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…
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: 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.
Biomechanical assessment of the injury risk of stomping.
Schirmer, Frederike; Muggenthaler, Holger; Hubig, Michael; Schenkl, Sebastian; Koch, Markus; Blickhan, Reinhard; Mall, Gita
2016-05-01
Forensic case work as well as literature shows that severe head injuries, e.g., with basilar fractures and cerebral hemorrhages due to stomps can be seen; however, there is no data basis concerning contact forces and potential influencing factors. The objective of this work was to generate a data basis of contact forces in stomping by performing experimental measurements and subsequent statistical analyses. Fifty-five volunteers participated in the present study. Each participant performed several stomps onto force plates with sturdy/soft footwear as well as with/without an elastic layer imitating the scalp. Ground reaction forces induced by jumps were also measured for sturdy and soft footwear. The results show statistically significant dependencies between maximum ground reaction forces and body weight and body height. A statistically significant influence of footwear on stomping force could only be found in tests with an elastic layer and in the jumping setup. Mean maximum stomping forces for the female volunteers were between 4694 and 5970 N; male volunteers were able to produce mean peak stomping forces between 8494 and 9016 N. Jumping forces were approximately twice the stomping forces for both male and female test persons. Regardless of footwear and gender, it can be claimed that a forceful stomp or jump to someone's head supported on the ground can cause facial and skull fractures. Thus, forceful stomps or jumps to someone's head can cause potential fatal injuries independent of footwear, gender, or fitness level. PMID:27075915
NASA Technical Reports Server (NTRS)
Otterman, J.; Susskind, J.; Brakke, T.; Kimes, D.; Pielke, R.; Lee, T. J.
1995-01-01
The thermal-infrared (longwave) emission from a vegetated terrain is generally anisotropic, i.e., the emission temperature varies with the view direction. If a directional measurement of temperature is considered to be equal to the effective temperature of the hemispheric emission, then the estimate of the latter can be significantly in error. The view-direction (zenith angle theta(sub eq) at which the emission equivalence does hold is determined in our modeling study. In a two-temperature field-of-view (soil and plants), theta(sub eq) falls in a narrow range depending on plant density and canopy architecture. Theta(sub eq) does not depend on soil and (uniform) plant temperatures nor on their ratio, even though the pattern of emission vs. the view direction depends crucially on this ratio. For a sparse canopy represented as thin, vertical cylindrical stalks (or vertical blades uniformly distributed in azimuth) with horizontal facets, theta(sub eq) ranges from 48 to 53 deg depending on the optical density of the vertical elements alone. When plant elements are modeled as small spheres, theta(sub eq) lies between 53 to 57 deg (for the same values of the canopy optical density). Only for horizontal leaves (a truly planophile canopy) is the temperature measured from any direction equal to the temperature of the hemispheric emission. When the emission temperature changes with optical depth within the canopy at a specified rate, theta(sub eq) depends to some extent on that rate. For practically any sparsely vegetated surface, a directional measurement at the zenith angle of 50 deg offers an appropriate evaluation of the hemispheric emission, since the error in the estimate will, at most, only slightly exceed 1% (around 4 W/sq m). Estimates of the hemispheric emission through a nadir measurement, on the other hand, can be in error in some cases by about 10%, i.e., on the order of 40 W/sq m.
Turbulent heat transfer from a sparsely vegetated surface - Two-component representation
NASA Technical Reports Server (NTRS)
Otterman, J.; Novak, M. D.; Starr, D. O'C.
