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Sample records for sparse support recovery

  1. 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.

  2. 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

  3. Behavior of greedy sparse representation algorithms on nested supports

    DEFF Research Database (Denmark)

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

    2013-01-01

    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...

  4. 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.

  5. 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....

  6. 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.

  7. 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.

  8. Distributed coding of multiview sparse sources with joint recovery

    DEFF Research Database (Denmark)

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

    2016-01-01

    In support of applications involving multiview sources in distributed object recognition using lightweight cameras, we propose a new method for the distributed coding of sparse sources as visual descriptor histograms extracted from multiview images. The problem is challenging due to the computati...... 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....

  9. Support agnostic Bayesian matching pursuit for block sparse signals

    KAUST Repository

    Masood, Mudassir; Al-Naffouri, Tareq Y.

    2013-01-01

    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

  10. 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.

  11. 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.

  12. Efficient coordinated recovery of sparse channels in massive MIMO

    KAUST Repository

    Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.

    2015-01-01

    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

  13. Optimal deep neural networks for sparse recovery via Laplace techniques

    OpenAIRE

    Limmer, Steffen; Stanczak, Slawomir

    2017-01-01

    This paper introduces Laplace techniques for designing a neural network, with the goal of estimating simplex-constraint sparse vectors from compressed measurements. To this end, we recast the problem of MMSE estimation (w.r.t. a pre-defined uniform input distribution) as the problem of computing the centroid of some polytope that results from the intersection of the simplex and an affine subspace determined by the measurements. Owing to the specific structure, it is shown that the centroid ca...

  14. Two-dimensional sparse wavenumber recovery for guided wavefields

    Science.gov (United States)

    Sabeti, Soroosh; Harley, Joel B.

    2018-04-01

    The multi-modal and dispersive behavior of guided waves is often characterized by their dispersion curves, which describe their frequency-wavenumber behavior. In prior work, compressive sensing based techniques, such as sparse wavenumber analysis (SWA), have been capable of recovering dispersion curves from limited data samples. A major limitation of SWA, however, is the assumption that the structure is isotropic. As a result, SWA fails when applied to composites and other anisotropic structures. There have been efforts to address this issue in the literature, but they either are not easily generalizable or do not sufficiently express the data. In this paper, we enhance the existing approaches by employing a two-dimensional wavenumber model to account for direction-dependent velocities in anisotropic media. We integrate this model with tools from compressive sensing to reconstruct a wavefield from incomplete data. Specifically, we create a modified two-dimensional orthogonal matching pursuit algorithm that takes an undersampled wavefield image, with specified unknown elements, and determines its sparse wavenumber characteristics. We then recover the entire wavefield from the sparse representations obtained with our small number of data samples.

  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. 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.

  17. Sparse Recovery via l1 and L1 Optimization

    Science.gov (United States)

    2014-11-01

    of China 1.1 (Mar. 2013), pp. 79–105. [13] H. Brezis. “Solutions with compact sup- port of variational inequalities ”. In: Rus- sian Mathematical...Surveys 29.2 (Apr. 30, 1974), pp. 103–108. [14] H. Brezis and A. Friedman. “Estimates on the support of solutions of parabolic varia- tional inequalities ...ized inverse problems (e.g., total variation image reconstruction [4]) have made `1 optimization a central tool in data processing problems. As the

  18. A Comparison of Compressed Sensing and Sparse Recovery Algorithms Applied to Simulation Data

    Directory of Open Access Journals (Sweden)

    Ya Ju Fan

    2016-08-01

    Full Text Available The move toward exascale computing for scientific simulations is placing new demands on compression techniques. It is expected that the I/O system will not be able to support the volume of data that is expected to be written out. To enable quantitative analysis and scientific discovery, we are interested in techniques that compress high-dimensional simulation data and can provide perfect or near-perfect reconstruction.  In this paper, we explore the use of compressed sensing (CS techniques to reduce the size of the data before they are written out. Using large-scale simulation data, we investigate how the sufficient sparsity condition and the contrast in the data affect the quality of reconstruction and the degree of compression.  We provide suggestions for the practical implementation of CS techniques and compare them with other sparse recovery methods. Our results show that despite longer times for reconstruction, compressed sensing techniques can provide near perfect reconstruction over a range of data with varying sparsity.

  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. 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.

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

    KAUST Repository

    Sana, Furrukh; Katterbauer, Klemens; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim

    2016-01-01

    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.

  2. 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.

  3. 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.

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

    Energy Technology Data Exchange (ETDEWEB)

    Wang, Kunpeng; Chai, Yi [College of Automation, Chongqing University, Chongqing 400044 (China); Su, Chunxiao [Research Center of Laser Fusion, CAEP, P. O. Box 919-983, Mianyang 621900 (China)

    2013-08-15

    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{sub 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.

  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. 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.

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

    KAUST Repository

    Sana, Furrukh; Ravanelli, Fabio; Al-Naffouri, Tareq Y.; Hoteit, Ibrahim

    2016-01-01

    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.

  8. 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...

  9. 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.

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

    International Nuclear Information System (INIS)

    Evastratova, Ekaterina S.; Petin, Vladislav; Kim, Jin Hong; Kim, Jin Kyu; Lim, Youg Khi

    2017-01-01

    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

  11. 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....

  12. Image denoising via collaborative support-agnostic recovery

    KAUST Repository

    Behzad, Muzammil; Masood, Mudassir; Ballal, Tarig; Shadaydeh, Maha; Al-Naffouri, Tareq Y.

    2017-01-01

    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.

  13. 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.

  14. Direction-of-Arrival Estimation Based on Sparse Recovery with Second-Order Statistics

    Directory of Open Access Journals (Sweden)

    H. Chen

    2015-04-01

    Full Text Available Traditional direction-of-arrival (DOA estimation techniques perform Nyquist-rate sampling of the received signals and as a result they require high storage. To reduce sampling ratio, we introduce level-crossing (LC sampling which captures samples whenever the signal crosses predetermined reference levels, and the LC-based analog-to-digital converter (LC ADC has been shown to efficiently sample certain classes of signals. In this paper, we focus on the DOA estimation problem by using second-order statistics based on the LC samplings recording on one sensor, along with the synchronous samplings of the another sensors, a sparse angle space scenario can be found by solving an $ell_1$ minimization problem, giving the number of sources and their DOA's. The experimental results show that our proposed method, when compared with some existing norm-based constrained optimization compressive sensing (CS algorithms, as well as subspace method, improves the DOA estimation performance, while using less samples when compared with Nyquist-rate sampling and reducing sensor activity especially for long time silence signal.

  15. 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.

  16. Promoting recovery through peer support: possibilities for social work practice.

    Science.gov (United States)

    Loumpa, Vasiliki

    2012-01-01

    The Recovery Approach has been adopted by mental health services worldwide and peer support constitutes one of the main elements of recovery-based services. This article discusses the relevancy of recovery and peer support to mental health social work practice through an exploration of social work ethics and values. Furthermore, it provides an exploration of how peer support can be maximized in groupwork to assist the social work clinician to promote recovery and well-being. More specifically, this article discusses how the narrative therapy concepts of "retelling" and "witnessing" can be used in the context of peer support to promote recovery, and also how social constructionist, dialogical, and systemic therapy approaches can assist the social work practitioner to enhance peer support in recovery oriented groupwork. Copyright © Taylor & Francis Group, LLC

  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. Sparse PCA with Oracle Property.

    Science.gov (United States)

    Gu, Quanquan; Wang, Zhaoran; Liu, Han

    In this paper, we study the estimation of the k -dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank- k , and attains a [Formula: see text] statistical rate of convergence with s being the subspace sparsity level and n the sample size. Compared to existing support recovery results for sparse PCA, our approach does not hinge on the spiked covariance model or the limited correlation condition. As a complement to the first estimator that enjoys the oracle property, we prove that, another estimator within the family achieves a sharper statistical rate of convergence than the standard semidefinite relaxation of sparse PCA, even when the previous assumption on the magnitude of the projection matrix is violated. We validate the theoretical results by numerical experiments on synthetic datasets.

  20. Socioeconomic Factors Affecting Local Support for Black Bear Recovery Strategies

    Science.gov (United States)

    Morzillo, Anita T.; Mertig, Angela G.; Hollister, Jeffrey W.; Garner, Nathan; Liu, Jianguo

    2010-06-01

    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 strategies prior to public release of a black bear conservation and management plan for eastern Texas, United States. Data were collected from 1,006 residents living in proximity to potential recovery locations, particularly Big Thicket National Preserve. In addition to traditional logistic regression analysis, we used conditional probability analysis to statistically and visually evaluate probabilities of public support for potential black bear recovery strategies based on socioeconomic characteristics. Allowing black bears to repopulate the region on their own (i.e., without active reintroduction) was the recovery strategy with the greatest probability of acceptance. Recovery strategy acceptance was influenced by many socioeconomic factors. Older and long-time local residents were most likely to want to exclude black bears from the area. Concern about the problems that black bears may cause was the only variable significantly related to support or non-support across all strategies. Lack of personal knowledge about black bears was the most frequent reason for uncertainty about preferred strategy. In order to reduce local uncertainty about possible recovery strategies, we suggest that wildlife managers focus outreach efforts on providing local residents with general information about black bears, as well as information pertinent to minimizing the potential for human-black bear conflict.

  1. Multi-stage classification method oriented to aerial image based on low-rank recovery and multi-feature fusion sparse representation.

    Science.gov (United States)

    Ma, Xu; Cheng, Yongmei; Hao, Shuai

    2016-12-10

    Automatic classification of terrain surfaces from an aerial image is essential for an autonomous unmanned aerial vehicle (UAV) landing at an unprepared site by using vision. Diverse terrain surfaces may show similar spectral properties due to the illumination and noise that easily cause poor classification performance. To address this issue, a multi-stage classification algorithm based on low-rank recovery and multi-feature fusion sparse representation is proposed. First, color moments and Gabor texture feature are extracted from training data and stacked as column vectors of a dictionary. Then we perform low-rank matrix recovery for the dictionary by using augmented Lagrange multipliers and construct a multi-stage terrain classifier. Experimental results on an aerial map database that we prepared verify the classification accuracy and robustness of the proposed method.

  2. 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.

  3. 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.

  4. On complex-valued deautoconvolution of compactly supported functions with sparse Fourier representation

    International Nuclear Information System (INIS)

    Bürger, Steven; Flemming, Jens; Hofmann, Bernd

    2016-01-01

    Convergence rates results for the Tikhonov regularization of nonlinear ill-posed operator equations are missing, even for a Hilbert space setting, if a range type source condition fails and if moreover nonlinearity conditions of tangential cone type cannot be shown. This situation applies for a deautoconvolution problem in complex-valued L 2 -spaces over finite real intervals, occurring in a slightly generalized version in laser optics. For this problem we show that the lack of applicable convergence rates results can be overcome under the assumption that the solution of the operator equation has a sparse Fourier representation. Precisely, we derive a variational source condition for that case, which implies a convergence rate immediately. The surprising observation is that a sparsity assumption imposed on the solution leads to success, although the used norm square is not known to be a sparsity promoting penalty in the Tikhonov functional. (paper)

  5. 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

  6. Renal Function Recovery with Total Artificial Heart Support.

    Science.gov (United States)

    Quader, Mohammed A; Goodreau, Adam M; Shah, Keyur B; Katlaps, Gundars; Cooke, Richard; Smallfield, Melissa C; Tchoukina, Inna F; Wolfe, Luke G; Kasirajan, Vigneshwar

    2016-01-01

    Heart failure patients requiring total artificial heart (TAH) support often have concomitant renal insufficiency (RI). We sought to quantify renal function recovery in patients supported with TAH at our institution. Renal function data at 30, 90, and 180 days after TAH implantation were analyzed for patients with RI, defined as hemodialysis supported or an estimated glomerular filtration rate (eGFR) less than 60 ml/min/1.73 m. Between January 2008 and December 2013, 20 of the 46 (43.5%) TAH recipients (age 51 ± 9 years, 85% men) had RI, mean preoperative eGFR of 48 ± 7 ml/min/1.73 m. Renal function recovery was noted at each follow-up interval: increment in eGFR (ml/min/1.73 m) at 30, 90, and 180 days was 21 ± 35 (p = 0.1), 16.5 ± 18 (p = 0.05), and 10 ± 9 (p = 0.1), respectively. Six patients (30%) required preoperative dialysis. Of these, four recovered renal function, one remained on dialysis, and one died. Six patients (30%) required new-onset dialysis. Of these, three recovered renal function and three died. Overall, 75% (15 of 20) of patients' renal function improved with TAH support. Total artificial heart support improved renal function in 75% of patients with pre-existing significant RI, including those who required preoperative dialysis.

  7. 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.

  8. 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.

  9. 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).

  10. Exploring the effect of organizational culture on consumer perceptions of agency support for mental health recovery.

    Science.gov (United States)

    Clossey, Laurene; Rheinheimer, David

    2014-05-01

    This research explores the impact of mental health agency culture on consumers' perceptions of agency support for their recovery. This study hypothesized that a constructive organizational culture must be present for consumers to perceive agency support for recovery. A sample of 12 mental health agencies in rural Pennsylvania participated in the research. Agency administrators completed an instrument called the recovery oriented service environment, which measured the number of recovery model program components offered by the agency. Consumers completed the recovery oriented services indicators, which taps into their perception of agency support for recovery. Direct service staff completed the organizational social context, which measured their agency's culture. Results showed that in this sample stronger consumer perceptions of agency support for recovery were correlated with higher ratings of agency constructive culture. The results suggest that agency culture is an important variable to target when implementing recovery model programming.

  11. 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...

  12. Sparse distributed memory overview

    Science.gov (United States)

    Raugh, Mike

    1990-01-01

    The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.

  13. The Alternative Peer Group: A Developmentally Appropriate Recovery Support Model for Adolescents.

    Science.gov (United States)

    Nash, Angela; Collier, Crystal

    2016-01-01

    Recovery as the goal for substance use disorder treatment has been a key component of the Substance Abuse and Mental Health Services Administration's mission for the past decade. Consistent with their mission, there is a call for research and development of recovery-oriented systems of care to support affected individuals through all stages of the recovery process. Evidence is emerging to support recovery practice and research for adults, but recovery-oriented models for adolescents are scant. The Alternative Peer Group (APG) is a comprehensive adolescent recovery support model that integrates recovering peers and prosocial activities into evidence-based clinical practice. Employing APG participants' own words, this article will describe the essential elements and three theoretical frameworks underlying the APG model to illustrate how the APG serves as a developmentally appropriate recovery support service for adolescents with substance use disorder.

  14. Sparse reconstruction using distribution agnostic bayesian matching pursuit

    KAUST Repository

    Masood, Mudassir; Al-Naffouri, Tareq Y.

    2013-01-01

    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

  15. 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......Introduction: Individual Placement and Support (IPS) is an evidence-based recovery-oriented intervention where employment specialists (ES) support persons with severe mental illness in achieving competitive employment. IPS is labelled a recovery-oriented intervention; although, the influence of IPS....... The other, clinical recovery is defined as symptom reduction and increased level of functioning. Aim: To investigate how an IPS-intervention influences the personal and clinical recovery in persons with severe mental illness. Method: A qualitative phenomenological study including interview of 12...

  16. 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.

  17. Social support in the post-abortion recovery room: evidence from patients, support persons and nurses in a Vancouver clinic.

    Science.gov (United States)

    Veiga, Mariana B; Lam, Melanie; Gemeinhardt, Carla; Houlihan, Edwina; Fitzsimmons, Brian P; Hodgson, Zoë G

    2011-03-01

    The benefits of social support in post-surgical recovery are well documented; social support decreases preoperative stress and postoperative recovery time. However, a paucity of studies have examined the effect of social support in the context of pregnancy termination. This study is the first to examine the effect of postoperative accompaniment from the patient, support person and nurses' perspective. This study was carried out in two phases. In Phase I, no accompaniment was allowed in the post-anesthesia recovery room (PAR); in Phase II, accompaniment was permitted. All participants completed pre- and postoperative questionnaires. The perception of accompaniment was overwhelmingly positive in patients and support people. Patients in Phase II demonstrated a high (over 95%) acceptance of accompaniment in the recovery room. It was found that 96.8% reported they would choose to be accompanied in the recovery room again if they had to have another abortion. Support persons felt very strongly that their presence was helpful to the patient. The decrease in pre- to postoperative anxiety levels was significantly greater in those women who were accompanied. However, overall, nurses demonstrated a negative attitude towards accompaniment in the recovery room. In summary, the presence of a support person in the PAR was perceived in a positive manner by patients and support people. However, the reasoning behind the negative opinion of nurses requires further study before PAR accompaniment can be considered a possibility in the context of pregnancy termination. Copyright © 2011 Elsevier Inc. All rights reserved.

  18. Recovery High Schools: Opportunities for Support and Personal Growth for Students in Recovery

    Science.gov (United States)

    Finch, Andrew; Wegman, Holly

    2012-01-01

    The time right after treatment for substance abuse is a particularly vulnerable time for adolescents; a time made more difficult by the expectation that they will return to their high school. Traditional high schools are often a high-risk environment for students who are working on maintaining their sobriety. Recovery schools offer an alternative…

  19. The Setting is the Service: How the Architecture of Sober Living Residences Supports Community Based Recovery.

    Science.gov (United States)

    Wittman, Fried; Jee, Babette; Polcin, Douglas L; Henderson, Diane

    2014-07-01

    The architecture of residential recovery settings is an important silent partner in the alcohol/drug recovery field. The settings significantly support or hinder recovery experiences of residents, and shape community reactions to the presence of sober living houses (SLH) in ordinary neighborhoods. Grounded in the principles of Alcoholics Anonymous, the SLH provides residents with settings designed to support peer based recovery; further, these settings operate in a community context that insists on sobriety and strongly encourages attendance at 12-step meetings. Little formal research has been conducted to show how architectural features of the recovery setting - building appearance, spatial layouts, furnishings and finishes, policies for use of the facilities, physical care and maintenance of the property, neighborhood features, aspects of location in the city - function to promote (or retard) recovery, and to build (or detract from) community support. This paper uses a case-study approach to analyze the architecture of a community-based residential recovery service that has demonstrated successful recovery outcomes for its residents, is popular in its community, and has achieved state-wide recognition. The Environmental Pattern Language (Alexander, Ishikawa, & Silverstein, 1977) is used to analyze its architecture in a format that can be tested, critiqued, and adapted for use by similar programs in many communities, providing a model for replication and further research.

  20. Development of the REFOCUS intervention to increase mental health team support for personal recovery.

    Science.gov (United States)

    Slade, Mike; Bird, Victoria; Le Boutillier, Clair; Farkas, Marianne; Grey, Barbara; Larsen, John; Leamy, Mary; Oades, Lindsay; Williams, Julie

    2015-12-01

    There is an emerging evidence base about best practice in supporting recovery. This is usually framed in relation to general principles, and specific pro-recovery interventions are lacking. To develop a theoretically based and empirically defensible new pro-recovery manualised intervention--called the REFOCUS intervention. Seven systematic and two narrative reviews were undertaken. Identified evidence gaps were addressed in three qualitative studies. The findings were synthesised to produce the REFOCUS intervention, manual and model. The REFOCUS intervention comprises two components: recovery-promoting relationships and working practices. Approaches to supporting relationships comprise coaching skills training for staff, developing a shared team understanding of recovery, exploring staff values, a Partnership Project with people who use the service and raising patient expectations. Working practices comprise the following: understanding values and treatment preferences; assessing strengths; and supporting goal-striving. The REFOCUS model describes the causal pathway from the REFOCUS intervention to improved recovery. The REFOCUS intervention is an empirically supported pro-recovery intervention for use in mental health services. It will be evaluated in a multisite cluster randomised controlled trial (ISRCTN02507940). © The Royal College of Psychiatrists 2015.

