

    2009-06-01

    Full Text Available Abstract Background In many rural regions in Germany, the proportion of the elderly population increases rapidly. Simultaneously, about one-third of the presently active GPs will retire until 2010. Often it is difficult to find successors for vacant GP-practices. These regions require innovative concepts to avoid the imminent shortage in primary health care. The AGnES-concept comprises the delegation of GP-home visits to qualified AGnES-practice assistants (AGnES: GP-supporting, community-based, e-health-assisted, systemic intervention. Main objectives were the assessment of the acceptance of the AGnES-concept by the participating GPs, patients, and AGnES-practice assistants, the kind of delegated tasks, and the feasibility of home telecare in a GP-practice. Methods In this paper, we report first results of the implementation of this concept in regular GP-practices, conducted November 2005 – March 2007 on the Island of Rügen, Mecklenburg-Western Pomerania, Germany. This study was meant as a proof of concept. The GP delegated routine home-visits to qualified practice employees (here: registered nurses. Eligible patients were provided with telecare-devices to monitor disease-related physiological values. All delegated tasks, modules conducted and questionnaire responses were documented. The participating patients were asked for their acceptance based on standardized questionnaires. The GPs and AGnES-practice assistants were asked for their judgement about different project components, the quality of health care provision and the competences of the AGnES-practice assistants. Results 550 home visits were conducted. 105 patients, two GPs and three AGnES-practice assistants (all registered nurses participated in the project. 48 patients used telecare-devices to monitor health parameters. 87.4% of the patients accepted AGnES-care as comparable to common GP-care. In the course of the project, the GPs delegated an increasing number of both monitoring

  16. Statistical Inference Methods for Sparse Biological Time Series Data

    Directory of Open Access Journals (Sweden)

    Voit Eberhard O

    2011-04-01

    Full Text Available Abstract Background Comparing metabolic profiles under different biological perturbations has become a powerful approach to investigating the functioning of cells. The profiles can be taken as single snapshots of a system, but more information is gained if they are measured longitudinally over time. The results are short time series consisting of relatively sparse data that cannot be analyzed effectively with standard time series techniques, such as autocorrelation and frequency domain methods. In this work, we study longitudinal time series profiles of glucose consumption in the yeast Saccharomyces cerevisiae under different temperatures and preconditioning regimens, which we obtained with methods of in vivo nuclear magnetic resonance (NMR spectroscopy. For the statistical analysis we first fit several nonlinear mixed effect regression models to the longitudinal profiles and then used an ANOVA likelihood ratio method in order to test for significant differences between the profiles. Results The proposed methods are capable of distinguishing metabolic time trends resulting from different treatments and associate significance levels to these differences. Among several nonlinear mixed-effects regression models tested, a three-parameter logistic function represents the data with highest accuracy. ANOVA and likelihood ratio tests suggest that there are significant differences between the glucose consumption rate profiles for cells that had been--or had not been--preconditioned by heat during growth. Furthermore, pair-wise t-tests reveal significant differences in the longitudinal profiles for glucose consumption rates between optimal conditions and heat stress, optimal and recovery conditions, and heat stress and recovery conditions (p-values Conclusion We have developed a nonlinear mixed effects model that is appropriate for the analysis of sparse metabolic and physiological time profiles. The model permits sound statistical inference procedures

  17. Supportive housing: an evidence-based intervention for reducing relapse among low income adults in addiction recovery.

    Science.gov (United States)

    Collard, Carol S; Lewinson, Terri; Watkins, Karen

    2014-01-01

    Within the ranks of the homeless are individuals coping with substance addiction and/or chronic physical or mental disability. Their special needs often pose significant barriers to successfully re-integrate into society. For these individuals, simply securing a roof overhead may not be an adequate solution. Supportive housing combines housing with access to on-site social services to assist persons coping with disabling physical and behavioral health conditions. This study examined whether an association could be found between length of residency in supportive housing and subjective well-being. For the purposes of this study, subjective well-being was measured by length of sobriety, self-efficacy, and employment.

  18. Fast wavelet based sparse approximate inverse preconditioner

    Energy Technology Data Exchange (ETDEWEB)

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

    1996-12-31

    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.

  19. Tunable Sparse Network Coding for Multicast Networks

    DEFF Research Database (Denmark)

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

    2014-01-01

    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...

  20. 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

    2017-01-01

    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.

  1. SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

    KAUST Repository

    Desmal, Abdulla

    2015-07-29

    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.

  2. Sparse regularization for force identification using dictionaries

    Science.gov (United States)

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

    2016-04-01

    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.

  3. Development of a 2nd Generation Decision Support Tool to Optimize Resource and Energy Recovery for Municipal Solid Waste

    Science.gov (United States)

    In 2012, EPA’s Office of Research and Development released the MSW decision support tool (MSW-DST) to help identify strategies for more sustainable MSW management. Depending upon local infrastructure, energy grid mix, population density, and waste composition and quantity, the m...

  4. Design criteria document, Maintenance Shop/Support Facility, K-Basin Essential Systems Recovery, Project W-405

    International Nuclear Information System (INIS)

    Strehlow, M.W.B.

    1994-01-01

    During the next 10 years a substantial amount of work is scheduled in the K-Basin Area related to the storage and eventual removal of irradiated N-Reactor fuel. Currently, maintenance support activities are housed in existing structures that were constructed in the early 1950's. These forty-year-old facilities and their supporting services are substandard, leading to inefficiencies. Because of numerous identified deficiencies and the planned increase in the numbers of K-Basin maintenance personnel, adequate maintenance support facilities that allow efficient operations are needed. The objective of this sub-project of Project W-405 is to provide a maintenance and storage facility which meets the K-Basin Maintenance Organization requirements as defined in Attachment 1. In Reference A, existing guidelines and requirements were used to allocate space for the maintenance activities and to provide a layout concept (See Attachment 2). The design solution includes modifying the existing 190 K-E building to provide space for shops, storage, and administration support functions. The primary reason for the modification is to simplify siting/permitting and make use of existing infrastructure. In addition, benefits relative to design loads will be realized by having the structure inside 190K-E. The new facility will meet the Maintenance Organization approved requirements in Attachment 1 relating to maintenance activities, storage areas, and personnel support services. This sub-project will also resolve outstanding findings and/or deficiencies relating to building fire protection, HVAC requirements, lighting replacement/upgrades, and personnel facilities. Compliance with building codes, local labor agreements and safety standards will result

  5. Fluctuations in percolation of sparse complex networks

    Science.gov (United States)

    Bianconi, Ginestra

    2017-07-01

    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.

  6. Multisnapshot Sparse Bayesian Learning for DOA

    DEFF Research Database (Denmark)

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

    2016-01-01

    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...

  7. 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....

  8. Scalable group level probabilistic sparse factor analysis

    DEFF Research Database (Denmark)

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

    2017-01-01

    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...

  9. Recovery of resources for advanced life support space applications: effect of retention time on biodegradation of two crop residues in a fed-batch, continuous stirred tank reactor

    Science.gov (United States)

    Strayer, R. F.; Finger, B. W.; Alazraki, M. P.; Cook, K.; Garland, J. L.

    2002-01-01

    Bioreactor retention time is a key process variable that will influence costs that are relevant to long distance space travel or long duration space habitation. However. little is known about the effects of this parameter on the microbiological treatment options that are being proposed for Advanced Life Support (ALS) systems. Two bioreactor studies were designed to examine this variable. In the first one, six retention times ranging from 1.3 to 21.3 days--were run in duplicate, 81 working-volume continuous stirred tank reactors (CSTR) that were fed ALS wheat residues. Ash-free dry weight loss, carbon mineralization, soluble TOC reduction, changes in fiber content (cellulose, hemicellulose, and lignin), bacterial numbers, and mineral recoveries were monitored. At short retention times--1.33 days--biodegradation was poor (total: 16-20%, cellulose - 12%, hemicellulose - 28%) but soluble TOC was decreased by 75-80% and recovery of major crop inorganic nutrients was adequate, except for phosphorus. A high proportion of the total bacteria (ca. 83%) was actively respiring. At the longest retention time tested, 21.3 days, biodegradation was good (total: 55-60%, cellulose ca. 70%, hemicellulose - ca. 55%) and soluble TOC was decreased by 80%. Recovery of major nutrients, except phosphorus, remained adequate. A very low proportion of total bacteria was actively respiring (ca. 16%). The second bioreactor study used potato residue to determine if even shorter retention times could be used (range 0.25-2.0 days). Although overall biodegradation deteriorated, the degradation of soluble TOC continued to be ca. 75%. We conclude that if the goal of ALS bioprocessing is maximal degradation of crop residues, including cellulose, then retention times of 10 days or longer will be needed. If the goal is to provide inorganic nutrients with the smallest volume/weight bioreactor possible, then a retention time of 1 day (or less) is sufficient.

  10. Effects of automated smartphone mobile recovery support and telephone continuing care in the treatment of alcohol use disorder: study protocol for a randomized controlled trial.

    Science.gov (United States)

    McKay, James R; Gustafson, David H; Ivey, Megan; McTavish, Fiona; Pe-Romashko, Klaren; Curtis, Brenda; Oslin, David A; Polsky, Daniel; Quanbeck, Andrew; Lynch, Kevin G

    2018-01-30

    New smartphone communication technology provides a novel way to provide personalized continuing care support following alcohol treatment. One such system is the Addiction version of the Comprehensive Health Enhancement Support System (A-CHESS), which provides a range of automated functions that support patients. A-CHESS improved drinking outcomes over standard continuing care when provided to patients leaving inpatient treatment. Effective continuing care can also be delivered via telephone calls with a counselor. Telephone Monitoring and Counseling (TMC) has demonstrated efficacy in two randomized trials with alcohol-dependent patients. A-CHESS and TMC have complementary strengths. A-CHESS provides automated 24/7 recovery support services and frequent assessment of symptoms and status, but does not involve regular contact with a counselor. TMC provides regular and sustained contact with the same counselor, but no ongoing support between calls. The future of continuing care for alcohol use disorders is likely to involve automated mobile technology and counselor contact, but little is known about how best to integrate these services. To address this question, the study will feature a 2 × 2 design (A-CHESS for 12 months [yes/no] × TMC for 12 months [yes/no]), in which 280 alcohol-dependent patients in intensive outpatient programs (IOPs) will be randomized to one of the four conditions and followed for 18 months. We will determine whether adding TMC to A-CHESS produces fewer heavy drinking days than TMC or A-CHESS alone and test for TMC and A-CHESS main effects. We will determine the costs of each of the four conditions and the incremental cost-effectiveness of the three active conditions. Analyses will also examine secondary outcomes, including a biological measure of alcohol use, and hypothesized moderation and mediation effects. The results of the study will yield important information on improving patient alcohol use outcomes by integrating mobile

  11. OPTIMAL COMPUTATIONAL AND STATISTICAL RATES OF CONVERGENCE FOR SPARSE NONCONVEX LEARNING PROBLEMS.

    Science.gov (United States)

    Wang, Zhaoran; Liu, Han; Zhang, Tong

    2014-01-01

    We provide theoretical analysis of the statistical and computational properties of penalized M -estimators that can be formulated as the solution to a possibly nonconvex optimization problem. Many important estimators fall in this category, including least squares regression with nonconvex regularization, generalized linear models with nonconvex regularization and sparse elliptical random design regression. For these problems, it is intractable to calculate the global solution due to the nonconvex formulation. In this paper, we propose an approximate regularization path-following method for solving a variety of learning problems with nonconvex objective functions. Under a unified analytic framework, we simultaneously provide explicit statistical and computational rates of convergence for any local solution attained by the algorithm. Computationally, our algorithm attains a global geometric rate of convergence for calculating the full regularization path, which is optimal among all first-order algorithms. Unlike most existing methods that only attain geometric rates of convergence for one single regularization parameter, our algorithm calculates the full regularization path with the same iteration complexity. In particular, we provide a refined iteration complexity bound to sharply characterize the performance of each stage along the regularization path. Statistically, we provide sharp sample complexity analysis for all the approximate local solutions along the regularization path. In particular, our analysis improves upon existing results by providing a more refined sample complexity bound as well as an exact support recovery result for the final estimator. These results show that the final estimator attains an oracle statistical property due to the usage of nonconvex penalty.

  12. Battleground Energy Recovery Project

    Energy Technology Data Exchange (ETDEWEB)

    Bullock, Daniel [USDOE Gulf Coast Clean Energy Application Center, Woodlands, TX (United States)

    2011-12-31

    In October 2009, the project partners began a 36-month effort to develop an innovative, commercial-scale demonstration project incorporating state-of-the-art waste heat recovery technology at Clean Harbors, Inc., a large hazardous waste incinerator site located in Deer Park, Texas. With financial support provided by the U.S. Department of Energy, the Battleground Energy Recovery Project was launched to advance waste heat recovery solutions into the hazardous waste incineration market, an area that has seen little adoption of heat recovery in the United States. The goal of the project was to accelerate the use of energy-efficient, waste heat recovery technology as an alternative means to produce steam for industrial processes. The project had three main engineering and business objectives: Prove Feasibility of Waste Heat Recovery Technology at a Hazardous Waste Incinerator Complex; Provide Low-cost Steam to a Major Polypropylene Plant Using Waste Heat; and Create a Showcase Waste Heat Recovery Demonstration Project.