1993-01-01
The conventional calculation of heat fluxes from a vegetated surface involving the coefficient of turbulent heat transfer which increases logarithmically with surface roughness, is inappropriate such highly structured surfaces as desert scrub or open forest. An approach is developed here for computing sensible heat flux from sparsely vegetated surfaces, where the absorption of insolation and the transfer of absorbed heat to the atmosphere are calculated separately for the plants and for the soil. This approach is applied to a desert-scrub surface in the northern Sinai, for which the turbulent transfer coefficient of sensible heat flux from the plants is much larger than that from the soil below, as shown by an analysis of plant, soil, and air temperatures. The plant density is expressed as the sum of products (plant-height) x (plant-diameter) of plants per unit horizontal surface area. The solar heat absorbed by the plants is assumed to be transferred immediately to the airflow. The effective turbulent transfer coefficient k(g-eff) for sensible heat from the desert-scrub/soil surface computed under this assumption increases sharply with increasing solar zenith angle, as the plants absorb a greater fraction of the incoming irradiation. The surface absorptivity (the coalbedo) also increases sharply with increasing solar zenith angle, and thus the sensible heat flux from such complex surfaces is a much broader function of time of day than when computed under constant k(g-eff) and constant albedo assumptions.
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.
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
NASA Astrophysics Data System (ADS)
Kumar, U.; Milesi, C.; Nemani, R. R.; Raja, S. Kumar; Ganguly, S.; Wang, W.
2015-06-01
In this paper, we explore the possibility of sparse regression, a new direction in unmixing, for vegetation and urban area classification. SUnSAL (Sparse unmixing via variable splitting and augmented Lagrangian) in both unconstrained and constrained forms (with the abundance non-negativity and abundance sum-to-one constraints) were used with a set of global endmembers (substrate, vegetation and dark objects) to unmix a set of computer simulated noise-free and noisy data (with Gaussian noise of different signal-to-noise ratio) in order to judge the robustness of the algorithm. The error in the fractional estimate was examined for varying noise power (variance): 2, 4, 8, 16, 32, 64, 128 and 256. In the second set of experiments, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data representing an agricultural setup in Fresno, California, USA were used. The corresponding ground data for validation were collected on the same days of satellite overpass. Finally in the third set of experiments, a clear sky Landsat-5 TM data for an area near the Golden Gate Bridge, San Francisco (an urbanized landscape), California, USA were used to assess the algorithm. The fractional estimates of the 30 m Landsat-5 TM data were compared with the fractional estimates of a high-resolution World View-2 data (2 m spatial resolution) obtained using a fully constrained least squares algorithm. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success and bivariate distribution function, which showed that constrained model was better than unconstrained form.
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)
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 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
STOMP: A Software Architecture for the Design and Simulation UAV-Based Sensor Networks
Jones, E D; Roberts, R S; Hsia, T C S
2002-10-28
This paper presents the Simulation, Tactical Operations and Mission Planning (STOMP) software architecture and framework for simulating, controlling and communicating with unmanned air vehicles (UAVs) servicing large distributed sensor networks. STOMP provides hardware-in-the-loop capability enabling real UAVs and sensors to feedback state information, route data and receive command and control requests while interacting with other real or virtual objects thereby enhancing support for simulation of dynamic and complex events.
Dimitrov, Boyan D; Gadeva, Polina G; Benova, Donka K; Bineva, Maria V
2006-11-01
The genotoxicities of the herbicides Roundup (glyphosate), Stomp (pendimethaline) and Reglone (diquat), were compared in plant (Crepis capillaris L.) and mouse bone marrow test systems using chromosomal aberrations and micronuclei. Roundup did not induce chromosomal aberrations or micronuclei in either test system. Reglone also did not induce chromosomal aberrations in either test system; however, it increased micronucleus frequency in both plant cells and mouse bone marrow polychromatic erythrocytes (PCEs). The responses of the two test systems to Stomp were quite different. Stomp did not induce chromosomal aberrations in the plant cells, but increased their incidence in mouse cells; Stomp increased the frequency of micronuclei in both test systems. The induction of micronuclei in plant cells may have been due to the spindle-destroying effect of the herbicide, since all concentrations of Stomp produced C-mitoses. The increased chromosomal aberration frequency in mouse bone marrow cells observed at later sampling times after administration of Stomp into animals suggests that the induction of aberrations may be due to biosynthesis of genotoxic metabolites. This conclusion was supported by the coincidence between the frequencies of chromosomal aberrations and of micronucleated PCEs in mouse cells. These data indicate that plant and animal assays are differentially responsive to some pesticides, and these differences may be due to metabolism and their responses to mitotic spindle disruption. PMID:16998229
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.