  1. Evaluating the INSPIRE measure of staff support for personal recovery in a Swedish psychiatric context.

    Science.gov (United States)

    Schön, Ulla-Karin; Svedberg, Petra; Rosenberg, David

    2015-05-01

    Recovery is understood to be an individual process that cannot be controlled, but can be supported and facilitated at the individual, organizational and system levels. Standardized measures of recovery may play a critical role in contributing to the development of a recovery-oriented system. The INSPIRE measure is a 28-item service user-rated measure of recovery support. INSPIRE assesses both the individual preferences of the user in the recovery process and their experience of support from staff. The aim of this study was to evaluate the psychometric properties of the Swedish version of the INSPIRE measure, for potential use in Swedish mental health services and in order to promote recovery in mental illness. The sample consisted of 85 participants from six community mental health services targeting people with a diagnosis of psychosis in a municipality in Sweden. For the test-retest evaluation, 78 participants completed the questionnaire 2 weeks later. The results in the present study indicate that the Swedish version of the INSPIRE measure had good face and content validity, satisfactory internal consistency and some level of instability in test-retest reliability. While further studies that test the instrument in a larger and more diverse clinical context are needed, INSPIRE can be considered a relevant and feasible instrument to utilize in supporting the development of a recovery-oriented system in Sweden.

  2. 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.

  3. Supported extractant membranes for americium and plutonium recovery

    International Nuclear Information System (INIS)

    Muscatello, A.C.; Navratil, J.D.; Killion, M.E.; Price, M.Y.

    1987-01-01

    Solid supported liquid membranes(SLM) are useful in transferring and concentrating americium and plutonium from nitrate solutions. Specifically, DHDECMP(dihexyl-N,N-diethylcarbamoylmethylphosphonate) supported on Accurel or Celgard polypropylene hollow fibers assembled in modular form transfers >95% of the americium and >70% of the plutonium from high nitrate (6.9 M), low acid (0.1 M) feeds into 0.25 M oxalic acid stripping solution. Membranes supporting TBP (tri-n-butylphosphate) also transfer these metal ions. Maximum permeabilities were observed to be 1 x 10 -3 cm sec -1 , similar to the values for other systems. The feed:strip volume ratio shows an inverse relationship to the fraction of metal ion transferred. Cation exchangers may be used to concentrate americium from the strip solution

  4. Grammar Engineering Support for Precedence Rule Recovery and Compatibility Checking

    NARCIS (Netherlands)

    Bouwers, E.; Bravenboer, M.; Visser, E.

    2007-01-01

    A wide range of parser generators are used to generate parsers for programming languages. The grammar formalisms that come with parser generators provide different approaches for defining operator precedence. Some generators (e.g. YACC) support precedence declarations, others require the grammar to

  5. 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,…

  6. Music-supported therapy for stroke motor recovery: theoretical and practical considerations.

    Science.gov (United States)

    Chen, Joyce L

    2018-05-08

    Music may confer benefits for well-being and health. What is the state of knowledge and evidence for a role of music in supporting the rehabilitation of movements after stroke? In this brief perspective, I provide background context and information about stroke recovery in general, in order to spark reflection and discussion for how we think music may impact motor recovery, given the current clinical milieu. © 2018 New York Academy of Sciences.

  7. Shearlets and Optimally Sparse Approximations

    DEFF Research Database (Denmark)

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

    2012-01-01

    Multivariate functions are typically governed by anisotropic features such as edges in images or shock fronts in solutions of transport-dominated equations. One major goal both for the purpose of compression as well as for an efficient analysis is the provision of optimally sparse approximations...... 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....

  8. Sparse structure regularized ranking

    KAUST Repository

    Wang, Jim Jing-Yan; Sun, Yijun; Gao, Xin

    2014-01-01

    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

  9. 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.

  10. 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.

  11. Self-efficacy as a mediator of the relationship between social support and recovery in serious mental illness.

    Science.gov (United States)

    Thomas, Elizabeth C; Muralidharan, Anjana; Medoff, Deborah; Drapalski, Amy L

    2016-12-01

    The purposes of this research were to assess relationships between social support and objective and subjective recovery in a sample of adults with serious mental illness and to examine self-efficacy as a potential mediator of these relationships. In this cross-sectional study, a sample of 250 individuals completed measures tapping social support network size, satisfaction with social support, perceived support from the mental health system, self-efficacy, objective recovery (i.e., psychiatric symptoms, social functioning), and subjective recovery. Pearson product-moment correlations and multiple linear regression analyses examined relationships among social support, self-efficacy, and recovery. A bootstrapping procedure was used to estimate the magnitude and significance of indirect effects in mediation analyses. All social support domains (i.e., social support network size, satisfaction with support, perceived support from the mental health system) were significantly related to at least 1 objective recovery outcome and to subjective recovery. Self-efficacy was a mediator of all relationships between social support and objective and subjective recovery. The present study aids in better understanding the relationship between social support and recovery in individuals with serious mental illness and paves the way for future research. Particularly relevant to mental health service providers, it highlights the importance of establishing and maintaining an effective therapeutic relationship as well as assisting consumers with developing supportive relationships with others. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

  12. 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.

  13. 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.

  14. 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…

  15. 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.

  16. Development and evaluation of the INSPIRE measure of staff support for personal recovery.

    Science.gov (United States)

    Williams, Julie; Leamy, Mary; Bird, Victoria; Le Boutillier, Clair; Norton, Sam; Pesola, Francesca; Slade, Mike

    2015-05-01

    No individualised standardised measure of staff support for mental health recovery exists. To develop and evaluate a measure of staff support for recovery. initial draft of measure based on systematic review of recovery processes; consultation (n = 61); and piloting (n = 20). Psychometric evaluation: three rounds of data collection from mental health service users (n = 92). INSPIRE has two sub-scales. The 20-item Support sub-scale has convergent validity (0.60) and adequate sensitivity to change. Exploratory factor analysis (variance 71.4-85.1 %, Kaiser-Meyer-Olkin 0.65-0.78) and internal consistency (range 0.82-0.85) indicate each recovery domain is adequately assessed. The 7-item Relationship sub-scale has convergent validity 0.69, test-retest reliability 0.75, internal consistency 0.89, a one-factor solution (variance 70.5 %, KMO 0.84) and adequate sensitivity to change. A 5-item Brief INSPIRE was also evaluated. INSPIRE and Brief INSPIRE demonstrate adequate psychometric properties, and can be recommended for research and clinical use.

  17. Medical Support for Aircraft Disaster Search and Recovery Operations at Sea: the RSN Experience.

    Science.gov (United States)

    Teo, Kok Ann Colin; Chong, Tse Feng Gabriel; Liow, Min Han Lincoln; Tang, Kong Choong

    2016-06-01

    The maritime environment presents a unique set of challenges to search and recovery (SAR) operations. There is a paucity of information available to guide provision of medical support for SAR operations for aircraft disasters at sea. The Republic of Singapore Navy (RSN) took part in two such SAR operations in 2014 which showcased the value of a military organization in these operations. Key considerations in medical support for similar operations include the resultant casualty profile and challenges specific to the maritime environment, such as large distances of area of operations from land, variable sea states, and space limitations. Medical support planning can be approached using well-established disaster management life cycle phases of preparedness, mitigation, response, and recovery, which all are described in detail. This includes key areas of dedicated training and exercises, force protection, availability of air assets and chamber support, psychological care, and the forensic handling of human remains. Relevant lessons learned by RSN from the Air Asia QZ8501 search operation are also included in the description of these key areas. Teo KAC , Chong TFG , Liow MHL , Tang KC . Medical support for aircraft disaster search and recovery operations at sea: the RSN experience. Prehosp Disaster Med. 2016; 31(3):294-299.

  18. Space Technology Game Changing Development- Next Generation Life Support: Spacecraft Oxygen Recovery (SCOR)

    Science.gov (United States)

    Abney, Morgan; Barta, Daniel

    2015-01-01

    The Next Generation Life Support Spacecraft Oxygen Recovery (SCOR) project element is dedicated to developing technology that enables oxygen recovery from metabolically produced carbon dioxide in space habitats. The state-of-the-art system on the International Space Station uses Sabatier technology to recover (is) approximately 50% oxygen from carbon dioxide. The remaining oxygen required for crew respiration is supplied from Earth. For long duration manned missions beyond low-Earth orbit, resupply of oxygen becomes economically and logistically prohibitive. To mitigate these challenges, the SCOR project element is targeting development of technology to increase the recovery of oxygen to 75% or more, thereby reducing the total oxygen resupply required for future missions.

  19. 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.

  20. Associations between the peer support relationship, service satisfaction and recovery-oriented outcomes: a correlational study.

    Science.gov (United States)

    Thomas, Elizabeth C; Salzer, Mark S

    2017-12-18

    The working alliance between non-peer providers and mental health consumers is associated with positive outcomes. It is hypothesized that this factor, in addition to other active support elements, is also positively related to peer support service outcomes. This study evaluates correlates of the peer-to-peer relationship and its unique association with service satisfaction and recovery-oriented outcomes. Participants were 46 adults with serious mental illnesses taking part in a peer-brokered self-directed care intervention. Pearson correlation analyses examined associations among peer relationship factors, services-related variables and recovery-oriented outcomes (i.e. empowerment, recovery and quality of life). Hierarchical multiple regression analyses evaluated associations between relationship factors and outcomes over time, controlling for other possible intervention effects. The peer relationship was not related to number of contacts. There were robust associations between the peer relationship and service satisfaction and some recovery-oriented outcomes at 24-months, but not at 12-months. These associations were not explained by other possible intervention effects. This study contributes to a better understanding of the positive, unique association between the peer-to-peer relationship and outcomes, similar to what is found in non-peer-delivered interventions. Implications for program administrators and policymakers seeking to integrate peer specialists into mental health service systems are discussed.

  1. 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.

  2. 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.

  3. Assessment of modularity architecture for recovery process of electric vehicle in supporting sustainable design

    Science.gov (United States)

    Baroroh, D. K.; Alfiah, D.

    2018-05-01

    The electric vehicle is one of the innovations to reduce the pollution of the vehicle. Nevertheless, it still has a problem, especially for disposal stage. In supporting product design and development strategy, which is the idea of sustainable design or problem solving of disposal stage, assessment of modularity architecture from electric vehicle in recovery process needs to be done. This research used Design Structure Matrix (DSM) approach to deciding interaction of components and assessment of modularity architecture using the calculation of value from 3 variables, namely Module Independence (MI), Module Similarity (MS), and Modularity for End of Life Stage (MEOL). The result of this research shows that existing design of electric vehicles has the architectural design which has a high value of modularity for recovery process on disposal stage. Accordingly, so it can be reused and recycled in component level or module without disassembly process to support the product that is environmentally friendly (sustainable design) and able reduce disassembly cost.

  4. Supporting Stroke Motor Recovery Through a Mobile Application: A Pilot Study.

    Science.gov (United States)

    Lawson, Sonia; Tang, Ziying; Feng, Jinjuan

    Neuroplasticity and motor learning are promoted with repetitive movement, appropriate challenge, and performance feedback. ARMStrokes, a smartphone application, incorporates these qualities to support motor recovery. Engaging exercises are easily accessible for improved compliance. In a multiple-case, mixed-methods pilot study, the potential of this technology for stroke motor recovery was examined. Exercises calibrated to the participant's skill level targeted forearm, elbow, and shoulder motions for a 6-wk protocol. Visual, auditory, and vibration feedback promoted self-assessment. Pre- and posttest data from 6 chronic stroke survivors who used the app in different ways (i.e., to measure active or passive motion, to track endurance) demonstrated improvements in accuracy of movements, fatigue, range of motion, and performance of daily activities. Statistically significant changes were not obtained with this pilot study. Further study on the efficacy of this technology is supported. Copyright © 2017 by the American Occupational Therapy Association, Inc.

  5. 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

  6. 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.

  7. 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.

  8. 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....

  9. 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.

  10. 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.

  11. 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.

  12. Therapy induces widespread reorganization of motor cortex after complete spinal transection that supports motor recovery.

    Science.gov (United States)

    Ganzer, Patrick D; Manohar, Anitha; Shumsky, Jed S; Moxon, Karen A

    2016-05-01

    Reorganization of the somatosensory system and its relationship to functional recovery after spinal cord injury (SCI) has been well studied. However, little is known about the impact of SCI on organization of the motor system. Recent studies suggest that step-training paradigms in combination with spinal stimulation, either electrically or through pharmacology, are more effective than step training alone at inducing recovery and that reorganization of descending corticospinal circuits is necessary. However, simpler, passive exercise combined with pharmacotherapy has also shown functional improvement after SCI and reorganization of, at least, the sensory cortex. In this study we assessed the effect of passive exercise and serotonergic (5-HT) pharmacological therapies on behavioral recovery and organization of the motor cortex. We compared the effects of passive hindlimb bike exercise to bike exercise combined with daily injections of 5-HT agonists in a rat model of complete mid-thoracic transection. 5-HT pharmacotherapy combined with bike exercise allowed the animals to achieve unassisted weight support in the open field. This combination of therapies also produced extensive expansion of the axial trunk motor cortex into the deafferented hindlimb motor cortex and, surprisingly, reorganization within the caudal and even the rostral forelimb motor cortex areas. The extent of the axial trunk expansion was correlated to improvement in behavioral recovery of hindlimbs during open field locomotion, including weight support. From a translational perspective, these data suggest a rationale for developing and optimizing cost-effective, non-invasive, pharmacological and passive exercise regimes to promote plasticity that supports restoration of movement after spinal cord injury. Copyright © 2016. Published by Elsevier Inc.

  13. Progressive recovery of osseoperception as a function of the combination of implant-supported prostheses.

    Science.gov (United States)

    Batista, Mauro; Bonachela, Wellington; Soares, Janir

    2008-06-01

    The extraction of teeth involves the elimination of extremely sensitive periodontal mechanoreceptors, which play an important role in oral sensory perception. The aim of this study was to evaluate the recovery of interocclusal sensory perception for micro-thickness in individuals with different types of implant-supported prostheses. Wearers of complete dentures (CDs) comprised the negative control group (group A, n=17). The experimental group consisted of wearers of prostheses supported by osseointegrated implants (Group B, n=29), which was subsequently divided into 4 subgroups: B(1) (n=5)--implant supported overdentures (ISO) occluding with CD; B(2) (n=6)--implant-supported fixed prostheses (ISFP) occluding with CD; B(3) (n=8)--wearers of maxillary and mandibular ISFP, and B(4) (n=10)--ISFP occluding with natural dentition (ND). Individuals with ND represented the positive control group (Group C, n=24). Aluminum foils measuring 10 microm, 24 microm, 30 microm, 50 microm, 80 microm, and 104 microm thickness were placed within the premolar area, adding up to 120 tests for each individual. The mean tactile thresholds of groups A, B1, B2, B3, B4, and C were 92 microm, 27 microm, 27 microm, 14 microm, 10 microm, and 10 microm, respectively. [Correction added after publication online 18 April 2008: in the preceding sentence 92 microm, 27 microm, 14 microm, 10 microm and 10 microm, was corrected to 92 microm, 27 microm, 27 microm, 14 microm, 10 microm and 10 microm]. The Kruskal-Wallis test revealed significant difference among groups (P<0.05). The Dunn test revealed that group A was statistically different from groups C, B(3), and B(4), and that B(1) and B(2) were statistically different from group C. Progressive recovery of osseoperception as a function of the combination of implant-supported prostheses could be observed. Moreover, ISO and/or ISFP combinations may similarly maximize the recovery of osseoperception.

  14. 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.

  15. 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.

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

    DEFF Research Database (Denmark)

    Rezanova, Natalia Jurjevna

    2009-01-01

    the proposed model and solution method is suitable for solving in real-time. Recovery duties are generated as resource constrained paths in duty networks, and the set partitioning problem is solved with a linear programming based branch-and-price algorithm. Dynamic column generation and problem space expansion...... driver decision support system in their operational environment. Besides solving a particular optimization problem, this thesis contributes with a description of the railway planning process, tactical crew scheduling and the real-time dispatching solutions, taking a starting point in DSB S....... Rezanova NJ, Ryan DM. The train driver recovery problem–A set partitioning based model and solution method. Computers and Operations Research, in press, 2009. doi: 10.1016/j.cor.2009.03.023. 2. Clausen J, Larsen A, Larsen J, Rezanova NJ. Disruption management in the airline industry–Concepts, models...

  17. 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.

  18. Structural Sparse Tracking

    KAUST Repository

    Zhang, Tianzhu; Yang, Ming-Hsuan; Ahuja, Narendra; Ghanem, Bernard; Yan, Shuicheng; Xu, Changsheng; Liu, Si

    2015-01-01

    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

  19. SparseM: A Sparse Matrix Package for R *

    Directory of Open Access Journals (Sweden)

    Roger Koenker

    2003-02-01

    Full Text Available SparseM provides some basic R functionality for linear algebra with sparse matrices. Use of the package is illustrated by a family of linear model fitting functions that implement least squares methods for problems with sparse design matrices. Significant performance improvements in memory utilization and computational speed are possible for applications involving large sparse matrices.

  20. 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

  1. 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...... a mechanism to perform efficient Gaussian elimination over sparse matrices going beyond belief propagation but maintaining low decoding complexity. Supporting simulation results are provided showing the trade-off between decoding complexity and completion time....

  2. 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.

  3. 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.

  4. Service users' expectations of treatment and support at the Community Mental Health Centre in their recovery.

    Science.gov (United States)

    Biringer, Eva; Davidson, Larry; Sundfør, Bengt; Ruud, Torleif; Borg, Marit

    2017-09-01

    Focus on service users' needs, coping and empowerment, user involvement, and comprehensiveness are supposed to be key elements of the Community Mental Health Centres in Norway. Taking a user-oriented approach means acknowledging the individual's own expectations, aims and hopes. However, studies that have investigated service users' expectations of treatment and support at Community Mental Health Centres are hard to find. The aim of the study was therefore to explore service users' expectations at the start of treatment at a Community Mental Health Centre. Within a collaborative framework, taking a hermeneutic-phenomenological approach, ten service users participated in in-depth interviews about their expectations, hopes and aims for treatment and recovery. The participants sought help due to various mental health issues that had interfered with their lives and created disability and suffering. A data-driven stepwise approach in line with thematic analysis was used. The study was approved by the Norwegian Social Science Data Services. The following four main themes representing participants' expectations at the start of treatment were elicited: hope for recovery, developing understanding, finding tools for coping and receiving counselling and practical assistance. Participants' expectations about treatment were tightly interwoven with their personal aims and hopes for their future life, and expectations were often related to practical and financial problems, the solution of which being deemed necessary to gain a safe basis for recovery in the long run. The transferability of the results may be limited by the small number of participants. The study emphasises how important it is that service users' personal aims and expectations guide the collaborative treatment process. In addition to providing treatment aimed at improving symptoms, Community Mental Health Centres should take a more comprehensive approach than today by providing more support with family issues

  5. 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

  6. Efficient convolutional sparse coding

    Science.gov (United States)

    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.

  7. 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...

  8. 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.

  9. 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....

  10. 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.

  11. Discrete Sparse Coding.

    Science.gov (United States)

    Exarchakis, Georgios; Lücke, Jörg

    2017-11-01

    Sparse coding algorithms with continuous latent variables have been the subject of a large number of studies. However, discrete latent spaces for sparse coding have been largely ignored. In this work, we study sparse coding with latents described by discrete instead of continuous prior distributions. We consider the general case in which the latents (while being sparse) can take on any value of a finite set of possible values and in which we learn the prior probability of any value from data. This approach can be applied to any data generated by discrete causes, and it can be applied as an approximation of continuous causes. As the prior probabilities are learned, the approach then allows for estimating the prior shape without assuming specific functional forms. To efficiently train the parameters of our probabilistic generative model, we apply a truncated expectation-maximization approach (expectation truncation) that we modify to work with a general discrete prior. We evaluate the performance of the algorithm by applying it to a variety of tasks: (1) we use artificial data to verify that the algorithm can recover the generating parameters from a random initialization, (2) use image patches of natural images and discuss the role of the prior for the extraction of image components, (3) use extracellular recordings of neurons to present a novel method of analysis for spiking neurons that includes an intuitive discretization strategy, and (4) apply the algorithm on the task of encoding audio waveforms of human speech. The diverse set of numerical experiments presented in this letter suggests that discrete sparse coding algorithms can scale efficiently to work with realistic data sets and provide novel statistical quantities to describe the structure of the data.