  13. Recovery of salicylic acid from aqueous solution by solvent extraction and supported liquid membrane using TOMAC as carrier

    International Nuclear Information System (INIS)

    Kouki, Noura; Tayeb, Rafik; Dhahbi, Mahmoud

    2009-01-01

    Conventional sewage treatment plants do not fully degrade residues of pharmaceuticals, so that they are introduced into the aquatic environment. On this basis, the demand for the development of efficient systems for removing these compounds from water has assumed a great research interest. Membrane operations are increasingly employed in many industrial sectors as important alternative technologies to the classical processes of separation. Among membrane-based separation processes, the use of supported liquid membranes (SLMs) has received growing attention during recent years. In our work we had tried to recover a pharmaceutical product, salicylic acid (S.A), from an aqueous solution by solvent extraction and supported liquid membrane using an ionic liquid: the tri octylmethylammonium chloride (TOMAC) as carrier. Ionic liquids has been revealed as interesting clean alternatives to classical solvents and their use as a liquid phase results in the stabilization of the SLMs duo to their negligible vapour pressure, the possibility of minimising their solubility in the surrounding phases by adequate selection of the cation and anion, and the greater capillary force associated with their high viscosity. For this reason we had studied the influence of different parameters which could affect the efficiency of the transport: pH of the feed phase, the nature of the strippant, the concentration of the strippant, the nature of the support and the initial concentration of the salicylic acid in the feed phase. We had noticed that the pH of the feed solution had no effect of the percentage extraction and after 24 hours we can extract completely our solute. TOMAC seemed to be a good extractant but we found difficult to strip salicylic acid from the TOMAC phase and this could be related to the formation of water micro environments in the ionic liquid membrane.

  14. Subspace Based Blind Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Hayashi, Kazunori; Matsushima, Hiroki; Sakai, Hideaki

    2012-01-01

    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...

  15. Better Size Estimation for Sparse Matrix Products

    DEFF Research Database (Denmark)

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

    2010-01-01

    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...

  16. Multilevel sparse functional principal component analysis.

    Science.gov (United States)

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

    2014-01-29

    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.

  17. Comparison of sparse point distribution models

    DEFF Research Database (Denmark)

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

    2010-01-01

    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...

  18. SAR Image Despeckling Via Structural Sparse Representation

    Science.gov (United States)

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

    2016-12-01

    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.

  19. Sparse DOA estimation with polynomial rooting

    DEFF Research Database (Denmark)

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

    2015-01-01

    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...

  20. Sparse acoustic imaging with a spherical array

    DEFF Research Database (Denmark)

    Fernandez Grande, Efren; Xenaki, Angeliki

    2015-01-01

    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...

  1. Sparse Text Indexing in Small Space

    DEFF Research Database (Denmark)

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

    2016-01-01

    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...

  2. Rotational image deblurring with sparse matrices

    DEFF Research Database (Denmark)

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

    2014-01-01

    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...

  3. Quantifying Registration Uncertainty With Sparse Bayesian Modelling.

    Science.gov (United States)

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

    2017-02-01

    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.

  4. A sparse-grid isogeometric solver

    KAUST Repository

    Beck, Joakim

    2018-02-28

    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.

  5. A sparse version of IGA solvers

    KAUST Repository

    Beck, Joakim

    2017-07-30

    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.

  6. An Approach for Hydrogen Recycling in a Closed-loop Life Support Architecture to Increase Oxygen Recovery Beyond State-of-the-Art

    Science.gov (United States)

    Abney, Morgan B.; Miller, Lee; Greenwood, Zachary; Alvarez, Giraldo

    2014-01-01

    State-of-the-art atmosphere revitalization life support technology on the International Space Station is theoretically capable of recovering 50% of the oxygen from metabolic carbon dioxide via the Carbon Dioxide Reduction Assembly (CRA). When coupled with a Plasma Pyrolysis Assembly (PPA), oxygen recovery increases dramatically, thus drastically reducing the logistical challenges associated with oxygen resupply. The PPA decomposes methane to predominantly form hydrogen and acetylene. Because of the unstable nature of acetylene, a down-stream separation system is required to remove acetylene from the hydrogen stream before it is recycled to the CRA. A new closed-loop architecture that includes a PPA and downstream Hydrogen Purification Assembly (HyPA) is proposed and discussed. Additionally, initial results of separation material testing are reported.

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

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1996-12-31

    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.

  8. Technical support for recovery phase decision-making in the event of a chemical warfare agent release

    Energy Technology Data Exchange (ETDEWEB)

    Watson, A.; Kistner, S.; Halbrook, R. [and others

    1995-12-31

    In late 1985, Congress mandated that the U.S. stockpile of lethal unitary chemical agents and munitions be destroyed by the Department of the Army in a manner that provides maximum protection to the environment, the general public and personnel involved in the disposal program (Public Law 99-1, Section 1412, Title 14, Part b). These unitary munitions were last manufactured in the late 1960`s. The stockpiled inventory is estimated to approximate 25,000-30,000 tons, an includes organophosphate ({open_quotes}nerves{close_quotes}) agents such as VX [O-ethylester of S-(diisopropyl aminoethyl) methyl phosphonothiolate, C{sub 11}H{sub 26}NO{sub 2}PS] and vesicant ({open_quotes}blister{close_quotes}) agents such as Hd [sulfur mustard; bis (2-chloroethyl sulfide), C{sub 4}H{sub 8}Cl{sub 2}S]. The method of agent destruction selected by the Department of the Army is combined high-temperature and high-residence time incineration at secured military installations where munitions are currently stockpiled. This program supports the research program to address: the biomonitoring of nerve agent exposure; agent detection limits in foods and milk; and permeation of agents through porous construction materials.

  9. Can nitrification bring us to Mars? The role of microbial interactions on nitrogen recovery in life support systems

    Science.gov (United States)

    Christiaens, Marlies E. R.; Lasseur, Christophe; Clauwaert, Peter; Boon, Nico; Ilgrande, Chiara; Vlaeminck, Siegfried

    2016-07-01

    Human habitation in space requires artificial environment recirculating fundamental elements to enable the highest degree of autonomy . The European Space Agency, supported by a large consortoium of European organisationsdevelop the Micro-Ecological Life Support System (MELiSSA) to transform the mission wastes waste (a.o. organic fibers, CO2, and urine) into water, oxygen, and food (Lasseur et al., 2010). Among these wastes, astronauts' urine has a high potential to provide nitrogen as a fertilizer for food production. As higher plant growth in space is typically proposed to be performed in hydroponics, liquid fertilizer containing nitrates is preferred. An Additional Unit for Water Treatment is developed for urine nitrification by means of a synthetic microbial community. The key players in this consortium are ureolytic bacteria to hydrolyse the main nitrogen source in urine, urea, to ammonium and carbon dioxide as well as oxidation of organic compounds present in urine, ammonium oxidizing bacteria (AOB) to convert ammonium to nitrite (nitritation), and the nitrate oxidizing bacteria (NOB) to produce nitrate (nitratation). Pure AOB strains Nitrosomonas ureae Nm10 and Nitrosomonas europaea ATCC 19718, pure NOB strains Nitrobacter winogradskyi Nb-255 and Nitrobacter vulgaris Z, and interactions within synthetic consortia of one AOB and one NOB or all together were tested. As the initial salinity of fresh urine can be as high as 30 mS/cm, the functionality of selected pure strains and synthetic consortia was evaluated by means of the nitritation and nitratation activity at varying NaCl salinities (5, 10, and 30 mS/cm). The nitritation activity of pure AOB strains was compared with the synthetic consortia. Both N. ureae and Ns. europaea benefit from the presence of Nb. winogradskyi as the ammonium oxidation rates of 1.7 ± 0.7 and 6.4 ± 0.6 mg N/L.d at 5 mS/cm, respectively, doubled. These results are in line with the findings of Perez et al (2015) observing a lower

  10. Improved Sparse Channel Estimation for Cooperative Communication Systems

    Directory of Open Access Journals (Sweden)

    Guan Gui

    2012-01-01

    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.

  11. Deploying temporary networks for upscaling of sparse network stations

    Science.gov (United States)

    Coopersmith, Evan J.; Cosh, Michael H.; Bell, Jesse E.; Kelly, Victoria; Hall, Mark; Palecki, Michael A.; Temimi, Marouane

    2016-10-01

    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.

  12. Social biases determine spatiotemporal sparseness of ciliate mating heuristics.

    Science.gov (United States)

    Clark, Kevin B

    2012-01-01

    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

  13. Sparse Bayesian Learning Based Three-Dimensional Imaging Algorithm for Off-Grid Air Targets in MIMO Radar Array

    Directory of Open Access Journals (Sweden)

    Zekun Jiao

    2018-02-01

    Full Text Available In recent years, the development of compressed sensing (CS and array signal processing provides us with a broader perspective of 3D imaging. The CS-based imaging algorithms have a better performance than traditional methods. In addition, the sparse array can overcome the limitation of aperture size and number of antennas. Since the signal to be reconstructed is sparse for air targets, many CS-based imaging algorithms using a sparse array are proposed. However, most of those algorithms assume that the scatterers are exactly located at the pre-discretized grids, which will not hold in real scene. Aiming at finding an accurate solution to off-grid target imaging, we propose an off-grid 3D imaging method based on improved sparse Bayesian learning (SBL. Besides, the Bayesian Cramér-Rao Bound (BCRB for off-grid bias estimator is provided. Different from previous algorithms, the proposed algorithm adopts a three-stage hierarchical sparse prior to introduce more degrees of freedom. Then variational expectation maximization method is applied to solve the sparse recovery problem through iteration, during each iteration joint sparsity is used to improve efficiency. Experimental results not only validate that the proposed method outperforms the existing off-grid imaging methods in terms of accuracy and resolution, but have compared the root mean square error with corresponding BCRB, proving effectiveness of the proposed method.

  14. Fingerprint Compression Based on Sparse Representation.

    Science.gov (United States)

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

    2014-02-01

    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.

  15. Sparse brain network using penalized linear regression

    Science.gov (United States)

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

    2011-03-01

    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.

  16. Recovery and money management.

    Science.gov (United States)

    Rowe, Michael; Serowik, Kristin L; Ablondi, Karen; Wilber, Charles; Rosen, Marc I

    2013-06-01

    Social recovery and external money management are important approaches in contemporary mental health care, but little research has been done on the relationship between the two or on application of recovery principles to money management for people at risk of being assigned a representative payee or conservator. Out of 49 total qualitative interviews, 25 transcripts with persons receiving Social Security insurance or Social Security disability insurance who were at risk of being assigned a money manager were analyzed to assess the presence of recognized recovery themes. The recovery principles of self-direction and responsibility were strong themes in participant comments related to money management. Money management interventions should incorporate peoples' recovery-related motivations to acquire financial management skills as a means to direct and assume responsibility for one's finances. Staff involved in money management should receive training to support client's recovery-related goals. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

  17. Greedy vs. L1 convex optimization in sparse coding

    DEFF Research Database (Denmark)

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

    2015-01-01

    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...

  18. 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)

    2008-07-15

    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

  19. Dictionary Learning Algorithms for Sparse Representation

    OpenAIRE

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

    2003-01-01

    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...

  20. SAR Image despeckling via sparse representation

    Science.gov (United States)

    Wang, Zhongmei; Yang, Xiaomei; Zheng, Liang

    2014-11-01

    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.

  1. Decentralized Sparse Multitask RLS Over Networks

    Science.gov (United States)

    Cao, Xuanyu; Liu, K. J. Ray

    2017-12-01

    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.

  2. Robust Fringe Projection Profilometry via Sparse Representation.

    Science.gov (United States)

    Budianto; Lun, Daniel P K

    2016-04-01

    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.

  3. Mindfulness-based cancer recovery and supportive-expressive therapy maintain telomere length relative to controls in distressed breast cancer survivors.

    Science.gov (United States)

    Carlson, Linda E; Beattie, Tara L; Giese-Davis, Janine; Faris, Peter; Tamagawa, Rie; Fick, Laura J; Degelman, Erin S; Speca, Michael

    2015-02-01

    Group psychosocial interventions including mindfulness-based cancer recovery (MBCR) and supportive-expressive group therapy (SET) can help breast cancer survivors decrease distress and influence cortisol levels. Although telomere length (TL) has been associated with breast cancer prognosis, the impact of these two interventions on TL has not been studied to date. The objective of the current study was to compare the effects of MBCR and SET with a minimal intervention control condition (a 1-day stress management seminar) on TL in distressed breast cancer survivors in a randomized controlled trial. MBCR focused on training in mindfulness meditation and gentle Hatha yoga whereas SET focused on emotional expression and group support. The primary outcome measure was relative TL, the telomere/single-copy gene ratio, assessed before and after each intervention. Secondary outcomes were self-reported mood and stress symptoms. Eighty-eight distressed breast cancer survivors with a diagnosis of stage I to III cancer (using the American Joint Committee on Cancer (AJCC) TNM staging system) who had completed treatment at least 3 months prior participated. Using analyses of covariance on a per-protocol sample, there were no differences noted between the MBCR and SET groups with regard to the telomere/single-copy gene ratio, but a trend effect was observed between the combined intervention group and controls (F [1,84], 3.82; P = .054; η(2)  = .043); TL in the intervention group was maintained whereas it was found to decrease for control participants. There were no associations noted between changes in TL and changes in mood or stress scores over time. Psychosocial interventions providing stress reduction and emotional support resulted in trends toward TL maintenance in distressed breast cancer survivors, compared with decreases in usual care. © 2014 The Authors. Cancer published by Wiley Periodicals, Inc. on behalf of American Cancer Society.

  4. Sparse discriminant manifold projections for bearing fault diagnosis

    Science.gov (United States)

    Chen, Gang; Liu, Fenglin; Huang, Wei

    2017-07-01

    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.

  5. Pedestrian detection from thermal images: A sparse representation based approach

    Science.gov (United States)

    Qi, Bin; John, Vijay; Liu, Zheng; Mita, Seiichi

    2016-05-01

    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.