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.
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
NASA Astrophysics Data System (ADS)
Baghzouz, Malika
One of the most critical issues associated with using satellite data-based products to study and estimate surface energy fluxes and other ecosystem processes, has been the lack of frequent acquisition at a spatial scale equivalent to or finer than the footprint of field measurements. In this study, we incorporated continuous field measurements based on using Normalized difference vegetation index (NDVI) time series analysis of individual shrub species and transect measurements within 625 m2 size plots equivalent to the Landsat-5 Thematic Mapper spatial resolution. The NDVI system was a dual channel SKR-1800 radiometer that simultaneously measured incident solar radiation and upward reflectance in two broadband red and near-infrared channels comparable to Landsat-5 TM band 3 and band 4, respectively. The two study sites identified as Spring Valley 1 site (SV1) and Snake Valley 1 site (SNK1) were chosen for having different species composition, soil texture and percent canopy cover. NDVI time-series of greasewood (Sarcobatus vermiculatus) from the SV1 site allowed for clear distinction between the main phenological stages of the entire growing season during the period from January to November, 2007. Comparison of greasewood NDVI values between the two sites revealed a significant temporal difference associated with early canopy development and early dry down of greasewood at the SNK1 site. NDVI time series values were also significantly different between sagebrush (Artemisia tridentata ) and rabbitbrush (Chrysothamnus viscidiflorus) at SV1 as well as between the two bare soil types at the two sites, indicating the ability of the ground-based NDVI to distinguish between different plant species as well as between different desert soils based on their moisture level and color. The difference in phenological characteristics of greasewood between the two sites and between sagebrush, rabbitbrush and greasewood within the same site were not captured by the spatially
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
Using the STOMP (Seismic TOMography Program) Program for tomography with strong ray bending
Beatty, J.A.; Berryman, J.G.
1987-08-31
Accurate tomographic reconstructions of sound wave speed and attenuation are more difficult to obtain than are the corresponding reconstructions for x-rays or high frequency electromagnetic probes. The source of the difficulty is the common occurrence of large contrasts in acoustic or seismic wave speeds, leading to refraction and ray-bending effects. A new algorithm based on Fermat's principle has been developed to treat these problems. A description of the code STOMP (for Seismic TOMography Program) implementing the new algorithm is presented here together with a brief users manual for applications to borehole-to-borehole tomography. 3 refs., 3 figs.
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.
Sparse Regression as a Sparse Eigenvalue Problem
NASA Technical Reports Server (NTRS)
Moghaddam, Baback; Gruber, Amit; Weiss, Yair; Avidan, Shai
2008-01-01
We extend the l0-norm "subspectral" algorithms for sparse-LDA [5] and sparse-PCA [6] to general quadratic costs such as MSE in linear (kernel) regression. The resulting "Sparse Least Squares" (SLS) problem is also NP-hard, by way of its equivalence to a rank-1 sparse eigenvalue problem (e.g., binary sparse-LDA [7]). Specifically, for a general quadratic cost we use a highly-efficient technique for direct eigenvalue computation using partitioned matrix inverses which leads to dramatic x103 speed-ups over standard eigenvalue decomposition. This increased efficiency mitigates the O(n4) scaling behaviour that up to now has limited the previous algorithms' utility for high-dimensional learning problems. Moreover, the new computation prioritizes the role of the less-myopic backward elimination stage which becomes more efficient than forward selection. Similarly, branch-and-bound search for Exact Sparse Least Squares (ESLS) also benefits from partitioned matrix inverse techniques. Our Greedy Sparse Least Squares (GSLS) generalizes Natarajan's algorithm [9] also known as Order-Recursive Matching Pursuit (ORMP). Specifically, the forward half of GSLS is exactly equivalent to ORMP but more efficient. By including the backward pass, which only doubles the computation, we can achieve lower MSE than ORMP. Experimental comparisons to the state-of-the-art LARS algorithm [3] show forward-GSLS is faster, more accurate and more flexible in terms of choice of regularization
NASA Astrophysics Data System (ADS)
Carroll, K. C.; Nguyen, B. N.; Fang, Y.; Richmond, M. C.; Murray, C. J.