  12. 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.

  13. 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.

  14. Green shoots of recovery: a realist evaluation of a team to support change in general practice.

    Science.gov (United States)

    Bartlett, Maggie; Basten, Ruth; McKinley, Robert K

    2017-02-08

    A multidisciplinary support team for general practice was established in April 2014 by a local National Health Service (NHS) England management team. This work evaluates the team's effectiveness in supporting and promoting change in its first 2 years, using realist methodology. Primary care in one area of England. Semistructured interviews were conducted with staff from 14 practices, 3 key senior NHS England personnel and 5 members of the support team. Sampling of practice staff was purposive to include representatives from relevant professional groups. The team worked with practices to identify areas for change, construct action plans and implement them. While there was no specified timescale for the team's work with practices, it was tailored to each. In realist evaluations, outcomes are contingent on mechanisms acting in contexts, and both an understanding of how an intervention leads to change in a socially constructed system and the resultant changes are outcomes. The principal positive mechanisms leading to change were the support team's expertise and its relationships with practice staff. The 'external view' provided by the team via its corroborative and normalising effects was an important mechanism for increasing morale in some practice contexts. A powerful negative mechanism was related to perceptions of 'being seen as a failing practice' which included expressions of 'shame'. Outcomes for practices as perceived by their staff were better communication, improvements in patients' access to appointments resulting from better clinical and managerial skill mix, and improvements in workload management. The support team promoted change within practices leading to signs of the 'green shoots of recovery' within the time frame of the evaluation. Such interventions need to be tailored and responsive to practices' needs. The team's expertise and relationships between team members and practice staff are central to success. Published by the BMJ Publishing Group

  15. Sparse inpainting and isotropy

    Energy Technology Data Exchange (ETDEWEB)

    Feeney, Stephen M.; McEwen, Jason D.; Peiris, Hiranya V. [Department of Physics and Astronomy, University College London, Gower Street, London, WC1E 6BT (United Kingdom); Marinucci, Domenico; Cammarota, Valentina [Department of Mathematics, University of Rome Tor Vergata, via della Ricerca Scientifica 1, Roma, 00133 (Italy); Wandelt, Benjamin D., E-mail: s.feeney@imperial.ac.uk, E-mail: marinucc@axp.mat.uniroma2.it, E-mail: jason.mcewen@ucl.ac.uk, E-mail: h.peiris@ucl.ac.uk, E-mail: wandelt@iap.fr, E-mail: cammarot@axp.mat.uniroma2.it [Kavli Institute for Theoretical Physics, Kohn Hall, University of California, 552 University Road, Santa Barbara, CA, 93106 (United States)

    2014-01-01

    Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.

  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. 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.

  18. Data analysis in high-dimensional sparse spaces

    DEFF Research Database (Denmark)

    Clemmensen, Line Katrine Harder

    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...... are applied to classifications of fish species, ear canal impressions used in the hearing aid industry, microbiological fungi species, and various cancerous tissues and healthy tissues. In addition, novel applications of sparse regressions (also called the elastic net) to the medical, concrete, and food...

  19. 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 solutions...

  20. 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) (paddiction. Copyright © 2018 Elsevier Inc. All rights reserved.

  1. 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

  2. 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.

  3. Semi-supervised sparse coding

    KAUST Repository

    Wang, Jim Jing-Yan; Gao, Xin

    2014-01-01

    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.

  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. 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.

  6. 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.

  7. 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.

  8. "That's what you do for people you love": A qualitative study of social support and recovery from a musculoskeletal injury.

    Science.gov (United States)

    Prang, Khic-Houy; Newnam, Sharon; Berecki-Gisolf, Janneke

    2018-01-01

    Social support has been identified as a significant factor in facilitating better health outcomes following injury. However, research has primarily focused on the role of social support from the perspective of the person experiencing an injury. Limited research has examined the experiences of the family members and friends of a person with injury. This study aims to explore the perceptions and experiences of social support and recovery following a transport-related musculoskeletal injury (MSI) in a population of injured persons and their family members and friends. This study was conducted using a phenomenological qualitative research design. In-depth semi-structured interviews were conducted with ten persons with MSI, recruited via the Transport Accident Commission (TAC) in Victoria, Australia. Seven family members and friends were also interviewed. The data was analysed using constant comparative method and thematic analysis. Several themes were identified including: (1) key sources and types of support received, (2) relationship development and (3) challenges of providing and receiving support. Participants with MSI reported stories about how the social network provided emotional and tangible support. Family members and friends confirmed the supportive acts provided to the participants with MSI. Positive iterative changes in relationships were reported by the participants with MSI. Participants with MSI, their family members and friends described several difficulties including loss of independence, feeling like a burden, and the impact of caring on health and well-being. The role of social support is complex given the multitude of people involved in the recovery process. The findings of this study suggest that persons with MSI may benefit from support groups and maintenance of existing support networks. Furthermore, family members and friends engaged in the recovery process may benefit from support in this role.

  9. A trial essay about studies of Grief Toward to recovery support for mothers who abuse their children

    OpenAIRE

    遠藤, 野ゆり

    2016-01-01

    This paper is a short essay about studies of Grief which are written by Sigmund Freud, Melanie Klein, John Bowlby, and Keigo Okonogi. The final aim is to describe parents’, mainly mother’s mechanism of recovery process from their abusing of children. This paper is one piece of this aim. The lag of support for abusing mother seems to be caused by the complexity and difficulty of their mechanism. They often have been abused in their childhood and can’t remember it. The process of recovery is pa...

  10. 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.

  11. 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

  12. 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

  13. 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.

  14. Progressing recovery-oriented care in psychiatric inpatient units: Occupational therapy’s role in supporting a stronger peer workforce

    Directory of Open Access Journals (Sweden)

    Chris Lloyd

    2017-10-01

    Full Text Available Purpose - Initiated by the service user movement, recovery-oriented practices are one of the keystones of modern mental health care. Over the past two decades, substantial gains have been made with introducing recovery-oriented practice in many areas of mental health practice, but there remain areas where progress is delayed, notably, the psychiatric inpatient environment. The peer support workforce can play a pivotal role in progressing recovery-oriented practices. The purpose of this paper is to provide a pragmatic consideration of how occupational therapists can influence mental health systems to work proactively with a peer workforce. Design/methodology/approach - The authors reviewed current literature and considered practical approaches to building a peer workforce in collaboration with occupational therapists. Findings - It is suggested that the peer support workforce should be consciously enhanced in the inpatient setting to support culture change as a matter of priority. Occupational therapists working on inpatient units should play a key role in promoting and supporting the growth in the peer support workforce. Doing so will enrich the Occupational Therapy profession as well as improving service user outcomes. Originality/value - This paper seeks to provide a pragmatic consideration of how occupational therapists can influence mental health systems to work proactively with a peer workforce.

  15. "Recovery" in bipolar disorder: how can service users be supported through a self-management intervention? A qualitative focus group study.

    Science.gov (United States)

    Todd, Nicholas J; Jones, Steven H; Lobban, Fiona A

    2012-04-01

    Bipolar disorder (BD) is a chronic and recurrent affective disorder. Recovery is defined as the process by which people can live fulfilling lives despite experiencing symptoms. To explore how an opportunistically recruited group of service users with BD experience recovery and self-management to understand more about how a service users' recovery may be supported. Twelve service users with BD took part in a series of focus groups. Service users' responses to questions about their personal experiences of self-management and recovery were analysed. Focus groups were transcribed verbatim and thematic analysis ([ Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101]) was employed to identify common themes in the data. Four key themes were identified: (1) Recovery is not about being symptom free; (2) Recovery requires taking responsibility for your own wellness; (3) Self-management: building on existing techniques; (4) Overcoming barriers to recovery: negativity, stigma and taboo. Service users with BD have provided further support for the concept of recovery and have suggested a number of ways recovery can be supported. A self-management approach informed by the recovery literature has been proposed as a way to support service users' recovery.

  16. 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

    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...... 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...

  17. 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.

  18. Sparse distributed memory

    Science.gov (United States)

    Denning, Peter J.

    1989-01-01

    Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.

  19. 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.

  20. 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.

  1. 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.

  2. 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...

  3. 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...

  4. 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

  5. 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.

  6. 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.

  7. Consensus Convolutional Sparse Coding

    KAUST Repository

    Choudhury, Biswarup; Swanson, Robin; Heide, Felix; Wetzstein, Gordon; Heidrich, Wolfgang

    2017-01-01

    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.

  8. Turbulent flows over sparse canopies

    Science.gov (United States)

    Sharma, Akshath; García-Mayoral, Ricardo

    2018-04-01

    Turbulent flows over sparse and dense canopies exerting a similar drag force on the flow are investigated using Direct Numerical Simulations. The dense canopies are modelled using a homogeneous drag force, while for the sparse canopy, the geometry of the canopy elements is represented. It is found that on using the friction velocity based on the local shear at each height, the streamwise velocity fluctuations and the Reynolds stress within the sparse canopy are similar to those from a comparable smooth-wall case. In addition, when scaled with the local friction velocity, the intensity of the off-wall peak in the streamwise vorticity for sparse canopies also recovers a value similar to a smooth-wall. This indicates that the sparse canopy does not significantly disturb the near-wall turbulence cycle, but causes its rescaling to an intensity consistent with a lower friction velocity within the canopy. In comparison, the dense canopy is found to have a higher damping effect on the turbulent fluctuations. For the case of the sparse canopy, a peak in the spectral energy density of the wall-normal velocity, and Reynolds stress is observed, which may indicate the formation of Kelvin-Helmholtz-like instabilities. It is also found that a sparse canopy is better modelled by a homogeneous drag applied on the mean flow alone, and not the turbulent fluctuations.

  9. Sparse Regression by Projection and Sparse Discriminant Analysis

    KAUST Repository

    Qi, Xin; Luo, Ruiyan; Carroll, Raymond J.; Zhao, Hongyu

    2015-01-01

    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

  10. In Defense of Sparse Tracking: Circulant Sparse Tracker

    KAUST Repository

    Zhang, Tianzhu; Bibi, Adel Aamer; Ghanem, Bernard

    2016-01-01

    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.

  11. 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.

  12. 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

  13. 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.

  14. 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

  15. Sparseness- and continuity-constrained seismic imaging

    Science.gov (United States)

    Herrmann, Felix J.

    2005-04-01

    Non-linear solution strategies to the least-squares seismic inverse-scattering problem with sparseness and continuity constraints are proposed. Our approach is designed to (i) deal with substantial amounts of additive noise (SNR formulating the solution of the seismic inverse problem in terms of an optimization problem. During the optimization, sparseness on the basis and continuity along the reflectors are imposed by jointly minimizing the l1- and anisotropic diffusion/total-variation norms on the coefficients and reflectivity, respectively. [Joint work with Peyman P. Moghaddam was carried out as part of the SINBAD project, with financial support secured through ITF (the Industry Technology Facilitator) from the following organizations: BG Group, BP, ExxonMobil, and SHELL. Additional funding came from the NSERC Discovery Grants 22R81254.

  16. Language Recognition via Sparse Coding

    Science.gov (United States)

    2016-09-08

    explanation is that sparse coding can achieve a near-optimal approximation of much complicated nonlinear relationship through local and piecewise linear...training examples, where x(i) ∈ RN is the ith example in the batch. Optionally, X can be normalized and whitened before sparse coding for better result...normalized input vectors are then ZCA- whitened [20]. Em- pirically, we choose ZCA- whitening over PCA- whitening , and there is no dimensionality reduction

  17. Efficient collaborative sparse channel estimation in massive MIMO

    KAUST Repository

    Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.

    2015-01-01

    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.

  18. 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

  19. 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.

  20. 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.

  1. Impact of preoperative change in physical function on postoperative recovery: argument supporting prehabilitation for colorectal surgery.

    Science.gov (United States)

    Mayo, Nancy E; Feldman, Liane; Scott, Susan; Zavorsky, Gerald; Kim, Do Jun; Charlebois, Patrick; Stein, Barry; Carli, Francesco

    2011-09-01

    Abdominal surgery represents a physiologic stress and is associated with a period of recovery during which functional capacity is often diminished. "Prehabilitation" is a program to increase functional capacity in anticipation of an upcoming stressor. We reported recently the results of a randomized trial comparing 2 prehabilitation programs before colorectal surgery (stationary cycling plus weight training versus a recommendation to increase walking coupled with breathing exercises); however, adherence to the programs was low. The objectives of this study were to estimate: (1) the extent to which physical function could be improved with either prehabilitation program and identify variables associated with response; and (2) the impact of change in preoperative function on postoperative recovery. This study involved a reanalysis of data arising from a randomized trial. The primary outcome measure was functional walking capacity measured by the Six-Minute Walk Test; secondary outcomes were anxiety, depression, health-related quality of life, and complications (Clavien classification). Multiple linear regression was used to estimate the extent to which key variables predicted change in functional walking capacity over the prehabilitation and follow-up periods. We included 95 people who completed the prehabilitation phase (median, 38 days; interquartile range, 22-60), and 75 who were also evaluated postoperatively (mean, 9 weeks). During prehabilitation, 33% improved their physical function, 38% stayed within 20 m of their baseline score, and 29% deteriorated. Among those who improved, mental health, vitality, self-perceived health, and peak exercise capacity also increased significantly. Women were less likely to improve; low baseline walking capacity, anxiety, and the belief that fitness aids recovery were associated with improvements during prehabilitation. In the postoperative phase, the patients who had improved during prehabilitation were also more likely to have

  2. 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.

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

    KAUST Repository

    Cho, Myung; Cai, Jian-Feng; Liu, Suhui; Eldar, Yonina C.; Xu, Weiyu

    2016-01-01

    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.

  4. 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

  5. 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.

  6. Supporting staff recovery and reintegration after a critical incident resulting in infant death.

    Science.gov (United States)

    Roesler, Roberta; Ward, Debra; Short, Mary

    2009-08-01

    A critical incident is described as any sudden unexpected event that has the power to overwhelm the usual effective coping skills of an individual or a group and can cause significant psychological distress in usually healthy persons. A Just Culture model to deal with critical incidents is an approach that seeks to identify and balance system events and personal accountability. This article reports a critical incident that occurred at the Neonatal Intensive Care Unit, Methodist Hospital of Indianapolis, when 5 infants received an overdose of heparin that resulted in the death of 3 infants. Although care of the family after the critical incident was the immediate priority, the focus of this article was on the recovery and reintegration of the NICU staff after a critical incident based on the Just Culture philosophy.

  7. 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.

  8. 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.

  9. Recovery from musculoskeletal injury: the role of social support following a transport accident.

    Science.gov (United States)

    Prang, Khic-Houy; Berecki-Gisolf, Janneke; Newnam, Sharon

    2015-07-03

    Social support can be an important coping resource for persons recovering from injury. In this study, we examined the effects of family structure and sources of social support on physical health, persistent pain and return to work (RTW) outcomes following musculoskeletal injury (MSI) sustained in a transport accident. Secondary analysis of Transport Accident Commission (TAC) cross-sectional surveys held in 2010 and 2011 was conducted. In total 1649 persons with MSI were identified and included. Family structure was determined by marital status and number of children. Sources of social support were measured as perceived help from family, friends, neighbours and employers. Physical health was measured with the Physical Component Summary (PCS) score of the Short-Form-12 Health Survey Version 2. Persistent pain was defined as self-reported persistent pain experienced in the last 3 months, and RTW was defined as being back at work for ≥3 months at time of interview. Multiple linear and logistic regressions were used for the analyses. Family and friends' support was associated with better physical health among persons with >1 day hospital stay. Being married or in a de facto relationship was associated with greater PCS score among non-hospitalised persons. Being widowed/separated/divorced was associated with more self-reported persistent pain (odds ratio 1.62 [95 % confidence intervals 1.11-2.37]). Support from family (0.40 [0.24-0.68]), friends (0.29 [0.17-0.47]) and neighbours (0.59 [0.41-0.84]) was associated with less persistent pain. Among women, support from family (0.09 [0.01-0.78]) was negatively associated with RTW, whereas support from friends (3.03 [1.15-8.02]) was positively associated with RTW. These associations were not observed among men. For both men (5.62 [2.77-11.38]) and women (7.22 [2.58-20.20]), support from employers was positively associated with RTW. Family structure and sources of social support had a positive impact on physical health

  10. 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.

  11. 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

  12. Sparse Representations of Hyperspectral Images

    KAUST Repository

    Swanson, Robin J.

    2015-01-01

    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.

  13. 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.

  14. 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.

  15. 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

  16. Limited-memory trust-region methods for sparse relaxation

    Science.gov (United States)

    Adhikari, Lasith; DeGuchy, Omar; Erway, Jennifer B.; Lockhart, Shelby; Marcia, Roummel F.

    2017-08-01

    In this paper, we solve the l2-l1 sparse recovery problem by transforming the objective function of this problem into an unconstrained differentiable function and applying a limited-memory trust-region method. Unlike gradient projection-type methods, which uses only the current gradient, our approach uses gradients from previous iterations to obtain a more accurate Hessian approximation. Numerical experiments show that our proposed approach eliminates spurious solutions more effectively while improving computational time.

  17. 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.

  18. 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

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

    Directory of Open Access Journals (Sweden)

    Mary Leamy

    Full Text Available OBJECTIVE: 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. DESIGN: Process evaluation nested within a cluster randomised controlled trial (RCT. PARTICIPANTS: 28 interviews with mental health care staff, 3 interviews with trainers, 4 focus groups with intervention teams and 28 written trainer reports. SETTING: 14 community-based mental health teams in two UK sites (one urban, one semi-rural who received the intervention. RESULTS: 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. CONCLUSIONS: 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

  20. 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.

  1. 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.

  2. The role of nutritional support in the physical and functional recovery of critically ill patients: a narrative review.

    Science.gov (United States)

    Bear, Danielle E; Wandrag, Liesl; Merriweather, Judith L; Connolly, Bronwen; Hart, Nicholas; Grocott, Michael P W

    2017-08-26

    The lack of benefit from randomised controlled trials has resulted in significant controversy regarding the role of nutrition during critical illness in terms of long-term recovery and outcome. Although methodological caveats with a failure to adequately appreciate biological mechanisms may explain these disappointing results, it must be acknowledged that nutritional support during early critical illness, when considered alone, may have limited long-term functional impact.This narrative review focuses specifically on recent clinical trials and evaluates the impact of nutrition during critical illness on long-term physical and functional recovery.Specific focus on the trial design and methodological limitations has been considered in detail. Limitations include delivery of caloric and protein targets, patient heterogeneity, short duration of intervention, inappropriate clinical outcomes and a disregard for baseline nutritional status and nutritional intake in the post-ICU period.With survivorship at the forefront of critical care research, it is imperative that nutrition studies carefully consider biological mechanisms and trial design because these factors can strongly influence outcomes, in particular long-term physical and functional outcome. Failure to do so may lead to inconclusive clinical trials and consequent rejection of the potentially beneficial effects of nutrition interventions during critical illness.

  3. Uniform sparse bounds for discrete quadratic phase Hilbert transforms

    Science.gov (United States)

    Kesler, Robert; Arias, Darío Mena

    2017-09-01

    For each α \\in T consider the discrete quadratic phase Hilbert transform acting on finitely supported functions f : Z → C according to H^{α }f(n):= \\sum _{m ≠ 0} e^{iα m^2} f(n - m)/m. We prove that, uniformly in α \\in T , there is a sparse bound for the bilinear form for every pair of finitely supported functions f,g : Z→ C . The sparse bound implies several mapping properties such as weighted inequalities in an intersection of Muckenhoupt and reverse Hölder classes.

