STOMP Sparse Vegetation Evapotranspiration Model for the Water-Air-Energy Operational Mode
Ward, Anderson L.; White, Mark D.; Freeman, Eugene J.; Zhang, Z. F.
2005-09-15
The Water-Air-Energy (WAE) Operational Mode of the Subsurface Transport Over Multiple Phases (STOMP) numerical simulator solves the coupled conservation equations for water mass, air mass, and thermal energy in multiple dimensions. This addendum describes the theory, input file formatting, and application of a soil-vegetation-atmosphere transfer (SVAT) scheme for STOMP that is based on a sparse vegetation evapotranspiration model. The SVAT scheme is implemented as a boundary condition on the upper surface of the computational domain and has capabilities for simulating evaporation from bare surfaces as well as evapotranspiration from sparsely vegetated surfaces populated with single or multiple plant species in response to meteorological forcings. With this extension, the model calculates water mass, air mass and thermal energy across a boundary surface in addition to root-water transport between the subsurface and atmosphere. This mode represents the barrier extension of the WAE mode and is designated as STOMP-WAE-B. Input for STOMP-WAE-B is specified via three input cards and include: atmospheric conditions through the Atmospheric Conditions Card; time-invariant plant species data through the Plant Properties Card; and time varying plant species data through the Boundary Conditions Card. Two optional cards, the Observed Data and UCODE Control Cards allow use of STOMP-WAE with UCODE in an inverse mode to estimate model parameters. STOMP-WAE was validated by solving a number of test problems from the literature that included experimental observations as well as analytical or numerical solutions. Several of the UNSAT-H verification problems are included along with a benchmark simulation derived from a recently published intercode comparison for barrier design tools. Results show that STOMP is able to meet, and in most cases, exceed performance of other commonly used simulation codes without having to resort to may of their simplifying assumptions. Use of the fully
ERIC Educational Resources Information Center
Martinez, Luz Adriana
2007-01-01
The Specialized Training of Military Parents, better known by many as STOMP, is a federally funded Parent Training and Information (PTI) center established to assist military families who have children with specialized education or health needs. STOMP exists to empower military parents, individuals with disabilities, and service providers with…
Partitioning evapotranspiration in sparsely vegetated rangeland using a portable chamber
Stannard, D.I.; Weltz, M.A.
2006-01-01
A portable chamber was used to separate evapotranspiration (ET) from a sparse, mixed-species shrub canopy in southeastern Arizona, United States, into vegetation and soil components. Chamber measurements were made of ET from the five dominant species, and from bare soil, on 3 days during the monsoon season when the soil surface was dry. The chamber measurements were assembled into landscape ET using a simple geometric model of the vegetated land surface. Chamber estimates of landscape ET were well correlated with, but about 26% greater than, simultaneous eddy-correlation measurements. Excessive air speed inside the chamber appears to be the primary cause of the overestimate. Overall, transpiration accounted for 84% of landscape ET, and bare soil evaporation for 16%. Desert zinnia, a small (???0.1 m high) but abundant species, was the greatest water user, both per unit area of shrub and of landscape. Partitioning of ET into components varied as a function of air temperature and shallow soil moisture. Transpiration from shorter species was more highly correlated with air temperature whereas transpiration from taller species was more highly correlated with shallow soil moisture. Application of these results to a full drying cycle between rainfalls at a similar site suggests that during the monsoon, ET at such sites may be about equally partitioned between transpiration and bare soil evaporation.
STOMP Subsurface Transport Over Multiple Phases: STOMP-CO2 and STOMP-CO2e Guide: Version 1.0
White, Mark D.; Bacon, Diana H.; McGrail, B. Peter; Watson, David J.; White, Signe K.; Zhang, Z. F.
2012-04-03
This STOMP (Subsurface Transport Over Multiple Phases) guide document describes the theory, use, and application of the STOMP-CO2 and STOMP-CO2e operational modes. These operational modes of the STOMP simulator are configured to solve problems involving the sequestration of CO2 in geologic saline reservoirs. STOMP-CO2 is the isothermal version and STOMP-CO2e is the nonisothermal version. These core operational modes solve the governing conservation equations for component flow and transport through geologic media; where, the STOMP-CO2 components are water, CO2 and salt and the STOMP-CO2e operational mode also includes an energy conservation equation. Geochemistry can be included in the problem solution via the ECKEChem (Equilibrium-Conservation-Kinetic-Equation Chemistry) module, and geomechanics via the EPRMech (Elastic-Plastic-Rock Mechanics) module. This addendum is designed to provide the new user with a full guide for the core capabilities of the STOMP-CO2 and -CO2e simulators, and to provide the experienced user with a quick reference on implementing features. Several benchmark problems are provided in this addendum, which serve as starting points for developing inputs for more complex problems and as demonstrations of the simulator’s capabilities.
Wu, Jun-Jun; Gao, Zhi-Hai; Li, Zeng-Yuan; Wang, Hong-Yan; Pang, Yong; Sun, Bin; Li, Chang-Long; Li, Xu-Zhi; Zhang, Jiu-Xing
2014-03-01
In order to estimate the sparse vegetation information accurately in desertification region, taking southeast of Sunite Right Banner, Inner Mongolia, as the test site and Tiangong-1 hyperspectral image as the main data, sparse vegetation coverage and biomass were retrieved based on normalized difference vegetation index (NDVI) and soil adjusted vegetation index (SAVI), combined with the field investigation data. Then the advantages and disadvantages between them were compared. Firstly, the correlation between vegetation indexes and vegetation coverage under different bands combination was analyzed, as well as the biomass. Secondly, the best bands combination was determined when the maximum correlation coefficient turned up between vegetation indexes (VI) and vegetation parameters. It showed that the maximum correlation coefficient between vegetation parameters and NDVI could reach as high as 0.7, while that of SAVI could nearly reach 0.8. The center wavelength of red band in the best bands combination for NDVI was 630nm, and that of the near infrared (NIR) band was 910 nm. Whereas, when the center wavelength was 620 and 920 nm respectively, they were the best combination for SAVI. Finally, the linear regression models were established to retrieve vegetation coverage and biomass based on Tiangong-1 VIs. R2 of all models was more than 0.5, while that of the model based on SAVI was higher than that based on NDVI, especially, the R2 of vegetation coverage retrieve model based on SAVI was as high as 0.59. By intersection validation, the standard errors RMSE based on SAVI models were lower than that of the model based on NDVI. The results showed that the abundant spectral information of Tiangong-1 hyperspectral image can reflect the actual vegetaion condition effectively, and SAVI can estimate the sparse vegetation information more accurately than NDVI in desertification region.
Energy budgets and resistances to energy transport in sparsely vegetated rangeland
Nichols, W.D.
1992-01-01
Partitioning available energy between plants and bare soil in sparsely vegetated rangelands will allow hydrologists and others to gain a greater understanding of water use by native vegetation, especially phreatophytes. Standard methods of conducting energy budget studies result in measurements of latent and sensible heat fluxes above the plant canopy which therefore include the energy fluxes from both the canopy and the soil. One-dimensional theoretical numerical models have been proposed recently for the partitioning of energy in sparse crops. Bowen ratio and other micrometeorological data collected over phreatophytes growing in areas of shallow ground water in central Nevada were used to evaluate the feasibility of using these models, which are based on surface and within-canopy aerodynamic resistances, to determine heat and water vapor transport in sparsely vegetated rangelands. The models appear to provide reasonably good estimates of sensible heat flux from the soil and latent heat flux from the canopy. Estimates of latent heat flux from the soil were less satisfactory. Sensible heat flux from the canopy was not well predicted by the present resistance formulations. Also, estimates of total above-canopy fluxes were not satisfactory when using a single value for above-canopy bulk aerodynamic resistance. ?? 1992.
STOMP, Subsurface Transport Over Multiple Phases, theory guide
White, M.D.; Oostrom, M.
1996-10-01
This guide describes the simulator`s governing equations, constitutive functions and numerical solution algorithms of the STOMP (Subsurface Transport Over Multiple Phases) simulator, a scientific tool for analyzing multiple phase subsurface flow and transport. The STOMP simulator`s fundamental purpose is to produce numerical predictions of thermal and hydrologic flow and transport phenomena in variably saturated subsurface environments, which are contaminated with volatile or nonvolatile organic compounds. Auxiliary applications include numerical predictions of solute transport processes including radioactive chain decay processes. In writing these guides for the STOMP simulator, the authors have assumed that the reader comprehends concepts and theories associated with multiple-phase hydrology, heat transfer, thermodynamics, radioactive chain decay, and nonhysteretic relative permeability, saturation-capillary pressure constitutive functions. The authors further assume that the reader is familiar with the computing environment on which they plan to compile and execute the STOMP simulator. The STOMP simulator requires an ANSI FORTRAN 77 compiler to generate an executable code. The memory requirements for executing the simulator are dependent on the complexity of physical system to be modeled and the size and dimensionality of the computational domain. Likewise execution speed depends on the problem complexity, size and dimensionality of the computational domain, and computer performance. One-dimensional problems of moderate complexity can be solved on conventional desktop computers, but multidimensional problems involving complex flow and transport phenomena typically require the power and memory capabilities of workstation or mainframe type computer systems.
STOMP Subsurface Transport Over Multiple Phases: User`s guide
White, M.D.; Oostrom, M.
1997-10-01
The U.S. Department of Energy, through the Office of Technology Development, has requested the demonstration of remediation technologies for the cleanup of volatile organic compounds and associated radionuclides within the soil and groundwater at arid sites. This demonstration program, called the VOC-Arid Soils Integrated Demonstration Program (Arid-ID), has been initially directed at a volume of unsaturated and saturated soil contaminated with carbon tetrachloride, on the Hanford Site near Richland, Washington. A principal subtask of the Arid-ID program involves the development of an integrated engineering simulator for evaluating the effectiveness and efficiency of various remediation technologies. The engineering simulator`s intended users include scientists and engineers who are investigating soil physics phenomena associated with remediation technologies. Principal design goals for the engineer simulator include broad applicability, verified algorithms, quality assurance controls, and validated simulations against laboratory and field-scale experiments. An important goal for the simulator development subtask involves the ability to scale laboratory and field-scale experiments to full-scale remediation technologies, and to transfer acquired technology to other arid sites. The STOMP (Subsurface Transport Over Multiple Phases) simulator has been developed by the Pacific Northwest National Laboratory for modeling remediation technologies. Information on the use, application, and theoretical basis of the STOMP simulator theory and discussions on the governing equations, constitutive relations, and numerical solution algorithms for the STOMP simulator.
STOMP Subsurface Transport Over Multiple Phases: Application guide
Nichols, W.E.; Aimo, N.J.; Oostrom, M.; White, M.D.
1997-09-01
The U.S. Department of Energy (DOE), through the Office of Technology Development, has requested the demonstration of remediation technologies for the cleanup of volatile organic compounds and associated radionuclides within the soil and ground water at arid sites. This demonstration program, called the VOC-Arid Soils Integrated Demonstrated Program (Arid-ID), has been initially directed at a volume of unsaturated and saturated soil contaminated with carbon tetrachloride on the Hanford Site near Richland, Washington. A principal subtask of the Arid-ID program involves the development of an integrated engineering simulator for evaluating the effectiveness and efficiency of various remediation technologies. The engineering simulator`s intended users include scientists and engineers who are investigating soil physics phenomena associated with remediation technologies. Principal design goals for the engineering simulator include broad applicability, verified algorithms, quality assurance controls, and validated simulations against laboratory and field-scale experiments. An important goal for the simulator development subtask involves the ability to scale laboratory and field-scale experiments to full-scale remediation technologies, and to transfer acquired technology to other arid sites. The STOMP (Subsurface Transport Over Multiple Phases) simulator has been developed by the Pacific Northwest Laboratory for modeling remediation technologies. Information on the use, application, and theoretical basis of the STOMP simulator are documented in three companion guide guides. This document, the Application Guide, provides a suite of example applications of the STOMP simulator.
NASA Astrophysics Data System (ADS)
Maltese, Antonino; Capodici, Fulvio; Ciraolo, Giuseppe; La Loggia, Goffredo
2013-01-01
A critical analysis of a thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data is reported. The study area is an experimental field located in Barrax, Spain. In situ data were acquired within the Barrax 2011 research project. An advanced hyperspectral scanner airborne imager provides images in the visible/near-infrared and thermal infrared bands. Images were acquired both in day and night times by the Instituto Nacional de Técnica Aeroespacial between 12th and 13th of June 2011. The scene covers a corn irrigation pivot surrounded by bare soil, where a set of in situ data have been collected both previously and simultaneously to overpasses. To validate remotely sensed estimations, an ad hoc dataset has been produced by measuring spectra, radiometric temperatures, surface soil water content, and soil thermal properties. These data were collected on two transects covering bare and sparsely vegetated soils. This ground dataset was used (1) to verify if a thermal inertia method can be applied to map the water content on soil covered by sparse vegetation and (2) to quantify a correction factor accounting for solar radiation reduction due to sky cloudiness. The experiment intended to test a spatially constant and a spatially distributed approach to estimate the phase difference. Both methods were then applied to the airborne images collected during the following days to obtain the spatial distribution of surface soil water content. Results confirm that the thermal inertia method can be applied to sparsely vegetated soil characterized by low fractional cover if the solar radiation reaching the ground is accurately estimated. A spatially constant value of the phase difference allows a good assessment of thermal inertia, whereas the comparison with the three-temperature approach did not give conclusive responses. Results also show that clear sky, only at the time of the acquisition, does not provide
NASA Astrophysics Data System (ADS)
Maltese, Antonino; Capodici, Fulvio; Corbari, Chiara; Ciraolo, Giuseppe; La Loggia, Goffredo; Sobrino, José Antonio
2012-09-01
The paper reports a critical analysis of the thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data. The study area is an experimental area located in Barrax (Spain). Field data were acquired within the Barrax 2011 research project. AHS airborne images including VIS/NIR and TIR bands were acquired both day and night time by the INTA (Instituto Nacional de Técnica Aeroespacial) between the 11th and 13rd of June 2011. Images cover a corn pivot surrounded by bare soil, where a set of in situ data have been collected previously and simultaneously to overpasses. To validate remotely sensed estimations, a preliminary proximity sensing set up has been arranged, measuring spectra and surface temperatures on transects by means of ASD hand-held spectroradiometer and an Everest Interscience radiometric thermometer respectively. These data were collected on two transects: the first one on bare soil and the second from bare to sparsely vegetated soil; soil water content in both transects ranged approximately between field and saturation values. Furthermore thermal inertia was measured using a KD2Pro probe, and surface water content of soil was measured using FDR and TDR probes. This ground dataset was used: 1) to verify if the thermal inertia method can be applied to map water content also on soil covered by sparse vegetation, and 2) to quantify a correction factor of the downwelling shortwave radiation taking into account sky cloudiness effects on thermal inertia assessment. The experiment tests both Xue and Cracknell approximation to retrieve the thermal inertia from a dumped value of the phase difference and the three-temperature approach of Sobrino to estimate the phase difference spatial distribution. Both methods were then applied on the remotely sensed airborne images collected during the following days, in order to obtain the spatial distribution of the surface soil moisture on bare soils and
Integrated Disposal Facility FY 2016: ILAW Verification and Validation of the eSTOMP Simulator
Freedman, Vicky L.; Bacon, Diana H.; Fang, Yilin
2016-05-13
This document describes two sets of simulations carried out to further verify and validate the eSTOMP simulator. In this report, a distinction is made between verification and validation, and the focus is on verifying eSTOMP through a series of published benchmarks on cementitious wastes, and validating eSTOMP based on a lysimeter experiment for the glassified waste. These activities are carried out within the context of a scientific view of validation that asserts that models can only be invalidated, and that model validation (and verification) is a subjective assessment.
STOMP Subsurface Transport Over Multiple Phases, Version 4.0, User’s Guide
White, Mark D.; Oostrom, Martinus
2006-06-09
This guide describes the general use, input file formatting, compilation and execution of the STOMP (Subsurface Transport Over Multiple Phases) simulator, a scientific tool for analyzing single and multiple phase subsurface flow and transport. A description of the simulator’s governing equations, constitutive functions and numerical solution algorithms are provided in a companion theory guide. In writing these guides for the STOMP simulator, the authors have assumed that the reader comprehends concepts and theories associated with multiple-phase hydrology, heat transfer, thermodynamics, radioactive chain decay, and relative permeability-saturation-capillary pressure constitutive relations. The authors further assume that the reader is familiar with the computing environment on which they plan to compile and execute the STOMP simulator. Source codes for the sequential versions of the simulator are available in pure FORTRAN 77 or mixed FORTRAN 77/90 forms. The pure FORTRAN 77 source code form requires a parameters file to define the memory requirements for the array elements. The mixed FORTRAN 77/90 form of the source code uses dynamic memory allocation to define memory requirements, based on a FORTRAN 90 preprocessor STEP, that reads the input files. The simulator utilizes a variable source code configuration, which allows the execution memory and speed to be tailored to the problem specifics, and essentially requires that the source code be assembled and compiled through a software maintenance utility. The memory requirements for executing the simulator are dependent on the complexity of physical system to be modeled and the size and dimensionality of the computational domain. Likewise execution speed depends on the problem complexity, size and dimensionality of the computational domain, and computer performance. Selected operational modes of the STOMP simulator are available for scalable execution on multiple processor (i.e., parallel) computers. These versions
Gallavotti, Andrea; Barazesh, Solmaz; Malcomber, Simon; Hall, Darren; Jackson, David; Schmidt, Robert J.; McSteen, Paula
2008-01-01
The plant growth hormone auxin plays a critical role in the initiation of lateral organs and meristems. Here, we identify and characterize a mutant, sparse inflorescence1 (spi1), which has defects in the initiation of axillary meristems and lateral organs during vegetative and inflorescence development in maize. Positional cloning shows that spi1 encodes a flavin monooxygenase similar to the YUCCA (YUC) genes of Arabidopsis, which are involved in local auxin biosynthesis in various plant tissues. In Arabidopsis, loss of function of single members of the YUC family has no obvious effect, but in maize the mutation of a single yuc locus causes severe developmental defects. Phylogenetic analysis of the different members of the YUC family in moss, monocot, and eudicot species shows that there have been independent expansions of the family in monocots and eudicots. spi1 belongs to a monocot-specific clade, within which the role of individual YUC genes has diversified. These observations, together with expression and functional data, suggest that spi1 has evolved a dominant role in auxin biosynthesis that is essential for normal maize inflorescence development. Analysis of the interaction between spi1 and genes regulating auxin transport indicate that auxin transport and biosynthesis function synergistically to regulate the formation of axillary meristems and lateral organs in maize. PMID:18799737
Gallavotti, Andrea; Barazesh, Solmaz; Malcomber, Simon; Hall, Darren; Jackson, David; Schmidt, Robert J; McSteen, Paula
2008-09-30
The plant growth hormone auxin plays a critical role in the initiation of lateral organs and meristems. Here, we identify and characterize a mutant, sparse inflorescence1 (spi1), which has defects in the initiation of axillary meristems and lateral organs during vegetative and inflorescence development in maize. Positional cloning shows that spi1 encodes a flavin monooxygenase similar to the YUCCA (YUC) genes of Arabidopsis, which are involved in local auxin biosynthesis in various plant tissues. In Arabidopsis, loss of function of single members of the YUC family has no obvious effect, but in maize the mutation of a single yuc locus causes severe developmental defects. Phylogenetic analysis of the different members of the YUC family in moss, monocot, and eudicot species shows that there have been independent expansions of the family in monocots and eudicots. spi1 belongs to a monocot-specific clade, within which the role of individual YUC genes has diversified. These observations, together with expression and functional data, suggest that spi1 has evolved a dominant role in auxin biosynthesis that is essential for normal maize inflorescence development. Analysis of the interaction between spi1 and genes regulating auxin transport indicate that auxin transport and biosynthesis function synergistically to regulate the formation of axillary meristems and lateral organs in maize.
NASA Astrophysics Data System (ADS)
Trautz, A.; Illangasekare, T. H.; Tilton, N.
2015-12-01
Soil moisture is a fundamental state variable that provides the water necessary for plant growth and evapotranspiration. Soil moisture has been extensively studied in the context of bare surface soils and root zones. Less attention has focused on the effects of sparse vegetation distributions, such as those typical of agricultural cropland and other natural surface environments, on soil moisture dynamics. The current study explores root zone, bulk soil, and near-surface atmosphere interactions in terms of soil moisture under different distributions of sparse vegetation using multi-scale laboratory experimentation and numerical simulation. This research is driven by the need to advance our fundamental understanding of soil moisture dynamics in the context of improving water conservation and next generation heat and mass transfer numerical models. Experimentation is performed in a two-dimensional 7.3 m long intermediate scale soil tank interfaced with a climate-controlled wind tunnel, both of which are outfitted with current sensor technologies for measuring atmospheric and soil variables. The soil tank is packed so that a sparsely vegetated soil is surrounded by bulk bare soil; the two regions are separated by porous membranes to isolate the root zone from the bulk soil. Results show that in the absence of vegetation, evaporation rates vary along the soil tank in response to longitudinal changes in humidity; soil dries fastest upstream where evaporation rates are highest. In the presence of vegetation, soil moisture in the bulk soil closest to a vegetated region decreases more rapidly than the bulk soil farther away. Evapotranspiration rates in this region are also higher than the bulk soil region. This study is the first step towards the development of more generalized models that account for non-uniformly distributed vegetation and land surfaces exhibiting micro-topology.
STOMP-ECKEChem: An Engineering Perspective on Reactive Transport in Geologic Media
White, Mark D.; Fang, Yilin
2012-04-04
ECKEChem (Equilibrium, Conservation, Kinetic Equation Chemistry) is a reactive transport module for the STOMP suite of multifluid subsurface flow and transport simulators that was developed from an engineering perspective. STOMP comprises a suite of operational modes that are distinguished by the solved coupled conservation equations with capabilities for a variety of subsurface applications (e.g., environmental remediation and stewardship, geologic sequestration of greenhouse gases, gas hydrate production, and oil shale production). The ECKEChem module was designed to provide integrated reactive transport capabilities across the suite of STOMP simulator operational modes. The initial application for the ECKEChem module was in the simulation of the mineralization reactions that occurred with the injection of supercritical carbon dioxide into deep Columbia River basalt formations, where it was implemented in the STOMP-CO2 simulator. The STOMP-ECKEChem solution approach to modeling reactive transport in multifluid geologic media is founded on an engineering perspective: (1) sequential non-iterative coupling between the flow and reactive transport is sufficient, (2) reactive transport can be modeled by operator splitting with local geochemistry and global transport, (3) geochemistry can be expressed as a system of coupled nonlinear equilibrium, conservation and kinetic equations, (4) a limited number of kinetic equation forms are used in geochemical practice. This chapter describes the conceptual approach to converting a geochemical reaction network into a series of equilibrium, conservation and kinetic equations, the implementation of ECKEChem in STOMP, the numerical solution approach, and a demonstration of the simulator on a complex application involving desorption of uranium from contaminated field-textured sediments.
Moustafa, Gihan G.; Shaaban, F. E.; Hadeed, A. H. Abo; Elhady, Walaa M.
2016-01-01
Aim: The current study was directed to investigate the immunotoxic and oxidative stress effects of Roundup and Stomp herbicides and their combination on Nile catfish (Clarias gariepinus). Materials and Methods: The experiment was carried out on 120 fish that randomly divided into four equal groups with three replicates: The first group kept as control, the second group exposed to 1/2 96 h lethal concentration 50 (LC50) of Roundup, the third group exposed to 1/2 96 h LC50 of Stomp, and the fourth one exposed to a combination of Roundup and Stomp at previously-mentioned doses. The experiment was terminated after 15 days; blood samples were obtained at 1st, 8th, and 15th days of treatment where the sera were separated for estimation of antioxidant enzymes. Meanwhile, at 15th day of exposure part of blood was collected from all groups with an anticoagulant for evaluation of phagocytic activity, then the fish were sacrificed, and specimens from the liver of all groups were obtained for histopathological examination. Results: Our results indicated that both herbicides either individually or in combination elucidated significant decrease in phagocytic activity that was highly marked in group exposed to both herbicides. Furthermore, our data elicited an obvious elevation in the levels of superoxide dismutase (SOD), catalase (CAT), and glutathione peroxidase (GPx). Meanwhile, the data depicted reduction in levels of reduced glutathione (GSH) and glutathione-S-transferase (GST). Histopathological investigation of liver proved the aforementioned results. Conclusion: It could be concluded that either Roundup or Stomp alone cause significant deleterious effects on aquatic vertebrates. However, the use of their combination enhanced their toxic effects. Toxicity can end up in humans through the food chain. PMID:27397989
Lu, Zhiming; Meyer, D.J.
2002-01-01
We used interferometric methods on a pair of repeat-pass ERS-1 synthetic aperture radar (SAR) images to study soil moisture changes over sparsely vegetated targets. The intensity of the SAR image acquired at one time was higher than that of an image acquired at an earlier time. We used a correlation image computed from the SAR image pair to study the cause of the observed changes in SAR intensity. Because a reduction of correlation over areas with intensity changes was not observed, we interpreted the intensity changes as not being caused by changes in roughness/structure, but by a change in soil moisture owing to rainfall. An increase in soil moisture ranging from 5% to 20% is the most likely explanation for the increase of intensity. These analyses imply that both intensity and phase information should be used in SAR change detection applications.
NASA Astrophysics Data System (ADS)
Powell, R. L.; Goulden, M.; Peterson, S.; Roberts, D. A.; Still, C. J.
2015-12-01
Temperature is a primary environmental control on biological systems and processes at a range of spatial and temporal scales, from controlling biochemical processes such as photosynthesis to influencing continental-scale species distribution. The Landsat satellite series provides a long record (since the mid-1980s) of relatively high spatial resolution thermal infrared (TIR) imagery, from which we derive land surface temperature (LST) grids. Here, we investigate fine spatial resolution factors that influence Landsat-derived LST over a spectrally and spatially heterogeneous landscape. We focus on paired sites (inside/outside a 1994 fire scar) within a pinyon-juniper scrubland in Southern California. The sites have nearly identical micro-meteorology and vegetation species composition, but distinctly different vegetation abundance and structure. The tower at the unburned site includes a number of in-situ imaging tools to quantify vegetation properties, including a thermal camera on a pan-tilt mount, allowing hourly characterization of landscape component temperatures (e.g., sunlit canopy, bare soil, leaf litter). We use these in-situ measurements to assess the impact of fine-scale landscape heterogeneity on estimates of LST, including sensitivity to (i) the relative abundance of component materials, (ii) directional effects due to solar and viewing geometry, (iii) duration of sunlit exposure for each compositional type, and (iv) air temperature. To scale these properties to Landsat spatial resolution (~100-m), we characterize the sub-pixel composition of landscape components (in addition to shade) by applying spectral mixture analysis (SMA) to the Landsat Operational Land Imager (OLI) spectral bands and test the sensitivity of the relationships established with the in-situ data at this coarser scale. The effects of vegetation abundance and cover height versus other controls on satellite-derived estimates of LST will be assessed by comparing estimates at the burned vs
Numerical Simulations of Urea Hydrolysis and Calcite Precipitation in Porous Media Using STOMP
Luanjing Guo; Hai Huang; Bill X. Hu
2010-11-01
Subsurface radionuclide and trace metal contaminants throughout the U.S. Department of Energy (DOE) complex pose one of DOE’s greatest challenges for long-term stewardship. One promising in situ immobilization approach of these contaminants is engineered mineral (co)precipitation of calcite driven by urea hydrolysis that is catalyzed by enzyme urease. The tight nonlinear coupling among flow, transport, reaction and reaction-induced property changes of media of this approach was studied by reactive transport simulations with systematically increasing level of complexities of reaction network and physical/chemical heterogeneities using a numerical simulator named STOMP. Sensitivity studies on the reaction rates of both urea hydrolysis and calcite precipitation are performed via controlling urease enzyme concentration and precipitation rate constant according to the rate models employed. We have found that the rate of ureolysis is a dominating factor in the amount of precipitated mineral; however, the spatial distribution of the precipitates depends on both rates of ureolysis and calcite precipitation. A maximum 5% reduction in the porosity was observed within the simulation time period of 6 pore volumes in our 1-dimensional (1D) column simulations. When a low permeability inclusion is considered in the 2D simulations, the altered flow fields redistribute mineral forming constituents, leading to a distorted precipitation reaction front. The simulations also indicate that mineral precipitation occurs along the boundary of the low permeability zone, which implies that contaminants in the low permeability zone could be encapsulated and isolated from the flow paths.
Ciro, CA; Poole, JL; Skipper, B; Hershey, LA
2017-01-01
Background Few studies have examined structured rehabilitation techniques for improving activities of daily living in people with mild-moderate dementia. We sought to examine the advantages to delivering the Skill-building through Task-Oriented Motor Practice (STOMP) intervention in the home environment (versus the clinic), hypothesizing that ADL improvement would be significantly better, time to meeting goals would be faster and fewer displays of behavior would be noted. Methods Compared results of two quasi-experimental studies of STOMP, one completed in the home, one completed previously in a clinic. Participants were English-speaking; community dwelling adults aged 50–90 diagnosed with mild-moderate dementia who could participate in an intensive rehabilitation program (5 days/week, 3 hours/day, for 2 weeks). Outcome measurements include examiner-observation of performance and proxy-report of performance and satisfaction with performance in patient-selected goals. Results No differences existed in the sociodemographic characteristics between the home and clinic groups where the groups were primarily white, married, had > high school education and had mild-moderate dementia. Results from the home group indicate that participants made significant improvement in ADL which was generally retained at the 90 day follow-up. These results were not significantly different than the clinic group. No significant advantages were noted for the home group in terms of time to meeting goals or exhibition of fewer behaviors. Discussion The STOMP intervention appeared to work equally as well in the home and in the clinic. Future studies should continue to examine the benefits of massed practice using high-dose regimens.
Ray, J.; Lee, J.; Yadav, V.; ...
2014-08-20
We present a sparse reconstruction scheme that can also be used to ensure non-negativity when fitting wavelet-based random field models to limited observations in non-rectangular geometries. The method is relevant when multiresolution fields are estimated using linear inverse problems. Examples include the estimation of emission fields for many anthropogenic pollutants using atmospheric inversion or hydraulic conductivity in aquifers from flow measurements. The scheme is based on three new developments. Firstly, we extend an existing sparse reconstruction method, Stagewise Orthogonal Matching Pursuit (StOMP), to incorporate prior information on the target field. Secondly, we develop an iterative method that uses StOMP tomore » impose non-negativity on the estimated field. Finally, we devise a method, based on compressive sensing, to limit the estimated field within an irregularly shaped domain. We demonstrate the method on the estimation of fossil-fuel CO2 (ffCO2) emissions in the lower 48 states of the US. The application uses a recently developed multiresolution random field model and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also reduces the overall computational cost by a factor of two. Further, the sparse reconstruction scheme imposes non-negativity without introducing strong nonlinearities, such as those introduced by employing log-transformed fields, and thus reaps the benefits of simplicity and computational speed that are characteristic of linear inverse problems.« less
Vectorized Sparse Elimination.
1984-03-01
Grids," Proc. 6th Symposium on Reservoir Simulation , New Orleans, Feb. 1-2, 1982, pp. 489-506. [51 Arya, S., and D. A. Calahan, "Optimal Scheduling of...of Computer Architecture on Direct Sparse Matrix Routines in Petroleum Reservoir Simulation ," Sparse Matrix Symposium, Fairfield Glade, TE, October
sparse-msrf:A package for sparse modeling and estimation of fossil-fuel CO2 emission fields
2014-10-06
The software is used to fit models of emission fields (e.g., fossil-fuel CO2 emissions) to sparse measurements of gaseous concentrations. Its primary aim is to provide an implementation and a demonstration for the algorithms and models developed in J. Ray, V. Yadav, A. M. Michalak, B. van Bloemen Waanders and S. A. McKenna, "A multiresolution spatial parameterization for the estimation of fossil-fuel carbon dioxide emissions via atmospheric inversions", accepted, Geoscientific Model Development, 2014. The software can be used to estimate emissions of non-reactive gases such as fossil-fuel CO2, methane etc. The software uses a proxy of the emission field being estimated (e.g., for fossil-fuel CO2, a population density map is a good proxy) to construct a wavelet model for the emission field. It then uses a shrinkage regression algorithm called Stagewise Orthogonal Matching Pursuit (StOMP) to fit the wavelet model to concentration measurements, using an atmospheric transport model to relate emission and concentration fields. Algorithmic novelties described in the paper above (1) ensure that the estimated emission fields are non-negative, (2) allow the use of guesses for emission fields to accelerate the estimation processes and (3) ensure that under/overestimates in the guesses do not skew the estimation.
Ray, J.; Lee, J.; Yadav, V.; ...
2015-04-29
Atmospheric inversions are frequently used to estimate fluxes of atmospheric greenhouse gases (e.g., biospheric CO2 flux fields) at Earth's surface. These inversions typically assume that flux departures from a prior model are spatially smoothly varying, which are then modeled using a multi-variate Gaussian. When the field being estimated is spatially rough, multi-variate Gaussian models are difficult to construct and a wavelet-based field model may be more suitable. Unfortunately, such models are very high dimensional and are most conveniently used when the estimation method can simultaneously perform data-driven model simplification (removal of model parameters that cannot be reliably estimated) and fitting.more » Such sparse reconstruction methods are typically not used in atmospheric inversions. In this work, we devise a sparse reconstruction method, and illustrate it in an idealized atmospheric inversion problem for the estimation of fossil fuel CO2 (ffCO2) emissions in the lower 48 states of the USA. Our new method is based on stagewise orthogonal matching pursuit (StOMP), a method used to reconstruct compressively sensed images. Our adaptations bestow three properties to the sparse reconstruction procedure which are useful in atmospheric inversions. We have modified StOMP to incorporate prior information on the emission field being estimated and to enforce non-negativity on the estimated field. Finally, though based on wavelets, our method allows for the estimation of fields in non-rectangular geometries, e.g., emission fields inside geographical and political boundaries. Our idealized inversions use a recently developed multi-resolution (i.e., wavelet-based) random field model developed for ffCO2 emissions and synthetic observations of ffCO2 concentrations from a limited set of measurement sites. We find that our method for limiting the estimated field within an irregularly shaped region is about a factor of 10 faster than conventional approaches. It also
Evolving sparse stellar populations
NASA Astrophysics Data System (ADS)
Bruzual, Gustavo; Gladis Magris, C.; Hernández-Pérez, Fabiola
2017-03-01
We examine the role that stochastic fluctuations in the IMF and in the number of interacting binaries have on the spectro-photometric properties of sparse stellar populations as a function of age and metallicity.
Grassmannian sparse representations
NASA Astrophysics Data System (ADS)
Azary, Sherif; Savakis, Andreas
2015-05-01
We present Grassmannian sparse representations (GSR), a sparse representation Grassmann learning framework for efficient classification. Sparse representation classification offers a powerful approach for recognition in a variety of contexts. However, a major drawback of sparse representation methods is their computational performance and memory utilization for high-dimensional data. A Grassmann manifold is a space that promotes smooth surfaces where points represent subspaces and the relationship between points is defined by the mapping of an orthogonal matrix. Grassmann manifolds are well suited for computer vision problems because they promote high between-class discrimination and within-class clustering, while offering computational advantages by mapping each subspace onto a single point. The GSR framework combines Grassmannian kernels and sparse representations, including regularized least squares and least angle regression, to improve high accuracy recognition while overcoming the drawbacks of performance and dependencies on high dimensional data distributions. The effectiveness of GSR is demonstrated on computationally intensive multiview action sequences, three-dimensional action sequences, and face recognition datasets.
Sparse distributed memory overview
NASA Technical Reports Server (NTRS)
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.
Multiple Sparse Representations Classification
Plenge, Esben; Klein, Stefan S.; Niessen, Wiro J.; Meijering, Erik
2015-01-01
Sparse representations classification (SRC) is a powerful technique for pixelwise classification of images and it is increasingly being used for a wide variety of image analysis tasks. The method uses sparse representation and learned redundant dictionaries to classify image pixels. In this empirical study we propose to further leverage the redundancy of the learned dictionaries to achieve a more accurate classifier. In conventional SRC, each image pixel is associated with a small patch surrounding it. Using these patches, a dictionary is trained for each class in a supervised fashion. Commonly, redundant/overcomplete dictionaries are trained and image patches are sparsely represented by a linear combination of only a few of the dictionary elements. Given a set of trained dictionaries, a new patch is sparse coded using each of them, and subsequently assigned to the class whose dictionary yields the minimum residual energy. We propose a generalization of this scheme. The method, which we call multiple sparse representations classification (mSRC), is based on the observation that an overcomplete, class specific dictionary is capable of generating multiple accurate and independent estimates of a patch belonging to the class. So instead of finding a single sparse representation of a patch for each dictionary, we find multiple, and the corresponding residual energies provides an enhanced statistic which is used to improve classification. We demonstrate the efficacy of mSRC for three example applications: pixelwise classification of texture images, lumen segmentation in carotid artery magnetic resonance imaging (MRI), and bifurcation point detection in carotid artery MRI. We compare our method with conventional SRC, K-nearest neighbor, and support vector machine classifiers. The results show that mSRC outperforms SRC and the other reference methods. In addition, we present an extensive evaluation of the effect of the main mSRC parameters: patch size, dictionary size, and
White, Mark D.; McGrail, B. Peter
2005-12-01
flow and transport simulator, STOMP (Subsurface Transport Over Multiple Phases). Prior to these code development activities, the STOMP simulator included sequential and scalable implementations for numerically simulating the injection of supercritical CO2 into deep saline aquifers. Additionally, the sequential implementations included operational modes that considered nonisothermal conditions and kinetic dissolution of CO2 into the saline aqueous phase. This addendum documents the advancement of these numerical simulation capabilities to include reactive transport in the STOMP simulator through the inclusion of the recently PNNL developed batch geochemistry solution module ECKEChem (Equilibrium-Conservation-Kinetic Equation Chemistry). Potential geologic reservoirs for sequestering CO2 include deep saline aquifers, hydrate-bearing formations, depleted or partially depleted natural gas and petroleum reservoirs, and coal beds. The mechanisms for sequestering carbon dioxide in geologic reservoirs include physical trapping, dissolution in the reservoir fluids, hydraulic trapping (hysteretic entrapment of nonwetting fluids), and chemical reaction. This document and the associated code development and verification work are concerned with the chemistry of injecting CO2 into geologic reservoirs. As geologic sequestration of CO2 via chemical reaction, namely precipitation reactions, are most dominate in deep saline aquifers, the principal focus of this document is the numerical simulation of CO2 injection, migration, and geochemical reaction in deep saline aquifers. The ECKEChem batch chemistry module was developed in a fashion that would allow its implementation into all operational modes of the STOMP simulator, making it a more versatile chemistry component. Additionally, this approach allows for verification of the ECKEChem module against more classical reactive transport problems involving aqueous systems.
Sparse inpainting and isotropy
NASA Astrophysics Data System (ADS)
Feeney, Stephen M.; Marinucci, Domenico; McEwen, Jason D.; Peiris, Hiranya V.; Wandelt, Benjamin D.; Cammarota, Valentina
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.
NASA Technical Reports Server (NTRS)
Kanerva, Pentti
1988-01-01
Theoretical models of the human brain and proposed neural-network computers are developed analytically. Chapters are devoted to the mathematical foundations, background material from computer science, the theory of idealized neurons, neurons as address decoders, and the search of memory for the best match. Consideration is given to sparse memory, distributed storage, the storage and retrieval of sequences, the construction of distributed memory, and the organization of an autonomous learning system.
Sparse matrix test collections
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.
Yin, Junming; Chen, Xi; Xing, Eric P.
2016-01-01
We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting (e.g., group lasso), or address the problem in the nonparametric setting without exploiting the structural information (e.g., sparse additive models). In this paper, we present a new method, called group sparse additive models (GroupSpAM), which can handle group sparsity in additive models. We generalize the ℓ1/ℓ2 norm to Hilbert spaces as the sparsity-inducing penalty in GroupSpAM. Moreover, we derive a novel thresholding condition for identifying the functional sparsity at the group level, and propose an efficient block coordinate descent algorithm for constructing the estimate. We demonstrate by simulation that GroupSpAM substantially outperforms the competing methods in terms of support recovery and prediction accuracy in additive models, and also conduct a comparative experiment on a real breast cancer dataset.
and Drayton Munster, Miroslav Stoyanov
2013-09-20
Sparse Grids are the family of methods of choice for multidimensional integration and interpolation in low to moderate number of dimensions. The method is to select extend a one dimensional set of abscissas, weights and basis functions by taking a subset of all possible tensor products. The module provides the ability to create global and local approximations based on polynomials and wavelets. The software has three components, a library, a wrapper for the library that provides a command line interface via text files ad a MATLAB interface via the command line tool.
Flexible sparse regularization
NASA Astrophysics Data System (ADS)
Lorenz, Dirk A.; Resmerita, Elena
2017-01-01
The seminal paper of Daubechies, Defrise, DeMol made clear that {{\\ell }}p spaces with p\\in [1,2) and p-powers of the corresponding norms are appropriate settings for dealing with reconstruction of sparse solutions of ill-posed problems by regularization. It seems that the case p = 1 provides the best results in most of the situations compared to the cases p\\in (1,2). An extensive literature gives great credit also to using {{\\ell }}p spaces with p\\in (0,1) together with the corresponding quasi-norms, although one has to tackle challenging numerical problems raised by the non-convexity of the quasi-norms. In any of these settings, either superlinear, linear or sublinear, the question of how to choose the exponent p has been not only a numerical issue, but also a philosophical one. In this work we introduce a more flexible way of sparse regularization by varying exponents. We introduce the corresponding functional analytic framework, that leaves the setting of normed spaces but works with so-called F-norms. One curious result is that there are F-norms which generate the ℓ 1 space, but they are strictly convex, while the ℓ 1-norm is just convex.
Percolation on Sparse Networks
NASA Astrophysics Data System (ADS)
Karrer, Brian; Newman, M. E. J.; Zdeborová, Lenka
2014-11-01
We study percolation on networks, which is used as a model of the resilience of networked systems such as the Internet to attack or failure and as a simple model of the spread of disease over human contact networks. We reformulate percolation as a message passing process and demonstrate how the resulting equations can be used to calculate, among other things, the size of the percolating cluster and the average cluster size. The calculations are exact for sparse networks when the number of short loops in the network is small, but even on networks with many short loops we find them to be highly accurate when compared with direct numerical simulations. By considering the fixed points of the message passing process, we also show that the percolation threshold on a network with few loops is given by the inverse of the leading eigenvalue of the so-called nonbacktracking matrix.
NASA Technical Reports Server (NTRS)
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.
Estimating sparse precision matrices
NASA Astrophysics Data System (ADS)
Padmanabhan, Nikhil; White, Martin; Zhou, Harrison H.; O'Connell, Ross
2016-08-01
We apply a method recently introduced to the statistical literature to directly estimate the precision matrix from an ensemble of samples drawn from a corresponding Gaussian distribution. Motivated by the observation that cosmological precision matrices are often approximately sparse, the method allows one to exploit this sparsity of the precision matrix to more quickly converge to an asymptotic 1/sqrt{N_sim} rate while simultaneously providing an error model for all of the terms. Such an estimate can be used as the starting point for further regularization efforts which can improve upon the 1/sqrt{N_sim} limit above, and incorporating such additional steps is straightforward within this framework. We demonstrate the technique with toy models and with an example motivated by large-scale structure two-point analysis, showing significant improvements in the rate of convergence. For the large-scale structure example, we find errors on the precision matrix which are factors of 5 smaller than for the sample precision matrix for thousands of simulations or, alternatively, convergence to the same error level with more than an order of magnitude fewer simulations.
Sparse Methods for Biomedical Data.
Ye, Jieping; Liu, Jun
2012-06-01
Following recent technological revolutions, the investigation of massive biomedical data with growing scale, diversity, and complexity has taken a center stage in modern data analysis. Although complex, the underlying representations of many biomedical data are often sparse. For example, for a certain disease such as leukemia, even though humans have tens of thousands of genes, only a few genes are relevant to the disease; a gene network is sparse since a regulatory pathway involves only a small number of genes; many biomedical signals are sparse or compressible in the sense that they have concise representations when expressed in a proper basis. Therefore, finding sparse representations is fundamentally important for scientific discovery. Sparse methods based on the [Formula: see text] norm have attracted a great amount of research efforts in the past decade due to its sparsity-inducing property, convenient convexity, and strong theoretical guarantees. They have achieved great success in various applications such as biomarker selection, biological network construction, and magnetic resonance imaging. In this paper, we review state-of-the-art sparse methods and their applications to biomedical data.
Sparse Methods for Biomedical Data
Ye, Jieping; Liu, Jun
2013-01-01
Following recent technological revolutions, the investigation of massive biomedical data with growing scale, diversity, and complexity has taken a center stage in modern data analysis. Although complex, the underlying representations of many biomedical data are often sparse. For example, for a certain disease such as leukemia, even though humans have tens of thousands of genes, only a few genes are relevant to the disease; a gene network is sparse since a regulatory pathway involves only a small number of genes; many biomedical signals are sparse or compressible in the sense that they have concise representations when expressed in a proper basis. Therefore, finding sparse representations is fundamentally important for scientific discovery. Sparse methods based on the ℓ1 norm have attracted a great amount of research efforts in the past decade due to its sparsity-inducing property, convenient convexity, and strong theoretical guarantees. They have achieved great success in various applications such as biomarker selection, biological network construction, and magnetic resonance imaging. In this paper, we review state-of-the-art sparse methods and their applications to biomedical data. PMID:24076585
Sparse and powerful cortical spikes.
Wolfe, Jason; Houweling, Arthur R; Brecht, Michael
2010-06-01
Activity in cortical networks is heterogeneous, sparse and often precisely timed. The functional significance of sparseness and precise spike timing is debated, but our understanding of the developmental and synaptic mechanisms that shape neuronal discharge patterns has improved. Evidence for highly specialized, selective and abstract cortical response properties is accumulating. Singe-cell stimulation experiments demonstrate a high sensitivity of cortical networks to the action potentials of some, but not all, single neurons. It is unclear how this sensitivity of cortical networks to small perturbations comes about and whether it is a generic property of cortex. The unforeseen sensitivity to cortical spikes puts serious constraints on the nature of neural coding schemes.
Sparse Regression by Projection and Sparse Discriminant Analysis.
Qi, Xin; Luo, Ruiyan; Carroll, Raymond J; Zhao, Hongyu
2015-04-01
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 to 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 supplemental materials available online, the details of the algorithms and theoretical proofs, and R codes for all simulation studies are provided.
1982-10-27
sparse matrices as well as other areas. Contents 1. operations on Sparse Matrices .. . . . . . . . . . . . . . . . . . . . . . . . I 1.1 Multi...22 2.1.1 Nonsymmetric systems ............................................. 22 2.1.1.1 General sparse matrices ...46 2.1.2.1 General sparse matrices ......................................... 46 2.1.2.2 Band or profile forms
Structured sparse models for classification
NASA Astrophysics Data System (ADS)
Castrodad, Alexey
The main focus of this thesis is the modeling and classification of high dimensional data using structured sparsity. Sparse 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 its use has led to state-of-the-art results in many signal and image processing tasks. The success of sparse modeling is highly due to its ability to efficiently use the redundancy of the data and find its underlying structure. On a classification setting, we capitalize on this advantage to properly model and separate the structure of the classes. We design and validate modeling solutions to challenging problems arising in computer vision and remote sensing. We propose both supervised and unsupervised schemes for the modeling of human actions from motion imagery under a wide variety of acquisition condi- tions. In the supervised case, the main goal is to classify the human actions in the video given a predefined set of actions to learn from. In the unsupervised case, the main goal is to an- alyze the spatio-temporal dynamics of the individuals in the scene without having any prior information on the actions themselves. We also propose a model for remotely sensed hysper- spectral imagery, where the main goal is to perform automatic spectral source separation and mapping at the subpixel level. Finally, we present a sparse model for sensor fusion to exploit the common structure and enforce collaboration of hyperspectral with LiDAR data for better mapping capabilities. In all these scenarios, we demonstrate that these data can be expressed as a combination of atoms from a class-structured dictionary. These data representation becomes essentially a "mixture of classes," and by directly exploiting the sparse codes, one can attain highly accurate classification performance with relatively unsophisticated classifiers.
Double shrinking sparse dimension reduction.
Zhou, Tianyi; Tao, Dacheng
2013-01-01
Learning tasks such as classification and clustering usually perform better and cost less (time and space) on compressed representations than on the original data. Previous works mainly compress data via dimension reduction. In this paper, we propose "double shrinking" to compress image data on both dimensionality and cardinality via building either sparse low-dimensional representations or a sparse projection matrix for dimension reduction. We formulate a double shrinking model (DSM) as an l(1) regularized variance maximization with constraint ||x||(2)=1, and develop a double shrinking algorithm (DSA) to optimize DSM. DSA is a path-following algorithm that can build the whole solution path of locally optimal solutions of different sparse levels. Each solution on the path is a "warm start" for searching the next sparser one. In each iteration of DSA, the direction, the step size, and the Lagrangian multiplier are deduced from the Karush-Kuhn-Tucker conditions. The magnitudes of trivial variables are shrunk and the importances of critical variables are simultaneously augmented along the selected direction with the determined step length. Double shrinking can be applied to manifold learning and feature selections for better interpretation of features, and can be combined with classification and clustering to boost their performance. The experimental results suggest that double shrinking produces efficient and effective data compression.
Highly parallel sparse Cholesky factorization
NASA Technical Reports Server (NTRS)
Gilbert, John R.; Schreiber, Robert
1990-01-01
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms.
Sparse Matrices in MATLAB: Design and Implementation
NASA Technical Reports Server (NTRS)
Gilbert, John R.; Moler, Cleve; Schreiber, Robert
1992-01-01
The matrix computation language and environment MATLAB is extended to include sparse matrix storage and operations. The only change to the outward appearance of the MATLAB language is a pair of commands to create full or sparse matrices. Nearly all the operations of MATLAB now apply equally to full or sparse matrices, without any explicit action by the user. The sparse data structure represents a matrix in space proportional to the number of nonzero entries, and most of the operations compute sparse results in time proportional to the number of arithmetic operations on nonzeros.
Sparse recovery via convex optimization
NASA Astrophysics Data System (ADS)
Randall, Paige Alicia
This thesis considers the problem of estimating a sparse signal from a few (possibly noisy) linear measurements. In other words, we have y = Ax + z where A is a measurement matrix with more columns than rows, x is a sparse signal to be estimated, z is a noise vector, and y is a vector of measurements. This setup arises frequently in many problems ranging from MRI imaging to genomics to compressed sensing.We begin by relating our setup to an error correction problem over the reals, where a received encoded message is corrupted by a few arbitrary errors, as well as smaller dense errors. We show that under suitable conditions on the encoding matrix and on the number of arbitrary errors, one is able to accurately recover the message.We next show that we are able to achieve oracle optimality for x, up to a log factor and a factor of sqrt{s}, when we require the matrix A to obey an incoherence property. The incoherence property is novel in that it allows the coherence of A to be as large as O(1/ log n) and still allows sparsities as large as O(m/log n). This is in contrast to other existing results involving coherence where the coherence can only be as large as O(1/sqrt{m}) to allow sparsities as large as O(sqrt{m}). We also do not make the common assumption that the matrix A obeys a restricted eigenvalue condition.We then show that we can recover a (non-sparse) signal from a few linear measurements when the signal has an exactly sparse representation in an overcomplete dictionary. We again only require that the dictionary obey an incoherence property.Finally, we introduce the method of l_1 analysis and show that it is guaranteed to give good recovery of a signal from a few measurements, when the signal can be well represented in a dictionary. We require that the combined measurement/dictionary matrix satisfies a uniform uncertainty principle and we compare our results with the more standard l_1 synthesis approach.All our methods involve solving an l_1 minimization
Sparse Coding for Alpha Matting.
Johnson, Jubin; Varnousfaderani, Ehsan Shahrian; Cholakkal, Hisham; Rajan, Deepu
2016-07-01
Existing color sampling-based alpha matting methods use the compositing equation to estimate alpha at a pixel from the pairs of foreground ( F ) and background ( B ) samples. The quality of the matte depends on the selected ( F,B ) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples. A non-parametric probabilistic segmentation provides a certainty measure on the pixel belonging to foreground or background, based on which a dictionary is formed for use in sparse coding. By removing the restriction to conform to ( F,B ) pairs, this method allows for better alpha estimation from multiple F and B samples. The same framework is extended to videos, where the requirement of temporal coherence is handled effectively. Here, the dictionary is formed by samples from multiple frames. A multi-frame graph model, as opposed to a single image as for image matting, is proposed that can be solved efficiently in closed form. Quantitative and qualitative evaluations on a benchmark dataset are provided to show that the proposed method outperforms the current stateoftheart in image and video matting.
Sparse Coding for Alpha Matting.
Johnson, Jubin; Varnousfaderani, Ehsan; Cholakkal, Hisham; Rajan, Deepu
2016-04-21
Existing color sampling based alpha matting methods use the compositing equation to estimate alpha at a pixel from pairs of foreground (F) and background (B) samples. The quality of the matte depends on the selected (F,B) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples. A non-parametric probabilistic segmentation provides a certainty measure on the pixel belonging to foreground or background, based on which a dictionary is formed for use in sparse coding. By removing the restriction to conform to (F,B) pairs, this method allows for better alpha estimation from multiple F and B samples. The same framework is extended to videos, where the requirement of temporal coherence is handled effectively. Here, the dictionary is formed by samples from multiple frames. A multi-frame graph model, as opposed to a single image as for image matting, is proposed that can be solved efficiently in closed form. Quantitative and qualitative evaluations on a benchmark dataset are provided to show that the proposed method outperforms current state-of-the-art in image and video matting.