  6. BOES: Building Occupancy Estimation System using sparse ambient vibration monitoring

    Science.gov (United States)

    Pan, Shijia; Bonde, Amelie; Jing, Jie; Zhang, Lin; Zhang, Pei; Noh, Hae Young

    2014-04-01

    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.

  7. Contracts for field projects and supporting research on enhanced oil recovery. Progress review No. 80. Quarterly report, July--September, 1994

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-11-01

    This report contains information on petroleum enhanced recovery projects. In addition to project descriptions, contract numbers, principal investigators and project management information is included.

  8. Activities of the Oil Implementation Task Force; Contracts for field projects and supporting research on enhanced oil recovery, July--September 1990

    Energy Technology Data Exchange (ETDEWEB)

    Tiedemann, H.A. (ed.) (USDOE Bartlesville Project Office, OK (USA))

    1991-05-01

    The report contains a general introduction and background to DOE's revised National Energy Strategy Advanced Oil Recovery Program and activities of the Oil Implementation Task Force; a detailed synopsis of the symposium, including technical presentations, comments and suggestions; a section of technical information on deltaic reservoirs; and appendices containing a comprehensive listing of references keyed to general deltaic and geological aspects of reservoirs and those relevant to six selected deltaic plays. Enhanced recovery processes include chemical floodings, gas displacement, thermal recovery, geoscience, and microbial recovery.

  9. Meeting the Science Needs of the Nation in the Wake of Hurricane Sandy-- A U.S. Geological Survey Science Plan for Support of Restoration and Recovery

    Science.gov (United States)

    Buxton, Herbert T.; Andersen, Matthew E.; Focazio, Michael J.; Haines, John W.; Hainly, Robert A.; Hippe, Daniel J.; Sugarbaker, Larry J.

    2013-01-01

    n late October 2012, Hurricane Sandy came ashore during a spring high tide on the New Jersey coastline, delivering hurricane-force winds, storm tides exceeding 19 feet, driving rain, and plummeting temperatures. Hurricane Sandy resulted in 72 direct fatalities in the mid-Atlantic and northeastern United States, and widespread and substantial physical, environmental, ecological, social, and economic impacts estimated at near $50 billion. Before the landfall of Hurricane Sandy, the USGS provided forecasts of potential coastal change; collected oblique aerial photography of pre-storm coastal morphology; deployed storm-surge sensors, rapid-deployment streamgages, wave sensors, and barometric pressure sensors; conducted Light Detection And Ranging (lidar) aerial topographic surveys of coastal areas; and issued a landslide alert for landslide prone areas. During the storm, Tidal Telemetry Networks provided real-time water-level information along the coast. Long-term network and rapid-deployment real-time streamgages and water-quality monitors reported on river levels and changes in water quality. Immediately after the storm, the USGS serviced real-time instrumentation, retrieved data from over 140 storm-surge sensors, and collected other essential environmental data, including more than 830 high-water marks mapping the extent and elevation of the storm surge. Post-storm lidar surveys documented storm impacts to coastal barriers informing response and recovery and providing a new baseline to assess vulnerability of the reconfigured coast. The USGS Hazard Data Distribution System served storm related information from many agencies on the Internet on a daily basis. This science plan was developed immediately following Hurricane Sandy to coordinate continuing USGS activities with other agencies and to guide continued data collection and analysis to ensure support for recovery and restoration efforts. The data, information, and tools that are produced by implementing this

  10. 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...

  11. 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)

    2018-01-01

    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.

  12. Solving large-scale sparse eigenvalue problems and linear systems of equations for accelerator modeling

    Energy Technology Data Exchange (ETDEWEB)

    Gene Golub; Kwok Ko

    2009-03-30

    The solutions of sparse eigenvalue problems and linear systems constitute one of the key computational kernels in the discretization of partial differential equations for the modeling of linear accelerators. The computational challenges faced by existing techniques for solving those sparse eigenvalue problems and linear systems call for continuing research to improve on the algorithms so that ever increasing problem size as required by the physics application can be tackled. Under the support of this award, the filter algorithm for solving large sparse eigenvalue problems was developed at Stanford to address the computational difficulties in the previous methods with the goal to enable accelerator simulations on then the world largest unclassified supercomputer at NERSC for this class of problems. Specifically, a new method, the Hemitian skew-Hemitian splitting method, was proposed and researched as an improved method for solving linear systems with non-Hermitian positive definite and semidefinite matrices.

  13. Recommendations from a global cross-company data sharing initiative on the incorporation of recovery phase animals in safety assessment studies to support first-in-human clinical trials.

    Science.gov (United States)

    Sewell, Fiona; Chapman, Kathryn; Baldrick, Paul; Brewster, David; Broadmeadow, Alan; Brown, Paul; Burns-Naas, Leigh Ann; Clarke, Janet; Constan, Alex; Couch, Jessica; Czupalla, Oliver; Danks, Andy; DeGeorge, Joseph; de Haan, Lolke; Hettinger, Klaudia; Hill, Marilyn; Festag, Matthias; Jacobs, Abby; Jacobson-Kram, David; Kopytek, Stephan; Lorenz, Helga; Moesgaard, Sophia Gry; Moore, Emma; Pasanen, Markku; Perry, Rick; Ragan, Ian; Robinson, Sally; Schmitt, Petra M; Short, Brian; Lima, Beatriz Silva; Smith, Diane; Sparrow, Sue; van Bekkum, Yvette; Jones, David

    2014-10-01

    An international expert group which includes 30 organisations (pharmaceutical companies, contract research organisations, academic institutions and regulatory bodies) has shared data on the use of recovery animals in the assessment of pharmaceutical safety for early development. These data have been used as an evidence-base to make recommendations on the inclusion of recovery animals in toxicology studies to achieve scientific objectives, while reducing animal use. Recovery animals are used in pharmaceutical development to provide information on the potential for a toxic effect to translate into long-term human risk. They are included on toxicology studies to assess whether effects observed during dosing persist or reverse once treatment ends. The group devised a questionnaire to collect information on the use of recovery animals in general regulatory toxicology studies to support first-in-human studies. Questions focused on study design, the rationale behind inclusion or exclusion and the impact this had on internal and regulatory decisions. Data on 137 compounds (including 53 biologicals and 78 small molecules) from 259 studies showed wide variation in where, when and why recovery animals were included. An analysis of individual study and programme design shows that there are opportunities to reduce the use of recovery animals without impacting drug development. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

  14. 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

    2015-01-01

    , 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...

  15. An integrated process for the recovery of high added-value compounds from olive oil using solid support free liquid-liquid extraction and chromatography techniques.

    Science.gov (United States)

    Angelis, Apostolis; Hamzaoui, Mahmoud; Aligiannis, Nektarios; Nikou, Theodora; Michailidis, Dimitris; Gerolimatos, Panagiotis; Termentzi, Aikaterini; Hubert, Jane; Halabalaki, Maria; Renault, Jean-Hugues; Skaltsounis, Alexios-Léandros

    2017-03-31

    An integrated extraction and purification process for the direct recovery of high added value compounds from extra virgin olive oil (EVOO) is proposed by using solid support free liquid-liquid extraction and chromatography techniques. Two different extraction methods were developed on a laboratory-scale Centrifugal Partition Extractor (CPE): a sequential strategy consisting of several "extraction-recovery" cycles and a continuous strategy based on stationary phase co-current elution. In both cases, EVOO was used as mobile phase diluted in food grade n-hexane (feed mobile phase) and the required biphasic system was obtained by adding ethanol and water as polar solvents. For the sequential process, 17.5L of feed EVOO containing organic phase (i.e. 7L of EVOO treated) were extracted yielding 9.5g of total phenolic fraction corresponding to a productivity of 5.8g/h/L of CPE column. Regarding the second approach, the co-current process, 2L of the feed oil phase (containing to 0.8L of EVOO) were treated at 100mL/min yielding 1.03g of total phenolic fraction corresponding to a productivity of 8.9g/h/L of CPE column. The total phenolic fraction was then fractionated by using stepwise gradient elution Centrifugal Partition Chromatography (CPC). The biphasic solvent systems were composed of n-hexane, ethyl acetate, ethanol and water in different proportions (X/Y/2/3, v/v). In a single run of 4h on a column with a capacity of 1L, 910mg of oleocanthal, 882mg of oleacein, 104mg of hydroxytyrosol were successfully recovered from 5g of phenolic extract with purities of 85%, 92% and 90%, respectively. CPC fractions were then submitted to orthogonal chromatographic steps (adsorption on silica gel or size exclusion chromatography) leading to the isolation of additional eleven compounds belonging to triterpens, phenolic compounds and secoiridoids. Among them, elenolic acid ethylester was found to be new compound. Thin Layer Chromatography (TLC), Nuclear magnetic Resonance (NMR) and

  16. Stochastic convex sparse principal component analysis.

    Science.gov (United States)

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

    2016-12-01

    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.

  17. Disaster Debris Recovery Database - Recovery

    Data.gov (United States)

    U.S. Environmental Protection Agency — The US EPA Disaster Debris Recovery Database (DDRD) promotes the proper recovery, recycling, and disposal of disaster debris for emergency responders at the federal,...

  18. A direct parallel sparse matrix solver

    International Nuclear Information System (INIS)

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

    1995-08-01

    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

  19. Functional fixedness in a technologically sparse culture.

    Science.gov (United States)

    German, Tim P; Barrett, H Clark

    2005-01-01

    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.

  20. Wavelets for Sparse Representation of Music

    DEFF Research Database (Denmark)

    Endelt, Line Ørtoft; Harbo, Anders La-Cour

    2004-01-01

    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...

  1. 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)

    1996-12-31

    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.

  2. A view of Kanerva's sparse distributed memory

    Science.gov (United States)

    Denning, P. J.

    1986-01-01

    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.

  3. Sparse Dataflow Analysis with Pointers and Reachability

    DEFF Research Database (Denmark)

    Madsen, Magnus; Møller, Anders

    2014-01-01

    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...

  4. Abnormal Event Detection Using Local Sparse Representation

    DEFF Research Database (Denmark)

    Ren, Huamin; Moeslund, Thomas B.

    2014-01-01

    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...

  5. Partitioning sparse rectangular matrices for parallel processing

    Energy Technology Data Exchange (ETDEWEB)

    Kolda, T.G.

    1998-05-01

    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.

  6. Dynamical analysis of Schrodinger operators with growing sparse potentials

    CERN Document Server

    Tcheremchantsev, S

    2003-01-01

    We consider Scr\\"odinger operators in l^2(Z^+) with potentials of the form V(n)=S(n)+Q(n). Here S is a sparse potential: S(n)=n^{1-\\eta \\over 2 \\eta}, 0<\\eta <1, for n=L_N and S(n)=0 else, where L_N is a very fast growing sequence. The real function Q(n) is compactly supported. We give a rather complete description of the (time-averaged) dynamics exp(-itH) \\psi for different initial states \\psi. In particular, for some \\psi we calculate explicitely the "intermittency function" \\beta_\\psi^- (p) which turns out to be nonconstant. As a particular corollary of obtained results, we show that the spectral measure restricted to (-2,2) has exact Hausdorff dimension \\eta for all boundary conditions, improving the result of Jitomirskaya and Last.

  7. Sparse adaptive finite elements for radiative transfer

    International Nuclear Information System (INIS)

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

    2008-01-01

    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

  8. Determining biosonar images using sparse representations.

    Science.gov (United States)

    Fontaine, Bertrand; Peremans, Herbert

    2009-05-01

    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.

  9. Interferometric interpolation of sparse marine data

    KAUST Repository

    Hanafy, Sherif M.

    2013-10-11

    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.

  10. Topological sparse learning of dynamic form patterns.

    Science.gov (United States)

    Guthier, T; Willert, V; Eggert, J

    2015-01-01

    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.

  11. Atmospheric inverse modeling via sparse reconstruction

    Directory of Open Access Journals (Sweden)

    N. Hase

    2017-10-01

    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.

  12. Balanced and sparse Tamo-Barg codes

    KAUST Repository

    Halbawi, Wael

    2017-08-29

    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.

  13. Sparse graphs using exchangeable random measures.

    Science.gov (United States)

    Caron, François; Fox, Emily B

    2017-11-01

    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.

  14. Atmospheric inverse modeling via sparse reconstruction

    Science.gov (United States)

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

    2017-10-01

    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.

  15. Activities of the Oil Implementation Task Force, December 1990--February 1991; Contracts for field projects and supporting research on enhanced oil recovery, April--June 1990

    Energy Technology Data Exchange (ETDEWEB)

    Tiedemann, H.A. (ed.) (USDOE Bartlesville Project Office, OK (USA))

    1991-03-01

    The Oil Implementation Task Force was appointed to implement the US DOE's new oil research program directed toward increasing domestic oil production by expanded research on near- or mid-term enhanced oil recovery methods. An added priority is to preserve access to reservoirs that have the largest potential for oil recovery, but that are threatened by the large number of wells abandoned each year. This report describes the progress of research activities in the following areas: chemical flooding; gas displacement; thermal recovery; resource assessment; microbial technology; geoscience technology; and environmental technology. (CK)

  16. The daily progress system: A proof of concept pilot study of a recovery support technology tool for outpatient substance abuse treatment.