  4. Distribution agnostic structured sparsity recovery algorithms

    KAUST Repository

    Al-Naffouri, Tareq Y.; Masood, Mudassir

    2013-01-01

    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

  5. 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

    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...... 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...... capacities of 1.2 and 3.4 mg ml(-1) were recorded for prototype diethylaminoethyl-and polyethylene imine-linked adsorbents which were respectively 25 and 70 fold higher than those of equivalently derivatised commercial expanded bed materials. The prototype polyethylene imine-coupled material exhibited severe...

  6. Acquisition of airborne imagery in support of Deepwater Horizon oil spill recovery assessments

    Science.gov (United States)

    Bostater, Charles R., Jr.; Muller-Karger, Frank E.

    2012-09-01

    Remote sensing imagery was collected from a low flying aircraft along the near coastal waters of the Florida Panhandle and northern Gulf of Mexico and into Barataria Bay, Louisiana, USA, during March 2011. Imagery was acquired from an aircraft that simultaneously collected traditional photogrammetric film imagery, digital video, digital still images, and digital hyperspectral imagery. The original purpose of the project was to collect airborne imagery to support assessment of weathered oil in littoral areas influenced by the Deepwater Horizon oil and gas spill that occurred during the spring and summer of 2010. This paper describes the data acquired and presents information that demonstrates the utility of small spatial scale imagery to detect the presence of weathered oil along littoral areas in the northern Gulf of Mexico. Flight tracks and examples of imagery collected are presented and methods used to plan and acquire the imagery are described. Results suggest weathered oil in littoral areas after the spill was contained at the source.

  7. 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.

  8. 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

  9. 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.

  10. 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

  11. 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...

  12. 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

  13. Recovery of cesium from nuclear waste using hollow fibre supported liquid membrane containing calix[4]arene-bis-(2,3-naphtho)-crown-6

    International Nuclear Information System (INIS)

    Kandwal, P.; Mohapatra, P.K.; Ansari, S.A.; Manchanda, Vijay K.

    2011-01-01

    Transport behaviour of cesium through hollow fibre supported liquid membrane (HFSLM) containing calix[4]arenebis-(2,3-naphtho)-crown-6 (CNC) as carrier extractant, has been investigated under various experimental conditions. At tracer concentration of cesium, > 99% recovery of cesium was achieved from 3M HNO 3 solution to distilled water with 1 mM of CNC in 80% NPOE + 20 % n-dodecane mixture. Effect of feed phase acidity, ligand concentration, metal ion concentration etc. has been investigated. Recovery of cesium from Pressurized Heavy Water Reactor Simulated High Level Waste (PHWR-SHLW) using 1 mM CNC dissolved proposed diluent as the extractant, was carried out and it was found that it takes 12 hours of continuous operation for 88% recovery of metal ion. Nevertheless, the complete recovery of cesium from SHLW was possible after neutralization of strip phase acidity with NaOH. (author)

  14. 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.

  15. 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.

  16. 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.

  17. A little goes a long way: the impact of distal social support on community integration and recovery of individuals with psychiatric disabilities.

    Science.gov (United States)

    Townley, Greg; Miller, Henry; Kloos, Bret

    2013-09-01

    Although an extensive body of literature highlights the important role of social support for individuals with psychiatric disabilities, definitions of support tend to be restricted-focusing on intimate relationships such as friend and family networks and ignoring the role of casual relationships existing naturally in the community. This mixed-methods study of 300 consumers of mental health services in the Southeastern US aims to better understand the impact of community supports, termed distal supports, on community integration and recovery from mental illness. Qualitative content analysis, tests of group mean differences, and hierarchical linear regression analyses revealed the following: (1) participants primarily reported receiving tangible support (e.g., free medication/discounted goods) from distal supports rather than emotional support (e.g., displays of warmth/affection) or informational support (e.g., provision of advice); (2) women and older participants reported more distal supports than men or younger participants; and (3) distal supports played a unique role in predicting community integration and recovery even after accounting for the influence of traditional support networks. Results highlight the importance of considering diverse types of social support in naturally occurring settings when designing treatment plans and interventions aimed at encouraging community participation and adaptive functioning for individuals with psychiatric disabilities.

  18. 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.

  19. 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.

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

    KAUST Repository

    Hussain, Z.; Muhammad, A.

    2013-01-01

    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.

  1. 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.

  2. 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.

  3. 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.

  4. 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

  5. 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...

  6. Diffusion Indexes with Sparse Loadings

    DEFF Research Database (Denmark)

    Kristensen, Johannes Tang

    The use of large-dimensional factor models in forecasting has received much attention in the literature with the consensus being that improvements on forecasts can be achieved when comparing with standard models. However, recent contributions in the literature have demonstrated that care needs...... to the problem by using the 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...... it to be an important alternative to PC....

  7. Sparse Linear Identifiable Multivariate Modeling

    DEFF Research Database (Denmark)

    Henao, Ricardo; Winther, Ole

    2011-01-01

    and bench-marked on artificial and real biological data sets. SLIM is closest in spirit to LiNGAM (Shimizu et al., 2006), but differs substantially in inference, Bayesian network structure learning and model comparison. Experimentally, SLIM performs equally well or better than LiNGAM with comparable......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...

  8. 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 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...

  9. 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

  10. Good recovery from aphasia is also supported by right basal ganglia: a longitudinal controlled PET study. EJPRM-ESPRM 2008 award winner.

    Science.gov (United States)

    De Boissezon, X; Marie, N; Castel-Lacanal, E; Marque, P; Bezy, C; Gros, H; Lotterie, J-A; Cardebat, D; Puel, M; Demonet, J-F

    2009-12-01

    It has long been a matter of debate whether recovery from aphasia after left perisylvian lesion is mediated by perilesional left hemispheric regions or by right homologous areas. To investigate the neural substrates of aphasia recovery, a longitudinal study in patients after a left single perisylvian stroke was performed. Thirteen aphasic patients were H2(15)O PET-scanned twice at a one year interval during a word generation task. Patients are divided into two groups according to language performance for the word generation task at PET2. For the Good Recovery (GR) group, patients' performances are indistinguishable from those of normal subjects, while patients from the Poor Recovery (PR) group keep language disorders. Using SPM2, Language-Rest contrast is computed for both groups at both PET stages. Then, Session Effect contrast (TEP2-TEP1>0) is calculated for both groups. For the GR group, the Session Effect contrast shows an increase of activations in the left Postero-Superior Temporal Gyrus PSTG but also in the right thalamus and lenticular nuclei; for PR patients, the right lenticular nucleus activation is more important at PET1 than PET2. The crucial role of the left temporal activation is confirmed and its increase is linked to behavioural recovery. The role of the right basal ganglia to support good recovery from aphasia is a new finding. Their activation may be more task-dependant and related to inhibition of the right frontal cortex.

  11. Approximated Function Based Spectral Gradient Algorithm for Sparse Signal Recovery

    Directory of Open Access Journals (Sweden)

    Weifeng Wang

    2014-02-01

    Full Text Available Numerical algorithms for the l0-norm regularized non-smooth non-convex minimization problems have recently became a topic of great interest within signal processing, compressive sensing, statistics, and machine learning. Nevertheless, the l0-norm makes the problem combinatorial and generally computationally intractable. In this paper, we construct a new surrogate function to approximate l0-norm regularization, and subsequently make the discrete optimization problem continuous and smooth. Then we use the well-known spectral gradient algorithm to solve the resulting smooth optimization problem. Experiments are provided which illustrate this method is very promising.

  12. 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

  13. Bayesian Inference Methods for Sparse Channel Estimation

    DEFF Research Database (Denmark)

    Pedersen, Niels Lovmand

    2013-01-01

    This thesis deals with sparse Bayesian learning (SBL) with application to radio channel estimation. As opposed to the classical approach for sparse signal representation, we focus on the problem of inferring complex signals. Our investigations within SBL constitute the basis for the development...... 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...... analysis of the complex prior representation, where we show that the ability to induce sparse estimates of a given prior heavily depends on the inference method used and, interestingly, whether real or complex variables are inferred. We also show that the Bayesian estimators derived from the proposed...

  14. Security-enhanced phase encryption assisted by nonlinear optical correlation via sparse phase

    International Nuclear Information System (INIS)

    Chen, Wen; Chen, Xudong; Wang, Xiaogang

    2015-01-01

    We propose a method for security-enhanced phase encryption assisted by a nonlinear optical correlation via a sparse phase. Optical configurations are established based on a phase retrieval algorithm for embedding an input image and the secret data into phase-only masks. We found that when one or a few phase-only masks generated during data hiding are sparse, it is possible to integrate these sparse masks into those phase-only masks generated during the encoding of the input image. Synthesized phase-only masks are used for the recovery, and sparse distributions (i.e., binary maps) for generating the incomplete phase-only masks are considered as additional parameters for the recovery of secret data. It is difficult for unauthorized receivers to know that a useful phase has been sparsely distributed in the finally generated phase-only masks for secret-data recovery. Only when the secret data are correctly verified can the input image obtained with valid keys be claimed as targeted information. (paper)

  15. 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.

  16. Acute cardiac support with intravenous milrinone promotes recovery from early brain injury in a murine model of severe subarachnoid haemorrhage.

    Science.gov (United States)

    Mutoh, Tomoko; Mutoh, Tatsushi; Nakamura, Kazuhiro; Yamamoto, Yukiko; Tsuru, Yoshiharu; Tsubone, Hirokazu; Ishikawa, Tatsuya; Taki, Yasuyuki

    2017-04-01

    Early brain injury/ischaemia (EBI) is a serious complication early after subarachnoid haemorrhage (SAH) that contributes to development of delayed cerebral ischaemia (DCI). This study aimed to determine the role of inotropic cardiac support using milrinone (MIL) on restoring acute cerebral hypoperfusion attributable to EBI and improving outcomes after experimental SAH. Forty-three male C57BL/6 mice were assigned to either sham surgery (SAH-sham), SAH induced by endovascular perforation plus postconditioning with 2% isoflurane (Control), or SAH plus isoflurane combined with MIL with and without hypoxia-inducible factor inhibitor (HIF-I) pretreatment. Cardiac output (CO) during intravenous MIL infusion (0.25-0.75 μg/kg/min) between 1.5 and 2.5 hours after SAH induction was monitored with Doppler echocardiography. Magnetic resonance imaging (MRI)-continuous arterial spin labelling was used for quantitative cerebral blood flow (CBF) measurements. Neurobehavioral function was assessed daily by neurological score and open field test. DCI was analyzed 3 days later by determining infarction on MRI. Mild reduction of cardiac output (CO) and global cerebral blood flow (CBF) depression were notable early after SAH. MIL increased CO in a dose-dependent manner (P<.001), which was accompanied by improved hypoperfusion, incidence of DCI and functional recovery than Control (P<.05). The neuroprotective effects afforded by MIL or Control were attenuated by hypoxia-inducible factor (HIF) inhibition (P<.05). These results suggest that MIL improves acute hypoperfusion by its inotropic effect, leading to neurobehavioral improvement in mice after severe SAH, in which HIF may be acting as a critical mediator. © 2017 John Wiley & Sons Australia, Ltd.

  17. 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.

  18. 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.

  19. Image fusion using sparse overcomplete feature dictionaries

    Science.gov (United States)

    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.

  20. 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...... applications. This thesis takes a systematic approach toward establishing quantitative understanding of conditions for sparse reconstruction to work well in CT. A general framework for analyzing sparse reconstruction methods in CT is introduced and two sets of computational tools are proposed: 1...... contributions to a general set of computational characterization tools. Thus, the thesis contributions help advance sparse reconstruction methods toward routine use in...

  1. 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.

  2. 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.

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

    KAUST Repository

    Wang, Jim Jing-Yan; Cui, Xuefeng; Yu, Ge; Guo, Lili; Gao, Xin

    2017-01-01

    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

  4. Neural Network for Sparse Reconstruction

    Directory of Open Access Journals (Sweden)

    Qingfa Li

    2014-01-01

    Full Text Available We construct a neural network based on smoothing approximation techniques and projected gradient method to solve a kind of sparse reconstruction problems. Neural network can be implemented by circuits and can be seen as an important method for solving optimization problems, especially large scale problems. Smoothing approximation is an efficient technique for solving nonsmooth optimization problems. We combine these two techniques to overcome the difficulties of the choices of the step size in discrete algorithms and the item in the set-valued map of differential inclusion. In theory, the proposed network can converge to the optimal solution set of the given problem. Furthermore, some numerical experiments show the effectiveness of the proposed network in this paper.

  5. 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...... in forecasting accuracy and thus find it to be an important alternative to PC. Supplementary materials for this article are available online....

  6. Sparse and stable Markowitz portfolios.

    Science.gov (United States)

    Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace

    2009-07-28

    We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio.

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

    KAUST Repository

    Nobile, Fabio; Tamellini, Lorenzo; Tesei, Francesco; Tempone, Raul

    2016-01-01

    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

  8. Heart Attack Recovery FAQs

    Science.gov (United States)

    ... recommendations to make a full recovery. View an animation of a heart attack . Heart Attack Recovery Questions ... Support Network Popular Articles 1 Understanding Blood Pressure Readings 2 Sodium and Salt 3 Heart Attack Symptoms ...

  9. 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.

  10. 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.

  11. 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

  12. 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

  13. 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

  14. 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.

  15. 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

  16. Opportunities in the American Recovery and Reinvestment Act for Supports and Services for Youth Transitioning from Foster Care

    Science.gov (United States)

    Flynn-Khan, Margaret; Langford, Barbara Hanson

    2009-01-01

    To address the economic crisis facing the country, the President signed the American Recovery and Reinvestment Act (ARRA) into law on February 17, 2009. This sweeping legislation provides $789 billion to jumpstart the economy and boost employment. This act includes $463 billion in new spending and $326 billion in tax relief directed at those…

  17. Orthogonal sparse linear discriminant analysis

    Science.gov (United States)

    Liu, Zhonghua; Liu, Gang; Pu, Jiexin; Wang, Xiaohong; Wang, Haijun

    2018-03-01

    Linear discriminant analysis (LDA) is a linear feature extraction approach, and it has received much attention. On the basis of LDA, researchers have done a lot of research work on it, and many variant versions of LDA were proposed. However, the inherent problem of LDA cannot be solved very well by the variant methods. The major disadvantages of the classical LDA are as follows. First, it is sensitive to outliers and noises. Second, only the global discriminant structure is preserved, while the local discriminant information is ignored. In this paper, we present a new orthogonal sparse linear discriminant analysis (OSLDA) algorithm. The k nearest neighbour graph is first constructed to preserve the locality discriminant information of sample points. Then, L2,1-norm constraint on the projection matrix is used to act as loss function, which can make the proposed method robust to outliers in data points. Extensive experiments have been performed on several standard public image databases, and the experiment results demonstrate the performance of the proposed OSLDA algorithm.

  18. Spectra of sparse random matrices

    International Nuclear Information System (INIS)

    Kuehn, Reimer

    2008-01-01

    We compute the spectral density for ensembles of sparse symmetric random matrices using replica. Our formulation of the replica-symmetric ansatz shares the symmetries of that suggested in a seminal paper by Rodgers and Bray (symmetry with respect to permutation of replica and rotation symmetry in the space of replica), but uses a different representation in terms of superpositions of Gaussians. It gives rise to a pair of integral equations which can be solved by a stochastic population-dynamics algorithm. Remarkably our representation allows us to identify pure-point contributions to the spectral density related to the existence of normalizable eigenstates. Our approach is not restricted to matrices defined on graphs with Poissonian degree distribution. Matrices defined on regular random graphs or on scale-free graphs, are easily handled. We also look at matrices with row constraints such as discrete graph Laplacians. Our approach naturally allows us to unfold the total density of states into contributions coming from vertices of different local coordinations and an example of such an unfolding is presented. Our results are well corroborated by numerical diagonalization studies of large finite random matrices

  19. Supporting recovery in patients with psychosis through care by community-based adult mental health teams (REFOCUS): a multisite, cluster, randomised, controlled trial.

    Science.gov (United States)

    Slade, Mike; Bird, Victoria; Clarke, Eleanor; Le Boutillier, Clair; McCrone, Paul; Macpherson, Rob; Pesola, Francesca; Wallace, Genevieve; Williams, Julie; Leamy, Mary

    2015-06-01

    Mental health policy in many countries is oriented around recovery, but the evidence base for service-level recovery-promotion interventions is lacking. We did a cluster, randomised, controlled trial in two National Health Service Trusts in England. REFOCUS is a 1-year team-level intervention targeting staff behaviour to increase focus on values, preferences, strengths, and goals of patients with psychosis, and staff-patient relationships, through coaching and partnership. Between April, 2011, and May, 2012, community-based adult mental health teams were randomly allocated to provide usual treatment plus REFOCUS or usual treatment alone (control). Baseline and 1-year follow-up outcomes were assessed in randomly selected patients. The primary outcome was recovery and was assessed with the Questionnaire about Processes of Recovery (QPR). We also calculated overall service costs. We used multiple imputation to estimate missing data, and the imputation model captured clustering at the team level. Analysis was by intention to treat. This trial is registered, number ISRCTN02507940. 14 teams were included in the REFOCUS group and 13 in the control group. Outcomes were assessed in 403 patients (88% of the target sample) at baseline and in 297 at 1 year. Mean QPR total scores did not differ between the two groups (REFOCUS group 40·6 [SD 10·1] vs control 40·0 [10·2], adjusted difference 0·68, 95% CI -1·7 to 3·1, p=0·58). High team participation was associated with higher staff-rated scores for recovery-promotion behaviour change (adjusted difference -0·4, 95% CI -0·7 to -0·2, p=0·001) and patient-rated QPR interpersonal scores (-1·6, -2·7 to -0·5, p=0·005) at follow-up than low participation. Patients treated in the REFOCUS group incurred £1062 (95% CI -1103 to 3017) lower adjusted costs than those in the control group. Although the primary endpoint was negative, supporting recovery might, from the staff perspective, improve functioning and reduce needs

  20. Discriminative sparse coding on multi-manifolds

    KAUST Repository

    Wang, J.J.-Y.; Bensmail, H.; Yao, N.; Gao, Xin

    2013-01-01

    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.

  1. 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.

  2. 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.

  3. Enhancing Scalability of Sparse Direct Methods

    International Nuclear Information System (INIS)

    Li, Xiaoye S.; Demmel, James; Grigori, Laura; Gu, Ming; Xia, Jianlin; Jardin, Steve; Sovinec, Carl; Lee, Lie-Quan

    2007-01-01

    TOPS is providing high-performance, scalable sparse direct solvers, which have had significant impacts on the SciDAC applications, including fusion simulation (CEMM), accelerator modeling (COMPASS), as well as many other mission-critical applications in DOE and elsewhere. Our recent developments have been focusing on new techniques to overcome scalability bottleneck of direct methods, in both time and memory. These include parallelizing symbolic analysis phase and developing linear-complexity sparse factorization methods. The new techniques will make sparse direct methods more widely usable in large 3D simulations on highly-parallel petascale computers

  4. 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...

  5. 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

  6. 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.

  7. 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.

  8. Improving sample recovery

    International Nuclear Information System (INIS)

    Blanchard, R.J.

    1995-09-01

    This Engineering Task Plan (ETP) describes the tasks, i.e., tests, studies, external support and modifications planned to increase the recovery of the recovery of the waste tank contents using combinations of improved techniques, equipment, knowledge, experience and testing to better the recovery rates presently being experienced

  9. 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.