Universal Priors for Sparse Modeling(PREPRINT)
2009-08-01
UNIVERSAL PRIORS FOR SPARSE MODELING By Ignacio Ramı́rez Federico Lecumberry and Guillermo Sapiro IMA Preprint Series # 2276 ( August 2009...8-98) Prescribed by ANSI Std Z39-18 Universal Priors for Sparse Modeling (Invited Paper) Ignacio Ramı́rez#1, Federico Lecumberry ∗2, Guillermo Sapiro...I. Ramirez, F. Lecumberry , and G. Sapiro. Sparse modeling with univer- sal priors and learned incoherent dictionaries. Submitted to NIPS, 2009. [22
Discriminative Sparse Representations in Hyperspectral Imagery
2010-03-01
classification , and unsupervised labeling (clustering) [2, 3, 4, 5, 6, 7, 8]. Recently, a non-parametric (Bayesian) approach to sparse modeling and com...DISCRIMINATIVE SPARSE REPRESENTATIONS IN HYPERSPECTRAL IMAGERY By Alexey Castrodad, Zhengming Xing John Greer, Edward Bosch Lawrence Carin and...00-00-2010 to 00-00-2010 4. TITLE AND SUBTITLE Discriminative Sparse Representations in Hyperspectral Imagery 5a. CONTRACT NUMBER 5b. GRANT
Image fusion using sparse overcomplete feature dictionaries
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.
Sparse and stable Markowitz portfolios.
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.
Approximate Orthogonal Sparse Embedding for Dimensionality Reduction.
Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Yang, Jian; Zhang, David
2016-04-01
Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1-norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes.
Sparse representation for the ISAR image reconstruction
NASA Astrophysics Data System (ADS)
Hu, Mengqi; Montalbo, John; Li, Shuxia; Sun, Ligang; Qiao, Zhijun G.
2016-05-01
In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.
Large-scale sparse singular value computations
NASA Technical Reports Server (NTRS)
Berry, Michael W.
1992-01-01
Four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture are presented. Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left and right-singular vectors) for sparse matrices arising from two practical applications: information retrieval and seismic reflection tomography are emphasized. The target architectures for implementations are the CRAY-2S/4-128 and Alliant FX/80. The sparse SVD problem is well motivated by recent information-retrieval techniques in which dominant singular values and their corresponding singular vectors of large sparse term-document matrices are desired, and by nonlinear inverse problems from seismic tomography applications which require approximate pseudo-inverses of large sparse Jacobian matrices.
Approximate inverse preconditioners for general sparse matrices
Chow, E.; Saad, Y.
1994-12-31
Preconditioned Krylov subspace methods are often very efficient in solving sparse linear matrices that arise from the discretization of elliptic partial differential equations. However, for general sparse indifinite matrices, the usual ILU preconditioners fail, often because of the fact that the resulting factors L and U give rise to unstable forward and backward sweeps. In such cases, alternative preconditioners based on approximate inverses may be attractive. We are currently developing a number of such preconditioners based on iterating on each column to get the approximate inverse. For this approach to be efficient, the iteration must be done in sparse mode, i.e., we must use sparse-matrix by sparse-vector type operatoins. We will discuss a few options and compare their performance on standard problems from the Harwell-Boeing collection.
Sparse-aperture adaptive optics
NASA Astrophysics Data System (ADS)
Tuthill, Peter; Lloyd, James; Ireland, Michael; Martinache, Frantz; Monnier, John; Woodruff, Henry; ten Brummelaar, Theo; Turner, Nils; Townes, Charles
2006-06-01
Aperture masking interferometry and Adaptive Optics (AO) are two of the competing technologies attempting to recover diffraction-limited performance from ground-based telescopes. However, there are good arguments that these techniques should be viewed as complementary, not competitive. Masking has been shown to deliver superior PSF calibration, rejection of atmospheric noise and robust recovery of phase information through the use of closure phases. However, this comes at the penalty of loss of flux at the mask, restricting the technique to bright targets. Adaptive optics, on the other hand, can reach a fainter class of objects but suffers from the difficulty of calibration of the PSF which can vary with observational parameters such as seeing, airmass and source brightness. Here we present results from a fusion of these two techniques: placing an aperture mask downstream of an AO system. The precision characterization of the PSF enabled by sparse-aperture interferometry can now be applied to deconvolution of AO images, recovering structure from the traditionally-difficult regime within the core of the AO-corrected transfer function. Results of this program from the Palomar and Keck adaptive optical systems are presented.
Resistant multiple sparse canonical correlation.
Coleman, Jacob; Replogle, Joseph; Chandler, Gabriel; Hardin, Johanna
2016-04-01
Canonical correlation analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables can be grouped together, thus better understanding multiple interactions that are otherwise difficult to compute or grasp intuitively. CCA appears to have quite powerful applications to high-throughput data, as we can use it to discover, for example, relationships between gene expression and gene copy number variation. One of the biggest problems of CCA is that the number of variables (often upwards of 10,000) makes biological interpretation of linear combinations nearly impossible. To limit variable output, we have employed a method known as sparse canonical correlation analysis (SCCA), while adding estimation which is resistant to extreme observations or other types of deviant data. In this paper, we have demonstrated the success of resistant estimation in variable selection using SCCA. Additionally, we have used SCCA to find multiple canonical pairs for extended knowledge about the datasets at hand. Again, using resistant estimators provided more accurate estimates than standard estimators in the multiple canonical correlation setting. R code is available and documented at https://github.com/hardin47/rmscca.
Sparse High Dimensional Models in Economics
Fan, Jianqing; Lv, Jinchi; Qi, Lei
2010-01-01
This paper reviews the literature on sparse high dimensional models and discusses some applications in economics and finance. Recent developments of theory, methods, and implementations in penalized least squares and penalized likelihood methods are highlighted. These variable selection methods are proved to be effective in high dimensional sparse modeling. The limits of dimensionality that regularization methods can handle, the role of penalty functions, and their statistical properties are detailed. Some recent advances in ultra-high dimensional sparse modeling are also briefly discussed. PMID:22022635
Structured sparse priors for image classification.
Srinivas, Umamahesh; Suo, Yuanming; Dao, Minh; Monga, Vishal; Tran, Trac D
2015-06-01
Model-based compressive sensing (CS) exploits the structure inherent in sparse signals for the design of better signal recovery algorithms. This information about structure is often captured in the form of a prior on the sparse coefficients, with the Laplacian being the most common such choice (leading to l1 -norm minimization). Recent work has exploited the discriminative capability of sparse representations for image classification by employing class-specific dictionaries in the CS framework. Our contribution is a logical extension of these ideas into structured sparsity for classification. We introduce the notion of discriminative class-specific priors in conjunction with class specific dictionaries, specifically the spike-and-slab prior widely applied in Bayesian sparse regression. Significantly, the proposed framework takes the burden off the demand for abundant training image samples necessary for the success of sparsity-based classification schemes. We demonstrate this practical benefit of our approach in important applications, such as face recognition and object categorization.
Robust feature point matching with sparse model.
Jiang, Bo; Tang, Jin; Luo, Bin; Lin, Liang
2014-12-01
Feature point matching that incorporates pairwise constraints can be cast as an integer quadratic programming (IQP) problem. Since it is NP-hard, approximate methods are required. The optimal solution for IQP matching problem is discrete, binary, and thus sparse in nature. This motivates us to use sparse model for feature point matching problem. The main advantage of the proposed sparse feature point matching (SPM) method is that it generates sparse solution and thus naturally imposes the discrete mapping constraints approximately in the optimization process. Therefore, it can optimize the IQP matching problem in an approximate discrete domain. In addition, an efficient algorithm can be derived to solve SPM problem. Promising experimental results on both synthetic points sets matching and real-world image feature sets matching tasks show the effectiveness of the proposed feature point matching method.
Deploying temporary networks for upscaling of sparse network stations
NASA Astrophysics Data System (ADS)
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.
Online Dictionary Learning for Sparse Coding
2009-04-01
cessing tasks such as denoising (Elad & Aharon, 2006) as well as higher-level tasks such as classification (Raina et al., 2007; Mairal et al., 2008a...Bruckstein, A. M. (2006). The K- SVD : An algorithm for designing of overcomplete dic- tionaries for sparse representations. IEEE Trans. SP...Tibshirani, R. (2004). Least angle regression. Ann. Statist. Elad, M., & Aharon, M. (2006). Image denoising via sparse and redundant representations
Sparse CSEM inversion driven by seismic coherence
NASA Astrophysics Data System (ADS)
Guo, Zhenwei; Dong, Hefeng; Kristensen, Åge
2016-12-01
Marine controlled source electromagnetic (CSEM) data inversion for hydrocarbon exploration is often challenging due to high computational cost, physical memory requirement and low resolution of the obtained resistivity map. This paper aims to enhance both the speed and resolution of CSEM inversion by introducing structural geological information in the inversion algorithm. A coarse mesh is generated for Occam’s inversion, where the parameters are fewer than in the fine regular mesh. This sparse mesh is defined as a coherence-based irregular (IC) sparse mesh, which is based on vertices extracted from available geological information. Inversion results on synthetic data illustrate that the IC sparse mesh has a smaller inversion computational cost compared to the regular dense (RD) mesh. It also has a higher resolution than with a regular sparse (RS) mesh for the same number of estimated parameters. In order to study how the IC sparse mesh reduces the computational time, four different meshes are generated for Occam’s inversion. As a result, an IC sparse mesh can reduce the computational cost while it keeps the resolution as good as a fine regular mesh. The IC sparse mesh reduces the computational cost of the matrix operation for model updates. When the number of estimated parameters reduces to a limited value, the computational cost is independent of the number of parameters. For a testing model with two resistive layers, the inversion result using an IC sparse mesh has higher resolution in both horizontal and vertical directions. Overall, the model representing significant geological information in the IC mesh can improve the resolution of the resistivity models obtained from inversion of CSEM data.
Sparse Extreme Learning Machine for Classification
Bai, Zuo; Huang, Guang-Bin; Wang, Danwei; Wang, Han; Westover, M. Brandon
2016-01-01
Extreme learning machine (ELM) was initially proposed for single-hidden-layer feedforward neural networks (SLFNs). In the hidden layer (feature mapping), nodes are randomly generated independently of training data. Furthermore, a unified ELM was proposed, providing a single framework to simplify and unify different learning methods, such as SLFNs, least square support vector machines, proximal support vector machines, and so on. However, the solution of unified ELM is dense, and thus, usually plenty of storage space and testing time are required for large-scale applications. In this paper, a sparse ELM is proposed as an alternative solution for classification, reducing storage space and testing time. In addition, unified ELM obtains the solution by matrix inversion, whose computational complexity is between quadratic and cubic with respect to the training size. It still requires plenty of training time for large-scale problems, even though it is much faster than many other traditional methods. In this paper, an efficient training algorithm is specifically developed for sparse ELM. The quadratic programming problem involved in sparse ELM is divided into a series of smallest possible sub-problems, each of which are solved analytically. Compared with SVM, sparse ELM obtains better generalization performance with much faster training speed. Compared with unified ELM, sparse ELM achieves similar generalization performance for binary classification applications, and when dealing with large-scale binary classification problems, sparse ELM realizes even faster training speed than unified ELM. PMID:25222727
Visual tracking via robust multitask sparse prototypes
NASA Astrophysics Data System (ADS)
Zhang, Huanlong; Hu, Shiqiang; Yu, Junyang
2015-03-01
Sparse representation has been applied to an online subspace learning-based tracking problem. To handle partial occlusion effectively, some researchers introduce l1 regularization to principal component analysis (PCA) reconstruction. However, in these traditional tracking methods, the representation of each object observation is often viewed as an individual task so the inter-relationship between PCA basis vectors is ignored. We propose a new online visual tracking algorithm with multitask sparse prototypes, which combines multitask sparse learning with PCA-based subspace representation. We first extend a visual tracking algorithm with sparse prototypes in multitask learning framework to mine inter-relations between subtasks. Then, to avoid the problem that enforcing all subtasks to share the same structure may result in degraded tracking results, we impose group sparse constraints on the coefficients of PCA basis vectors and element-wise sparse constraints on the error coefficients, respectively. Finally, we show that the proposed optimization problem can be effectively solved using the accelerated proximal gradient method with the fast convergence. Experimental results compared with the state-of-the-art tracking methods demonstrate that the proposed algorithm achieves favorable performance when the object undergoes partial occlusion, motion blur, and illumination changes.
Finding Nonoverlapping Substructures of a Sparse Matrix
Pinar, Ali; Vassilevska, Virginia
2005-08-11
Many applications of scientific computing rely on computations on sparse matrices. The design of efficient implementations of sparse matrix kernels is crucial for the overall efficiency of these applications. Due to the high compute-to-memory ratio and irregular memory access patterns, the performance of sparse matrix kernels is often far away from the peak performance on a modern processor. Alternative data structures have been proposed, which split the original matrix A into A{sub d} and A{sub s}, so that A{sub d} contains all dense blocks of a specified size in the matrix, and A{sub s} contains the remaining entries. This enables the use of dense matrix kernels on the entries of A{sub d} producing better memory performance. In this work, we study the problem of finding a maximum number of nonoverlapping dense blocks in a sparse matrix, which is previously not studied in the sparse matrix community. We show that the maximum nonoverlapping dense blocks problem is NP-complete by using a reduction from the maximum independent set problem on cubic planar graphs. We also propose a 2/3-approximation algorithm that runs in linear time in the number of nonzeros in the matrix. This extended abstract focuses on our results for 2x2 dense blocks. However we show that our results can be generalized to arbitrary sized dense blocks, and many other oriented substructures, which can be exploited to improve the memory performance of sparse matrix operations.
Sparse extreme learning machine for classification.
Bai, Zuo; Huang, Guang-Bin; Wang, Danwei; Wang, Han; Westover, M Brandon
2014-10-01
Extreme learning machine (ELM) was initially proposed for single-hidden-layer feedforward neural networks (SLFNs). In the hidden layer (feature mapping), nodes are randomly generated independently of training data. Furthermore, a unified ELM was proposed, providing a single framework to simplify and unify different learning methods, such as SLFNs, least square support vector machines, proximal support vector machines, and so on. However, the solution of unified ELM is dense, and thus, usually plenty of storage space and testing time are required for large-scale applications. In this paper, a sparse ELM is proposed as an alternative solution for classification, reducing storage space and testing time. In addition, unified ELM obtains the solution by matrix inversion, whose computational complexity is between quadratic and cubic with respect to the training size. It still requires plenty of training time for large-scale problems, even though it is much faster than many other traditional methods. In this paper, an efficient training algorithm is specifically developed for sparse ELM. The quadratic programming problem involved in sparse ELM is divided into a series of smallest possible sub-problems, each of which are solved analytically. Compared with SVM, sparse ELM obtains better generalization performance with much faster training speed. Compared with unified ELM, sparse ELM achieves similar generalization performance for binary classification applications, and when dealing with large-scale binary classification problems, sparse ELM realizes even faster training speed than unified ELM.
Efficient, sparse biological network determination
August, Elias; Papachristodoulou, Antonis
2009-01-01
Background Determining the interaction topology of biological systems is a topic that currently attracts significant research interest. Typical models for such systems take the form of differential equations that involve polynomial and rational functions. Such nonlinear models make the problem of determining the connectivity of biochemical networks from time-series experimental data much harder. The use of linear dynamics and linearization techniques that have been proposed in the past can circumvent this, but the general problem of developing efficient algorithms for models that provide more accurate system descriptions remains open. Results We present a network determination algorithm that can treat model descriptions with polynomial and rational functions and which does not make use of linearization. For this purpose, we make use of the observation that biochemical networks are in general 'sparse' and minimize the 1-norm of the decision variables (sum of weighted network connections) while constraints keep the error between data and the network dynamics small. The emphasis of our methodology is on determining the interconnection topology rather than the specific reaction constants and it takes into account the necessary properties that a chemical reaction network should have – something that techniques based on linearization can not. The problem can be formulated as a Linear Program, a convex optimization problem, for which efficient algorithms are available that can treat large data sets efficiently and uncertainties in data or model parameters. Conclusion The presented methodology is able to predict with accuracy and efficiency the connectivity structure of a chemical reaction network with mass action kinetics and of a gene regulatory network from simulation data even if the dynamics of these systems are non-polynomial (rational) and uncertainties in the data are taken into account. It also produces a network structure that can explain the real experimental
NASA Astrophysics Data System (ADS)
Trisakti, Bambang
2017-01-01
Open green space in the urban area has aims to maintain the availability of land as a water catchment area, creating aspects of urban planning through a balance between the natural environment and the built environment that are useful for the public needs. Local governments have to make the green zone plan map and monitor the green space changes in their territory. Medium and high resolution satellite imageries have been widely utilized to map and monitor the changes of vegetation cover as an indicator of green space area. This paper describes the use of pleaides imagery to classify vegetation types and estimate vegetation cover percentage in the green zone. Vegetation cover was mapped using a combination of NDVI and blue band. Furthermore, vegetation types in the green space were classified using unsupervised and supervised (ISODATA and MLEN) methods. Vegetation types in the study area were divided into sparse vegetation, low-medium vegetation and medium-high vegetation. The classification accuracies were 97.9% and 98.9% for unsupervised and supervised method respectively. The vegetation cover percentage was determined by calculating the ratio between the vegetation type area and the green zone area. These information are useful to support green zone management activities.
Finding nonoverlapping substructures of a sparse matrix
Pinar, Ali; Vassilevska, Virginia
2004-08-09
Many applications of scientific computing rely on computations on sparse matrices, thus the design of efficient implementations of sparse matrix kernels is crucial for the overall efficiency of these applications. Due to the high compute-to-memory ratio and irregular memory access patterns, the performance of sparse matrix kernels is often far away from the peak performance on a modern processor. Alternative data structures have been proposed, which split the original matrix A into A{sub d} and A{sub s}, so that A{sub d} contains all dense blocks of a specified size in the matrix, and A{sub s} contains the remaining entries. This enables the use of dense matrix kernels on the entries of A{sub d} producing better memory performance. In this work, we study the problem of finding a maximum number of non overlapping rectangular dense blocks in a sparse matrix, which has not been studied in the sparse matrix community. We show that the maximum non overlapping dense blocks problem is NP-complete by using a reduction from the maximum independent set problem on cubic planar graphs. We also propose a 2/3-approximation algorithm for 2 times 2 blocks that runs in linear time in the number of nonzeros in the matrix. We discuss alternatives to rectangular blocks such as diagonal blocks and cross blocks and present complexity analysis and approximation algorithms.
Fast wavelet based sparse approximate inverse preconditioner
Wan, W.L.
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.
A unified approach to sparse signal processing
NASA Astrophysics Data System (ADS)
Marvasti, Farokh; Amini, Arash; Haddadi, Farzan; Soltanolkotabi, Mahdi; Khalaj, Babak Hossein; Aldroubi, Akram; Sanei, Saeid; Chambers, Janathon
2012-12-01
A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, component analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing, and rate of innovation. The redundancy introduced by channel coding in finite and real Galois fields is then related to over-sampling with similar reconstruction algorithms. The error locator polynomial (ELP) and iterative methods are shown to work quite effectively for both sampling and coding applications. The methods of Prony, Pisarenko, and MUltiple SIgnal Classification (MUSIC) are next shown to be targeted at analyzing signals with sparse frequency domain representations. Specifically, the relations of the approach of Prony to an annihilating filter in rate of innovation and ELP in coding are emphasized; the Pisarenko and MUSIC methods are further improvements of the Prony method under noisy environments. The iterative methods developed for sampling and coding applications are shown to be powerful tools in spectral estimation. Such narrowband spectral estimation is then related to multi-source location and direction of arrival estimation in array processing. Sparsity in unobservable source signals is also shown to facilitate source separation in sparse component analysis; the algorithms developed in this area such as linear programming and matching pursuit are also widely used in compressed sensing. Finally
Protein family classification using sparse Markov transducers.
Eskin, E; Grundy, W N; Singer, Y
2000-01-01
In this paper we present a method for classifying proteins into families using sparse Markov transducers (SMTs). Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Because substitutions of amino acids are common in protein families, incorporating wildcards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. We also present efficient data structures to improve the memory usage of the models. We evaluate SMTs by building protein family classifiers using the Pfam database and compare our results to previously published results.
Analog system for computing sparse codes
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.
Tensor methods for large, sparse unconstrained optimization
Bouaricha, A.
1996-11-01
Tensor methods for unconstrained optimization were first introduced by Schnabel and Chow [SIAM J. Optimization, 1 (1991), pp. 293-315], who describe these methods for small to moderate size problems. This paper extends these methods to large, sparse unconstrained optimization problems. This requires an entirely new way of solving the tensor model that makes the methods suitable for solving large, sparse optimization problems efficiently. We present test results for sets of problems where the Hessian at the minimizer is nonsingular and where it is singular. These results show that tensor methods are significantly more efficient and more reliable than standard methods based on Newton`s method.
Second SIAM conference on sparse matrices: Abstracts. Final technical report
1996-12-31
This report contains abstracts on the following topics: invited and long presentations (IP1 & LP1); sparse matrix reordering & graph theory I; sparse matrix tools & environments I; eigenvalue computations I; iterative methods & acceleration techniques I; applications I; parallel algorithms I; sparse matrix reordering & graphy theory II; sparse matrix tool & environments II; least squares & optimization I; iterative methods & acceleration techniques II; applications II; eigenvalue computations II; least squares & optimization II; parallel algorithms II; sparse direct methods; iterative methods & acceleration techniques III; eigenvalue computations III; and sparse matrix reordering & graph theory III.
Structured Sparse Method for Hyperspectral Unmixing
NASA Astrophysics Data System (ADS)
Zhu, Feiyun; Wang, Ying; Xiang, Shiming; Fan, Bin; Pan, Chunhong
2014-02-01
Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.
Sparse matrix orderings for factorized inverse preconditioners
Benzi, M.; Tuama, M.
1998-09-01
The effect of reorderings on the performance of factorized sparse approximate inverse preconditioners is considered. It is shown that certain reorderings can be very beneficial both in the preconditioner construction phase and in terms of the rate of convergence of the preconditioned iteration.
Multilevel sparse functional principal component analysis.
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.
Learning Stable Multilevel Dictionaries for Sparse Representations.
Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas
2015-09-01
Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The increasing need for learning sparse models in large-scale applications motivates the development of efficient, robust, and provably good dictionary learning algorithms. Algorithmic stability and generalizability are desirable characteristics for dictionary learning algorithms that aim to build global dictionaries, which can efficiently model any test data similar to the training samples. In this paper, we propose an algorithm to learn dictionaries for sparse representations from large scale data, and prove that the proposed learning algorithm is stable and generalizable asymptotically. The algorithm employs a 1-D subspace clustering procedure, the K-hyperline clustering, to learn a hierarchical dictionary with multiple levels. We also propose an information-theoretic scheme to estimate the number of atoms needed in each level of learning and develop an ensemble approach to learn robust dictionaries. Using the proposed dictionaries, the sparse code for novel test data can be computed using a low-complexity pursuit procedure. We demonstrate the stability and generalization characteristics of the proposed algorithm using simulations. We also evaluate the utility of the multilevel dictionaries in compressed recovery and subspace learning applications.
SAR Image Despeckling Via Structural Sparse Representation
NASA Astrophysics Data System (ADS)
Lu, Ting; Li, Shutao; Fang, Leyuan; Benediktsson, Jón Atli
2016-12-01
A novel synthetic aperture radar (SAR) image despeckling method based on structural sparse representation is introduced. The proposed method utilizes the fact that different regions in SAR images correspond to varying terrain reflectivity. Therefore, SAR images can be split into a heterogeneous class (with a varied terrain reflectivity) and a homogeneous class (with a constant terrain reflectivity). In the proposed method, different sparse representation based despeckling schemes are designed by combining the different region characteristics in SAR images. For heterogeneous regions with rich structure and texture information, structural dictionaries are learned to appropriately represent varied structural characteristics. Specifically, each patch in these regions is sparsely coded with the best fitted structural dictionary, thus good structure preservation can be obtained. For homogenous regions without rich structure and texture information, the highly redundant photometric self-similarity is exploited to suppress speckle noise without introducing artifacts. That is achieved by firstly learning the sub-dictionary, then simultaneously sparsely coding for each group of photometrically similar image patches. Visual and objective experimental results demonstrate the superiority of the proposed method over the-state-of-the-art methods.
Maxdenominator Reweighted Sparse Representation for Tumor Classification
Li, Weibiao; Liao, Bo; Zhu, Wen; Chen, Min; Peng, Li; Wei, Xiaohui; Gu, Changlong; Li, Keqin
2017-01-01
The classification of tumors is crucial for the proper treatment of cancer. Sparse representation-based classifier (SRC) exhibits good classification performance and has been successfully used to classify tumors using gene expression profile data. In this study, we propose a three-step maxdenominator reweighted sparse representation classification (MRSRC) method to classify tumors. First, we extract a set of metagenes from the training samples. These metagenes can capture the structures inherent to the data and are more effective for classification than the original gene expression data. Second, we use a reweighted regularization method to obtain the sparse representation coefficients. Reweighted regularization can enhance sparsity and obtain better sparse representation coefficients. Third, we classify the data by utilizing a maxdenominator residual error function. Maxdenominator strategy can reduce the residual error and improve the accuracy of the final classification. Extensive experiments using publicly available gene expression profile data sets show that the performance of MRSRC is comparable with or better than many existing representative methods. PMID:28393883
Solving large sparse eigenvalue problems on supercomputers
NASA Technical Reports Server (NTRS)
Philippe, Bernard; Saad, Youcef
1988-01-01
An important problem in scientific computing consists in finding a few eigenvalues and corresponding eigenvectors of a very large and sparse matrix. The most popular methods to solve these problems are based on projection techniques on appropriate subspaces. The main attraction of these methods is that they only require the use of the matrix in the form of matrix by vector multiplications. The implementations on supercomputers of two such methods for symmetric matrices, namely Lanczos' method and Davidson's method are compared. Since one of the most important operations in these two methods is the multiplication of vectors by the sparse matrix, methods of performing this operation efficiently are discussed. The advantages and the disadvantages of each method are compared and implementation aspects are discussed. Numerical experiments on a one processor CRAY 2 and CRAY X-MP are reported. Possible parallel implementations are also discussed.
Sparse representation for color image restoration.
Mairal, Julien; Elad, Michael; Sapiro, Guillermo
2008-01-01
Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.
Fingerprint Compression Based on Sparse Representation.
Shao, Guangqi; Wu, Yanping; A, Yong; Liu, Xiao; Guo, Tiande
2014-02-01
A new fingerprint compression algorithm based on sparse representation is introduced. Obtaining an overcomplete dictionary from a set of fingerprint patches allows us to represent them as a sparse linear combination of dictionary atoms. In the algorithm, we first construct a dictionary for predefined fingerprint image patches. For a new given fingerprint images, represent its patches according to the dictionary by computing l(0)-minimization and then quantize and encode the representation. In this paper, we consider the effect of various factors on compression results. Three groups of fingerprint images are tested. The experiments demonstrate that our algorithm is efficient compared with several competing compression techniques (JPEG, JPEG 2000, and WSQ), especially at high compression ratios. The experiments also illustrate that the proposed algorithm is robust to extract minutiae.
Feature selection using sparse Bayesian inference
NASA Astrophysics Data System (ADS)
Brandes, T. Scott; Baxter, James R.; Woodworth, Jonathan
2014-06-01
A process for selecting a sparse subset of features that maximize discrimination between target classes is described in a Bayesian framework. Demonstrated on high range resolution radar (HRR) signature data, this has the effect of selecting the most informative range bins for a classification task. The sparse Bayesian classifier (SBC) model is directly compared against Fisher's linear discriminant analysis (LDA), showing a clear performance gain with the Bayesian framework using HRRs from the publicly available MSTAR data set. The discriminative power of the selected features from the SBC is shown to be particularly dominant over LDA when only a few features are selected or when there is a shift in training and testing data sets, as demonstrated by training on a specific target type and testing on a slightly different target type.
Sparse brain network using penalized linear regression
NASA Astrophysics Data System (ADS)
Lee, Hyekyoung; Lee, Dong Soo; Kang, Hyejin; Kim, Boong-Nyun; Chung, Moo K.
2011-03-01
Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.
Statistical prediction with Kanerva's sparse distributed memory
NASA Technical Reports Server (NTRS)
Rogers, David
1989-01-01
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory.
Causal Network Inference Via Group Sparse Regularization
Bolstad, Andrew; Van Veen, Barry D.; Nowak, Robert
2011-01-01
This paper addresses the problem of inferring sparse causal networks modeled by multivariate autoregressive (MAR) processes. Conditions are derived under which the Group Lasso (gLasso) procedure consistently estimates sparse network structure. The key condition involves a “false connection score” ψ. In particular, we show that consistent recovery is possible even when the number of observations of the network is far less than the number of parameters describing the network, provided that ψ < 1. The false connection score is also demonstrated to be a useful metric of recovery in nonasymptotic regimes. The conditions suggest a modified gLasso procedure which tends to improve the false connection score and reduce the chances of reversing the direction of causal influence. Computational experiments and a real network based electrocorticogram (ECoG) simulation study demonstrate the effectiveness of the approach. PMID:21918591
Multispectral vegetative canopy parameter retrieval
NASA Astrophysics Data System (ADS)
Borel, Christoph C.; Bunker, David J.
2011-11-01
Precision agriculture, forestry and environmental remote sensing are applications uniquely suited to the 8 bands that DigitalGlobe's WorldView-2 provides. At the fine spatial resolution of 0.5 m (panchromatic) and 2 m (multispectral) individual trees can be readily resolved. Recent research [1] has shown that it is possible for hyper-spectral data to invert plant reflectance spectra and estimate nitrogen content, leaf water content, leaf structure, canopy leaf area index and, for sparse canopies, also soil reflectance. The retrieval is based on inverting the SAIL (Scattering by Arbitrary Inclined Leaves) vegetation radiative transfer model for the canopy structure and the reflectance model PROSPECT4/5 for the leaf reflectance. Working on the paper [1] confirmed that a limited number of adjacent bands covering just the visible and near infrared can retrieve the parameters as well, opening up the possibility that this method can be used to analyze multi-spectral WV-2 data. Thus it seems possible to create WV-2 specific inversions using 8 bands and apply them to imagery of various vegetation covered surfaces of agricultural and environmental interest. The capability of retrieving leaf water content and nitrogen content has important applications in determining the health of vegetation, e.g. plant growth status, disease mapping, quantitative drought assessment, nitrogen deficiency, plant vigor, yield, etc.
Inpainting with sparse linear combinations of exemplars
Wohlberg, Brendt
2008-01-01
We introduce a new exemplar-based inpainting algorithm based on representing the region to be inpainted as a sparse linear combination of blocks extracted from similar parts of the image being inpainted. This method is conceptually simple, being computed by functional minimization, and avoids the complexity of correctly ordering the filling in of missing regions of other exemplar-based methods. Initial performance comparisons on small inpainting regions indicate that this method provides similar or better performance than other recent methods.
Hyperspectral Image Classification via Kernel Sparse Representation
2013-01-01
and sparse representations, image processing, wavelets, multirate systems , and filter banks . Nasser M. Nasrabadi (S’80–M’84–SM’92–F’01) received the...ery, sampling, multirate systems , filter banks , transforms, wavelets, and their applications in signal analysis, compression, processing, and...University of Pavia and the Center of Pavia images, are urban images acquired by the Reflective Optics System Imaging Spectrom- eter (ROSIS). The ROSIS
Sparse Representation for Time-Series Classification
2015-02-08
February 8, 2015 16:49 World Scientific Review Volume - 9in x 6in ” time - series classification” page 1 Chapter 1 Sparse Representation for Time - Series ...studies the problem of time - series classification and presents an overview of recent developments in the area of feature extraction and information...problem of target classification, and more generally time - series classification, in two main directions, feature extraction and information fusion. 1
Notes on implementation of sparsely distributed memory
NASA Technical Reports Server (NTRS)
Keeler, J. D.; Denning, P. J.
1986-01-01
The Sparsely Distributed Memory (SDM) developed by Kanerva is an unconventional memory design with very interesting and desirable properties. The memory works in a manner that is closely related to modern theories of human memory. The SDM model is discussed in terms of its implementation in hardware. Two appendices discuss the unconventional approaches of the SDM: Appendix A treats a resistive circuit for fast, parallel address decoding; and Appendix B treats a systolic array for high throughput read and write operations.
Sparseness- and continuity-constrained seismic imaging
NASA Astrophysics Data System (ADS)
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 < 0 dB); (ii) use the sparseness and locality (both in position and angle) of directional basis functions (such as curvelets and contourlets) on the model: the reflectivity; and (iii) exploit the near invariance of these basis functions under the normal operator, i.e., the scattering-followed-by-imaging operator. Signal-to-noise ratio and the continuity along the imaged reflectors are significantly enhanced by 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.
Imaging black holes with sparse modeling
NASA Astrophysics Data System (ADS)
Honma, Mareki; Akiyama, Kazunori; Tazaki, Fumie; Kuramochi, Kazuki; Ikeda, Shiro; Hada, Kazuhiro; Uemura, Makoto
2016-03-01
We introduce a new imaging method for radio interferometry based on sparse- modeling. The direct observables in radio interferometry are visibilities, which are Fourier transformation of an astronomical image on the sky-plane, and incomplete sampling of visibilities in the spatial frequency domain results in an under-determined problem, which has been usually solved with 0 filling to un-sampled grids. In this paper we propose to directly solve this under-determined problem using sparse modeling without 0 filling, which realizes super resolution, i.e., resolution higher than the standard refraction limit. We show simulation results of sparse modeling for the Event Horizon Telescope (EHT) observations of super-massive black holes and demonstrate that our approach has significant merit in observations of black hole shadows expected to be realized in near future. We also present some results with the method applied to real data, and also discuss more advanced techniques for practical observations such as imaging with closure phase as well as treating the effect of interstellar scattering effect.
Automatic target recognition via sparse representations
NASA Astrophysics Data System (ADS)
Estabridis, Katia
2010-04-01
Automatic target recognition (ATR) based on the emerging technology of Compressed Sensing (CS) can considerably improve accuracy, speed and cost associated with these types of systems. An image based ATR algorithm has been built upon this new theory, which can perform target detection and recognition in a low dimensional space. Compressed dictionaries (A) are formed to include rotational information for a scale of interest. The algorithm seeks to identify y(test sample) as a linear combination of the dictionary elements : y=Ax, where A ∈ Rnxm(n<
Aerial Scene Recognition using Efficient Sparse Representation
Cheriyadat, Anil M
2012-01-01
Advanced scene recognition systems for processing large volumes of high-resolution aerial image data are in great demand today. However, automated scene recognition remains a challenging problem. Efficient encoding and representation of spatial and structural patterns in the imagery are key in developing automated scene recognition algorithms. We describe an image representation approach that uses simple and computationally efficient sparse code computation to generate accurate features capable of producing excellent classification performance using linear SVM kernels. Our method exploits unlabeled low-level image feature measurements to learn a set of basis vectors. We project the low-level features onto the basis vectors and use simple soft threshold activation function to derive the sparse features. The proposed technique generates sparse features at a significantly lower computational cost than other methods~\\cite{Yang10, newsam11}, yet it produces comparable or better classification accuracy. We apply our technique to high-resolution aerial image datasets to quantify the aerial scene classification performance. We demonstrate that the dense feature extraction and representation methods are highly effective for automatic large-facility detection on wide area high-resolution aerial imagery.
Efficient visual tracking via low-complexity sparse representation
NASA Astrophysics Data System (ADS)
Lu, Weizhi; Zhang, Jinglin; Kpalma, Kidiyo; Ronsin, Joseph
2015-12-01
Thanks to its good performance on object recognition, sparse representation has recently been widely studied in the area of visual object tracking. Up to now, little attention has been paid to the complexity of sparse representation, while most works are focused on the performance improvement. By reducing the computation load related to sparse representation hundreds of times, this paper proposes by far the most computationally efficient tracking approach based on sparse representation. The proposal simply consists of two stages of sparse representation, one is for object detection and the other for object validation. Experimentally, it achieves better performance than some state-of-the-art methods in both accuracy and speed.
Noor, Amina; Ahmad, Aitzaz; Serpedin, Erchin
2015-10-27
Network component analysis (NCA) is an important method for inferring transcriptional regulatory networks (TRNs) and recovering transcription factor activities (TFAs) using gene expression data, and the prior information about the connectivity matrix. The algorithms currently available crucially depend on the completeness of this prior information. However, inaccuracies in the measurement process may render incompleteness in the available knowledge about the connectivity matrix. Hence, computationally efficient algorithms are needed to overcome the possible incompleteness in the available data. We present a sparse network component analysis algorithm (sparseNCA), which incorporates the effect of incompleteness in the estimation of TRNs by imposing an additional sparsity constraint using the `1 norm, which results in a greater estimation accuracy. In order to improve the computational efficiency, an iterative re-weighted `2 method is proposed for the NCA problem which not only promotes sparsity but is hundreds of times faster than the `1 norm based solution. The performance of sparseNCA is rigorously compared to that of FastNCA and NINCA using synthetic data as well as real data. It is shown that sparseNCA outperforms the existing state-of-the-art algorithms both in terms of estimation accuracy and consistency with the added advantage of low computational complexity. The performance of sparseNCA compared to its predecessors is particularly pronounced in case of incomplete prior information about the sparsity of the network. Subnetwork analysis is performed on the E.coli data which reiterates the superior consistency of the proposed algorithm.
Yao, Jincao; Yu, Huimin; Hu, Roland
2017-01-01
This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.
Dynamics of self-organized vegetation patterns
NASA Astrophysics Data System (ADS)
Foti, R.; Ramirez, J. A.
2011-12-01
Vegetation patterns are a common and well-defined characteristic of many arid and semi-arid landscapes. In this study we explore some of the physical mechanisms responsible for the establishment of self-organized, non-random vegetation patterns that arise at the hillslope scale in many areas of the world, especially in arid and semi-arid regions. In doing so we use a water and energy balance model and provide a fundamental mechanistic understanding of the dynamics of vegetation pattern formation and development. Within the modeling, reciprocal effects of vegetation on the hillslope energy balance, runoff production and run-on infiltration, root density, surface albedo and soil moisture content are analyzed. In particular, we: 1) present a physically based mechanistic description of the processes leading to vegetation pattern formation; 2) Compare simulated vegetation coverage at the hillslope scale with observations; 3) quantify the relative impact of pattern-inducing dynamics on pattern formation; and 4) describe the relationships between vegetation patterns and the climatic, hydraulic and topographic characteristic of the system. The model is validated by comparing hillslope-scale simulations with available observations for the areas of Niger near Niamey and Somalia near Garoowe, where respectively tiger bushes and banded vegetation patterns are present. The model validation includes comparison of simulated and observed vegetation coverage as well as simulated and measured water fluxes, showing both qualitative and quantitative agreement between simulations and observations. The analysis of the system suggests that the main driver of pattern establishment is climate, in terms of average annual precipitation and incoming solar radiation. In particular, decreasing precipitation or, conversely, increasing incoming radiation are responsible for the system departure from fully vegetated with indistinguishable vegetation structures to sparsely vegetated with (self
Adaptive feature extraction using sparse coding for machinery fault diagnosis
NASA Astrophysics Data System (ADS)
Liu, Haining; Liu, Chengliang; Huang, Yixiang
2011-02-01
In the signal processing domain, there has been growing interest in sparse coding with a learned dictionary instead of a predefined one, which is advocated as an effective mathematical description for the underlying principle of mammalian sensory systems in processing information. In this paper, sparse coding is introduced as a feature extraction technique for machinery fault diagnosis and an adaptive feature extraction scheme is proposed based on it. The two core problems of sparse coding, i.e., dictionary learning and coefficients solving, are discussed in detail. A natural extension of sparse coding, shift-invariant sparse coding, is also introduced. Then, the vibration signals of rolling element bearings are taken as the target signals to verify the proposed scheme, and shift-invariant sparse coding is used for vibration analysis. With the purpose of diagnosing the different fault conditions of bearings, features are extracted following the proposed scheme: basis functions are separately learned from each class of vibration signals trying to capture the defective impulses; a redundant dictionary is built by merging all the learned basis functions; based on the redundant dictionary, the diagnostic information is made explicit in the solved sparse representations of vibration signals; sparse features are formulated in terms of activations of atoms. The multiclass linear discriminant analysis (LDA) classifier is used to test the discriminability of the extracted sparse features and the adaptability of the learned atoms. The experiments show that sparse coding is an effective feature extraction technique for machinery fault diagnosis.
Stochastic convex sparse principal component analysis.
Baytas, Inci M; Lin, Kaixiang; Wang, Fei; Jain, Anil K; Zhou, Jiayu
2016-12-01
Principal component analysis (PCA) is a dimensionality reduction and data analysis tool commonly used in many areas. The main idea of PCA is to represent high-dimensional data with a few representative components that capture most of the variance present in the data. However, there is an obvious disadvantage of traditional PCA when it is applied to analyze data where interpretability is important. In applications, where the features have some physical meanings, we lose the ability to interpret the principal components extracted by conventional PCA because each principal component is a linear combination of all the original features. For this reason, sparse PCA has been proposed to improve the interpretability of traditional PCA by introducing sparsity to the loading vectors of principal components. The sparse PCA can be formulated as an ℓ1 regularized optimization problem, which can be solved by proximal gradient methods. However, these methods do not scale well because computation of the exact gradient is generally required at each iteration. Stochastic gradient framework addresses this challenge by computing an expected gradient at each iteration. Nevertheless, stochastic approaches typically have low convergence rates due to the high variance. In this paper, we propose a convex sparse principal component analysis (Cvx-SPCA), which leverages a proximal variance reduced stochastic scheme to achieve a geometric convergence rate. We further show that the convergence analysis can be significantly simplified by using a weak condition which allows a broader class of objectives to be applied. The efficiency and effectiveness of the proposed method are demonstrated on a large-scale electronic medical record cohort.
Learn Sparse Dictionaries for Edit Propagation.
Xiaowu Chen; Jianwei Li; Dongqing Zou; Qinping Zhao
2016-04-01
With the increasing availability of high-resolution images, videos, and 3D models, the demand for scalable large data processing techniques increases. We introduce a method of sparse dictionary learning for edit propagation of large input data. Previous approaches for edit propagation typically employ a global optimization over the whole set of pixels (or vertexes), incurring a prohibitively high memory and time-consumption for large input data. Rather than propagating an edit pixel by pixel, we follow the principle of sparse representation to obtain a representative and compact dictionary and perform edit propagation on the dictionary instead. The sparse dictionary provides an intrinsic basis for input data, and the coding coefficients capture the linear relationship between all pixels and the dictionary atoms. The learned dictionary is then optimized by a novel scheme, which maximizes the Kullback-Leibler divergence between each atom pair to remove redundant atoms. To enable local edit propagation for images or videos with similar appearance, a dictionary learning strategy is proposed by considering range constraint to better account for the global distribution of pixels in their feature space. We show several applications of the sparsity-based edit propagation, including video recoloring, theme editing, and seamless cloning, operating on both color and texture features. Our approach can also be applied to computer graphics tasks, such as 3D surface deformation. We demonstrate that with an atom-to-pixel ratio in the order of 0.01% signifying a significant reduction on memory consumption, our method still maintains a high degree of visual fidelity.
Space-Time Approximation with Sparse Grids
Griebel, M; Oeltz, D; Vassilevski, P S
2005-04-14
In this article we introduce approximation spaces for parabolic problems which are based on the tensor product construction of a multiscale basis in space and a multiscale basis in time. Proper truncation then leads to so-called space-time sparse grid spaces. For a uniform discretization of the spatial space of dimension d with O(N{sup d}) degrees of freedom, these spaces involve for d > 1 also only O(N{sup d}) degrees of freedom for the discretization of the whole space-time problem. But they provide the same approximation rate as classical space-time Finite Element spaces which need O(N{sup d+1}) degrees of freedoms. This makes these approximation spaces well suited for conventional parabolic and for time-dependent optimization problems. We analyze the approximation properties and the dimension of these sparse grid space-time spaces for general stable multiscale bases. We then restrict ourselves to an interpolatory multiscale basis, i.e. a hierarchical basis. Here, to be able to handle also complicated spatial domains {Omega}, we construct the hierarchical basis from a given spatial Finite Element basis as follows: First we determine coarse grid points recursively over the levels by the coarsening step of the algebraic multigrid method. Then, we derive interpolatory prolongation operators between the respective coarse and fine grid points by a least squares approach. This way we obtain an algebraic hierarchical basis for the spatial domain which we then use in our space-time sparse grid approach. We give numerical results on the convergence rate of the interpolation error of these spaces for various space-time problems with two spatial dimensions. Also implementational issues, data structures and questions of adaptivity are addressed to some extent.
All scale-free networks are sparse.
Del Genio, Charo I; Gross, Thilo; Bassler, Kevin E
2011-10-21
We study the realizability of scale-free networks with a given degree sequence, showing that the fraction of realizable sequences undergoes two first-order transitions at the values 0 and 2 of the power-law exponent. We substantiate this finding by analytical reasoning and by a numerical method, proposed here, based on extreme value arguments, which can be applied to any given degree distribution. Our results reveal a fundamental reason why large scale-free networks without constraints on minimum and maximum degree must be sparse.
Effective dimension reduction for sparse functional data.
Yao, F; Lei, E; Wu, Y
2015-06-01
We propose a method of effective dimension reduction for functional data, emphasizing the sparse design where one observes only a few noisy and irregular measurements for some or all of the subjects. The proposed method borrows strength across the entire sample and provides a way to characterize the effective dimension reduction space, via functional cumulative slicing. Our theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures of the predictor process and the effective dimension reduction space. A simulation study and an application illustrate the superior finite-sample performance of the method.
Guided wavefield reconstruction from sparse measurements
NASA Astrophysics Data System (ADS)
Mesnil, Olivier; Ruzzene, Massimo
2016-02-01
Guided wave measurements are at the basis of several Non-Destructive Evaluation (NDE) techniques. Although sparse measurements of guided wave obtained using piezoelectric sensors can efficiently detect and locate defects, extensive informa-tion on the shape and subsurface location of defects can be extracted from full-field measurements acquired by Laser Doppler Vibrometers (LDV). Wavefield acquisition from LDVs is generally a slow operation due to the fact that the wave propagation to record must be repeated for each point measurement and the initial conditions must be reached between each measurement. In this research, a Sparse Wavefield Reconstruction (SWR) process using Compressed Sensing is developed. The goal of this technique is to reduce the number of point measurements needed to apply NDE techniques by at least one order of magnitude by extrapolating the knowledge of a few randomly chosen measured pixels over an over-sampled grid. To achieve this, the Lamb wave propagation equation is used to formulate a basis of shape functions in which the wavefield has a sparse representation, in order to comply with the Compressed Sensing requirements and use l1-minimization solvers. The main assumption of this reconstruction process is that every material point of the studied area is a potential source. The Compressed Sensing matrix is defined as being the contribution that would have been received at a measurement location from each possible source, using the dispersion relations of the specimen computed using a Semi-Analytical Finite Element technique. The measurements are then processed through an l1-minimizer to find a minimum corresponding to the set of active sources and their corresponding excitation functions. This minimum represents the best combination of the parameters of the model matching the sparse measurements. Wavefields are then reconstructed using the propagation equation. The set of active sources found by minimization contains all the wave
Effective dimension reduction for sparse functional data
YAO, F.; LEI, E.; WU, Y.
2015-01-01
Summary We propose a method of effective dimension reduction for functional data, emphasizing the sparse design where one observes only a few noisy and irregular measurements for some or all of the subjects. The proposed method borrows strength across the entire sample and provides a way to characterize the effective dimension reduction space, via functional cumulative slicing. Our theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures of the predictor process and the effective dimension reduction space. A simulation study and an application illustrate the superior finite-sample performance of the method. PMID:26566293
Parallel preconditioning techniques for sparse CG solvers
Basermann, A.; Reichel, B.; Schelthoff, C.
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.
A view of Kanerva's sparse distributed memory
NASA Technical Reports Server (NTRS)
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.
Partitioning sparse rectangular matrices for parallel processing
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.
Distributed memory compiler design for sparse problems
NASA Technical Reports Server (NTRS)
Wu, Janet; Saltz, Joel; Berryman, Harry; Hiranandani, Seema
1991-01-01
A compiler and runtime support mechanism is described and demonstrated. The methods presented are capable of solving a wide range of sparse and unstructured problems in scientific computing. The compiler takes as input a FORTRAN 77 program enhanced with specifications for distributing data, and the compiler outputs a message passing program that runs on a distributed memory computer. The runtime support for this compiler is a library of primitives designed to efficiently support irregular patterns of distributed array accesses and irregular distributed array partitions. A variety of Intel iPSC/860 performance results obtained through the use of this compiler are presented.
Sparse dynamics for partial differential equations
Schaeffer, Hayden; Caflisch, Russel; Hauck, Cory D.; Osher, Stanley
2013-01-01
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. PMID:23533273
Learning sparse discriminative representations for land cover classification in the Arctic
NASA Astrophysics Data System (ADS)
Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Gangodagamage, Chandana
2012-10-01
Neuroscience-inspired machine vision algorithms are of current interest in the areas of detection and monitoring of climate change impacts, and general Land Use/Land Cover classification using satellite image data. We describe an approach for automatic classification of land cover in multispectral satellite imagery of the Arctic using sparse representations over learned dictionaries. We demonstrate our method using DigitalGlobe Worldview-2 8-band visible/near infrared high spatial resolution imagery of the MacKenzie River basin. We use an on-line batch Hebbian learning rule to build spectral-textural dictionaries that are adapted to this multispectral data. We learn our dictionaries from millions of overlapping image patches and then use a pursuit search to generate sparse classification features. We explore unsupervised clustering in the sparse representation space to produce land-cover category labels. This approach combines spectral and spatial textural characteristics to detect geologic, vegetative, and hydrologic features. We compare our technique to standard remote sensing algorithms. Our results suggest that neuroscience-based models are a promising approach to practical pattern recognition problems in remote sensing, even for datasets using spectral bands not found in natural visual systems.
Ward, Andy L.
2007-11-26
Fluor Hanford (FH) is designing and assessing the performance of engineered barriers for final closure of 200-UW-1 waste sites. Engineered barriers must minimize the intrusion and water, plants and animals into the underlying waste to provide protection for human health and the environment. The Pacific Northwest National Laboratory (PNNL) developed Subsurface Transport Over Multiple Phases (STOMP) simulator is being used to optimize the performance of candidate barriers. Simulating barrier performance involves computation of mass and energy transfer within a soil-atmosphere-vegetation continuum and requires a variety of input parameters, some of which are more readily available than others. Required input includes parameter values for the geotechnical, physical, hydraulic, and thermal properties of the materials comprising the barrier and the structural fill on which it will be constructed as well as parameters to allow simulation of plant effects. This report provides a data package of the required parameters as well as the technical basis, rationale and methodology used to obtain the parameter values.
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
Vuduc, R; Demmel, J W; Yelick, K A
2005-07-19
The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned computational kernels on sparse matrices, for use by solver libraries and applications. These kernels include sparse matrix-vector multiply and sparse triangular solve, among others. The primary aim of this interface is to hide the complex decision-making process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine, while also exposing the steps and potentially non-trivial costs of tuning at run-time. This paper provides an overview of OSKI, which is based on our research on automatically tuned sparse kernels for modern cache-based superscalar machines.
Neonatal atlas construction using sparse representation.
Shi, Feng; Wang, Li; Wu, Guorong; Li, Gang; Gilmore, John H; Lin, Weili; Shen, Dinggang
2014-09-01
Atlas construction generally includes first an image registration step to normalize all images into a common space and then an atlas building step to fuse the information from all the aligned images. Although numerous atlas construction studies have been performed to improve the accuracy of the image registration step, unweighted or simply weighted average is often used in the atlas building step. In this article, we propose a novel patch-based sparse representation method for atlas construction after all images have been registered into the common space. By taking advantage of local sparse representation, more anatomical details can be recovered in the built atlas. To make the anatomical structures spatially smooth in the atlas, the anatomical feature constraints on group structure of representations and also the overlapping of neighboring patches are imposed to ensure the anatomical consistency between neighboring patches. The proposed method has been applied to 73 neonatal MR images with poor spatial resolution and low tissue contrast, for constructing a neonatal brain atlas with sharp anatomical details. Experimental results demonstrate that the proposed method can significantly enhance the quality of the constructed atlas by discovering more anatomical details especially in the highly convoluted cortical regions. The resulting atlas demonstrates superior performance of our atlas when applied to spatially normalizing three different neonatal datasets, compared with other start-of-the-art neonatal brain atlases.
Protein family classification using sparse markov transducers.
Eskin, Eleazar; Noble, William Stafford; Singer, Yoram
2003-01-01
We present a method for classifying proteins into families based on short subsequences of amino acids using a new probabilistic model called sparse Markov transducers (SMT). We classify a protein by estimating probability distributions over subsequences of amino acids from the protein. Sparse Markov transducers, similar to probabilistic suffix trees, estimate a probability distribution conditioned on an input sequence. SMTs generalize probabilistic suffix trees by allowing for wild-cards in the conditioning sequences. Since substitutions of amino acids are common in protein families, incorporating wild-cards into the model significantly improves classification performance. We present two models for building protein family classifiers using SMTs. As protein databases become larger, data driven learning algorithms for probabilistic models such as SMTs will require vast amounts of memory. We therefore describe and use efficient data structures to improve the memory usage of SMTs. We evaluate SMTs by building protein family classifiers using the Pfam and SCOP databases and compare our results to previously published results and state-of-the-art protein homology detection methods. SMTs outperform previous probabilistic suffix tree methods and under certain conditions perform comparably to state-of-the-art protein homology methods.
Sparse spectrum model for a turbulent phase.
Charnotskii, Mikhail
2013-03-01
Monte Carlo (MC) simulation of phase front perturbations by atmospheric turbulence finds numerous applications for design and modeling of the adaptive optics systems, laser beam propagation simulations, and evaluating the performance of the various optical systems operating in the open air environment. Accurate generation of two-dimensional random fields of turbulent phase is complicated by the enormous diversity of scales that can reach five orders of magnitude in each coordinate. In addition there is a need for generation of the long "ribbons" of turbulent phase that are used to represent the time evolution of the wave front. This makes it unfeasible to use the standard discrete Fourier transform-based technique as a basis for the MC simulation algorithm. We propose a new model for turbulent phase: the sparse spectrum (SS) random field. The principal assumption of the SS model is that each realization of the random field has a discrete random spectral support. Statistics of the random amplitudes and wave vectors of the SS model are arranged to provide the required spectral and correlation properties of the random field. The SS-based MC model offers substantial reduction of computer costs for simulation of the wide-band random fields and processes, and is capable of generating long aperiodic phase "ribbons." We report the results of model trials that determine the number of sparse components, and the range of wavenumbers that is necessary to accurately reproduce the random field with a power-law spectrum.
Image reconstruction from photon sparse data
Mertens, Lena; Sonnleitner, Matthias; Leach, Jonathan; Agnew, Megan; Padgett, Miles J.