    Science.gov (United States)

    Carswell, S B; Gordon, M S; Gryczynski, J; Tangires, S A

    2018-01-01

    Illicit substance use remains highly prevalent in the US, and epidemiological surveillance surveys estimate that in 2015, over 27 million individuals (10.1% of the US population) 12 years of age or older used illicit drugs in the past 30 days. 1 Outpatient treatment delivered in community-based settings is the dominant modality for addiction treatment, typically involving weekly psychosocial counseling sessions in an individual and/or group format. 2,3 Unfortunately, relapse and premature treatment discontinuation are quite common in outpatient treatment. 3-5 Objectives: This is a pilot proof of concept feasibility study involving clients presenting for outpatient SUD treatment. This study sought to examine the feasibility and acceptability of the Daily Progress System (DPS), a telephone-based software program, using interactive voice response (IVR), designed to enhance quality care and improve client outcomes. Individuals who presented at the participating treatment clinic, who met study eligibility criteria, and who provided written informed consent to participate were included in the study (N = 15; 53.3% females). Incentives were paid to participants for calls completed. Participants completed 65% of scheduled daily call-ins, representing 273 person-days of data on client cravings, mood, substance use, and involvement in recovery support activities. The average call duration was approximately 2 minutes and 42 seconds. There was a high degree of client and counselor acceptance and satisfaction using the system. Conclusions and Clinical Significance: Findings suggest that the DPS appears to be a feasible means of potentially addressing relapse and treatment engagement issues based on client and counselor engagement and satisfaction with the system.

  17. Parallel sparse direct solver for integrated circuit simulation

    CERN Document Server

    Chen, Xiaoming; Yang, Huazhong

    2017-01-01

    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...

  18. Short-Term Operations Plan for Collection of Bulk Quantity CBP Liquid in Support of a Pilot-Scale Treatabilty Evaluation with Water Recovery Inc

    Science.gov (United States)

    June 3, 2011 work plan for a pilot-scale treatability evaluation with a commercial wastewater treatment facility, Water Recovery Inc. (WRI) located in Jacksonville, Florida. Region ID: 04 DocID: 10749927, DocDate: 06-03-2011

  19. Dose-shaping using targeted sparse optimization.

    Science.gov (United States)

    Sayre, George A; Ruan, Dan

    2013-07-01

    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

  20. Dose-shaping using targeted sparse optimization

    International Nuclear Information System (INIS)

    Sayre, George A.; Ruan, Dan

    2013-01-01

    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

  1. Approach to improve compound recovery in a high-throughput Caco-2 permeability assay supported by liquid chromatography-tandem mass spectrometry.

    Science.gov (United States)

    Cai, Xianmei; Walker, Aaron; Cheng, Charles; Paiva, Anthony; Li, Ying; Kolb, Janet; Herbst, John; Shou, Wilson; Weller, Harold

    2012-08-01

    The Caco-2 cell culture system is widely employed as an in vitro model for prediction of intestinal absorption of test compounds in early drug discovery. Poor recovery is a commonly encountered issue in Caco-2 assay, which can lead to difficulty in data interpretation and underestimation of the apparent permeability of affected compounds. In this study, we systematically investigated the potential sources of compound loss in our automated, high-throughput Caco-2 assay, sample storage, and analysis processes, and as a result found the nonspecific binding to various plastic surfaces to be the major cause of poor compound recovery. To minimize the nonspecific binding, we implemented a simple and practical approach in our assay automation by preloading collection plates with organic solvent containing internal standard prior to transferring incubations samples. The implementation of this new method has been shown to significantly increase recovery in many compounds previously identified as having poor recovery in the Caco-2 permeability assay. With improved recovery, permeability results were obtained for many compounds that were previously not detected in the basolateral samples. In addition to recovery improvement, this new approach also simplified sample preparation for liquid chromatography-tandem mass spectrometric analysis and therefore achieved time and cost savings for the bioanalyst. Copyright © 2012 Wiley Periodicals, Inc.

  2. Sparse principal component analysis in hyperspectral change detection

    DEFF Research Database (Denmark)

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

    2011-01-01

    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....

  3. Hand posture recognition via joint feature sparse representation

    Science.gov (United States)

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

    2011-12-01

    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.

  4. Eigensolver for a Sparse, Large Hermitian Matrix

    Science.gov (United States)

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

    2003-01-01

    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).

  5. Sparse random matrices: The eigenvalue spectrum revisited

    International Nuclear Information System (INIS)

    Semerjian, Guilhem; Cugliandolo, Leticia F.

    2003-08-01

    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)

  6. Better Size Estimation for Sparse Matrix Products

    DEFF Research Database (Denmark)

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

    2010-01-01

    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....

  7. Sparse suffix tree construction in small space

    DEFF Research Database (Denmark)

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

    2013-01-01

    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....

  8. Fast Generation of Sparse Random Kernel Graphs.

    Science.gov (United States)

    Hagberg, Aric; Lemons, Nathan

    2015-01-01

    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.

  9. Multiplication method for sparse interferometric fringes.

    Science.gov (United States)

    Liu, Cong; Zhang, Xingyi; Zhou, Youhe

    2016-04-04

    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.

  10. Cardiac Rotational Mechanics As a Predictor of Myocardial Recovery in Heart Failure Patients Undergoing Chronic Mechanical Circulatory Support: A Pilot Study.

    Science.gov (United States)

    Bonios, Michael J; Koliopoulou, Antigone; Wever-Pinzon, Omar; Taleb, Iosif; Stehlik, Josef; Xu, Weining; Wever-Pinzon, James; Catino, Anna; Kfoury, Abdallah G; Horne, Benjamin D; Nativi-Nicolau, Jose; Adamopoulos, Stamatis N; Fang, James C; Selzman, Craig H; Bax, Jeroen J; Drakos, Stavros G

    2018-04-01

    Impaired qualitative and quantitative left ventricular (LV) rotational mechanics predict cardiac remodeling progression and prognosis after myocardial infarction. We investigated whether cardiac rotational mechanics can predict cardiac recovery in chronic advanced cardiomyopathy patients. Sixty-three patients with advanced and chronic dilated cardiomyopathy undergoing implantation of LV assist device (LVAD) were prospectively investigated using speckle tracking echocardiography. Acute heart failure patients were prospectively excluded. We evaluated LV rotational mechanics (apical and basal LV twist, LV torsion) and deformational mechanics (circumferential and longitudinal strain) before LVAD implantation. Cardiac recovery post-LVAD implantation was defined as (1) final resulting LV ejection fraction ≥40%, (2) relative LV ejection fraction increase ≥50%, (iii) relative LV end-systolic volume decrease ≥50% (all 3 required). Twelve patients fulfilled the criteria for cardiac recovery (Rec Group). The Rec Group had significantly less impaired pre-LVAD peak LV torsion compared with the Non-Rec Group. Notably, both groups had similarly reduced pre-LVAD LV ejection fraction. By receiver operating characteristic curve analysis, pre-LVAD peak LV torsion of 0.35 degrees/cm had a 92% sensitivity and a 73% specificity in predicting cardiac recovery. Peak LV torsion before LVAD implantation was found to be an independent predictor of cardiac recovery after LVAD implantation (odds ratio, 0.65 per 0.1 degrees/cm [0.49-0.87]; P =0.014). LV rotational mechanics seem to be useful in selecting patients prone to cardiac recovery after mechanical unloading induced by LVADs. Future studies should investigate the utility of these markers in predicting durable cardiac recovery after the explantation of the cardiac assist device. © 2018 American Heart Association, Inc.

  11. Bayesian learning of sparse multiscale image representations.

    Science.gov (United States)

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

    2013-12-01

    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.

  12. 5D whole-heart sparse MRI.

    Science.gov (United States)

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

    2018-02-01

    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.

  13. Generalized Recovery

    DEFF Research Database (Denmark)

    Skov Jensen, Christian; Lando, David; Heje Pedersen, Lasse

    of Ross (2015). Our characterization is simple and intuitive, linking recovery to the relation between the number of time periods on the number of states. When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement our model empirically, testing...... the predictive power of the recovered expected return, crash risk, and other recovered statistics....

  14. Backup & Recovery

    CERN Document Server

    Preston, W

    2009-01-01

    Packed with practical, freely available backup and recovery solutions for Unix, Linux, Windows, and Mac OS X systems -- as well as various databases -- this new guide is a complete overhaul of Unix Backup & Recovery by the same author, now revised and expanded with over 75% new material.

  15. Single-image super-resolution reconstruction via learned geometric dictionaries and clustered sparse coding.

    Science.gov (United States)

    Yang, Shuyuan; Wang, Min; Chen, Yiguang; Sun, Yaxin

    2012-09-01

    Recently, single image super-resolution reconstruction (SISR) via sparse coding has attracted increasing interest. In this paper, we proposed a multiple-geometric-dictionaries-based clustered sparse coding scheme for SISR. Firstly, a large number of high-resolution (HR) image patches are randomly extracted from a set of example training images and clustered into several groups of "geometric patches," from which the corresponding "geometric dictionaries" are learned to further sparsely code each local patch in a low-resolution image. A clustering aggregation is performed on the HR patches recovered by different dictionaries, followed by a subsequent patch aggregation to estimate the HR image. Considering that there are often many repetitive image structures in an image, we add a self-similarity constraint on the recovered image in patch aggregation to reveal new features and details. Finally, the HR residual image is estimated by the proposed recovery method and compensated to better preserve the subtle details of the images. Some experiments test the proposed method on natural images, and the results show that the proposed method outperforms its counterparts in both visual fidelity and numerical measures.

  16. Low-Rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging.

    Science.gov (United States)

    Ravishankar, Saiprasad; Moore, Brian E; Nadakuditi, Raj Rao; Fessler, Jeffrey A

    2017-05-01

    Sparsity-based approaches have been popular in many applications in image processing and imaging. Compressed sensing exploits the sparsity of images in a transform domain or dictionary to improve image recovery fromundersampledmeasurements. In the context of inverse problems in dynamic imaging, recent research has demonstrated the promise of sparsity and low-rank techniques. For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements. In this work, we investigate a data-adaptive extension of the L+S model, dubbed LASSI, where the temporal image sequence is decomposed into a low-rank component and a component whose spatiotemporal (3D) patches are sparse in some adaptive dictionary domain. We investigate various formulations and efficient methods for jointly estimating the underlying dynamic signal components and the spatiotemporal dictionary from limited measurements. We also obtain efficient sparsity penalized dictionary-blind compressed sensing methods as special cases of our LASSI approaches. Our numerical experiments demonstrate the promising performance of LASSI schemes for dynamicmagnetic resonance image reconstruction from limited k-t space data compared to recent methods such as k-t SLR and L+S, and compared to the proposed dictionary-blind compressed sensing method.

  17. On-Chip Neural Data Compression Based On Compressed Sensing With Sparse Sensing Matrices.

    Science.gov (United States)

    Zhao, Wenfeng; Sun, Biao; Wu, Tong; Yang, Zhi

    2018-02-01

    On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and -sparse random binary matrix [-SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and -SRBM encoders with reduced area and total power consumption.

  18. MR image super-resolution reconstruction using sparse representation, nonlocal similarity and sparse derivative prior.

    Science.gov (United States)

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

    2015-03-01

    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.

  19. Unbundling payments for radioisotopes from radiopharmaceuticals and from diagnostic procedures: A tool to support the implementation of full-cost recovery. NEA discussion document

    International Nuclear Information System (INIS)

    2012-01-01

    The objective of the NEA's HLG-MR policy approach is to ensure a long-term secure supply. The HLG-MR has determined that to attain that objective, a necessary (but not sufficient) requirement is that irradiation services in the 99 Mo/ 99m Tc supply chain must be provided on a full-cost recovery (FCR) basis (OECD-NEA, 2011). The HLG-MR policy approach also recommended that supply chain participants should implement payment reforms that promote full-cost recovery within their reimbursement systems. Reforms might include separate radioisotope pricing or auditing, separate radioisotope payment, differential radioisotope payment for FCR, or other approaches to promote a complete transition to full-cost recovery

  20. 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

    2018-01-02

    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.

  1. Local posterior concentration rate for multilevel sparse sequences

    NARCIS (Netherlands)

    Belitser, E.N.; Nurushev, N.

    2017-01-01

    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

  2. In-Storage Embedded Accelerator for Sparse Pattern Processing

    Science.gov (United States)

    2016-09-13

    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

  3. Joint Group Sparse PCA for Compressed Hyperspectral Imaging.

    Science.gov (United States)

    Khan, Zohaib; Shafait, Faisal; Mian, Ajmal

    2015-12-01

    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.

  4. Robust Face Recognition Via Gabor Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    Hao Yu-Juan

    2016-01-01

    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.

  5. Comparison of Methods for Sparse Representation of Musical Signals

    DEFF Research Database (Denmark)

    Endelt, Line Ørtoft; la Cour-Harbo, Anders

    2005-01-01

    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....

  6. Convergence results for 3D sparse grid approaches

    NARCIS (Netherlands)

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

    1997-01-01

    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

  7. Confidence of model based shape reconstruction from sparse data

    DEFF Research Database (Denmark)

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

    2010-01-01

    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...

  8. Enhancement of snow cover change detection with sparse representation and dictionary learning

    Science.gov (United States)

    Varade, D.; Dikshit, O.

    2014-11-01

    Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representation of a snow cover change map using the K-SVD trained dictionary and sparse decoding to enhance the change map. The pixels often falsely characterized as "changes" are eliminated using this approach. The preliminary change map was generated using differenced NDSI or S3 maps in case of Resourcesat-2 and Landsat 8 OLI imagery respectively. These maps are extracted into patches for compressed sensing using Discrete Cosine Transform (DCT) to generate an initial dictionary which is trained by the K-SVD approach. The trained dictionary is used for sparse coding of the change map using the Orthogonal Matching Pursuit (OMP) algorithm. The reconstructed change map incorporates a greater degree of smoothing and represents the features (snow cover changes) with better accuracy. The enhanced change map is segmented using kmeans to discriminate between the changed and non-changed pixels. The segmented enhanced change map is compared, firstly with the difference of Support Vector Machine (SVM) classified NDSI maps and secondly with a reference data generated as a mask by visual interpretation of the two input images. The methodology is evaluated using multi-spectral datasets from Resourcesat-2 and Landsat-8. The k-hat statistic is computed to determine the accuracy of the proposed approach.