  10. Relaxations to Sparse Optimization Problems and Applications

    Science.gov (United States)

    Skau, Erik West

    Parsimony is a fundamental property that is applied to many characteristics in a variety of fields. Of particular interest are optimization problems that apply rank, dimensionality, or support in a parsimonious manner. In this thesis we study some optimization problems and their relaxations, and focus on properties and qualities of the solutions of these problems. The Gramian tensor decomposition problem attempts to decompose a symmetric tensor as a sum of rank one tensors.We approach the Gramian tensor decomposition problem with a relaxation to a semidefinite program. We study conditions which ensure that the solution of the relaxed semidefinite problem gives the minimal Gramian rank decomposition. Sparse representations with learned dictionaries are one of the leading image modeling techniques for image restoration. When learning these dictionaries from a set of training images, the sparsity parameter of the dictionary learning algorithm strongly influences the content of the dictionary atoms.We describe geometrically the content of trained dictionaries and how it changes with the sparsity parameter.We use statistical analysis to characterize how the different content is used in sparse representations. Finally, a method to control the structure of the dictionaries is demonstrated, allowing us to learn a dictionary which can later be tailored for specific applications. Variations of dictionary learning can be broadly applied to a variety of applications.We explore a pansharpening problem with a triple factorization variant of coupled dictionary learning. Another application of dictionary learning is computer vision. Computer vision relies heavily on object detection, which we explore with a hierarchical convolutional dictionary learning model. Data fusion of disparate modalities is a growing topic of interest.We do a case study to demonstrate the benefit of using social media data with satellite imagery to estimate hazard extents. In this case study analysis we

  11. Structure-based bayesian sparse reconstruction

    KAUST Repository

    Quadeer, Ahmed Abdul; Al-Naffouri, Tareq Y.

    2012-01-01

    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

  12. 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.

  13. SPARSE ELECTROMAGNETIC IMAGING USING NONLINEAR LANDWEBER ITERATIONS

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2015-01-01

    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

  14. Learning sparse generative models of audiovisual signals

    OpenAIRE

    Monaci, Gianluca; Sommer, Friedrich T.; Vandergheynst, Pierre

    2008-01-01

    This paper presents a novel framework to learn sparse represen- tations for audiovisual signals. An audiovisual signal is modeled as a sparse sum of audiovisual kernels. The kernels are bimodal functions made of synchronous audio and video components that can be positioned independently and arbitrarily in space and time. We design an algorithm capable of learning sets of such audiovi- sual, synchronous, shift-invariant functions by alternatingly solving a coding and a learning pr...

  15. 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

  16. 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....

  17. Low perceived social support and post-myocardial infarction prognosis in the enhancing recovery in coronary heart disease clinical trial: the effects of treatment.

    Science.gov (United States)

    Burg, Matthew M; Barefoot, John; Berkman, Lisa; Catellier, Diane J; Czajkowski, Susan; Saab, Patrice; Huber, Marc; DeLillo, Vicki; Mitchell, Pamela; Skala, Judy; Taylor, C Barr

    2005-01-01

    In post hoc analyses, to examine in low perceived social support (LPSS) patients enrolled in the Enhancing Recovery in Coronary Heart Disease (ENRICHD) clinical trial (n = 1503), the pattern of social support following myocardial infarction (MI), the impact of psychosocial intervention on perceived support, the relationship of perceived support at the time of MI to subsequent death and recurrent MI, and the relationship of change in perceived support 6 months after MI to subsequent mortality. Partner status (partner, no partner) and score (12 = moderate support) on the ENRICHD Social Support Instrument (ESSI) were used post hoc to define four levels of risk. The resulting 4 LPSS risk groups were compared on baseline characteristics, changes in social support, and medical outcomes to a group of concurrently enrolled acute myocardial infarction patients without depression or LPSS (MI comparison group, n = 408). Effects of treatment assignment on LPSS and death/recurrent MI were also examined. All 4 LPSS risk groups demonstrated improvement in perceived support, regardless of treatment assignment, with a significant treatment effect only seen in the LPSS risk group with no partner and moderate support at baseline. During an average 29-month follow-up, the combined end point of death/nonfatal MI was 10% in the MI comparison group and 23% in the ENRICHD LPSS patients; LPSS conferred a greater risk in unadjusted and adjusted models (HR = 1.74-2.39). Change in ESSI score and/or improvement in perceived social support were not found to predict subsequent mortality. Baseline LPSS predicted death/recurrent MI in the ENRICHD cohort, independent of treatment assignment. Intervention effects indicated a partner surrogacy role for the interventionist and the need for a moderate level of support at baseline for the intervention to be effective.

  18. Hyperspectral Unmixing with Robust Collaborative Sparse Regression

    Directory of Open Access Journals (Sweden)

    Chang Li

    2016-07-01

    Full Text Available Recently, sparse unmixing (SU of hyperspectral data has received particular attention for analyzing remote sensing images. However, most SU methods are based on the commonly admitted linear mixing model (LMM, which ignores the possible nonlinear effects (i.e., nonlinearity. In this paper, we propose a new method named robust collaborative sparse regression (RCSR based on the robust LMM (rLMM for hyperspectral unmixing. The rLMM takes the nonlinearity into consideration, and the nonlinearity is merely treated as outlier, which has the underlying sparse property. The RCSR simultaneously takes the collaborative sparse property of the abundance and sparsely distributed additive property of the outlier into consideration, which can be formed as a robust joint sparse regression problem. The inexact augmented Lagrangian method (IALM is used to optimize the proposed RCSR. The qualitative and quantitative experiments on synthetic datasets and real hyperspectral images demonstrate that the proposed RCSR is efficient for solving the hyperspectral SU problem compared with the other four state-of-the-art algorithms.

  19. Towards "Precision Mining" of wastewater: Selective recovery of Cu from acid mine drainage onto diatomite supported nanoscale zerovalent iron particles.

    Science.gov (United States)

    Crane, R A; Sapsford, D J

    2018-07-01

    This paper introduces the concept of 'Precision Mining' of metals which can be defined as a process for the selective in situ uptake of a metal from a material or media, with subsequent retrieval and recovery of the target metal. In order to demonstrate this concept nanoscale zerovalent iron (nZVI) was loaded onto diatomaceous earth (DE) and tested for the selective uptake of Cu from acid mine drainage (AMD) and subsequent release. Batch experiments were conducted using the AMD and nZVI-DE at 4.0-16.0 g/L. Results demonstrate nZVI-DE as highly selective for Cu removal with >99% uptake recorded after 0.25 h when using nZVI-DE concentrations ≥12.0 g/L, despite appreciable concentrations of numerous other metals in the AMD, namely: Co, Ni, Mn and Zn. Cu uptake was maintained in excess of 4 and 24 h when using nZVI-DE concentrations of 12.0 and 16.0 g/L respectively. Near-total Cu release from the nZVI-DE was then recorded and attributed to the depletion of the nZVI component and the subsequent Eh, DO and pH recovery. This novel Cu uptake and release mechanism, once appropriately engineered, holds great promise as a novel 'Precision Mining' process for the rapid and selective Cu recovery from acidic wastewater, process effluents and leach liquors. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

  20. 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.

  1. 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.

  2. Early enteral immune nutrition support after radical operation for gastric cancer on promoting the recovery of gastrointestinal function and immune function

    Directory of Open Access Journals (Sweden)

    Zhi-Gang Li

    2016-05-01

    Full Text Available Objective: To analyze the effect of early enteral immune nutrition support after radical operation for gastric cancer on the recovery of gastrointestinal function and immune function. Methods: A total of 106 cases of patients received radical operation for gastric cancer in our hospital were selected as research subjects, and according to different ways of postoperative nutrition intervention, all patients were divided into observation group (n=50 and control group (n=56. Control group received conventional enteral nutrition intervention, observation group received postoperative early enteral immune nutrition support, and then differences in postoperative intestinal mucosa barrier function, gastrointestinal hormone levels, immune function levels and nutrition-related indicator values were compared between two groups. Results: After observation group received enteral immune nutrition intervention, serum DAO, PS and D-lactate levels as well as urine L/M ratio were lower than those of control group; serum GAS, CCK, MTL and SP values of observation group after intervention were higher than those of control group, and GLU, VIP, GIP and SS values were lower than those of control group; CD4, IgG, NK cell, C3, C4, CH50 and S-IgA levels of observation group after intervention were higher than those of control group; serum ALB, PRE, TRF and RBP levels of observation group after intervention were higher than those of control group. Conclusion: Early enteral immune nutrition support after radical operation for gastric cancer is conducive to the recovery of gastrointestinal function and the promotion of immune state, eventually promotes patients’ postoperative overall recovery and has active clinical significance.

  3. Long-Term Social Reintegration Outcomes for Burn Survivors With and Without Peer Support Attendance: A Life Impact Burn Recovery Evaluation (LIBRE) Study.

    Science.gov (United States)

    Grieve, Brian; Shapiro, Gabriel D; Wibbenmeyer, Lucy; Acton, Amy; Lee, Austin; Marino, Molly; Jette, Alan; Schneider, Jeffrey C; Kazis, Lewis E; Ryan, Colleen M

    2017-10-31

    To examine differences in long-term social reintegration outcomes for burn survivors with and without peer support attendance. Cross-sectional survey. Community-dwelling burn survivors. Burn survivors (N=601) aged ≥18 years with injuries to ≥5% total body surface area (TBSA) or burns to critical areas (hands, feet, face, or genitals). Not applicable. The Life Impact Burn Recovery Evaluation Profile was used to examine the following previously validated 6 scale scores of social participation: Family and Friends, Social Interactions, Social Activities, Work and Employment, Romantic Relationships, and Sexual Relationships. Burn support group attendance was reported by 330 (55%) of 596 respondents who responded to this item. Attendees had larger burn size (43.4%±23.6% vs 36.8%±23.4% TBSA burned, P10 years from injury (50% vs 42.5%, Preintegration in burn survivors. This cross-sectional study prompts further exploration into the potential benefits of peer support groups on burn recovery with future intervention studies. Copyright © 2017 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

  4. Recovery from schizophrenia and the recovery model.

    Science.gov (United States)

    Warner, Richard

    2009-07-01

    The recovery model refers to subjective experiences of optimism, empowerment and interpersonal support, and to a focus on collaborative treatment approaches, finding productive roles for user/consumers, peer support and reducing stigma. The model is influencing service development around the world. This review will assess whether optimism about outcome from serious mental illness and other tenets of the recovery model are borne out by recent research. Remission of symptoms has been precisely defined, but the definition of 'recovery' is a more diffuse concept that includes such factors as being productive and functioning independently. Recent research and a large, earlier body of data suggest that optimism about outcome from schizophrenia is justified. A substantial proportion of people with the illness will recover completely and many more will regain good social functioning. Outcome is better for people in the developing world. Mortality for people with schizophrenia is increasing but is lower in the developing world. Working appears to help people recover from schizophrenia, and recent advances in vocational rehabilitation have been shown to be effective in countries with differing economies and labor markets. A growing body of research supports the concept that empowerment is an important component of the recovery process. Key tenets of the recovery model - optimism about recovery from schizophrenia, the importance of access to employment and the value of empowerment of user/consumers in the recovery process - are supported by the scientific research. Attempts to reduce the internalized stigma of mental illness should enhance the recovery process.

  5. 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.

  6. On A Nonlinear Generalization of Sparse Coding and Dictionary Learning.

    Science.gov (United States)

    Xie, Yuchen; Ho, Jeffrey; Vemuri, Baba

    2013-01-01

    Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝ d , and the dictionary is learned from the training data using the vector space structure of ℝ d and its Euclidean L 2 -metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis.

  7. 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.

  8. 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.

  9. Seed banks as a source of vegetation regeneration to support the recovery of degraded rivers: A comparison of river reaches of varying condition.

    Science.gov (United States)

    O'Donnell, Jessica; Fryirs, Kirstie A; Leishman, Michelle R

    2016-01-15

    Anthropogenic disturbance has contributed to widespread geomorphic adjustment and the degradation of many rivers. This research compares for river reaches of varying condition, the potential for seed banks to support geomorphic river recovery through vegetation regeneration. Seven river reaches in the lower Hunter catchment of south-eastern Australia were assessed as being in poor, moderate, or good condition, based on geomorphic and ecological indicators. Seed bank composition within the channel and floodplain (determined in a seedling emergence study) was compared to standing vegetation. Seed bank potential for supporting geomorphic recovery was assessed by measuring native species richness, and the abundance of different plant growth forms, with consideration of the roles played by different growth forms in geomorphic adjustment. The exotic seed bank was considered a limiting factor for achieving ecological restoration goals, and similarly analysed. Seed bank native species richness was comparable between the reaches, and regardless of condition, early successional and pioneer herbs, sedges, grasses and rushes dominated the seed bank. The capacity for these growth forms to colonise and stabilise non-cohesive sediments and initiate biogeomorphic succession, indicates high potential for the seed banks of even highly degraded reaches to contribute to geomorphic river recovery. However, exotic propagules increasingly dominated the seed banks of moderate and poor condition reaches and reflected increasing encroachment by terrestrial exotic vegetation associated with riparian degradation. As the degree of riparian degradation increases, the resources required to control the regeneration of exotic species will similarly increase, if seed bank-based regeneration is to contribute to both geomorphic and ecological restoration goals. Copyright © 2015 Elsevier B.V. All rights reserved.

  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. Sparse Learning with Stochastic Composite Optimization.

    Science.gov (United States)

    Zhang, Weizhong; Zhang, Lijun; Jin, Zhongming; Jin, Rong; Cai, Deng; Li, Xuelong; Liang, Ronghua; He, Xiaofei

    2017-06-01

    In this paper, we study Stochastic Composite Optimization (SCO) for sparse learning that aims to learn a sparse solution from a composite function. Most of the recent SCO algorithms have already reached the optimal expected convergence rate O(1/λT), but they often fail to deliver sparse solutions at the end either due to the limited sparsity regularization during stochastic optimization (SO) or due to the limitation in online-to-batch conversion. Even when the objective function is strongly convex, their high probability bounds can only attain O(√{log(1/δ)/T}) with δ is the failure probability, which is much worse than the expected convergence rate. To address these limitations, we propose a simple yet effective two-phase Stochastic Composite Optimization scheme by adding a novel powerful sparse online-to-batch conversion to the general Stochastic Optimization algorithms. We further develop three concrete algorithms, OptimalSL, LastSL and AverageSL, directly under our scheme to prove the effectiveness of the proposed scheme. Both the theoretical analysis and the experiment results show that our methods can really outperform the existing methods at the ability of sparse learning and at the meantime we can improve the high probability bound to approximately O(log(log(T)/δ)/λT).

  12. In-place sparse suffix sorting

    DEFF Research Database (Denmark)

    Prezza, Nicola

    2018-01-01

    information regarding the lexicographical order of a size-b subset of all n text suffixes is often needed. Such information can be stored space-efficiently (in b words) in the sparse suffix array (SSA). The SSA and its relative sparse LCP array (SLCP) can be used as a space-efficient substitute of the sparse...... suffix tree. Very recently, Gawrychowski and Kociumaka [11] showed that the sparse suffix tree (and therefore SSA and SLCP) can be built in asymptotically optimal O(b) space with a Monte Carlo algorithm running in O(n) time. The main reason for using the SSA and SLCP arrays in place of the sparse suffix...... tree is, however, their reduced space of b words each. This leads naturally to the quest for in-place algorithms building these arrays. Franceschini and Muthukrishnan [8] showed that the full suffix array can be built in-place and in optimal running time. On the other hand, finding sub-quadratic in...

  13. Superresolution radar imaging based on fast inverse-free sparse Bayesian learning for multiple measurement vectors

    Science.gov (United States)

    He, Xingyu; Tong, Ningning; Hu, Xiaowei

    2018-01-01

    Compressive sensing has been successfully applied to inverse synthetic aperture radar (ISAR) imaging of moving targets. By exploiting the block sparse structure of the target image, sparse solution for multiple measurement vectors (MMV) can be applied in ISAR imaging and a substantial performance improvement can be achieved. As an effective sparse recovery method, sparse Bayesian learning (SBL) for MMV involves a matrix inverse at each iteration. Its associated computational complexity grows significantly with the problem size. To address this problem, we develop a fast inverse-free (IF) SBL method for MMV. A relaxed evidence lower bound (ELBO), which is computationally more amiable than the traditional ELBO used by SBL, is obtained by invoking fundamental property for smooth functions. A variational expectation-maximization scheme is then employed to maximize the relaxed ELBO, and a computationally efficient IF-MSBL algorithm is proposed. Numerical results based on simulated and real data show that the proposed method can reconstruct row sparse signal accurately and obtain clear superresolution ISAR images. Moreover, the running time and computational complexity are reduced to a great extent compared with traditional SBL methods.

  14. 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...... shows that noise is reduced in areas typically associated with activation by the experimental design. The psFA model identifies sparse components and the probabilistic setting provides a natural way to handle parameter uncertainties. The variational Bayesian framework easily extends to more complex...

  15. 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.

  16. 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.

  17. 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

  18. Dynamic Stochastic Superresolution of sparsely observed turbulent systems

    International Nuclear Information System (INIS)

    Branicki, M.; Majda, A.J.

    2013-01-01

    Real-time capture of the relevant features of the unresolved turbulent dynamics of complex natural systems from sparse noisy observations and imperfect models is a notoriously difficult problem. The resulting lack of observational resolution and statistical accuracy in estimating the important turbulent processes, which intermittently send significant energy to the large-scale fluctuations, hinders efficient parameterization and real-time prediction using discretized PDE models. This issue is particularly subtle and important when dealing with turbulent geophysical systems with an vast range of interacting spatio-temporal scales and rough energy spectra near the mesh scale of numerical models. Here, we introduce and study a suite of general Dynamic Stochastic Superresolution (DSS) algorithms and show that, by appropriately filtering sparse regular observations with the help of cheap stochastic exactly solvable models, one can derive stochastically ‘superresolved’ velocity fields and gain insight into the important characteristics of the unresolved dynamics, including the detection of the so-called black swans. The DSS algorithms operate in Fourier domain and exploit the fact that the coarse observation network aliases high-wavenumber information into the resolved waveband. It is shown that these cheap algorithms are robust and have significant skill on a test bed of turbulent solutions from realistic nonlinear turbulent spatially extended systems in the presence of a significant model error. In particular, the DSS algorithms are capable of successfully capturing time-localized extreme events in the unresolved modes, and they provide good and robust skill for recovery of the unresolved processes in terms of pattern correlation. Moreover, we show that DSS improves the skill for recovering the primary modes associated with the sparse observation mesh which is equally important in applications. The skill of the various DSS algorithms depends on the energy spectrum

  19. 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.

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

    International Nuclear Information System (INIS)

    Ziritt, Jose Luis

    1999-01-01

    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 and 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

  1. 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.

  2. 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.

  3. Analog system for computing sparse codes

    Science.gov (United States)

    Rozell, Christopher John; Johnson, Don Herrick; Baraniuk, Richard Gordon; Olshausen, Bruno A.; Ortman, Robert Lowell

    2010-08-24

    A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.

  4. Parallel transposition of sparse data structures

    DEFF Research Database (Denmark)

    Wang, Hao; Liu, Weifeng; Hou, Kaixi

    2016-01-01

    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 tr...... transposition in the latest vendor-supplied library on an Intel multicore CPU platform, and the MergeTrans approach achieves on average of 3.4-fold (up to 11.7-fold) speedup on an Intel Xeon Phi many-core processor....

  5. 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...

  6. 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

  7. 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.