2017-01-01
We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected. PMID:28169363
Image reconstruction from photon sparse data
NASA Astrophysics Data System (ADS)
Mertens, Lena; Sonnleitner, Matthias; Leach, Jonathan; Agnew, Megan; Padgett, Miles J.
2017-02-01
We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected.
Sparse estimation of a covariance matrix.
Bien, Jacob; Tibshirani, Robert J
2011-12-01
We suggest a method for estimating a covariance matrix on the basis of a sample of vectors drawn from a multivariate normal distribution. In particular, we penalize the likelihood with a lasso penalty on the entries of the covariance matrix. This penalty plays two important roles: it reduces the effective number of parameters, which is important even when the dimension of the vectors is smaller than the sample size since the number of parameters grows quadratically in the number of variables, and it produces an estimate which is sparse. In contrast to sparse inverse covariance estimation, our method's close relative, the sparsity attained here is in the covariance matrix itself rather than in the inverse matrix. Zeros in the covariance matrix correspond to marginal independencies; thus, our method performs model selection while providing a positive definite estimate of the covariance. The proposed penalized maximum likelihood problem is not convex, so we use a majorize-minimize approach in which we iteratively solve convex approximations to the original nonconvex problem. We discuss tuning parameter selection and demonstrate on a flow-cytometry dataset how our method produces an interpretable graphical display of the relationship between variables. We perform simulations that suggest that simple elementwise thresholding of the empirical covariance matrix is competitive with our method for identifying the sparsity structure. Additionally, we show how our method can be used to solve a previously studied special case in which a desired sparsity pattern is prespecified.
Learning doubly sparse transforms for images.
Ravishankar, Saiprasad; Bresler, Yoram
2013-12-01
The sparsity of images in a transform domain or dictionary has been exploited in many applications in image processing. For example, analytical sparsifying transforms, such as wavelets and discrete cosine transform (DCT), have been extensively used in compression standards. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular especially in applications such as image denoising. Following up on our recent research, where we introduced the idea of learning square sparsifying transforms, we propose here novel problem formulations for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learnt, stored, and implemented efficiently. We show the superior promise of our learnt transforms as compared with analytical sparsifying transforms such as the DCT for image representation. We also show promising performance in image denoising that compares favorably with approaches involving learnt synthesis dictionaries such as the K-SVD algorithm. The proposed approach is also much faster than K-SVD denoising.
Sparse Identification of Nonlinear Dynamics (SINDy)
NASA Astrophysics Data System (ADS)
Brunton, Steven; Proctor, Joshua; Kutz, Nathan
2016-11-01
This work develops a general new framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity techniques and machine learning. The so-called sparse identification of nonlinear dynamics (SINDy) method results in models that are parsimonious, balancing model complexity with descriptive ability while avoiding over fitting. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions; this assumption holds for many physical systems in an appropriate basis. We demonstrate the algorithm on a wide range of problems, from simple canonical systems, including the chaotic Lorenz system, to the canonical fluid vortex shedding behind an circular cylinder at Re=100. We also show that this method generalizes to parameterized systems and systems that are time-varying or have external forcing. With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an increasingly important role in the characterization and control of fluid dynamics.
Inferring sparse networks for noisy transient processes
NASA Astrophysics Data System (ADS)
Tran, Hoang M.; Bukkapatnam, Satish T. S.
2016-02-01
Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the -min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of -min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues.
Fast Sparse Level Sets on Graphics Hardware.
Jalba, Andrei C; van der Laan, Wladimir J; Roerdink, Jos B T M
2013-01-01
The level-set method is one of the most popular techniques for capturing and tracking deformable interfaces. Although level sets have demonstrated great potential in visualization and computer graphics applications, such as surface editing and physically based modeling, their use for interactive simulations has been limited due to the high computational demands involved. In this paper, we address this computational challenge by leveraging the increased computing power of graphics processors, to achieve fast simulations based on level sets. Our efficient, sparse GPU level-set method is substantially faster than other state-of-the-art, parallel approaches on both CPU and GPU hardware. We further investigate its performance through a method for surface reconstruction, based on GPU level sets. Our novel multiresolution method for surface reconstruction from unorganized point clouds compares favorably with recent, existing techniques and other parallel implementations. Finally, we point out that both level-set computations and rendering of level-set surfaces can be performed at interactive rates, even on large volumetric grids. Therefore, many applications based on level sets can benefit from our sparse level-set method.
Sparse and accurate high resolution SAR imaging
NASA Astrophysics Data System (ADS)
Vu, Duc; Zhao, Kexin; Rowe, William; Li, Jian
2012-05-01
We investigate the usage of an adaptive method, the Iterative Adaptive Approach (IAA), in combination with a maximum a posteriori (MAP) estimate to reconstruct high resolution SAR images that are both sparse and accurate. IAA is a nonparametric weighted least squares algorithm that is robust and user parameter-free. IAA has been shown to reconstruct SAR images with excellent side lobes suppression and high resolution enhancement. We first reconstruct the SAR images using IAA, and then we enforce sparsity by using MAP with a sparsity inducing prior. By coupling these two methods, we can produce a sparse and accurate high resolution image that are conducive for feature extractions and target classification applications. In addition, we show how IAA can be made computationally efficient without sacrificing accuracies, a desirable property for SAR applications where the size of the problems is quite large. We demonstrate the success of our approach using the Air Force Research Lab's "Gotcha Volumetric SAR Data Set Version 1.0" challenge dataset. Via the widely used FFT, individual vehicles contained in the scene are barely recognizable due to the poor resolution and high side lobe nature of FFT. However with our approach clear edges, boundaries, and textures of the vehicles are obtained.
Image reconstruction from photon sparse data.
Mertens, Lena; Sonnleitner, Matthias; Leach, Jonathan; Agnew, Megan; Padgett, Miles J
2017-02-07
We report an algorithm for reconstructing images when the average number of photons recorded per pixel is of order unity, i.e. photon-sparse data. The image optimisation algorithm minimises a cost function incorporating both a Poissonian log-likelihood term based on the deviation of the reconstructed image from the measured data and a regularization-term based upon the sum of the moduli of the second spatial derivatives of the reconstructed image pixel intensities. The balance between these two terms is set by a bootstrapping technique where the target value of the log-likelihood term is deduced from a smoothed version of the original data. When compared to the original data, the processed images exhibit lower residuals with respect to the true object. We use photon-sparse data from two different experimental systems, one system based on a single-photon, avalanche photo-diode array and the other system on a time-gated, intensified camera. However, this same processing technique could most likely be applied to any low photon-number image irrespective of how the data is collected.
Feature Selection and Pedestrian Detection Based on Sparse Representation
Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei
2015-01-01
Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony. PMID:26295480
Improving sparse representation algorithms for maritime video processing
NASA Astrophysics Data System (ADS)
Smith, L. N.; Nichols, J. M.; Waterman, J. R.; Olson, C. C.; Judd, K. P.
2012-06-01
We present several improvements to published algorithms for sparse image modeling with the goal of improving processing of imagery of small watercraft in littoral environments. The first improvement is to the K-SVD algorithm for training over-complete dictionaries, which are used in sparse representations. It is shown that the training converges significantly faster by incorporating multiple dictionary (i.e., codebook) update stages in each training iteration. The paper also provides several useful and practical lessons learned from our experience with sparse representations. Results of three applications of sparse representation are presented and compared to the state-of-the-art methods; image compression, image denoising, and super-resolution.
Local structure preserving sparse coding for infrared target recognition
Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa
2017-01-01
Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824
Multimodal visual dictionary learning via heterogeneous latent semantic sparse coding
NASA Astrophysics Data System (ADS)
Li, Chenxiao; Ding, Guiguang; Zhou, Jile; Guo, Yuchen; Liu, Qiang
2014-11-01
Visual dictionary learning as a crucial task of image representation has gained increasing attention. Specifically, sparse coding is widely used due to its intrinsic advantage. In this paper, we propose a novel heterogeneous latent semantic sparse coding model. The central idea is to bridge heterogeneous modalities by capturing their common sparse latent semantic structure so that the learned visual dictionary is able to describe both the visual and textual properties of training data. Experiments on both image categorization and retrieval tasks demonstrate that our model shows superior performance over several recent methods such as K-means and Sparse Coding.
Dose-shaping using targeted sparse optimization
Sayre, George A.; Ruan, Dan
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
ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES.
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.
Sparse distributed memory and related models
NASA Technical Reports Server (NTRS)
Kanerva, Pentti
1992-01-01
Described here is sparse distributed memory (SDM) as a neural-net associative memory. It is characterized by two weight matrices and by a large internal dimension - the number of hidden units is much larger than the number of input or output units. The first matrix, A, is fixed and possibly random, and the second matrix, C, is modifiable. The SDM is compared and contrasted to (1) computer memory, (2) correlation-matrix memory, (3) feet-forward artificial neural network, (4) cortex of the cerebellum, (5) Marr and Albus models of the cerebellum, and (6) Albus' cerebellar model arithmetic computer (CMAC). Several variations of the basic SDM design are discussed: the selected-coordinate and hyperplane designs of Jaeckel, the pseudorandom associative neural memory of Hassoun, and SDM with real-valued input variables by Prager and Fallside. SDM research conducted mainly at the Research Institute for Advanced Computer Science (RIACS) in 1986-1991 is highlighted.
Surface reconstruction from sparse fringe contours
Cong, G.; Parvin, B.
1998-08-10
A new approach for reconstruction of 3D surfaces from 2D cross-sectional contours is presented. By using the so-called ''Equal Importance Criterion,'' we reconstruct the surface based on the assumption that every point in the region contributes equally to the surface reconstruction process. In this context, the problem is formulated in terms of a partial differential equation (PDE), and we show that the solution for dense contours can be efficiently derived from distance transform. In the case of sparse contours, we add a regularization term to insure smoothness in surface recovery. The proposed technique allows for surface recovery at any desired resolution. The main advantage of the proposed method is that inherent problems due to correspondence, tiling, and branching are avoided. Furthermore, the computed high resolution surface is better represented for subsequent geometric analysis. We present results on both synthetic and real data.
Preserving sparseness in multivariate polynominal factorization
NASA Technical Reports Server (NTRS)
Wang, P. S.
1977-01-01
Attempts were made to factor these ten polynomials on MACSYMA. However it did not get very far with any of the larger polynomials. At that time, MACSYMA used an algorithm created by Wang and Rothschild. This factoring algorithm was also implemented for the symbolic manipulation system, SCRATCHPAD of IBM. A closer look at this old factoring algorithm revealed three problem areas, each of which contribute to losing sparseness and intermediate expression growth. This study led to effective ways of avoiding these problems and actually to a new factoring algorithm. The three problems are known as the extraneous factor problem, the leading coefficient problem, and the bad zero problem. These problems are examined separately. Their causes and effects are set forth in detail; the ways to avoid or lessen these problems are described.
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less
Predicting structure in nonsymmetric sparse matrix factorizations
Gilbert, J.R. ); Ng, E.G. )
1992-10-01
Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.
ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES
Fan, Jianqing; Rigollet, Philippe; Wang, Weichen
2016-01-01
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 ℓr 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. PMID:26806986
Eigensolver for a Sparse, Large Hermitian Matrix
NASA Technical Reports Server (NTRS)
Tisdale, E. Robert; Oyafuso, Fabiano; Klimeck, Gerhard; Brown, R. Chris
2003-01-01
A parallel-processing computer program finds a few eigenvalues in a sparse Hermitian matrix that contains as many as 100 million diagonal elements. This program finds the eigenvalues faster, using less memory, than do other, comparable eigensolver programs. This program implements a Lanczos algorithm in the American National Standards Institute/ International Organization for Standardization (ANSI/ISO) C computing language, using the Message Passing Interface (MPI) standard to complement an eigensolver in PARPACK. [PARPACK (Parallel Arnoldi Package) is an extension, to parallel-processing computer architectures, of ARPACK (Arnoldi Package), which is a collection of Fortran 77 subroutines that solve large-scale eigenvalue problems.] The eigensolver runs on Beowulf clusters of computers at the Jet Propulsion Laboratory (JPL).
Classification of sparse high-dimensional vectors.
Ingster, Yuri I; Pouet, Christophe; Tsybakov, Alexandre B
2009-11-13
We study the problem of classification of d-dimensional vectors into two classes (one of which is 'pure noise') based on a training sample of size m. The main specific feature is that the dimension d can be very large. We suppose that the difference between the distribution of the population and that of the noise is only in a shift, which is a sparse vector. For Gaussian noise, fixed sample size m, and dimension d that tends to infinity, we obtain the sharp classification boundary, i.e. the necessary and sufficient conditions for the possibility of successful classification. We propose classifiers attaining this boundary. We also give extensions of the result to the case where the sample size m depends on d and satisfies the condition (log m)/log d --> gamma, 0
A scalable sparse eigensolver for petascale applications
NASA Astrophysics Data System (ADS)
Keceli, Murat; Zhang, Hong; Zapol, Peter; Dixon, David; Wagner, Albert
2015-03-01
Exploiting locality of chemical interactions and therefore sparsity is necessary to push the limits of quantum simulations beyond petascale. However, sparse numerical algorithms are known to have poor strong scaling. Here, we show that shift-and-invert parallel spectral transformations (SIPs) method can scale up to two-hundred thousand cores for density functional based tight-binding (DFTB), or semi-empirical molecular orbital (SEMO) applications. We demonstrated the robustness and scalability of the SIPs method on various kinds of systems including metallic carbon nanotubes, diamond crystals and water clusters. We analyzed how sparsity patterns and eigenvalue spectrums of these different type of applications affect the computational performance of the SIPs. The SIPs method enables us to perform simulations with more than five hundred thousands of basis functions utilizing more than hundreds of thousands of cores. SIPs has a better scaling for memory and computational time in contrast to dense eigensolvers, and it does not require fast interconnects.
Blind source separation by sparse decomposition
NASA Astrophysics Data System (ADS)
Zibulevsky, Michael; Pearlmutter, Barak A.
2000-04-01
The blind source separation problem is to extract the underlying source signals from a set of their linear mixtures, where the mixing matrix is unknown. This situation is common, eg in acoustics, radio, and medical signal processing. We exploit the property of the sources to have a sparse representation in a corresponding signal dictionary. Such a dictionary may consist of wavelets, wavelet packets, etc., or be obtained by learning from a given family of signals. Starting from the maximum a posteriori framework, which is applicable to the case of more sources than mixtures, we derive a few other categories of objective functions, which provide faster and more robust computations, when there are an equal number of sources and mixtures. Our experiments with artificial signals and with musical sounds demonstrate significantly better separation than other known techniques.
Integer sparse distributed memory: analysis and results.
Snaider, Javier; Franklin, Stan; Strain, Steve; George, E Olusegun
2013-10-01
Sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean vectors. Here we present an extension of the original SDM, the Integer SDM that uses modular arithmetic integer vectors rather than binary vectors. This extension preserves many of the desirable properties of the original SDM: auto-associativity, content addressability, distributed storage, and robustness over noisy inputs. In addition, it improves the representation capabilities of the memory and is more robust over normalization. It can also be extended to support forgetting and reliable sequence storage. We performed several simulations that test the noise robustness property and capacity of the memory. Theoretical analyses of the memory's fidelity and capacity are also presented.
Miniature Laboratory for Detecting Sparse Biomolecules
NASA Technical Reports Server (NTRS)
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.
Blind spectral unmixing based on sparse nonnegative matrix factorization.
Yang, Zuyuan; Zhou, Guoxu; Xie, Shengli; Ding, Shuxue; Yang, Jun-Mei; Zhang, Jun
2011-04-01
Nonnegative matrix factorization (NMF) is a widely used method for blind spectral unmixing (SU), which aims at obtaining the endmembers and corresponding fractional abundances, knowing only the collected mixing spectral data. It is noted that the abundance may be sparse (i.e., the endmembers may be with sparse distributions) and sparse NMF tends to lead to a unique result, so it is intuitive and meaningful to constrain NMF with sparseness for solving SU. However, due to the abundance sum-to-one constraint in SU, the traditional sparseness measured by L0/L1-norm is not an effective constraint any more. A novel measure (termed as S-measure) of sparseness using higher order norms of the signal vector is proposed in this paper. It features the physical significance. By using the S-measure constraint (SMC), a gradient-based sparse NMF algorithm (termed as NMF-SMC) is proposed for solving the SU problem, where the learning rate is adaptively selected, and the endmembers and abundances are simultaneously estimated. In the proposed NMF-SMC, there is no pure index assumption and no need to know the exact sparseness degree of the abundance in prior. Yet, it does not require the preprocessing of dimension reduction in which some useful information may be lost. Experiments based on synthetic mixtures and real-world images collected by AVIRIS and HYDICE sensors are performed to evaluate the validity of the proposed method.
Sparse representations for online-learning-based hyperspectral image compression.
Ülkü, İrem; Töreyin, Behçet Uğur
2015-10-10
Sparse models provide data representations in the fewest possible number of nonzero elements. This inherent characteristic enables sparse models to be utilized for data compression purposes. Hyperspectral data is large in size. In this paper, a framework for sparsity-based hyperspectral image compression methods using online learning is proposed. There are various sparse optimization models. A comparative analysis of sparse representations in terms of their hyperspectral image compression performance is presented. For this purpose, online-learning-based hyperspectral image compression methods are proposed using four different sparse representations. Results indicate that, independent of the sparsity models, online-learning-based hyperspectral data compression schemes yield the best compression performances for data rates of 0.1 and 0.3 bits per sample, compared to other state-of-the-art hyperspectral data compression techniques, in terms of image quality measured as average peak signal-to-noise ratio.
Classification of vegetation types in military region
NASA Astrophysics Data System (ADS)
Gonçalves, Miguel; Silva, Jose Silvestre; Bioucas-Dias, Jose
2015-10-01
In decision-making process regarding planning and execution of military operations, the terrain is a determining factor. Aerial photographs are a source of vital information for the success of an operation in hostile region, namely when the cartographic information behind enemy lines is scarce or non-existent. The objective of present work is the development of a tool capable of processing aerial photos. The methodology implemented starts with feature extraction, followed by the application of an automatic selector of features. The next step, using the k-fold cross validation technique, estimates the input parameters for the following classifiers: Sparse Multinomial Logist Regression (SMLR), K Nearest Neighbor (KNN), Linear Classifier using Principal Component Expansion on the Joint Data (PCLDC) and Multi-Class Support Vector Machine (MSVM). These classifiers were used in two different studies with distinct objectives: discrimination of vegetation's density and identification of vegetation's main components. It was found that the best classifier on the first approach is the Sparse Logistic Multinomial Regression (SMLR). On the second approach, the implemented methodology applied to high resolution images showed that the better performance was achieved by KNN classifier and PCLDC. Comparing the two approaches there is a multiscale issue, in which for different resolutions, the best solution to the problem requires different classifiers and the extraction of different features.
ERIC Educational Resources Information Center
Sadler, Peter G.
The Institute for the Study of Sparsely Populated Areas is a multidisciplinary research unit which acts to coordinate, further, and initiate studies of the economic and social conditions of sparsely populated areas. Short summaries of the eight studies completed in the session of 1977-78 indicate work in such areas as the study of political life…
Prostate segmentation by sparse representation based classification
Gao, Yaozong; Liao, Shu; Shen, Dinggang
2012-01-01
Purpose: The segmentation of prostate in CT images is of essential importance to external beam radiotherapy, which is one of the major treatments for prostate cancer nowadays. During the radiotherapy, the prostate is radiated by high-energy x rays from different directions. In order to maximize the dose to the cancer and minimize the dose to the surrounding healthy tissues (e.g., bladder and rectum), the prostate in the new treatment image needs to be accurately localized. Therefore, the effectiveness and efficiency of external beam radiotherapy highly depend on the accurate localization of the prostate. However, due to the low contrast of the prostate with its surrounding tissues (e.g., bladder), the unpredicted prostate motion, and the large appearance variations across different treatment days, it is challenging to segment the prostate in CT images. In this paper, the authors present a novel classification based segmentation method to address these problems. Methods: To segment the prostate, the proposed method first uses sparse representation based classification (SRC) to enhance the prostate in CT images by pixel-wise classification, in order to overcome the limitation of poor contrast of the prostate images. Then, based on the classification results, previous segmented prostates of the same patient are used as patient-specific atlases to align onto the current treatment image and the majority voting strategy is finally adopted to segment the prostate. In order to address the limitations of the traditional SRC in pixel-wise classification, especially for the purpose of segmentation, the authors extend SRC from the following four aspects: (1) A discriminant subdictionary learning method is proposed to learn a discriminant and compact representation of training samples for each class so that the discriminant power of SRC can be increased and also SRC can be applied to the large-scale pixel-wise classification. (2) The L1 regularized sparse coding is replaced by
Local Sparse Structure Denoising for Low-Light-Level Image.
Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa
2015-12-01
Sparse and redundant representations perform well in image denoising. However, sparsity-based methods fail to denoise low-light-level (LLL) images because of heavy and complex noise. They consider sparsity on image patches independently and tend to lose the texture structures. To suppress noises and maintain textures simultaneously, it is necessary to embed noise invariant features into the sparse decomposition process. We, therefore, used a local structure preserving sparse coding (LSPSc) formulation to explore the local sparse structures (both the sparsity and local structure) in image. It was found that, with the introduction of spatial local structure constraint into the general sparse coding algorithm, LSPSc could improve the robustness of sparse representation for patches in serious noise. We further used a kernel LSPSc (K-LSPSc) formulation, which extends LSPSc into the kernel space to weaken the influence of linear structure constraint in nonlinear data. Based on the robust LSPSc and K-LSPSc algorithms, we constructed a local sparse structure denoising (LSSD) model for LLL images, which was demonstrated to give high performance in the natural LLL images denoising, indicating that both the LSPSc- and K-LSPSc-based LSSD models have the stable property of noise inhibition and texture details preservation.
Transformer fault diagnosis using continuous sparse autoencoder.
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.
Sparse Superpixel Unmixing for Hyperspectral Image Analysis
NASA Technical Reports Server (NTRS)
Castano, Rebecca; Thompson, David R.; Gilmore, Martha
2010-01-01
Software was developed that automatically detects minerals that are present in each pixel of a hyperspectral image. An algorithm based on sparse spectral unmixing with Bayesian Positive Source Separation is used to produce mineral abundance maps from hyperspectral images. A superpixel segmentation strategy enables efficient unmixing in an interactive session. The algorithm computes statistically likely combinations of constituents based on a set of possible constituent minerals whose abundances are uncertain. A library of source spectra from laboratory experiments or previous remote observations is used. A superpixel segmentation strategy improves analysis time by orders of magnitude, permitting incorporation into an interactive user session (see figure). Mineralogical search strategies can be categorized as supervised or unsupervised. Supervised methods use a detection function, developed on previous data by hand or statistical techniques, to identify one or more specific target signals. Purely unsupervised results are not always physically meaningful, and may ignore subtle or localized mineralogy since they aim to minimize reconstruction error over the entire image. This algorithm offers advantages of both methods, providing meaningful physical interpretations and sensitivity to subtle or unexpected minerals.
A sparse Ising model with covariates.
Cheng, Jie; Levina, Elizaveta; Wang, Pei; Zhu, Ji
2014-12-01
There has been a lot of work fitting Ising models to multivariate binary data in order to understand the conditional dependency relationships between the variables. However, additional covariates are frequently recorded together with the binary data, and may influence the dependence relationships. Motivated by such a dataset on genomic instability collected from tumor samples of several types, we propose a sparse covariate dependent Ising model to study both the conditional dependency within the binary data and its relationship with the additional covariates. This results in subject-specific Ising models, where the subject's covariates influence the strength of association between the genes. As in all exploratory data analysis, interpretability of results is important, and we use ℓ1 penalties to induce sparsity in the fitted graphs and in the number of selected covariates. Two algorithms to fit the model are proposed and compared on a set of simulated data, and asymptotic results are established. The results on the tumor dataset and their biological significance are discussed in detail.
Index statistical properties of sparse random graphs
NASA Astrophysics Data System (ADS)
Metz, F. L.; Stariolo, Daniel A.
2015-10-01
Using the replica method, we develop an analytical approach to compute the characteristic function for the probability PN(K ,λ ) that a large N ×N adjacency matrix of sparse random graphs has K eigenvalues below a threshold λ . The method allows to determine, in principle, all moments of PN(K ,λ ) , from which the typical sample-to-sample fluctuations can be fully characterized. For random graph models with localized eigenvectors, we show that the index variance scales linearly with N ≫1 for |λ |>0 , with a model-dependent prefactor that can be exactly calculated. Explicit results are discussed for Erdös-Rényi and regular random graphs, both exhibiting a prefactor with a nonmonotonic behavior as a function of λ . These results contrast with rotationally invariant random matrices, where the index variance scales only as lnN , with an universal prefactor that is independent of λ . Numerical diagonalization results confirm the exactness of our approach and, in addition, strongly support the Gaussian nature of the index fluctuations.
Sparse alignment for robust tensor learning.
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.
Sparse distributed memory: Principles and operation
NASA Technical Reports Server (NTRS)
Flynn, M. J.; Kanerva, P.; Bhadkamkar, N.
1989-01-01
Sparse distributed memory is a generalized random access memory (RAM) for long (1000 bit) binary words. Such words can be written into and read from the memory, and they can also be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original write address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech recognition and scene analysis, in signal detection and verification, and in adaptive control of automated equipment, in general, in dealing with real world information in real time. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. Major design issues were resolved which were faced in building the memories. The design is described of a prototype memory with 256 bit addresses and from 8 to 128 K locations for 256 bit words. A key aspect of the design is extensive use of dynamic RAM and other standard components.
Sparse distributed memory prototype: Principles of operation
NASA Technical Reports Server (NTRS)
Flynn, Michael J.; Kanerva, Pentti; Ahanin, Bahram; Bhadkamkar, Neal; Flaherty, Paul; Hickey, Philip
1988-01-01
Sparse distributed memory is a generalized random access memory (RAM) for long binary words. Such words can be written into and read from the memory, and they can be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original right address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech and scene analysis, in signal detection and verification, and in adaptive control of automated equipment. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. The research is aimed at resolving major design issues that have to be faced in building the memories. The design of a prototype memory with 256-bit addresses and from 8K to 128K locations for 256-bit words is described. A key aspect of the design is extensive use of dynamic RAM and other standard components.
Partitioning sparse matrices with eigenvectors of graphs
NASA Technical Reports Server (NTRS)
Pothen, Alex; Simon, Horst D.; Liou, Kang-Pu
1990-01-01
The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorithms for computing separators. Finally, the time required to compute the Laplacian eigenvector is reported, and the accuracy with which the eigenvector must be computed to obtain good separators is considered. The spectral algorithm has the advantage that it can be implemented on a medium-size multiprocessor in a straightforward manner.
Partially sparse imaging of stationary indoor scenes
NASA Astrophysics Data System (ADS)
Ahmad, Fauzia; Amin, Moeness G.; Dogaru, Traian
2014-12-01
In this paper, we exploit the notion of partial sparsity for scene reconstruction associated with through-the-wall radar imaging of stationary targets under reduced data volume. Partial sparsity implies that the scene being imaged consists of a sparse part and a dense part, with the support of the latter assumed to be known. For the problem at hand, sparsity is represented by a few stationary indoor targets, whereas the high scene density is defined by exterior and interior walls. Prior knowledge of wall positions and extent may be available either through building blueprints or from prior surveillance operations. The contributions of the exterior and interior walls are removed from the data through the use of projection matrices, which are determined from wall- and corner-specific dictionaries. The projected data, with enhanced sparsity, is then processed using l 1 norm reconstruction techniques. Numerical electromagnetic data is used to demonstrate the effectiveness of the proposed approach for imaging stationary indoor scenes using a reduced set of measurements.
Visual Exploration of Sparse Traffic Trajectory Data.
Wang, Zuchao; Ye, Tangzhi; Lu, Min; Yuan, Xiaoru; Qu, Huamin; Yuan, Jacky; Wu, Qianliang
2014-12-01
In this paper, we present a visual analysis system to explore sparse traffic trajectory data recorded by transportation cells. Such data contains the movements of nearly all moving vehicles on the major roads of a city. Therefore it is very suitable for macro-traffic analysis. However, the vehicle movements are recorded only when they pass through the cells. The exact tracks between two consecutive cells are unknown. To deal with such uncertainties, we first design a local animation, showing the vehicle movements only in the vicinity of cells. Besides, we ignore the micro-behaviors of individual vehicles, and focus on the macro-traffic patterns. We apply existing trajectory aggregation techniques to the dataset, studying cell status pattern and inter-cell flow pattern. Beyond that, we propose to study the correlation between these two patterns with dynamic graph visualization techniques. It allows us to check how traffic congestion on one cell is correlated with traffic flows on neighbouring links, and with route selection in its neighbourhood. Case studies show the effectiveness of our system.
Optimal parallel solution of sparse triangular systems
NASA Technical Reports Server (NTRS)
Alvarado, Fernando L.; Schreiber, Robert
1990-01-01
A method for the parallel solution of triangular sets of equations is described that is appropriate when there are many right-handed sides. By preprocessing, the method can reduce the number of parallel steps required to solve Lx = b compared to parallel forward or backsolve. Applications are to iterative solvers with triangular preconditioners, to structural analysis, or to power systems applications, where there may be many right-handed sides (not all available a priori). The inverse of L is represented as a product of sparse triangular factors. The problem is to find a factored representation of this inverse of L with the smallest number of factors (or partitions), subject to the requirement that no new nonzero elements be created in the formation of these inverse factors. A method from an earlier reference is shown to solve this problem. This method is improved upon by constructing a permutation of the rows and columns of L that preserves triangularity and allow for the best possible such partition. A number of practical examples and algorithmic details are presented. The parallelism attainable is illustrated by means of elimination trees and clique trees.
Optimized design and analysis of sparse-sampling FMRI experiments.
Perrachione, Tyler K; Ghosh, Satrajit S
2013-01-01
Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase
Arizona Vegetation Resource Inventory (AVRI) accuracy assessment
Szajgin, John; Pettinger, L.R.; Linden, D.S.; Ohlen, D.O.
1982-01-01
A quantitative accuracy assessment was performed for the vegetation classification map produced as part of the Arizona Vegetation Resource Inventory (AVRI) project. This project was a cooperative effort between the Bureau of Land Management (BLM) and the Earth Resources Observation Systems (EROS) Data Center. The objective of the accuracy assessment was to estimate (with a precision of ?10 percent at the 90 percent confidence level) the comission error in each of the eight level II hierarchical vegetation cover types. A stratified two-phase (double) cluster sample was used. Phase I consisted of 160 photointerpreted plots representing clusters of Landsat pixels, and phase II consisted of ground data collection at 80 of the phase I cluster sites. Ground data were used to refine the phase I error estimates by means of a linear regression model. The classified image was stratified by assigning each 15-pixel cluster to the stratum corresponding to the dominant cover type within each cluster. This method is known as stratified plurality sampling. Overall error was estimated to be 36 percent with a standard error of 2 percent. Estimated error for individual vegetation classes ranged from a low of 10 percent ?6 percent for evergreen woodland to 81 percent ?7 percent for cropland and pasture. Total cost of the accuracy assessment was $106,950 for the one-million-hectare study area. The combination of the stratified plurality sampling (SPS) method of sample allocation with double sampling provided the desired estimates within the required precision levels. The overall accuracy results confirmed that highly accurate digital classification of vegetation is difficult to perform in semiarid environments, due largely to the sparse vegetation cover. Nevertheless, these techniques show promise for providing more accurate information than is presently available for many BLM-administered lands.
Real-time Algorithms for Sparse Neuronal System Identification.
Sheikhattar, Alireza; Babadi, Behtash
2016-08-01
We consider the problem of sparse adaptive neuronal system identification, where the goal is to estimate the sparse time-varying neuronal model parameters in an online fashion from neural spiking observations. We develop two adaptive filters based on greedy estimation techniques and regularized log-likelihood maximization. We apply the proposed algorithms to simulated spiking data as well as experimentally recorded data from the ferret's primary auditory cortex during performance of auditory tasks. Our results reveal significant performance gains achieved by the proposed algorithms in terms of sparse identification and trackability, compared to existing algorithms.
SPARSKIT: A basic tool kit for sparse matrix computations
NASA Technical Reports Server (NTRS)
Saad, Youcef
1990-01-01
Presented here are the main features of a tool package for manipulating and working with sparse matrices. One of the goals of the package is to provide basic tools to facilitate the exchange of software and data between researchers in sparse matrix computations. The starting point is the Harwell/Boeing collection of matrices for which the authors provide a number of tools. Among other things, the package provides programs for converting data structures, printing simple statistics on a matrix, plotting a matrix profile, and performing linear algebra operations with sparse matrices.
Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays
NASA Technical Reports Server (NTRS)
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.
Vegetation mapping of the Mond Protected Area of Bushehr Province (south-west Iran).
Mehrabian, Ahmadreza; Naqinezhad, Alireza; Mahiny, Abdolrassoul Salman; Mostafavi, Hossein; Liaghati, Homan; Kouchekzadeh, Mohsen
2009-03-01
Arid regions of the world occupy up to 35% of the earth's surface, the basis of various definitions of climatic conditions, vegetation types or potential for food production. Due to their high ecological value, monitoring of arid regions is necessary and modern vegetation studies can help in the conservation and management of these areas. The use of remote sensing for mapping of desert vegetation is difficult due to mixing of the spectral reflectance of bright desert soils with the weak spectral response of sparse vegetation. We studied the vegetation types in the semiarid to arid region of Mond Protected Area, south-west Iran, based on unsupervised classification of the Spot XS bands and then produced updated maps. Sixteen map units covering 12 vegetation types were recognized in the area based on both field works and satellite mapping. Halocnemum strobilaceum and Suaeda fruticosa vegetation types were the dominant types and Ephedra foliata, Salicornia europaea-Suaeda heterophylla vegetation types were the smallest. Vegetation coverage decreased sharply with the increase in salinity towards the coastal areas of the Persian Gulf. The highest vegetation coverage belonged to the riparian vegetation along the Mond River, which represents the northern boundary of the protected area. The location of vegetation types was studied on the separate soil and habitat diversity maps of the study area, which helped in final refinements of the vegetation map produced.
Method and apparatus for distinguishing actual sparse events from sparse event false alarms
Spalding, Richard E.; Grotbeck, Carter L.
2000-01-01
Remote sensing method and apparatus wherein sparse optical events are distinguished from false events. "Ghost" images of actual optical phenomena are generated using an optical beam splitter and optics configured to direct split beams to a single sensor or segmented sensor. True optical signals are distinguished from false signals or noise based on whether the ghost image is presence or absent. The invention obviates the need for dual sensor systems to effect a false target detection capability, thus significantly reducing system complexity and cost.
Effects of vegetation cover on landscape denudation rates
NASA Astrophysics Data System (ADS)
Torres Acosta, Veronica; Schildgen, Taylor; Clarke, Brian; Scherler, Dirk; Bookhagen, Bodo; Wittmann, Hella; von Blankenburg, Friedhelm; Strecker, Manfred
2014-05-01
Increasing slope or relief in a landscape has long been known to correlate with faster denudation rates. Despite a number of studies that have attempted to clarify the additional role of precipitation on denudation, deciphering the complex influence of climate on erosion rates in a landscape with variable slope and relief has remained difficult. The eastern and western branches of the East African Rift System (EARS) constitute first-order tectonic and topographic features in East Africa, which have a profound influence on the distribution and amount of rainfall. The Kenya Rift is an integral part of the eastern branch and is characterized by pronounced differences in morphology, rainfall, and vegetation cover. While paleoclimatic studies in this region reveal general stability of the precipitation and vegetation patterns, short-term changes on timescales of ca. 104 yrs have affected the area multiple times throughout the Pleistocene. We present 20 10Be-derived catchment-wide mean denudation rates from various morphotectonic sectors of the Kenya Rift. The sampling locations include steep rift escarpments, step-faulted composite escarpments, and gently inclined rift-shoulder areas. These different environments also span a rainfall gradient of 0.004 to 4 m/yr, and vegetation covers that range from very sparse to dense. For comparison, 10Be-derived denudation rates are also available from the Rwenzori Mountains in the western branch of the rift system. There, rainfall is high and the vegetation cover is denser than the studied sites in Kenya, but the range of relief and slopes is similar. A first-order comparison of our new denudation rates from Kenya with climatic and topographic characteristics of the catchments show no obvious correlations. However, denudation rates from sparsely vegetated environments in the Kenya Rift define a steep trend in the denudation rate-slope relationship, while denudation rates from the densely vegetated portion of the Kenya Rift and the
Finding One Community in a Sparse Graph
NASA Astrophysics Data System (ADS)
Montanari, Andrea
2015-10-01
We consider a random sparse graph with bounded average degree, in which a subset of vertices has higher connectivity than the background. In particular, the average degree inside this subset of vertices is larger than outside (but still bounded). Given a realization of such graph, we aim at identifying the hidden subset of vertices. This can be regarded as a model for the problem of finding a tightly knitted community in a social network, or a cluster in a relational dataset. In this paper we present two sets of contributions: ( i) We use the cavity method from spin glass theory to derive an exact phase diagram for the reconstruction problem. In particular, as the difference in edge probability increases, the problem undergoes two phase transitions, a static phase transition and a dynamic one. ( ii) We establish rigorous bounds on the dynamic phase transition and prove that, above a certain threshold, a local algorithm (belief propagation) correctly identify most of the hidden set. Below the same threshold no local algorithm can achieve this goal. However, in this regime the subset can be identified by exhaustive search. For small hidden sets and large average degree, the phase transition for local algorithms takes an intriguingly simple form. Local algorithms succeed with high probability for deg _in - deg _out > √{deg _out/e} and fail for deg _in - deg _out < √{deg _out/e} (with deg _in, deg _out the average degrees inside and outside the community). We argue that spectral algorithms are also ineffective in the latter regime. It is an open problem whether any polynomial time algorithms might succeed for deg _in - deg _out < √{deg _out/e}.
Optical double-image encryption and authentication by sparse representation.
Mohammed, Emad A; Saadon, H L
2016-12-10
An optical double-image encryption and authentication method by sparse representation is proposed. The information from double-image encryption can be integrated into a sparse representation. Unlike the traditional double-image encryption technique, only sparse (partial) data from the encrypted data is adopted for the authentication process. Simulation results demonstrate that the correct authentication results are achieved even with partial information from the encrypted data. The randomly selected sparse encrypted information will be used as an effective key for a security system. Therefore, the proposed method is feasible, effective, and can provide an additional security layer for optical security systems. In addition, the method also achieved the general requirements of storage and transmission due to a high reduction of the encrypted information.
Ensemble polarimetric SAR image classification based on contextual sparse representation
NASA Astrophysics Data System (ADS)
Zhang, Lamei; Wang, Xiao; Zou, Bin; Qiao, Zhijun
2016-05-01
Polarimetric SAR image interpretation has become one of the most interesting topics, in which the construction of the reasonable and effective technique of image classification is of key importance. Sparse representation represents the data using the most succinct sparse atoms of the over-complete dictionary and the advantages of sparse representation also have been confirmed in the field of PolSAR classification. However, it is not perfect, like the ordinary classifier, at different aspects. So ensemble learning is introduced to improve the issue, which makes a plurality of different learners training and obtained the integrated results by combining the individual learner to get more accurate and ideal learning results. Therefore, this paper presents a polarimetric SAR image classification method based on the ensemble learning of sparse representation to achieve the optimal classification.
Image denoising via group Sparse representation over learned dictionary
NASA Astrophysics Data System (ADS)
Cheng, Pan; Deng, Chengzhi; Wang, Shengqian; Zhang, Chunfeng
2013-10-01
Images are one of vital ways to get information for us. However, in the practical application, images are often subject to a variety of noise, so that solving the problem of image denoising becomes particularly important. The K-SVD algorithm can improve the denoising effect by sparse coding atoms instead of the traditional method of sparse coding dictionary. In order to further improve the effect of denoising, we propose to extended the K-SVD algorithm via group sparse representation. The key point of this method is dividing the sparse coefficients into groups, so that adjusts the correlation among the elements by controlling the size of the groups. This new approach can improve the local constraints between adjacent atoms, thereby it is very important to increase the correlation between the atoms. The experimental results show that our method has a better effect on image recovery, which is efficient to prevent the block effect and can get smoother images.
A healthy diet includes adding vegetables and fruit every day. Vegetables like broccoli, green beans, leafy greens, zucchini, cauliflower, cabbage, carrots, and tomatoes are low in calories and high in fiber, ...
Sparse Event Modeling with Hierarchical Bayesian Kernel Methods
2016-01-05
events (and subsequently, their likelihood of occurrence) based on historical evidence of the counts of previous event occurrences. The novel Bayesian...Aug-2014 22-May-2015 Approved for Public Release; Distribution Unlimited Final Report: Sparse Event Modeling with Hierarchical Bayesian Kernel Methods...Sparse Event Modeling with Hierarchical Bayesian Kernel Methods Report Title The research objective of this proposal was to develop a predictive Bayesian
Testing of Error-Correcting Sparse Permutation Channel Codes
NASA Technical Reports Server (NTRS)
Shcheglov, Kirill, V.; Orlov, Sergei S.
2008-01-01
A computer program performs Monte Carlo direct numerical simulations for testing sparse permutation channel codes, which offer strong error-correction capabilities at high code rates and are considered especially suitable for storage of digital data in holographic and volume memories. A word in a code of this type is characterized by, among other things, a sparseness parameter (M) and a fixed number (K) of 1 or "on" bits in a channel block length of N.
Representation-Independent Iteration of Sparse Data Arrays
NASA Technical Reports Server (NTRS)
James, Mark
2007-01-01
An approach is defined that describes a method of iterating over massively large arrays containing sparse data using an approach that is implementation independent of how the contents of the sparse arrays are laid out in memory. What is unique and important here is the decoupling of the iteration over the sparse set of array elements from how they are internally represented in memory. This enables this approach to be backward compatible with existing schemes for representing sparse arrays as well as new approaches. What is novel here is a new approach for efficiently iterating over sparse arrays that is independent of the underlying memory layout representation of the array. A functional interface is defined for implementing sparse arrays in any modern programming language with a particular focus for the Chapel programming language. Examples are provided that show the translation of a loop that computes a matrix vector product into this representation for both the distributed and not-distributed cases. This work is directly applicable to NASA and its High Productivity Computing Systems (HPCS) program that JPL and our current program are engaged in. The goal of this program is to create powerful, scalable, and economically viable high-powered computer systems suitable for use in national security and industry by 2010. This is important to NASA for its computationally intensive requirements for analyzing and understanding the volumes of science data from our returned missions.
Visual Tracking Based on Extreme Learning Machine and Sparse Representation
Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen
2015-01-01
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. PMID:26506359
Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.
Feng, Yunlong; Lv, Shao-Gao; Hang, Hanyuan; Suykens, Johan A K
2016-03-01
Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens (2014) showed that KENReg has some nice properties including stability, sparseness, and generalization. In this letter, we continue our study on KENReg by conducting a refined learning theory analysis. This letter makes the following three main contributions. First, we present refined error analysis on the generalization performance of KENReg. The main difficulty of analyzing the generalization error of KENReg lies in characterizing the population version of its empirical target function. We overcome this by introducing a weighted Banach space associated with the elastic net regularization. We are then able to conduct elaborated learning theory analysis and obtain fast convergence rates under proper complexity and regularity assumptions. Second, we study the sparse recovery problem in KENReg with fixed design and show that the kernelization may improve the sparse recovery ability compared to the classical elastic net regularization. Finally, we discuss the interplay among different properties of KENReg that include sparseness, stability, and generalization. We show that the stability of KENReg leads to generalization, and its sparseness confidence can be derived from generalization. Moreover, KENReg is stable and can be simultaneously sparse, which makes it attractive theoretically and practically.
Visual tracking based on extreme learning machine and sparse representation.
Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen
2015-10-22
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker.
Sparseness Analysis in the Pretraining of Deep Neural Networks.
Li, Jun; Zhang, Tong; Luo, Wei; Yang, Jian; Yuan, Xiao-Tong; Zhang, Jian
2016-03-31
A major progress in deep multilayer neural networks (DNNs) is the invention of various unsupervised pretraining methods to initialize network parameters which lead to good prediction accuracy. This paper presents the sparseness analysis on the hidden unit in the pretraining process. In particular, we use the L₁-norm to measure sparseness and provide some sufficient conditions for that pretraining leads to sparseness with respect to the popular pretraining models--such as denoising autoencoders (DAEs) and restricted Boltzmann machines (RBMs). Our experimental results demonstrate that when the sufficient conditions are satisfied, the pretraining models lead to sparseness. Our experiments also reveal that when using the sigmoid activation functions, pretraining plays an important sparseness role in DNNs with sigmoid (Dsigm), and when using the rectifier linear unit (ReLU) activation functions, pretraining becomes less effective for DNNs with ReLU (Drelu). Luckily, Drelu can reach a higher recognition accuracy than DNNs with pretraining (DAEs and RBMs), as it can capture the main benefit (such as sparseness-encouraging) of pretraining in Dsigm. However, ReLU is not adapted to the different firing rates in biological neurons, because the firing rate actually changes along with the varying membrane resistances. To address this problem, we further propose a family of rectifier piecewise linear units (RePLUs) to fit the different firing rates. The experimental results show that the performance of RePLU is better than ReLU, and is comparable with those with some pretraining techniques, such as RBMs and DAEs.
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
Ji, Lei; Zhang, Li; Rover, Jennifer R.; Wylie, Bruce K.; Chen, Xuexia
2014-01-01
In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.
Geostatistical estimation of signal-to-noise ratios for spectral vegetation indices
NASA Astrophysics Data System (ADS)
Ji, Lei; Zhang, Li; Rover, Jennifer; Wylie, Bruce K.; Chen, Xuexia
2014-10-01
In the past 40 years, many spectral vegetation indices have been developed to quantify vegetation biophysical parameters. An ideal vegetation index should contain the maximum level of signal related to specific biophysical characteristics and the minimum level of noise such as background soil influences and atmospheric effects. However, accurate quantification of signal and noise in a vegetation index remains a challenge, because it requires a large number of field measurements or laboratory experiments. In this study, we applied a geostatistical method to estimate signal-to-noise ratio (S/N) for spectral vegetation indices. Based on the sample semivariogram of vegetation index images, we used the standardized noise to quantify the noise component of vegetation indices. In a case study in the grasslands and shrublands of the western United States, we demonstrated the geostatistical method for evaluating S/N for a series of soil-adjusted vegetation indices derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The soil-adjusted vegetation indices were found to have higher S/N values than the traditional normalized difference vegetation index (NDVI) and simple ratio (SR) in the sparsely vegetated areas. This study shows that the proposed geostatistical analysis can constitute an efficient technique for estimating signal and noise components in vegetation indices.
Pryde, E.H.
1982-01-01
Suggested standards for vegetable oils and ester fuels, as well as ASTM specifications for No. 2 diesel oil are given. The following physical properties were discussed: cetane number, cloud point, distillation temperatures, flash point, pour point, turbidity, viscosity, free fatty acids, iodine value, phosphorus, and wax. It was apparent that vegetable oils and their esters cannot meet ASTM specifications D975 for No. 2 diesel oil for use in the diesel engine. Vegetable oil modification or engine design modification may make it possible eventually for vegetable oils to become suitable alternative fuels. Vegetable oils must be recognized as experimental fuels until modifications have been tested thoroughly and generally accepted. 1 table. (DP)
Wang, Li-wen; Wei, Ya-xing; Niu, Zheng
2008-06-01
1 km MODIS NDVI time series data combining with decision tree classification, supervised classification and unsupervised classification was used to classify land cover type of Qinghai Province into 14 classes. In our classification system, sparse grassland and sparse shrub were emphasized, and their spatial distribution locations were labeled. From digital elevation model (DEM) of Qinghai Province, five elevation belts were achieved, and we utilized geographic information system (GIS) software to analyze vegetation cover variation on different elevation belts. Our research result shows that vegetation cover in Qinghai Province has been improved in recent five years. Vegetation cover area increases from 370047 km2 in 2001 to 374576 km2 in 2006, and vegetation cover rate increases by 0.63%. Among five grade elevation belts, vegetation cover ratio of high mountain belt is the highest (67.92%). The area of middle density grassland in high mountain belt is the largest, of which area is 94 003 km2. Increased area of dense grassland in high mountain belt is the greatest (1280 km2). During five years, the biggest variation is the conversion from sparse grassland to middle density grassland in high mountain belt, of which area is 15931 km2.
Sparse modeling of spatial environmental variables associated with asthma.
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.
[Investigation of Multi-Angle Polarization Properties of Vegetation Based on RSP].
Jiao, Jian-nan; Zhao, Hai-meng; Yang, Bin; Yan, Lei
2016-02-01
Polarization detection provides us with novel information to reflect the target attribute. Compared with traditional remote sensing methods, multi-angle polarization has relatively stable correlation and regularity. RSP(research scanning polarimeter)is an airborne prototype for the APS(aerosol polarimetery sensor) developed by the USA, which can provide with us the polarization detection information of 9 channels. We can get optical properties and physical characteristics of vegetation by analyzing stable multi-angle and multi-band polarization detection information from preprocessing scanning polarization data of flight test. In this paper, after making registration based on flight attitude information, a comparative analysis is made between characteristics of reflectance and polarization reflectance with visible light and near infrared band of the view zenith angles between--30 degree and 65 degree, based on dense area and sparse area(close to bare field) of vegetation. The results show that both dense area and sparse area demonstrate regular characteristics of polarization degree. The area close to hot spot area has highest reflectance energy. In contrast,. it has relatively least energy of polarization degree, which can prevent strong reflectance energy from influencing the stability of detector. Because the degree of polarization in dense area of vegetation is higher than that in sparse area at visible light band while that in concentration area of vegetation is lower than sparse area at near infrared light band, it shows that the visible light band information of dense area of vegetation that the sensor received is dominated by single scattering while the near infrared light band information of dense area of vegetation is dominated by multiple scattering.
Environment identification in flight using sparse approximation of wing strain
NASA Astrophysics Data System (ADS)
Manohar, Krithika; Brunton, Steven L.; Kutz, J. Nathan
2017-04-01
This paper addresses the problem of identifying different flow environments from sparse data collected by wing strain sensors. Insects regularly perform this feat using a sparse ensemble of noisy strain sensors on their wing. First, we obtain strain data from numerical simulation of a Manduca sexta hawkmoth wing undergoing different flow environments. Our data-driven method learns low-dimensional strain features originating from different aerodynamic environments using proper orthogonal decomposition (POD) modes in the frequency domain, and leverages sparse approximation to classify a set of strain frequency signatures using a dictionary of POD modes. This bio-inspired machine learning architecture for dictionary learning and sparse classification permits fewer costly physical strain sensors while being simultaneously robust to sensor noise. A measurement selection algorithm identifies frequencies that best discriminate the different aerodynamic environments in low-rank POD feature space. In this manner, sparse and noisy wing strain data can be exploited to robustly identify different aerodynamic environments encountered in flight, providing insight into the stereotyped placement of neurons that act as strain sensors on a Manduca sexta hawkmoth wing.
X-ray computed tomography using curvelet sparse regularization
Wieczorek, Matthias Vogel, Jakob; Lasser, Tobias; Frikel, Jürgen; Demaret, Laurent; Eggl, Elena; Pfeiffer, Franz; Kopp, Felix; Noël, Peter B.
2015-04-15
Purpose: 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. Methods: In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Results: 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. Conclusions: The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.
Image fusion via nonlocal sparse K-SVD dictionary learning.
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.
A temporal channel for information in sparse sensory coding
Gupta, Nitin; Stopfer, Mark
2014-01-01
Summary Background Sparse codes are found in nearly every sensory system, but the role of spike timing in sparse sensory coding is unclear. Here we used the olfactory system of awake locusts to test whether the timing of spikes in Kenyon cells, a population of neurons that responds sparsely to odors, carries sensory information to, and influences the responses of, follower neurons. Results We characterized two major classes of direct followers of Kenyon cells. With paired intracellular and field potential recordings made during odor presentations, we found these followers contain information about odor identity in the temporal patterns of their spikes, rather than in the spike rate, the spike phase or the identities of the responsive neurons. Subtly manipulating the relative timing of Kenyon cell spikes with temporally and spatially structured microstimulation reliably altered the response patterns of the followers. Conclusions Our results show that even remarkably sparse spiking responses can provide information through stimulus-specific variations in timing on the order of tens to hundreds of milliseconds, and that these variations can determine the responses of downstream neurons. These results establish the importance of spike timing in a sparse sensory code. PMID:25264257
Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking
Qu, Shiru
2016-01-01
Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness. PMID:27630710
A sparse matrix based full-configuration interaction algorithm.
Rolik, Zoltán; Szabados, Agnes; Surján, Péter R
2008-04-14
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.
P-SPARSLIB: A parallel sparse iterative solution package
Saad, Y.
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.
Sparse-based multispectral image encryption via ptychography
NASA Astrophysics Data System (ADS)
Rawat, Nitin; Shi, Yishi; Kim, Byoungho; Lee, Byung-Geun
2015-12-01
Recently, we proposed a model of securing a ptychography-based monochromatic image encryption system via the classical Photon-counting imaging (PCI) technique. In this study, we examine a single-channel multispectral sparse-based photon-counting ptychography imaging (SMPI)-based cryptosystem. A ptychography-based cryptosystem creates a complex object wave field, which can be reconstructed by a series of diffraction intensity patterns through an aperture movement. The PCI sensor records only a few complex Bayer patterned samples that have been utilized in the decryption process. Sparse sensing and nonlinear properties of the classical PCI system, together with the scanning probes, enlarge the key space, and such a combination therefore enhances the system's security. We demonstrate that the sparse samples have adequate information for image decryption, as well as information authentication by means of optical correlation.
Sparse representation-based image restoration via nonlocal supervised coding
NASA Astrophysics Data System (ADS)
Li, Ao; Chen, Deyun; Sun, Guanglu; Lin, Kezheng
2016-10-01
Sparse representation (SR) and nonlocal technique (NLT) have shown great potential in low-level image processing. However, due to the degradation of the observed image, SR and NLT may not be accurate enough to obtain a faithful restoration results when they are used independently. To improve the performance, in this paper, a nonlocal supervised coding strategy-based NLT for image restoration is proposed. The novel method has three main contributions. First, to exploit the useful nonlocal patches, a nonnegative sparse representation is introduced, whose coefficients can be utilized as the supervised weights among patches. Second, a novel objective function is proposed, which integrated the supervised weights learning and the nonlocal sparse coding to guarantee a more promising solution. Finally, to make the minimization tractable and convergence, a numerical scheme based on iterative shrinkage thresholding is developed to solve the above underdetermined inverse problem. The extensive experiments validate the effectiveness of the proposed method.