2011-12-01
Geomechanical alteration of porous media is generally ignored for most shallow subsurface applications, whereas CO2 injection, migration, and trapping in deep saline aquifers will be controlled by coupled multifluid flow, energy transfer, geomechanical, and geochemical processes. The accurate assessment of the risks associated with potential leakage of injected CO2 and the design of effective injection systems requires that we represent these coupled processes within numerical simulators. The objective of this study was to examine the coupling of hydraulic and geomechanical processes for simulation of CO2 injection into the subsurface for carbon sequestration. The impact of nonisothermal multifluid flow and porous media deformation mechanics on CO2 migration and storage was evaluated. We present a sequentially coupled approach for multifluid and geomechanical simulation using STOMP and ABAQUS that has been developed and validated through comparison to the solutions for benchmark problems that were solved with a coupled TOUGH-FLAC simulator. The poroelastic model was implemented with user-subroutines in ABAQUS. We also compare the STOMP-ABAQUS simulator to a new version of STOMP that includes the fully coupled poroelastic simulation within the multifluid flow and transport simulator. The poroelastic model computes stiffness, stresses, and strains using aqueous and gas pressures as well as saturations from STOMP output, and provides STOMP with the updated permeability, porosity, and capillary pressure over time during the simulation. The hydraulic only (uncoupled from mechanics) simulation and the hydrogeomechanical (coupled) simulation results using STOMP-ABAQUS were comparable to the previous results of a TOUGH-FLAC simulator. Results from the STOMP-ABAQUS coupled simulator were essentially identical to the fully coupled STOMP hydrogeomechanical simulator when the sequential coupling occurred at small time steps, and deviations between results increased with
A Modified Sparse Representation Method for Facial Expression Recognition.
Wang, Wei; Xu, LiHong
2016-01-01
In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan's JAFFE and CMU's CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result. PMID:26880878
A Modified Sparse Representation Method for Facial Expression Recognition
Wang, Wei; Xu, LiHong
2016-01-01
In this paper, we carry on research on a facial expression recognition method, which is based on modified sparse representation recognition (MSRR) method. On the first stage, we use Haar-like+LPP to extract feature and reduce dimension. On the second stage, we adopt LC-K-SVD (Label Consistent K-SVD) method to train the dictionary, instead of adopting directly the dictionary from samples, and add block dictionary training into the training process. On the third stage, stOMP (stagewise orthogonal matching pursuit) method is used to speed up the convergence of OMP (orthogonal matching pursuit). Besides, a dynamic regularization factor is added to iteration process to suppress noises and enhance accuracy. We verify the proposed method from the aspect of training samples, dimension, feature extraction and dimension reduction methods and noises in self-built database and Japan's JAFFE and CMU's CK database. Further, we compare this sparse method with classic SVM and RVM and analyze the recognition effect and time efficiency. The result of simulation experiment has shown that the coefficient of MSRR method contains classifying information, which is capable of improving the computing speed and achieving a satisfying recognition result. PMID:26880878
Ray, J.; Lee, J.; Yadav, V.; Lefantzi, S.; Michalak, A. M.; van Bloemen Waanders, B.
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
Sparse representation with kernels.
Gao, Shenghua; Tsang, Ivor Wai-Hung; Chia, Liang-Tien
2013-02-01
Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks. PMID:23014744
sparse-msrf:A package for sparse modeling and estimation of fossil-fuel CO2 emission fields
Energy Science and Technology Software Center (ESTSC)
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 softwaremore » 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.« less
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.
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.
Ray, J.; Lee, J.; Yadav, V.; Lefantzi, S.; Michalak, A. M.; van Bloemen Waanders, B.