  8. 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

  9. From recovery values to recovery-oriented practice?

    DEFF Research Database (Denmark)

    Dalum, Helle; Pedersen, Inge Kryger; Cunningham, Harry

    2015-01-01

    Introduction: The recovery model has influenced mental health services and fostered new standards for best practice. However, knowledge about how mental health care professionals (HCPs) experience recoveryoriented programs is sparse. Aim/Question: This paper explores HCPs' experiences when...... facilitating a recovery-oriented rehabilitation program. The research question is howdo HCPs experience a change in their attitude and practicewhen applying recovery-oriented programs? Methods: This paper draws on semi-structured in-depth qualitative interviews conducted with 16 HCPs experienced...... in facilitating a recovery-oriented rehabilitation program in either the USA or Denmark. Results: Three themes emerged from the HCPs' reflections on changes in attitudes and practices: “Hopeful Attitude” captures a change in the HCPs' attitude toward a more positive view on the future for clients' living...

  10. 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...

  11. 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.

  12. Continuous speech recognition with sparse coding

    CSIR Research Space (South Africa)

    Smit, WJ

    2009-04-01

    Full Text Available generative model. The spike train is classified by making use of a spike train model and dynamic programming. It is computationally expensive to find a sparse code. We use an iterative subset selection algorithm with quadratic programming for this process...

  13. 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...

  14. 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...

  15. 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...

  16. Feature based omnidirectional sparse visual path following

    OpenAIRE

    Goedemé, Toon; Tuytelaars, Tinne; Van Gool, Luc; Vanacker, Gerolf; Nuttin, Marnix

    2005-01-01

    Goedemé T., Tuytelaars T., Van Gool L., Vanacker G., Nuttin M., ''Feature based omnidirectional sparse visual path following'', Proceedings IEEE/RSJ international conference on intelligent robots and systems - IROS2005, pp. 1003-1008, August 2-6, 2005, Edmonton, Alberta, Canada.

  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. 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.

  19. 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.

  20. A sparse-grid isogeometric solver

    KAUST Repository

    Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

    2018-01-01

    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.

  1. A sparse version of IGA solvers

    KAUST Repository

    Beck, Joakim; Sangalli, Giancarlo; Tamellini, Lorenzo

    2017-01-01

    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.

  2. New methods for sampling sparse populations

    Science.gov (United States)

    Anna Ringvall

    2007-01-01

    To improve surveys of sparse objects, methods that use auxiliary information have been suggested. Guided transect sampling uses prior information, e.g., from aerial photographs, for the layout of survey strips. Instead of being laid out straight, the strips will wind between potentially more interesting areas. 3P sampling (probability proportional to prediction) uses...

  3. 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.

  4. 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.

  5. 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.

  6. Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint.

    Science.gov (United States)

    Gao, Zhi; Lao, Mingjie; Sang, Yongsheng; Wen, Fei; Ramesh, Bharath; Zhai, Ruifang

    2018-05-06

    Light detection and ranging (LiDAR) sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs) and unmanned aerial vehicles (UAVs) to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

  7. A sparse electromagnetic imaging scheme using nonlinear landweber iterations

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2015-01-01

    Development and use of electromagnetic inverse scattering techniques for imagining sparse domains have been on the rise following the recent advancements in solving sparse optimization problems. Existing techniques rely on iteratively converting

  8. Efficient Pseudorecursive Evaluation Schemes for Non-adaptive Sparse Grids

    KAUST Repository

    Buse, Gerrit; Pflü ger, Dirk; Jacob, Riko

    2014-01-01

    In this work we propose novel algorithms for storing and evaluating sparse grid functions, operating on regular (not spatially adaptive), yet potentially dimensionally adaptive grid types. Besides regular sparse grids our approach includes truncated

  9. 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.

  10. 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

  11. 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

  12. 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.

  13. Social biases determine spatiotemporal sparseness of ciliate mating heuristics

    Science.gov (United States)

    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

  14. 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 highresol......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...... highresolution imaging. Utilizing the dual optimal variables of the CS optimization problem, it is shown with Monte Carlo simulations that the DOAs are accurately reconstructed through polynomial rooting (Root-CS). Polynomial rooting is known to improve the resolution in several other DOA estimation methods...

  15. Sparse learning of stochastic dynamical equations

    Science.gov (United States)

    Boninsegna, Lorenzo; Nüske, Feliks; Clementi, Cecilia

    2018-06-01

    With the rapid increase of available data for complex systems, there is great interest in the extraction of physically relevant information from massive datasets. Recently, a framework called Sparse Identification of Nonlinear Dynamics (SINDy) has been introduced to identify the governing equations of dynamical systems from simulation data. In this study, we extend SINDy to stochastic dynamical systems which are frequently used to model biophysical processes. We prove the asymptotic correctness of stochastic SINDy in the infinite data limit, both in the original and projected variables. We discuss algorithms to solve the sparse regression problem arising from the practical implementation of SINDy and show that cross validation is an essential tool to determine the right level of sparsity. We demonstrate the proposed methodology on two test systems, namely, the diffusion in a one-dimensional potential and the projected dynamics of a two-dimensional diffusion process.

  16. A density functional for sparse matter

    DEFF Research Database (Denmark)

    Langreth, D.C.; Lundqvist, Bengt; Chakarova-Kack, S.D.

    2009-01-01

    forces in molecules, to adsorbed molecules, like benzene, naphthalene, phenol and adenine on graphite, alumina and metals, to polymer and carbon nanotube (CNT) crystals, and hydrogen storage in graphite and metal-organic frameworks (MOFs), and to the structure of DNA and of DNA with intercalators......Sparse matter is abundant and has both strong local bonds and weak nonbonding forces, in particular nonlocal van der Waals (vdW) forces between atoms separated by empty space. It encompasses a broad spectrum of systems, like soft matter, adsorption systems and biostructures. Density-functional...... theory (DFT), long since proven successful for dense matter, seems now to have come to a point, where useful extensions to sparse matter are available. In particular, a functional form, vdW-DF (Dion et al 2004 Phys. Rev. Lett. 92 246401; Thonhauser et al 2007 Phys. Rev. B 76 125112), has been proposed...

  17. 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.

  18. 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).

  19. Solving Sparse Polynomial Optimization Problems with Chordal Structure Using the Sparse, Bounded-Degree Sum-of-Squares Hierarchy

    NARCIS (Netherlands)

    Marandi, Ahmadreza; de Klerk, Etienne; Dahl, Joachim

    The sparse bounded degree sum-of-squares (sparse-BSOS) hierarchy of Weisser, Lasserre and Toh [arXiv:1607.01151,2016] constructs a sequence of lower bounds for a sparse polynomial optimization problem. Under some assumptions, it is proven by the authors that the sequence converges to the optimal

  20. CT Image Sequence Restoration Based on Sparse and Low-Rank Decomposition

    Science.gov (United States)

    Gou, Shuiping; Wang, Yueyue; Wang, Zhilong; Peng, Yong; Zhang, Xiaopeng; Jiao, Licheng; Wu, Jianshe

    2013-01-01

    Blurry organ boundaries and soft tissue structures present a major challenge in biomedical image restoration. In this paper, we propose a low-rank decomposition-based method for computed tomography (CT) image sequence restoration, where the CT image sequence is decomposed into a sparse component and a low-rank component. A new point spread function of Weiner filter is employed to efficiently remove blur in the sparse component; a wiener filtering with the Gaussian PSF is used to recover the average image of the low-rank component. And then we get the recovered CT image sequence by combining the recovery low-rank image with all recovery sparse image sequence. Our method achieves restoration results with higher contrast, sharper organ boundaries and richer soft tissue structure information, compared with existing CT image restoration methods. The robustness of our method was assessed with numerical experiments using three different low-rank models: Robust Principle Component Analysis (RPCA), Linearized Alternating Direction Method with Adaptive Penalty (LADMAP) and Go Decomposition (GoDec). Experimental results demonstrated that the RPCA model was the most suitable for the small noise CT images whereas the GoDec model was the best for the large noisy CT images. PMID:24023764

  1. 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.

  2. 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.

  3. On the role of sparseness in the evolution of modularity in gene regulatory networks.

    Science.gov (United States)

    Espinosa-Soto, Carlos

    2018-05-01

    Modularity is a widespread property in biological systems. It implies that interactions occur mainly within groups of system elements. A modular arrangement facilitates adjustment of one module without perturbing the rest of the system. Therefore, modularity of developmental mechanisms is a major factor for evolvability, the potential to produce beneficial variation from random genetic change. Understanding how modularity evolves in gene regulatory networks, that create the distinct gene activity patterns that characterize different parts of an organism, is key to developmental and evolutionary biology. One hypothesis for the evolution of modules suggests that interactions between some sets of genes become maladaptive when selection favours additional gene activity patterns. The removal of such interactions by selection would result in the formation of modules. A second hypothesis suggests that modularity evolves in response to sparseness, the scarcity of interactions within a system. Here I simulate the evolution of gene regulatory networks and analyse diverse experimentally sustained networks to study the relationship between sparseness and modularity. My results suggest that sparseness alone is neither sufficient nor necessary to explain modularity in gene regulatory networks. However, sparseness amplifies the effects of forms of selection that, like selection for additional gene activity patterns, already produce an increase in modularity. That evolution of new gene activity patterns is frequent across evolution also supports that it is a major factor in the evolution of modularity. That sparseness is widespread across gene regulatory networks indicates that it may have facilitated the evolution of modules in a wide variety of cases.

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

    International Nuclear Information System (INIS)

    Gene Golub; Kwok Ko

    2009-01-01

    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.

  5. 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.

  6. 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.

  7. 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.

  8. 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

  9. Removing flicker based on sparse color correspondences in old film restoration

    Science.gov (United States)

    Huang, Xi; Ding, Youdong; Yu, Bing; Xia, Tianran

    2018-04-01

    In the long history of human civilization, archived film is an indispensable part of it, and using digital method to repair damaged film is also a mainstream trend nowadays. In this paper, we propose a sparse color correspondences based technique to remove fading flicker for old films. Our model, combined with multi frame images to establish a simple correction model, includes three key steps. Firstly, we recover sparse color correspondences in the input frames to build a matrix with many missing entries. Secondly, we present a low-rank matrix factorization approach to estimate the unknown parameters of this model. Finally, we adopt a two-step strategy that divide the estimated parameters into reference frame parameters for color recovery correction and other frame parameters for color consistency correction to remove flicker. Our method combined multi-frames takes continuity of the input sequence into account, and the experimental results show the method can remove fading flicker efficiently.

  10. Noniterative MAP reconstruction using sparse matrix representations.

    Science.gov (United States)

    Cao, Guangzhi; Bouman, Charles A; Webb, Kevin J

    2009-09-01

    We present a method for noniterative maximum a posteriori (MAP) tomographic reconstruction which is based on the use of sparse matrix representations. Our approach is to precompute and store the inverse matrix required for MAP reconstruction. This approach has generally not been used in the past because the inverse matrix is typically large and fully populated (i.e., not sparse). In order to overcome this problem, we introduce two new ideas. The first idea is a novel theory for the lossy source coding of matrix transformations which we refer to as matrix source coding. This theory is based on a distortion metric that reflects the distortions produced in the final matrix-vector product, rather than the distortions in the coded matrix itself. The resulting algorithms are shown to require orthonormal transformations of both the measurement data and the matrix rows and columns before quantization and coding. The second idea is a method for efficiently storing and computing the required orthonormal transformations, which we call a sparse-matrix transform (SMT). The SMT is a generalization of the classical FFT in that it uses butterflies to compute an orthonormal transform; but unlike an FFT, the SMT uses the butterflies in an irregular pattern, and is numerically designed to best approximate the desired transforms. We demonstrate the potential of the noniterative MAP reconstruction with examples from optical tomography. The method requires offline computation to encode the inverse transform. However, once these offline computations are completed, the noniterative MAP algorithm is shown to reduce both storage and computation by well over two orders of magnitude, as compared to a linear iterative reconstruction methods.

  11. Galaxy redshift surveys with sparse sampling

    International Nuclear Information System (INIS)

    Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro; Jee, Inh; Jeong, Donghui; Blanc, Guillermo A.; Ciardullo, Robin; Gronwall, Caryl; Hagen, Alex; Schneider, Donald P.; Drory, Niv; Fabricius, Maximilian; Landriau, Martin; Finkelstein, Steven; Jogee, Shardha; Cooper, Erin Mentuch; Tuttle, Sarah; Gebhardt, Karl; Hill, Gary J.

    2013-01-01

    Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., V survey ∼ 10Gpc 3 ) to be covered, and thus tends to be expensive. A ''sparse sampling'' method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, V survey , we observe only a fraction of the volume. The distribution of observed regions should be chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of V survey (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by V survey (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. On the other hand, we show that the two-point correlation function (pair counting) is not affected by sparse sampling. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys

  12. 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.

  13. 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...

  14. Sparse dynamics for partial differential equations.

    Science.gov (United States)

    Schaeffer, Hayden; Caflisch, Russel; Hauck, Cory D; Osher, Stanley

    2013-04-23

    We investigate the approximate dynamics of several differential equations when the solutions are restricted to a sparse subset of a given basis. The restriction is enforced at every time step by simply applying soft thresholding to the coefficients of the basis approximation. By reducing or compressing the information needed to represent the solution at every step, only the essential dynamics are represented. In many cases, there are natural bases derived from the differential equations, which promote sparsity. We find that our method successfully reduces the dynamics of convection equations, diffusion equations, weak shocks, and vorticity equations with high-frequency source terms.

  15. 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 measurem...... is found that satisfies: the distance between its local space and the normal space is large. We evaluate our method on two public benchmark datasets: UCSD and Subway Entrance datasets. The comparison to the state-of-the-art methods validate our method's effectiveness....

  16. 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.

  17. 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.

  18. 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.

  19. 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,...

  20. 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.

  1. 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

    We propose a general technique for obtaining sparse solutions to generalized eigenvalue problems, and call it Regularized Generalized Eigen-Decomposition (RGED). For decades, Fisher's discriminant criterion has been applied in supervised feature extraction and discriminant analysis, and it is for...

  2. Fast Sparse Coding for Range Data Denoising with Sparse Ridges Constraint

    Directory of Open Access Journals (Sweden)

    Zhi Gao

    2018-05-01

    Full Text Available Light detection and ranging (LiDAR sensors have been widely deployed on intelligent systems such as unmanned ground vehicles (UGVs and unmanned aerial vehicles (UAVs to perform localization, obstacle detection, and navigation tasks. Thus, research into range data processing with competitive performance in terms of both accuracy and efficiency has attracted increasing attention. Sparse coding has revolutionized signal processing and led to state-of-the-art performance in a variety of applications. However, dictionary learning, which plays the central role in sparse coding techniques, is computationally demanding, resulting in its limited applicability in real-time systems. In this study, we propose sparse coding algorithms with a fixed pre-learned ridge dictionary to realize range data denoising via leveraging the regularity of laser range measurements in man-made environments. Experiments on both synthesized data and real data demonstrate that our method obtains accuracy comparable to that of sophisticated sparse coding methods, but with much higher computational efficiency.

  3. 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.

  4. Balanced and sparse Tamo-Barg codes

    KAUST Repository

    Halbawi, Wael; Duursma, Iwan; Dau, Hoang; Hassibi, Babak

    2017-01-01

    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.

  5. 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.

  6. 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.

  7. 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...

  8. 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)

  9. Carbon Cloth Supported Nano-Mg(OH)2 for the Enrichment and Recovery of Rare Earth Element Eu(III) From Aqueous Solution.

    Science.gov (United States)

    Li, Yinong; Tian, Chen; Liu, Weizhen; Xu, Si; Xu, Yunyun; Cui, Rongxin; Lin, Zhang

    2018-01-01

    Nano-Mg(OH) 2 is attracting great attention as adsorbent for pre-concentration and recovery of rare earth elements (REEs) from low-concentration solution, due to its superior removal efficiency for REEs and environmental friendliness. However, the nanoparticles also cause some severe problems during application, including aggregation, blockage in fixed-bed column, as well as the difficulties in separation and reuse. Herein, in order to avoid the mentioned problems, a carbon cloth (CC) supported nano-Mg(OH) 2 (nano-Mg(OH) 2 @CC) was synthesized by electrodeposition. The X-ray diffraction and scanning electron microscopy analysis demonstrated that the interlaced nano-sheet of Mg(OH) 2 grew firmly and uniformly on the surface of carbon cloth fibers. Batch adsorption experiments of Eu(III) indicated that the nano-Mg(OH) 2 @CC composite maintained the excellent adsorption performance of nano-Mg(OH) 2 toward Eu(III). After adsorption, the Eu containing composite was calcined under nitrogen atmosphere. The content of Eu 2 O 3 in the calcined material was as high as 99.66%. Fixed-bed column experiments indicated that no blockage for Mg(OH) 2 @CC composite was observed during the treatment, while the complete blockage of occurred to nano-Mg(OH) 2 at an effluent volume of 240 mL. Moreover, the removal efficiency of Mg(OH) 2 @CC was still higher than 90% until 4,200 mL of effluent volume. This work provides a promising method for feasible application of nanoadsorbents in fixed-bed process to recycle low-concentration REEs from wastewater.

  10. A Sparse Approximate Inverse Preconditioner for Nonsymmetric Linear Systems

    Czech Academy of Sciences Publication Activity Database

    Benzi, M.; Tůma, Miroslav

    1998-01-01

    Roč. 19, č. 3 (1998), s. 968-994 ISSN 1064-8275 R&D Projects: GA ČR GA201/93/0067; GA AV ČR IAA230401 Keywords : large sparse systems * interative methods * preconditioning * approximate inverse * sparse linear systems * sparse matrices * incomplete factorizations * conjugate gradient -type methods Subject RIV: BA - General Mathematics Impact factor: 1.378, year: 1998

  11. Dose-shaping using targeted sparse optimization

    Energy Technology Data Exchange (ETDEWEB)

    Sayre, George A.; Ruan, Dan [Department of Radiation Oncology, University of California - Los Angeles School of Medicine, 200 Medical Plaza, Los Angeles, California 90095 (United States)

    2013-07-15

    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{sub tot}{sup 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{sub 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{sub tot

  12. 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

  13. 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

  14. 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

  15. 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...... 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 from various aspects to better understand their applicability, including computation time, reconstruction error, sparsity, detection...

  16. Sparse Bayesian Learning for Nonstationary Data Sources

    Science.gov (United States)

    Fujimaki, Ryohei; Yairi, Takehisa; Machida, Kazuo

    This paper proposes an online Sparse Bayesian Learning (SBL) algorithm for modeling nonstationary data sources. Although most learning algorithms implicitly assume that a data source does not change over time (stationary), one in the real world usually does due to such various factors as dynamically changing environments, device degradation, sudden failures, etc (nonstationary). The proposed algorithm can be made useable for stationary online SBL by setting time decay parameters to zero, and as such it can be interpreted as a single unified framework for online SBL for use with stationary and nonstationary data sources. Tests both on four types of benchmark problems and on actual stock price data have shown it to perform well.

  17. Modern algorithms for large sparse eigenvalue problems

    International Nuclear Information System (INIS)

    Meyer, A.

    1987-01-01

    The volume is written for mathematicians interested in (numerical) linear algebra and in the solution of large sparse eigenvalue problems, as well as for specialists in engineering, who use the considered algorithms in the investigation of eigenoscillations of structures, in reactor physics, etc. Some variants of the algorithms based on the idea of a gradient-type direction of movement are presented and their convergence properties are discussed. From this, a general strategy for the direct use of preconditionings for the eigenvalue problem is derived. In this new approach the necessity of the solution of large linear systems is entirely avoided. Hence, these methods represent a new alternative to some other modern eigenvalue algorithms, as they show a slightly slower convergence on the one hand but essentially lower numerical and data processing problems on the other hand. A brief description and comparison of some well-known methods (i.e. simultaneous iteration, Lanczos algorithm) completes this volume. (author)

  18. 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)

  19. ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES.

    Science.gov (United States)

    Fan, Jianqing; Rigollet, Philippe; Wang, Weichen

    High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓ r norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics.