Joint sparse representation for robust multimodal biometrics recognition.
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.
A note on rank reduction in sparse multivariate regression.
Chen, Kun; Chan, Kung-Sik
A reduced-rank regression with sparse singular value decomposition (RSSVD) approach was proposed by Chen et al. for conducting variable selection in a reduced-rank model. To jointly model the multivariate response, the method efficiently constructs a prespecified number of latent variables as some sparse linear combinations of the predictors. Here, we generalize the method to also perform rank reduction, and enable its usage in reduced-rank vector autoregressive (VAR) modeling to perform automatic rank determination and order selection. We show that in the context of stationary time-series data, the generalized approach correctly identifies both the model rank and the sparse dependence structure between the multivariate response and the predictors, with probability one asymptotically. We demonstrate the efficacy of the proposed method by simulations and analyzing a macro-economical multivariate time series using a reduced-rank VAR model.
Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging
Zhang, Yichun; Shi, Tielin; Su, Lei; Wang, Xiao; Hong, Yuan; Chen, Kepeng; Liao, Guanglan
2016-01-01
Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l1-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging. PMID:27783040
Automatic landslide and mudflow detection method via multichannel sparse representation
NASA Astrophysics Data System (ADS)
Chao, Chen; Zhou, Jianjun; Hao, Zhuo; Sun, Bo; He, Jun; Ge, Fengxiang
2015-10-01
Landslide and mudflow detection is an important application of aerial images and high resolution remote sensing images, which is crucial for national security and disaster relief. Since the high resolution images are often large in size, it's necessary to develop an efficient algorithm for landslide and mudflow detection. Based on the theory of sparse representation and, we propose a novel automatic landslide and mudflow detection method in this paper, which combines multi-channel sparse representation and eight neighbor judgment methods. The whole process of the detection is totally automatic. We make the experiment on a high resolution image of ZhouQu district of Gansu province in China on August, 2010 and get a promising result which proved the effective of using sparse representation on landslide and mudflow detection.
Combining sparseness and smoothness improves classification accuracy and interpretability.
de Brecht, Matthew; Yamagishi, Noriko
2012-04-02
Sparse logistic regression (SLR) has been shown to be a useful method for decoding high-dimensional fMRI and MEG data by automatically selecting relevant feature dimensions. However, when applied to signals with high spatio-temporal correlations, SLR often over-prunes the feature space, which can result in overfitting and weight vectors that are difficult to interpret. To overcome this problem, we investigate a modification of ℓ₁-normed sparse logistic regression, called smooth sparse logistic regression (SSLR), which has a spatio-temporal "smoothing" prior that encourages weights that are close in time and space to have similar values. This causes the classifier to select spatio-temporally continuous groups of features, whereas SLR classifiers often select a scattered collection of independent features. We applied the method to both simulation data and real MEG data. We found that SSLR consistently increases classification accuracy, and produces weight vectors that are more meaningful from a neuroscientific perspective.
Sparse Partial Equilibrium Tables in Chemically Resolved Reactive Flow
NASA Astrophysics Data System (ADS)
Vitello, Peter; Fried, Laurence E.; Pudliner, Brian; McAbee, Tom
2004-07-01
The detonation of an energetic material is the result of a complex interaction between kinetic chemical reactions and hydrodynamics. Unfortunately, little is known concerning the detailed chemical kinetics of detonations in energetic materials. CHEETAH uses rate laws to treat species with the slowest chemical reactions, while assuming other chemical species are in equilibrium. CHEETAH supports a wide range of elements and condensed detonation products and can also be applied to gas detonations. A sparse hash table of equation of state values is used in CHEETAH to enhance the efficiency of kinetic reaction calculations. For large-scale parallel hydrodynamic calculations, CHEETAH uses parallel communication to updates to the cache. We present here details of the sparse caching model used in the CHEETAH coupled to an ALE hydrocode. To demonstrate the efficiency of modeling using a sparse cache model we consider detonations in energetic materials.
Sparse nonnegative matrix factorization with ℓ0-constraints
Peharz, Robert; Pernkopf, Franz
2012-01-01
Although nonnegative matrix factorization (NMF) favors a sparse and part-based representation of nonnegative data, there is no guarantee for this behavior. Several authors proposed NMF methods which enforce sparseness by constraining or penalizing the ℓ1-norm of the factor matrices. On the other hand, little work has been done using a more natural sparseness measure, the ℓ0-pseudo-norm. In this paper, we propose a framework for approximate NMF which constrains the ℓ0-norm of the basis matrix, or the coefficient matrix, respectively. For this purpose, techniques for unconstrained NMF can be easily incorporated, such as multiplicative update rules, or the alternating nonnegative least-squares scheme. In experiments we demonstrate the benefits of our methods, which compare to, or outperform existing approaches. PMID:22505792
Sparse Partial Equilibrium Tables in Chemically Resolved Reactive Flow
Vitello, P; Fried, L E; Pudliner, B; McAbee, T
2003-07-14
The detonation of an energetic material is the result of a complex interaction between kinetic chemical reactions and hydrodynamics. Unfortunately, little is known concerning the detailed chemical kinetics of detonations in energetic materials. CHEETAH uses rate laws to treat species with the slowest chemical reactions, while assuming other chemical species are in equilibrium. CHEETAH supports a wide range of elements and condensed detonation products and can also be applied to gas detonations. A sparse hash table of equation of state values, called the ''cache'' is used in CHEETAH to enhance the efficiency of kinetic reaction calculations. For large-scale parallel hydrodynamic calculations, CHEETAH uses MPI communication to updates to the cache. We present here details of the sparse caching model used in the CHEETAH. To demonstrate the efficiency of modeling using a sparse cache model we consider detonations in energetic materials.
Sparse Reconstruction for Micro Defect Detection in Acoustic Micro Imaging.
Zhang, Yichun; Shi, Tielin; Su, Lei; Wang, Xiao; Hong, Yuan; Chen, Kepeng; Liao, Guanglan
2016-10-24
Acoustic micro imaging has been proven to be sufficiently sensitive for micro defect detection. In this study, we propose a sparse reconstruction method for acoustic micro imaging. A finite element model with a micro defect is developed to emulate the physical scanning. Then we obtain the point spread function, a blur kernel for sparse reconstruction. We reconstruct deblurred images from the oversampled C-scan images based on l₁-norm regularization, which can enhance the signal-to-noise ratio and improve the accuracy of micro defect detection. The method is further verified by experimental data. The results demonstrate that the sparse reconstruction is effective for micro defect detection in acoustic micro imaging.
A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.
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.
Sparse Sensing of Aerodynamic Loads on Insect Wings
NASA Astrophysics Data System (ADS)
Manohar, Krithika; Brunton, Steven; Kutz, J. Nathan
2015-11-01
We investigate how insects use sparse sensors on their wings to detect aerodynamic loading and wing deformation using a coupled fluid-structure model given periodically flapping input motion. Recent observations suggest that insects collect sensor information about their wing deformation to inform control actions for maneuvering and rejecting gust disturbances. Given a small number of point measurements of the chordwise aerodynamic loads from the sparse sensors, we reconstruct the entire chordwise loading using sparsesensing - a signal processing technique that reconstructs a signal from a small number of measurements using l1 norm minimization of sparse modal coefficients in some basis. We compare reconstructions from sensors randomly sampled from probability distributions biased toward different regions along the wing chord. In this manner, we determine the preferred regions along the chord for sensor placement and for estimating chordwise loads to inform control decisions in flight.
Sparse grid techniques for particle-in-cell schemes
NASA Astrophysics Data System (ADS)
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.
High Angular Resolution Microwave Sensing with Large, Sparse, Random Arrays.
1982-12-01
b.cnuainas saldaatv an quired at microwaves to achieve the rec0n(pwro cam’ forming or seti -colternng or phas. synchronzing. After the moo optical...AD A126 866 HIGH ANGULAR RESOLUTICN MICROWAVE SENSING WITH LARGE 1/ SPARSE RANDOM ARRAYS..U) MOORE SCHOOL OF ELECTRICAL ENGINEERING PHILADELPHIAPA...RESOLUTION TEST CHART N4ATIONAL BUREAU Of SrANDARDS 1963 A iOSR-TR- 83-0225 HIGH ANGULAR RESOLUTION MICROWAVE SENSING WITH LARGE, SPARSE, RANDOM ARRAYS Annual
Moving target detection for frequency agility radar by sparse reconstruction.
Quan, Yinghui; Li, YaChao; Wu, Yaojun; Ran, Lei; Xing, Mengdao; Liu, Mengqi
2016-09-01
Frequency agility radar, with randomly varied carrier frequency from pulse to pulse, exhibits superior performance compared to the conventional fixed carrier frequency pulse-Doppler radar against the electromagnetic interference. A novel moving target detection (MTD) method is proposed for the estimation of the target's velocity of frequency agility radar based on pulses within a coherent processing interval by using sparse reconstruction. Hardware implementation of orthogonal matching pursuit algorithm is executed on Xilinx Virtex-7 Field Programmable Gata Array (FPGA) to perform sparse optimization. Finally, a series of experiments are performed to evaluate the performance of proposed MTD method for frequency agility radar systems.
Towards an Accurate Performance Modeling of Parallel SparseFactorization
Grigori, Laura; Li, Xiaoye S.
2006-05-26
We present a performance model to analyze a parallel sparseLU factorization algorithm on modern cached-based, high-end parallelarchitectures. Our model characterizes the algorithmic behavior bytakingaccount the underlying processor speed, memory system performance, aswell as the interconnect speed. The model is validated using theSuperLU_DIST linear system solver, the sparse matrices from realapplications, and an IBM POWER3 parallel machine. Our modelingmethodology can be easily adapted to study performance of other types ofsparse factorizations, such as Cholesky or QR.
Communication requirements of sparse Cholesky factorization with nested dissection ordering
NASA Technical Reports Server (NTRS)
Naik, Vijay K.; Patrick, Merrell L.
1989-01-01
Load distribution schemes for minimizing the communication requirements of the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems are presented. The total data traffic in factoring an n x n sparse symmetric positive definite matrix representing an n-vertex regular two-dimensional grid graph using n exp alpha, alpha not greater than 1, processors are shown to be O(n exp 1 + alpha/2). It is O(n), when n exp alpha, alpha not smaller than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal.
A LONE code for the sparse control of quantum systems
NASA Astrophysics Data System (ADS)
Ciaramella, G.; Borzì, A.
2016-03-01
In many applications with quantum spin systems, control functions with a sparse and pulse-shaped structure are often required. These controls can be obtained by solving quantum optimal control problems with L1-penalized cost functionals. In this paper, the MATLAB package LONE is presented aimed to solving L1-penalized optimal control problems governed by unitary-operator quantum spin models. This package implements a new strategy that includes a globalized semi-smooth Krylov-Newton scheme and a continuation procedure. Results of numerical experiments demonstrate the ability of the LONE code in computing accurate sparse optimal control solutions.
Sparse coding based feature representation method for remote sensing images
NASA Astrophysics Data System (ADS)
Oguslu, Ender
In this dissertation, we study sparse coding based feature representation method for the classification of multispectral and hyperspectral images (HSI). The existing feature representation systems based on the sparse signal model are computationally expensive, requiring to solve a convex optimization problem to learn a dictionary. A sparse coding feature representation framework for the classification of HSI is presented that alleviates the complexity of sparse coding through sub-band construction, dictionary learning, and encoding steps. In the framework, we construct the dictionary based upon the extracted sub-bands from the spectral representation of a pixel. In the encoding step, we utilize a soft threshold function to obtain sparse feature representations for HSI. Experimental results showed that a randomly selected dictionary could be as effective as a dictionary learned from optimization. The new representation usually has a very high dimensionality requiring a lot of computational resources. In addition, the spatial information of the HSI data has not been included in the representation. Thus, we modify the framework by incorporating the spatial information of the HSI pixels and reducing the dimension of the new sparse representations. The enhanced model, called sparse coding based dense feature representation (SC-DFR), is integrated with a linear support vector machine (SVM) and a composite kernels SVM (CKSVM) classifiers to discriminate different types of land cover. We evaluated the proposed algorithm on three well known HSI datasets and compared our method to four recently developed classification methods: SVM, CKSVM, simultaneous orthogonal matching pursuit (SOMP) and image fusion and recursive filtering (IFRF). The results from the experiments showed that the proposed method can achieve better overall and average classification accuracies with a much more compact representation leading to more efficient sparse models for HSI classification. To further
An evaluation of GPU acceleration for sparse reconstruction
NASA Astrophysics Data System (ADS)
Braun, Thomas R.
2010-04-01
Image processing applications typically parallelize well. This gives a developer interested in data throughput several different implementation options, including multiprocessor machines, general purpose computation on the graphics processor, and custom gate-array designs. Herein, we will investigate these first two options for dictionary learning and sparse reconstruction, specifically focusing on the K-SVD algorithm for dictionary learning and the Batch Orthogonal Matching Pursuit for sparse reconstruction. These methods have been shown to provide state of the art results for image denoising, classification, and object recognition. We'll explore the GPU implementation and show that GPUs are not significantly better or worse than CPUs for this application.
Multiscale Sparse Image Representation with Learned Dictionaries (PREPRINT)
2007-01-01
age processing, e.g., image denoising [5]. In [1] the K- SVD is proposed for learning a single-scale dic- tionary for sparse representation of image...performance we obtain. 2. THE SINGLE-SCALE K- SVD DENOISING ALGORITHM In this section, we briefly review the main ideas of the K- SVD frame- work for sparse...weighted average: x̂ = “ λI + X ij R T ijRij ”−1“ λy + X ij R T ijD̂α̂ij ” . (4) Fig. 1. The single-scale K- SVD -based image denoising algorithm. Fig
Accelerated nonlinear multichannel ultrasonic tomographic imaging using target sparseness.
Chengdong Dong; Yuanwei Jin; Enyue Lu
2014-03-01
This paper presents an accelerated iterative Landweber method for nonlinear ultrasonic tomographic imaging in a multiple-input multiple-output (MIMO) configuration under a sparsity constraint on the image. The proposed method introduces the emerging MIMO signal processing techniques and target sparseness constraints in the traditional computational imaging field, thus significantly improves the speed of image reconstruction compared with the conventional imaging method while producing high quality images. Using numerical examples, we demonstrate that incorporating prior knowledge about the imaging field such as target sparseness accelerates significantly the convergence of the iterative imaging method, which provides considerable benefits to real-time tomographic imaging applications.
Moving target detection for frequency agility radar by sparse reconstruction
NASA Astrophysics Data System (ADS)
Quan, Yinghui; Li, YaChao; Wu, Yaojun; Ran, Lei; Xing, Mengdao; Liu, Mengqi
2016-09-01
Frequency agility radar, with randomly varied carrier frequency from pulse to pulse, exhibits superior performance compared to the conventional fixed carrier frequency pulse-Doppler radar against the electromagnetic interference. A novel moving target detection (MTD) method is proposed for the estimation of the target's velocity of frequency agility radar based on pulses within a coherent processing interval by using sparse reconstruction. Hardware implementation of orthogonal matching pursuit algorithm is executed on Xilinx Virtex-7 Field Programmable Gata Array (FPGA) to perform sparse optimization. Finally, a series of experiments are performed to evaluate the performance of proposed MTD method for frequency agility radar systems.
Reconstruction Techniques for Sparse Multistatic Linear Array Microwave Imaging
Sheen, David M.; Hall, Thomas E.
2014-06-09
Sequentially-switched linear arrays are an enabling technology for a number of near-field microwave imaging applications. Electronically sequencing along the array axis followed by mechanical scanning along an orthogonal axis allows dense sampling of a two-dimensional aperture in near real-time. In this paper, a sparse multi-static array technique will be described along with associated Fourier-Transform-based and back-projection-based image reconstruction algorithms. Simulated and measured imaging results are presented that show the effectiveness of the sparse array technique along with the merits and weaknesses of each image reconstruction approach.
BIRD: A general interface for sparse distributed memory simulators
NASA Technical Reports Server (NTRS)
Rogers, David
1990-01-01
Kanerva's sparse distributed memory (SDM) has now been implemented for at least six different computers, including SUN3 workstations, the Apple Macintosh, and the Connection Machine. A common interface for input of commands would both aid testing of programs on a broad range of computer architectures and assist users in transferring results from research environments to applications. A common interface also allows secondary programs to generate command sequences for a sparse distributed memory, which may then be executed on the appropriate hardware. The BIRD program is an attempt to create such an interface. Simplifying access to different simulators should assist developers in finding appropriate uses for SDM.
Sparse Matrix for ECG Identification with Two-Lead Features
Tseng, Kuo-Kun; Luo, Jiao; Wang, Wenmin; Haiting, Dong
2015-01-01
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. PMID:25961074
Vegetation Identification With LIDAR
2005-09-01
Quercus agrifolia ).....27 3. Eucalyptus Tree (Eucalyptus Globus).........28 E. IDENTIFYING LOCATIONS WITHOUT VEGETATION.........30 F. IDENTIFYING...Relative First Return 25 ( Quercus dumosa), and the California Live Oak ( Quercus agrifolia ). These three species of trees are very abundant in this...ELEVATION OF TERRAIN...23 D. TYPES OF VEGETATION..............................26 1. California Scrub Oak ( Quercus dumosa).......26 2. California Live Oak
Soil and vegetation surveillance
Antonio, E.J.
1995-06-01
Soil sampling and analysis evaluates long-term contamination trends and monitors environmental radionuclide inventories. This section of the 1994 Hanford Site Environmental Report summarizes the soil and vegetation surveillance programs which were conducted during 1994. Vegetation surveillance is conducted offsite to monitor atmospheric deposition of radioactive materials in areas not under cultivation and onsite at locations adjacent to potential sources of radioactivity.
Modelling vegetated dune landscapes
NASA Astrophysics Data System (ADS)
Baas, A. C. W.; Nield, J. M.
2007-03-01
This letter presents a self-organising cellular automaton model capable of simulating the evolution of vegetated dunes with multiple types of plant response in the environment. It can successfully replicate hairpin, or long-walled, parabolic dunes with trailing ridges as well as nebkha dunes with distinctive deposition tails. Quantification of simulated landscapes with eco-geomorphic state variables and subsequent cluster analysis and PCA yields a phase diagram of different types of coastal dunes developing from blow-outs as a function of vegetation vitality. This diagram indicates the potential sensitivity of dormant dune fields to reactivation under declining vegetation vitality, e.g. due to climatic changes. Nebkha simulations with different grid resolutions demonstrate that the interaction between the (abiotic) geomorphic processes and the biological vegetation component (life) introduces a characteristic length scale on the resultant landforms that breaks the typical self-similar scaling of (un-vegetated) bare-sand dunes.
Sparse feature fidelity for perceptual image quality assessment.
Chang, Hua-Wen; Yang, Hua; Gan, Yong; Wang, Ming-Hui
2013-10-01
The prediction of an image quality metric (IQM) should be consistent with subjective human evaluation. As the human visual system (HVS) is critical to visual perception, modeling of the HVS is regarded as the most suitable way to achieve perceptual quality predictions. Sparse coding that is equivalent to independent component analysis (ICA) can provide a very good description of the receptive fields of simple cells in the primary visual cortex, which is the most important part of the HVS. With this inspiration, a quality metric called sparse feature fidelity (SFF) is proposed for full-reference image quality assessment (IQA) on the basis of transformation of images into sparse representations in the primary visual cortex. The proposed method is based on the sparse features that are acquired by a feature detector, which is trained on samples of natural images by an ICA algorithm. In addition, two strategies are designed to simulate the properties of the visual perception: 1) visual attention and 2) visual threshold. The computation of SFF has two stages: training and fidelity computation, in addition, the fidelity computation consists of two components: feature similarity and luminance correlation. The feature similarity measures the structure differences between the two images, whereas the luminance correlation evaluates brightness distortions. SFF also reflects the chromatic properties of the HVS, and it is very effective for color IQA. The experimental results on five image databases show that SFF has a better performance in matching subjective ratings compared with the leading IQMs.
Stable Restoration and Separation of Approximately Sparse Signals
2011-07-02
dictionary. Particular applications covered by our framework include the restoration of signals impaired by impulse noise , narrowband interference, or...representation in a second general dictionary. Particular applications covered by our framework include the restoration of signals impaired by impulse noise ...applications (see [1–17] and references therein), including: • Impulse noise : The recovery of approximately sparse signals corrupted by impulse noise [13
Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering
Wang, Changqing; Kipping, Judy; Bao, Chenglong; Ji, Hui; Qiu, Anqi
2016-01-01
The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children. PMID:27199650
Robust Methods for Sensing and Reconstructing Sparse Signals
ERIC Educational Resources Information Center
Carrillo, Rafael E.
2012-01-01
Compressed sensing (CS) is an emerging signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are…
Feature-Enhanced, Model-Based Sparse Aperture Imaging
2008-03-01
imaging, anisotropy characterization, feature-enhanced imaging, inverse problems, superresolution , anisotropy, sparse signal representation, overcomplete...number of such activities ourselves, and we provide very brief information on some of them here. We have developed a superresolution technique for...enhanced, superresolution image reconstruction. This framework provides a number of desirable features including preservation of anisotropic scatterers
Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation.
Aram, Parham; Kadirkamanathan, Visakan; Anderson, Sean R
2015-11-01
We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; 2) the model allows for spatial heterogeneity; 3) the model representation is continuous over space; and 4) the model parameters can be identified in a simple and sparse estimation procedure. The model identification procedure we propose has four steps: 1) decomposition of the continuous spatial field using a finite set of basis functions where spatial frequency analysis is used to determine basis function width and spacing, such that the main spatial frequency contents of the underlying field can be captured; 2) initialization of states in closed form; 3) initialization of state-transition and input matrix model parameters using sparse regression-the least absolute shrinkage and selection operator method; and 4) joint state and parameter estimation using an iterative Kalman-filter/sparse-regression algorithm. To investigate the performance of the proposed algorithm we use data generated by the Kuramoto model of spatiotemporal cortical dynamics. The identification algorithm performs successfully, predicting the spatiotemporal field with high accuracy, whilst the sparse regression leads to a compact model.
Two demonstrators and a simulator for a sparse, distributed memory
NASA Technical Reports Server (NTRS)
Brown, Robert L.
1987-01-01
Described are two programs demonstrating different aspects of Kanerva's Sparse, Distributed Memory (SDM). These programs run on Sun 3 workstations, one using color, and have straightforward graphically oriented user interfaces and graphical output. Presented are descriptions of the programs, how to use them, and what they show. Additionally, this paper describes the software simulator behind each program.
Remote sensing image fusion via wavelet transform and sparse representation
NASA Astrophysics Data System (ADS)
Cheng, Jian; Liu, Haijun; Liu, Ting; Wang, Feng; Li, Hongsheng
2015-06-01
In this paper, we propose a remote sensing image fusion method which combines the wavelet transform and sparse representation to obtain fusion images with high spectral resolution and high spatial resolution. Firstly, intensity-hue-saturation (IHS) transform is applied to Multi-Spectral (MS) images. Then, wavelet transform is used to the intensity component of MS images and the Panchromatic (Pan) image to construct the multi-scale representation respectively. With the multi-scale representation, different fusion strategies are taken on the low-frequency and the high-frequency sub-images. Sparse representation with training dictionary is introduced into the low-frequency sub-image fusion. The fusion rule for the sparse representation coefficients of the low-frequency sub-images is defined by the spatial frequency maximum. For high-frequency sub-images with prolific detail information, the fusion rule is established by the images information fusion measurement indicator. Finally, the fused results are obtained through inverse wavelet transform and inverse IHS transform. The wavelet transform has the ability to extract the spectral information and the global spatial details from the original pairwise images, while sparse representation can extract the local structures of images effectively. Therefore, our proposed fusion method can well preserve the spectral information and the spatial detail information of the original images. The experimental results on the remote sensing images have demonstrated that our proposed method could well maintain the spectral characteristics of fusion images with a high spatial resolution.
Medical Image Fusion Based on Feature Extraction and Sparse Representation
Wei, Gao; Zongxi, Song
2017-01-01
As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. PMID:28321246
Immunogenicity is preferentially induced in sparse dendritic cell cultures
Nasi, Aikaterini; Bollampalli, Vishnu Priya; Sun, Meng; Chen, Yang; Amu, Sylvie; Nylén, Susanne; Eidsmo, Liv; Rothfuchs, Antonio Gigliotti; Réthi, Bence
2017-01-01
We have previously shown that human monocyte-derived dendritic cells (DCs) acquired different characteristics in dense or sparse cell cultures. Sparsity promoted the development of IL-12 producing migratory DCs, whereas dense cultures increased IL-10 production. Here we analysed whether the density-dependent endogenous breaks could modulate DC-based vaccines. Using murine bone marrow-derived DC models we show that sparse cultures were essential to achieve several key functions required for immunogenic DC vaccines, including mobility to draining lymph nodes, recruitment and massive proliferation of antigen-specific CD4+ T cells, in addition to their TH1 polarization. Transcription analyses confirmed higher commitment in sparse cultures towards T cell activation, whereas DCs obtained from dense cultures up-regulated immunosuppressive pathway components and genes suggesting higher differentiation plasticity towards osteoclasts. Interestingly, we detected a striking up-regulation of fatty acid and cholesterol biosynthesis pathways in sparse cultures, suggesting an important link between DC immunogenicity and lipid homeostasis regulation. PMID:28276533
Sparse-flowering orchardgrass is stable across temperate North America
Technology Transfer Automated Retrieval System (TEKTRAN)
Three sparse-flowering orchardgrass populations were developed by selective breeding as a mechanism to reduce stem production during the early spring season in management-intensive grazing systems. These populations and three check cultivars were evaluated under frequent- and infrequent-harvest syst...
Dictionary Learning and Sparse Coding for Unsupervised Clustering
2009-09-01
the fully unsupervised setting of data clustering. Work supported by ONR, NGA, ARO, DARPA, and NSF. We thank I. Ramirez, F. Lecumberry , and J. Mairal...Ramirez, F. Lecumberry , and G. Sapiro, “Uni- versal priors for sparse modeling,” in IMA Preprint, http://www.ima.umn.edu/preprints/aug2009/2276.pdf, August 2009.
Sparsely sampling the sky: a Bayesian experimental design approach
NASA Astrophysics Data System (ADS)
Paykari, P.; Jaffe, A. H.
2013-08-01
The next generation of galaxy surveys will observe millions of galaxies over large volumes of the Universe. These surveys are expensive both in time and cost, raising questions regarding the optimal investment of this time and money. In this work, we investigate criteria for selecting amongst observing strategies for constraining the galaxy power spectrum and a set of cosmological parameters. Depending on the parameters of interest, it may be more efficient to observe a larger, but sparsely sampled, area of sky instead of a smaller contiguous area. In this work, by making use of the principles of Bayesian experimental design, we will investigate the advantages and disadvantages of the sparse sampling of the sky and discuss the circumstances in which a sparse survey is indeed the most efficient strategy. For the Dark Energy Survey (DES), we find that by sparsely observing the same area in a smaller amount of time, we only increase the errors on the parameters by a maximum of 0.45 per cent. Conversely, investing the same amount of time as the original DES to observe a sparser but larger area of sky, we can in fact constrain the parameters with errors reduced by 28 per cent.
Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection
Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max; Burkardt, John
2016-08-04
This work proposes a hyperspherical sparse approximation framework for detecting jump discontinuities in functions in high-dimensional spaces. The need for a novel approach results from the theoretical and computational inefficiencies of well-known approaches, such as adaptive sparse grids, for discontinuity detection. Our approach constructs the hyperspherical coordinate representation of the discontinuity surface of a function. Then sparse approximations of the transformed function are built in the hyperspherical coordinate system, with values at each point estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the new technique can identify jump discontinuities with significantly reduced computational cost, compared to existing methods. Several approaches are used to approximate the transformed discontinuity surface in the hyperspherical system, including adaptive sparse grid and radial basis function interpolation, discrete least squares projection, and compressed sensing approximation. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. In conclusion, rigorous complexity analyses of the new methods are provided, as are several numerical examples that illustrate the effectiveness of our approach.
Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation
Grossi, Giuliano; Lin, Jianyi
2017-01-01
In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD’s robustness and wide applicability. PMID:28103283
Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering
Sicat, Ronell; Krüger, Jens; Möller, Torsten; Hadwiger, Markus
2015-01-01
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 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. PMID:26146475
Sparse Recovery via l1 and L1 Optimization
2014-11-01
compactly supported approximations to eigenfunctions of the Schrodinger equation [18, 19]. These have long been sought [20] and are called Wannier...and are intimately con- nected to the Schrodinger equation. 4 Computing Paths of Sparse Solutions When the vector b is corrupted due to noise at an
Optimized Color Filter Arrays for Sparse Representation Based Demosaicking.
Li, Jia; Bai, Chenyan; Lin, Zhouchen; Yu, Jian
2017-03-08
Demosaicking is the problem of reconstructing a color image from the raw image captured by a digital color camera that covers its only imaging sensor with a color filter array (CFA). Sparse representation based demosaicking has been shown to produce superior reconstruction quality. However, almost all existing algorithms in this category use the CFAs which are not specifically optimized for the algorithms. In this paper, we consider optimally designing CFAs for sparse representation based demosaicking, where the dictionary is well-chosen. The fact that CFAs correspond to the projection matrices used in compressed sensing inspires us to optimize CFAs via minimizing the mutual coherence. This is more challenging than that for traditional projection matrices because CFAs have physical realizability constraints. However, most of the existing methods for minimizing the mutual coherence require that the projection matrices should be unconstrained, making them inapplicable for designing CFAs. We consider directly minimizing the mutual coherence with the CFA's physical realizability constraints as a generalized fractional programming problem, which needs to find sufficiently accurate solutions to a sequence of nonconvex nonsmooth minimization problems. We adapt the redistributed proximal bundle method to address this issue. Experiments on benchmark images testify to the superiority of the proposed method. In particular, we show that a simple sparse representation based demosaicking algorithm with our specifically optimized CFA can outperform LSSC [1]. To the best of our knowledge, it is the first sparse representation based demosaicking algorithm that beats LSSC in terms of CPSNR.
Efficient Coordinated Recovery of Sparse Channels in Massive MIMO
NASA Astrophysics Data System (ADS)
Masood, Mudassir; Afify, Laila H.; Al-Naffouri, Tareq Y.
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 require a small number of pilots. Two algorithms based on this approach have been developed which 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.
Block sparse Cholesky algorithms on advanced uniprocessor computers
Ng, E.G.; Peyton, B.W.
1991-12-01
As with many other linear algebra algorithms, devising a portable implementation of sparse Cholesky factorization that performs well on the broad range of computer architectures currently available is a formidable challenge. Even after limiting our attention to machines with only one processor, as we have done in this report, there are still several interesting issues to consider. For dense matrices, it is well known that block factorization algorithms are the best means of achieving this goal. We take this approach for sparse factorization as well. This paper has two primary goals. First, we examine two sparse Cholesky factorization algorithms, the multifrontal method and a blocked left-looking sparse Cholesky method, in a systematic and consistent fashion, both to illustrate the strengths of the blocking techniques in general and to obtain a fair evaluation of the two approaches. Second, we assess the impact of various implementation techniques on time and storage efficiency, paying particularly close attention to the work-storage requirement of the two methods and their variants.
HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION
Mukherjee, Rajarshi; Pillai, Natesh S.; Lin, Xihong
2015-01-01
In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression. We derive the detection boundary as a function of two components: a design matrix sparsity index and signal strength, each of which is a function of the sparsity of the alternative. For any alternative, if the design matrix sparsity index is too high, any test is asymptotically powerless irrespective of the magnitude of signal strength. For binary design matrices with the sparsity index that is not too high, our results are parallel to those in the Gaussian case. In this context, we derive detection boundaries for both dense and sparse regimes. For the dense regime, we show that the generalized likelihood ratio is rate optimal; for the sparse regime, we propose an extended Higher Criticism Test and show it is rate optimal and sharp. We illustrate the finite sample properties of the theoretical results using simulation studies. PMID:26246645
Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules
Sacramento, João; Wichert, Andreas; van Rossum, Mark C. W.
2015-01-01
It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L 1-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum. PMID:26046817
Sparse matrix methods based on orthogonality and conjugacy
NASA Technical Reports Server (NTRS)
Lawson, C. L.
1973-01-01
A matrix having a high percentage of zero elements is called spares. In the solution of systems of linear equations or linear least squares problems involving large sparse matrices, significant saving of computer cost can be achieved by taking advantage of the sparsity. The conjugate gradient algorithm and a set of related algorithms are described.
New Algorithms and Sparse Regularization for Synthetic Aperture Radar Imaging
2015-10-26
Demanet Department of Mathematics Massachusetts Institute of Technology. • Grant title: New Algorithms and Sparse Regularization for Synthetic Aperture...statistical analysis of one such method, the so-called MUSIC algorithm (multiple signal classification). We have a publication that mathematically justifies...called MUSIC algorithm (multiple signal classification). We have a publication that mathematically justifies the scaling of the phase transition
Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection
Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max; ...
2016-08-04
This work proposes a hyperspherical sparse approximation framework for detecting jump discontinuities in functions in high-dimensional spaces. The need for a novel approach results from the theoretical and computational inefficiencies of well-known approaches, such as adaptive sparse grids, for discontinuity detection. Our approach constructs the hyperspherical coordinate representation of the discontinuity surface of a function. Then sparse approximations of the transformed function are built in the hyperspherical coordinate system, with values at each point estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the new technique can identify jump discontinuities with significantly reduced computationalmore » cost, compared to existing methods. Several approaches are used to approximate the transformed discontinuity surface in the hyperspherical system, including adaptive sparse grid and radial basis function interpolation, discrete least squares projection, and compressed sensing approximation. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. In conclusion, rigorous complexity analyses of the new methods are provided, as are several numerical examples that illustrate the effectiveness of our approach.« less
Beam hardening correction for sparse-view CT reconstruction
NASA Astrophysics Data System (ADS)
Liu, Wenlei; Rong, Junyan; Gao, Peng; Liao, Qimei; Lu, HongBing
2015-03-01
Beam hardening, which is caused by spectrum polychromatism of the X-ray beam, may result in various artifacts in the reconstructed image and degrade image quality. The artifacts would be further aggravated for the sparse-view reconstruction due to insufficient sampling data. Considering the advantages of the total-variation (TV) minimization in CT reconstruction with sparse-view data, in this paper, we propose a beam hardening correction method for sparse-view CT reconstruction based on Brabant's modeling. In this correction model for beam hardening, the attenuation coefficient of each voxel at the effective energy is modeled and estimated linearly, and can be applied in an iterative framework, such as simultaneous algebraic reconstruction technique (SART). By integrating the correction model into the forward projector of the algebraic reconstruction technique (ART), the TV minimization can recover images when only a limited number of projections are available. The proposed method does not need prior information about the beam spectrum. Preliminary validation using Monte Carlo simulations indicates that the proposed method can provide better reconstructed images from sparse-view projection data, with effective suppression of artifacts caused by beam hardening. With appropriate modeling of other degrading effects such as photon scattering, the proposed framework may provide a new way for low-dose CT imaging.
Neogene biomarker record of vegetation change in eastern Africa.
Uno, Kevin T; Polissar, Pratigya J; Jackson, Kevin E; deMenocal, Peter B
2016-06-07
The evolution of C4 grassland ecosystems in eastern Africa has been intensely studied because of the potential influence of vegetation on mammalian evolution, including that of our own lineage, hominins. Although a handful of sparse vegetation records exists from middle and early Miocene terrestrial fossil sites, there is no comprehensive record of vegetation through the Neogene. Here we present a vegetation record spanning the Neogene and Quaternary Periods that documents the appearance and subsequent expansion of C4 grasslands in eastern Africa. Carbon isotope ratios from terrestrial plant wax biomarkers deposited in marine sediments indicate constant C3 vegetation from ∼24 Ma to 10 Ma, when C4 grasses first appeared. From this time forward, C4 vegetation increases monotonically to present, with a coherent signal between marine core sites located in the Somali Basin and the Red Sea. The response of mammalian herbivores to the appearance of C4 grasses at 10 Ma is immediate, as evidenced from existing records of mammalian diets from isotopic analyses of tooth enamel. The expansion of C4 vegetation in eastern Africa is broadly mirrored by increasing proportions of C4-based foods in hominin diets, beginning at 3.8 Ma in Australopithecus and, slightly later, Kenyanthropus This continues into the late Pleistocene in Paranthropus, whereas Homo maintains a flexible diet. The biomarker vegetation record suggests the increase in open, C4 grassland ecosystems over the last 10 Ma may have operated as a selection pressure for traits and behaviors in Homo such as bipedalism, flexible diets, and complex social structure.
Neogene biomarker record of vegetation change in eastern Africa
Polissar, Pratigya J.; Jackson, Kevin E.; deMenocal, Peter B.
2016-01-01
The evolution of C4 grassland ecosystems in eastern Africa has been intensely studied because of the potential influence of vegetation on mammalian evolution, including that of our own lineage, hominins. Although a handful of sparse vegetation records exists from middle and early Miocene terrestrial fossil sites, there is no comprehensive record of vegetation through the Neogene. Here we present a vegetation record spanning the Neogene and Quaternary Periods that documents the appearance and subsequent expansion of C4 grasslands in eastern Africa. Carbon isotope ratios from terrestrial plant wax biomarkers deposited in marine sediments indicate constant C3 vegetation from ∼24 Ma to 10 Ma, when C4 grasses first appeared. From this time forward, C4 vegetation increases monotonically to present, with a coherent signal between marine core sites located in the Somali Basin and the Red Sea. The response of mammalian herbivores to the appearance of C4 grasses at 10 Ma is immediate, as evidenced from existing records of mammalian diets from isotopic analyses of tooth enamel. The expansion of C4 vegetation in eastern Africa is broadly mirrored by increasing proportions of C4-based foods in hominin diets, beginning at 3.8 Ma in Australopithecus and, slightly later, Kenyanthropus. This continues into the late Pleistocene in Paranthropus, whereas Homo maintains a flexible diet. The biomarker vegetation record suggests the increase in open, C4 grassland ecosystems over the last 10 Ma may have operated as a selection pressure for traits and behaviors in Homo such as bipedalism, flexible diets, and complex social structure. PMID:27274042
Deformable segmentation via sparse representation and dictionary learning.
Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N
2012-10-01
"Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy.
Cruciferous Vegetables and Cancer Prevention
... cruciferous vegetables? Cruciferous vegetables are part of the Brassica genus of plants. They include the following vegetables, ... others: Arugula Bok choy Broccoli Brussels sprouts Cabbage Cauliflower Collard greens Horseradish Kale Radishes Rutabaga Turnips Watercress ...
EFFECTS OF VEGETATION ON TURBULENCE, SEDIMENT TRANSPORT AND STREAM MORPHOLOGY
Neary, Vincent S
2012-01-01
Vegetation, from an individual stem to multiple stems in various configurations, profoundly alters turbulent flows. These alterations influence sediment transport and stream morphology, but depend on complex interactions and relationships between flow, plants and sediment properties. This is illustrated for three case studies that represent a variety of macrophyte patterns and scales in the environment: flows through simulated uniformly distributed plant stems, emergent and submerged; flows with alternating simulated stem patches; and flow around an isolated stem in a flood plain. The emergent case demonstrates that when density is sparse the mean velocity and turbulence intensities vary horizontally around the stems, which would promote a heterogeneous bedform morphology. However, it is still unclear how density, submergence ratio, and flow Reynolds number, in combination, influence interference effects, vortex shedding and dissipation, and velocity, pressure and lift fluctuations that affect sediment entrainment. The submerged case demonstrates significant reduction of the mean velocity, turbulence intensities, and turbulent shear near the bed compared to an unobstructed flow and supports numerous observations that vegetation promotes deposition or stabilizes bed sediments. The case of alternating emergent vegetation patches illustrates how vegetation adjusts the bed promoting scour in open water and deposition within the patches. The isolated stem case illustrates the variety of coherent structures generated, their complex interaction, and their role in specific sediment transport phenomena observed. Additional research is required, however, to quantify thresholds and relationships for flow-vegetation-sediment interactions so that aquatic macrophyte plantings can be used more effectively in water resource management.
Vegetable Production System (Veggie)
NASA Technical Reports Server (NTRS)
Levine, Howard G.; Smith, Trent M.
2016-01-01
The Vegetable Production System (Veggie) was developed by Orbital Technologies Corp. to be a simple, easily stowed, and high growth volume yet low resource facility capable of producing fresh vegetables on the International Space Station (ISS). In addition to growing vegetables in space, Veggie can support a variety of experiments designed to determine how plants respond to microgravity, provide real-time psychological benefits for the crew, and conduct outreach activities. Currently, Veggie provides the largest volume available for plant growth on the ISS.
Vegetation against dune mobility.
Durán, Orencio; Herrmann, Hans J
2006-11-03
Vegetation is the most common and most reliable stabilizer of loose soil or sand. This ancient technique is for the first time cast into a set of equations of motion describing the competition between aeolian sand transport and vegetation growth. Our set of equations is then applied to study quantitatively the transition between barchans and parabolic dunes driven by the dimensionless fixation index theta which is the ratio between the dune characteristic erosion rate and vegetation growth velocity. We find a fixation index theta(c) below which the dunes are stabilized, characterized by scaling laws.
NASA Technical Reports Server (NTRS)
vanLeeuwen, W. J. D.; Huete, A. R.; Duncan, J.; Franklin, J.
1994-01-01
A shrub savannah landscape in Niger was optically characterized utilizing blue, green, red and near-infrared wavelengths. Selected vegetation indices were evaluated for their performance and sensitivity to describe the complex Sahelian soil/vegetation canopies. Bidirectional reflectance factors (BRF) of plants and soils were measured at several view angles, and used as input to various vegetation indices. Both soil and vegetation targets had strong anisotropic reflectance properties, rendering all vegetation index (6) responses to be a direct function of sun and view geometry. Soil background influences were shown to alter the response of most vegetation indices. N-space greenness had the smallest dynamic range in VI response, but the n-space brightness index provided additional useful information. The global environmental monitoring index (GEMI) showed a large 6 dynamic range for bare soils, which was undesirable for a vegetation index. The view angle response of the normalized difference vegetation index (NDVI), atmosphere resistant vegetation index (ARVI) and soil atmosphere resistant vegetation index (SARVI) were asymmetric about nadir for multiple view angles, and were, except for the SARVI, altered seriously by soil moisture and/or soil brightness effects. The soil adjusted vegetation index (SAVI) was least affected by surface soil moisture and was symmetric about nadir for grass vegetation covers. Overall the SAVI, SARVI and the n-space vegetation index performed best under all adverse conditions and were recommended to monitor vegetation growth in the sparsely vegetated Sahelian zone.
... Vegetative State Legal Issues Sleeping Problems Anxiety & Stress Concussion / Mild TBI Living with Traumatic Brain Injury Speech & ... Conscious States After Severe Brain Injury Brain Trauma, Concussion, and Coma What Is the Glasgow Coma Scale? ...
Global Enhanced Vegetation Index
NASA Technical Reports Server (NTRS)
2002-01-01
By carefully measuring the wavelengths and intensity of visible and near-infrared light reflected by the land surface back up into space, the Moderate-resolution Imaging Spectroradiometer (MODIS) Team can quantify the concentrations of green leaf vegetation around the world. The above MODIS Enhanced Vegetation Index (EVI) map shows the density of plant growth over the entire globe. Very low values of EVI (white and brown areas) correspond to barren areas of rock, sand, or snow. Moderate values (light greens) represent shrub and grassland, while high values indicate temperate and tropical rainforests (dark greens). The MODIS EVI gives scientists a new tool for monitoring major fluctuations in vegetation and understanding how they affect, and are affected by, regional climate trends. For more information, read NASA Unveils Spectacular Suite of New Global Data Products from MODIS. Image courtesy MODIS Land Group/Vegetation Indices, Alfredo Huete, Principal Investigator, and Kamel Didan, University of Arizona
Vegetative pyoderma gangrenosum.
Kim, Randie H; Lewin, Jesse; Hale, Christopher S; Meehan, Shane A; Stein, Jennifer; Ramachandran, Sarika
2014-12-16
Vegetative pyoderma gangrenosum is a rare, superficial variant of pyoderma gangrenosum that is more commonly found on the trunk as single or multiple, non-painful lesions. There is typically no associated underlying systemic disease. Compared to classic pyoderma gangrenosum, vegetative lesions are more likely to heal without the use of systemic glucocorticoids, although up to 39% of patients required a short course of prednisone in a review of 46 cases. Treatments for vegetative pyoderma gangrenosum include topical and intralesional glucocorticoids, minocycline or doxycycline, dapsone, colchicine, and, rarely, alternative steroid-sparing immunosuppressants. We present a case of multiple vegetative pyoderma gangrenosum lesions arising in prior surgical sites in a patient found to have IgA monoclonal gammopathy and abnormal urinary protein electrophoresis.
Pudel, Frank; Wiesen, Sebastian
2017-03-07
Conventional vegetable oil mills are complex plants, processing oil, fruits, or seeds to vegetable fats and oils of high quality and predefined properties. Nearly all by-products are used. However, most of the high valuable plant substances occurring in oil fruits or seeds besides the oil are used only in low price applications (proteins as animal feeding material) or not at all (e.g., phenolics). This chapter describes the state-of-the-art of extraction and use of oilseed/oil fruit proteins and phyto-nutrients in order to move from a conventional vegetable oil processing plant to a proper vegetable oil-biorefinery producing a wide range of different high value bio-based products.
Implementations and applications of the sparse Radon transform
NASA Astrophysics Data System (ADS)
Trad, Daniel Osvaldo
The Radon transform (RT) has many desirable properties which make it particularly useful for multiple removal and interpolation of seismic reflections. However many practical difficulties arise as a consequence of poor sampling and limited aperture in the offset dimension. Furthermore the ever increasing volumes of seismic data make computing time a key issue in any practical implementation. The standard implementations of the Radon transform used in seismic processing fulfill very well the requirement of a fast transform but do not allow proper handling of problems associated with sampling and aperture. Many of these difficulties can be partially solved by using inverse theory to compute the transform subject to a sparseness constraint. The main thrust and contribution of this thesis lies in the exploration of the sparseness constraint and the design and implementation of the RT. Although this technique reduces sampling and aperture problems, it also considerably increases the computation time. Some of the possible solutions to decrease computation time discussed in this thesis are conjugate gradient methods, irregularly sampled model space and efficient operations with sparse matrices. Real data often have very complicated structure that makes sparseness criteria difficult to implement. Noise, non hyperbolic events and amplitude variation with offset conspire against sparseness. Therefore, the success of this method depends on the applied algorithms and on a delicate balance between sparseness and fit of data. Several real data examples of multiple removal and interpolation show the success of the proposed algorithms to achieve this balance. Another aspect of this thesis is the design of new applications for the Radon transform in seismic processing. Many new applications can be developed by designing new types of Radon operators. Some new applications implemented in this thesis are elliptical and hybrid linear-hyperbolic Radon transforms that should prove to be
NASA Technical Reports Server (NTRS)
Macdonald, R. B.; Houston, A. G.; Heydorn, R. P.; Botkin, D. B.; Estes, J. E.; Strahler, A. H.
1981-01-01
An attempt is made to identify the need for, and the current capability of, a technology which could aid in monitoring the Earth's vegetation resource on a global scale. Vegetation is one of our most critical natural resources, and accurate timely information on its current status and temporal dynamics is essential to understand many basic and applied environmental interrelationships which exist on the small but complex planet Earth.
Burke, M.K.; King, S.L.; Eisenbies, M.H.; Gartner, D.
2000-01-01
Intro paragraph: Characterization of bottomland hardwood vegetation in relatively undisturbed forests can provide critical information for developing effective wetland creation and restoration techniques and for assessing the impacts of management and development. Classification is a useful technique in characterizing vegetation because it summarizes complex data sets, assists in hypothesis generation about factors influencing community variation, and helps refine models of community structure. Hierarchical classification of communities is particularly useful for showing relationships among samples (Gauche 1982).
Not Available
1982-01-01
Fifty contributions (presentations) involving more than one hundred people worldwide were given at the International Conference on Plant and Vegetable Oils as Fuels. The proceedings were in Fargo, North Dakota, from August 2-4, 1982. The conference helped to promote renewable fuels, bio-oils, from plant and vegetable oils. Separate abstracts were prepared for 44 items for inclusion in the Energy Data Base.
ERIC Educational Resources Information Center
Piippo, Teuvo; Lihr, Silja
Since half of Finland is sparsely populated, the Finland National Board of Education has initiated a preschool project for sparsely populated areas. Project goals are defined as the acquisition through research, experiment, and planning of information relative to sparsely populated areas and special problems of distance, small population base,…
Optimal Dictionaries for Sparse Solutions of Multi-frame Blind Deconvolution
2014-09-01
Optimal Dictionaries for Sparse Solutions of Multi-frame Blind Deconvolution B. R. Hunt...overcomplete dictionaries from atmospheric turbulence data. Implications for blind - deconvolution of turbulent images are discussed. The application of sparse...dictionaries is demonstrated by the employment of sparse PSF representations to formulate a multi-frame blind deconvolution (MFBD) algorithm. We
Disentangling giant component and finite cluster contributions in sparse random matrix spectra.
Kühn, Reimer
2016-04-01
We describe a method for disentangling giant component and finite cluster contributions to sparse random matrix spectra, using sparse symmetric random matrices defined on Erdős-Rényi graphs as an example and test bed. Our methods apply to sparse matrices defined in terms of arbitrary graphs in the configuration model class, as long as they have finite mean degree.
Parallel Sparse Linear System and Eigenvalue Problem Solvers: From Multicore to Petascale Computing
2015-06-01
addressed designing: (1) Tools for sparse matrix primtives such as sparse matrix‐ vector multiplication (and sparse matrix‐multivector...of the Harwell Subroutine Library (HSL), while our symmetric reordering is based on the Fiedler vector of the corresponding...and extended through this grant) for obtaining the Fiedler vector rather than the multilevel eigensolver used in MC73 which
Aranceta, Javier
2004-06-01
Fruits and vegetables are particularly interesting for health for their content in minerals, antioxidant vitamins, phytochemicals and dietary fiber. All these substances are related to lower risk for the development of health probems, such as certain types of cancer, cardiovascular diseases, type 2 diabetes, obesity, constipation or diverticolsys. The sound basis of scientific evidence led European and American scientific organizations and societies to recommend an intake up to 150-200 g of vegetables every day; ie. 2 or more portions daily and 3 or more portions of fruit; five portions of fruit and vegetables all together. According to the consumer panel from the Spanish Ministry of Agriculture, Fisheries and Food, between the late 80s and the end of the 90s. consumption of fruit and vegetables decreased. However, in late years this trend has slow down and even reversed. Results from food consumption studies based on individual level assessment in Spain estimate an average consumption of fruit and vegetables of 154 g/per person/day in adults aged 25-60 yr. Prevalence of inadequate intake of fruit and vegetables is high among children and young people. In this age group above 70% of the population consume less than 3 portions of fruit every day on average. Reorientation of prevailing food patterns nowadays require investment in measures aimed at increasing the consumption of plant foods and estimulate healthy food habits in families.
Bessler, T.R.
1986-05-13
A process is described for preparing an injectable vegetable oil selected from the group consisting of soybean oil and sunflower oil and mixtures thereof which comprise: (a) first treating the vegetable oil at a temperature of 80/sup 0/C to about 130/sup 0/C with an acid clay; (b) deodorizing the vegetable oil with steam at a temperature of 220/sup 0/C to about 280/sup 0/C and applying a vacuum to remove volatilized components; (c) treating the deodorized vegetable oil, at a temperature of from about 10/sup 0/C to about 60/sup 0/C, with an acid clay to reduce the content of a member selected from the group consisting of diglycerides, tocopherol components, and trilinolenin and mixtures thereof, wherein the acid clay is added in a weight ratio to the deoderized vegetable oil of from about 1:99 to about 1:1; and (d) thereafter conducting a particulate filtration to remove a substantial portion of the acid clay from the vegetable oil, wherein the filtration is accomplished with filters having a pore size of from about 0.1 to 0.45 microns, thereby obtaining the injectable oil.
Sparse matrix methods research using the CSM testbed software system
NASA Technical Reports Server (NTRS)
Chu, Eleanor; George, J. Alan
1989-01-01
Research is described on sparse matrix techniques for the Computational Structural Mechanics (CSM) Testbed. The primary objective was to compare the performance of state-of-the-art techniques for solving sparse systems with those that are currently available in the CSM Testbed. Thus, one of the first tasks was to become familiar with the structure of the testbed, and to install some or all of the SPARSPAK package in the testbed. A suite of subroutines to extract from the data base the relevant structural and numerical information about the matrix equations was written, and all the demonstration problems distributed with the testbed were successfully solved. These codes were documented, and performance studies comparing the SPARSPAK technology to the methods currently in the testbed were completed. In addition, some preliminary studies were done comparing some recently developed out-of-core techniques with the performance of the testbed processor INV.
Nucleotide sequence alignment using sparse coding and belief propagation.
Roozgard, Aminmohammad; Barzigar, Nafise; Wang, Shuang; Jiang, Xiaoqian; Ohno-Machado, Lucila; Cheng, Samuel
2013-01-01
Advances in DNA information extraction techniques have led to huge sequenced genomes from organisms spanning the tree of life. This increasing amount of genomic information requires tools for comparison of the nucleotide sequences. In this paper, we propose a novel nucleotide sequence alignment method based on sparse coding and belief propagation to compare the similarity of the nucleotide sequences. We used the neighbors of each nucleotide as features, and then we employed sparse coding to find a set of candidate nucleotides. To select optimum matches, belief propagation was subsequently applied to these candidate nucleotides. Experimental results show that the proposed approach is able to robustly align nucleotide sequences and is competitive to SOAPaligner [1] and BWA [2].