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
Banerjee, Abhirup; Maji, Pradipta
2015-12-01
The segmentation of brain MR images into different tissue classes is an important task for automatic image analysis technique, particularly due to the presence of intensity inhomogeneity artifact in MR images. In this regard, this paper presents a novel approach for simultaneous segmentation and bias field correction in brain MR images. It integrates judiciously the concept of rough sets and the merit of a novel probability distribution, called stomped normal (SN) distribution. The intensity distribution of a tissue class is represented by SN distribution, where each tissue class consists of a crisp lower approximation and a probabilistic boundary region. The intensity distribution of brain MR image is modeled as a mixture of finite number of SN distributions and one uniform distribution. The proposed method incorporates both the expectation-maximization and hidden Markov random field frameworks to provide an accurate and robust segmentation. The performance of the proposed approach, along with a comparison with related methods, is demonstrated on a set of synthetic and real brain MR images for different bias fields and noise levels. PMID:26462197
LOFAR sparse image reconstruction
NASA Astrophysics Data System (ADS)
Garsden, H.; Girard, J. N.; Starck, J. L.; Corbel, S.; Tasse, C.; Woiselle, A.; McKean, J. P.; van Amesfoort, A. S.; Anderson, J.; Avruch, I. M.; Beck, R.; Bentum, M. J.; Best, P.; Breitling, F.; Broderick, J.; Brüggen, M.; Butcher, H. R.; Ciardi, B.; de Gasperin, F.; de Geus, E.; de Vos, M.; Duscha, S.; Eislöffel, J.; Engels, D.; Falcke, H.; Fallows, R. A.; Fender, R.; Ferrari, C.; Frieswijk, W.; Garrett, M. A.; Grießmeier, J.; Gunst, A. W.; Hassall, T. E.; Heald, G.; Hoeft, M.; Hörandel, J.; van der Horst, A.; Juette, E.; Karastergiou, A.; Kondratiev, V. I.; Kramer, M.; Kuniyoshi, M.; Kuper, G.; Mann, G.; Markoff, S.; McFadden, R.; McKay-Bukowski, D.; Mulcahy, D. D.; Munk, H.; Norden, M. J.; Orru, E.; Paas, H.; Pandey-Pommier, M.; Pandey, V. N.; Pietka, G.; Pizzo, R.; Polatidis, A. G.; Renting, A.; Röttgering, H.; Rowlinson, A.; Schwarz, D.; Sluman, J.; Smirnov, O.; Stappers, B. W.; Steinmetz, M.; Stewart, A.; Swinbank, J.; Tagger, M.; Tang, Y.; Tasse, C.; Thoudam, S.; Toribio, C.; Vermeulen, R.; Vocks, C.; van Weeren, R. J.; Wijnholds, S. J.; Wise, M. W.; Wucknitz, O.; Yatawatta, S.; Zarka, P.; Zensus, A.
2015-03-01
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods. Aims: Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework. Methods: We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data. Results: We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions: Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKA.
Structured Multifrontal Sparse Solver
Energy Science and Technology Software Center (ESTSC)
2014-05-01
StruMF is an algebraic structured preconditioner for the interative solution of large sparse linear systems. The preconditioner corresponds to a multifrontal variant of sparse LU factorization in which some dense blocks of the factors are approximated with low-rank matrices. It is algebraic in that it only requires the linear system itself, and the approximation threshold that determines the accuracy of individual low-rank approximations. Favourable rank properties are obtained using a block partitioning which is amore » refinement of the partitioning induced by nested dissection ordering.« less
Sparse inpainting and isotropy
Feeney, Stephen M.; McEwen, Jason D.; Peiris, Hiranya V.; Marinucci, Domenico; Cammarota, Valentina; Wandelt, Benjamin D. E-mail: marinucc@axp.mat.uniroma2.it E-mail: h.peiris@ucl.ac.uk E-mail: cammarot@axp.mat.uniroma2.it
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.
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.
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.
Kanerva, P.
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. 63 refs.
Optical sparse aperture imaging.