  9. Infrared small target tracking based on sample constrained particle filtering and sparse representation

    Science.gov (United States)

    Zhang, Xiaomin; Ren, Kan; Wan, Minjie; Gu, Guohua; Chen, Qian

    2017-12-01

    Infrared search and track technology for small target plays an important role in infrared warning and guidance. In view of the tacking randomness and uncertainty caused by background clutter and noise interference, a robust tracking method for infrared small target based on sample constrained particle filtering and sparse representation is proposed in this paper. Firstly, to distinguish the normal region and interference region in target sub-blocks, we introduce a binary support vector, and combine it with the target sparse representation model, after which a particle filtering observation model based on sparse reconstruction error differences between sample targets is developed. Secondly, we utilize saliency extraction to obtain the high frequency area in infrared image, and make it as a priori knowledge of the transition probability model to limit the particle filtering sampling process. Lastly, the tracking result is brought about via target state estimation and the Bayesian posteriori probability calculation. Theoretical analyses and experimental results show that our method can enhance the state estimation ability of stochastic particles, improve the sparse representation adaptabilities for infrared small targets, and optimize the tracking accuracy for infrared small moving targets.

  10. Information filtering in sparse online systems: recommendation via semi-local diffusion.

    Science.gov (United States)

    Zeng, Wei; Zeng, An; Shang, Ming-Sheng; Zhang, Yi-Cheng

    2013-01-01

    With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users' decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot accurately recommend objects for users. This data sparsity problem makes many well-known recommendation algorithms perform poorly. To solve the problem, we propose a recommendation algorithm based on the semi-local diffusion process on the user-object bipartite network. The simulation results on two sparse datasets, Amazon and Bookcross, show that our method significantly outperforms the state-of-the-art methods especially for those small-degree users. Two personalized semi-local diffusion methods are proposed which further improve the recommendation accuracy. Finally, our work indicates that sparse online systems are essentially different from the dense online systems, so it is necessary to reexamine former algorithms and conclusions based on dense data in sparse systems.

  11. Speckle suppression via sparse representation for wide-field imaging through turbid media.

    Science.gov (United States)

    Jang, Hwanchol; Yoon, Changhyeong; Chung, Euiheon; Choi, Wonshik; Lee, Heung-No

    2014-06-30

    Speckle suppression is one of the most important tasks in the image transmission through turbid media. Insufficient speckle suppression requires an additional procedure such as temporal ensemble averaging over multiple exposures. In this paper, we consider the image recovery process based on the so-called transmission matrix (TM) of turbid media for the image transmission through the media. We show that the speckle left unremoved in the TM-based image recovery can be suppressed effectively via sparse representation (SR). SR is a relatively new signal reconstruction framework which works well even for ill-conditioned problems. This is the first study to show the benefit of using the SR as compared to the phase conjugation (PC) a de facto standard method to date for TM-based imaging through turbid media including a live cell through tissue slice.

  12. Sparse Frequency Waveform Design for Radar-Embedded Communication

    Directory of Open Access Journals (Sweden)

    Chaoyun Mai

    2016-01-01

    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. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

    Science.gov (United States)

    Tang, Xin; Feng, Guo-Can; Li, Xiao-Xin; Cai, Jia-Xin

    2015-01-01

    Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC) achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC). Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis) of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our method achieves the

  14. Learning Low-Rank Class-Specific Dictionary and Sparse Intra-Class Variant Dictionary for Face Recognition.

    Directory of Open Access Journals (Sweden)

    Xin Tang

    Full Text Available Face recognition is challenging especially when the images from different persons are similar to each other due to variations in illumination, expression, and occlusion. If we have sufficient training images of each person which can span the facial variations of that person under testing conditions, sparse representation based classification (SRC achieves very promising results. However, in many applications, face recognition often encounters the small sample size problem arising from the small number of available training images for each person. In this paper, we present a novel face recognition framework by utilizing low-rank and sparse error matrix decomposition, and sparse coding techniques (LRSE+SC. Firstly, the low-rank matrix recovery technique is applied to decompose the face images per class into a low-rank matrix and a sparse error matrix. The low-rank matrix of each individual is a class-specific dictionary and it captures the discriminative feature of this individual. The sparse error matrix represents the intra-class variations, such as illumination, expression changes. Secondly, we combine the low-rank part (representative basis of each person into a supervised dictionary and integrate all the sparse error matrix of each individual into a within-individual variant dictionary which can be applied to represent the possible variations between the testing and training images. Then these two dictionaries are used to code the query image. The within-individual variant dictionary can be shared by all the subjects and only contribute to explain the lighting conditions, expressions, and occlusions of the query image rather than discrimination. At last, a reconstruction-based scheme is adopted for face recognition. Since the within-individual dictionary is introduced, LRSE+SC can handle the problem of the corrupted training data and the situation that not all subjects have enough samples for training. Experimental results show that our

  15. Fast method of sparse acquisition and reconstruction of view and illumination dependent datasets

    Czech Academy of Sciences Publication Activity Database

    Filip, Jiří; Vávra, Radomír

    2013-01-01

    Roč. 37, č. 5 (2013), s. 376-388 ISSN 0097-8493 R&D Projects: GA ČR GAP103/11/0335 Grant - others:EC ERG (European Reintegration Grant) FP7(BE) 239294 Institutional support: RVO:67985556 Keywords : apparent BRDF * measurement * reconstruction * sparse sampling * portable setup Subject RIV: BD - Theory of Information Impact factor: 1.029, year: 2013 http://library.utia.cas.cz/separaty/2013/RO/filip-0392214.pdf

  16. Transformer fault diagnosis using continuous sparse autoencoder.

    Science.gov (United States)

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

    2016-01-01

    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.

  17. Sparse alignment for robust tensor learning.

    Science.gov (United States)

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

    2014-10-01

    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.

  18. Link Prediction via Sparse Gaussian Graphical Model

    Directory of Open Access Journals (Sweden)

    Liangliang Zhang

    2016-01-01

    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.

  19. Sparse Superpixel Unmixing for Hyperspectral Image Analysis

    Science.gov (United States)

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

    2010-01-01

    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.

  20. Manifold regularization for sparse unmixing of hyperspectral images.

    Science.gov (United States)

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

    2016-01-01

    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.

  1. Object tracking by occlusion detection via structured sparse learning

    KAUST Repository

    Zhang, Tianzhu

    2013-06-01

    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.

  2. Uses and abuses of recovery: implementing recovery-oriented practices in mental health systems

    Science.gov (United States)

    Slade, Mike; Amering, Michaela; Farkas, Marianne; Hamilton, Bridget; O'Hagan, Mary; Panther, Graham; Perkins, Rachel; Shepherd, Geoff; Tse, Samson; Whitley, Rob

    2014-01-01

    An understanding of recovery as a personal and subjective experience has emerged within mental health systems. This meaning of recovery now underpins mental health policy in many countries. Developing a focus on this type of recovery will involve transformation within mental health systems. Human systems do not easily transform. In this paper, we identify seven mis-uses (“abuses”) of the concept of recovery: recovery is the latest model; recovery does not apply to “my” patients; services can make people recover through effective treatment; compulsory detention and treatment aid recovery; a recovery orientation means closing services; recovery is about making people independent and normal; and contributing to society happens only after the person is recovered. We then identify ten empirically-validated interventions which support recovery, by targeting key recovery processes of connectedness, hope, identity, meaning and empowerment (the CHIME framework). The ten interventions are peer support workers, advance directives, wellness recovery action planning, illness management and recovery, REFOCUS, strengths model, recovery colleges or recovery education programs, individual placement and support, supported housing, and mental health trialogues. Finally, three scientific challenges are identified: broadening cultural understandings of recovery, implementing organizational transformation, and promoting citizenship. PMID:24497237

  3. Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays

    Science.gov (United States)

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

    2004-01-01

    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.

  4. Sparse Principal Component Analysis in Medical Shape Modeling

    DEFF Research Database (Denmark)

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

    2006-01-01

    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...

  5. Accelerating Dynamic Cardiac MR Imaging Using Structured Sparse Representation

    Directory of Open Access Journals (Sweden)

    Nian Cai

    2013-01-01

    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.

  6. A comprehensive study of sparse codes on abnormality detection

    DEFF Research Database (Denmark)

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

    2017-01-01

    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...

  7. Effect of Low Perceived Social Support on Health Outcomes in Young Patients With Acute Myocardial Infarction: Results From the VIRGO (Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients) Study

    Science.gov (United States)

    Bucholz, Emily M.; Strait, Kelly M.; Dreyer, Rachel P.; Geda, Mary; Spatz, Erica S.; Bueno, Hector; Lichtman, Judith H.; D'Onofrio, Gail; Spertus, John A.; Krumholz, Harlan M.

    2014-01-01

    Background Social support is an important predictor of health outcomes after acute myocardial infarction (AMI), but social support varies by sex and age. Differences in social support could account for sex differences in outcomes of young patients with AMI. Methods and Results Data from the Variation in Recovery: Role of Gender on Outcomes of Young AMI Patients (VIRGO) study, an observational study of AMI patients aged ≤55 years in the United States and Spain, were used for this study. Patients were categorized as having low versus moderate/high perceived social support using the ENRICHD Social Support Inventory. Outcomes included health status (Short Form‐12 physical and mental component scores), depressive symptoms (Patient Health Questionnaire), and angina‐related quality of life (Seattle Angina Questionnaire) evaluated at baseline and 12 months. Among 3432 patients, 21.2% were classified as having low social support. Men and women had comparable levels of social support at baseline. On average, patients with low social support reported lower functional status and quality of life and more depressive symptoms at baseline and 12 months post‐AMI. After multivariable adjustment, including baseline health status, low social support was associated with lower mental functioning, lower quality of life, and more depressive symptoms at 12 months (all P<0.001). The relationship between low social support and worse physical functioning was nonsignificant after adjustment (P=0.6). No interactions were observed between social support, sex, or country. Conclusion Lower social support is associated with worse health status and more depressive symptoms 12 months after AMI in both young men and women. Sex did not modify the effect of social support. PMID:25271209

  8. Recovery Spirituality

    Directory of Open Access Journals (Sweden)

    Ernest Kurtz

    2015-01-01

    Full Text Available There is growing interest in Alcoholics Anonymous (A.A. and other secular, spiritual, and religious frameworks of long-term addiction recovery. The present paper explores the varieties of spiritual experience within A.A., with particular reference to the growth of a wing of recovery spirituality promoted within A.A. It is suggested that the essence of secular spirituality is reflected in the experience of beyond (horizontal and vertical transcendence and between (connection and mutuality and in six facets of spirituality (Release, Gratitude, Humility, Tolerance, Forgiveness, and a Sense of Being-at-home shared across religious, spiritual, and secular pathways of addiction recovery. The growing varieties of A.A. spirituality (spanning the “Christianizers” and “Seculizers” reflect A.A.’s adaptation to the larger diversification of religious experience and the growing secularization of spirituality across the cultural contexts within which A.A. is nested.

  9. Sparse reconstruction methods in x-ray CT

    Science.gov (United States)

    Abascal, J. F. P. J.; Abella, M.; Mory, C.; Ducros, N.; de Molina, C.; Marinetto, E.; Peyrin, F.; Desco, M.

    2017-10-01

    Recent progress in X-ray CT is contributing to the advent of new clinical applications. A common challenge for these applications is the need for new image reconstruction methods that meet tight constraints in radiation dose and geometrical limitations in the acquisition. The recent developments in sparse reconstruction methods provide a framework that permits obtaining good quality images from drastically reduced signal-to-noise-ratio and limited-view data. In this work, we present our contributions in this field. For dynamic studies (3D+Time), we explored the possibility of extending the exploitation of sparsity to the temporal dimension: a temporal operator based on modelling motion between consecutive temporal points in gated-CT and based on experimental time curves in contrast-enhanced CT. In these cases, we also exploited sparsity by using a prior image estimated from the complete acquired dataset and assessed the effect on image quality of using different sparsity operators. For limited-view CT, we evaluated total-variation regularization in different simulated limited-data scenarios from a real small animal acquisition with a cone-beam microCT scanner, considering different angular span and number of projections. For other emerging imaging modalities, such as spectral CT, the image reconstruction problem is nonlinear, so we explored new efficient approaches to exploit sparsity for multi-energy CT data. In conclusion, we review our approaches to challenging CT data reconstruction problems and show results that support the feasibility for new clinical applications.

  10. Sparse spectral deconvolution algorithm for noncartesian MR spectroscopic imaging.

    Science.gov (United States)

    Bhave, Sampada; Eslami, Ramin; Jacob, Mathews

    2014-02-01

    To minimize line shape distortions and spectral leakage artifacts in MR spectroscopic imaging (MRSI). A spatially and spectrally regularized non-Cartesian MRSI algorithm that uses the line shape distortion priors, estimated from water reference data, to deconvolve the spectra is introduced. Sparse spectral regularization is used to minimize noise amplification associated with deconvolution. A spiral MRSI sequence that heavily oversamples the central k-space regions is used to acquire the MRSI data. The spatial regularization term uses the spatial supports of brain and extracranial fat regions to recover the metabolite spectra and nuisance signals at two different resolutions. Specifically, the nuisance signals are recovered at the maximum resolution to minimize spectral leakage, while the point spread functions of metabolites are controlled to obtain acceptable signal-to-noise ratio. The comparisons of the algorithm against Tikhonov regularized reconstructions demonstrates considerably reduced line-shape distortions and improved metabolite maps. The proposed sparsity constrained spectral deconvolution scheme is effective in minimizing the line-shape distortions. The dual resolution reconstruction scheme is capable of minimizing spectral leakage artifacts. Copyright © 2013 Wiley Periodicals, Inc.