Partial Correlation Estimation by Joint Sparse Regression Models
Peng, Jie; Wang, Pei; Zhou, Nengfeng; Zhu, Ji
2009-01-01
In this paper, we propose a computationally efficient approach —space(Sparse PArtial Correlation Estimation)— for selecting non-zero partial correlations under the high-dimension-low-sample-size setting. This method assumes the overall sparsity of the partial correlation matrix and employs sparse regression techniques for model fitting. We illustrate the performance of space by extensive simulation studies. It is shown that space performs well in both non-zero partial correlation selection and the identification of hub variables, and also outperforms two existing methods. We then apply space to a microarray breast cancer data set and identify a set of hub genes which may provide important insights on genetic regulatory networks. Finally, we prove that, under a set of suitable assumptions, the proposed procedure is asymptotically consistent in terms of model selection and parameter estimation. PMID:19881892
Non-convex Statistical Optimization for Sparse Tensor Graphical Model
Sun, Wei; Wang, Zhaoran; Liu, Han; Cheng, Guang
2016-01-01
We consider the estimation of sparse graphical models that characterize the dependency structure of high-dimensional tensor-valued data. To facilitate the estimation of the precision matrix corresponding to each way of the tensor, we assume the data follow a tensor normal distribution whose covariance has a Kronecker product structure. The penalized maximum likelihood estimation of this model involves minimizing a non-convex objective function. In spite of the non-convexity of this estimation problem, we prove that an alternating minimization algorithm, which iteratively estimates each sparse precision matrix while fixing the others, attains an estimator with the optimal statistical rate of convergence as well as consistent graph recovery. Notably, such an estimator achieves estimation consistency with only one tensor sample, which is unobserved in previous work. Our theoretical results are backed by thorough numerical studies.
Accelerated Gibbs Sampling for Infinite Sparse Factor Analysis
Andrzejewski, D M
2011-09-12
The Indian Buffet Process (IBP) gives a probabilistic model of sparse binary matrices with an unbounded number of columns. This construct can be used, for example, to model a fixed numer of observed data points (rows) associated with an unknown number of latent features (columns). Markov Chain Monte Carlo (MCMC) methods are often used for IBP inference, and in this technical note, we provide a detailed review of the derivations of collapsed and accelerated Gibbs samplers for the linear-Gaussian infinite latent feature model. We also discuss and explain update equations for hyperparameter resampling in a 'full Bayesian' treatment and present a novel slice sampler capable of extending the accelerated Gibbs sampler to the case of infinite sparse factor analysis by allowing the use of real-valued latent features.
Sparse model selection in the highly under-sampled regime
NASA Astrophysics Data System (ADS)
Bulso, Nicola; Marsili, Matteo; Roudi, Yasser
2016-09-01
We propose a method for recovering the structure of a sparse undirected graphical model when very few samples are available. The method decides about the presence or absence of bonds between pairs of variable by considering one pair at a time and using a closed form formula, analytically derived by calculating the posterior probability for every possible model explaining a two body system using Jeffreys prior. The approach does not rely on the optimization of any cost functions and consequently is much faster than existing algorithms. Despite this time and computational advantage, numerical results show that for several sparse topologies the algorithm is comparable to the best existing algorithms, and is more accurate in the presence of hidden variables. We apply this approach to the analysis of US stock market data and to neural data, in order to show its efficiency in recovering robust statistical dependencies in real data with non-stationary correlations in time and/or space.
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices.
Cai, Tony; Ma, Zongming; Wu, Yihong
2015-04-01
This paper considers a sparse spiked covariancematrix model in the high-dimensional setting and studies the minimax estimation of the covariance matrix and the principal subspace as well as the minimax rank detection. The optimal rate of convergence for estimating the spiked covariance matrix under the spectral norm is established, which requires significantly different techniques from those for estimating other structured covariance matrices such as bandable or sparse covariance matrices. We also establish the minimax rate under the spectral norm for estimating the principal subspace, the primary object of interest in principal component analysis. In addition, the optimal rate for the rank detection boundary is obtained. This result also resolves the gap in a recent paper by Berthet and Rigollet [2] where the special case of rank one is considered.
Hierarchy-Direction Selective Approach for Locally Adaptive Sparse Grids
Stoyanov, Miroslav K
2013-09-01
We consider the problem of multidimensional adaptive hierarchical interpolation. We use sparse grids points and functions that are induced from a one dimensional hierarchical rule via tensor products. The classical locally adaptive sparse grid algorithm uses an isotropic refinement from the coarser to the denser levels of the hierarchy. However, the multidimensional hierarchy provides a more complex structure that allows for various anisotropic and hierarchy selective refinement techniques. We consider the more advanced refinement techniques and apply them to a number of simple test functions chosen to demonstrate the various advantages and disadvantages of each method. While there is no refinement scheme that is optimal for all functions, the fully adaptive family-direction-selective technique is usually more stable and requires fewer samples.
Estimation of Sparse Directed Acyclic Graphs for Multivariate Counts Data
Han, Sung Won; Zhong, Hua
2016-01-01
Summary The next-generation sequencing data, called high throughput sequencing data, are recorded as count data, which is generally far from normal distribution. Under the assumption that the count data follow the Poisson log-normal distribution, this paper provides an L1-penalized likelihood framework and an efficient search algorithm to estimate the structure of sparse directed acyclic graphs (DAGs) for multivariate counts data. In searching for the solution, we use iterative optimization procedures to estimate the adjacency matrix and the variance matrix of the latent variables. The simulation result shows that our proposed method outperforms the approach which assumes multivariate normal distributions, and the log-transformation approach. It also shows that the proposed method outperforms the rank-based PC method under sparse network or hub network structures. As a real data example, we demonstrate the efficiency of the proposed method in estimating the gene regulatory networks of the ovarian cancer study. PMID:26849781
Sparse cortical source localization using spatio-temporal atoms.
Korats, Gundars; Ranta, Radu; Le Cam, Steven; Louis-Dorr, Valérie
2015-01-01
This paper addresses the problem of sparse localization of cortical sources from scalp EEG recordings. Localization algorithms use propagation model under spatial and/or temporal constraints, but their performance highly depends on the data signal-to-noise ratio (SNR). In this work we propose a dictionary based sparse localization method which uses a data driven spatio-temporal dictionary to reconstruct the measurements using Single Best Replacement (SBR) and Continuation Single Best Replacement (CSBR) algorithms. We tested and compared our methods with the well-known MUSIC and RAP-MUSIC algorithms on simulated realistic data. Tests were carried out for different noise levels. The results show that our method has a strong advantage over MUSIC-type methods in case of synchronized sources.
Sparse representation-based spectral clustering for SAR image segmentation
NASA Astrophysics Data System (ADS)
Zhang, Xiangrong; Wei, Zhengli; Feng, Jie; Jiao, Licheng
2011-12-01
A new method, sparse representation based spectral clustering (SC) with Nyström method, is proposed for synthetic aperture radar (SAR) image segmentation. Different from the conventional SC, this proposed technique is developed by using the sparse coefficients which obtained by solving l1 minimization problem to construct the affinity matrix and the Nyström method is applied to alleviate the segmentation process. The advantage of our proposed method is that we do not need to select the scaling parameter in the Gaussian kernel function artificially. We apply the proposed method, k-means and the classic spectral clustering algorithm with Nyström method to SAR image segmentation. The results show that compared with the other two methods, the proposed method can obtain much better segmentation results.
Mineral identification in hyperspectral imaging using Sparse-PCA
NASA Astrophysics Data System (ADS)
Yousefi, Bardia; Sojasi, Saeed; Ibarra Castanedo, Clemente; Beaudoin, Georges; Huot, François; Maldague, Xavier P. V.; Chamberland, Martin; Lalonde, Erik
2016-05-01
Hyperspectral imaging has been considerably developed during the recent decades. The application of hyperspectral imagery and infrared thermography, particularly for the automatic identification of minerals from satellite images, has been the subject of several interesting researches. In this study, a method is presented for the automated identification of the mineral grains typically used from satellite imagery and adapted for analyzing collected sample grains in a laboratory environment. For this, an approach involving Sparse Principle Components Analysis (SPCA) based on spectral abundance mapping techniques (i.e. SAM, SID, NormXCorr) is proposed for extraction of the representative spectral features. It develops an approximation of endmember as a reference spectrum process through the highest sparse principle component of the pure mineral grains. Subsequently, the features categorized by kernel Extreme Learning Machine (Kernel- ELM) classify and identify the mineral grains in a supervised manner. Classification is conducted in the binary scenario and the results indicate the dependency to the training spectra.
A Study of Filled and Sparse Line Array Beamformers.
1980-09-01
RESOLUTION TESI CHART NAIII NAt RItRI Al SI 1TANT ART 1161 A - LEVEL($ 0met 0o 1 A STUDY OF FILLED AND SPARSE LINE ARRAY BEAMFORMERS y! C- I L1 0___...8217 deviennent importantes . L’auteur d~montre que le PSB posse~de des avantages par rapport au CB pour produire le diagraune de directiviteA. Toutefois
Reconstruction techniques for sparse multistatic linear array microwave imaging
NASA Astrophysics Data System (ADS)
Sheen, David M.; Hall, Thomas E.
2014-06-01
Sequentially-switched linear arrays are an enabling technology for a number of near-field microwave imaging applications. Electronically sequencing along the array axis followed by mechanical scanning along an orthogonal axis allows dense sampling of a two-dimensional aperture in near real-time. The Pacific Northwest National Laboratory (PNNL) has developed this technology for several applications including concealed weapon detection, groundpenetrating radar, and non-destructive inspection and evaluation. These techniques form three-dimensional images by scanning a diverging beam swept frequency transceiver over a two-dimensional aperture and mathematically focusing or reconstructing the data into three-dimensional images. Recently, a sparse multi-static array technology has been developed that reduces the number of antennas required to densely sample the linear array axis of the spatial aperture. This allows a significant reduction in cost and complexity of the linear-array-based imaging system. The sparse array has been specifically designed to be compatible with Fourier-Transform-based image reconstruction techniques; however, there are limitations to the use of these techniques, especially for extreme near-field operation. In the extreme near-field of the array, back-projection techniques have been developed that account for the exact location of each transmitter and receiver in the linear array and the 3-D image location. In this paper, the sparse array technique will be described along with associated Fourier-Transform-based and back-projection-based image reconstruction algorithms. Simulated imaging results are presented that show the effectiveness of the sparse array technique along with the merits and weaknesses of each image reconstruction approach.
Mathematical strategies for filtering complex systems: Regularly spaced sparse observations
Harlim, J. Majda, A.J.
2008-05-01
Real time filtering of noisy turbulent signals through sparse observations on a regularly spaced mesh is a notoriously difficult and important prototype filtering problem. Simpler off-line test criteria are proposed here as guidelines for filter performance for these stiff multi-scale filtering problems in the context of linear stochastic partial differential equations with turbulent solutions. Filtering turbulent solutions of the stochastically forced dissipative advection equation through sparse observations is developed as a stringent test bed for filter performance with sparse regular observations. The standard ensemble transform Kalman filter (ETKF) has poor skill on the test bed and even suffers from filter divergence, surprisingly, at observable times with resonant mean forcing and a decaying energy spectrum in the partially observed signal. Systematic alternative filtering strategies are developed here including the Fourier Domain Kalman Filter (FDKF) and various reduced filters called Strongly Damped Approximate Filter (SDAF), Variance Strongly Damped Approximate Filter (VSDAF), and Reduced Fourier Domain Kalman Filter (RFDKF) which operate only on the primary Fourier modes associated with the sparse observation mesh while nevertheless, incorporating into the approximate filter various features of the interaction with the remaining modes. It is shown below that these much cheaper alternative filters have significant skill on the test bed of turbulent solutions which exceeds ETKF and in various regimes often exceeds FDKF, provided that the approximate filters are guided by the off-line test criteria. The skill of the various approximate filters depends on the energy spectrum of the turbulent signal and the observation time relative to the decorrelation time of the turbulence at a given spatial scale in a precise fashion elucidated here.
High Angular Resolution Microwave Sensing with Large, Sparse, Random Arrays
1983-11-01
MICROWAVE SENSING WITH LARGE, SPARSE, RANDOM ARRAYS Final Scientific Report AIR FORCE OFFICE OF SCIENTIFIC RESEARCH AFOSR 82-0012 Valley Forge Research ...CONTROLLING OFFICE NAME AND ADDRESS 12. REPORT DATE Air Force Office of Scientific Research /NE Nov 1983 - . Bildin 41073. NUMBER Or PAG ES BOllinZ AFB, DIC...Air Force Office of Scientific Research , under Grant No. AFOSR-78-3688. March, 1981 QPR No. 37 VFRC QPR No. 37 A-1 S
Learning Multiscale Sparse Representations for Image and Video Restoration (PREPRINT)
2007-07-01
video denoising [35]. In this paper, we extend the basic K- SVD work, providing a framework for learning multiscale and sparse image representation. In... denoising algorithm [1], the extensions to color image denoising , non-homogeneous noise, and inpainting [25], and the K- SVD for denoising videos [35]. Section...improvements to the original single-scale K- SVD . Section 6 presents some applications of the multiscale K- SVD , covering grayscale and color image denoising
Sparse Coding and Dictionary Learning Based on the MDL Principle
2010-10-01
dependencies, in a natural way. We demonstrate the performance of the proposed framework with results for image denoising and classification tasks...The idea of using MDL for sparse signal coding was explored in the context of wavelet-based image denoising [6, 7]. These pioneer- ing works were...restricted to denoising using fixed orthonormal basis (wavelets). In addition, the underlying probabilistic models used to describe the transform
Sparse Modeling of Human Actions from Motion Imagery
2011-09-02
classification [23, 24], hyperspectral imag- ing [5, 6], among numerous other applications. It has also been applied recently for motion imagery analysis... CLASSIFICATION OF: 17. LIMITATION OF ABSTRACT Same as Report (SAR) 18. NUMBER OF PAGES 26 19a. NAME OF RESPONSIBLE PERSON a. REPORT...Actions from Motion Imagery Alexey Castrodad and Guillermo Sapiro ∗ September 2, 2011 Abstract An efficient sparse modeling pipeline for the classification
A multi-level method for sparse linear systems
Shapira, Y.
1997-09-01
A multi-level method for the solution of sparse linear systems is introduced. The definition of the method is based on data from the coefficient matrix alone. An upper bound for the condition number is available for certain symmetric positive definite (SPD) problems. Numerical experiments confirm the analysis and illustrate the efficiency of the method for diffusion problems with discontinuous coefficients with discontinuities which are not aligned with the coarse meshes.
Partitioning Rectangular and Structurally Nonsymmetric Sparse Matrices for Parallel Processing
B. Hendrickson; T.G. Kolda
1998-09-01
A common operation in scientific computing is the multiplication of a sparse, rectangular or structurally nonsymmetric matrix and a vector. In many applications the matrix- transpose-vector product is also required. This paper addresses the efficient parallelization of these operations. We show that the problem can be expressed in terms of partitioning bipartite graphs. We then introduce several algorithms for this partitioning problem and compare their performance on a set of test matrices.
Informative Feature Selection for Object Recognition via Sparse PCA
2011-04-07
the BMW database [17] are used for training. For each image pair in SfM, SURF features are deemed informative if the consensus of the corresponding...observe that the first two sparse PVs are sufficient for selecting in- formative features that lie on the foreground objects in the BMW database (as... BMW ) database [17]. The database consists of multiple-view images of 20 landmark buildings on the Berkeley campus. For each building, wide-baseline
Eddy-current NDE inverse problem with sparse grid algorithm
NASA Astrophysics Data System (ADS)
Zhou, Liming; Sabbagh, Harold A.; Sabbagh, Elias H.; Murphy, R. Kim; Bernacchi, William; Aldrin, John C.; Forsyth, David; Lindgren, Eric
2016-02-01
In model-based inverse problems, the unknown parameters (such as length, width, depth) need to be estimated. When the unknown parameters are few, the conventional mathematical methods are suitable. But the increasing number of unknown parameters will make the computation become heavy. To reduce the burden of computation, the sparse grid algorithm was used in our work. As a result, we obtain a powerful interpolation method that requires significantly fewer support nodes than conventional interpolation on a full grid.
Iterative algorithms for large sparse linear systems on parallel computers
NASA Technical Reports Server (NTRS)
Adams, L. M.
1982-01-01
Algorithms for assembling in parallel the sparse system of linear equations that result from finite difference or finite element discretizations of elliptic partial differential equations, such as those that arise in structural engineering are developed. Parallel linear stationary iterative algorithms and parallel preconditioned conjugate gradient algorithms are developed for solving these systems. In addition, a model for comparing parallel algorithms on array architectures is developed and results of this model for the algorithms are given.
Sparse repulsive coupling enhances synchronization in complex networks.
Leyva, I; Sendiña-Nadal, I; Almendral, J A; Sanjuán, M A F
2006-11-01
Through the last years, different strategies to enhance synchronization in complex networks have been proposed. In this work, we show that synchronization of nonidentical dynamical units that are attractively coupled in a small-world network is strongly improved by just making phase-repulsive a tiny fraction of the couplings. By a purely topological analysis that does not depend on the dynamical model, we link the emerging dynamical behavior with the structural properties of the sparsely coupled repulsive network.
Predicting cognitive data from medical images using sparse linear regression.
Kandel, Benjamin M; Wolk, David A; Gee, James C; Avants, Brian
2013-01-01
We present a new framework for predicting cognitive or other continuous-variable data from medical images. Current methods of probing the connection between medical images and other clinical data typically use voxel-based mass univariate approaches. These approaches do not take into account the multivariate, network-based interactions between the various areas of the brain and do not give readily interpretable metrics that describe how strongly cognitive function is related to neuroanatomical structure. On the other hand, high-dimensional machine learning techniques do not typically provide a direct method for discovering which parts of the brain are used for making predictions. We present a framework, based on recent work in sparse linear regression, that addresses both drawbacks of mass univariate approaches, while preserving the direct spatial interpretability that they provide. In addition, we present a novel optimization algorithm that adapts the conjugate gradient method for sparse regression on medical imaging data. This algorithm produces coefficients that are more interpretable than existing sparse regression techniques.
Extracting pure endmembers using symmetric sparse representation for hyperspectral imagery
NASA Astrophysics Data System (ADS)
Sun, Weiwei; Liu, Chun; Sun, Yanwei; Li, Weiyue; Li, Jialin
2016-10-01
This article proposes a symmetric sparse representation (SSR) method to extract pure endmembers from hyperspectral imagery (HSI). The SSR combines the features of the linear unmixing model and the sparse subspace clustering model of endmembers, and it assumes that the desired endmembers and all the HSI pixel points can be sparsely represented by each other. It formulates the endmember extraction problem into a famous program of archetypal analysis, and accordingly, extracting pure endmembers can be transformed as finding the archetypes in the minimal convex hull containing all the HSI pixel points. The vector quantization scheme is adopted to help in carefully choosing the initial pure endmembers, and the archetypal analysis program is solved using the simple projected gradient algorithm. Seven state-of-the-art methods are implemented to make comparisons with the SSR on both synthetic and real hyperspectral images. Experimental results show that the SSR outperforms all the seven methods in spectral angle distance and root-mean-square error, and it can be a good alternative choice for extracting pure endmembers from HSI data.
Detail-preserving controllable deformation from sparse examples.
Huang, Haoda; Yin, KangKang; Zhao, Ling; Qi, Yue; Yu, Yizhou; Tong, Xin
2012-08-01
Recent advances in laser scanning technology have made it possible to faithfully scan a real object with tiny geometric details, such as pores and wrinkles. However, a faithful digital model should not only capture static details of the real counterpart but also be able to reproduce the deformed versions of such details. In this paper, we develop a data-driven model that has two components; the first accommodates smooth large-scale deformations and the second captures high-resolution details. Large-scale deformations are based on a nonlinear mapping between sparse control points and bone transformations. A global mapping, however, would fail to synthesize realistic geometries from sparse examples, for highly deformable models with a large range of motion. The key is to train a collection of mappings defined over regions locally in both the geometry and the pose space. Deformable fine-scale details are generated from a second nonlinear mapping between the control points and per-vertex displacements. We apply our modeling scheme to scanned human hand models, scanned face models, face models reconstructed from multiview video sequences, and manually constructed dinosaur models. Experiments show that our deformation models, learned from extremely sparse training data, are effective and robust in synthesizing highly deformable models with rich fine features, for keyframe animation as well as performance-driven animation. We also compare our results with those obtained by alternative techniques.
Compressed Sensing Doppler Ultrasound Reconstruction Using Block Sparse Bayesian Learning.
Lorintiu, Oana; Liebgott, Herve; Friboulet, Denis
2016-04-01
In this paper we propose a framework for using duplex Doppler ultrasound systems. These type of systems need to interleave the acquisition and display of a B-mode image and of the pulsed Doppler spectrogram. In a recent study (Richy , 2013), we have shown that compressed sensing-based reconstruction of Doppler signal allowed reducing the number of Doppler emissions and yielded better results than traditional interpolation and at least equivalent or even better depending on the configuration than the study estimating the signal from sparse data sets given in Jensen, 2006. We propose here to improve over this study by using a novel framework for randomly interleaving Doppler and US emissions. The proposed method reconstructs the Doppler signal segment by segment using a block sparse Bayesian learning (BSBL) algorithm based CS reconstruction. The interest of using such framework in the context of duplex Doppler is linked to the unique ability of BSBL to exploit block-correlated signals and to recover non-sparse signals. The performance of the technique is evaluated from simulated data as well as experimental in vivo data and compared to the recent results in Richy , 2013.
Vigilance detection based on sparse representation of EEG.
Yu, Hongbin; Lu, Hongtao; Ouyang, Tian; Liu, Hongjun; Lu, Bao-Liang
2010-01-01
Electroencephalogram (EEG) based vigilance detection of those people who engage in long time attention demanding tasks such as monotonous monitoring or driving is a key field in the research of brain-computer interface (BCI). However, robust detection of human vigilance from EEG is very difficult due to the low SNR nature of EEG signals. Recently, compressive sensing and sparse representation become successful tools in the fields of signal reconstruction and machine learning. In this paper, we propose to use the sparse representation of EEG to the vigilance detection problem. We first use continuous wavelet transform to extract the rhythm features of EEG data, and then employ the sparse representation method to the wavelet transform coefficients. We collect five subjects' EEG recordings in a simulation driving environment and apply the proposed method to detect the vigilance of the subjects. The experimental results show that the algorithm framework proposed in this paper can successfully estimate driver's vigilance with the average accuracy about 94.22 %. We also compare our algorithm framework with other vigilance estimation methods using different feature extraction and classifier selection approaches, the result shows that the proposed method has obvious advantages in the classification accuracy.
A comparison of methods for representing sparsely sampled random quantities.
Romero, Vicente Jose; Swiler, Laura Painton; Urbina, Angel; Mullins, Joshua
2013-09-01
This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the desired percentile range of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem interesting and difficult. In this report, five uncertainty representation techniques are characterized for their performance on twenty-one test problems (over thousands of trials for each problem) according to these two opposing objectives and other performance measures. Two of the methods, statistical Tolerance Intervals and a kernel density approach specifically developed for handling sparse data, exhibit significantly better overall performance than the others.
Finger vein verification system based on sparse representation.
Xin, Yang; Liu, Zhi; Zhang, Haixia; Zhang, Hong
2012-09-01
Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.
Cellular Adaptation Facilitates Sparse and Reliable Coding in Sensory Pathways
Farkhooi, Farzad; Froese, Anja; Muller, Eilif; Menzel, Randolf; Nawrot, Martin P.
2013-01-01
Most neurons in peripheral sensory pathways initially respond vigorously when a preferred stimulus is presented, but adapt as stimulation continues. It is unclear how this phenomenon affects stimulus coding in the later stages of sensory processing. Here, we show that a temporally sparse and reliable stimulus representation develops naturally in sequential stages of a sensory network with adapting neurons. As a modeling framework we employ a mean-field approach together with an adaptive population density treatment, accompanied by numerical simulations of spiking neural networks. We find that cellular adaptation plays a critical role in the dynamic reduction of the trial-by-trial variability of cortical spike responses by transiently suppressing self-generated fast fluctuations in the cortical balanced network. This provides an explanation for a widespread cortical phenomenon by a simple mechanism. We further show that in the insect olfactory system cellular adaptation is sufficient to explain the emergence of the temporally sparse and reliable stimulus representation in the mushroom body. Our results reveal a generic, biophysically plausible mechanism that can explain the emergence of a temporally sparse and reliable stimulus representation within a sequential processing architecture. PMID:24098101
Sparse codes of harmonic natural sounds and their modulatory interactions.
Terashima, Hiroki; Hosoya, Haruo
2009-01-01
Sparse coding and its related theories have been successful to explain various response properties of early stages of sensory information processing such as primary visual cortex and peripheral auditory system, which suggests that the emergence of such properties results from adaptation of the nerve system to natural stimuli. The present study continues this line of research in a higher stage of auditory processing, focusing on harmonic structures that are often found in behaviourally important natural sound like animal vocalization. It has been physiologically shown that monkey primary auditory cortices (A1) have neurons with response properties capturing such harmonic structures: their response and modulation peaks are often found at frequencies that are harmonically related to each other. We hypothesize that such relations emerge from sparse coding of harmonic natural sounds. Our simulation shows that similar harmonic relations emerge from frequency-domain sparse codes of harmonic sounds, namely, piano performance and human speech. Moreover, the modulatory behaviours can be explained by competitive interactions of model neurons that capture partially common harmonic structures.
Inversion of magnetotelluric data in a sparse model domain
NASA Astrophysics Data System (ADS)
Nittinger, Christian G.; Becken, Michael
2016-08-01
The inversion of magnetotelluric data into subsurface electrical conductivity poses an ill-posed problem. Smoothing constraints are widely employed to estimate a regularized solution. Here, we present an alternative inversion scheme that estimates a sparse representation of the model in a wavelet basis. The objective of the inversion is to determine the few non-zero wavelet coefficients which are required to fit the data. This approach falls into the class of sparsity constrained inversion schemes and minimizes the combination of the data misfit in a least-squares ℓ2 sense and of a model coefficient norm in an ℓ1 sense (ℓ2-ℓ1 minimization). The ℓ1 coefficient norm renders the solution sparse in a suitable representation such as the multiresolution wavelet basis, but does not impose explicit structural penalties on the model as it is the case for ℓ2 regularization. The presented numerical algorithm solves the mixed ℓ2-ℓ1 norm minimization problem for the nonlinear magnetotelluric inverse problem. We demonstrate the feasibility of our algorithm on synthetic 2-D MT data as well as on a real data example. We found that sparse models can be estimated by inversion and that the spatial distribution of non-vanishing coefficients indicates regions in the model which are resolved.
Sparse Neural Network Models of Antimicrobial Peptide-Activity Relationships.
Müller, Alex T; Kaymaz, Aral C; Gabernet, Gisela; Posselt, Gernot; Wessler, Silja; Hiss, Jan A; Schneider, Gisbert
2016-12-01
We present an adaptive neural network model for chemical data classification. The method uses an evolutionary algorithm for optimizing the network structure by seeking sparsely connected architectures. The number of hidden layers, the number of neurons in each layer and their connectivity are free variables of the system. We used the method for predicting antimicrobial peptide activity from the amino acid sequence. Visualization of the evolved sparse network structures suggested a high charge density and a low aggregation potential in solution as beneficial for antimicrobial activity. However, different training data sets and peptide representations resulted in greatly varying network structures. Overall, the sparse network models turned out to be less accurate than fully-connected networks. In a prospective application, we synthesized and tested 10 de novo generated peptides that were predicted to either possess antimicrobial activity, or to be inactive. Two of the predicted antibacterial peptides showed cosiderable bacteriostatic effects against both Staphylococcus aureus and Escherichia coli. None of the predicted inactive peptides possessed antibacterial properties. Molecular dynamics simulations of selected peptide structures in water and TFE suggest a pronounced peptide helicity in a hydrophobic environment. The results of this study underscore the applicability of neural networks for guiding the computer-assisted design of new peptides with desired properties.
Blind deconvolution using an improved L0 sparse representation
NASA Astrophysics Data System (ADS)
Ye, Pengzhao; Feng, Huajun; Li, Qi; Xu, Zhihai; Chen, Yueting
2014-09-01
In this paper, we present a method for single image blind deconvolution. Many common forms of blind deconvolution methods need to previously generate a salient image, while the paper presents a novel L0 sparse expression to directly solve the ill-positioned problem. It has no need to filter the blurred image as a restoration step and can use the gradient information as a fidelity term during optimization. The key to blind deconvolution problem is to estimate an accurate kernel. First, based on L2 sparse expression using gradient operator as a prior, the kernel can be estimated roughly and efficiently in the frequency domain. We adopt the multi-scale scheme which can estimate blur kernel from coarser level to finer level. After the estimation of this level's kernel, L0 sparse representation is employed as the fidelity term during restoration. After derivation, L0 norm can be approximately converted to a sum term and L1 norm term which can be addressed by the Split-Bregman method. By using the estimated blur kernel and the TV deconvolution model, the final restoration image is obtained. Experimental results show that the proposed method is fast and can accurately reconstruct the kernel, especially when the blur is motion blur, defocus blur or the superposition of the two. The restored image is of higher quality than that of some of the art algorithms.
Medical image registration using sparse coding of image patches.
Afzali, Maryam; Ghaffari, Aboozar; Fatemizadeh, Emad; Soltanian-Zadeh, Hamid
2016-06-01
Image registration is a basic task in medical image processing applications like group analysis and atlas construction. Similarity measure is a critical ingredient of image registration. Intensity distortion of medical images is not considered in most previous similarity measures. Therefore, in the presence of bias field distortions, they do not generate an acceptable registration. In this paper, we propose a sparse based similarity measure for mono-modal images that considers non-stationary intensity and spatially-varying distortions. The main idea behind this measure is that the aligned image is constructed by an analysis dictionary trained using the image patches. For this purpose, we use "Analysis K-SVD" to train the dictionary and find the sparse coefficients. We utilize image patches to construct the analysis dictionary and then we employ the proposed sparse similarity measure to find a non-rigid transformation using free form deformation (FFD). Experimental results show that the proposed approach is able to robustly register 2D and 3D images in both simulated and real cases. The proposed method outperforms other state-of-the-art similarity measures and decreases the transformation error compared to the previous methods. Even in the presence of bias field distortion, the proposed method aligns images without any preprocessing.
Sparse matrix-vector multiplication on a reconfigurable supercomputer
Dubois, David H; Dubois, Andrew J; Boorman, Thomas M; Connor, Carolyn M; Poole, Steve
2008-01-01
Double precision floating point Sparse Matrix-Vector Multiplication (SMVM) is a critical computational kernel used in iterative solvers for systems of sparse linear equations. The poor data locality exhibited by sparse matrices along with the high memory bandwidth requirements of SMVM result in poor performance on general purpose processors. Field Programmable Gate Arrays (FPGAs) offer a possible alternative with their customizable and application-targeted memory sub-system and processing elements. In this work we investigate two separate implementations of the SMVM on an SRC-6 MAPStation workstation. The first implementation investigates the peak performance capability, while the second implementation balances the amount of instantiated logic with the available sustained bandwidth of the FPGA subsystem. Both implementations yield the same sustained performance with the second producing a much more efficient solution. The metrics of processor and application balance are introduced to help provide some insight into the efficiencies of the FPGA and CPU based solutions explicitly showing the tight coupling of the available bandwidth to peak floating point performance. Due to the FPGA's ability to balance the amount of implemented logic to the available memory bandwidth it can provide a much more efficient solution. Finally, making use of the lessons learned implementing the SMVM, we present an fully implemented nonpreconditioned Conjugate Gradient Algorithm utilizing the second SMVM design.
Clutter Mitigation in Echocardiography Using Sparse Signal Separation
Turek, Javier S.; Elad, Michael; Yavneh, Irad
2015-01-01
In ultrasound imaging, clutter artifacts degrade images and may cause inaccurate diagnosis. In this paper, we apply a method called Morphological Component Analysis (MCA) for sparse signal separation with the objective of reducing such clutter artifacts. The MCA approach assumes that the two signals in the additive mix have each a sparse representation under some dictionary of atoms (a matrix), and separation is achieved by finding these sparse representations. In our work, an adaptive approach is used for learning the dictionary from the echo data. MCA is compared to Singular Value Filtering (SVF), a Principal Component Analysis- (PCA-) based filtering technique, and to a high-pass Finite Impulse Response (FIR) filter. Each filter is applied to a simulated hypoechoic lesion sequence, as well as experimental cardiac ultrasound data. MCA is demonstrated in both cases to outperform the FIR filter and obtain results comparable to the SVF method in terms of contrast-to-noise ratio (CNR). Furthermore, MCA shows a lower impact on tissue sections while removing the clutter artifacts. In experimental heart data, MCA obtains in our experiments clutter mitigation with an average CNR improvement of 1.33 dB. PMID:26199622
Treatment of non-sparse cratering in planetary surface dating
NASA Astrophysics Data System (ADS)
Kneissl, T.; Michael, G. G.; Schmedemann, N.
2016-10-01
We here propose a new technique to derive crater size-frequency distributions (CSFDs) from non-sparsely cratered surfaces, by accounting for the loss of craters due to subsequent crater/ejecta coverage. This approach, which we refer to as the buffered non-sparseness correction (BNSC), relates each crater to a measurement area found by excluding regions in the study area that have been resurfaced by larger craters and their ejecta blankets. The approach includes the well-known buffered crater counting (BCC) technique in order to consider the potential identification of craters whose centers are located outside the counting area. We demonstrate the new approach at two test sites on the Moon, one on the ancient lunar highlands outside the South Pole Aitken basin and the other on the much younger surface of lunar Mare Serenitatis. As expected, the correction has a much stronger effect on ancient, densely cratered surfaces than on younger, sparsely cratered surfaces. Furthermore, these first results indicate that the shapes of CSFDs on ancient terrains are actually very similar to the shapes of CSFDs on younger terrains.
Efficient MATLAB computations with sparse and factored tensors.
Bader, Brett William; Kolda, Tamara Gibson (Sandia National Lab, Livermore, CA)
2006-12-01
In this paper, the term tensor refers simply to a multidimensional or N-way array, and we consider how specially structured tensors allow for efficient storage and computation. First, we study sparse tensors, which have the property that the vast majority of the elements are zero. We propose storing sparse tensors using coordinate format and describe the computational efficiency of this scheme for various mathematical operations, including those typical to tensor decomposition algorithms. Second, we study factored tensors, which have the property that they can be assembled from more basic components. We consider two specific types: a Tucker tensor can be expressed as the product of a core tensor (which itself may be dense, sparse, or factored) and a matrix along each mode, and a Kruskal tensor can be expressed as the sum of rank-1 tensors. We are interested in the case where the storage of the components is less than the storage of the full tensor, and we demonstrate that many elementary operations can be computed using only the components. All of the efficiencies described in this paper are implemented in the Tensor Toolbox for MATLAB.
Scalable Library for the Parallel Solution of Sparse Linear Systems
Jones, Mark; Plassmann, Paul E.
1993-07-14
BlockSolve is a scalable parallel software library for the solution of large sparse, symmetric systems of linear equations. It runs on a variety of parallel architectures and can easily be ported to others. BlockSovle is primarily intended for the solution of sparse linear systems that arise from physical problems having multiple degrees of freedom at each node point. For example, when the finite element method is used to solve practical problems in structural engineering, each node will typically have anywhere from 3-6 degrees of freedom associated with it. BlockSolve is written to take advantage of problems of this nature; however, it is still reasonably efficient for problems that have only one degree of freedom associated with each node, such as the three-dimensional Poisson problem. It does not require that the matrices have any particular structure other than being sparse and symmetric. BlockSolve is intended to be used within real application codes. It is designed to work best in the context of our experience which indicated that most application codes solve the same linear systems with several different right-hand sides and/or linear systems with the same structure, but different matrix values multiple times.
Monitoring global vegetation using Nimbus-7 37 GHz data - Some empirical relations
NASA Technical Reports Server (NTRS)
Choudhury, B. J.; Tucker, C. J.
1987-01-01
The difference of the vertically and horizontally polarized brightness temperatures observed by the 37 GHz channel of the SMMR on board the Nimbus-7 satellite are correlated temporally with three indicators of vegetation density, namely the temporal variation of the atmospheric CO2 concentration at Mauna Loa (Hawaii), rainfall over the Sahel and the normalized difference vegetation index derived from the AVHRR on board the NOAA-7 satellite. SMMR 37 GHz and AVHRR provide complementary data sets for monitoring global vegetation, the 37 GHz data being more suitable for arid and semiarid regions as these data are more sensitive to changes in sparse vegetation. The 37-GHz data might be useful for understanding desertification and indexing Co2 exchange between the biosphere and the atmosphere.
A LANDSAT study of ephemeral and perennial rangeland vegetation and soils
NASA Technical Reports Server (NTRS)
Bentley, R. G., Jr. (Principal Investigator); Salmon-Drexler, B. C.; Bonner, W. J.; Vincent, R. K.
1976-01-01
The author has identified the following significant results. Several methods of computer processing were applied to LANDSAT data for mapping vegetation characteristics of perennial rangeland in Montana and ephemeral rangeland in Arizona. The choice of optimal processing technique was dependent on prescribed mapping and site condition. Single channel level slicing and ratioing of channels were used for simple enhancement. Predictive models for mapping percent vegetation cover based on data from field spectra and LANDSAT data were generated by multiple linear regression of six unique LANDSAT spectral ratios. Ratio gating logic and maximum likelihood classification were applied successfully to recognize plant communities in Montana. Maximum likelihood classification did little to improve recognition of terrain features when compared to a single channel density slice in sparsely vegetated Arizona. LANDSAT was found to be more sensitive to differences between plant communities based on percentages of vigorous vegetation than to actual physical or spectral differences among plant species.
NASA Technical Reports Server (NTRS)
Kimes, D. S.
1981-01-01
A mathematical method is presented which allows the determination of vertical temperature profiles of vegetation canopies from multiple sensor view angles and some knowledge of the vegetation geometric structure. The technique was evaluated with data from several wheat canopies at different stages of development, and shown to be most useful in the separation of vegetation and substrate temperatures with greater accuracy in the case of intermediate and dense vegetation canopies than in sparse ones. The converse is true for substrate temperatures. Root-mean-square prediction accuracies of temperatures for intermediate-density wheat canopies were 1.8 C and 1.4 C for an exact and an overdeterminate system, respectively. The findings have implication for remote sensing research in agriculture, geology or other earth resources disciplines.
An Efficient Scheme for Updating Sparse Cholesky Factors
NASA Technical Reports Server (NTRS)
Raghavan, Padma
2002-01-01
Raghavan had earlier developed the software package DCSPACK which can be used for solving sparse linear systems where the coefficient matrix is symmetric and positive definite (this project was not funded by NASA but by agencies such as NSF). DSCPACK-S is the serial code and DSCPACK-P is a parallel implementation suitable for multiprocessors or networks-of-workstations with message passing using MCI. The main algorithm used is the Cholesky factorization of a sparse symmetric positive positive definite matrix A = LL(T). The code can also compute the factorization A = LDL(T). The complexity of the software arises from several factors relating to the sparsity of the matrix A. A sparse N x N matrix A has typically less that cN nonzeroes where c is a small constant. If the matrix were dense, it would have O(N2) nonzeroes. The most complicated part of such sparse Cholesky factorization relates to fill-in, i.e., zeroes in the original matrix that become nonzeroes in the factor L. An efficient implementation depends to a large extent on complex data structures and on techniques from graph theory to reduce, identify, and manage fill. DSCPACK is based on an efficient multifrontal implementation with fill-managing algorithms and implementation arising from earlier research by Raghavan and others. Sparse Cholesky factorization is typically a four step process: (1) ordering to compute a fill-reducing numbering, (2) symbolic factorization to determine the nonzero structure of L, (3) numeric factorization to compute L, and, (4) triangular solution to solve L(T)x = y and Ly = b. The first two steps are symbolic and are performed using the graph of the matrix. The numeric factorization step is of dominant cost and there are several schemes for improving performance by exploiting the nested and dense structure of groups of columns in the factor. The latter are aimed at better utilization of the cache-memory hierarchy on modem processors to prevent cache-misses and provide execution
Fast Solution in Sparse LDA for Binary Classification
NASA Technical Reports Server (NTRS)
Moghaddam, Baback
2010-01-01
An algorithm that performs sparse linear discriminant analysis (Sparse-LDA) finds near-optimal solutions in far less time than the prior art when specialized to binary classification (of 2 classes). Sparse-LDA is a type of feature- or variable- selection problem with numerous applications in statistics, machine learning, computer vision, computational finance, operations research, and bio-informatics. Because of its combinatorial nature, feature- or variable-selection problems are NP-hard or computationally intractable in cases involving more than 30 variables or features. Therefore, one typically seeks approximate solutions by means of greedy search algorithms. The prior Sparse-LDA algorithm was a greedy algorithm that considered the best variable or feature to add/ delete to/ from its subsets in order to maximally discriminate between multiple classes of data. The present algorithm is designed for the special but prevalent case of 2-class or binary classification (e.g. 1 vs. 0, functioning vs. malfunctioning, or change versus no change). The present algorithm provides near-optimal solutions on large real-world datasets having hundreds or even thousands of variables or features (e.g. selecting the fewest wavelength bands in a hyperspectral sensor to do terrain classification) and does so in typical computation times of minutes as compared to days or weeks as taken by the prior art. Sparse LDA requires solving generalized eigenvalue problems for a large number of variable subsets (represented by the submatrices of the input within-class and between-class covariance matrices). In the general (fullrank) case, the amount of computation scales at least cubically with the number of variables and thus the size of the problems that can be solved is limited accordingly. However, in binary classification, the principal eigenvalues can be found using a special analytic formula, without resorting to costly iterative techniques. The present algorithm exploits this analytic
Bagby, M.O.
1995-12-01
Vegetable oils serve various industrial applications such as plasticizers, emulsifiers, surfactants, plastics and resins. Research and development approaches may take advantage of natural properties of the oils. More often it is advantageous to modify those properties for specific applications. One example is the preparation of ink vehicles using vegetable oils in the absence of petroleum. They are cost competitive with petroleum-based inks with similar quality factors. Vegetable oils have potential as renewable sources of fuels for the diesel engine. However, several characteristics can restrict their use. These include poor cold-engine startup, misfire and for selected fuels, high pour point and cloud point temperatures. Other characteristics include incomplete combustion causing carbon buildup, lube oil dilution and degradation, and elevated NO{sub x} emissions. Precombustion and fuel quality data are presented as a tool for understanding and solving these operational and durability problems.
NASA Astrophysics Data System (ADS)
Moody, Daniela I.; Wilson, Cathy J.; Rowland, Joel C.; Altmann, Garrett L.
2015-06-01
Advanced pattern recognition and computer vision algorithms are of great interest for landscape characterization, change detection, and change monitoring in satellite imagery, in support of global climate change science and modeling. We present results from an ongoing effort to extend neuroscience-inspired models for feature extraction to the environmental sciences, and we demonstrate our work using Worldview-2 multispectral satellite imagery. We use a Hebbian learning rule to derive multispectral, multiresolution dictionaries directly from regional satellite normalized band difference index data. These feature dictionaries are used to build sparse scene representations, from which we automatically generate land cover labels via our CoSA algorithm: Clustering of Sparse Approximations. These data adaptive feature dictionaries use joint spectral and spatial textural characteristics to help separate geologic, vegetative, and hydrologic features. Land cover labels are estimated in example Worldview-2 satellite images of Barrow, Alaska, taken at two different times, and are used to detect and discuss seasonal surface changes. Our results suggest that an approach that learns from both spectral and spatial features is promising for practical pattern recognition problems in high resolution satellite imagery.
Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; ...
2014-12-09
We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labelsmore » are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.« less
Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.
2014-12-09
We present results from an ongoing effort to extend neuromimetic machine vision algorithms to multispectral data using adaptive signal processing combined with compressive sensing and machine learning techniques. Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties, and topographic/geomorphic characteristics. We use a Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using unsupervised clustering of sparse approximations (CoSA). We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska. We explore learning from both raw multispectral imagery and normalized band difference indices. We explore a quantitative metric to evaluate the spectral properties of the clusters in order to potentially aid in assigning land cover categories to the cluster labels. In this study, our results suggest CoSA is a promising approach to unsupervised land cover classification in high-resolution satellite imagery.
NASA Astrophysics Data System (ADS)
Moody, Daniela I.; Brumby, Steven P.; Rowland, Joel C.; Altmann, Garrett L.
2014-05-01
Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. We present results from an ongoing effort to extend machine vision methodologies to the environmental sciences, using state-of-theart adaptive signal processing, combined with compressive sensing and machine learning techniques. We use a modified Hebbian learning rule to build spectral-textural dictionaries that are tailored for classification. We learn our dictionaries from millions of overlapping multispectral image patches and then use a pursuit search to generate classification features. Land cover labels are automatically generated using CoSA: unsupervised Clustering of Sparse Approximations. We demonstrate our method on multispectral WorldView-2 data from a coastal plain ecosystem in Barrow, Alaska (USA). Our goal is to develop a robust classification methodology that will allow for automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties (e.g., soil moisture and inundation), and topographic/geomorphic characteristics. In this paper, we explore learning from both raw multispectral imagery, as well as normalized band difference indexes. We explore a quantitative metric to evaluate the spectral properties of the clusters, in order to potentially aid in assigning land cover categories to the cluster labels.
Feng, Huihui
2016-01-01
Climate and vegetation change are two dominating factors for soil moisture trend. However, their individual contributions remain unknown due to their complex interaction. Here, I separated their contributions through a trajectory-based method across the global, regional and local scales. Our results demonstrated that climate change accounted for 98.78% and 114.64% of the global drying and wetting trend. Vegetation change exhibited a relatively weak influence (contributing 1.22% and −14.64% of the global drying and wetting) because it occurred in a limited area on land. Regionally, the impact of vegetation change cannot be neglected, which contributed −40.21% of the soil moisture change in the wetting zone. Locally, the contributions strongly correlated to the local environmental characteristics. Vegetation negatively affected soil moisture trends in the dry and sparsely vegetated regions and positively in the wet and densely vegetated regions. I conclude that individual contributions of climate and vegetation change vary at the global, regional and local scales. Climate change dominates the soil moisture trends, while vegetation change acts as a regulator to drying or wetting the soil under the changing climate. PMID:27600157
Ringrose, S; Chipanshi, A C; Matheson, W; Chanda, R; Motoma, L; Magole, I; Jellema, A
2002-07-01
For purposes of suggesting adaptive and policy options regarding the sustained use of forestry resources in Botswana, an analysis of the whole countrywide satellite data (showing the mean present distribution of vegetation in terms of species abundance and over all density) and the projection of vegetation cover changes using a simulation approach under different climatic scenarios were undertaken. The analysis revealed that changes in vegetation cover types due to human and natural causes have taken place since the first vegetation map was produced in 1971. In the southwest, the changes appear to be more towards an increasing prevalence of thorn trees; in the eastern part of the country where widespread bush encroachment is taking place, the higher population density suggests more human induced (agrarian-degradation) effects, while in the sparsely settled central Kalahari region, changes from tree savanna to shrubs may be indicative of the possible influence of climate with the associated effects of fires and local adaptations. Projection of future vegetation changes to about 2050 indicates degeneration of the major vegetation types due to the expected drying. Based on the projected changes in vegetation, current adaptive and policy arrangements are not adequate and as such a shift from the traditional adaptive approaches to community-based types is suggested. Defining forestry management units and adopting different management plans for the main vegetation stands that are found in Botswana are the major policy options.
Drought influence on vegetation behavior in Mediterranean basin
NASA Astrophysics Data System (ADS)
Gouveia, C. M.; Trigo, R. M.; Begueria, S. M.; Vicente-Serrano, S.
2012-04-01
The strong dependence of Mediterranean vegetation on water availability has been for long known. Drought events are relatively frequent in Mediterranean countries and prolonged intense drought episodes are responsible for the most negative impacts on vegetation, such as losses in crop yields, increases of fire risk, declines of forest growth and land degradation and desertification. The aim of the present work is to analyze in detail the impact of drought episodes on vegetation behavior in the Mediterranean region during the last three decades. For this purpose we use the Normalized Difference Vegetation Index (NDVI) from the Global Inventory Modeling and Mapping Studies (GIMMS) dataset, as obtained from NOAA-AVHRR sensor and the recently developed multi-scale drought index Standardised Precipitation-Evapotranspiration Index (SPEI, Vicente-Serrano et al, 2010). The study aims to analyze the drought impacts on vegetation dynamics since the early 1980s over the entire Mediterranean region, with the purpose of determining the most sensitive areas and land cover types. Additionally we need to evaluate this impact on a seasonal basis and identify which drought-time scales are more prone to cause negative effects on vegetation. Thus, correlation maps between fields of monthly NDVI and SPEI for time scales ranging between 1 and 24 months were computed in order to identify the regions and seasons most affected by climatic droughts. The role played by vegetation density and aridity on drought impacts on vegetation were also analyzed for different regions of the Mediterranean basin. Vegetation affected by drought presents high spatial and seasonal variability, with a maximum in summer and a minimum in winter. During February half of these affected pixels correspond to time scale of 6 months, while in November the most frequent time scale corresponds to just 3 months, representing more than 40% of the pixels affected by drought. While in February sparse vegetation is the most
Not Available
1980-11-01
A review is presented of various experiments undertaken over the past few years in the U.S. to test the performance of vegetable oils in diesel engines, mainly with a view to on-farm energy self-sufficiency. The USDA Northern Regional Research Center in Peoria, Illinois, is screening native U.S. plant species as potential fuel oil sources.
Fermented and Acidified Vegetables
Technology Transfer Automated Retrieval System (TEKTRAN)
Vegetables may be preserved by fermentation, direct acidification, or a combination of these along with pasteurization or refrigeration and selected additives to yield products with an extended shelf life and enhanced safety. Organic acids such as lactic, acetic, sorbic and benzoic acids along with ...
Social biases determine spatiotemporal sparseness of ciliate mating heuristics
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
Sparse and compositionally robust inference of microbial ecological networks.
Kurtz, Zachary D; Müller, Christian L; Miraldi, Emily R; Littman, Dan R; Blaser, Martin J; Bonneau, Richard A
2015-05-01
16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC
Face sketch synthesis via sparse representation-based greedy search.
Shengchuan Zhang; Xinbo Gao; Nannan Wang; Jie Li; Mingjin Zhang
2015-08-01
Face sketch synthesis has wide applications in digital entertainment and law enforcement. Although there is much research on face sketch synthesis, most existing algorithms cannot handle some nonfacial factors, such as hair style, hairpins, and glasses if these factors are excluded in the training set. In addition, previous methods only work on well controlled conditions and fail on images with different backgrounds and sizes as the training set. To this end, this paper presents a novel method that combines both the similarity between different image patches and prior knowledge to synthesize face sketches. Given training photo-sketch pairs, the proposed method learns a photo patch feature dictionary from the training photo patches and replaces the photo patches with their sparse coefficients during the searching process. For a test photo patch, we first obtain its sparse coefficient via the learnt dictionary and then search its nearest neighbors (candidate patches) in the whole training photo patches with sparse coefficients. After purifying the nearest neighbors with prior knowledge, the final sketch corresponding to the test photo can be obtained by Bayesian inference. The contributions of this paper are as follows: 1) we relax the nearest neighbor search area from local region to the whole image without too much time consuming and 2) our method can produce nonfacial factors that are not contained in the training set and is robust against image backgrounds and can even ignore the alignment and image size aspects of test photos. Our experimental results show that the proposed method outperforms several state-of-the-arts in terms of perceptual and objective metrics.
A sparse Gaussian process framework for photometric redshift estimation
NASA Astrophysics Data System (ADS)
Almosallam, Ibrahim A.; Lindsay, Sam N.; Jarvis, Matt J.; Roberts, Stephen J.
2016-01-01
Accurate photometric redshifts are a lynchpin for many future experiments to pin down the cosmological model and for studies of galaxy evolution. In this study, a novel sparse regression framework for photometric redshift estimation is presented. Synthetic data set simulating the Euclid survey and real data from SDSS DR12 are used to train and test the proposed models. We show that approaches which include careful data preparation and model design offer a significant improvement in comparison with several competing machine learning algorithms. Standard implementations of most regression algorithms use the minimization of the sum of squared errors as the objective function. For redshift inference, this induces a bias in the posterior mean of the output distribution, which can be problematic. In this paper, we directly minimize the target metric Δz = (zs - zp)/(1 + zs) and address the bias problem via a distribution-based weighting scheme, incorporated as part of the optimization objective. The results are compared with other machine learning algorithms in the field such as artificial neural networks (ANN), Gaussian processes (GPs) and sparse GPs. The proposed framework reaches a mean absolute Δz = 0.0026(1 + zs), over the redshift range of 0 ≤ zs ≤ 2 on the simulated data, and Δz = 0.0178(1 + zs) over the entire redshift range on the SDSS DR12 survey, outperforming the standard ANNz used in the literature. We also investigate how the relative size of the training sample affects the photometric redshift accuracy. We find that a training sample of >30 per cent of total sample size, provides little additional constraint on the photometric redshifts, and note that our GP formalism strongly outperforms ANNz in the sparse data regime for the simulated data set.
Analysing Local Sparseness in the Macaque Brain Network
Singh, Raghavendra; Nagar, Seema; Nanavati, Amit A.
2015-01-01
Understanding the network structure of long distance pathways in the brain is a necessary step towards developing an insight into the brain’s function, organization and evolution. Dense global subnetworks of these pathways have often been studied, primarily due to their functional implications. Instead we study sparse local subnetworks of the pathways to establish the role of a brain area in enabling shortest path communication between its non-adjacent topological neighbours. We propose a novel metric to measure the topological communication load on a vertex due to its immediate neighbourhood, and show that in terms of distribution of this local communication load, a network of Macaque long distance pathways is substantially different from other real world networks and random graph models. Macaque network contains the entire range of local subnetworks, from star-like networks to clique-like networks, while other networks tend to contain a relatively small range of subnetworks. Further, sparse local subnetworks in the Macaque network are not only found across topographical super-areas, e.g., lobes, but also within a super-area, arguing that there is conservation of even relatively short-distance pathways. To establish the communication role of a vertex we borrow the concept of brokerage from social science, and present the different types of brokerage roles that brain areas play, highlighting that not only the thalamus, but also cingulate gyrus and insula often act as “relays” for areas in the neocortex. These and other analysis of communication load and roles of the sparse subnetworks of the Macaque brain provide new insights into the organisation of its pathways. PMID:26437077
Supervised Discriminative Group Sparse Representation for Mild Cognitive Impairment Diagnosis
Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang
2014-01-01
Research on an early detection of Mild Cognitive Impairment (MCI), a prodromal stage of Alzheimer’s Disease (AD), with resting-state functional Magnetic Resonance Imaging (rs-fMRI) has been of great interest for the last decade. Witnessed by recent studies, functional connectivity is a useful concept in extracting brain network features and finding biomarkers for brain disease diagnosis. However, it still remains challenging for the estimation of functional connectivity from rs-fMRI due to the inevitable high dimensional problem. In order to tackle this problem, we utilize a group sparse representation along with a structural equation model. Unlike the conventional group sparse representation method that does not explicitly consider class-label information, which can help enhance the diagnostic performance, in this paper, we propose a novel supervised discriminative group sparse representation method by penalizing a large within-class variance and a small between-class variance of connectivity coefficients. Thanks to the newly devised penalization terms, we can learn connectivity coefficients that are similar within the same class and distinct between classes, thus helping enhance the diagnostic accuracy. The proposed method also allows the learned common network structure to preserve the network specific and label-related characteristics. In our experiments on the rs-fMRI data of 37 subjects (12 MCI; 25 healthy normal control) with a cross-validation technique, we demonstrated the validity and effectiveness of the proposed method, showing the diagnostic accuracy of 89.19% and the sensitivity of 0.9167. PMID:25501275
Analysing Local Sparseness in the Macaque Brain Network.