  20. Miniature Laboratory for Detecting Sparse Biomolecules

    Science.gov (United States)

    Lin, Ying; Yu, Nan

    2005-01-01

    A miniature laboratory system has been proposed for use in the field to detect sparsely distributed biomolecules. By emphasizing concentration and sorting of specimens prior to detection, the underlying system concept would make it possible to attain high detection sensitivities without the need to develop ever more sensitive biosensors. The original purpose of the proposal is to aid the search for signs of life on a remote planet by enabling the detection of specimens as sparse as a few molecules or microbes in a large amount of soil, dust, rocks, water/ice, or other raw sample material. Some version of the system could prove useful on Earth for remote sensing of biological contamination, including agents of biological warfare. Processing in this system would begin with dissolution of the raw sample material in a sample-separation vessel. The solution in the vessel would contain floating microscopic magnetic beads coated with substances that could engage in chemical reactions with various target functional groups that are parts of target molecules. The chemical reactions would cause the targeted molecules to be captured on the surfaces of the beads. By use of a controlled magnetic field, the beads would be concentrated in a specified location in the vessel. Once the beads were thus concentrated, the rest of the solution would be discarded. This procedure would obviate the filtration steps and thereby also eliminate the filter-clogging difficulties of typical prior sample-concentration schemes. For ferrous dust/soil samples, the dissolution would be done first in a separate vessel before the solution is transferred to the microbead-containing vessel.

  1. Framing U-Net via Deep Convolutional Framelets: Application to Sparse-View CT.

    Science.gov (United States)

    Han, Yoseob; Ye, Jong Chul

    2018-06-01

    X-ray computed tomography (CT) using sparse projection views is a recent approach to reduce the radiation dose. However, due to the insufficient projection views, an analytic reconstruction approach using the filtered back projection (FBP) produces severe streaking artifacts. Recently, deep learning approaches using large receptive field neural networks such as U-Net have demonstrated impressive performance for sparse-view CT reconstruction. However, theoretical justification is still lacking. Inspired by the recent theory of deep convolutional framelets, the main goal of this paper is, therefore, to reveal the limitation of U-Net and propose new multi-resolution deep learning schemes. In particular, we show that the alternative U-Net variants such as dual frame and tight frame U-Nets satisfy the so-called frame condition which makes them better for effective recovery of high frequency edges in sparse-view CT. Using extensive experiments with real patient data set, we demonstrate that the new network architectures provide better reconstruction performance.

  2. 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.

  3. 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.

  4. Efficient Model Selection for Sparse Least-Square SVMs

    Directory of Open Access Journals (Sweden)

    Xiao-Lei Xia

    2013-01-01

    Full Text Available The Forward Least-Squares Approximation (FLSA SVM is a newly-emerged Least-Square SVM (LS-SVM whose solution is extremely sparse. The algorithm uses the number of support vectors as the regularization parameter and ensures the linear independency of the support vectors which span the solution. This paper proposed a variant of the FLSA-SVM, namely, Reduced FLSA-SVM which is of reduced computational complexity and memory requirements. The strategy of “contexts inheritance” is introduced to improve the efficiency of tuning the regularization parameter for both the FLSA-SVM and the RFLSA-SVM algorithms. Experimental results on benchmark datasets showed that, compared to the SVM and a number of its variants, the RFLSA-SVM solutions contain a reduced number of support vectors, while maintaining competitive generalization abilities. With respect to the time cost for tuning of the regularize parameter, the RFLSA-SVM algorithm was empirically demonstrated fastest compared to FLSA-SVM, the LS-SVM, and the SVM algorithms.

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

    Science.gov (United States)

    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.

  6. 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

  7. 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.

  8. 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...

  9. 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....

  10. 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.

  11. The effective recovery of praseodymium from mixed rare earths via a hollow fiber supported liquid membrane and its mass transfer related

    International Nuclear Information System (INIS)

    Wannachod, Pharannalak; Chaturabul, Srestha; Pancharoen, Ura; Lothongkum, Anchaleeporn W.; Patthaveekongka, Weerawat

    2011-01-01

    Graphical abstract: Display Omitted Research highlights: → Maximum percentage of praseodymium extraction at 91.7% from 10% (v/v) bis (2,4,4-trimethylpentyl) phosphinic acid as extractant carrier in multi cycle operation through single HFLSM module. → Mass transfer mechanism of this system was investigated. → The rate-controlling step of this system was the diffusion of praseodymium ions through the film layer between the feed solution and the liquid membrane. → Model prediction of the dimensionless concentrations and separation factors showed promising agreement with the experimental data. - Abstract: The recovery of praseodymium from mixed rare earths via a hollow fiber supported liquid membrane (HFSLM) was examined. Bis(2,4,4-trimethylpentyl) phosphinic acid - known as Cyanex 272 - was used as an extractant carrier. The stripping solution was hydrochloric acid solution. The experiments examined in functions of the concentrations of the carrier in liquid membrane, the (initial) pH's of initial feed solution within the acidic-pH range, the concentrations of hydrochloric acid, the flow rates of feed and stripping solution, and the operation mode of runs through the hollow fiber module. In addition, the influence of circulation of the stripping solution at various numbers of runs through the HFSLM on the outlet concentration of praseodymium ions in the stripping solution was observed. Mass transfer mechanism in the system was investigated. Extraction equilibrium constant (K ex ), distribution ratio (D), permeability (P) and mass transfer coefficients were determined. The aqueous-phase mass-transfer coefficient (k i ) and organic-phase mass-transfer coefficient (k m ) were reported to 0.0103 and 0.788 cm s -1 , respectively, in which k m is much higher than the k i . Thus it suggests the rate-controlling step is the diffusion of praseodymium ions through the film layer between the feed solution and the liquid membrane. Model prediction of the dimensionless

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. Spatially Dispersed Employee Recovery

    DEFF Research Database (Denmark)

    Hvass, Kristian Anders; Torfadóttir, Embla

    2014-01-01

    Employee recovery addresses either employee well-being or management's practices in aiding employees in recovering themselves following a service failure. This paper surveys the cabin crew at a small, European, low-cost carrier and investigates employees' perceptions of management practices to aid...... personnel achieve service recovery. Employee recovery within service research often focuses on front-line employees that work in a fixed location, however a contribution to the field is made by investigating the recovery of spatially dispersed personnel, such as operational personnel in the transport sector......, who have a work place away from a fixed or central location and have minimal management contact. Results suggest that the support employees receive from management, such as recognition, information sharing, training, and strategic awareness are all important for spatially dispersed front...

  18. 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

  19. 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

  20. 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

  1. 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...

  2. 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

  3. 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.

  4. Regression analysis of sparse asynchronous longitudinal data.

    Science.gov (United States)

    Cao, Hongyuan; Zeng, Donglin; Fine, Jason P

    2015-09-01

    We consider estimation of regression models for sparse asynchronous longitudinal observations, where time-dependent responses and covariates are observed intermittently within subjects. Unlike with synchronous data, where the response and covariates are observed at the same time point, with asynchronous data, the observation times are mismatched. Simple kernel-weighted estimating equations are proposed for generalized linear models with either time invariant or time-dependent coefficients under smoothness assumptions for the covariate processes which are similar to those for synchronous data. For models with either time invariant or time-dependent coefficients, the estimators are consistent and asymptotically normal but converge at slower rates than those achieved with synchronous data. Simulation studies evidence that the methods perform well with realistic sample sizes and may be superior to a naive application of methods for synchronous data based on an ad hoc last value carried forward approach. The practical utility of the methods is illustrated on data from a study on human immunodeficiency virus.

  5. Duplex scanning using sparse data sequences

    DEFF Research Database (Denmark)

    Møllenbach, S. K.; Jensen, Jørgen Arendt

    2008-01-01

    reconstruction of the missing samples possible. The periodic pattern has the length T = M + A samples, where M are for B-mode and A for velocity estimation. The missing samples can now be reconstructed using a filter bank. One filter bank reconstructs one missing sample, so the number of filter banks corresponds...... to M. The number of sub filters in every filter bank is the same as A. Every sub filter contains fractional delay (FD) filter and an interpolation function. Many different sequences can be selected to adapt the B-mode frame rate needed. The drawback of the method is that the maximum velocity detectable......, the fprf and the resolution are 15 MHz, 3.5 kHz, and 12 bit sample (8 kHz and 16 bit for the Carotid artery). The resulting data contains 8000 RF lines with 128 samples at a depth of 45 mm for the vein and 50 mm for Aorta. Sparse sequences are constructed from the full data sequences to have both...

  6. 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.

  7. SLAP, Large Sparse Linear System Solution Package

    International Nuclear Information System (INIS)

    Greenbaum, A.

    1987-01-01

    1 - Description of program or function: SLAP is a set of routines for solving large sparse systems of linear equations. One need not store the entire matrix - only the nonzero elements and their row and column numbers. Any nonzero structure is acceptable, so the linear system solver need not be modified when the structure of the matrix changes. Auxiliary storage space is acquired and released within the routines themselves by use of the LRLTRAN POINTER statement. 2 - Method of solution: SLAP contains one direct solver, a band matrix factorization and solution routine, BAND, and several interactive solvers. The iterative routines are as follows: JACOBI, Jacobi iteration; GS, Gauss-Seidel Iteration; ILUIR, incomplete LU decomposition with iterative refinement; DSCG and ICCG, diagonal scaling and incomplete Cholesky decomposition with conjugate gradient iteration (for symmetric positive definite matrices only); DSCGN and ILUGGN, diagonal scaling and incomplete LU decomposition with conjugate gradient interaction on the normal equations; DSBCG and ILUBCG, diagonal scaling and incomplete LU decomposition with bi-conjugate gradient iteration; and DSOMN and ILUOMN, diagonal scaling and incomplete LU decomposition with ORTHOMIN iteration

  8. 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.

  9. 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.

  10. Science support for evaluating natural recovery of polychlorinated biphenyl concentrations in fish from Crab Orchard Lake, Crab Orchard National Wildlife Refuge, Illinois

    Science.gov (United States)

    Kunz, Bethany K.; Hinck, Jo E.; Calfee, Robin D.; Linder, Greg L.; Little, Edward E.

    2018-05-11

    IntroductionCrab Orchard Lake in southern Illinois is one of the largest and most popular recreational lakes in the state. Construction of the nearly 7,000-acre reservoir in the late 1930s created employment opportunities through the Works Progress Administration, and the lake itself was intended to supply water, control flooding, and provide recreational opportunities for local communities (Stall, 1954). In 1942, the Department of War appropriated or purchased more than 20,000 acres of land around Crab Orchard Lake and constructed the Illinois Ordnance Plant, which manufactured bombs and anti-tank mines during World War II. After the war, an Act of Congress transferred the property to the U.S. Department of the Interior. Crab Orchard National Wildlife Refuge was established on August 5, 1947, for the joint purposes of wildlife conservation, agriculture, recreation, and industry. Production of explosives continued, but new industries also moved onsite. More than 200 tenants have held leases with Crab Orchard National Wildlife Refuge and have operated a variety of manufacturing plants (electrical components, plated metal parts, ink, machined parts, painted products, and boats) on-site. Soils, water, and sediments in several areas of the refuge were contaminated with hazardous substances from handling and disposal methods that are no longer acceptable environmental practice (for example, direct discharge to surface water, use of unlined landfills).Polychlorinated biphenyl (PCB) contamination at the refuge was identified in the 1970s, and a PCB-based fish-consumption advisory has been in effect since 1988 for Crab Orchard Lake. The present advisory covers common carp (Cyprinus carpio) and channel catfish (Ictalurus punctatus); see Illinois Department of Public Health (2017). Some of the most contaminated areas of the refuge were actively remediated, and natural ecosystem recovery processes are expected to further reduce residual PCB concentrations in the lake. The U

  11. The Role of Social Supports, Spirituality, Religiousness, Life Meaning and Affiliation with 12-Step Fellowships in Quality of Life Satisfaction Among Individuals in Recovery from Alcohol and Drug Problems

    Science.gov (United States)

    Laudet, Alexandre B.; Morgen, Keith; White, William L.

    2006-01-01

    SUMMARY Many recovering substance users report quitting drugs because they wanted a better life. The road of recovery is the path to a better life but a challenging and stressful path for most. There has been little research among recovering persons in spite of the numbers involved, and most research has focused on substance use outcomes. This study examines stress and quality of life as a function of time in recovery, and uses structural equation modeling to test the hypothesis that social supports, spirituality, religiousness, life meaning, and 12-step affiliation buffer stress toward enhanced life satisfaction. Recovering persons (N = 353) recruited in New York City were mostly inner-city ethnic minority members whose primary substance had been crack or heroin. Longer recovery time was significantly associated with lower stress and with higher quality of life. Findings supported the study hypothesis; the ‘buffer’ constructs accounted for 22% of the variance in life satisfaction. Implications for research and clinical practice are discussed. PMID:16892161

  12. 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...

  13. 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.

  14. 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...

  15. 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

  16. 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.

  17. High Order Tensor Formulation for Convolutional Sparse Coding

    KAUST Repository

    Bibi, Adel Aamer; Ghanem, Bernard

    2017-01-01

    Convolutional sparse coding (CSC) has gained attention for its successful role as a reconstruction and a classification tool in the computer vision and machine learning community. Current CSC methods can only reconstruct singlefeature 2D images

  18. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2014-01-01

    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

  19. Multiple instance learning tracking method with local sparse representation

    KAUST Repository

    Xie, Chengjun; Tan, Jieqing; Chen, Peng; Zhang, Jie; Helg, Lei

    2013-01-01

    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

  20. Low-rank sparse learning for robust visual tracking

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    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

  1. Robust visual tracking via multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    2012-01-01

    In this paper, we formulate object tracking in a particle filter framework as a multi-task sparse learning problem, which we denote as Multi-Task Tracking (MTT). Since we model particles as linear combinations of dictionary templates

  2. 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...

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

    KAUST Repository

    Sicat, Ronell Barrera; Kruger, Jens; Moller, Torsten; Hadwiger, Markus

    2014-01-01

    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

  4. Sparse Linear Solver for Power System Analysis Using FPGA

    National Research Council Canada - National Science Library

    Johnson, J. R; Nagvajara, P; Nwankpa, C

    2005-01-01

    .... Numerical solution to load flow equations are typically computed using Newton-Raphson iteration, and the most time consuming component of the computation is the solution of a sparse linear system...

  5. 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.

  6. Sparse logistic principal components analysis for binary data

    KAUST Repository

    Lee, Seokho; Huang, Jianhua Z.; Hu, Jianhua

    2010-01-01

    with a criterion function motivated from a penalized Bernoulli likelihood. A Majorization-Minimization algorithm is developed to efficiently solve the optimization problem. The effectiveness of the proposed sparse logistic PCA method is illustrated

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

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Xu, Changsheng; Ahuja, Narendra

    2014-01-01

    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

  8. Object tracking by occlusion detection via structured sparse learning

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Xu, Changsheng; Ahuja, Narendra

    2013-01-01

    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

  9. Sparse encoding of automatic visual association in hippocampal networks

    DEFF Research Database (Denmark)

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

    2014-01-01

    Intelligent action entails exploiting predictions about associations between elements of ones environment. The hippocampus and mediotemporal cortex are endowed with the network topology, physiology, and neurochemistry to automatically and sparsely code sensori-cognitive associations that can...

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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

  16. 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

  17. In-Storage Embedded Accelerator for Sparse Pattern Processing

    OpenAIRE

    Jun, Sang-Woo; Nguyen, Huy T.; Gadepally, Vijay N.; Arvind

    2016-01-01

    We present a novel architecture for sparse pattern processing, using flash storage with embedded accelerators. Sparse pattern processing on large data sets is the essence of applications such as document search, natural language processing, bioinformatics, subgraph matching, machine learning, and graph processing. One slice of our prototype accelerator is capable of handling up to 1TB of data, and experiments show that it can outperform C/C++ software solutions on a 16-core system at a fracti...

  18. Process Knowledge Discovery Using Sparse Principal Component Analysis

    DEFF Research Database (Denmark)

    Gao, Huihui; Gajjar, Shriram; Kulahci, Murat

    2016-01-01

    As the goals of ensuring process safety and energy efficiency become ever more challenging, engineers increasingly rely on data collected from such processes for informed decision making. During recent decades, extracting and interpreting valuable process information from large historical data sets...... SPCA approach that helps uncover the underlying process knowledge regarding variable relations. This approach systematically determines the optimal sparse loadings for each sparse PC while improving interpretability and minimizing information loss. The salient features of the proposed approach...

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. 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.

  10. 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.

  11. 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

  12. Multi-information fusion sparse coding with preserving local structure for hyperspectral image classification

    Science.gov (United States)

    Wei, Xiaohui; Zhu, Wen; Liao, Bo; Gu, Changlong; Li, Weibiao

    2017-10-01

    The key question of sparse coding (SC) is how to exploit the information that already exists to acquire the robust sparse representations (SRs) of distinguishing different objects for hyperspectral image (HSI) classification. We propose a multi-information fusion SC framework, which fuses the spectral, spatial, and label information in the same level, to solve the above question. In particular, pixels from disjointed spatial clusters, which are obtained by cutting the given HSI in space, are individually and sparsely encoded. Then, due to the importance of spatial structure, graph- and hypergraph-based regularizers are enforced to motivate the obtained representations smoothness and to preserve the local consistency for each spatial cluster. The latter simultaneously considers the spectrum, spatial, and label information of multiple pixels that have a great probability with the same label. Finally, a linear support vector machine is selected as the final classifier with the learned SRs as input. Experiments conducted on three frequently used real HSIs show that our methods can achieve satisfactory results compared with other state-of-the-art methods.

  13. Cloud-In-Cell modeling of shocked particle-laden flows at a ``SPARSE'' cost

    Science.gov (United States)

    Taverniers, Soren; Jacobs, Gustaaf; Sen, Oishik; Udaykumar, H. S.

    2017-11-01

    A common tool for enabling process-scale simulations of shocked particle-laden flows is Eulerian-Lagrangian Particle-Source-In-Cell (PSIC) modeling where each particle is traced in its Lagrangian frame and treated as a mathematical point. Its dynamics are governed by Stokes drag corrected for high Reynolds and Mach numbers. The computational burden is often reduced further through a ``Cloud-In-Cell'' (CIC) approach which amalgamates groups of physical particles into computational ``macro-particles''. CIC does not account for subgrid particle fluctuations, leading to erroneous predictions of cloud dynamics. A Subgrid Particle-Averaged Reynolds-Stress Equivalent (SPARSE) model is proposed that incorporates subgrid interphase velocity and temperature perturbations. A bivariate Gaussian source distribution, whose covariance captures the cloud's deformation to first order, accounts for the particles' momentum and energy influence on the carrier gas. SPARSE is validated by conducting tests on the interaction of a particle cloud with the accelerated flow behind a shock. The cloud's average dynamics and its deformation over time predicted with SPARSE converge to their counterparts computed with reference PSIC models as the number of Gaussians is increased from 1 to 16. This work was supported by AFOSR Grant No. FA9550-16-1-0008.

  14. Hybrid sparse blind deconvolution: an implementation of SOOT algorithm to real data

    Science.gov (United States)

    Pakmanesh, Parvaneh; Goudarzi, Alireza; Kourki, Meisam

    2018-06-01

    Getting information of seismic data depends on deconvolution as an important processing step; it provides the reflectivity series by signal compression. This compression can be obtained by removing the wavelet effects on the traces. The recently blind deconvolution has provided reliable performance for sparse signal recovery. In this study, two deconvolution methods have been implemented to the seismic data; the convolution of these methods provides a robust spiking deconvolution approach. This hybrid deconvolution is applied using the sparse deconvolution (MM algorithm) and the Smoothed-One-Over-Two algorithm (SOOT) in a chain. The MM algorithm is based on the minimization of the cost function defined by standards l1 and l2. After applying the two algorithms to the seismic data, the SOOT algorithm provided well-compressed data with a higher resolution than the MM algorithm. The SOOT algorithm requires initial values to be applied for real data, such as the wavelet coefficients and reflectivity series that can be achieved through the MM algorithm. The computational cost of the hybrid method is high, and it is necessary to be implemented on post-stack or pre-stack seismic data of complex structure regions.