  11. Linearithmic time sparse and convex maximum margin clustering.

    Science.gov (United States)

    Zhang, Xiao-Lei; Wu, Ji

    2012-12-01

    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

  12. Generalized Recovery

    DEFF Research Database (Denmark)

    Skov Jensen, Christian; Lando, David; Heje Pedersen, Lasse

    We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. Our characterization makes no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model...... of Ross (2015). Our characterization is simple and intuitive, linking recovery to the relation between the number of time periods and the number of states. When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement our model empirically, testing...

  13. Generalized Recovery

    DEFF Research Database (Denmark)

    Jensen, Christian Skov; Lando, David; Pedersen, Lasse Heje

    We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. Our characterization makes no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model...... of Ross (2015). Our characterization is simple and intuitive, linking recovery to the relation between the number of time periods on the number of states. When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement our model empirically, testing...

  14. Generalized Recovery

    DEFF Research Database (Denmark)

    Jensen, Christian Skov; Lando, David; Pedersen, Lasse Heje

    We characterize when physical probabilities, marginal utilities, and the discount rate can be recovered from observed state prices for several future time periods. We make no assumptions of the probability distribution, thus generalizing the time-homogeneous stationary model of Ross (2015......). Recovery is feasible when the number of maturities with observable prices is higher than the number of states of the economy (or the number of parameters characterizing the pricing kernel). When recovery is feasible, our model is easy to implement, allowing a closed-form linearized solution. We implement...... our model empirically, testing the predictive power of the recovered expected return and other recovered statistics....

  15. Sparse Representations for Pattern Classification using Learned Dictionaries

    Science.gov (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.

  16. Sparse Machine Learning Methods for Understanding Large Text Corpora

    Data.gov (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. Image classification by semisupervised sparse coding with confident unlabeled samples

    Science.gov (United States)

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

    2017-09-01

    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.

  18. Deep Marginalized Sparse Denoising Auto-Encoder for Image Denoising

    Science.gov (United States)

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

    2018-01-01

    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.

  19. 3rd Workshop on Sparse Grids and Applications

    CERN Document Server

    Pflüger, Dirk

    2016-01-01

    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.

  20. 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...

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

    Directory of Open Access Journals (Sweden)

    Yongzhi Qu

    2017-05-01

    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.

  2. Beam Combination for Sparse Aperture Telescopes, Phase I

    Data.gov (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...

  3. A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM

    Directory of Open Access Journals (Sweden)

    Xin-Sheng Zhang

    2014-01-01

    Full Text Available In digital mammograms, an early sign of breast cancer is the existence of microcalcification clusters (MCs, which is very important to the early breast cancer detection. In this paper, a new approach is proposed to classify and detect MCs. We formulate this classification problem as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts. A visual information-rich vocabulary of training samples is manually built up from a set of samples, which include MCs parts and no-MCs parts. With the prior ground truth of MCs in mammograms, the sparse feature learning is acquired by the lP-regularized least square approach with the interior-point method. Then we designed the sparse feature learning based MCs classification algorithm using twin support vector machines (TWSVMs. To investigate its performance, the proposed method is applied to DDSM datasets and compared with support vector machines (SVMs with the same dataset. Experiments have shown that performance of the proposed method is more efficient or better than the state-of-art methods.

  4. An ensemble based nonlinear orthogonal matching pursuit algorithm for sparse history matching of reservoir models

    KAUST Repository

    Fsheikh, Ahmed H.

    2013-01-01

    A nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of reservoir models is presented. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated components of the basis functions with the residual. The discovered basis (aka support) is augmented across the nonlinear iterations. Once the basis functions are selected from the dictionary, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on approximate gradient estimation using an iterative stochastic ensemble method (ISEM). ISEM utilizes an ensemble of directional derivatives to efficiently approximate gradients. In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm.

  5. Hyperspectral Image Classification Based on the Combination of Spatial-spectral Feature and Sparse Representation

    Directory of Open Access Journals (Sweden)

    YANG Zhaoxia

    2015-07-01

    Full Text Available In order to avoid the problem of being over-dependent on high-dimensional spectral feature in the traditional hyperspectral image classification, a novel approach based on the combination of spatial-spectral feature and sparse representation is proposed in this paper. Firstly, we extract the spatial-spectral feature by reorganizing the local image patch with the first d principal components(PCs into a vector representation, followed by a sorting scheme to make the vector invariant to local image rotation. Secondly, we learn the dictionary through a supervised method, and use it to code the features from test samples afterwards. Finally, we embed the resulting sparse feature coding into the support vector machine(SVM for hyperspectral image classification. Experiments using three hyperspectral data show that the proposed method can effectively improve the classification accuracy comparing with traditional classification methods.

  6. Fault Diagnosis of Complex Industrial Process Using KICA and Sparse SVM

    Directory of Open Access Journals (Sweden)

    Jie Xu

    2013-01-01

    Full Text Available New approaches are proposed for complex industrial process monitoring and fault diagnosis based on kernel independent component analysis (KICA and sparse support vector machine (SVM. The KICA method is a two-phase algorithm: whitened kernel principal component analysis (KPCA. The data are firstly mapped into high-dimensional feature subspace. Then, the ICA algorithm seeks the projection directions in the KPCA whitened space. Performance monitoring is implemented through constructing the statistical index and control limit in the feature space. If the statistical indexes exceed the predefined control limit, a fault may have occurred. Then, the nonlinear score vectors are calculated and fed into the sparse SVM to identify the faults. The proposed method is applied to the simulation of Tennessee Eastman (TE chemical process. The simulation results show that the proposed method can identify various types of faults accurately and rapidly.

  7. Phase Behavior, Solid Organic Precipitation, and Mobility Characterization Studies in Support of Enhanced Heavy Oil Recovery on the Alaska North Slope

    Energy Technology Data Exchange (ETDEWEB)

    Shirish Patil; Abhijit Dandekar; Santanu Khataniar

    2008-12-31

    The medium-heavy oil (viscous oil) resources in the Alaska North Slope are estimated at 20 to 25 billion barrels. These oils are viscous, flow sluggishly in the formations, and are difficult to recover. Recovery of this viscous oil requires carefully designed enhanced oil recovery processes. Success of these recovery processes is critically dependent on accurate knowledge of the phase behavior and fluid properties, especially viscosity, of these oils under variety of pressure and temperature conditions. This project focused on predicting phase behavior and viscosity of viscous oils using equations of state and semi-empirical correlations. An experimental study was conducted to quantify the phase behavior and physical properties of viscous oils from the Alaska North Slope oil field. The oil samples were compositionally characterized by the simulated distillation technique. Constant composition expansion and differential liberation tests were conducted on viscous oil samples. Experiment results for phase behavior and reservoir fluid properties were used to tune the Peng-Robinson equation of state and predict the phase behavior accurately. A comprehensive literature search was carried out to compile available compositional viscosity models and their modifications, for application to heavy or viscous oils. With the help of meticulously amassed new medium-heavy oil viscosity data from experiments, a comparative study was conducted to evaluate the potential of various models. The widely used corresponding state viscosity model predictions deteriorate when applied to heavy oil systems. Hence, a semi-empirical approach (the Lindeloff model) was adopted for modeling the viscosity behavior. Based on the analysis, appropriate adjustments have been suggested: the major one is the division of the pressure-viscosity profile into three distinct regions. New modifications have improved the overall fit, including the saturated viscosities at low pressures. However, with the limited

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

    Directory of Open Access Journals (Sweden)

    Van Deun Katrijn

    2011-11-01

    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.

    Science.gov (United States)

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

    2011-11-15

    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

    2014-01-01

    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. 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)

    2017-03-07

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

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

    Science.gov (United States)

    2011-01-01

    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

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

    OpenAIRE

    Lacerda, Pedro

    2007-01-01

    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...

  14. Sparse Reconstruction Schemes for Nonlinear Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla

    2016-03-01

    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.

  15. Exhaustive Search for Sparse Variable Selection in Linear Regression

    Science.gov (United States)

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

    2018-04-01

    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.

  16. A Preference-Based Multiobjective Evolutionary Approach for Sparse Optimization.

    Science.gov (United States)

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

    2017-03-29

    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.

  17. Visual tracking based on extreme learning machine and sparse representation.

    Science.gov (United States)

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

    2015-10-22

    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.

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

    Directory of Open Access Journals (Sweden)

    Xiangwei Xing

    2014-01-01

    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.

  19. Structure-aware Local Sparse Coding for Visual Tracking

    KAUST Repository

    Qi, Yuankai

    2018-01-24

    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.

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

    KAUST Repository

    Zhang, Tianzhu

    2014-01-01

    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.

  1. Vector sparse representation of color image using quaternion matrix analysis.

    Science.gov (United States)

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

    2015-04-01

    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.

  2. Hardware Acceleration of Sparse Cognitive Algorithms

    Science.gov (United States)

    2016-05-01

    is clear that these emerging algorithms that can support unsupervised , or lightly supervised learning , as well as incremental learning , map poorly...distribution unlimited. 8.0 CONCLUDING REMARKS These emerging algorithms that can support unsupervised , or lightly supervised learning , as well as...15. SUBJECT TERMS Cortical Algorithms; Machine Learning ; Hardware; VLSI; ASIC 16. SECURITY CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT: SAR

  3. Sparse calibration of subsurface flow models using nonlinear orthogonal matching pursuit and an iterative stochastic ensemble method

    KAUST Repository

    Elsheikh, Ahmed H.

    2013-06-01

    We introduce a nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of subsurface flow models. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated basis function with the residual from a large pool of basis functions. The discovered basis (aka support) is augmented across the nonlinear iterations. Once a set of basis functions are selected, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on stochastically approximated gradient using an iterative stochastic ensemble method (ISEM). In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm. The proposed algorithm is the first ensemble based algorithm that tackels the sparse nonlinear parameter estimation problem. © 2013 Elsevier Ltd.

  4. The MUSIC algorithm for sparse objects: a compressed sensing analysis

    International Nuclear Information System (INIS)

    Fannjiang, Albert C

    2011-01-01

    The multiple signal classification (MUSIC) algorithm, and its extension for imaging sparse extended objects, with noisy data is analyzed by compressed sensing (CS) techniques. A thresholding rule is developed to augment the standard MUSIC algorithm. The notion of restricted isometry property (RIP) and an upper bound on the restricted isometry constant (RIC) are employed to establish sufficient conditions for the exact localization by MUSIC with or without noise. In the noiseless case, the sufficient condition gives an upper bound on the numbers of random sampling and incident directions necessary for exact localization. In the noisy case, the sufficient condition assumes additionally an upper bound for the noise-to-object ratio in terms of the RIC and the dynamic range of objects. This bound points to the super-resolution capability of the MUSIC algorithm. Rigorous comparison of performance between MUSIC and the CS minimization principle, basis pursuit denoising (BPDN), is given. In general, the MUSIC algorithm guarantees to recover, with high probability, s scatterers with n=O(s 2 ) random sampling and incident directions and sufficiently high frequency. For the favorable imaging geometry where the scatterers are distributed on a transverse plane MUSIC guarantees to recover, with high probability, s scatterers with a median frequency and n=O(s) random sampling/incident directions. Moreover, for the problems of spectral estimation and source localizations both BPDN and MUSIC guarantee, with high probability, to identify exactly the frequencies of random signals with the number n=O(s) of sampling times. However, in the absence of abundant realizations of signals, BPDN is the preferred method for spectral estimation. Indeed, BPDN can identify the frequencies approximately with just one realization of signals with the recovery error at worst linearly proportional to the noise level. Numerical results confirm that BPDN outperforms MUSIC in the well-resolved case while

  5. Infrastructure to support learning health systems: are we there yet? Innovative solutions and lessons learned from American Recovery and Reinvestment Act CER investments.

    Science.gov (United States)

    Holve, Erin; Segal, Courtney

    2014-11-01

    The 11 big health data networks participating in the AcademyHealth Electronic Data Methods Forum represent cutting-edge efforts to harness the power of big health data for research and quality improvement. This paper is a comparative case study based on site visits conducted with a subset of these large infrastructure grants funded through the Recovery Act, in which four key issues emerge that can inform the evolution of learning health systems, including the importance of acknowledging the challenges of scaling specialized expertise needed to manage and run CER networks; the delicate balance between privacy protections and the utility of distributed networks; emerging community engagement strategies; and the complexities of developing a robust business model for multi-use networks.

  6. Sparse modeling of spatial environmental variables associated with asthma.

    Science.gov (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

    2015-02-01

    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.

  7. Single-Trial Decoding of Bistable Perception Based on Sparse Nonnegative Tensor Decomposition

    Science.gov (United States)

    Wang, Zhisong; Maier, Alexander; Logothetis, Nikos K.; Liang, Hualou

    2008-01-01

    The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper, we develop a sparse nonnegative tensor factorization-(NTF)-based method to extract features from the local field potential (LFP), collected from the middle temporal (MT) visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM) perception. We apply the feature extraction approach to the multichannel time-frequency representation of the intracortical LFP data. The advantages of the sparse NTF-based feature extraction approach lies in its capability to yield components common across the space, time, and frequency domains yet discriminative across different conditions without prior knowledge of the discriminating frequency bands and temporal windows for a specific subject. We employ the support vector machines (SVMs) classifier based on the features of the NTF components for single-trial decoding the reported perception. Our results suggest that although other bands also have certain discriminability, the gamma band feature carries the most discriminative information for bistable perception, and that imposing the sparseness constraints on the nonnegative tensor factorization improves extraction of this feature. PMID:18528515

  8. Robust Pedestrian Tracking and Recognition from FLIR Video: A Unified Approach via Sparse Coding

    Directory of Open Access Journals (Sweden)

    Xin Li

    2014-06-01

    Full Text Available Sparse coding is an emerging method that has been successfully applied to both robust object tracking and recognition in the vision literature. In this paper, we propose to explore a sparse coding-based approach toward joint object tracking-and-recognition and explore its potential in the analysis of forward-looking infrared (FLIR video to support nighttime machine vision systems. A key technical contribution of this work is to unify existing sparse coding-based approaches toward tracking and recognition under the same framework, so that they can benefit from each other in a closed-loop. On the one hand, tracking the same object through temporal frames allows us to achieve improved recognition performance through dynamical updating of template/dictionary and combining multiple recognition results; on the other hand, the recognition of individual objects facilitates the tracking of multiple objects (i.e., walking pedestrians, especially in the presence of occlusion within a crowded environment. We report experimental results on both the CASIAPedestrian Database and our own collected FLIR video database to demonstrate the effectiveness of the proposed joint tracking-and-recognition approach.