Miller, Nicholas J; Dierking, Matthew P; Duncan, Bradley D
2007-08-10
The resolution of a conventional diffraction-limited imaging system is proportional to its pupil diameter. A primary goal of sparse aperture imaging is to enhance resolution while minimizing the total light collection area; the latter being desirable, in part, because of the cost of large, monolithic apertures. Performance metrics are defined and used to evaluate several sparse aperture arrays constructed from multiple, identical, circular subapertures. Subaperture piston and/or tilt effects on image quality are also considered. We selected arrays with compact nonredundant autocorrelations first described by Golay. We vary both the number of subapertures and their relative spacings to arrive at an optimized array. We report the results of an experiment in which we synthesized an image from multiple subaperture pupil fields by masking a large lens with a Golay array. For this experiment we imaged a slant edge feature of an ISO12233 resolution target in order to measure the modulation transfer function. We note the contrast reduction inherent in images formed through sparse aperture arrays and demonstrate the use of a Wiener-Helstrom filter to restore contrast in our experimental images. Finally, we describe a method to synthesize images from multiple subaperture focal plane intensity images using a phase retrieval algorithm to obtain estimates of subaperture pupil fields. Experimental results from synthesizing an image of a point object from multiple subaperture images are presented, and weaknesses of the phase retrieval method for this application are discussed. PMID:17694146
Energy Science and Technology Software Center (ESTSC)
2007-04-12
The Sparse Image Format (SIF) is a file format for storing spare raster images. It works by breaking an image down into tiles. Space is savid by only storing non-uniform tiles, i.e. tiles with at least two different pixel values. If a tile is completely uniform, its common pixel value is stored instead of the complete tile raster. The software is a library in the C language used for manipulating files in SIF format. Itmore » supports large files (> 2GB) and is designed to build in Windows and Linux environments.« less
Eads, Damian Ryan
2007-04-12
The Sparse Image Format (SIF) is a file format for storing spare raster images. It works by breaking an image down into tiles. Space is savid by only storing non-uniform tiles, i.e. tiles with at least two different pixel values. If a tile is completely uniform, its common pixel value is stored instead of the complete tile raster. The software is a library in the C language used for manipulating files in SIF format. It supports large files (> 2GB) and is designed to build in Windows and Linux environments.
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.
Energy Science and Technology Software Center (ESTSC)
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 thatmore » provides a command line interface via text files ad a MATLAB interface via the command line tool.« less
Compressed Sampling of Spectrally Sparse Signals Using Sparse Circulant Matrices
NASA Astrophysics Data System (ADS)
Xu, Guangjie; Wang, Huali; Sun, Lei; Zeng, Weijun; Wang, Qingguo
2014-11-01
Circulant measurement matrices constructed by partial cyclically shifts of one generating sequence, are easier to be implemented in hardware than widely used random measurement matrices; however, the diminishment of randomness makes it more sensitive to signal noise. Selecting a deterministic sequence with optimal periodic autocorrelation property (PACP) as generating sequence, would enhance the noise robustness of circulant measurement matrix, but this kind of deterministic circulant matrices only exists in the fixed periodic length. Actually, the selection of generating sequence doesn't affect the compressive performance of circulant measurement matrix but the subspace energy in spectrally sparse signals. Sparse circulant matrices, whose generating sequence is a sparse sequence, could keep the energy balance of subspaces and have similar noise robustness to deterministic circulant matrices. In addition, sparse circulant matrices have no restriction on length and are more suitable for the compressed sampling of spectrally sparse signals at arbitrary dimensionality.
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.
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.
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.
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.