Singh, Raghavendra; Nagar, Seema; Nanavati, Amit A
2015-01-01
Understanding the network structure of long distance pathways in the brain is a necessary step towards developing an insight into the brain's function, organization and evolution. Dense global subnetworks of these pathways have often been studied, primarily due to their functional implications. Instead we study sparse local subnetworks of the pathways to establish the role of a brain area in enabling shortest path communication between its non-adjacent topological neighbours. We propose a novel metric to measure the topological communication load on a vertex due to its immediate neighbourhood, and show that in terms of distribution of this local communication load, a network of Macaque long distance pathways is substantially different from other real world networks and random graph models. Macaque network contains the entire range of local subnetworks, from star-like networks to clique-like networks, while other networks tend to contain a relatively small range of subnetworks. Further, sparse local subnetworks in the Macaque network are not only found across topographical super-areas, e.g., lobes, but also within a super-area, arguing that there is conservation of even relatively short-distance pathways. To establish the communication role of a vertex we borrow the concept of brokerage from social science, and present the different types of brokerage roles that brain areas play, highlighting that not only the thalamus, but also cingulate gyrus and insula often act as "relays" for areas in the neocortex. These and other analysis of communication load and roles of the sparse subnetworks of the Macaque brain provide new insights into the organisation of its pathways.
Sparse and Compositionally Robust Inference of Microbial Ecological Networks
Kurtz, Zachary D.; Müller, Christian L.; Miraldi, Emily R.; Littman, Dan R.; Blaser, Martin J.; Bonneau, Richard A.
2015-01-01
16S ribosomal RNA (rRNA) gene and other environmental sequencing techniques provide snapshots of microbial communities, revealing phylogeny and the abundances of microbial populations across diverse ecosystems. While changes in microbial community structure are demonstrably associated with certain environmental conditions (from metabolic and immunological health in mammals to ecological stability in soils and oceans), identification of underlying mechanisms requires new statistical tools, as these datasets present several technical challenges. First, the abundances of microbial operational taxonomic units (OTUs) from amplicon-based datasets are compositional. Counts are normalized to the total number of counts in the sample. Thus, microbial abundances are not independent, and traditional statistical metrics (e.g., correlation) for the detection of OTU-OTU relationships can lead to spurious results. Secondly, microbial sequencing-based studies typically measure hundreds of OTUs on only tens to hundreds of samples; thus, inference of OTU-OTU association networks is severely under-powered, and additional information (or assumptions) are required for accurate inference. Here, we present SPIEC-EASI (SParse InversE Covariance Estimation for Ecological Association Inference), a statistical method for the inference of microbial ecological networks from amplicon sequencing datasets that addresses both of these issues. SPIEC-EASI combines data transformations developed for compositional data analysis with a graphical model inference framework that assumes the underlying ecological association network is sparse. To reconstruct the network, SPIEC-EASI relies on algorithms for sparse neighborhood and inverse covariance selection. To provide a synthetic benchmark in the absence of an experimentally validated gold-standard network, SPIEC-EASI is accompanied by a set of computational tools to generate OTU count data from a set of diverse underlying network topologies. SPIEC
A Comparison of Methods for Ocean Reconstruction from Sparse Observations
NASA Astrophysics Data System (ADS)
Streletz, G. J.; Kronenberger, M.; Weber, C.; Gebbie, G.; Hagen, H.; Garth, C.; Hamann, B.; Kreylos, O.; Kellogg, L. H.; Spero, H. J.
2014-12-01
We present a comparison of two methods for developing reconstructions of oceanic scalar property fields from sparse scattered observations. Observed data from deep sea core samples provide valuable information regarding the properties of oceans in the past. However, because the locations of sample sites are distributed on the ocean floor in a sparse and irregular manner, developing a global ocean reconstruction is a difficult task. Our methods include a flow-based and a moving least squares -based approximation method. The flow-based method augments the process of interpolating or approximating scattered scalar data by incorporating known flow information. The scheme exploits this additional knowledge to define a non-Euclidean distance measure between points in the spatial domain. This distance measure is used to create a reconstruction of the desired scalar field on the spatial domain. The resulting reconstruction thus incorporates information from both the scattered samples and the known flow field. The second method does not assume a known flow field, but rather works solely with the observed scattered samples. It is based on a modification of the moving least squares approach, a weighted least squares approximation method that blends local approximations into a global result. The modifications target the selection of data used for these local approximations and the construction of the weighting function. The definition of distance used in the weighting function is crucial for this method, so we use a machine learning approach to determine a set of near-optimal parameters for the weighting. We have implemented both of the reconstruction methods and have tested them using several sparse oceanographic datasets. Based upon these studies, we discuss the advantages and disadvantages of each method and suggest possible ways to combine aspects of both methods in order to achieve an overall high-quality reconstruction.
Parallel solution of sparse one-dimensional dynamic programming problems
NASA Technical Reports Server (NTRS)
Nicol, David M.
1989-01-01
Parallel computation offers the potential for quickly solving large computational problems. However, it is often a non-trivial task to effectively use parallel computers. Solution methods must sometimes be reformulated to exploit parallelism; the reformulations are often more complex than their slower serial counterparts. We illustrate these points by studying the parallelization of sparse one-dimensional dynamic programming problems, those which do not obviously admit substantial parallelization. We propose a new method for parallelizing such problems, develop analytic models which help us to identify problems which parallelize well, and compare the performance of our algorithm with existing algorithms on a multiprocessor.
Sparse Representation for Computer Vision and Pattern Recognition
2009-05-01
with 40% occlusion. Figure 2 (right) shows the validation perfor - mance of the various methods, under 30% contiguous occlu- sion, plotted as a...performance. B. Sparse Modeling for Image Reconstruction Let X ∈ Rm×N be a set of N column data vectors xj ∈ Rm (e.g., image patches ), D ∈ Rm×K be a dictionary...K > m, and the patch sizes vary from 7× 7, m = 49, to 20× 20, m = 400 (in the multiscale case), with a sparsity of about 1/10th of the signal
Universal Collaboration Strategies for Signal Detection: A Sparse Learning Approach
NASA Astrophysics Data System (ADS)
Khanduri, Prashant; Kailkhura, Bhavya; Thiagarajan, Jayaraman J.; Varshney, Pramod K.
2016-10-01
This paper considers the problem of high dimensional signal detection in a large distributed network whose nodes can collaborate with their one-hop neighboring nodes (spatial collaboration). We assume that only a small subset of nodes communicate with the Fusion Center (FC). We design optimal collaboration strategies which are universal for a class of deterministic signals. By establishing the equivalence between the collaboration strategy design problem and sparse PCA, we solve the problem efficiently and evaluate the impact of collaboration on detection performance.
Routing Protocol of Sparse Urban Vehicular Ad Hoc Networks
NASA Astrophysics Data System (ADS)
Li, Huxiong
Vehicular ad hoc network (VANET) is an application of mobile ad hoc technology in transportation systems, it has become an important part of ITS. Since multi-hop link is hard to set up in sparse VANET, a traffic-aware routing (TAR) protocol is proposed which estimates vehicle average neighbors (VAN) of roads by exchanging beacon messages between encounter vehicles. Road with high VAN is preferred to be selected as part of forwarding path at intersection. Packets are forwarded to the next intersection in road in a greedy manner. Simulations show that TAR outperforms the compared protocols in terms of both packet delivery ratio and average end-to-end delay.
Multi-frame blind deconvolution using sparse priors
NASA Astrophysics Data System (ADS)
Dong, Wende; Feng, Huajun; Xu, Zhihai; Li, Qi
2012-05-01
In this paper, we propose a method for multi-frame blind deconvolution. Two sparse priors, i.e., the natural image gradient prior and an l1-norm based prior are used to regularize the latent image and point spread functions (PSFs) respectively. An alternating minimization approach is adopted to solve the resulted optimization problem. We use both gray scale blurred frames from a data set and some colored ones which are captured by a digital camera to verify the robustness of our approach. Experimental results show that the proposed method can accurately reconstruct PSFs with complex structures and the restored images are of high quality.
Sparse Superpixel Unmixing for Exploratory Analysis of CRISM Hyperspectral Images
NASA Technical Reports Server (NTRS)
Thompson, David R.; Castano, Rebecca; Gilmore, Martha S.
2009-01-01
Fast automated analysis of hyperspectral imagery can inform observation planning and tactical decisions during planetary exploration. Products such as mineralogical maps can focus analysts' attention on areas of interest and assist data mining in large hyperspectral catalogs. In this work, sparse spectral unmixing drafts mineral abundance maps with Compact Reconnaissance Imaging Spectrometer (CRISM) images from the Mars Reconnaissance Orbiter. We demonstrate a novel "superpixel" segmentation strategy enabling efficient unmixing in an interactive session. Tests correlate automatic unmixing results based on redundant spectral libraries against hand-tuned summary products currently in use by CRISM researchers.
Self-organizing control for space-based sparse antennas
NASA Technical Reports Server (NTRS)
Hadaegh, Fred Y.; Jamnejad, Vaharaz; Scharf, Daniel P.; Ploen, Scott R.
2003-01-01
An integrated control and electromagnetic/antenna formulation is presented for evaluating the performance of a distributed antenna system as a function of formation geometry. A distributed and self-organizing control law for the control law for the control of multiple antennas in Low Earth Orbit (LEO) is presented. The control system provides collaborative commanding and performance optimization to configure and operate the distributed formation system. A large aperture antenna is thereby realized by a collection of miniature sparse antennas in formation. A case study consisting of a simulation of four antennas in Low Earth orbit (LEO)is presented to demonstrate the concept.
Robust compressive sensing of sparse signals: a review
NASA Astrophysics Data System (ADS)
Carrillo, Rafael E.; Ramirez, Ana B.; Arce, Gonzalo R.; Barner, Kenneth E.; Sadler, Brian M.
2016-12-01
Compressive sensing generally relies on the ℓ 2 norm for data fidelity, whereas in many applications, robust estimators are needed. Among the scenarios in which robust performance is required, applications where the sampling process is performed in the presence of impulsive noise, i.e., measurements are corrupted by outliers, are of particular importance. This article overviews robust nonlinear reconstruction strategies for sparse signals based on replacing the commonly used ℓ 2 norm by M-estimators as data fidelity functions. The derived methods outperform existing compressed sensing techniques in impulsive environments, while achieving good performance in light-tailed environments, thus offering a robust framework for CS.
Kanerva's sparse distributed memory with multiple hamming thresholds
NASA Technical Reports Server (NTRS)
Pohja, Seppo; Kaski, Kimmo
1992-01-01
If the stored input patterns of Kanerva's Sparse Distributed Memory (SDM) are highly correlated, utilization of the storage capacity is very low compared to the case of uniformly distributed random input patterns. We consider a variation of SDM that has a better storage capacity utilization for correlated input patterns. This approach uses a separate selection threshold for each physical storage address or hard location. The selection of the hard locations for reading or writing can be done in parallel of which SDM implementations can benefit.
Estimating the vegetation water content using a radar vegetation index
Technology Transfer Automated Retrieval System (TEKTRAN)
Vegetation water content is an important biophysical parameter. Here, the Radar Vegetation Index (RVI) based on polarimetric backscatter observations was evaluated for estimating vegetation water content. Analysis utilized a data set obtained by a ground-based multi-frequency polarimetric scatterome...
User's Manual for PCSMS (Parallel Complex Sparse Matrix Solver). Version 1.
NASA Technical Reports Server (NTRS)
Reddy, C. J.
2000-01-01
PCSMS (Parallel Complex Sparse Matrix Solver) is a computer code written to make use of the existing real sparse direct solvers to solve complex, sparse matrix linear equations. PCSMS converts complex matrices into real matrices and use real, sparse direct matrix solvers to factor and solve the real matrices. The solution vector is reconverted to complex numbers. Though, this utility is written for Silicon Graphics (SGI) real sparse matrix solution routines, it is general in nature and can be easily modified to work with any real sparse matrix solver. The User's Manual is written to make the user acquainted with the installation and operation of the code. Driver routines are given to aid the users to integrate PCSMS routines in their own codes.
Precession missile feature extraction using sparse component analysis of radar measurements
NASA Astrophysics Data System (ADS)
Liu, Lihua; Du, Xiaoyong; Ghogho, Mounir; Hu, Weidong; McLernon, Des
2012-12-01
According to the working mode of the ballistic missile warning radar (BMWR), the radar return from the BMWR is usually sparse. To recognize and identify the warhead, it is necessary to extract the precession frequency and the locations of the scattering centers of the missile. This article first analyzes the radar signal model of the precessing conical missile during flight and develops the sparse dictionary which is parameterized by the unknown precession frequency. Based on the sparse dictionary, the sparse signal model is then established. A nonlinear least square estimation is first applied to roughly extract the precession frequency in the sparse dictionary. Based on the time segmented radar signal, a sparse component analysis method using the orthogonal matching pursuit algorithm is then proposed to jointly estimate the precession frequency and the scattering centers of the missile. Simulation results illustrate the validity of the proposed method.
Sparse, Decorrelated Odor Coding in the Mushroom Body Enhances Learned Odor Discrimination
Lin, Andrew C.; Bygrave, Alexei; de Calignon, Alix; Lee, Tzumin; Miesenböck, Gero
2014-01-01
Summary Sparse coding may be a general strategy of neural systems to augment memory capacity. In Drosophila, sparse odor coding by the Kenyon cells of the mushroom body is thought to generate a large number of precisely addressable locations for the storage of odor-specific memories. However, it remains untested how sparse coding relates to behavioral performance. Here we demonstrate that sparseness is controlled by a negative feedback circuit between Kenyon cells and the GABAergic anterior paired lateral (APL) neuron. Systematic activation and blockade of each leg of this feedback circuit show that Kenyon cells activate APL and APL inhibits Kenyon cells. Disrupting the Kenyon cell-APL feedback loop decreases the sparseness of Kenyon cell odor responses, increases inter-odor correlations, and prevents flies from learning to discriminate similar, but not dissimilar, odors. These results suggest that feedback inhibition suppresses Kenyon cell activity to maintain sparse, decorrelated odor coding and thus the odor-specificity of memories. PMID:24561998
Sparse spectrum model for the turbulent phase simulations
NASA Astrophysics Data System (ADS)
Charnotskii, Mikhail
2013-05-01
Monte-Carlo simulation of phase front perturbations by atmospheric turbulence finds numerous applications for design and modeling of the adaptive optics systems, laser beams propagation simulations, and evaluating the performance of the various optical systems operating in the open air environment. Accurate generation of two-dimensional random fields of turbulent phase is complicated by the enormous diversity of scales that can reach five orders in magnitude in each coordinate. In addition there is a need for generation of the long "ribbons" of turbulent phase that are used to represent the time evolution of the wave front. This makes it unfeasible to use the standard discrete Fourier transform-based technique as a basis for the Monte-Carlo simulation algorithm. We propose a novel concept for turbulent phase - the Sparse Spectrum (SS) random field. The principle assumption of the SS model is that each realization of the random field has a discrete random spectral support. Statistics of the random amplitudes and wave vectors of the SS model are arranged to provide the required spectral and correlation properties of the random field. The SS-based Monte-Carlo model offers substantial reduction of computer costs for simulation of the wide-band random fields and processes, and is capable of generating long aperiodic phase "ribbons". We report the results of model trials that determine the number of sparse components, and the range of wavenumbers that is necessary to accurately reproduce the random field with a power-law spectrum.
Robot-Assisted Measurements in Data Sparse Regions
NASA Astrophysics Data System (ADS)
Peschel, J.; Young, S. N.; Penny, G.; Thompson, S. E.; Srinivasan, V.
2015-12-01
This work presents a methodology for topographic and bathymetric data collection using multiple robot platforms in the data sparse Arkavathy region around Bangalore, India. In the late 20th century, Arkavathy River flows began declining; consequently, a dependence on the Cauvery River has occurred while the reasons for the drying of the Arkavathy remain unknown. Understanding this shift is critical for managing local water resources, specifically for quantifying the socio-hydrologic effects of human intervention through the use of tanks which serve as a controlled method of irrigation for farmers. Determining the potential volume of water capable of being stored in these tanks can aid investigators to better understand hydrologic parameters such as recharge and streamflow. At present, satellite and LiDAR data are the two methods to collect topographic and bathymetric data for this region, but both options are either too poor of resolution or too costly. Small unmanned aerial vehicles (UAVs) and unmanned surface vehicles (USVs) were demonstrated as low-cost and reliable, high-resolution alternatives for surface data gathering at two locations in the Arkavathy basin during a Summer 2015 field campaign: i) Hadonahalli, and ii) SM Gollahalli. This robot-assisted approach for data gathering will be of interest to investigators in the geophysical sciences, especially those operating with budget constraints in data sparse regions.
High-performance parallel sparse-direct triangular solves (Invited)
NASA Astrophysics Data System (ADS)
Poulson, J.; Ying, L.
2013-12-01
Geophysical inverse problems are increasingly posed in the frequency domain in a manner which requires solving many challenging heterogeneous 3D Helmholtz or linear elastic wave equations at each iteration. One effective means of solving such problems, at least when there is no large-scale internal resonance, is to use moving-PML "sweeping preconditioners". Each application of the sweeping preconditioner involves performing many modest-sized sparse-direct triangular solves -- unfortunately, one at a time. While P. et al. have shown that, with a careful implementation of a distributed sparse-direct solver [1,2], challenging 3D problems approaching a billion degrees of freedom can be solved in a few minutes using less than 10,000 cores, this talk discusses how to leverage the existence of many right-hand sides in order to increase the performance of the preconditioner applications by orders of magnitude. [1] http://github.com/poulson/Clique [2] http://github.com/poulson/PSP
Sparse Bayesian Classification of EEG for Brain-Computer Interface.
Zhang, Yu; Zhou, Guoxu; Jin, Jing; Zhao, Qibin; Wang, Xingyu; Cichocki, Andrzej
2016-11-01
Regularization has been one of the most popular approaches to prevent overfitting in electroencephalogram (EEG) classification of brain-computer interfaces (BCIs). The effectiveness of regularization is often highly dependent on the selection of regularization parameters that are typically determined by cross-validation (CV). However, the CV imposes two main limitations on BCIs: 1) a large amount of training data is required from the user and 2) it takes a relatively long time to calibrate the classifier. These limitations substantially deteriorate the system's practicability and may cause a user to be reluctant to use BCIs. In this paper, we introduce a sparse Bayesian method by exploiting Laplace priors, namely, SBLaplace, for EEG classification. A sparse discriminant vector is learned with a Laplace prior in a hierarchical fashion under a Bayesian evidence framework. All required model parameters are automatically estimated from training data without the need of CV. Extensive comparisons are carried out between the SBLaplace algorithm and several other competing methods based on two EEG data sets. The experimental results demonstrate that the SBLaplace algorithm achieves better overall performance than the competing algorithms for EEG classification.
Sparse Downscaling and Adaptive Fusion of Multi-sensor Precipitation
NASA Astrophysics Data System (ADS)
Ebtehaj, M.; Foufoula, E.
2011-12-01
The past decades have witnessed a remarkable emergence of new sources of multiscale multi-sensor precipitation data including data from global spaceborne active and passive sensors, regional ground based weather surveillance radars and local rain-gauges. Resolution enhancement of remotely sensed rainfall and optimal integration of multi-sensor data promise a posteriori estimates of precipitation fluxes with increased accuracy and resolution to be used in hydro-meteorological applications. In this context, new frameworks are proposed for resolution enhancement and multiscale multi-sensor precipitation data fusion, which capitalize on two main observations: (1) sparseness of remotely sensed precipitation fields in appropriately chosen transformed domains, (e.g., in wavelet space) which promotes the use of the newly emerged theory of sparse representation and compressive sensing for resolution enhancement; (2) a conditionally Gaussian Scale Mixture (GSM) parameterization in the wavelet domain which allows exploiting the efficient linear estimation methodologies, while capturing the non-Gaussian data structure of rainfall. The proposed methodologies are demonstrated using a data set of coincidental observations of precipitation reflectivity images by the spaceborne precipitation radar (PR) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite and ground-based NEXRAD weather surveillance Doppler radars. Uniqueness and stability of the solution, capturing non-Gaussian singular structure of rainfall, reduced uncertainty of estimation and efficiency of computation are the main advantages of the proposed methodologies over the commonly used standard Gaussian techniques.
Objective sea level pressure analysis for sparse data areas
NASA Technical Reports Server (NTRS)
Druyan, L. M.
1972-01-01
A computer procedure was used to analyze the pressure distribution over the North Pacific Ocean for eleven synoptic times in February, 1967. Independent knowledge of the central pressures of lows is shown to reduce the analysis errors for very sparse data coverage. The application of planned remote sensing of sea-level wind speeds is shown to make a significant contribution to the quality of the analysis especially in the high gradient mid-latitudes and for sparse coverage of conventional observations (such as over Southern Hemisphere oceans). Uniform distribution of the available observations of sea-level pressure and wind velocity yields results far superior to those derived from a random distribution. A generalization of the results indicates that the average lower limit for analysis errors is between 2 and 2.5 mb based on the perfect specification of the magnitude of the sea-level pressure gradient from a known verification analysis. A less than perfect specification will derive from wind-pressure relationships applied to satellite observed wind speeds.
Permuting sparse rectangular matrices into block-diagonal form
Aykanat, Cevdet; Pinar, Ali; Catalyurek, Umit V.
2002-12-09
This work investigates the problem of permuting a sparse rectangular matrix into block diagonal form. Block diagonal form of a matrix grants an inherent parallelism for the solution of the deriving problem, as recently investigated in the context of mathematical programming, LU factorization and QR factorization. We propose graph and hypergraph models to represent the nonzero structure of a matrix, which reduce the permutation problem to those of graph partitioning by vertex separator and hypergraph partitioning, respectively. Besides proposing the models to represent sparse matrices and investigating related combinatorial problems, we provide a detailed survey of relevant literature to bridge the gap between different societies, investigate existing techniques for partitioning and propose new ones, and finally present a thorough empirical study of these techniques. Our experiments on a wide range of matrices, using state-of-the-art graph and hypergraph partitioning tools MeTiS and PaT oH, revealed that the proposed methods yield very effective solutions both in terms of solution quality and run time.
Synthesizing spatiotemporally sparse smartphone sensor data for bridge modal identification
NASA Astrophysics Data System (ADS)
Ozer, Ekin; Feng, Maria Q.
2016-08-01
Smartphones as vibration measurement instruments form a large-scale, citizen-induced, and mobile wireless sensor network (WSN) for system identification and structural health monitoring (SHM) applications. Crowdsourcing-based SHM is possible with a decentralized system granting citizens with operational responsibility and control. Yet, citizen initiatives introduce device mobility, drastically changing SHM results due to uncertainties in the time and the space domains. This paper proposes a modal identification strategy that fuses spatiotemporally sparse SHM data collected by smartphone-based WSNs. Multichannel data sampled with the time and the space independence is used to compose the modal identification parameters such as frequencies and mode shapes. Structural response time history can be gathered by smartphone accelerometers and converted into Fourier spectra by the processor units. Timestamp, data length, energy to power conversion address temporal variation, whereas spatial uncertainties are reduced by geolocation services or determining node identity via QR code labels. Then, parameters collected from each distributed network component can be extended to global behavior to deduce modal parameters without the need of a centralized and synchronous data acquisition system. The proposed method is tested on a pedestrian bridge and compared with a conventional reference monitoring system. The results show that the spatiotemporally sparse mobile WSN data can be used to infer modal parameters despite non-overlapping sensor operation schedule.
Relational-Regularized Discriminative Sparse Learning for Alzheimer's Disease Diagnosis.
Lei, Baiying; Yang, Peng; Wang, Tianfu; Chen, Siping; Ni, Dong
2017-01-16
Accurate identification and understanding informative feature is important for early Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel discriminative sparse learning method with relational regularization to jointly predict the clinical score and classify AD disease stages using multimodal features. Specifically, we apply a discriminative learning technique to expand the class-specific difference and include geometric information for effective feature selection. In addition, two kind of relational information are incorporated to explore the intrinsic relationships among features and training subjects in terms of similarity learning. We map the original feature into the target space to identify the informative and predictive features by sparse learning technique. A unique loss function is designed to include both discriminative learning and relational regularization methods. Experimental results based on a total of 805 subjects [including 226 AD patients, 393 mild cognitive impairment (MCI) subjects, and 186 normal controls (NCs)] from AD neuroimaging initiative database show that the proposed method can obtain a classification accuracy of 94.68% for AD versus NC, 80.32% for MCI versus NC, and 74.58% for progressive MCI versus stable MCI, respectively. In addition, we achieve remarkable performance for the clinical scores prediction and classification label identification, which has efficacy for AD disease diagnosis and prognosis. The algorithm comparison demonstrates the effectiveness of the introduced learning techniques and superiority over the state-of-the-arts methods.
Data traffic reduction schemes for sparse Cholesky factorizations
NASA Technical Reports Server (NTRS)
Naik, Vijay K.; Patrick, Merrell L.
1988-01-01
Load distribution schemes are presented which minimize the total data traffic in the Cholesky factorization of dense and sparse, symmetric, positive definite matrices on multiprocessor systems with local and shared memory. The total data traffic in factoring an n x n sparse, symmetric, positive definite matrix representing an n-vertex regular 2-D grid graph using n (sup alpha), alpha is equal to or less than 1, processors are shown to be O(n(sup 1 + alpha/2)). It is O(n(sup 3/2)), when n (sup alpha), alpha is equal to or greater than 1, processors are used. Under the conditions of uniform load distribution, these results are shown to be asymptotically optimal. The schemes allow efficient use of up to O(n) processors before the total data traffic reaches the maximum value of O(n(sup 3/2)). The partitioning employed within the scheme, allows a better utilization of the data accessed from shared memory than those of previously published methods.
Sparse Representation of Electrodermal Activity With Knowledge-Driven Dictionaries
Tsiartas, Andreas; Stein, Leah I.; Cermak, Sharon A.; Narayanan, Shrikanth S.
2015-01-01
Biometric sensors and portable devices are being increasingly embedded into our everyday life, creating the need for robust physiological models that efficiently represent, analyze, and interpret the acquired signals. We propose a knowledge-driven method to represent electrodermal activity (EDA), a psychophysiological signal linked to stress, affect, and cognitive processing. We build EDA-specific dictionaries that accurately model both the slow varying tonic part and the signal fluctuations, called skin conductance responses (SCR), and use greedy sparse representation techniques to decompose the signal into a small number of atoms from the dictionary. Quantitative evaluation of our method considers signal reconstruction, compression rate, and information retrieval measures, that capture the ability of the model to incorporate the main signal characteristics, such as SCR occurrences. Compared to previous studies fitting a predetermined structure to the signal, results indicate that our approach provides benefits across all aforementioned criteria. This paper demonstrates the ability of appropriate dictionaries along with sparse decomposition methods to reliably represent EDA signals and provides a foundation for automatic measurement of SCR characteristics and the extraction of meaningful EDA features. PMID:25494494
Sparse tensor discriminant color space for face verification.
Wang, Su-Jing; Yang, Jian; Sun, Ming-Fang; Peng, Xu-Jun; Sun, Ming-Ming; Zhou, Chun-Guang
2012-06-01
As one of the fundamental features, color provides useful information and plays an important role for face recognition. Generally, the choice of a color space is different for different visual tasks. How can a color space be sought for the specific face recognition problem? To address this problem, we propose a sparse tensor discriminant color space (STDCS) model that represents a color image as a third-order tensor in this paper. The model cannot only keep the underlying spatial structure of color images but also enhance robustness and give intuitionistic or semantic interpretation. STDCS transforms the eigenvalue problem to a series of regression problems. Then one spare color space transformation matrix and two sparse discriminant projection matrices are obtained by applying lasso or elastic net on the regression problems. The experiments on three color face databases, AR, Georgia Tech, and Labeled Faces in the Wild face databases, show that both the performance and the robustness of the proposed method outperform those of the state-of-the-art TDCS model.
Guided-mode resonance nanophotonics in materially sparse architectures
NASA Astrophysics Data System (ADS)
Magnusson, Robert; Niraula, Manoj; Yoon, Jae W.; Ko, Yeong H.; Lee, Kyu J.
2016-03-01
The guided-mode resonance (GMR) concept refers to lateral quasi-guided waveguide modes induced in periodic layers. Whereas these effects have been known for a long time, new attributes and innovations continue to appear. Here, we review some recent progress in this field with emphasis on sparse, or minimal, device embodiments. We discuss properties of wideband resonant reflectors designed with gratings in which the grating ridges are matched to an identical material to eliminate local reflections and phase changes. This critical interface therefore possesses zero refractive-index contrast; hence we call them "zero-contrast gratings." Applying this architecture, we present single-layer, wideband reflectors that are robust under experimentally realistic parametric variations. We introduce a new class of reflectors and polarizers fashioned with dielectric nanowire grids that are mostly empty space. Computed results predict high reflection and attendant polarization extinction for these sparse lattices. Experimental verification with Si nanowire grids yields ~200-nm-wide band of high reflection for one polarization state and free transmission of the orthogonal state. Finally, we present bandpass filters using all-dielectric resonant gratings. We design, fabricate, and test nanostructured single layer filters exhibiting high efficiency and sub-nanometer-wide passbands surrounded by 100-nm-wide stopbands.
Method and apparatus for optimized processing of sparse matrices
Taylor, Valerie E.
1993-01-01
A computer architecture for processing a sparse matrix is disclosed. The apparatus stores a value-row vector corresponding to nonzero values of a sparse matrix. Each of the nonzero values is located at a defined row and column position in the matrix. The value-row vector includes a first vector including nonzero values and delimiting characters indicating a transition from one column to another. The value-row vector also includes a second vector which defines row position values in the matrix corresponding to the nonzero values in the first vector and column position values in the matrix corresponding to the column position of the nonzero values in the first vector. The architecture also includes a circuit for detecting a special character within the value-row vector. Matrix-vector multiplication is executed on the value-row vector. This multiplication is performed by multiplying an index value of the first vector value by a column value from a second matrix to form a matrix-vector product which is added to a previous matrix-vector product.
Slowness and sparseness have diverging effects on complex cell learning.
Lies, Jörn-Philipp; Häfner, Ralf M; Bethge, Matthias
2014-03-01
Following earlier studies which showed that a sparse coding principle may explain the receptive field properties of complex cells in primary visual cortex, it has been concluded that the same properties may be equally derived from a slowness principle. In contrast to this claim, we here show that slowness and sparsity drive the representations towards substantially different receptive field properties. To do so, we present complete sets of basis functions learned with slow subspace analysis (SSA) in case of natural movies as well as translations, rotations, and scalings of natural images. SSA directly parallels independent subspace analysis (ISA) with the only difference that SSA maximizes slowness instead of sparsity. We find a large discrepancy between the filter shapes learned with SSA and ISA. We argue that SSA can be understood as a generalization of the Fourier transform where the power spectrum corresponds to the maximally slow subspace energies in SSA. Finally, we investigate the trade-off between slowness and sparseness when combined in one objective function.
Sparse regression and marginal testing using cluster prototypes
Reid, Stephen; Tibshirani, Robert
2016-01-01
We propose a new approach for sparse regression and marginal testing, for data with correlated features. Our procedure first clusters the features, and then chooses as the cluster prototype the most informative feature in that cluster. Then we apply either sparse regression (lasso) or marginal significance testing to these prototypes. While this kind of strategy is not entirely new, a key feature of our proposal is its use of the post-selection inference theory of Taylor and others (2014, Exact post-selection inference for forward stepwise and least angle regression, Preprint, arXiv:1401.3889) and Lee and others (2014, Exact post-selection inference with the lasso, Preprint, arXiv:1311.6238v5) to compute exact \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{upgreek} \\usepackage{mathrsfs} \\setlength{\\oddsidemargin}{-69pt} \\begin{document} }{}$p$\\end{document}-values and confidence intervals that properly account for the selection of prototypes. We also apply the recent “knockoff” idea of Barber and Candès (2014, Controlling the false discovery rate via knockoffs, Preprint, arXiv:1404.5609) to provide exact finite sample control of the FDR of our regression procedure. We illustrate our proposals on both real and simulated data. PMID:26614384
Sparse regression and marginal testing using cluster prototypes.
Reid, Stephen; Tibshirani, Robert
2016-04-01
We propose a new approach for sparse regression and marginal testing, for data with correlated features. Our procedure first clusters the features, and then chooses as the cluster prototype the most informative feature in that cluster. Then we apply either sparse regression (lasso) or marginal significance testing to these prototypes. While this kind of strategy is not entirely new, a key feature of our proposal is its use of the post-selection inference theory of Taylor and others (2014, Exact post-selection inference for forward stepwise and least angle regression, Preprint, arXiv:1401.3889) and Lee and others (2014, Exact post-selection inference with the lasso, Preprint, arXiv:1311.6238v5) to compute exact [Formula: see text]-values and confidence intervals that properly account for the selection of prototypes. We also apply the recent "knockoff" idea of Barber and Candès (2014, Controlling the false discovery rate via knockoffs, Preprint, arXiv:1404.5609) to provide exact finite sample control of the FDR of our regression procedure. We illustrate our proposals on both real and simulated data.
Sparse representation based image interpolation with nonlocal autoregressive modeling.
Dong, Weisheng; Zhang, Lei; Lukac, Rastislav; Shi, Guangming
2013-04-01
Sparse representation is proven to be a promising approach to image super-resolution, where the low-resolution (LR) image is usually modeled as the down-sampled version of its high-resolution (HR) counterpart after blurring. When the blurring kernel is the Dirac delta function, i.e., the LR image is directly down-sampled from its HR counterpart without blurring, the super-resolution problem becomes an image interpolation problem. In such cases, however, the conventional sparse representation models (SRM) become less effective, because the data fidelity term fails to constrain the image local structures. In natural images, fortunately, many nonlocal similar patches to a given patch could provide nonlocal constraint to the local structure. In this paper, we incorporate the image nonlocal self-similarity into SRM for image interpolation. More specifically, a nonlocal autoregressive model (NARM) is proposed and taken as the data fidelity term in SRM. We show that the NARM-induced sampling matrix is less coherent with the representation dictionary, and consequently makes SRM more effective for image interpolation. Our extensive experimental results demonstrate that the proposed NARM-based image interpolation method can effectively reconstruct the edge structures and suppress the jaggy/ringing artifacts, achieving the best image interpolation results so far in terms of PSNR as well as perceptual quality metrics such as SSIM and FSIM.
Relative species abundance of replicator dynamics with sparse interactions
NASA Astrophysics Data System (ADS)
Obuchi, Tomoyuki; Kabashima, Yoshiyuki; Tokita, Kei
2016-11-01
A theory of relative species abundance on sparsely-connected networks is presented by investigating the replicator dynamics with symmetric interactions. Sparseness of a network involves difficulty in analyzing the fixed points of the equation, and we avoid this problem by treating large self interaction u, which allows us to construct a perturbative expansion. Based on this perturbation, we find that the nature of the interactions is directly connected to the abundance distribution, and some characteristic behaviors, such as multiple peaks in the abundance distribution and all species coexistence at moderate values of u, are discovered in a wide class of the distribution of the interactions. The all species coexistence collapses at a critical value of u, u c , and this collapsing is regarded as a phase transition. To get more quantitative information, we also construct a non-perturbative theory on random graphs based on techniques of statistical mechanics. The result shows those characteristic behaviors are sustained well even for not large u. For even smaller values of u, extinct species start to appear and the abundance distribution becomes rounded and closer to a standard functional form. Another interesting finding is the non-monotonic behavior of diversity, which quantifies the number of coexisting species, when changing the ratio of mutualistic relations Δ . These results are examined by numerical simulations, which show that our theory is exact for the case without extinct species, but becomes less and less precise as the proportion of extinct species grows.
SPARSE INTEGRATIVE CLUSTERING OF MULTIPLE OMICS DATA SETS
Wang, Sijian; Mo, Qianxing
2012-01-01
High resolution microarrays and second-generation sequencing platforms are powerful tools to investigate genome-wide alterations in DNA copy number, methylation, and gene expression associated with a disease. An integrated genomic profiling approach measuring multiple omics data types simultaneously in the same set of biological samples would render an integrated data resolution that would not be available with any single data type. In this study, we use penalized latent variable regression methods for joint modeling of multiple omics data types to identify common latent variables that can be used to cluster patient samples into biologically and clinically relevant disease subtypes. We consider lasso (Tibshirani, 1996), elastic net (Zou and Hastie, 2005), and fused lasso (Tibshirani et al., 2005) methods to induce sparsity in the coefficient vectors, revealing important genomic features that have significant contributions to the latent variables. An iterative ridge regression is used to compute the sparse coefficient vectors. In model selection, a uniform design (Fang and Wang, 1994) is used to seek “experimental” points that scattered uniformly across the search domain for efficient sampling of tuning parameter combinations. We compared our method to sparse singular value decomposition (SVD) and penalized Gaussian mixture model (GMM) using both real and simulated data sets. The proposed method is applied to integrate genomic, epigenomic, and transcriptomic data for subtype analysis in breast and lung cancer data sets. PMID:24587839
Dictionary learning and sparse recovery for electrodermal activity analysis
NASA Astrophysics Data System (ADS)
Kelsey, Malia; Dallal, Ahmed; Eldeeb, Safaa; Akcakaya, Murat; Kleckner, Ian; Gerard, Christophe; Quigley, Karen S.; Goodwin, Matthew S.
2016-05-01
Measures of electrodermal activity (EDA) have advanced research in a wide variety of areas including psychophysiology; however, the majority of this research is typically undertaken in laboratory settings. To extend the ecological validity of laboratory assessments, researchers are taking advantage of advances in wireless biosensors to gather EDA data in ambulatory settings, such as in school classrooms. While measuring EDA in naturalistic contexts may enhance ecological validity, it also introduces analytical challenges that current techniques cannot address. One limitation is the limited efficiency and automation of analysis techniques. Many groups either analyze their data by hand, reviewing each individual record, or use computationally inefficient software that limits timely analysis of large data sets. To address this limitation, we developed a method to accurately and automatically identify SCRs using curve fitting methods. Curve fitting has been shown to improve the accuracy of SCR amplitude and location estimations, but have not yet been used to reduce computational complexity. In this paper, sparse recovery and dictionary learning methods are combined to improve computational efficiency of analysis and decrease run time, while maintaining a high degree of accuracy in detecting SCRs. Here, a dictionary is first created using curve fitting methods for a standard SCR shape. Then, orthogonal matching pursuit (OMP) is used to detect SCRs within a dataset using the dictionary to complete sparse recovery. Evaluation of our method, including a comparison to for speed and accuracy with existing software, showed an accuracy of 80% and a reduced run time.
Configurable hardware integrate and fire neurons for sparse approximation.
Shapero, Samuel; Rozell, Christopher; Hasler, Paul
2013-09-01
Sparse approximation is an important optimization problem in signal and image processing applications. A Hopfield-Network-like system of integrate and fire (IF) neurons is proposed as a solution, using the Locally Competitive Algorithm (LCA) to solve an overcomplete L1 sparse approximation problem. A scalable system architecture is described, including IF neurons with a nonlinear firing function, and current-based synapses to provide linear computation. A network of 18 neurons with 12 inputs is implemented on the RASP 2.9v chip, a Field Programmable Analog Array (FPAA) with directly programmable floating gate elements. Said system uses over 1400 floating gates, the largest system programmed on a FPAA to date. The circuit successfully reproduced the outputs of a digital optimization program, converging to within 4.8% RMS, and an objective cost only 1.7% higher on average. The active circuit consumed 559 μA of current at 2.4 V and converges on solutions in 25 μs, with measurement of the converged spike rate taking an additional 1 ms. Extrapolating the scaling trends to a N=1000 node system, the spiking LCA compares favorably with state-of-the-art digital solutions, and analog solutions using a non-spiking approach.
Music emotion detection using hierarchical sparse kernel machines.
Chin, Yu-Hao; Lin, Chang-Hong; Siahaan, Ernestasia; Wang, Jia-Ching
2014-01-01
For music emotion detection, this paper presents a music emotion verification system based on hierarchical sparse kernel machines. With the proposed system, we intend to verify if a music clip possesses happiness emotion or not. There are two levels in the hierarchical sparse kernel machines. In the first level, a set of acoustical features are extracted, and principle component analysis (PCA) is implemented to reduce the dimension. The acoustical features are utilized to generate the first-level decision vector, which is a vector with each element being a significant value of an emotion. The significant values of eight main emotional classes are utilized in this paper. To calculate the significant value of an emotion, we construct its 2-class SVM with calm emotion as the global (non-target) side of the SVM. The probability distributions of the adopted acoustical features are calculated and the probability product kernel is applied in the first-level SVMs to obtain first-level decision vector feature. In the second level of the hierarchical system, we merely construct a 2-class relevance vector machine (RVM) with happiness as the target side and other emotions as the background side of the RVM. The first-level decision vector is used as the feature with conventional radial basis function kernel. The happiness verification threshold is built on the probability value. In the experimental results, the detection error tradeoff (DET) curve shows that the proposed system has a good performance on verifying if a music clip reveals happiness emotion.
Sparse Gamma Rhythms Arising through Clustering in Adapting Neuronal Networks
Kilpatrick, Zachary P.; Ermentrout, Bard
2011-01-01
Gamma rhythms (30–100 Hz) are an extensively studied synchronous brain state responsible for a number of sensory, memory, and motor processes. Experimental evidence suggests that fast-spiking interneurons are responsible for carrying the high frequency components of the rhythm, while regular-spiking pyramidal neurons fire sparsely. We propose that a combination of spike frequency adaptation and global inhibition may be responsible for this behavior. Excitatory neurons form several clusters that fire every few cycles of the fast oscillation. This is first shown in a detailed biophysical network model and then analyzed thoroughly in an idealized model. We exploit the fact that the timescale of adaptation is much slower than that of the other variables. Singular perturbation theory is used to derive an approximate periodic solution for a single spiking unit. This is then used to predict the relationship between the number of clusters arising spontaneously in the network as it relates to the adaptation time constant. We compare this to a complementary analysis that employs a weak coupling assumption to predict the first Fourier mode to destabilize from the incoherent state of an associated phase model as the external noise is reduced. Both approaches predict the same scaling of cluster number with respect to the adaptation time constant, which is corroborated in numerical simulations of the full system. Thus, we develop several testable predictions regarding the formation and characteristics of gamma rhythms with sparsely firing excitatory neurons. PMID:22125486
Magnetic resonance brain tissue segmentation based on sparse representations
NASA Astrophysics Data System (ADS)
Rueda, Andrea
2015-12-01
Segmentation or delineation of specific organs and structures in medical images is an important task in the clinical diagnosis and treatment, since it allows to characterize pathologies through imaging measures (biomarkers). In brain imaging, segmentation of main tissues or specific structures is challenging, due to the anatomic variability and complexity, and the presence of image artifacts (noise, intensity inhomogeneities, partial volume effect). In this paper, an automatic segmentation strategy is proposed, based on sparse representations and coupled dictionaries. Image intensity patterns are singly related to tissue labels at the level of small patches, gathering this information in coupled intensity/segmentation dictionaries. This dictionaries are used within a sparse representation framework to find the projection of a new intensity image onto the intensity dictionary, and the same projection can be used with the segmentation dictionary to estimate the corresponding segmentation. Preliminary results obtained with two publicly available datasets suggest that the proposal is capable of estimating adequate segmentations for gray matter (GM) and white matter (WM) tissues, with an average overlapping of 0:79 for GM and 0:71 for WM (with respect to original segmentations).
Bayesian modeling of temporal dependence in large sparse contingency tables
Kunihama, Tsuyoshi; Dunson, David B.
2013-01-01
In many applications, it is of interest to study trends over time in relationships among categorical variables, such as age group, ethnicity, religious affiliation, political party and preference for particular policies. At each time point, a sample of individuals provide responses to a set of questions, with different individuals sampled at each time. In such settings, there tends to be abundant missing data and the variables being measured may change over time. At each time point, one obtains a large sparse contingency table, with the number of cells often much larger than the number of individuals being surveyed. To borrow information across time in modeling large sparse contingency tables, we propose a Bayesian autoregressive tensor factorization approach. The proposed model relies on a probabilistic Parafac factorization of the joint pmf characterizing the categorical data distribution at each time point, with autocorrelation included across times. Efficient computational methods are developed relying on MCMC. The methods are evaluated through simulation examples and applied to social survey data. PMID:24482548
Brain at work: time, sparseness and superposition principles.
Molotchnikoff, Stephane; Rouat, Jean
2012-01-01
Many studies explored mechanisms through which the brain encodes sensory inputs allowing a coherent behavior. The brain could identify stimuli via a hierarchical stream of activity leading to a cardinal neuron responsive to one particular object. The opportunity to record from numerous neurons offered investigators the capability of examining simultaneously the functioning of many cells. These approaches suggested encoding processes that are parallel rather than serial. Binding the many features of a stimulus may be accomplished through an induced synchronization of cell's action potentials. These interpretations are supported by experimental data and offer many advantages but also several shortcomings. We argue for a coding mechanism based on a sparse synchronization paradigm. We show that synchronization of spikes is a fast and efficient mode to encode the representation of objects based on feature bindings. We introduce the view that sparse synchronization coding presents an interesting venue in probing brain encoding mechanisms as it allows the functional establishment of multi-layered and time-conditioned neuronal networks or multislice networks. We propose a model based on integrate-and-fire spiking neurons.
3D ear identification based on sparse representation.
Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying
2014-01-01
Biometrics based personal authentication is an effective way for automatically recognizing, with a high confidence, a person's identity. Recently, 3D ear shape has attracted tremendous interests in research field due to its richness of feature and ease of acquisition. However, the existing ICP (Iterative Closet Point)-based 3D ear matching methods prevalent in the literature are not quite efficient to cope with the one-to-many identification case. In this paper, we aim to fill this gap by proposing a novel effective fully automatic 3D ear identification system. We at first propose an accurate and efficient template-based ear detection method. By utilizing such a method, the extracted ear regions are represented in a common canonical coordinate system determined by the ear contour template, which facilitates much the following stages of feature extraction and classification. For each extracted 3D ear, a feature vector is generated as its representation by making use of a PCA-based local feature descriptor. At the stage of classification, we resort to the sparse representation based classification approach, which actually solves an l1-minimization problem. To the best of our knowledge, this is the first work introducing the sparse representation framework into the field of 3D ear identification. Extensive experiments conducted on a benchmark dataset corroborate the effectiveness and efficiency of the proposed approach. The associated Matlab source code and the evaluation results have been made publicly online available at http://sse.tongji.edu.cn/linzhang/ear/srcear/srcear.htm.
Unsupervised analysis of polyphonic music by sparse coding.
Abdallah, Samer A; Plumbley, Mark D
2006-01-01
We investigate a data-driven approach to the analysis and transcription of polyphonic music, using a probabilistic model which is able to find sparse linear decompositions of a sequence of short-term Fourier spectra. The resulting system represents each input spectrum as a weighted sum of a small number of "atomic" spectra chosen from a larger dictionary; this dictionary is, in turn, learned from the data in such a way as to represent the given training set in an (information theoretically) efficient way. When exposed to examples of polyphonic music, most of the dictionary elements take on the spectral characteristics of individual notes in the music, so that the sparse decomposition can be used to identify the notes in a polyphonic mixture. Our approach differs from other methods of polyphonic analysis based on spectral decomposition by combining all of the following: (a) a formulation in terms of an explicitly given probabilistic model, in which the process estimating which notes are present corresponds naturally with the inference of latent variables in the model; (b) a particularly simple generative model, motivated by very general considerations about efficient coding, that makes very few assumptions about the musical origins of the signals being processed; and (c) the ability to learn a dictionary of atomic spectra (most of which converge to harmonic spectral profiles associated with specific notes) from polyphonic examples alone-no separate training on monophonic examples is required.
Robust Pedestrian Classification Based on Hierarchical Kernel Sparse Representation
Sun, Rui; Zhang, Guanghai; Yan, Xiaoxing; Gao, Jun
2016-01-01
Vision-based pedestrian detection has become an active topic in computer vision and autonomous vehicles. It aims at detecting pedestrians appearing ahead of the vehicle using a camera so that autonomous vehicles can assess the danger and take action. Due to varied illumination and appearance, complex background and occlusion pedestrian detection in outdoor environments is a difficult problem. In this paper, we propose a novel hierarchical feature extraction and weighted kernel sparse representation model for pedestrian classification. Initially, hierarchical feature extraction based on a CENTRIST descriptor is used to capture discriminative structures. A max pooling operation is used to enhance the invariance of varying appearance. Then, a kernel sparse representation model is proposed to fully exploit the discrimination information embedded in the hierarchical local features, and a Gaussian weight function as the measure to effectively handle the occlusion in pedestrian images. Extensive experiments are conducted on benchmark databases, including INRIA, Daimler, an artificially generated dataset and a real occluded dataset, demonstrating the more robust performance of the proposed method compared to state-of-the-art pedestrian classification methods. PMID:27537888
Threshold partitioning of sparse matrices and applications to Markov chains
Choi, Hwajeong; Szyld, D.B.
1996-12-31
It is well known that the order of the variables and equations of a large, sparse linear system influences the performance of classical iterative methods. In particular if, after a symmetric permutation, the blocks in the diagonal have more nonzeros, classical block methods have a faster asymptotic rate of convergence. In this paper, different ordering and partitioning algorithms for sparse matrices are presented. They are modifications of PABLO. In the new algorithms, in addition to the location of the nonzeros, the values of the entries are taken into account. The matrix resulting after the symmetric permutation has dense blocks along the diagonal, and small entries in the off-diagonal blocks. Parameters can be easily adjusted to obtain, for example, denser blocks, or blocks with elements of larger magnitude. In particular, when the matrices represent Markov chains, the permuted matrices are well suited for block iterative methods that find the corresponding probability distribution. Applications to three types of methods are explored: (1) Classical block methods, such as Block Gauss Seidel. (2) Preconditioned GMRES, where a block diagonal preconditioner is used. (3) Iterative aggregation method (also called aggregation/disaggregation) where the partition obtained from the ordering algorithm with certain parameters is used as an aggregation scheme. In all three cases, experiments are presented which illustrate the performance of the methods with the new orderings. The complexity of the new algorithms is linear in the number of nonzeros and the order of the matrix, and thus adding little computational effort to the overall solution.
Sparse signal representation and its applications in ultrasonic NDE.
Zhang, Guang-Ming; Zhang, Cheng-Zhong; Harvey, David M
2012-03-01
Many sparse signal representation (SSR) algorithms have been developed in the past decade. The advantages of SSR such as compact representations and super resolution lead to the state of the art performance of SSR for processing ultrasonic non-destructive evaluation (NDE) signals. Choosing a suitable SSR algorithm and designing an appropriate overcomplete dictionary is a key for success. After a brief review of sparse signal representation methods and the design of overcomplete dictionaries, this paper addresses the recent accomplishments of SSR for processing ultrasonic NDE signals. The advantages and limitations of SSR algorithms and various overcomplete dictionaries widely-used in ultrasonic NDE applications are explored in depth. Their performance improvement compared to conventional signal processing methods in many applications such as ultrasonic flaw detection and noise suppression, echo separation and echo estimation, and ultrasonic imaging is investigated. The challenging issues met in practical ultrasonic NDE applications for example the design of a good dictionary are discussed. Representative experimental results are presented for demonstration.
Sparse Bayesian extreme learning machine for multi-classification.
Luo, Jiahua; Vong, Chi-Man; Wong, Pak-Kin
2014-04-01
Extreme learning machine (ELM) has become a popular topic in machine learning in recent years. ELM is a new kind of single-hidden layer feedforward neural network with an extremely low computational cost. ELM, however, has two evident drawbacks: 1) the output weights solved by Moore-Penrose generalized inverse is a least squares minimization issue, which easily suffers from overfitting and 2) the accuracy of ELM is drastically sensitive to the number of hidden neurons so that a large model is usually generated. This brief presents a sparse Bayesian approach for learning the output weights of ELM in classification. The new model, called Sparse Bayesian ELM (SBELM), can resolve these two drawbacks by estimating the marginal likelihood of network outputs and automatically pruning most of the redundant hidden neurons during learning phase, which results in an accurate and compact model. The proposed SBELM is evaluated on wide types of benchmark classification problems, which verifies that the accuracy of SBELM model is relatively insensitive to the number of hidden neurons; and hence a much more compact model is always produced as compared with other state-of-the-art neural network classifiers.
Pedestrian detection from thermal images: A sparse representation based approach
NASA Astrophysics Data System (ADS)
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.