  9. Robust Sparse Sensing Using Weather Radar

    Science.gov (United States)

    Mishra, K. V.; Kruger, A.; Krajewski, W. F.; Xu, W.

    2014-12-01

    The ability of a weather radar to detect weak echoes is limited by the presence of noise or unwanted echoes. Some of these unwanted signals originate externally to the radar system, such as cosmic noise, radome reflections, interference from co-located radars, and power transmission lines. The internal source of noise in microwave radar receiver is mainly thermal. The thermal noise from various microwave devices in the radar receiver tends to lower the signal-to-noise ratio, thereby masking the weaker signals. Recently, the compressed sensing (CS) technique has emerged as a novel signal sampling paradigm that allows perfect reconstruction of signals sampled at frequencies lower than the Nyquist rate. Many radar and remote sensing applications require efficient and rapid data acquisition. The application of CS to weather radars may allow for faster target update rates without compromising the accuracy of target information. In our previous work, we demonstrated recovery of an entire precipitation scene from its compressed-sensed version by using the matrix completion approach. In this study, we characterize the performance of such a CS-based weather radar in the presence of additive noise. We use a signal model where the precipitation signals form a low-rank matrix that is corrupted with (bounded) noise. Using recent advances in algorithms for matrix completion from few noisy observations, we reconstruct the precipitation scene with reasonable accuracy. We test and demonstrate our approach using the data collected by Iowa X-band Polarimetric (XPOL) weather radars.

  10. Regularized Data Assimilation and Fusion of non-Gaussian States Exhibiting Sparse Prior in Transform Domains

    Science.gov (United States)

    Ebtehaj, M.; Foufoula, E.

    2012-12-01

    Improved estimation of geophysical state variables in a noisy environment from down-sampled observations and background model forecasts has been the subject of growing research in the past decades. Often the number of degrees of freedom in high-dimensional non-Gaussian natural states is quite small compared to their ambient dimensionality, a property often revealed as a sparse representation in an appropriately chosen domain. Aiming to increase the hydrometeorological forecast skill and motivated by wavelet-domain sparsity of some land-surface geophysical states, new framework is presented that recast the classical variational data assimilation/fusion (DA/DF) problem via L_1 regularization in the wavelet domain. Our results suggest that proper regularization can lead to more accurate recovery of a wide range of smooth/non-smooth geophysical states exhibiting remarkable non-Gaussian features. The promise of the proposed framework is demonstrated in multi-sensor satellite and land-based precipitation data fusion, while the regularized DA is performed on the heat equation in a 4D-VAR context, using sparse regularization in the wavelet domain.; ; Top panel: Noisy observations of the linear advection diffusion equation at five consecutive snapshots, middle panel: Classical 4D-VAR and bottom panel: l_1 regularized 4D-VAR with improved results.

  11. A fast and accurate sparse continuous signal reconstruction by homotopy DCD with non-convex regularization.

    Science.gov (United States)

    Wang, Tianyun; Lu, Xinfei; Yu, Xiaofei; Xi, Zhendong; Chen, Weidong

    2014-03-26

    In recent years, various applications regarding sparse continuous signal recovery such as source localization, radar imaging, communication channel estimation, etc., have been addressed from the perspective of compressive sensing (CS) theory. However, there are two major defects that need to be tackled when considering any practical utilization. The first issue is off-grid problem caused by the basis mismatch between arbitrary located unknowns and the pre-specified dictionary, which would make conventional CS reconstruction methods degrade considerably. The second important issue is the urgent demand for low-complexity algorithms, especially when faced with the requirement of real-time implementation. In this paper, to deal with these two problems, we have presented three fast and accurate sparse reconstruction algorithms, termed as HR-DCD, Hlog-DCD and Hlp-DCD, which are based on homotopy, dichotomous coordinate descent (DCD) iterations and non-convex regularizations, by combining with the grid refinement technique. Experimental results are provided to demonstrate the effectiveness of the proposed algorithms and related analysis.

  12. Sparse signals recovered by non-convex penalty in quasi-linear systems.

    Science.gov (United States)

    Cui, Angang; Li, Haiyang; Wen, Meng; Peng, Jigen

    2018-01-01

    The goal of compressed sensing is to reconstruct a sparse signal under a few linear measurements far less than the dimension of the ambient space of the signal. However, many real-life applications in physics and biomedical sciences carry some strongly nonlinear structures, and the linear model is no longer suitable. Compared with the compressed sensing under the linear circumstance, this nonlinear compressed sensing is much more difficult, in fact also NP-hard, combinatorial problem, because of the discrete and discontinuous nature of the [Formula: see text]-norm and the nonlinearity. In order to get a convenience for sparse signal recovery, we set the nonlinear models have a smooth quasi-linear nature in this paper, and study a non-convex fraction function [Formula: see text] in this quasi-linear compressed sensing. We propose an iterative fraction thresholding algorithm to solve the regularization problem [Formula: see text] for all [Formula: see text]. With the change of parameter [Formula: see text], our algorithm could get a promising result, which is one of the advantages for our algorithm compared with some state-of-art algorithms. Numerical experiments show that our method performs much better than some state-of-the-art methods.

  13. Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions

    Energy Technology Data Exchange (ETDEWEB)

    Huan, Xun; Safta, Cosmin; Sargsyan, Khachik; Vane, Zachary Phillips; Lacaze, Guilhem; Oefelein, Joseph C.; Najm, Habib N.

    2017-07-01

    Compressive sensing is a powerful technique for recovering sparse solutions of underdetermined linear systems, which is often encountered in uncertainty quanti cation analysis of expensive and high-dimensional physical models. We perform numerical investigations employing several com- pressive sensing solvers that target the unconstrained LASSO formulation, with a focus on linear systems that arise in the construction of polynomial chaos expansions. With core solvers of l1 ls, SpaRSA, CGIST, FPC AS, and ADMM, we develop techniques to mitigate over tting through an automated selection of regularization constant based on cross-validation, and a heuristic strategy to guide the stop-sampling decision. Practical recommendations on parameter settings for these tech- niques are provided and discussed. The overall method is applied to a series of numerical examples of increasing complexity, including large eddy simulations of supersonic turbulent jet-in-cross flow involving a 24-dimensional input. Through empirical phase-transition diagrams and convergence plots, we illustrate sparse recovery performance under structures induced by polynomial chaos, accuracy and computational tradeoffs between polynomial bases of different degrees, and practi- cability of conducting compressive sensing for a realistic, high-dimensional physical application. Across test cases studied in this paper, we find ADMM to have demonstrated empirical advantages through consistent lower errors and faster computational times.

  14. Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.

    Science.gov (United States)

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

    2017-07-01

    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.

  15. Low-count PET image restoration using sparse representation

    Science.gov (United States)

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

    2018-04-01

    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.

  16. Sparse approximation problem: how rapid simulated annealing succeeds and fails

    Science.gov (United States)

    Obuchi, Tomoyuki; Kabashima, Yoshiyuki

    2016-03-01

    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.

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

    Directory of Open Access Journals (Sweden)

    Honghong Yang

    2016-01-01

    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.

  18. Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing.

    Science.gov (United States)

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

    2014-02-01

    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.

  19. Sparse representation-based color visualization method for hyperspectral imaging

    Science.gov (United States)

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

    2013-06-01

    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.

  20. Sparse recovery of undersampled intensity patterns for coherent diffraction imaging at high X-ray energies

    OpenAIRE

    Maddali, Siddharth; Calvo-Almazan, Irene; Almer, Jonathan; Kenesei, Peter; Park, Jun-Sang; Harder, Ross; Nashed, Youssef; Hruszkewycz, Stephan

    2017-01-01

    Coherent X-ray photons with energies higher than 50 keV offer new possibilities for imaging nanoscale lattice distortions in bulk crystalline materials using Bragg peak phase retrieval methods. However, the compression of reciprocal space at high energies typically results in poorly resolved fringes on an area detector, rendering the diffraction data unsuitable for the three-dimensional reconstruction of compact crystals. To address this problem, we propose a method by which to recover fine f...

  1. Matching pursuit and source deflation for sparse EEG/MEG dipole moment estimation.

    Science.gov (United States)

    Wu, Shun Chi; Swindlehurst, A Lee

    2013-08-01

    In this paper, we propose novel matching pursuit (MP)-based algorithms for EEG/MEG dipole source localization and parameter estimation for multiple measurement vectors with constant sparsity. The algorithms combine the ideas of MP for sparse signal recovery and source deflation, as employed in estimation via alternating projections. The source-deflated matching pursuit (SDMP) approach mitigates the problem of residual interference inherent in sequential MP-based methods or recursively applied (RAP)-MUSIC. Furthermore, unlike prior methods based on alternating projection, SDMP allows one to efficiently estimate the dipole orientation in addition to its location. Simulations show that the proposed algorithms outperform existing techniques under various conditions, including those with highly correlated sources. Results using real EEG data from auditory experiments are also presented to illustrate the performance of these algorithms.

  2. Sparse Reconstruction of Regional Gravity Signal Based on Stabilized Orthogonal Matching Pursuit (SOMP)

    Science.gov (United States)

    Saadat, S. A.; Safari, A.; Needell, D.

    2016-06-01

    The main role of gravity field recovery is the study of dynamic processes in the interior of the Earth especially in exploration geophysics. In this paper, the Stabilized Orthogonal Matching Pursuit (SOMP) algorithm is introduced for sparse reconstruction of regional gravity signals of the Earth. In practical applications, ill-posed problems may be encountered regarding unknown parameters that are sensitive to the data perturbations. Therefore, an appropriate regularization method needs to be applied to find a stabilized solution. The SOMP algorithm aims to regularize the norm of the solution vector, while also minimizing the norm of the corresponding residual vector. In this procedure, a convergence point of the algorithm that specifies optimal sparsity-level of the problem is determined. The results show that the SOMP algorithm finds the stabilized solution for the ill-posed problem at the optimal sparsity-level, improving upon existing sparsity based approaches.

  3. When 'exact recovery' is exact recovery in compressed sensing simulation

    DEFF Research Database (Denmark)

    Sturm, Bob L.

    2012-01-01

    In a simulation of compressed sensing (CS), one must test whether the recovered solution \\(\\vax\\) is the true solution \\(\\vx\\), i.e., ``exact recovery.'' Most CS simulations employ one of two criteria: 1) the recovered support is the true support; or 2) the normalized squared error is less than...... \\(\\epsilon^2\\). We analyze these exact recovery criteria independent of any recovery algorithm, but with respect to signal distributions that are often used in CS simulations. That is, given a pair \\((\\vax,\\vx)\\), when does ``exact recovery'' occur with respect to only one or both of these criteria...... for a given distribution of \\(\\vx\\)? We show that, in a best case scenario, \\(\\epsilon^2\\) sets a maximum allowed missed detection rate in a majority sense....

  4. Algorithms for Sparse Non-negative Tucker Decompositions

    DEFF Research Database (Denmark)

    Mørup, Morten; Hansen, Lars Kai

    2008-01-01

    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...

  5. Sparse-view Reconstruction of Dynamic Processes by Neutron Tomography

    Science.gov (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.

  6. Massively parallel sparse matrix function calculations with NTPoly

    Science.gov (United States)

    Dawson, William; Nakajima, Takahito

    2018-04-01

    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.

  7. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla

    2014-05-04

    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.

  8. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla

    2014-01-06

    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.

  9. Identification of MIMO systems with sparse transfer function coefficients

    Science.gov (United States)

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

    2012-12-01

    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.

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

    Energy Technology Data Exchange (ETDEWEB)

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

    1994-12-31

    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.

  11. MULTISCALE SPARSE APPEARANCE MODELING AND SIMULATION OF PATHOLOGICAL DEFORMATIONS

    Directory of Open Access Journals (Sweden)

    Rami Zewail

    2017-08-01

    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.

  12. Data-driven initialization of SParSE

    Science.gov (United States)

    Roh, Min K.; Proctor, Joshua L.

    2017-07-01

    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.

  13. Dictionaries for Sparse Neural Network Approximation

    Czech Academy of Sciences Publication Activity Database

    Kůrková, Věra; Sanguineti, M.

    submitted 27.12.2017 (2018) ISSN 2162-237X R&D Projects: GA ČR GA15-18108S Institutional support: RVO:67985807 Keywords : measures of sparsity * feedforward networks * binary classification * dictionaries of computational units * Chernoff-Hoeffding Bound Subject RIV: IN - Informatics, Computer Science OBOR OECD: Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8) Impact factor: 6.108, year: 2016

  14. A new type of self-supported, polymeric Ru-carbene complex for homogeneous catalysis and heterogeneous recovery: synthesis and catalytic activities for ring-closing metathesis.