James, Gareth M.; Sabatti, Chiara; Zhou, Nengfeng; Zhu, Ji
2011-01-01
In many organisms the expression levels of each gene are controlled by the activation levels of known “Transcription Factors” (TF). A problem of considerable interest is that of estimating the “Transcription Regulation Networks” (TRN) relating the TFs and genes. While the expression levels of genes can be observed, the activation levels of the corresponding TFs are usually unknown, greatly increasing the difficulty of the problem. Based on previous experimental work, it is often the case that partial information about the TRN is available. For example, certain TFs may be known to regulate a given gene or in other cases a connection may be predicted with a certain probability. In general, the biology of the problem indicates there will be very few connections between TFs and genes. Several methods have been proposed for estimating TRNs. However, they all suffer from problems such as unrealistic assumptions about prior knowledge of the network structure or computational limitations. We propose a new approach that can directly utilize prior information about the network structure in conjunction with observed gene expression data to estimate the TRN. Our approach uses L1 penalties on the network to ensure a sparse structure. This has the advantage of being computationally efficient as well as making many fewer assumptions about the network structure. We use our methodology to construct the TRN for E. coli and show that the estimate is biologically sensible and compares favorably with previous estimates. PMID:21625366
Estimating sparse precision matrices
NASA Astrophysics Data System (ADS)
Padmanabhan, Nikhil; White, Martin; Zhou, Harrison H.; O'Connell, Ross
2016-05-01
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/√{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/√{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.
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/√{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/√{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.
Completeness for sparse potential scattering
Shen, Zhongwei
2014-01-15
The present paper is devoted to the scattering theory of a class of continuum Schrödinger operators with deterministic sparse potentials. We first establish the limiting absorption principle for both modified free resolvents and modified perturbed resolvents. This actually is a weak form of the classical limiting absorption principle. We then prove the existence and completeness of local wave operators, which, in particular, imply the existence of wave operators. Under additional assumptions on the sparse potential, we prove the completeness of wave operators. In the context of continuum Schrödinger operators with sparse potentials, this paper gives the first proof of the completeness of wave operators.
Threaded Operations on Sparse Matrices
Sneed, Brett
2015-09-01
We investigate the use of sparse matrices and OpenMP multi-threading on linear algebra operations involving them. Several sparse matrix data structures are presented. Implementation of the multi- threading primarily occurs in the level one and two BLAS functions used within the four algorithms investigated{the Power Method, Conjugate Gradient, Biconjugate Gradient, and Jacobi's Method. The bene ts of launching threads once per high level algorithm are explored.
Wavelet Sparse Approximate Inverse Preconditioners
NASA Technical Reports Server (NTRS)
Chan, Tony F.; Tang, W.-P.; Wan, W. L.
1996-01-01
There is an increasing interest in using sparse approximate inverses as preconditioners for Krylov subspace iterative methods. Recent studies of Grote and Huckle and Chow and Saad also show that sparse approximate inverse preconditioner can be effective for a variety of matrices, e.g. Harwell-Boeing collections. Nonetheless a drawback is that it requires rapid decay of the inverse entries so that sparse approximate inverse is possible. However, for the class of matrices that, come from elliptic PDE problems, this assumption may not necessarily hold. Our main idea is to look for a basis, other than the standard one, such that a sparse representation of the inverse is feasible. A crucial observation is that the kind of matrices we are interested in typically have a piecewise smooth inverse. We exploit this fact, by applying wavelet techniques to construct a better sparse approximate inverse in the wavelet basis. We shall justify theoretically and numerically that our approach is effective for matrices with smooth inverse. We emphasize that in this paper we have only presented the idea of wavelet approximate inverses and demonstrated its potential but have not yet developed a highly refined and efficient algorithm.
Learning Sparse Representations of Depth
NASA Astrophysics Data System (ADS)
Tosic, Ivana; Olshausen, Bruno A.; Culpepper, Benjamin J.
2011-09-01
This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer graphical model for inferring depth from stereo, by including a sparsity prior on the learned features. Since they capture higher-order dependencies in the depth structure, these priors can complement smoothness priors commonly used in depth inference based on Markov Random Field (MRF) models. Inference on the proposed graph is achieved using an alternating iterative optimization technique, where the first layer is solved using an existing MRF-based stereo matching algorithm, then held fixed as the second layer is solved using the proposed non-stationary sparse coding algorithm. This leads to a general method for improving solutions of state of the art MRF-based depth estimation algorithms. Our experimental results first show that depth inference using learned representations leads to state of the art denoising of depth maps obtained from laser range scanners and a time of flight camera. Furthermore, we show that adding sparse priors improves the results of two depth estimation methods: the classical graph cut algorithm by Boykov et al. and the more recent algorithm of Woodford et al.