  15. 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.

  16. 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.

  17. 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.

  18. A sparse matrix based full-configuration interaction algorithm

    International Nuclear Information System (INIS)

    Rolik, Zoltan; Szabados, Agnes; Surjan, Peter R.

    2008-01-01

    We present an algorithm related to the full-configuration interaction (FCI) method that makes complete use of the sparse nature of the coefficient vector representing the many-electron wave function in a determinantal basis. Main achievements of the presented sparse FCI (SFCI) algorithm are (i) development of an iteration procedure that avoids the storage of FCI size vectors; (ii) development of an efficient algorithm to evaluate the effect of the Hamiltonian when both the initial and the product vectors are sparse. As a result of point (i) large disk operations can be skipped which otherwise may be a bottleneck of the procedure. At point (ii) we progress by adopting the implementation of the linear transformation by Olsen et al. [J. Chem Phys. 89, 2185 (1988)] for the sparse case, getting the algorithm applicable to larger systems and faster at the same time. The error of a SFCI calculation depends only on the dropout thresholds for the sparse vectors, and can be tuned by controlling the amount of system memory passed to the procedure. The algorithm permits to perform FCI calculations on single node workstations for systems previously accessible only by supercomputers

  19. X-ray computed tomography using curvelet sparse regularization.

    Science.gov (United States)

    Wieczorek, Matthias; Frikel, Jürgen; Vogel, Jakob; Eggl, Elena; Kopp, Felix; Noël, Peter B; Pfeiffer, Franz; Demaret, Laurent; Lasser, Tobias

    2015-04-01

    Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method's strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.

  20. 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.

  1. 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.

  2. 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.

  3. 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.

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

    KAUST Repository

    Ali, Anum; Masood, Mudassir; Sohail, Muhammad; Al-Ghadhban, Samir; Al-Naffouri, Tareq Y.

    2016-01-01

    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

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

    KAUST Repository

    Sana, Furrukh

    2016-01-01

    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

  6. Recovery from cannabis use disorders: Abstinence versus moderation and treatment-assisted recovery versus natural recovery.

    Science.gov (United States)

    Stea, Jonathan N; Yakovenko, Igor; Hodgins, David C

    2015-09-01

    The present study of recovery from cannabis use disorders was undertaken with 2 primary objectives that address gaps in the literature. The first objective was to provide an exploratory portrait of the recovery process from cannabis use disorders, comparing individuals who recovered naturally with those who were involved in treatment. The second objective was to explore systematically the similarities and differences between abstinence and moderation recoveries. Adults who have recovered from a cannabis use disorder were recruited in the community (N = 119). The abstinence and treatment-assisted participants exhibited higher levels of lifetime cannabis problem severity than the moderation and natural recovery participants, respectively. As well, cognitive factors were identified as the most useful strategies for recovery (e.g., thinking about benefits and negative consequences of cannabis), followed by behavioral factors (e.g., avoidance of triggers for use and high-risk situations). Findings lend further support to the effectiveness of cognitive, motivational, and behavioral strategies as helpful actions and maintenance factors involved in the recovery process. The findings also generally support the idea that cannabis use disorders lie on a continuum of problem severity, with moderation and natural recoveries more likely to occur at the lower end of the continuum and abstinence and treatment-assisted recoveries more likely to occur at the upper end. (c) 2015 APA, all rights reserved).

  7. On the Automatic Parallelization of Sparse and Irregular Fortran Programs

    Directory of Open Access Journals (Sweden)

    Yuan Lin

    1999-01-01

    Full Text Available Automatic parallelization is usually believed to be less effective at exploiting implicit parallelism in sparse/irregular programs than in their dense/regular counterparts. However, not much is really known because there have been few research reports on this topic. In this work, we have studied the possibility of using an automatic parallelizing compiler to detect the parallelism in sparse/irregular programs. The study with a collection of sparse/irregular programs led us to some common loop patterns. Based on these patterns new techniques were derived that produced good speedups when manually applied to our benchmark codes. More importantly, these parallelization methods can be implemented in a parallelizing compiler and can be applied automatically.

  8. Joint sparse representation for robust multimodal biometrics recognition.

    Science.gov (United States)

    Shekhar, Sumit; Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama

    2014-01-01

    Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods.

  9. Sparse Representation Denoising for Radar High Resolution Range Profiling

    Directory of Open Access Journals (Sweden)

    Min Li

    2014-01-01

    Full Text Available Radar high resolution range profile has attracted considerable attention in radar automatic target recognition. In practice, radar return is usually contaminated by noise, which results in profile distortion and recognition performance degradation. To deal with this problem, in this paper, a novel denoising method based on sparse representation is proposed to remove the Gaussian white additive noise. The return is sparsely described in the Fourier redundant dictionary and the denoising problem is described as a sparse representation model. Noise level of the return, which is crucial to the denoising performance but often unknown, is estimated by performing subspace method on the sliding subsequence correlation matrix. Sliding window process enables noise level estimation using only one observation sequence, not only guaranteeing estimation efficiency but also avoiding the influence of profile time-shift sensitivity. Experimental results show that the proposed method can effectively improve the signal-to-noise ratio of the return, leading to a high-quality profile.

  10. A Projected Conjugate Gradient Method for Sparse Minimax Problems

    DEFF Research Database (Denmark)

    Madsen, Kaj; Jonasson, Kristjan

    1993-01-01

    A new method for nonlinear minimax problems is presented. The method is of the trust region type and based on sequential linear programming. It is a first order method that only uses first derivatives and does not approximate Hessians. The new method is well suited for large sparse problems...... 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...... with the method are presented. In fact, we find that the number of iterations required is comparable to that of state-of-the-art quasi-Newton codes....

  11. A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

    Science.gov (United States)

    Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi

    2015-12-01

    Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features.

  12. 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.

  13. Preconditioned Inexact Newton for Nonlinear Sparse Electromagnetic Imaging

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2014-01-01

    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.

  14. 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.

  15. 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.

  16. 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.

  17. Sparse dictionary learning of resting state fMRI networks.

    Science.gov (United States)

    Eavani, Harini; Filipovych, Roman; Davatzikos, Christos; Satterthwaite, Theodore D; Gur, Raquel E; Gur, Ruben C

    2012-07-02

    Research in resting state fMRI (rsfMRI) has revealed the presence of stable, anti-correlated functional subnetworks in the brain. Task-positive networks are active during a cognitive process and are anti-correlated with task-negative networks, which are active during rest. In this paper, based on the assumption that the structure of the resting state functional brain connectivity is sparse, we utilize sparse dictionary modeling to identify distinct functional sub-networks. We propose two ways of formulating the sparse functional network learning problem that characterize the underlying functional connectivity from different perspectives. Our results show that the whole-brain functional connectivity can be concisely represented with highly modular, overlapping task-positive/negative pairs of sub-networks.

  18. Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model

    Directory of Open Access Journals (Sweden)

    Qi Yuan(Alan

    2010-01-01

    Full Text Available Abstract The problem of uncovering transcriptional regulation by transcription factors (TFs based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ( status and Estrogen Receptor negative ( status, respectively.

  19. 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.

  20. 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.

  1. 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 * fee dforward 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

  2. Universal Regularizers For Robust Sparse Coding and Modeling

    OpenAIRE

    Ramirez, Ignacio; Sapiro, Guillermo

    2010-01-01

    Sparse data models, where data is assumed to be well represented as a linear combination of a few elements from a dictionary, have gained considerable attention in recent years, and their use has led to state-of-the-art results in many signal and image processing tasks. It is now well understood that the choice of the sparsity regularization term is critical in the success of such models. Based on a codelength minimization interpretation of sparse coding, and using tools from universal coding...

  3. Sparse reconstruction by means of the standard Tikhonov regularization

    International Nuclear Information System (INIS)

    Lu Shuai; Pereverzev, Sergei V

    2008-01-01

    It is a common belief that Tikhonov scheme with || · ||L 2 -penalty fails in sparse reconstruction. We are going to show, however, that this standard regularization can help if the stability measured in L 1 -norm will be properly taken into account in the choice of the regularization parameter. The crucial point is that now a stability bound may depend on the bases with respect to which the solution of the problem is assumed to be sparse. We discuss how such a stability can be estimated numerically and present the results of computational experiments giving the evidence of the reliability of our approach.

  4. Sparse electromagnetic imaging using nonlinear iterative shrinkage thresholding

    KAUST Repository

    Desmal, Abdulla; Bagci, Hakan

    2015-01-01

    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.

  5. 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.

  6. Sparse grid techniques for particle-in-cell schemes

    Science.gov (United States)

    Ricketson, L. F.; Cerfon, A. J.

    2017-02-01

    We propose the use of sparse grids to accelerate particle-in-cell (PIC) schemes. By using the so-called ‘combination technique’ from the sparse grids literature, we are able to dramatically increase the size of the spatial cells in multi-dimensional PIC schemes while paying only a slight penalty in grid-based error. The resulting increase in cell size allows us to reduce the statistical noise in the simulation without increasing total particle number. We present initial proof-of-principle results from test cases in two and three dimensions that demonstrate the new scheme’s efficiency, both in terms of computation time and memory usage.

  7. Ordering sparse matrices for cache-based systems

    International Nuclear Information System (INIS)

    Biswas, Rupak; Oliker, Leonid

    2001-01-01

    The Conjugate Gradient (CG) algorithm is the oldest and best-known Krylov subspace method used to solve sparse linear systems. Most of the coating-point operations within each CG iteration is spent performing sparse matrix-vector multiplication (SPMV). We examine how various ordering and partitioning strategies affect the performance of CG and SPMV when different programming paradigms are used on current commercial cache-based computers. However, a multithreaded implementation on the cacheless Cray MTA demonstrates high efficiency and scalability without any special ordering or partitioning

  8. 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.

  9. 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.

  10. A Bandwidth-Efficient Service for Local Information Dissemination in Sparse to Dense Roadways

    Directory of Open Access Journals (Sweden)

    Patricia Noriega-Vivas

    2013-07-01

    Full Text Available Thanks to the research on Vehicular Ad Hoc Networks (VANETs, we will be able to deploy applications on roadways that will contribute to energy efficiency through a better planning of long trips. With this goal in mind, we have designed a gas/charging station advertising system, which takes advantage of the broadcast nature of the network. We have found that reducing the number of total sent packets is important, as it allows for a better use of the available bandwidth. We have designed improvements for a distance-based flooding scheme, so that it can support the advertising application with good results in sparse to dense roadway scenarios.

  11. A bandwidth-efficient service for local information dissemination in sparse to dense roadways.

    Science.gov (United States)

    Garcia-Lozano, Estrella; Campo, Celeste; Garcia-Rubio, Carlos; Cortes-Martin, Alberto; Rodriguez-Carrion, Alicia; Noriega-Vivas, Patricia

    2013-07-05

    Thanks to the research on Vehicular Ad Hoc Networks (VANETs), we will be able to deploy applications on roadways that will contribute to energy efficiency through a better planning of long trips. With this goal in mind, we have designed a gas/charging station advertising system, which takes advantage of the broadcast nature of the network. We have found that reducing the number of total sent packets is important, as it allows for a better use of the available bandwidth. We have designed improvements for a distance-based flooding scheme, so that it can support the advertising application with good results in sparse to dense roadway scenarios.

  12. Convolutional Sparse Coding for Static and Dynamic Images Analysis

    Directory of Open Access Journals (Sweden)

    B. A. Knyazev

    2014-01-01

    Full Text Available The objective of this work is to improve performance of static and dynamic objects recognition. For this purpose a new image representation model and a transformation algorithm are proposed. It is examined and illustrated that limitations of previous methods make it difficult to achieve this objective. Static images, specifically handwritten digits of the widely used MNIST dataset, are the primary focus of this work. Nevertheless, preliminary qualitative results of image sequences analysis based on the suggested model are presented.A general analytical form of the Gabor function, often employed to generate filters, is described and discussed. In this research, this description is required for computing parameters of responses returned by our algorithm. The recursive convolution operator is introduced, which allows extracting free shape features of visual objects. The developed parametric representation model is compared with sparse coding based on energy function minimization.In the experimental part of this work, errors of estimating the parameters of responses are determined. Also, parameters statistics and their correlation coefficients for more than 106 responses extracted from the MNIST dataset are calculated. It is demonstrated that these data correspond well with previous research studies on Gabor filters as well as with works on visual cortex primary cells of mammals, in which similar responses were observed. A comparative test of the developed model with three other approaches is conducted; speed and accuracy scores of handwritten digits classification are presented. A support vector machine with a linear or radial basic function is used for classification of images and their representations while principal component analysis is used in some cases to prepare data beforehand. High accuracy is not attained due to the specific difficulties of combining our model with a support vector machine (a 3.99% error rate. However, another method is

  13. 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...

  14. Sparse Generalized Fourier Series via Collocation-based Optimization

    Science.gov (United States)

    2014-11-01

    Theory 51, 12 (2005) 4203– 4215. [6] P. CONSTANTINE , M. ELDRED AND E. PHIPPS, Sparse pseu- dospectral approximation method. Comput. Methods Appl. Mech...Visition XVI: Algorithms, Techniques, Active Vision , and Materials Handling, 224 (1997). [15] J. SHEN AND L. WANG, Some recent advances on spectral methods

  15. 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...

  16. Robust visual tracking via structured multi-task sparse learning

    KAUST Repository

    Zhang, Tianzhu; Ghanem, Bernard; Liu, Si; Ahuja, Narendra

    2012-01-01

    In this paper, we formulate object tracking in a particle filter framework as a structured multi-task sparse learning problem, which we denote as Structured Multi-Task Tracking (S-MTT). Since we model particles as linear combinations of dictionary

  17. Sparse linear models: Variational approximate inference and Bayesian experimental design

    International Nuclear Information System (INIS)

    Seeger, Matthias W

    2009-01-01

    A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

  18. Inference algorithms and learning theory for Bayesian sparse factor analysis

    International Nuclear Information System (INIS)

    Rattray, Magnus; Sharp, Kevin; Stegle, Oliver; Winn, John

    2009-01-01

    Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

  19. Sparse linear models: Variational approximate inference and Bayesian experimental design

    Energy Technology Data Exchange (ETDEWEB)

    Seeger, Matthias W [Saarland University and Max Planck Institute for Informatics, Campus E1.4, 66123 Saarbruecken (Germany)

    2009-12-01

    A wide range of problems such as signal reconstruction, denoising, source separation, feature selection, and graphical model search are addressed today by posterior maximization for linear models with sparsity-favouring prior distributions. The Bayesian posterior contains useful information far beyond its mode, which can be used to drive methods for sampling optimization (active learning), feature relevance ranking, or hyperparameter estimation, if only this representation of uncertainty can be approximated in a tractable manner. In this paper, we review recent results for variational sparse inference, and show that they share underlying computational primitives. We discuss how sampling optimization can be implemented as sequential Bayesian experimental design. While there has been tremendous recent activity to develop sparse estimation, little attendance has been given to sparse approximate inference. In this paper, we argue that many problems in practice, such as compressive sensing for real-world image reconstruction, are served much better by proper uncertainty approximations than by ever more aggressive sparse estimation algorithms. Moreover, since some variational inference methods have been given strong convex optimization characterizations recently, theoretical analysis may become possible, promising new insights into nonlinear experimental design.

  20. Inference algorithms and learning theory for Bayesian sparse factor analysis

    Energy Technology Data Exchange (ETDEWEB)

    Rattray, Magnus; Sharp, Kevin [School of Computer Science, University of Manchester, Manchester M13 9PL (United Kingdom); Stegle, Oliver [Max-Planck-Institute for Biological Cybernetics, Tuebingen (Germany); Winn, John, E-mail: magnus.rattray@manchester.ac.u [Microsoft Research Cambridge, Roger Needham Building, Cambridge, CB3 0FB (United Kingdom)

    2009-12-01

    Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.

  1. 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.

  2. Structure-aware Local Sparse Coding for Visual Tracking

    KAUST Repository

    Qi, Yuankai; Qin, Lei; Zhang, Jian; Zhang, Shengping; Huang, Qingming; Yang, Ming-Hsuan

    2018-01-01

    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

  3. 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 perfor...

  4. Parallel and Scalable Sparse Basic Linear Algebra Subprograms

    DEFF Research Database (Denmark)

    Liu, Weifeng

    and heterogeneous processors. The thesis compares the proposed methods with state-of-the-art approaches on six homogeneous and five heterogeneous processors from Intel, AMD and nVidia. Using in total 38 sparse matrices as a benchmark suite, the experimental results show that the proposed methods obtain significant...

  5. SparseBeads data: benchmarking sparsity-regularized computed tomography

    DEFF Research Database (Denmark)

    Jørgensen, Jakob Sauer; Coban, Sophia B.; Lionheart, William R. B.

    2017-01-01

    -regularized reconstruction. A collection of 48 x-ray CT datasets called SparseBeads was designed for benchmarking SR reconstruction algorithms. Beadpacks comprising glass beads of five different sizes as well as mixtures were scanned in a micro-CT scanner to provide structured datasets with variable image sparsity levels...

  6. Hierarchical Bayesian sparse image reconstruction with application to MRFM.

    Science.gov (United States)

    Dobigeon, Nicolas; Hero, Alfred O; Tourneret, Jean-Yves

    2009-09-01

    This paper presents a hierarchical Bayesian model to reconstruct sparse images when the observations are obtained from linear transformations and corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is well suited to such naturally sparse image applications as it seamlessly accounts for properties such as sparsity and positivity of the image via appropriate Bayes priors. We propose a prior that is based on a weighted mixture of a positive exponential distribution and a mass at zero. The prior has hyperparameters that are tuned automatically by marginalization over the hierarchical Bayesian model. To overcome the complexity of the posterior distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be used to estimate the image to be recovered, e.g., by maximizing the estimated posterior distribution. In our fully Bayesian approach, the posteriors of all the parameters are available. Thus, our algorithm provides more information than other previously proposed sparse reconstruction methods that only give a point estimate. The performance of the proposed hierarchical Bayesian sparse reconstruction method is illustrated on synthetic data and real data collected from a tobacco virus sample using a prototype MRFM instrument.

  7. 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.

  8. Fast sparse matrix-vector multiplication by partitioning and reordering

    NARCIS (Netherlands)

    Yzelman, A.N.

    2011-01-01

    The thesis introduces a cache-oblivious method for the sparse matrix-vector (SpMV) multiplication, which is an important computational kernel in many applications. The method works by permuting rows and columns of the input matrix so that the resulting reordered matrix induces cache-friendly

  9. 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

  10. 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.

  11. 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...

  12. 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.

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

    KAUST Repository

    Zhang, Tianzhu; Liu, Si; Ahuja, Narendra; Yang, Ming-Hsuan; Ghanem, Bernard

    2014-01-01

    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

  14. 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

  15. 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...

  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. 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.

  19. EEG Source Reconstruction using Sparse Basis Function Representations

    DEFF Research Database (Denmark)

    Hansen, Sofie Therese; Hansen, Lars Kai

    2014-01-01

    -validation this approach is more automated than competing approaches such as Multiple Sparse Priors (Friston et al., 2008) or Champagne (Wipf et al., 2010) that require manual selection of noise level and auxiliary signal free data, respectively. Finally, we propose an unbiased estimator of the reproducibility...

  20. Aliasing-free wideband beamforming using sparse signal representation

    NARCIS (Netherlands)

    Tang, Z.; Blacquière, G.; Leus, G.

    2011-01-01

    Sparse signal representation (SSR) is considered to be an appealing alternative to classical beamforming for direction-of-arrival (DOA) estimation. For wideband signals, the SSR-based approach constructs steering matrices, referred to as dictionaries in this paper, corresponding to different