    Science.gov (United States)

    Chen, Shu-Wei; Kim, Ju Hyun; Shin, Hyunik; Lee, Sang-Gi

    2008-08-07

    A novel 2nd generation Grubbs-type catalyst tethering an isopropoxystyrene has been synthesized and automatically polymerized in solution to form a self-supported polymeric Ru-carbene complex, which catalyzed ring-closing metathesis homogeneously, but was recovered heterogeneously.

  15. BigSparse: High-performance external graph analytics

    OpenAIRE

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

    2017-01-01

    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...

  16. Airborne LIDAR Points Classification Based on Tensor Sparse Representation

    Science.gov (United States)

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

    2017-09-01

    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.

  17. Adaptive identification of acoustic multichannel systems using sparse representations

    CERN Document Server

    Helwani, Karim

    2014-01-01

    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

  18. A Projected Conjugate Gradient Method for Sparse Minimax Problems

    DEFF Research Database (Denmark)

    Madsen, Kaj; Jonasson, Kristjan

    1993-01-01

    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...

  19. Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

    KAUST Repository

    Desmal, Abdulla

    2015-04-13

    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.

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

    Directory of Open Access Journals (Sweden)

    Kuo-Kun Tseng

    2015-01-01

    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.

  1. Source term identification in atmospheric modelling via sparse optimization

    Science.gov (United States)

    Adam, Lukas; Branda, Martin; Hamburger, Thomas

    2015-04-01

    concept of sparsity. In the paper, we summarize several optimization techniques which are used for finding sparse solutions and propose their modifications to handle selected constraints such as nonnegativity constraints and simple linear constraints, for example the minimal or maximal amount of total release. These techniques range from successive convex approximations to solution of one nonconvex problem. On simple examples, we explain these techniques and compare them from the point of implementation simplicity, approximation capability and convergence properties. Finally, these methods will be applied on the European Tracer Experiment (ETEX) data and the results will be compared with the current state of arts techniques such as regularized least squares or Bayesian approach. The obtained results show the surprisingly good results of these techniques. This research is supported by EEA/Norwegian Financial Mechanism under project 7F14287 STRADI.

  2. American Recovery and Reinvestment Act (ARRA) FEMP Technical Assistance Federal Aviation Administration Project 209 - Control Tower and Support Building, Las Vegas, NV

    Energy Technology Data Exchange (ETDEWEB)

    Arends, J.; Sandusky, William F.

    2010-03-31

    This report represents findings of a design review team that evaluated construction documents (at the 70% level) and operating specifications for a new control tower and support building that will be built in Las Vegas, Nevada by the Federal Aviation Administration (FAA). The focus of the review was to identify measures that could be incorporated into the final design and operating specification that would result in additional energy savings for the FAA that would not have otherwise occurred.

  3. American Recovery and Reinvestment Act (ARRA) FEMP Technical Assistance Federal Aviation Administration – Project 209 Control Tower and Support Building Oakland, CA

    Energy Technology Data Exchange (ETDEWEB)

    Arends, J.; Sandusky, William F.

    2010-03-01

    This report represents findings of a design review team that evaluated construction documents (at the 70% level) and operating specifications for a new control tower and support building that will be build at Oakland, California by the Federal Aviation Administration (FAA). The focus of the review was to identify measures that could be incorporated into the final design and operating specification that would result in additional energy savings for the FAA that would not have otherwise occurred.

  4. 30 CFR 75.207 - Pillar recovery.

    Science.gov (United States)

    2010-07-01

    ... SAFETY STANDARDS-UNDERGROUND COAL MINES Roof Support § 75.207 Pillar recovery. Pillar recovery shall be... be left in place. (b) Before mining is started in a pillar split or lift— (1) At least two rows of breaker posts or equivalent support shall be installed— (i) As close to the initial intended breakline as...

  5. Iterative support detection-based split Bregman method for wavelet frame-based image inpainting.

    Science.gov (United States)

    He, Liangtian; Wang, Yilun

    2014-12-01

    The wavelet frame systems have been extensively studied due to their capability of sparsely approximating piece-wise smooth functions, such as images, and the corresponding wavelet frame-based image restoration models are mostly based on the penalization of the l1 norm of wavelet frame coefficients for sparsity enforcement. In this paper, we focus on the image inpainting problem based on the wavelet frame, propose a weighted sparse restoration model, and develop a corresponding efficient algorithm. The new algorithm combines the idea of iterative support detection method, first proposed by Wang and Yin for sparse signal reconstruction, and the split Bregman method for wavelet frame l1 model of image inpainting, and more important, naturally makes use of the specific multilevel structure of the wavelet frame coefficients to enhance the recovery quality. This new algorithm can be considered as the incorporation of prior structural information of the wavelet frame coefficients into the traditional l1 model. Our numerical experiments show that the proposed method is superior to the original split Bregman method for wavelet frame-based l1 norm image inpainting model as well as some typical l(p) (0 ≤ p wavelet frame coefficients.

  6. Sparse Decomposition and Modeling of Anatomical Shape Variation

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Rostrup, Egill; Ryberg, Charlotte

    2007-01-01

    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...

  7. Sparse decomposition and modeling of anatomical shape variation

    DEFF Research Database (Denmark)

    Sjöstrand, Karl; Rostrup, Egill; Ryberg, Charlotte

    2007-01-01

    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...

  8. Sparse principal component analysis in medical shape modeling

    Science.gov (United States)

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

    2006-03-01

    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.

  9. A Practical View on Tunable Sparse Network Coding

    DEFF Research Database (Denmark)

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

    2015-01-01

    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....

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

    Directory of Open Access Journals (Sweden)

    Yin Fei

    2017-01-01

    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.

  11. Discriminative object tracking via sparse representation and online dictionary learning.

    Science.gov (United States)

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

    2014-04-01

    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.

  12. The equivalent source method as a sparse signal reconstruction

    DEFF Research Database (Denmark)

    Fernandez Grande, Efren; Xenaki, Angeliki

    2015-01-01

    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...

  13. LOW-DIMENSIONAL STRUCTURES: SPARSE CODING FOR NEURONAL ACTIVITY

    Directory of Open Access Journals (Sweden)

    YUNHUA XU

    2013-01-01

    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.

  14. Sparsely-Packetized Predictive Control by Orthogonal Matching Pursuit

    DEFF Research Database (Denmark)

    Nagahara, Masaaki; Quevedo, Daniel; Østergaard, Jan

    2012-01-01

    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...

  15. Robust visual tracking of infrared object via sparse representation model

    Science.gov (United States)

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

    2014-11-01

    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.

  16. Proportionate Minimum Error Entropy Algorithm for Sparse System Identification

    Directory of Open Access Journals (Sweden)

    Zongze Wu

    2015-08-01

    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.

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

    KAUST Repository

    Zhang, Tianzhu

    2014-06-19

    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.

  18. Multiple kernel sparse representations for supervised and unsupervised learning.

    Science.gov (United States)

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

    2014-07-01

    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.

  19. Graph Regularized Nonnegative Matrix Factorization with Sparse Coding

    Directory of Open Access Journals (Sweden)

    Chuang Lin

    2015-01-01

    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.

  20. 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

  1. New Algorithms and Sparse Regularization for Synthetic Aperture Radar Imaging

    Science.gov (United States)

    2015-10-26

    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

  2. Low-rank sparse learning for robust visual tracking

    KAUST Repository

    Zhang, Tianzhu

    2012-01-01

    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.

  3. A Sparse Bayesian Learning Algorithm With Dictionary Parameter Estimation

    DEFF Research Database (Denmark)

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

    2014-01-01

    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...

  4. Fast Estimation of Optimal Sparseness of Music Signals

    DEFF Research Database (Denmark)

    la Cour-Harbo, Anders

    2006-01-01

    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...

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

    NARCIS (Netherlands)

    Wajer, F.T.A.W.

    2001-01-01

    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

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

    NARCIS (Netherlands)

    F. Sprengel

    1998-01-01

    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

  7. Sobol indices for dimension adaptivity in sparse grids

    NARCIS (Netherlands)

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

    2016-01-01

    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

  8. Superpixel sparse representation for target detection in hyperspectral imagery

    Science.gov (United States)

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

    2017-05-01

    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.

  9. Sparse group lasso and high dimensional multinomial classification

    DEFF Research Database (Denmark)

    Vincent, Martin; Hansen, N.R.

    2014-01-01

    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...

  10. 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 ...

  11. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun

    2013-10-01

    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.

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

    KAUST Repository

    Sicat, Ronell Barrera

    2014-12-31

    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.

  13. 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 ...

  14. Effects of Arm Weight Support Training to Promote Recovery of Upper Limb Function for Subacute Patients after Stroke with Different Levels of Arm Impairments

    Directory of Open Access Journals (Sweden)

    Irene H. L. Chan

    2016-01-01

    Full Text Available Purpose. The goal of this study was to investigate the effects of arm weight support training using the ArmeoSpring for subacute patients after stroke with different levels of hemiplegic arm impairments. Methods. 48 inpatients with subacute stroke, stratified into 3 groups from mild to severe upper extremity impairment, were engaged in ArmeoSpring training for 45 minutes daily, 5 days per week for 3 weeks, in addition to conventional rehabilitation. Evaluations were conducted at three measurement occasions: immediately before training (T1; immediately after training (T2; and at a 3-week follow-up (T3 by a blind rater. Results. Shoulder flexion active range of motion, Upper Extremity Scores in the Fugl-Meyer Assessment (FMA, and Vertical Catch had the greatest differences in gain scores for patients between severe and moderate impairments, whereas FMA Hand Scores had significant differences in gain scores between moderate and mild impairments. There was no significant change in muscle tone or hand-path ratios between T1, T2, and T3 within the groups. Conclusion. Arm weight support training is beneficial for subacute stroke patients with moderate to severe arm impairments, especially to improve vertical control such as shoulder flexion, and there were no adverse effects in muscle tone.

  15. Sparse magnetic resonance imaging reconstruction using the bregman iteration

    Science.gov (United States)

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

    2013-01-01

    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.

  16. Deformable segmentation via sparse representation and dictionary learning.

    Science.gov (United States)

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

    2012-10-01

    "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.

  17. Transport of Zn (II by TDDA-Polypropylene Supported Liquid Membranes and Recovery from Waste Discharge Liquor of Galvanizing Plant of Zn (II

    Directory of Open Access Journals (Sweden)

    Hanif Ur Rehman

    2017-01-01

    Full Text Available The facilitated passage of Zn (II across flat sheet supported liquid membrane saturated with TDDA (tri-n-dodecylamine in xylene membrane phase has been investigated. The effect of acid and metal ion concentration in the feed solution, the carrier concentration in membrane phase, stripping agent concentration in stripping phase, and coions on the extraction of Zn (II was investigated. The stoichiometry of the extracted species, that is, complex, was investigated on slope analysis method and it was found that the complex (LH2·Zn(Cl2 is responsible for transport of Zn (II. A mathematical model was developed for transport of Zn (II, and the predicted results strongly agree with experimental ones. The mechanism of transport was determined by coupled coion transport mechanism with H+ and Cl− coupled ions. The optimized SLM was effectively used for elimination of Zn (II from waste discharge liquor of galvanizing plant of Zn (II.

  18. A data-driven sparse GLM for fMRI analysis using sparse dictionary learning with MDL criterion.

    Science.gov (United States)

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

    2011-05-01

    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.

  19. Separation of seismic blended data by sparse inversion over dictionary learning

    Science.gov (United States)

    Zhou, Yanhui; Chen, Wenchao; Gao, Jinghuai

    2014-07-01

    Recent development of blended acquisition calls for the new procedure to process blended seismic measurements. Presently, deblending and reconstructing unblended data followed by conventional processing is the most practical processing workflow. We study seismic deblending by advanced sparse inversion with a learned dictionary in this paper. To make our method more effective, hybrid acquisition and time-dithering sequential shooting are introduced so that clean single-shot records can be used to train the dictionary to favor the sparser representation of data to be recovered. Deblending and dictionary learning with l1-norm based sparsity are combined to construct the corresponding problem with respect to unknown recovery, dictionary, and coefficient sets. A two-step optimization approach is introduced. In the step of dictionary learning, the clean single-shot data are selected as trained data to learn the dictionary. For deblending, we fix the dictionary and employ an alternating scheme to update the recovery and coefficients separately. Synthetic and real field data were used to verify the performance of our method. The outcome can be a significant reference in designing high-efficient and low-cost blended acquisition.

  20. Bayesian sparse-based reconstruction in bioluminescence tomography improves localization accuracy and reduces computational time.

    Science.gov (United States)

    Feng, Jinchao; Jia, Kebin; Li, Zhe; Pogue, Brian W; Yang, Mingjie; Wang, Yaqi

    2017-11-09

    Bioluminescence tomography (BLT) provides fundamental insight into biological processes in vivo. To fully realize its potential, it is important to develop image reconstruction algorithms that accurately visualize and quantify the bioluminescence signals taking advantage of limited boundary measurements. In this study, a new 2-step reconstruction method for BLT is developed by taking advantage of the sparse a priori information of the light emission using multispectral measurements. The first step infers a wavelength-dependent prior by using all multi-wavelength measurements. The second step reconstructs the source distribution based on this developed prior. Simulation, phantom and in vivo results were performed to assess and compare the accuracy and the computational efficiency of this algorithm with conventional sparsity-promoting BLT reconstruction algorithms, and results indicate that the position errors are reduced from a few millimeters down to submillimeter, and reconstruction time is reduced by 3 orders of magnitude in most cases, to just under a few seconds. The recovery of single objects and multiple (2 and 3) small objects is simulated, and the recovery of images of a mouse phantom and an experimental animal with an existing luminescent source in the abdomen is demonstrated. Matlab code is available at https://github.com/jinchaofeng/code/tree/master. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.