Kinetics of diffusion-controlled annihilation with sparse initial conditions
Ben-Naim, Eli; Krapivsky, Paul
2016-12-16
Here, we study diffusion-controlled single-species annihilation with sparse initial conditions. In this random process, particles undergo Brownian motion, and when two particles meet, both disappear. We also focus on sparse initial conditions where particles occupy a subspace of dimension δ that is embedded in a larger space of dimension d. Furthermore, we find that the co-dimension Δ = d - δ governs the behavior. All particles disappear when the co-dimension is sufficiently small, Δ ≤ 2; otherwise, a finite fraction of particles indefinitely survive. We establish the asymptotic behavior of the probability S(t) that a test particle survives until time t. When the subspace is a line, δ = 1, we find inverse logarithmic decay,more » $$S\\sim {(\\mathrm{ln}t)}^{-1}$$, in three dimensions, and a modified power-law decay, $$S\\sim (\\mathrm{ln}t){t}^{-1/2}$$, in two dimensions. In general, the survival probability decays algebraically when Δ < 2, and there is an inverse logarithmic decay at the critical co-dimension Δ = 2.« less
Kinetics of diffusion-controlled annihilation with sparse initial conditions
Ben-Naim, Eli; Krapivsky, Paul
2016-12-16
Here, we study diffusion-controlled single-species annihilation with sparse initial conditions. In this random process, particles undergo Brownian motion, and when two particles meet, both disappear. We also focus on sparse initial conditions where particles occupy a subspace of dimension δ that is embedded in a larger space of dimension d. Furthermore, we find that the co-dimension Δ = d - δ governs the behavior. All particles disappear when the co-dimension is sufficiently small, Δ ≤ 2; otherwise, a finite fraction of particles indefinitely survive. We establish the asymptotic behavior of the probability S(t) that a test particle survives until time t. When the subspace is a line, δ = 1, we find inverse logarithmic decay, $S\\sim {(\\mathrm{ln}t)}^{-1}$, in three dimensions, and a modified power-law decay, $S\\sim (\\mathrm{ln}t){t}^{-1/2}$, in two dimensions. In general, the survival probability decays algebraically when Δ < 2, and there is an inverse logarithmic decay at the critical co-dimension Δ = 2.
Sparse expression bases in cancer reveal tumor drivers
Logsdon, Benjamin A.; Gentles, Andrew J.; Miller, Chris P.; Blau, C. Anthony; Becker, Pamela S.; Lee, Su-In
2015-01-01
We define a new category of candidate tumor drivers in cancer genome evolution: ‘selected expression regulators’ (SERs)—genes driving dysregulated transcriptional programs in cancer evolution. The SERs are identified from genome-wide tumor expression data with a novel method, namely SPARROW (SPARse selected expRessiOn regulators identified With penalized regression). SPARROW uncovers a previously unknown connection between cancer expression variation and driver events, by using a novel sparse regression technique. Our results indicate that SPARROW is a powerful complementary approach to identify candidate genes containing driver events that are hard to detect from sequence data, due to a large number of passenger mutations and lack of comprehensive sequence information from a sufficiently large number of samples. SERs identified by SPARROW reveal known driver mutations in multiple human cancers, along with known cancer-associated processes and survival-associated genes, better than popular methods for inferring gene expression networks. We demonstrate that when applied to acute myeloid leukemia expression data, SPARROW identifies an apoptotic biomarker (PYCARD) for an investigational drug obatoclax. The PYCARD and obatoclax association is validated in 30 AML patient samples. PMID:25583238
Optimization-based multiple-point geostatistics: A sparse way
NASA Astrophysics Data System (ADS)
Kalantari, Sadegh; Abdollahifard, Mohammad Javad
2016-10-01
In multiple-point simulation the image should be synthesized consistent with the given training image and hard conditioning data. Existing sequential simulation methods usually lead to error accumulation which is hardly manageable in future steps. Optimization-based methods are capable of handling inconsistencies by iteratively refining the simulation grid. In this paper, the multiple-point stochastic simulation problem is formulated in an optimization-based framework using a sparse model. Sparse model allows each patch to be constructed as a superposition of a few atoms of a dictionary formed using training patterns, leading to a significant increase in the variability of the patches. To control the creativity of the model, a local histogram matching method is proposed. Furthermore, effective solutions are proposed for different issues arisen in multiple-point simulation. In order to handle hard conditioning data a weighted matching pursuit method is developed in this paper. Moreover, a simple and efficient thresholding method is developed which allows working with categorical variables. The experiments show that the proposed method produces acceptable realizations in terms of pattern reproduction, increases the variability of the realizations, and properly handles numerous conditioning data.
essaMEM: finding maximal exact matches using enhanced sparse suffix arrays.
Vyverman, Michaël; De Baets, Bernard; Fack, Veerle; Dawyndt, Peter
2013-03-15
We have developed essaMEM, a tool for finding maximal exact matches that can be used in genome comparison and read mapping. essaMEM enhances an existing sparse suffix array implementation with a sparse child array. Tests indicate that the enhanced algorithm for finding maximal exact matches is much faster, while maintaining the same memory footprint. In this way, sparse suffix arrays remain competitive with the more complex compressed suffix arrays.
Karaosmanoglu, F.
1999-04-01
Using vegetable oils as fuel alternatives has economic, environmental, and energy benefits for Turkey. The present work provides insight to the status of vegetable oil fuels in Turkey. A brief historical background of the issue, as well as an up to date review of the research carried out on vegetable oil fuels, is given and the future of their production and application is discussed.
Nonlinearities in vegetation functioning
NASA Astrophysics Data System (ADS)
Ceballos-Núñez, Verónika; Müller, Markus; Metzler, Holger; Sierra, Carlos
2016-04-01
Given the current drastic changes in climate and atmospheric CO2 concentrations, and the role of vegetation in the global carbon cycle, there is increasing attention to the carbon allocation component in biosphere terrestrial models. Improving the representation of C allocation in models could be the key to having better predictions of the fate of C once it enters the vegetation and is partitioned to C pools of different residence times. C allocation has often been modeled using systems of ordinary differential equations, and it has been hypothesized that most models can be generalized with a specific form of a linear dynamical system. However, several studies have highlighted discrepancies between empirical observations and model predictions, attributing these differences to problems with model structure. Although efforts have been made to compare different models, the outcome of these qualitative assessments has been a conceptual categorization of them. In this contribution, we introduce a new effort to identify the main properties of groups of models by studying their mathematical structure. For this purpose, we performed a literature research of the relevant models of carbon allocation in vegetation and developed a database with their representation in symbolic mathematics. We used the Python package SymPy for symbolic mathematics as a common language and manipulated the models to calculate their Jacobian matrix at fixed points and their eigenvalues, among other mathematical analyses. Our preliminary results show a tendency of inverse proportionality between model complexity and size of time/space scale; complex interactions between the variables controlling carbon allocation in vegetation tend to operate at shorter time/space scales, and vice-versa. Most importantly, we found that although the linear structure is common, other structures with non-linearities have been also proposed. We, therefore, propose a new General Model that can accommodate these
Wave Dissipation by Vegetation
2011-09-01
relative to conditions without vegetation. During Hurricanes Charley and Wilma, water levels recorded in two Florida mangrove ecosystems were...reduced by as much as 9.4 cm per km inland. Although water levels were reduced as the surge moved through the coastal mangroves , the relative contribution...of mangroves was still unclear (Krauss et al. 2009). Numerical simulations by Loder et al. (2009) and ERDC/CHL CHETN-I-82 September 2011 2
Choose a variety of fruits and vegetables daily: understanding the complexities.
Krebs-Smith, S M; Kantor, L S
2001-02-01
The 2000 edition of Nutrition and Your Health: Dietary Guidelines for Americans is the first to include a recommendation aimed specifically at fruits and vegetables, apart from grains. This paper discusses these changes in the Dietary Guidelines, summarizes the methods of assessment pertaining to fruit and vegetable intakes and their related factors, and reviews the data available on current levels and trends over time. Recent methodological advances in the measurement of both the aggregate U.S. food supply and foods consumed by individuals have allowed for better estimates with which recommendations can be compared. The data on individual intakes suggest the following: Americans are consuming fruits and vegetables at a level near the minimum recommendations; to be in concordance with energy-based recommendations, they would have to consume approximately 2 more servings per day; and dark green and deep yellow vegetables are accounting for a disproportionately small share of the total. Fruit and vegetable consumption appears to be rising, but only slightly, and this increase might be only an artifact of shifts in the population demographics. A number of studies suggest that low income households in poor central cities and sparsely populated rural areas often have less access to food stores and face higher prices for food, including fruits and vegetables, compared with other households. At the aggregate level, supplying enough fruits and vegetables to meet dietary recommendations for all U.S. consumers would require adjustments in U.S. agricultural production, trade, marketing practices and prices of these commodities.
Analysis of image content recognition algorithm based on sparse coding and machine learning
NASA Astrophysics Data System (ADS)
Xiao, Yu
2017-03-01
This paper presents an image classification algorithm based on spatial sparse coding model and random forest. Firstly, SIFT feature extraction of the image; and then use the sparse encoding theory to generate visual vocabulary based on SIFT features, and using the visual vocabulary of SIFT features into a sparse vector; through the combination of regional integration and spatial sparse vector, the sparse vector gets a fixed dimension is used to represent the image; at last random forest classifier for image sparse vectors for training and testing, using the experimental data set for standard test Caltech-101 and Scene-15. The experimental results show that the proposed algorithm can effectively represent the features of the image and improve the classification accuracy. In this paper, we propose an innovative image recognition algorithm based on image segmentation, sparse coding and multi instance learning. This algorithm introduces the concept of multi instance learning, the image as a multi instance bag, sparse feature transformation by SIFT images as instances, sparse encoding model generation visual vocabulary as the feature space is mapped to the feature space through the statistics on the number of instances in bags, and then use the 1-norm SVM to classify images and generate sample weights to select important image features.
Sparse representation based multi-threshold segmentation for hyperspectral target detection
NASA Astrophysics Data System (ADS)
Feng, Wei-yi; Chen, Qian; Miao, Zhuang; He, Wei-ji; Gu, Guo-hua; Zhuang, Jia-yan
2013-08-01
A sparse representation based multi-threshold segmentation (SRMTS) algorithm for target detection in hyperspectral images is proposed. Benefiting from the sparse representation, the high-dimensional spectral data can be characterized into a series of sparse feature vectors which has only a few nonzero coefficients. Through setting an appropriate threshold, the noise removed sparse spectral vectors are divided into two subspaces in the sparse domain consistent with the sample spectrum to separate the target from the background. Then a correlation and a vector 1-norm are calculated respectively in the subspaces. The sparse characteristic of the target is used to ext ract the target with a multi -threshold method. Unlike the conventional hyperspectral dimensionality reduction methods used in target detection algorithms, like Principal Components Analysis (PCA) and Maximum Noise Fraction (MNF), this algorithm maintains the spectral characteristics while removing the noise due to the sparse representation. In the experiments, an orthogonal wavelet sparse base is used to sparse the spectral information and a best contraction threshold to remove the hyperspectral image noise according to the noise estimation of the test images. Compared with co mmon algorithms, such as Adaptive Cosine Estimator (ACE), Constrained Energy Minimizat ion (CEM) and the noise removed MNF-CEM algorithm, the proposed algorithm demonstrates higher detection rates and robustness via the ROC curves.
Rastmanesh, F; Moore, F; Kharrati-Kopaei, M; Behrouz, M
2010-04-01
Simple statistical methods on Normalized Difference Vegetation Index (NDVI) and bands 3 and 4 data of relatively coarse resolution Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM(+)) imageries were used to investigate the impacts of air pollution on the deterioration of the vegetation cover in the Sarcheshmeh copper complex of central Iran. Descriptive statistics and k-means cluster analysis indicated that vegetation deterioration had already started in the prevailing wind directions. The results show that combination of simple statistical methods and satellite imageries can be used as effective monitoring tools to indicate vegetation stress even in regions of sparse vegetation. Despite various possible perturbing factors upon NDVI, this index remains to be a valuable quantitative vegetation monitoring tool.
NASA Astrophysics Data System (ADS)
Masmoudi, Atef; Zouari, Sonia; Ghribi, Abdelaziz
2015-11-01
We propose a new adaptive block-wise lossless image compression algorithm, which is based on the so-called alphabet reduction scheme combined with an adaptive arithmetic coding (AC). This new encoding algorithm is particularly efficient for lossless compression of images with sparse and locally sparse histograms. AC is a very efficient technique for lossless data compression and produces a rate that is close to the entropy; however, a compression performance loss occurs when encoding images or blocks with a limited number of active symbols by comparison with the number of symbols in the nominal alphabet, which consists in the amplification of the zero frequency problem. Generally, most methods add one to the frequency count of each symbol from the nominal alphabet, which leads to a statistical model distortion, and therefore reduces the efficiency of the AC. The aim of this work is to overcome this drawback by assigning to each image block the smallest possible set including all the existing symbols called active symbols. This is an alternative of using the nominal alphabet when applying the conventional arithmetic encoders. We show experimentally that the proposed method outperforms several lossless image compression encoders and standards including the conventional arithmetic encoders, JPEG2000, and JPEG-LS.
The red edge in arid region vegetation: 340-1060 nm spectra
NASA Technical Reports Server (NTRS)
Ray, Terrill W.; Murray, Bruce C.; Chehbouni, A.; Njoku, Eni
1993-01-01
The remote sensing study of vegetated regions of the world has typically been focused on the use of broad-band vegetation indices such as NDVI. Various modifications of these indices have been developed in attempts to minimize the effect of soil background, e.g., SAVI, or to reduce the effect of the atmosphere, e.g., ARVI. Most of these indices depend on the so-called 'red edge,' the sharp transition between the strong absorption of chlorophyll pigment in visible wavelengths and the strong scattering in the near-infrared from the cellular structure of leaves. These broadband indices tend to become highly inaccurate as the green canopy cover becomes sparse. The advent of high spectral resolution remote sensing instrument such as the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS) has allowed the detection of narrow spectral features in vegetation and there are reports of detection of the red edge even for pixels with very low levels of green vegetation cover by Vane et al. and Elvidge et al., and to characterize algal biomass in coastal areas. Spectral mixing approaches similar to those of Smith et al. can be extended into the high spectral resolution domain allowing for the analysis of more endmembers, and potentially, discrimination between material with narrow spectral differences. Vegetation in arid regions tends to be sparse, often with small leaves such as the creosote bush. Many types of arid region vegetation spend much of the year with their leaves in a senescent state, i.e., yellow, with lowered chlorophyll pigmentation. The sparseness of the leaves of many arid region plants has the dual effect of lowering the green leaf area which can be observed and of allowing more of the sub-shrub soil to be visible which further complicates the spectrum of a region covered with arid region vegetation. Elvidge examined the spectral characteristics of dry plant materials showing significant differences in the region of the red edge and the diagnostic ligno
NASA Astrophysics Data System (ADS)
Rowland, J. C.; Moody, D. I.; Brumby, S.; Gangodagamage, C.
2012-12-01
Techniques for automated feature extraction, including neuroscience-inspired machine vision, are of great interest for landscape characterization and change detection in support of global climate change science and modeling. Successful application of novel unsupervised feature extraction and clustering algorithms for use in Land Cover Classification requires the ability to determine what landscape attributes are represented by automated clustering. A closely related challenge is learning how to precondition the input data streams to the unsupervised classification algorithms in order to obtain clusters that represent Land Cover category of relevance to landsurface change and modeling applications. We present results from an ongoing effort to apply novel clustering methodologies developed primarily for neuroscience machine vision applications to the environmental sciences. We use a Hebbian learning rule to build spectral-textural dictionaries that are adapted to the data. We learn our dictionaries from millions of overlapping image patches and then use a pursuit search to generate sparse classification features. These sparse representations of pixel patches are used to perform unsupervised k-means clustering. In our application, we use 8-band multispectral Worldview-2 data from three arctic study areas: Barrow, Alaska; the Selawik River, Alaska; and a watershed near the Mackenzie River delta in northwest Canada. Our goal is to develop a robust classification methodology that will allow for the automated discretization of the landscape into distinct units based on attributes such as vegetation, surface hydrological properties (e.g. soil moisture and inundation), and topographic/geomorphic characteristics. The challenge of developing a meaningful land cover classification includes both learning how optimize the clustering algorithm and successfully interpreting the results. In applying the unsupervised clustering, we have the flexibility of selecting both the window
Sparse regularization techniques provide novel insights into outcome integration processes.
Mohr, Holger; Wolfensteller, Uta; Frimmel, Steffi; Ruge, Hannes
2015-01-01
By exploiting information that is contained in the spatial arrangement of neural activations, multivariate pattern analysis (MVPA) can detect distributed brain activations which are not accessible by standard univariate analysis. Recent methodological advances in MVPA regularization techniques have made it feasible to produce sparse discriminative whole-brain maps with highly specific patterns. Furthermore, the most recent refinement, the Graph Net, explicitly takes the 3D-structure of fMRI data into account. Here, these advanced classification methods were applied to a large fMRI sample (N=70) in order to gain novel insights into the functional localization of outcome integration processes. While the beneficial effect of differential outcomes is well-studied in trial-and-error learning, outcome integration in the context of instruction-based learning has remained largely unexplored. In order to examine neural processes associated with outcome integration in the context of instruction-based learning, two groups of subjects underwent functional imaging while being presented with either differential or ambiguous outcomes following the execution of varying stimulus-response instructions. While no significant univariate group differences were found in the resulting fMRI dataset, L1-regularized (sparse) classifiers performed significantly above chance and also clearly outperformed the standard L2-regularized (dense) Support Vector Machine on this whole-brain between-subject classification task. Moreover, additional L2-regularization via the Elastic Net and spatial regularization by the Graph Net improved interpretability of discriminative weight maps but were accompanied by reduced classification accuracies. Most importantly, classification based on sparse regularization facilitated the identification of highly specific regions differentially engaged under ambiguous and differential outcome conditions, comprising several prefrontal regions previously associated with
Object-oriented algorithmic laboratory for ordering sparse matrices
Kumfert, Gary Karl
2000-05-01
We focus on two known NP-hard problems that have applications in sparse matrix computations: the envelope/wavefront reduction problem and the fill reduction problem. Envelope/wavefront reducing orderings have a wide range of applications including profile and frontal solvers, incomplete factorization preconditioning, graph reordering for cache performance, gene sequencing, and spatial databases. Fill reducing orderings are generally limited to--but an inextricable part of--sparse matrix factorization. Our major contribution to this field is the design of new and improved heuristics for these NP-hard problems and their efficient implementation in a robust, cross-platform, object-oriented software package. In this body of research, we (1) examine current ordering algorithms, analyze their asymptotic complexity, and characterize their behavior in model problems, (2) introduce new and improved algorithms that address deficiencies found in previous heuristics, (3) implement an object-oriented library of these algorithms in a robust, modular fashion without significant loss of efficiency, and (4) extend our algorithms and software to address both generalized and constrained problems. We stress that the major contribution is the algorithms and the implementation; the whole being greater than the sum of its parts. The initial motivation for implementing our algorithms in object-oriented software was to manage the inherent complexity. During our research came the realization that the object-oriented implementation enabled new possibilities augmented algorithms that would not have been as natural to generalize from a procedural implementation. Some extensions are constructed from a family of related algorithmic components, thereby creating a poly-algorithm that can adapt its strategy to the properties of the specific problem instance dynamically. Other algorithms are tailored for special constraints by aggregating algorithmic components and having them collaboratively
Surface Pollen Distribution from Alpine Vegetation in Eastern Tibet, China.
Zhang, Yun; Kong, Zhaochen; Yang, Zhenjing; Wang, Li; Duan, Xiaohong
2017-04-03
We explore the relationship between modern pollen spectra and vegetation patterns in the Eastern Tibet, China in order to provide information on the representation of pollen taxa and improve the general knowledge of vertical pollen transport. Forty-two modern pollen samples collected in surface soil along two altitudinal transects allowed conclusions on vertical pollen dispersal from the alpine region of Dingqing County, Changdu district in Tibet. Discriminant analyses and detrended correspondence analysis (DCA) of 24 pollen taxa were used to further discuss the difference of modern pollen spectra in these alpine vegetation zones. The surface pollen assemblage is divided into three pollen zones, such as subalpine shrub meadow, montane coniferous forest and shrub steppe with sparse trees. Altitude and precipitation are two primary factors contributing to changes in surface pollen assemblage from alpine vegetation in the eastern Tibet. Large amounts of spruce pollen at higher elevations above the timberline might be introduced from lower elevations by upslope winds. Therefore, the interpretation of spruce pollen in the fossil record must take into account long distance upward wind transport. Moreover, the destruction of coniferous forest in the study area is well illustrated in the modern pollen rain.
Simulation of the vegetation spatial heterogeneity over the semiarid grassland
NASA Astrophysics Data System (ADS)
Zeng, X.; Lian, R.
2013-12-01
Along the transition zones between large-scale grassland and desert in arid and semiarid regions, vegetation is usually sparse and forms various fine scale vegetation patches. This work attempts to better understand the underlying ecohydrology of these observations by relating the fine scale patterns to the large-scale ecosystem behaviors, including the following issues: (1) what is the variability (and diversity) of vegetation pattern formation? (2) how is the fine scale heterogeneity relevant to the mean ecosystem state variables in large scale? and (3) how does the fine scale heterogeneity influence the system behaviors such as transition and resilience? In the first study, fine-scale horizontal interaction terms are introduced to a single column model of large scale grassland to explicitly reproduce the vegetation patterns of spots and stripes. High diversities are found in two very different spatial scales. (1) In terms of spatial vegetation formation (microscale), the ecosystem undergoes the transition from uniform grassland to regular and irregular vegetation patterns, and then to pure desert, as the moisture index (i.e., the ratio of precipitation over potential evaporation) decreases. An infinite number of equilibrium states coexist in the transition zone. (2) In terms of spatially averaged biomass (macroscale), the transition zone from grassland to desert consists of a narrow region with erratic vegetation patterns and high diversity of averaged biomass, in contrast to just a single critical point in representing the transition in the single column model. In the second study, a spatial implicit model is developed by the introducing of different leaf/root allocation strategy. By allocating higher percentage of its photosynthetic product to root and creating a root area lager than the crown area (i.e., the area occupied by leaves), plant can absorb water from surrounding bare soil and hence take the advantage over plant with lower root area. The optimal
The onset of sediment transport in vegetated channels predicted by turbulent kinetic energy
NASA Astrophysics Data System (ADS)
Yang, J. Q.; Chung, H.; Nepf, H. M.
2016-11-01
This laboratory study advances our understanding of sediment transport in vegetated regions, by describing the impact of stem density on the critical velocity, Ucrit, at which sediment motion is initiated. Sparse emergent vegetation was modeled with rigid cylinders arranged in staggered arrays of different stem densities. The sediment transport rate, Qs, was measured over a range of current speeds using digital imaging, and the critical velocity was selected as the condition at which the magnitude of Qs crossed the noise threshold. For both grain sizes considered here (0.6-0.85 mm and 1.7-2 mm), Ucrit decreased with increasing stem density. This dependence can be explained by a threshold condition based on turbulent kinetic energy, kt, suggesting that near-bed turbulence intensity may be a more important control than bed shear stress on the initiation of sediment motion. The turbulent kinetic energy model unified the bare bed and vegetated channel measurements.
CARS Spectral Fitting with Multiple Resonant Species using Sparse Libraries
NASA Technical Reports Server (NTRS)
Cutler, Andrew D.; Magnotti, Gaetano
2010-01-01
The dual pump CARS technique is often used in the study of turbulent flames. Fast and accurate algorithms are needed for fitting dual-pump CARS spectra for temperature and multiple chemical species. This paper describes the development of such an algorithm. The algorithm employs sparse libraries, whose size grows much more slowly with number of species than a conventional library. The method was demonstrated by fitting synthetic "experimental" spectra containing 4 resonant species (N2, O2, H2 and CO2), both with noise and without it, and by fitting experimental spectra from a H2-air flame produced by a Hencken burner. In both studies, weighted least squares fitting of signal, as opposed to least squares fitting signal or square-root signal, was shown to produce the least random error and minimize bias error in the fitted parameters.
Constraints on Fluctuations in Sparsely Characterized Biological Systems
Hilfinger, Andreas; Norman, Thomas M.; Vinnicombe, Glenn
2016-01-01
Biochemical processes are inherently stochastic, creating molecular fluctuations in otherwise identical cells. Such “noise” is widespread but has proven difficult to analyze because most systems are sparsely characterized at the single cell level and because nonlinear stochastic models are analytically intractable. Here, we exactly relate average abundances, lifetimes, step sizes, and covariances for any pair of components in complex stochastic reaction systems even when the dynamics of other components are left unspecified. Using basic mathematical inequalities, we then establish bounds for whole classes of systems. These bounds highlight fundamental trade-offs that show how efficient assembly processes must invariably exhibit large fluctuations in subunit levels and how eliminating fluctuations in one cellular component requires creating heterogeneity in another. PMID:26894735
Mandala Networks: ultra-small-world and highly sparse graphs
NASA Astrophysics Data System (ADS)
Sampaio Filho, Cesar I. N.; Moreira, André A.; Andrade, Roberto F. S.; Herrmann, Hans J.; Andrade, José S.
2015-03-01
The increasing demands in security and reliability of infrastructures call for the optimal design of their embedded complex networks topologies. The following question then arises: what is the optimal layout to fulfill best all the demands? Here we present a general solution for this problem with scale-free networks, like the Internet and airline networks. Precisely, we disclose a way to systematically construct networks which are robust against random failures. Furthermore, as the size of the network increases, its shortest path becomes asymptotically invariant and the density of links goes to zero, making it ultra-small world and highly sparse, respectively. The first property is ideal for communication and navigation purposes, while the second is interesting economically. Finally, we show that some simple changes on the original network formulation can lead to an improved topology against malicious attacks.
Sparse Bayesian Learning for DOA Estimation with Mutual Coupling
Dai, Jisheng; Hu, Nan; Xu, Weichao; Chang, Chunqi
2015-01-01
Sparse Bayesian learning (SBL) has given renewed interest to the problem of direction-of-arrival (DOA) estimation. It is generally assumed that the measurement matrix in SBL is precisely known. Unfortunately, this assumption may be invalid in practice due to the imperfect manifold caused by unknown or misspecified mutual coupling. This paper describes a modified SBL method for joint estimation of DOAs and mutual coupling coefficients with uniform linear arrays (ULAs). Unlike the existing method that only uses stationary priors, our new approach utilizes a hierarchical form of the Student t prior to enforce the sparsity of the unknown signal more heavily. We also provide a distinct Bayesian inference for the expectation-maximization (EM) algorithm, which can update the mutual coupling coefficients more efficiently. Another difference is that our method uses an additional singular value decomposition (SVD) to reduce the computational complexity of the signal reconstruction process and the sensitivity to the measurement noise. PMID:26501284
Sparse pre-Columbian human habitation in western Amazonia.
McMichael, C H; Piperno, D R; Bush, M B; Silman, M R; Zimmerman, A R; Raczka, M F; Lobato, L C
2012-06-15
Locally extensive pre-Columbian human occupation and modification occurred in the forests of the central and eastern Amazon Basin, but whether comparable impacts extend westward and into the vast terra firme (interfluvial) zones, remains unclear. We analyzed soils from 55 sites across central and western Amazonia to assess the history of human occupation. Sparse occurrences of charcoal and the lack of phytoliths from agricultural and disturbance species in the soils during pre-Columbian times indicated that human impacts on interfluvial forests were small, infrequent, and highly localized. No human artifacts or modified soils were found at any site surveyed. Riverine bluff areas also appeared less heavily occupied and disturbed than similar settings elsewhere. Our data indicate that human impacts on Amazonian forests were heterogeneous across this vast landscape.
CCD photometry of the sparse halo cluster E3
Mcclure, R.D.; Hesser, J.E.; Stetson, P.B.; Stryker, L.L.
1985-08-01
New photometry in B and V for the sparse halo cluster E3 has been obtained with the prime-focus CCD camera at the CTIO 4-m telescope. The principal sequences are better defined than in the previous color-magnitude (C-M) diagrams, but the large scatter of evolved stars above the turnoff remains. The C-M morphology, inferred old age, and position in the halo definitely indicate that E3 is a globular cluster. Evidence for possible main-sequence binaries appears in the C-M diagram in the form of a sequence parallel to the cluster main sequence and three-quarters of a magnitude above it. The luminosity function drops off sharply, about 2.5 mag below the turnoff, supporting the suggestion by van den Bergh, Demers, and Kunkel (1980), that the cluster is severely truncated by tidal forces. 34 references.
Sparse activity of identified dentate granule cells during spatial exploration
Diamantaki, Maria; Frey, Markus; Berens, Philipp; Preston-Ferrer, Patricia; Burgalossi, Andrea
2016-01-01
In the dentate gyrus – a key component of spatial memory circuits – granule cells (GCs) are known to be morphologically diverse and to display heterogeneous activity profiles during behavior. To resolve structure–function relationships, we juxtacellularly recorded and labeled single GCs in freely moving rats. We found that the vast majority of neurons were silent during exploration. Most active GCs displayed a characteristic spike waveform, fired at low rates and showed spatial activity. Primary dendritic parameters were sufficient for classifying neurons as active or silent with high accuracy. Our data thus support a sparse coding scheme in the dentate gyrus and provide a possible link between structural and functional heterogeneity among the GC population. DOI: http://dx.doi.org/10.7554/eLife.20252.001 PMID:27692065
Automated identification of crystallographic ligands using sparse-density representations
Carolan, C. G.; Lamzin, V. S.
2014-01-01
A novel procedure for the automatic identification of ligands in macromolecular crystallographic electron-density maps is introduced. It is based on the sparse parameterization of density clusters and the matching of the pseudo-atomic grids thus created to conformationally variant ligands using mathematical descriptors of molecular shape, size and topology. In large-scale tests on experimental data derived from the Protein Data Bank, the procedure could quickly identify the deposited ligand within the top-ranked compounds from a database of candidates. This indicates the suitability of the method for the identification of binding entities in fragment-based drug screening and in model completion in macromolecular structure determination. PMID:25004962
Compressive Fresnel digital holography using Fresnelet based sparse representation
NASA Astrophysics Data System (ADS)
Ramachandran, Prakash; Alex, Zachariah C.; Nelleri, Anith
2015-04-01
Compressive sensing (CS) in digital holography requires only very less number of pixel level detections in hologram plane for accurate image reconstruction and this is achieved by exploiting the sparsity of the object wave. When the input object fields are non-sparse in spatial domain, CS demands a suitable sparsification method like wavelet decomposition. The Fresnelet, a suitable wavelet basis for processing Fresnel digital holograms is an efficient sparsifier for the complex Fresnel field obtained by the Fresnel transform of the object field and minimizes the mutual coherence between sensing and sparsifying matrices involved in CS. The paper demonstrates the merits of Fresnelet based sparsification in compressive digital Fresnel holography over conventional method of sparsifying the input object field. The phase shifting digital Fresnel holography (PSDH) is used to retrieve the complex Fresnel field for the chosen problem. The results are presented from a numerical experiment to show the proof of the concept.
Root Sparse Bayesian Learning for Off-Grid DOA Estimation
NASA Astrophysics Data System (ADS)
Dai, Jisheng; Bao, Xu; Xu, Weichao; Chang, Chunqi
2017-01-01
The performance of the existing sparse Bayesian learning (SBL) methods for off-gird DOA estimation is dependent on the trade off between the accuracy and the computational workload. To speed up the off-grid SBL method while remain a reasonable accuracy, this letter describes a computationally efficient root SBL method for off-grid DOA estimation, where a coarse refinable grid, whose sampled locations are viewed as the adjustable parameters, is adopted. We utilize an expectation-maximization (EM) algorithm to iteratively refine this coarse grid, and illustrate that each updated grid point can be simply achieved by the root of a certain polynomial. Simulation results demonstrate that the computational complexity is significantly reduced and the modeling error can be almost eliminated.
Sparse Coding and Counting for Robust Visual Tracking
Liu, Risheng; Wang, Jing; Shang, Xiaoke; Wang, Yiyang; Su, Zhixun; Cai, Yu
2016-01-01
In this paper, we propose a novel sparse coding and counting method under Bayesian framework for visual tracking. In contrast to existing methods, the proposed method employs the combination of L0 and L1 norm to regularize the linear coefficients of incrementally updated linear basis. The sparsity constraint enables the tracker to effectively handle difficult challenges, such as occlusion or image corruption. To achieve real-time processing, we propose a fast and efficient numerical algorithm for solving the proposed model. Although it is an NP-hard problem, the proposed accelerated proximal gradient (APG) approach is guaranteed to converge to a solution quickly. Besides, we provide a closed solution of combining L0 and L1 regularized representation to obtain better sparsity. Experimental results on challenging video sequences demonstrate that the proposed method achieves state-of-the-art results both in accuracy and speed. PMID:27992474
Performance evaluation of a parallel sparse lattice Boltzmann solver
Axner, L. Bernsdorf, J. Zeiser, T. Lammers, P. Linxweiler, J. Hoekstra, A.G.
2008-05-01
We develop a performance prediction model for a parallelized sparse lattice Boltzmann solver and present performance results for simulations of flow in a variety of complex geometries. A special focus is on partitioning and memory/load balancing strategy for geometries with a high solid fraction and/or complex topology such as porous media, fissured rocks and geometries from medical applications. The topology of the lattice nodes representing the fluid fraction of the computational domain is mapped on a graph. Graph decomposition is performed with both multilevel recursive-bisection and multilevel k-way schemes based on modified Kernighan-Lin and Fiduccia-Mattheyses partitioning algorithms. Performance results and optimization strategies are presented for a variety of platforms, showing a parallel efficiency of almost 80% for the largest problem size. A good agreement between the performance model and experimental results is demonstrated.
Multiple peaks of species abundance distributions induced by sparse interactions
NASA Astrophysics Data System (ADS)
Obuchi, Tomoyuki; Kabashima, Yoshiyuki; Tokita, Kei
2016-08-01
We investigate the replicator dynamics with "sparse" symmetric interactions which represent specialist-specialist interactions in ecological communities. By considering a large self-interaction u , we conduct a perturbative expansion which manifests that the nature of the interactions has a direct impact on the species abundance distribution. The central results are all species coexistence in a realistic range of the model parameters and that a certain discrete nature of the interactions induces multiple peaks in the species abundance distribution, providing the possibility of theoretically explaining multiple peaks observed in various field studies. To get more quantitative information, we also construct a non-perturbative theory which becomes exact on tree-like networks if all the species coexist, providing exact critical values of u below which extinct species emerge. Numerical simulations in various different situations are conducted and they clarify the robustness of the presented mechanism of all species coexistence and multiple peaks in the species abundance distributions.
Dense and Sparse Matrix Operations on the Cell Processor
Williams, Samuel W.; Shalf, John; Oliker, Leonid; Husbands,Parry; Yelick, Katherine
2005-05-01
The slowing pace of commodity microprocessor performance improvements combined with ever-increasing chip power demands has become of utmost concern to computational scientists. Therefore, the high performance computing community is examining alternative architectures that address the limitations of modern superscalar designs. In this work, we examine STI's forthcoming Cell processor: a novel, low-power architecture that combines a PowerPC core with eight independent SIMD processing units coupled with a software-controlled memory to offer high FLOP/s/Watt. Since neither Cell hardware nor cycle-accurate simulators are currently publicly available, we develop an analytic framework to predict Cell performance on dense and sparse matrix operations, using a variety of algorithmic approaches. Results demonstrate Cell's potential to deliver more than an order of magnitude better GFLOP/s per watt performance, when compared with the Intel Itanium2 and Cray X1 processors.
Learning partial differential equations via data discovery and sparse optimization
NASA Astrophysics Data System (ADS)
Schaeffer, Hayden
2017-01-01
We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.
Image denoising via sparse and redundant representations over learned dictionaries.
Elad, Michael; Aharon, Michal
2006-12-01
We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.
Learned dictionaries for sparse image representation: properties and results
NASA Astrophysics Data System (ADS)
Skretting, Karl; Engan, Kjersti
2011-09-01
Sparse representation of images using learned dictionaries have been shown to work well for applications like image denoising, impainting, image compression, etc. In this paper dictionary properties are reviewed from a theoretical approach, and experimental results for learned dictionaries are presented. The main dictionary properties are the upper and lower frame (dictionary) bounds, and (mutual) coherence properties based on the angle between dictionary atoms. Both l0 sparsity and l1 sparsity are considered by using a matching pursuit method, order recursive matching Pursuit (ORMP), and a basis pursuit method, i.e. LARS or Lasso. For dictionary learning the following methods are considered: Iterative least squares (ILS-DLA or MOD), recursive least squares (RLS-DLA), K-SVD and online dictionary learning (ODL). Finally, it is shown how these properties relate to an image compression example.
Face recognition under variable illumination via sparse representation of patches
NASA Astrophysics Data System (ADS)
Fan, Shouke; Liu, Rui; Feng, Weiguo; Zhu, Ming
2013-10-01
The objective of this work is to recognize faces under variations in illumination. Previous works have indicated that the variations in illumination can dramatically reduce the performance of face recognition. To this end - ;an efficient method for face recognition which is robust under variable illumination is proposed in this paper. First of all, a discrete cosine transform(DCT) in the logarithm domain is employed to preprocess the images, removing the illumination variations by discarding an appropriate number of low-frequency DCT coefficients. Then, a face image is partitioned into several patches, and we classify the patches using Sparse Representation-based Classification, respectively. At last, the identity of a test image can be determined by the classification results of its patches. Experimental results on the Yale B database and the CMU PIE database show that excellent recognition rates can be achieved by the proposed method.
A Spectral Algorithm for Envelope Reduction of Sparse Matrices
NASA Technical Reports Server (NTRS)
Barnard, Stephen T.; Pothen, Alex; Simon, Horst D.
1993-01-01
The problem of reordering a sparse symmetric matrix to reduce its envelope size is considered. A new spectral algorithm for computing an envelope-reducing reordering is obtained by associating a Laplacian matrix with the given matrix and then sorting the components of a specified eigenvector of the Laplacian. This Laplacian eigenvector solves a continuous relaxation of a discrete problem related to envelope minimization called the minimum 2-sum problem. The permutation vector computed by the spectral algorithm is a closest permutation vector to the specified Laplacian eigenvector. Numerical results show that the new reordering algorithm usually computes smaller envelope sizes than those obtained from the current standard algorithms such as Gibbs-Poole-Stockmeyer (GPS) or SPARSPAK reverse Cuthill-McKee (RCM), in some cases reducing the envelope by more than a factor of two.
Sparse matrix transform for fast projection to reduced dimension
Theiler, James P; Cao, Guangzhi; Bouman, Charles A
2010-01-01
We investigate three algorithms that use the sparse matrix transform (SMT) to produce variance-maximizing linear projections to a lower-dimensional space. The SMT expresses the projection as a sequence of Givens rotations and this enables computationally efficient implementation of the projection operator. The baseline algorithm uses the SMT to directly approximate the optimal solution that is given by principal components analysis (PCA). A variant of the baseline begins with a standard SMT solution, but prunes the sequence of Givens rotations to only include those that contribute to the variance maximization. Finally, a simpler and faster third algorithm is introduced; this also estimates the projection operator with a sequence of Givens rotations, but in this case, the rotations are chosen to optimize a criterion that more directly expresses the dimension reduction criterion.
Inferring propagation paths for sparsely observed perturbations on complex networks
Massucci, Francesco Alessandro; Wheeler, Jonathan; Beltrán-Debón, Raúl; Joven, Jorge; Sales-Pardo, Marta; Guimerà, Roger
2016-01-01
In a complex system, perturbations propagate by following paths on the network of interactions among the system’s units. In contrast to what happens with the spreading of epidemics, observations of general perturbations are often very sparse in time (there is a single observation of the perturbed system) and in “space” (only a few perturbed and unperturbed units are observed). A major challenge in many areas, from biology to the social sciences, is to infer the propagation paths from observations of the effects of perturbation under these sparsity conditions. We address this problem and show that it is possible to go beyond the usual approach of using the shortest paths connecting the known perturbed nodes. Specifically, we show that a simple and general probabilistic model, which we solved using belief propagation, provides fast and accurate estimates of the probabilities of nodes being perturbed. PMID:27819038
Time-frequency signature sparse reconstruction using chirp dictionary
NASA Astrophysics Data System (ADS)
Nguyen, Yen T. H.; Amin, Moeness G.; Ghogho, Mounir; McLernon, Des
2015-05-01
This paper considers local sparse reconstruction of time-frequency signatures of windowed non-stationary radar returns. These signals can be considered instantaneously narrow-band, thus the local time-frequency behavior can be recovered accurately with incomplete observations. The typically employed sinusoidal dictionary induces competing requirements on window length. It confronts converse requests on the number of measurements for exact recovery, and sparsity. In this paper, we use chirp dictionary for each window position to determine the signal instantaneous frequency laws. This approach can considerably mitigate the problems of sinusoidal dictionary, and enable the utilization of longer windows for accurate time-frequency representations. It also reduces the picket fence by introducing a new factor, the chirp rate α. Simulation examples are provided, demonstrating the superior performance of local chirp dictionary over its sinusoidal counterpart.
Learning partial differential equations via data discovery and sparse optimization.
Schaeffer, Hayden
2017-01-01
We investigate the problem of learning an evolution equation directly from some given data. This work develops a learning algorithm to identify the terms in the underlying partial differential equations and to approximate the coefficients of the terms only using data. The algorithm uses sparse optimization in order to perform feature selection and parameter estimation. The features are data driven in the sense that they are constructed using nonlinear algebraic equations on the spatial derivatives of the data. Several numerical experiments show the proposed method's robustness to data noise and size, its ability to capture the true features of the data, and its capability of performing additional analytics. Examples include shock equations, pattern formation, fluid flow and turbulence, and oscillatory convection.
Sparse label-indicator optimization methods for image classification.
Jing, Liping; Ng, Michael K
2014-03-01
Image label prediction is a critical issue in computer vision and machine learning. In this paper, we propose and develop sparse label-indicator optimization methods for image classification problems. Sparsity is introduced in the label-indicator such that relevant and irrelevant images with respect to a given class can be distinguished. Also, when we deal with multi-class image classification problems, the number of possible classes of a given image can also be constrained to be small in which it is valid for natural images. The resulting sparsity model can be formulated as a convex optimization problem, and it can be solved very efficiently. Experimental results are reported to illustrate the effectiveness of the proposed model, and demonstrate that the classification performance of the proposed method is better than the other testing methods in this paper.
Sparse Parallel MRI Based on Accelerated Operator Splitting Schemes
Xie, Weisi; Su, Zhenghang
2016-01-01
Recently, the sparsity which is implicit in MR images has been successfully exploited for fast MR imaging with incomplete acquisitions. In this paper, two novel algorithms are proposed to solve the sparse parallel MR imaging problem, which consists of l1 regularization and fidelity terms. The two algorithms combine forward-backward operator splitting and Barzilai-Borwein schemes. Theoretically, the presented algorithms overcome the nondifferentiable property in l1 regularization term. Meanwhile, they are able to treat a general matrix operator that may not be diagonalized by fast Fourier transform and to ensure that a well-conditioned optimization system of equations is simply solved. In addition, we build connections between the proposed algorithms and the state-of-the-art existing methods and prove their convergence with a constant stepsize in Appendix. Numerical results and comparisons with the advanced methods demonstrate the efficiency of proposed algorithms. PMID:27746824
A strategy of car detection via sparse dictionary
NASA Astrophysics Data System (ADS)
Jin, Guo-Qing; Dong, Ying-Hui
2011-06-01
In recent years there is a growing interest in the study of sparse representation for object detection. These approaches heavily depend on local salient image patches, thus weakening the global contribution to the object identification of other less informative signals.Our generic approach not only employs the informative representation by linear transform, but also keeps all the spatial dependence implied among the objects. As an example,car images can be represented using parts from a vocabulary, along with spatial relations observed among them.Our approach is conducted with the quantitative measurement in developing the car detector at every stage. The theory underneath the optimal solution is the maximal mutual information carried out by the system. Our goal is to keep the maximal mutual information transmitted from stage to stage so that only the least uncertainty about the class identification remains based on the observation of classifier's output.
Adaptive dictionary learning in sparse gradient domain for image recovery.
Liu, Qiegen; Wang, Shanshan; Ying, Leslie; Peng, Xi; Zhu, Yanjie; Liang, Dong
2013-12-01
Image recovery from undersampled data has always been challenging due to its implicit ill-posed nature but becomes fascinating with the emerging compressed sensing (CS) theory. This paper proposes a novel gradient based dictionary learning method for image recovery, which effectively integrates the popular total variation (TV) and dictionary learning technique into the same framework. Specifically, we first train dictionaries from the horizontal and vertical gradients of the image and then reconstruct the desired image using the sparse representations of both derivatives. The proposed method enables local features in the gradient images to be captured effectively, and can be viewed as an adaptive extension of the TV regularization. The results of various experiments on MR images consistently demonstrate that the proposed algorithm efficiently recovers images and presents advantages over the current leading CS reconstruction approaches.
Maximizing sparse matrix vector product performance in MIMD computers
McLay, R.T.; Kohli, H.S.; Swift, S.L.; Carey, G.F.
1994-12-31
A considerable component of the computational effort involved in conjugate gradient solution of structured sparse matrix systems is expended during the Matrix-Vector Product (MVP), and hence it is the focus of most efforts at improving performance. Such efforts are hindered on MIMD machines due to constraints on memory, cache and speed of memory-cpu data transfer. This paper describes a strategy for maximizing the performance of the local computations associated with the MVP. The method focuses on single stride memory access, and the efficient use of cache by pre-loading it with data that is re-used while bypassing it for other data. The algorithm is designed to behave optimally for varying grid sizes and number of unknowns per gridpoint. Results from an assembly language implementation of the strategy on the iPSC/860 show a significant improvement over the performance using FORTRAN.
Learning feature representations with a cost-relevant sparse autoencoder.
Längkvist, Martin; Loutfi, Amy
2015-02-01
There is an increasing interest in the machine learning community to automatically learn feature representations directly from the (unlabeled) data instead of using hand-designed features. The autoencoder is one method that can be used for this purpose. However, for data sets with a high degree of noise, a large amount of the representational capacity in the autoencoder is used to minimize the reconstruction error for these noisy inputs. This paper proposes a method that improves the feature learning process by focusing on the task relevant information in the data. This selective attention is achieved by weighting the reconstruction error and reducing the influence of noisy inputs during the learning process. The proposed model is trained on a number of publicly available image data sets and the test error rate is compared to a standard sparse autoencoder and other methods, such as the denoising autoencoder and contractive autoencoder.
Technology Transfer Automated Retrieval System (TEKTRAN)
A change detection experiment for an invasive species, saltcedar, near Lovelock, Nevada, was conducted with multi-date Compact Airborne Spectrographic Imager (CASI) hyperspectral datasets. Classification and NDVI differencing change detection methods were tested, In the classification strategy, a p...
Sinogram denoising via simultaneous sparse representation in learned dictionaries.
Karimi, Davood; Ward, Rabab K
2016-05-07
Reducing the radiation dose in computed tomography (CT) is highly desirable but it leads to excessive noise in the projection measurements. This can significantly reduce the diagnostic value of the reconstructed images. Removing the noise in the projection measurements is, therefore, essential for reconstructing high-quality images, especially in low-dose CT. In recent years, two new classes of patch-based denoising algorithms proved superior to other methods in various denoising applications. The first class is based on sparse representation of image patches in a learned dictionary. The second class is based on the non-local means method. Here, the image is searched for similar patches and the patches are processed together to find their denoised estimates. In this paper, we propose a novel denoising algorithm for cone-beam CT projections. The proposed method has similarities to both these algorithmic classes but is more effective and much faster. In order to exploit both the correlation between neighboring pixels within a projection and the correlation between pixels in neighboring projections, the proposed algorithm stacks noisy cone-beam projections together to form a 3D image and extracts small overlapping 3D blocks from this 3D image for processing. We propose a fast algorithm for clustering all extracted blocks. The central assumption in the proposed algorithm is that all blocks in a cluster have a joint-sparse representation in a well-designed dictionary. We describe algorithms for learning such a dictionary and for denoising a set of projections using this dictionary. We apply the proposed algorithm on simulated and real data and compare it with three other algorithms. Our results show that the proposed algorithm outperforms some of the best denoising algorithms, while also being much faster.
Sinogram denoising via simultaneous sparse representation in learned dictionaries
NASA Astrophysics Data System (ADS)
Karimi, Davood; Ward, Rabab K.
2016-05-01
Reducing the radiation dose in computed tomography (CT) is highly desirable but it leads to excessive noise in the projection measurements. This can significantly reduce the diagnostic value of the reconstructed images. Removing the noise in the projection measurements is, therefore, essential for reconstructing high-quality images, especially in low-dose CT. In recent years, two new classes of patch-based denoising algorithms proved superior to other methods in various denoising applications. The first class is based on sparse representation of image patches in a learned dictionary. The second class is based on the non-local means method. Here, the image is searched for similar patches and the patches are processed together to find their denoised estimates. In this paper, we propose a novel denoising algorithm for cone-beam CT projections. The proposed method has similarities to both these algorithmic classes but is more effective and much faster. In order to exploit both the correlation between neighboring pixels within a projection and the correlation between pixels in neighboring projections, the proposed algorithm stacks noisy cone-beam projections together to form a 3D image and extracts small overlapping 3D blocks from this 3D image for processing. We propose a fast algorithm for clustering all extracted blocks. The central assumption in the proposed algorithm is that all blocks in a cluster have a joint-sparse representation in a well-designed dictionary. We describe algorithms for learning such a dictionary and for denoising a set of projections using this dictionary. We apply the proposed algorithm on simulated and real data and compare it with three other algorithms. Our results show that the proposed algorithm outperforms some of the best denoising algorithms, while also being much faster.
Modeling Disease Progression via Fused Sparse Group Lasso
Zhou, Jiayu; Liu, Jun; Narayan, Vaibhav A.; Ye, Jieping
2013-01-01
Alzheimer’s Disease (AD) is the most common neurodegenerative disorder associated with aging. Understanding how the disease progresses and identifying related pathological biomarkers for the progression is of primary importance in the clinical diagnosis and prognosis of Alzheimer’s disease. In this paper, we develop novel multi-task learning techniques to predict the disease progression measured by cognitive scores and select biomarkers predictive of the progression. In multi-task learning, the prediction of cognitive scores at each time point is considered as a task, and multiple prediction tasks at different time points are performed simultaneously to capture the temporal smoothness of the prediction models across different time points. Specifically, we propose a novel convex fused sparse group Lasso (cFSGL) formulation that allows the simultaneous selection of a common set of biomarkers for multiple time points and specific sets of biomarkers for different time points using the sparse group Lasso penalty and in the meantime incorporates the temporal smoothness using the fused Lasso penalty. The proposed formulation is challenging to solve due to the use of several non-smooth penalties. One of the main technical contributions of this paper is to show that the proximal operator associated with the proposed formulation exhibits a certain decomposition property and can be computed efficiently; thus cFSGL can be solved efficiently using the accelerated gradient method. To further improve the model, we propose two non-convex formulations to reduce the shrinkage bias inherent in the convex formulation. We employ the difference of convex (DC) programming technique to solve the non-convex formulations. We have performed extensive experiments using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Results demonstrate the effectiveness of the proposed progression models in comparison with existing methods for disease progression. We also perform
3D Ear Identification Based on Sparse Representation
Zhang, Lin; Ding, Zhixuan; Li, Hongyu; Shen, Ying
2014-01-01
Biometrics based personal authentication is an effective way for automatically recognizing, with a high confidence, a person’s identity. Recently, 3D ear shape has attracted tremendous interests in research field due to its richness of feature and ease of acquisition. However, the existing ICP (Iterative Closet Point)-based 3D ear matching methods prevalent in the literature are not quite efficient to cope with the one-to-many identification case. In this paper, we aim to fill this gap by proposing a novel effective fully automatic 3D ear identification system. We at first propose an accurate and efficient template-based ear detection method. By utilizing such a method, the extracted ear regions are represented in a common canonical coordinate system determined by the ear contour template, which facilitates much the following stages of feature extraction and classification. For each extracted 3D ear, a feature vector is generated as its representation by making use of a PCA-based local feature descriptor. At the stage of classification, we resort to the sparse representation based classification approach, which actually solves an l1-minimization problem. To the best of our knowledge, this is the first work introducing the sparse representation framework into the field of 3D ear identification. Extensive experiments conducted on a benchmark dataset corroborate the effectiveness and efficiency of the proposed approach. The associated Matlab source code and the evaluation results have been made publicly online available at http://sse.tongji.edu.cn/linzhang/ear/srcear/srcear.htm. PMID:24740247
An Assessment of Iterative Reconstruction Methods for Sparse Ultrasound Imaging
Valente, Solivan A.; Zibetti, Marcelo V. W.; Pipa, Daniel R.; Maia, Joaquim M.; Schneider, Fabio K.
2017-01-01
Ultrasonic image reconstruction using inverse problems has recently appeared as an alternative to enhance ultrasound imaging over beamforming methods. This approach depends on the accuracy of the acquisition model used to represent transducers, reflectivity, and medium physics. Iterative methods, well known in general sparse signal reconstruction, are also suited for imaging. In this paper, a discrete acquisition model is assessed by solving a linear system of equations by an ℓ1-regularized least-squares minimization, where the solution sparsity may be adjusted as desired. The paper surveys 11 variants of four well-known algorithms for sparse reconstruction, and assesses their optimization parameters with the goal of finding the best approach for iterative ultrasound imaging. The strategy for the model evaluation consists of using two distinct datasets. We first generate data from a synthetic phantom that mimics real targets inside a professional ultrasound phantom device. This dataset is contaminated with Gaussian noise with an estimated SNR, and all methods are assessed by their resulting images and performances. The model and methods are then assessed with real data collected by a research ultrasound platform when scanning the same phantom device, and results are compared with beamforming. A distinct real dataset is finally used to further validate the proposed modeling. Although high computational effort is required by iterative methods, results show that the discrete model may lead to images closer to ground-truth than traditional beamforming. However, computing capabilities of current platforms need to evolve before frame rates currently delivered by ultrasound equipments are achievable. PMID:28282862
Multi-shell diffusion signal recovery from sparse measurements
Rathi, Y.; Michailovich, O.; Laun, F.; Setsompop, K.; Grant, P. E.; Westin, C-F
2014-01-01
For accurate estimation of the ensemble average diffusion propagator (EAP), traditional multi-shell diffusion imaging (MSDI) approaches require acquisition of diffusion signals for a range of b-values. However, this makes the acquisition time too long for several types of patients, making it difficult to use in a clinical setting. In this work, we propose a new method for the reconstruction of diffusion signals in the entire q-space from highly under-sampled sets of MSDI data, thus reducing the scan time significantly. In particular, to sparsely represent the diffusion signal over multiple q-shells, we propose a novel extension to the framework of spherical ridgelets by accurately modeling the monotonically decreasing radial component of the diffusion signal. Further, we enforce the reconstructed signal to have smooth spatial regularity in the brain, by minimizing the total variation (TV) norm. We combine these requirements into a novel cost function and derive an optimal solution using the Alternating Directions Method of Multipliers (ADMM) algorithm. We use a physical phantom data set with known fiber crossing angle of 45° to determine the optimal number of measurements (gradient directions and b-values) needed for accurate signal recovery. We compare our technique with a state-of-the-art sparse reconstruction method (i.e., the SHORE method of (Cheng et al., 2010)) in terms of angular error in estimating the crossing angle, incorrect number of peaks detected, normalized mean squared error in signal recovery as well as error in estimating the return-to-origin probability (RTOP). Finally, we also demonstrate the behavior of the proposed technique on human in-vivo data sets. Based on these experiments, we conclude that using the proposed algorithm, at least 60 measurements (spread over three b-value shells) are needed for proper recovery of MSDI data in the entire q-space. PMID:25047866
Sparse multivariate autoregressive modeling for mild cognitive impairment classification.
Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen; Shen, Dinggang
2014-07-01
Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach.
Tomographic image reconstruction via estimation of sparse unidirectional gradients.
Polak, Adam G; Mroczka, Janusz; Wysoczański, Dariusz
2017-02-01
Since computed tomography (CT) was developed over 35 years ago, new mathematical ideas and computational algorithms have been continuingly elaborated to improve the quality of reconstructed images. In recent years, a considerable effort can be noticed to apply the sparse solution of underdetermined system theory to the reconstruction of CT images from undersampled data. Its significance stems from the possibility of obtaining good quality CT images from low dose projections. Among diverse approaches, total variation (TV) minimizing 2D gradients of an image, seems to be the most popular method. In this paper, a new method for CT image reconstruction via sparse gradients estimation (SGE), is proposed. It consists in estimating 1D gradients specified in four directions using the iterative reweighting algorithm. To investigate its properties and to compare it with TV and other related methods, numerical simulations were performed according to the Monte Carlo scheme, using the Shepp-Logan and more realistic brain phantoms scanned at 9-60 directions in the range from 0 to 179°, with measurement data disturbed by additive Gaussians noise characterized by the relative level of 0.1%, 0.2%, 0.5%, 1%, 2% and 5%. The accuracy of image reconstruction was assessed in terms of the relative root-mean-square (RMS) error. The results show that the proposed SGE algorithm has returned more accurate images than TV for the cases fulfilling the sparsity conditions. Particularly, it preserves sharp edges of regions representing different tissues or organs and yields images of much better quality reconstructed from a small number of projections disturbed by relatively low measurement noise.
Sparse imaging of cortical electrical current densities via wavelet transforms
NASA Astrophysics Data System (ADS)
Liao, Ke; Zhu, Min; Ding, Lei; Valette, Sébastien; Zhang, Wenbo; Dickens, Deanna
2012-11-01
While the cerebral cortex in the human brain is of functional importance, functions defined on this structure are difficult to analyze spatially due to its highly convoluted irregular geometry. This study developed a novel L1-norm regularization method using a newly proposed multi-resolution face-based wavelet method to estimate cortical electrical activities in electroencephalography (EEG) and magnetoencephalography (MEG) inverse problems. The proposed wavelets were developed based on multi-resolution models built from irregular cortical surface meshes, which were realized in this study too. The multi-resolution wavelet analysis was used to seek sparse representation of cortical current densities in transformed domains, which was expected due to the compressibility of wavelets, and evaluated using Monte Carlo simulations. The EEG/MEG inverse problems were solved with the use of the novel L1-norm regularization method exploring the sparseness in the wavelet domain. The inverse solutions obtained from the new method using MEG data were evaluated by Monte Carlo simulations too. The present results indicated that cortical current densities could be efficiently compressed using the proposed face-based wavelet method, which exhibited better performance than the vertex-based wavelet method. In both simulations and auditory experimental data analysis, the proposed L1-norm regularization method showed better source detection accuracy and less estimation errors than other two classic methods, i.e. weighted minimum norm (wMNE) and cortical low-resolution electromagnetic tomography (cLORETA). This study suggests that the L1-norm regularization method with the use of face-based wavelets is a promising tool for studying functional activations of the human brain.
M-estimation for robust sparse unmixing of hyperspectral images
NASA Astrophysics Data System (ADS)
Toomik, Maria; Lu, Shijian; Nelson, James D. B.
2016-10-01
Hyperspectral unmixing methods often use a conventional least squares based lasso which assumes that the data follows the Gaussian distribution. The normality assumption is an approximation which is generally invalid for real imagery data. We consider a robust (non-Gaussian) approach to sparse spectral unmixing of remotely sensed imagery which reduces the sensitivity of the estimator to outliers and relaxes the linearity assumption. The method consists of several appropriate penalties. We propose to use an lp norm with 0 < p < 1 in the sparse regression problem, which induces more sparsity in the results, but makes the problem non-convex. On the other hand, the problem, though non-convex, can be solved quite straightforwardly with an extensible algorithm based on iteratively reweighted least squares. To deal with the huge size of modern spectral libraries we introduce a library reduction step, similar to the multiple signal classification (MUSIC) array processing algorithm, which not only speeds up unmixing but also yields superior results. In the hyperspectral setting we extend the traditional least squares method to the robust heavy-tailed case and propose a generalised M-lasso solution. M-estimation replaces the Gaussian likelihood with a fixed function ρ(e) that restrains outliers. The M-estimate function reduces the effect of errors with large amplitudes or even assigns the outliers zero weights. Our experimental results on real hyperspectral data show that noise with large amplitudes (outliers) often exists in the data. This ability to mitigate the influence of such outliers can therefore offer greater robustness. Qualitative hyperspectral unmixing results on real hyperspectral image data corroborate the efficacy of the proposed method.
Auditory Sketches: Very Sparse Representations of Sounds Are Still Recognizable
Isnard, Vincent; Taffou, Marine; Viaud-Delmon, Isabelle; Suied, Clara
2016-01-01
Sounds in our environment like voices, animal calls or musical instruments are easily recognized by human listeners. Understanding the key features underlying this robust sound recognition is an important question in auditory science. Here, we studied the recognition by human listeners of new classes of sounds: acoustic and auditory sketches, sounds that are severely impoverished but still recognizable. Starting from a time-frequency representation, a sketch is obtained by keeping only sparse elements of the original signal, here, by means of a simple peak-picking algorithm. Two time-frequency representations were compared: a biologically grounded one, the auditory spectrogram, which simulates peripheral auditory filtering, and a simple acoustic spectrogram, based on a Fourier transform. Three degrees of sparsity were also investigated. Listeners were asked to recognize the category to which a sketch sound belongs: singing voices, bird calls, musical instruments, and vehicle engine noises. Results showed that, with the exception of voice sounds, very sparse representations of sounds (10 features, or energy peaks, per second) could be recognized above chance. No clear differences could be observed between the acoustic and the auditory sketches. For the voice sounds, however, a completely different pattern of results emerged, with at-chance or even below-chance recognition performances, suggesting that the important features of the voice, whatever they are, were removed by the sketch process. Overall, these perceptual results were well correlated with a model of auditory distances, based on spectro-temporal excitation patterns (STEPs). This study confirms the potential of these new classes of sounds, acoustic and auditory sketches, to study sound recognition. PMID:26950589
SAMBA: Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos
Ahlfeld, R. Belkouchi, B.; Montomoli, F.
2016-09-01
A new arbitrary Polynomial Chaos (aPC) method is presented for moderately high-dimensional problems characterised by limited input data availability. The proposed methodology improves the algorithm of aPC and extends the method, that was previously only introduced as tensor product expansion, to moderately high-dimensional stochastic problems. The fundamental idea of aPC is to use the statistical moments of the input random variables to develop the polynomial chaos expansion. This approach provides the possibility to propagate continuous or discrete probability density functions and also histograms (data sets) as long as their moments exist, are finite and the determinant of the moment matrix is strictly positive. For cases with limited data availability, this approach avoids bias and fitting errors caused by wrong assumptions. In this work, an alternative way to calculate the aPC is suggested, which provides the optimal polynomials, Gaussian quadrature collocation points and weights from the moments using only a handful of matrix operations on the Hankel matrix of moments. It can therefore be implemented without requiring prior knowledge about statistical data analysis or a detailed understanding of the mathematics of polynomial chaos expansions. The extension to more input variables suggested in this work, is an anisotropic and adaptive version of Smolyak's algorithm that is solely based on the moments of the input probability distributions. It is referred to as SAMBA (PC), which is short for Sparse Approximation of Moment-Based Arbitrary Polynomial Chaos. It is illustrated that for moderately high-dimensional problems (up to 20 different input variables or histograms) SAMBA can significantly simplify the calculation of sparse Gaussian quadrature rules. SAMBA's efficiency for multivariate functions with regard to data availability is further demonstrated by analysing higher order convergence and accuracy for a set of nonlinear test functions with 2, 5 and 10
Sparse Multivariate Autoregressive Modeling for Mild Cognitive Impairment Classification
Li, Yang; Wee, Chong-Yaw; Jie, Biao; Peng, Ziwen
2014-01-01
Brain connectivity network derived from functional magnetic resonance imaging (fMRI) is becoming increasingly prevalent in the researches related to cognitive and perceptual processes. The capability to detect causal or effective connectivity is highly desirable for understanding the cooperative nature of brain network, particularly when the ultimate goal is to obtain good performance of control-patient classification with biological meaningful interpretations. Understanding directed functional interactions between brain regions via brain connectivity network is a challenging task. Since many genetic and biomedical networks are intrinsically sparse, incorporating sparsity property into connectivity modeling can make the derived models more biologically plausible. Accordingly, we propose an effective connectivity modeling of resting-state fMRI data based on the multivariate autoregressive (MAR) modeling technique, which is widely used to characterize temporal information of dynamic systems. This MAR modeling technique allows for the identification of effective connectivity using the Granger causality concept and reducing the spurious causality connectivity in assessment of directed functional interaction from fMRI data. A forward orthogonal least squares (OLS) regression algorithm is further used to construct a sparse MAR model. By applying the proposed modeling to mild cognitive impairment (MCI) classification, we identify several most discriminative regions, including middle cingulate gyrus, posterior cingulate gyrus, lingual gyrus and caudate regions, in line with results reported in previous findings. A relatively high classification accuracy of 91.89 % is also achieved, with an increment of 5.4 % compared to the fully-connected, non-directional Pearson-correlation-based functional connectivity approach. PMID:24595922
Using sparse photometric data sets for asteroid lightcurve studies
NASA Astrophysics Data System (ADS)
Warner, Brian D.; Harris, Alan W.
2011-12-01
With the advent of wide-field imagers, it has become possible to conduct a photometric lightcurve survey of many asteroids simultaneously, either for that single purpose (e.g., Dermawan, B., Nakamura, T., Yoshida, F. [2011]. Publ. Astron. Soc. Japan 63, S555-S576; Masiero, J., Jedicke, R., Ďurech, J., Gwyn, S., Denneau, L., Larsen, J. [2009]. Icarus 204, 145-171), or as a part of a multipurpose survey (e.g., Pan-STARRS, LSST). Such surveys promise to yield photometric data for many thousands of asteroids, but these data sets will be “sparse” compared to most of those taken in a “targeted” mode directed to one asteroid at a time. We consider the potential limitations of sparse data sets using different sampling rates with respect to specific research questions that might be addressed with lightcurve data. For our study we created synthetic sparse data sets similar to those from wide-field surveys by generating more than 380,000 individual lightcurves that were combined into more than 47,000 composite lightcurves. The variables in generating the data included the number of observations per night, number of nights, noise, and the intervals between observations and nights, in addition to periods ranging from 0.1 to 400 h and amplitudes ranging from 0.1 to 2.0 mag. A Fourier analysis pipeline was used to find the period for each composite lightcurve and then review the derived period and period spectrum to gauge how well an automated analysis of sparse data sets would perform in finding the true period. For this part of the analysis, a normally distributed noise level of 0.03 mag was added to the data, regardless of amplitude, thus simulating a relatively high SNR for the observations. For the second part of the analysis, a smaller set of composite curves was generated with fixed core parameters of eight observations per night, 8 nights within a 14-day span, periods ranging from 2 to 6 h, and an amplitude of either 0.3 mag or 0.4 mag. Individual data sets using
Sparse parallel transmission on randomly perturbed spiral k-space trajectory.
Pang, Yong; Jiang, Xiaohua; Zhang, Xiaoliang
2014-04-01
Combination of parallel transmission and sparse pulse is able to shorten the excitation by using both the coil sensitivity and sparse k-space, showing improved fast excitation capability over the use of parallel transmission alone. However, to design an optimal k-space trajectory for sparse parallel transmission is a challenging task. In this work, a randomly perturbed sparse k-space trajectory is designed by modifying the path of a spiral trajectory along the sparse k-space data, and the sparse parallel transmission RF pulses are subsequently designed based on this optimal trajectory. This method combines the parallel transmission and sparse spiral k-space trajectory, potentially to further reduce the RF transmission time. Bloch simulation of 90° excitation by using a four channel coil array is performed to demonstrate its feasibility. Excitation performance of the sparse parallel transmission technique at different reduction factors of 1, 2, and 4 is evaluated. For comparison, parallel excitation using regular spiral trajectory is performed. The passband errors of the excitation profiles of each transmission are calculated for quantitative assessment of the proposed excitation method.
Bayesian Learning in Sparse Graphical Factor Models via Variational Mean-Field Annealing.
Yoshida, Ryo; West, Mike
2010-05-01
We describe a class of sparse latent factor models, called graphical factor models (GFMs), and relevant sparse learning algorithms for posterior mode estimation. Linear, Gaussian GFMs have sparse, orthogonal factor loadings matrices, that, in addition to sparsity of the implied covariance matrices, also induce conditional independence structures via zeros in the implied precision matrices. We describe the models and their use for robust estimation of sparse latent factor structure and data/signal reconstruction. We develop computational algorithms for model exploration and posterior mode search, addressing the hard combinatorial optimization involved in the search over a huge space of potential sparse configurations. A mean-field variational technique coupled with annealing is developed to successively generate "artificial" posterior distributions that, at the limiting temperature in the annealing schedule, define required posterior modes in the GFM parameter space. Several detailed empirical studies and comparisons to related approaches are discussed, including analyses of handwritten digit image and cancer gene expression data.
NASA Technical Reports Server (NTRS)
Rogers, David
1988-01-01
The advent of the Connection Machine profoundly changes the world of supercomputers. The highly nontraditional architecture makes possible the exploration of algorithms that were impractical for standard Von Neumann architectures. Sparse distributed memory (SDM) is an example of such an algorithm. Sparse distributed memory is a particularly simple and elegant formulation for an associative memory. The foundations for sparse distributed memory are described, and some simple examples of using the memory are presented. The relationship of sparse distributed memory to three important computational systems is shown: random-access memory, neural networks, and the cerebellum of the brain. Finally, the implementation of the algorithm for sparse distributed memory on the Connection Machine is discussed.
A performance study of sparse Cholesky factorization on INTEL iPSC/860
NASA Technical Reports Server (NTRS)
Zubair, M.; Ghose, M.
1992-01-01
The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.
NASA Astrophysics Data System (ADS)
Fang, Jun; Zhang, Lizao; Duan, Huiping; Huang, Lei; Li, Hongbin
2016-05-01
The application of sparse representation to SAR/ISAR imaging has attracted much attention over the past few years. This new class of sparse representation based imaging methods present a number of unique advantages over conventional range-Doppler methods, the basic idea behind these works is to formulate SAR/ISAR imaging as a sparse signal recovery problem. In this paper, we propose a new two-dimensional pattern-coupled sparse Bayesian learning(SBL) method to capture the underlying cluster patterns of the ISAR target images. Based on this model, an expectation-maximization (EM) algorithm is developed to infer the maximum a posterior (MAP) estimate of the hyperparameters, along with the posterior distribution of the sparse signal. Experimental results demonstrate that the proposed method is able to achieve a substantial performance improvement over existing algorithms, including the conventional SBL method.
Technical note: A significance test for data-sparse zones in scatter plots
NASA Astrophysics Data System (ADS)
Vetrova, V. V.; Bardsley, W. E.
2012-04-01
Data-sparse zones in scatter plots of hydrological variables can be of interest in various contexts. For example, a well-defined data-sparse zone may indicate inhibition of one variable by another. It is of interest therefore to determine whether data-sparse regions in scatter plots are of sufficient extent to be beyond random chance. We consider the specific situation of data-sparse regions defined by a linear internal boundary within a scatter plot defined over a rectangular region. An Excel VBA macro is provided for carrying out a randomisation-based significance test of the data-sparse region, taking into account both the within-region number of data points and the extent of the region. Example applications are given with respect to a rainfall time series from Israel and also to validation scatter plots from a seasonal forecasting model for lake inflows in New Zealand.
Technical Note: A significance test for data-sparse zones in scatter plots
NASA Astrophysics Data System (ADS)
Vetrova, V. V.; Bardsley, W. E.
2012-01-01
Data-sparse zones in scatter plots of hydrological variables can be of interest in various contexts. For example, a well-defined data-sparse zone may indicate inhibition of one variable by another. It is of interest therefore to determine whether data-sparse regions in scatter plots are of sufficient extent to be beyond random chance. We consider the specific situation of data-sparse regions defined by a linear internal boundary within a scatter plot defined over a rectangular region. An Excel VBA macro is provided for carrying out a randomisation-based significance test of the data-sparse region, taking into account both the within-region number of data points and the extent of the region. Example applications are given with respect to a rainfall time series from Israel and to validation scatter plots from a seasonal forecasting model for lake inflows in New Zealand.
Magliano, Patricio N; Breshears, David D; Fernández, Roberto J; Jobbágy, Esteban G
2015-12-01
Effectively managing net primary productivity in drylands for grazing and other uses depends on understanding how limited rainfall input is redistributed by runoff and runon among vegetation patches, particularly for patches that contrast between lesser and greater amounts of vegetation cover. Due in part to data limitations, ecohydrologists generally have focused on rainfall event size to characterize water redistribution processes. Here we use soil moisture data from a semiarid woodland to highlight how, when event size is controlled and runoff and interception are negligible at the stand scale, rainfall intensity drives the relationship between water redistribution and canopy and soil patch attributes. Horizontal water redistribution variability increased with rainfall intensity and differed between patches with contrasting vegetation cover. Sparsely vegetated patches gained relatively more water during lower intensity events, whereas densely vegetated ones gained relatively more water during higher intensity events. Consequently, range managers need to account for the distribution of rainfall event intensity, as well as event size, to assess the consequences of climate variability and change on net primary productivity. More generally, our results suggest that rainfall intensity needs to be considered in addition to event size to understand vegetation patch dynamics in drylands.
A comparison of methods to assess long-term changes in Sonoran Desert vegetation
Munson, S.M.; Webb, R.H.; Hubbard, J.A.
2011-01-01
Knowledge about the condition of vegetation cover and composition is critical for assessing the structure and function of ecosystems. To effectively quantify the impacts of a rapidly changing environment, methods to track long-term trends of vegetation must be precise, repeatable, and time- and cost-efficient. Measuring vegetation cover and composition in arid and semiarid regions is especially challenging because vegetation is typically sparse, discontinuous, and individual plants are widely spaced. To meet the goal of long-term vegetation monitoring in the Sonoran Desert and other arid and semiarid regions, we determined how estimates of plant species, total vegetation, and soil cover obtained using a widely-implemented monitoring protocol compared to a more time- and resource-intensive plant census. We also assessed how well this protocol tracked changes in cover through 82 years compared to the plant census. Results from the monitoring protocol were comparable to those from the plant census, despite low and variable plant species cover. Importantly, this monitoring protocol could be used as a rapid, "off-the shelf" tool for assessing land degradation (or desertification) in arid and semiarid ecosystems.
Paukert, C.P.; Willis, D.W.; Holland, R.S.
2002-01-01
We assessed the precision of visual estimates of vegetation and substrate along transects in 15 shallow, natural Nebraska lakes. Vegetation type (submergent or emergent), vegetation density (sparse, moderate, or dense), and substrate composition (percentage sand, muck, and clay; to the nearest 10%) were estimated at 25-70 sampling sites per lake by two independent observers. Observer agreement for vegetation type was 92%. Agreement ranged from 62.5% to 90.1% for substrate composition. Agreement was also high (72%) for vegetation density estimates. The relatively high agreement between estimates was likely attributable to the homogeneity of the lake habitats. Nearly 90% of the substrate sites were classified as 0% clay, and over 68% as either 0% or 100% sand. When habitats were homogeneous, less than 40 sampling sites per lake were required for 95% confidence that habitat composition was within 10% of the true mean, and over 100 sites were required when habitats were heterogeneous. Our results suggest that relatively high precision is attainable for vegetation and substrate mapping in shallow, natural lakes.
NASA Astrophysics Data System (ADS)
Shen, Li; He, Yuhong; Guo, Xulin
2013-01-01
Habitat loss has become one major cause of prairie loggerhead shrike population decline, which is associated with some important grassland biophysical features. However, our understanding of what and how biophysical variables can spatially characterize shrike habitats is poor. The purpose of this study is to investigate the suitability of two vegetation indices (VIs) for spatially characterizing shrike habitats in North American mixed prairies. Our research, conducted in Grasslands National Park of Canada, is based on the normalized difference vegetation index (NDVI) and the adjusted transformed soil-adjusted vegetation index (ATSAVI) as derived from both in situ measurements and SPOT imagery for three types of nesting categories at three spatial scales. Our results demonstrated that shrikes in mixed North American prairies prefer sparsely vegetated areas with a leaf area index less than 2.01 and shrub cover of around 25%. Our results also demonstrated that ATSAVI is superior to NDVI in estimating vegetation abundance and structure. Loggerhead shrikes seems to prefer habitats characterized by NDVI ranging from 0.562 to 0.616 and ATSAVI ranging from 0.319 to 0.372 with the spatial scale varying from 100 to 20 m. ATSAVI also had better performance in detecting the spatial variation of shrike habitats due to its higher sensitivity to background information.
White vegetables: glycemia and satiety.
Anderson, G Harvey; Soeandy, Chesarahmia Dojo; Smith, Christopher E
2013-05-01
The objective of this review is to discuss the effect of white vegetable consumption on glycemia, satiety, and food intake. White vegetables is a term used to refer to vegetables that are white or near white in color and include potatoes, cauliflowers, turnips, onions, parsnips, white corn, kohlrabi, and mushrooms (technically fungi but generally considered a vegetable). They vary greatly in their contribution to the energy and nutrient content of the diet and glycemia and satiety. As with other foods, the glycemic effect of many white vegetables has been measured. The results illustrate that interpretation of the semiquantitative comparative ratings of white vegetables as derived by the glycemic index must be context dependent. As illustrated by using the potato as an example, the glycemic index of white vegetables can be misleading if not interpreted in the context of the overall contribution that the white vegetable makes to the carbohydrate and nutrient composition of the diet and their functionality in satiety and metabolic control within usual meals. It is concluded that application of the glycemic index in isolation to judge the role of white vegetables in the diet and, specifically in the case of potato as consumed in ad libitum meals, has led to premature and possibly counterproductive dietary guidance.
A MRI-CT prostate registration using sparse representation technique
NASA Astrophysics Data System (ADS)
Yang, Xiaofeng; Jani, Ashesh B.; Rossi, Peter J.; Mao, Hui; Curran, Walter J.; Liu, Tian
2016-03-01
Purpose: To develop a new MRI-CT prostate registration using patch-based deformation prediction framework to improve MRI-guided prostate radiotherapy by incorporating multiparametric MRI into planning CT images. Methods: The main contribution is to estimate the deformation between prostate MRI and CT images in a patch-wise fashion by using the sparse representation technique. We assume that two image patches should follow the same deformation if their patch-wise appearance patterns are similar. Specifically, there are two stages in our proposed framework, i.e., the training stage and the application stage. In the training stage, each prostate MR images are carefully registered to the corresponding CT images and all training MR and CT images are carefully registered to a selected CT template. Thus, we obtain the dense deformation field for each training MR and CT image. In the application stage, for registering a new subject MR image with the same subject CT image, we first select a small number of key points at the distinctive regions of this subject CT image. Then, for each key point in the subject CT image, we extract the image patch, centered at the underlying key point. Then, we adaptively construct the coupled dictionary for the underlying point where each atom in the dictionary consists of image patches and the respective deformations obtained from training pair-wise MRI-CT images. Next, the subject image patch can be sparsely represented by a linear combination of training image patches in the dictionary, where we apply the same sparse coefficients to the respective deformations in the dictionary to predict the deformation for the subject MR image patch. After we repeat the same procedure for each subject CT key point, we use B-splines to interpolate a dense deformation field, which is used as the initialization to allow the registration algorithm estimating the remaining small segment of deformations from MRI to CT image
The Effect of Vegetation on Soil Moisture Retrievals from GPS Signal-to-Noise Ratio Data
NASA Astrophysics Data System (ADS)
Chew, C. C.; Small, E. E.; Larson, K. M.; Zavorotny, V.
2012-12-01
GPS-Interferometric Reflectometry (GPS-IR) is a method of environmental monitoring that relates changes in ground-reflected (multipath) GPS signals to changes in surface soil moisture and vegetative state for an area of approximately 1000 m2 surrounding a GPS antenna. GPS-IR operates as a bi-static radar: L2C frequency signals transmitted by GPS satellites and subsequent reflections (multipath) are measured by antennas at permanent GPS stations. Changes in multipath signals are seen in signal-to-noise ratio (SNR) interferograms, which are recorded by the GPS receiver. Results from previous field studies have shown that shallow soil moisture can be estimated from SNR phase for bare soil conditions or when vegetation is sparse. Vegetation surrounding a GPS antenna affects the phase shift, amplitude, and frequency/apparent reflector height of SNR oscillations. Therefore, it is necessary to quantify the vegetation conditions, for example vegetation height or water content, that preclude retrieval of soil moisture estimates using GPS-IR. We use both field data and an electrodynamic model that simulates SNR interferograms for variable soil and vegetation conditions to: 1. Determine how changes in vegetation height, biomass, and water content affect GPS phase, amplitude, and apparent reflector height and 2. Quantify the amount of vegetation that obscures the soil moisture signal in SNR data. We report results for rangeland and agricultural sites. At the rangeland sites, vegetation water content only varies between 0 and 0.6 kg/m2. Both observed and simulated SNR data from these sites show that apparent reflector height is nearly constant. Therefore, SNR interferograms are strongly affected by permittivity at the soil surface, and thus soil moisture can be retrieved. Even though reflector height does not change, SNR phase shift and amplitude are affected by fluctuations in rangeland vegetation and must be accounted for in soil moisture retrievals. At several agricultural
Fruits and vegetables dehydration
NASA Astrophysics Data System (ADS)
de Ita, A.; Flores, G.; Franco, F.
2015-01-01
Dehydration diagrams were determined by means of Differential Thermal Analysis, DTA, and Thermo Gravimetric Analysis, TGA, curves of several simultaneous fruits and vegetables, all under the same conditions. The greater mass loss is associated with water containing in the structure of the investigated materials at low temperature. In poblano chile water is lost in a single step. The banana shows a very sharply two stages, while jicama can be observed although with a little difficulty three stages. The major mass loss occurs in the poblano chile and the lower in banana. The velocity and temperature of dehydration vary within a small range for most materials investigated, except for banana and cactus how are very different.
Sparse-view proton computed tomography using modulated proton beams
Lee, Jiseoc; Kim, Changhwan; Cho, Seungryong; Min, Byungjun; Kwak, Jungwon; Park, Seyjoon; Lee, Se Byeong; Park, Sungyong
2015-02-15
Purpose: Proton imaging that uses a modulated proton beam and an intensity detector allows a relatively fast image acquisition compared to the imaging approach based on a trajectory tracking detector. In addition, it requires a relatively simple implementation in a conventional proton therapy equipment. The model of geometric straight ray assumed in conventional computed tomography (CT) image reconstruction is however challenged by multiple-Coulomb scattering and energy straggling in the proton imaging. Radiation dose to the patient is another important issue that has to be taken care of for practical applications. In this work, the authors have investigated iterative image reconstructions after a deconvolution of the sparsely view-sampled data to address these issues in proton CT. Methods: Proton projection images were acquired using the modulated proton beams and the EBT2 film as an intensity detector. Four electron-density cylinders representing normal soft tissues and bone were used as imaged object and scanned at 40 views that are equally separated over 360°. Digitized film images were converted to water-equivalent thickness by use of an empirically derived conversion curve. For improving the image quality, a deconvolution-based image deblurring with an empirically acquired point spread function was employed. They have implemented iterative image reconstruction algorithms such as adaptive steepest descent-projection onto convex sets (ASD-POCS), superiorization method–projection onto convex sets (SM-POCS), superiorization method–expectation maximization (SM-EM), and expectation maximization-total variation minimization (EM-TV). Performance of the four image reconstruction algorithms was analyzed and compared quantitatively via contrast-to-noise ratio (CNR) and root-mean-square-error (RMSE). Results: Objects of higher electron density have been reconstructed more accurately than those of lower density objects. The bone, for example, has been reconstructed
Fast sparse Raman spectral unmixing for chemical fingerprinting and quantification
NASA Astrophysics Data System (ADS)
Yaghoobi, Mehrdad; Wu, Di; Clewes, Rhea J.; Davies, Mike E.
2016-10-01
Raman spectroscopy is a well-established spectroscopic method for the detection of condensed phase chemicals. It is based on scattered light from exposure of a target material to a narrowband laser beam. The information generated enables presumptive identification from measuring correlation with library spectra. Whilst this approach is successful in identification of chemical information of samples with one component, it is more difficult to apply to spectral mixtures. The capability of handling spectral mixtures is crucial for defence and security applications as hazardous materials may be present as mixtures due to the presence of degradation, interferents or precursors. A novel method for spectral unmixing is proposed here. Most modern decomposition techniques are based on the sparse decomposition of mixture and the application of extra constraints to preserve the sum of concentrations. These methods have often been proposed for passive spectroscopy, where spectral baseline correction is not required. Most successful methods are computationally expensive, e.g. convex optimisation and Bayesian approaches. We present a novel low complexity sparsity based method to decompose the spectra using a reference library of spectra. It can be implemented on a hand-held spectrometer in near to real-time. The algorithm is based on iteratively subtracting the contribution of selected spectra and updating the contribution of each spectrum. The core algorithm is called fast non-negative orthogonal matching pursuit, which has been proposed by the authors in the context of nonnegative sparse representations. The iteration terminates when the maximum number of expected chemicals has been found or the residual spectrum has a negligible energy, i.e. in the order of the noise level. A backtracking step removes the least contributing spectrum from the list of detected chemicals and reports it as an alternative component. This feature is particularly useful in detection of chemicals
Snow and Vegetation Interactions at Boundaries in Alaska's Boreal Forest
NASA Astrophysics Data System (ADS)
Hiemstra, C. A.; Sturm, M.
2012-12-01
There has been increased attention on snow-vegetation interactions in Arctic tundra because of rapid climate-driven changes affecting that snow-dominated ecosystem. Yet, far less attention is paid to boreal forest snow-vegetation interactions even though climatic conditions are changing there as well. Further, it is the prevalent terrestrial biome on the planet. The forest is a variable patchwork of trees, shrubs, grasses, and forbs shaped by wind, fire, topography, water drainage, and permafrost. These patches and their boundaries have a corresponding effect on boreal snow distributions; however, measurements characterizing boreal snow are sparse and focus within patches (vs. between patches). Unfortunately, remote sensing approaches in such forested areas frequently fall short due to coarse footprint size and dense canopy cover. Over the last several years we have been examining the characteristics of snow cover within and across boundaries in the boreal forest, seeking to identify gradients in snow depth due to snow-vegetation interactions as well identifying methods whereby boreal forest surveys could be improved. Specifically, we collected end-of-season snow measurements in the Alaska boreal forest during long-distance traverses in the Tanana Basin in 2010 (26 sites) and within the Yukon Flats National Wildlife Refuge in 2011 (26 sites). At each site (all relatively flat), hundreds of snow depths were collected using a GPS-equipped Magnaprobe, which is an automated tool for measuring and locating individual snow depths. Corresponding canopy properties included NDVI determined from high-resolution satellite imagery; canopy properties were variable among the 1ha sites and some areas had recently burned. Among sites, NDVI had the largest correlation with snow depths; elevation was not significant. Vegetation transition zones play important roles in explaining observed snow depth. Similar to treeline work showing nutrient and energy gradients are influenced by
2015-06-01
Wide- Band Antennas from Sparse Measurements by Patrick Debroux and Berenice Verdin Approved for public release...Army Research Laboratory The Use of Compressive Sensing to Reconstruct Radiation Characteristics of Wide- Band Antennas from Sparse Measurements...Compressive Sensing to Reconstruct Radiation Characteristics of Wide-Band Antennas from Sparse Measurements 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c
NASA Technical Reports Server (NTRS)
Wohlen, R. L.
1976-01-01
A listing of the source deck of each sparse FORMA subroutine is given to remove the 'black-box' aura of the subroutines so that the analyst may better understand the detailed operations of each subroutine. The format of a sparse matrix on a utility tape is also presented. The FORTRAN 4 programming language is used in all sparse FORMA subroutines.
Yang, Alice C; Kretzler, Madison; Sudarski, Sonja; Gulani, Vikas; Seiberlich, Nicole
2016-06-01
The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in magnetic resonance imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be used to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MRI, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold standards, are discussed.
Feature selection and multi-kernel learning for sparse representation on a manifold.
Wang, Jim Jing-Yan; Bensmail, Halima; Gao, Xin
2014-03-01
Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods.
NASA Astrophysics Data System (ADS)
Lee, O. A.; Eicken, H.; Weyapuk, W., Jr.; Adams, B.; Mohoney, A. R.
2015-12-01
The significance of highly dispersed, remnant Arctic sea ice as a platform for marine mammals and indigenous hunters in spring and summer may have increased disproportionately with changes in the ice cover. As dispersed remnant ice becomes more common in the future it will be increasingly important to understand its ecological role for upper trophic levels such as marine mammals and its role for supporting primary productivity of ice-associated algae. Potential sparse ice habitat at sea ice concentrations below 15% is difficult to detect using remote sensing data alone. A combination of high resolution satellite imagery (including Synthetic Aperture Radar), data from the Barrow sea ice radar, and local observations from indigenous sea ice experts was used to detect sparse sea ice in the Alaska Arctic. Traditional knowledge on sea ice use by marine mammals was used to delimit the scales where sparse ice could still be used as habitat for seals and walrus. Potential sparse ice habitat was quantified with respect to overall spatial extent, size of ice floes, and density of floes. Sparse ice persistence offshore did not prevent the occurrence of large coastal walrus haul outs, but the lack of sparse ice and early sea ice retreat coincided with local observations of ringed seal pup mortality. Observations from indigenous hunters will continue to be an important source of information for validating remote sensing detections of sparse ice, and improving understanding of marine mammal adaptations to sea ice change.
Pattern-oriented memory interpolation of sparse historical rainfall records
NASA Astrophysics Data System (ADS)
Matos, J. P.; Cohen Liechti, T.; Portela, M. M.; Schleiss, A. J.
2014-03-01
The pattern-oriented memory (POM) is a novel historical rainfall interpolation method that explicitly takes into account the time dimension in order to interpolate areal rainfall maps. The method is based on the idea that rainfall patterns exist and can be identified over a certain area by means of non-linear regressions. Having been previously benchmarked with a vast array of interpolation methods using proxy satellite data under different time and space availabilities, in the scope of the present contribution POM is applied to rain gauge data in order to produce areal rainfall maps. Tested over the Zambezi River Basin for the period from 1979 to 1997 (accurate satellite rainfall estimates based on spaceborne instruments are not available for dates prior to 1998), the novel pattern-oriented memory historical interpolation method has revealed itself as a better alternative than Kriging or Inverse Distance Weighing in the light of a Monte Carlo cross-validation procedure. Superior in most metrics to the other tested interpolation methods, in terms of the Pearson correlation coefficient and bias the accuracy of POM's historical interpolation results are even comparable with that of recent satellite rainfall products. The new method holds the possibility of calculating detailed and performing daily areal rainfall estimates, even in the case of sparse rain gauging grids. Besides their performance, the similarity to satellite rainfall estimates inherent to POM interpolations can contribute to substantially extend the length of the rainfall series used in hydrological models and water availability studies in remote areas.
Sparse non-negative generalized PCA with applications to metabolomics
Allen, Genevera I.; Maletić-Savatić, Mirjana
2011-01-01
Motivation: Nuclear magnetic resonance (NMR) spectroscopy has been used to study mixtures of metabolites in biological samples. This technology produces a spectrum for each sample depicting the chemical shifts at which an unknown number of latent metabolites resonate. The interpretation of this data with common multivariate exploratory methods such as principal components analysis (PCA) is limited due to high-dimensionality, non-negativity of the underlying spectra and dependencies at adjacent chemical shifts. Results: We develop a novel modification of PCA that is appropriate for analysis of NMR data, entitled Sparse Non-Negative Generalized PCA. This method yields interpretable principal components and loading vectors that select important features and directly account for both the non-negativity of the underlying spectra and dependencies at adjacent chemical shifts. Through the reanalysis of experimental NMR data on five purified neural cell types, we demonstrate the utility of our methods for dimension reduction, pattern recognition, sample exploration and feature selection. Our methods lead to the identification of novel metabolites that reflect the differences between these cell types. Availability: www.stat.rice.edu/~gallen/software.html Contact: gallen@rice.edu Supplementary Information: Supplementary data are available at Bioinformatics online. PMID:21930672
Analysis of oscillator neural networks for sparsely coded phase patterns
NASA Astrophysics Data System (ADS)
Nomura, Masaki; Aoyagi, Toshio
2000-12-01
We study a simple extended model of oscillator neural networks capable of storing sparsely coded phase patterns, in which information is encoded both in the mean activity level and in the timing of spikes. Applying the methods of statistical neurodynamics to our model, we investigate theoretically the model's associative memory capability by evaluating its maximum storage capacities and deriving its basins of attraction. It is shown that, as in the Hopfield model, the storage capacity diverges as the activity level decreases. We consider various practically and theoretically important cases. For example, it is revealed that a dynamically adjusted threshold mechanism enhances the retrieval ability of the associative memory. It is also found that, under suitable conditions, the network can recall patterns even in the case that patterns with different activity levels are stored at the same time. In addition, we examine the robustness with respect to damage of the synaptic connections. The validity of these theoretical results is confirmed by reasonable agreement with numerical simulations.
Novel Calibration System with Sparse Wires for CMB Polarization Receivers
NASA Astrophysics Data System (ADS)
Tajima, O.; Nguyen, H.; Bischoff, C.; Brizius, A.; Buder, I.; Kusaka, A.
2012-06-01
A curl competent (also known as B-modes) in the cosmic microwave background (CMB) polarization is a smoking gun signature of the inflationary universe. To achieve better sensitivity to this faint signal, CMB polarization experiments aim to maximize the number of detector elements, resulting in a large focal plane receiver. Detector calibration of the polarization response becomes essential. It is extremely useful to be able to calibrate "simultaneously" all detectors on the large focal plane. We developed a novel calibration system that rotates a large "sparse" grid of metal wires, in front of and fully covering the field of view of the focal plane receiver. Polarized radiation is created via the reflection of ambient temperature photons from the wire surface. Since the detector has a finite beam size, the observed signal is convolved with the beam property. The intensity of the of the calibrator is reasonable (a few Kelvin or less) compared to sky temperature for typical observing conditions (˜10 K). The system played a successful role for receiver calibration of QUIET, a CMB polarization experiment located in the Atacama desert in Chile. The successful performance revealed that this system is applicable to other experiments based on different technologies, e.g. TES bolometers.
Efficient Hessian computation using sparse matrix derivatives in RAM notation.
von Oertzen, Timo; Brick, Timothy R
2014-06-01
This article proposes a new, more efficient method to compute the minus two log likelihood, its gradient, and the Hessian for structural equation models (SEMs) in reticular action model (RAM) notation. The method exploits the beneficial aspect of RAM notation that the matrix derivatives used in RAM are sparse. For an SEM with K variables, P parameters, and P' entries in the symmetrical or asymmetrical matrix of the RAM notation filled with parameters, the asymptotical run time of the algorithm is O(P ' K (2) + P (2) K (2) + K (3)). The naive implementation and numerical implementations are both O(P (2) K (3)), so that for typical applications of SEM, the proposed algorithm is asymptotically K times faster than the best previously known algorithm. A simulation comparison with a numerical algorithm shows that the asymptotical efficiency is transferred to an applied computational advantage that is crucial for the application of maximum likelihood estimation, even in small, but especially in moderate or large, SEMs.
Status of air-shower measurements with sparse radio arrays
NASA Astrophysics Data System (ADS)
Schröder, Frank G.
2017-03-01
This proceeding gives a summary of the current status and open questions of the radio technique for cosmic-ray air showers, assuming that the reader is already familiar with the principles. It includes recent results of selected experiments not present at this conference, e.g., LOPES and TREND. Current radio arrays like AERA or Tunka-Rex have demonstrated that areas of several km2 can be instrumented for reasonable costs with antenna spacings of the order of 200m. For the energy of the primary particle such sparse antenna arrays can already compete in absolute accuracy with other precise techniques, like the detection of air-fluorescence or air-Cherenkov light. With further improvements in the antenna calibration, the radio detection might become even more accurate. For the atmospheric depth of the shower maximum, Xmax, currently only the dense array LOFAR features a precision similar to the fluorescence technique, but analysis methods for the radio measurement of Xmax are still under development. Moreover, the combination of radio and muon measurements is expected to increase the accuracy of the mass composition, and this around-the-clock recording is not limited to clear nights as are the light-detection methods. Consequently, radio antennas will be a valuable add-on for any air shower array targeting the energy range above 100 PeV.
Gaussian kernel width optimization for sparse Bayesian learning.
Mohsenzadeh, Yalda; Sheikhzadeh, Hamid
2015-04-01
Sparse kernel methods have been widely used in regression and classification applications. The performance and the sparsity of these methods are dependent on the appropriate choice of the corresponding kernel functions and their parameters. Typically, the kernel parameters are selected using a cross-validation approach. In this paper, a learning method that is an extension of the relevance vector machine (RVM) is presented. The proposed method can find the optimal values of the kernel parameters during the training procedure. This algorithm uses an expectation-maximization approach for updating kernel parameters as well as other model parameters; therefore, the speed of convergence and computational complexity of the proposed method are the same as the standard RVM. To control the convergence of this fully parameterized model, the optimization with respect to the kernel parameters is performed using a constraint on these parameters. The proposed method is compared with the typical RVM and other competing methods to analyze the performance. The experimental results on the commonly used synthetic data, as well as benchmark data sets, demonstrate the effectiveness of the proposed method in reducing the performance dependency on the initial choice of the kernel parameters.
Pavement crack characteristic detection based on sparse representation
NASA Astrophysics Data System (ADS)
Sun, Xiaoming; Huang, Jianping; Liu, Wanyu; Xu, Mantao
2012-12-01
Pavement crack detection plays an important role in pavement maintaining and management. The three-dimensional (3D) pavement crack detection technique based on laser is a recent trend due to its ability of discriminating dark areas, which are not caused by pavement distress such as tire marks, oil spills and shadows. In the field of 3D pavement crack detection, the most important thing is the accurate extraction of cracks in individual pavement profile without destroying pavement profile. So after analyzing the pavement profile signal characteristics and the changeability of pavement crack characteristics, a new method based on the sparse representation is developed to decompose pavement profile signal into a summation of the mainly pavement profile and cracks. Based on the characteristics of the pavement profile signal and crack, the mixed dictionary is constructed with an over-complete exponential function and an over-complete trapezoidal membership function, and the signal is separated by learning in this mixed dictionary with a matching pursuit algorithm. Some experiments were conducted and promising results were obtained, showing that we can detect the pavement crack efficiently and achieve a good separation of crack from pavement profile without destroying pavement profile.
A linear geospatial streamflow modeling system for data sparse environments
Asante, Kwabena O.; Arlan, Guleid A.; Pervez, Md Shahriar; Rowland, James
2008-01-01
In many river basins around the world, inaccessibility of flow data is a major obstacle to water resource studies and operational monitoring. This paper describes a geospatial streamflow modeling system which is parameterized with global terrain, soils and land cover data and run operationally with satellite‐derived precipitation and evapotranspiration datasets. Simple linear methods transfer water through the subsurface, overland and river flow phases, and the resulting flows are expressed in terms of standard deviations from mean annual flow. In sample applications, the modeling system was used to simulate flow variations in the Congo, Niger, Nile, Zambezi, Orange and Lake Chad basins between 1998 and 2005, and the resulting flows were compared with mean monthly values from the open‐access Global River Discharge Database. While the uncalibrated model cannot predict the absolute magnitude of flow, it can quantify flow anomalies in terms of relative departures from mean flow. Most of the severe flood events identified in the flow anomalies were independently verified by the Dartmouth Flood Observatory (DFO) and the Emergency Disaster Database (EM‐DAT). Despite its limitations, the modeling system is valuable for rapid characterization of the relative magnitude of flood hazards and seasonal flow changes in data sparse settings.
Stacked Predictive Sparse Decomposition for Classification of Histology Sections.
Chang, Hang; Zhou, Yin; Borowsky, Alexander; Barner, Kenneth; Spellman, Paul; Parvin, Bahram
2015-05-01
Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), provides a series of indices for histology composition (e.g., the percentage of each distinct components in histology sections), and enables the study of nuclear properties within each component. Furthermore, the study of these indices, constructed from each whole slide image in a large cohort, has the potential to provide predictive models of clinical outcome. For example, correlations can be established between the constructed indices and the patients' survival information at cohort level, which is a fundamental step towards personalized medicine. However, performance of the existing techniques is hindered as a result of large technical variations (e.g., variations of color/textures in tissue images due to non-standard experimental protocols) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. We propose a system that automatically learns a series of dictionary elements for representing the underlying spatial distribution using stacked predictive sparse decomposition. The learned representation is then fed into the spatial pyramid matching framework with a linear support vector machine classifier. The system has been evaluated for classification of distinct histological components for two cohorts of tumor types. Throughput has been increased by using of graphical processing unit (GPU), and evaluation indicates a superior performance results, compared with previous research.
Fast estimation of sparse doubly spread acoustic channels.
Zeng, Wen-Jun; Xu, Wen
2012-01-01
The estimation of doubly spread underwater acoustic channels is addressed. By exploiting the sparsity in the delay-Doppler domain, this paper proposes a fast projected gradient method (FPGM) that can handle complex-valued data for estimating the delay-Doppler spread function of a time-varying channel. The proposed FPGM formulates the sparse channel estimation as a complex-valued convex optimization using an [script-l](1)-norm constraint. Conventional approaches to complex-valued optimization split the complex variables into their real and imaginary parts; this doubles the dimension compared with the original problem and may break the special data structure. Unlike the conventional methods, the proposed method directly handles the complex variables as a whole without splitting them into real numbers; hence the dimension will not increase. By exploiting the block Toeplitz-like structure of the coefficient matrix, the computational complexity of the FPGM is reduced to O(LNlogN), where L is the dimension of the Doppler shift and N is the signal length. Simulation results verify the accuracy and efficiency of the FPGM, indicating that is robust to parameter selection and is orders-of-magnitude faster than standard convex optimization algorithms. The Kauai experimental data processing results are also provided to demonstrate the performance of the proposed algorithm.
Stacked Predictive Sparse Decomposition for Classification of Histology Sections
Zhou, Yin; Borowsky, Alexander; Barner, Kenneth; Spellman, Paul
2016-01-01
Image-based classification of histology sections, in terms of distinct components (e.g., tumor, stroma, normal), provides a series of indices for histology composition (e.g., the percentage of each distinct components in histology sections), and enables the study of nuclear properties within each component. Furthermore, the study of these indices, constructed from each whole slide image in a large cohort, has the potential to provide predictive models of clinical outcome. For example, correlations can be established between the constructed indices and the patients’ survival information at cohort level, which is a fundamental step towards personalized medicine. However, performance of the existing techniques is hindered as a result of large technical variations (e.g., variations of color/textures in tissue images due to non-standard experimental protocols) and biological heterogeneities (e.g., cell type, cell state) that are always present in a large cohort. We propose a system that automatically learns a series of dictionary elements for representing the underlying spatial distribution using stacked predictive sparse decomposition. The learned representation is then fed into the spatial pyramid matching framework with a linear support vector machine classifier. The system has been evaluated for classification of distinct histological components for two cohorts of tumor types. Throughput has been increased by using of graphical processing unit (GPU), and evaluation indicates a superior performance results, compared with previous research. PMID:27721567
Sparse Nonnegative Matrix Factorization Strategy for Cochlear Implants
Lutman, Mark E.; Ewert, Stephan D.; Li, Guoping; Bleeck, Stefan
2015-01-01
Current cochlear implant (CI) strategies carry speech information via the waveform envelope in frequency subbands. CIs require efficient speech processing to maximize information transfer to the brain, especially in background noise, where the speech envelope is not robust to noise interference. In such conditions, the envelope, after decomposition into frequency bands, may be enhanced by sparse transformations, such as nonnegative matrix factorization (NMF). Here, a novel CI processing algorithm is described, which works by applying NMF to the envelope matrix (envelopogram) of 22 frequency channels in order to improve performance in noisy environments. It is evaluated for speech in eight-talker babble noise. The critical sparsity constraint parameter was first tuned using objective measures and then evaluated with subjective speech perception experiments for both normal hearing and CI subjects. Results from vocoder simulations with 10 normal hearing subjects showed that the algorithm significantly enhances speech intelligibility with the selected sparsity constraints. Results from eight CI subjects showed no significant overall improvement compared with the standard advanced combination encoder algorithm, but a trend toward improvement of word identification of about 10 percentage points at +15 dB signal-to-noise ratio (SNR) was observed in the eight CI subjects. Additionally, a considerable reduction of the spread of speech perception performance from 40% to 93% for advanced combination encoder to 80% to 100% for the suggested NMF coding strategy was observed. PMID:26721919
Extreme learning machine and adaptive sparse representation for image classification.
Cao, Jiuwen; Zhang, Kai; Luo, Minxia; Yin, Chun; Lai, Xiaoping
2016-09-01
Recent research has shown the speed advantage of extreme learning machine (ELM) and the accuracy advantage of sparse representation classification (SRC) in the area of image classification. Those two methods, however, have their respective drawbacks, e.g., in general, ELM is known to be less robust to noise while SRC is known to be time-consuming. Consequently, ELM and SRC complement each other in computational complexity and classification accuracy. In order to unify such mutual complementarity and thus further enhance the classification performance, we propose an efficient hybrid classifier to exploit the advantages of ELM and SRC in this paper. More precisely, the proposed classifier consists of two stages: first, an ELM network is trained by supervised learning. Second, a discriminative criterion about the reliability of the obtained ELM output is adopted to decide whether the query image can be correctly classified or not. If the output is reliable, the classification will be performed by ELM; otherwise the query image will be fed to SRC. Meanwhile, in the stage of SRC, a sub-dictionary that is adaptive to the query image instead of the entire dictionary is extracted via the ELM output. The computational burden of SRC thus can be reduced. Extensive experiments on handwritten digit classification, landmark recognition and face recognition demonstrate that the proposed hybrid classifier outperforms ELM and SRC in classification accuracy with outstanding computational efficiency.
Design and evaluation of sparse quantization index modulation watermarking schemes
NASA Astrophysics Data System (ADS)
Cornelis, Bruno; Barbarien, Joeri; Dooms, Ann; Munteanu, Adrian; Cornelis, Jan; Schelkens, Peter
2008-08-01
In the past decade the use of digital data has increased significantly. The advantages of digital data are, amongst others, easy editing, fast, cheap and cross-platform distribution and compact storage. The most crucial disadvantages are the unauthorized copying and copyright issues, by which authors and license holders can suffer considerable financial losses. Many inexpensive methods are readily available for editing digital data and, unlike analog information, the reproduction in the digital case is simple and robust. Hence, there is great interest in developing technology that helps to protect the integrity of a digital work and the copyrights of its owners. Watermarking, which is the embedding of a signal (known as the watermark) into the original digital data, is one method that has been proposed for the protection of digital media elements such as audio, video and images. In this article, we examine watermarking schemes for still images, based on selective quantization of the coefficients of a wavelet transformed image, i.e. sparse quantization-index modulation (QIM) watermarking. Different grouping schemes for the wavelet coefficients are evaluated and experimentally verified for robustness against several attacks. Wavelet tree-based grouping schemes yield a slightly improved performance over block-based grouping schemes. Additionally, the impact of the deployment of error correction codes on the most promising configurations is examined. The utilization of BCH-codes (Bose, Ray-Chaudhuri, Hocquenghem) results in an improved robustness as long as the capacity of the error codes is not exceeded (cliff-effect).
Multi-Class Sparse Bayesian Regression for Neuroimaging Data Analysis
NASA Astrophysics Data System (ADS)
Michel, Vincent; Eger, Evelyn; Keribin, Christine; Thirion, Bertrand
The use of machine learning tools is gaining popularity in neuroimaging, as it provides a sensitive assessment of the information conveyed by brain images. In particular, finding regions of the brain whose functional signal reliably predicts some behavioral information makes it possible to better understand how this information is encoded or processed in the brain. However, such a prediction is performed through regression or classification algorithms that suffer from the curse of dimensionality, because a huge number of features (i.e. voxels) are available to fit some target, with very few samples (i.e. scans) to learn the informative regions. A commonly used solution is to regularize the weights of the parametric prediction function. However, model specification needs a careful design to balance adaptiveness and sparsity. In this paper, we introduce a novel method, Multi - Class Sparse Bayesian Regression(MCBR), that generalizes classical approaches such as Ridge regression and Automatic Relevance Determination. Our approach is based on a grouping of the features into several classes, where each class is regularized with specific parameters. We apply our algorithm to the prediction of a behavioral variable from brain activation images. The method presented here achieves similar prediction accuracies than reference methods, and yields more interpretable feature loadings.
Convergence and Rate Analysis of Neural Networks for Sparse Approximation
Balavoine, Aurèle; Romberg, Justin; Rozell, Christopher J.
2013-01-01
We present an analysis of the Locally Competitive Algotihm (LCA), which is a Hopfield-style neural network that efficiently solves sparse approximation problems (e.g., approximating a vector from a dictionary using just a few nonzero coefficients). This class of problems plays a significant role in both theories of neural coding and applications in signal processing. However, the LCA lacks analysis of its convergence properties, and previous results on neural networks for nonsmooth optimization do not apply to the specifics of the LCA architecture. We show that the LCA has desirable convergence properties, such as stability and global convergence to the optimum of the objective function when it is unique. Under some mild conditions, the support of the solution is also proven to be reached in finite time. Furthermore, some restrictions on the problem specifics allow us to characterize the convergence rate of the system by showing that the LCA converges exponentially fast with an analytically bounded convergence rate. We support our analysis with several illustrative simulations. PMID:24199030