A scalable 2-D parallel sparse solver
Kothari, S.C.; Mitra, S.
1995-12-01
Scalability beyond a small number of processors, typically 32 or less, is known to be a problem for existing parallel general sparse (PGS) direct solvers. This paper presents a parallel general sparse PGS direct solver for general sparse linear systems on distributed memory machines. The algorithm is based on the well-known sequential sparse algorithm Y12M. To achieve efficient parallelization, a 2-D scattered decomposition of the sparse matrix is used. The proposed algorithm is more scalable than existing parallel sparse direct solvers. Its scalability is evaluated on a 256 processor nCUBE2s machine using Boeing/Harwell benchmark matrices.
Amesos2 Templated Direct Sparse Solver Package
Energy Science and Technology Software Center (ESTSC)
2011-05-24
Amesos2 is a templated direct sparse solver package. Amesos2 provides interfaces to direct sparse solvers, rather than providing native solver capabilities. Amesos2 is a derivative work of the Trilinos package Amesos.
SparsePZ: Sparse Representation of Photometric Redshift PDFs
NASA Astrophysics Data System (ADS)
Carrasco Kind, Matias; Brunner, R. J.
2015-11-01
SparsePZ uses sparse basis representation to fully represent individual photometric redshift probability density functions (PDFs). This approach requires approximately half the parameters for the same multi-Gaussian fitting accuracy, and has the additional advantage that an entire PDF can be stored by using a 4-byte integer per basis function. Only 10-20 points per galaxy are needed to reconstruct both the individual PDFs and the ensemble redshift distribution, N(z), to an accuracy of 99.9 per cent when compared to the one built using the original PDFs computed with a resolution of δz = 0.01, reducing the required storage of 200 original values by a factor of 10-20. This basis representation can be directly extended to a cosmological analysis, thereby increasing computational performance without losing resolution or accuracy.
Sparse Biclustering of Transposable Data
Tan, Kean Ming
2013-01-01
We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ1 penalty to the means of the biclusters in order to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of k-means clustering. We show that k-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for bi-clustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression data set. This article has supplementary material online. PMID:25364221
Sparse Biclustering of Transposable Data.
Tan, Kean Ming; Witten, Daniela M
2014-01-01
We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ1 penalty to the means of the biclusters in order to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of k-means clustering. We show that k-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for bi-clustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression data set. This article has supplementary material online. PMID:25364221
Finding communities in sparse networks
Singh, Abhinav; Humphries, Mark D.
2015-01-01
Spectral algorithms based on matrix representations of networks are often used to detect communities, but classic spectral methods based on the adjacency matrix and its variants fail in sparse networks. New spectral methods based on non-backtracking random walks have recently been introduced that successfully detect communities in many sparse networks. However, the spectrum of non-backtracking random walks ignores hanging trees in networks that can contain information about their community structure. We introduce the reluctant backtracking operators that explicitly account for hanging trees as they admit a small probability of returning to the immediately previous node, unlike the non-backtracking operators that forbid an immediate return. We show that the reluctant backtracking operators can detect communities in certain sparse networks where the non-backtracking operators cannot, while performing comparably on benchmark stochastic block model networks and real world networks. We also show that the spectrum of the reluctant backtracking operator approximately optimises the standard modularity function. Interestingly, for this family of non- and reluctant-backtracking operators the main determinant of performance on real-world networks is whether or not they are normalised to conserve probability at each node. PMID:25742951
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 Coding for Alpha Matting
NASA Astrophysics Data System (ADS)
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 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.
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 representation for vehicle recognition
NASA Astrophysics Data System (ADS)
Monnig, Nathan D.; Sakla, Wesam
2014-06-01
The Sparse Representation for Classification (SRC) algorithm has been demonstrated to be a state-of-the-art algorithm for facial recognition applications. Wright et al. demonstrate that under certain conditions, the SRC algorithm classification performance is agnostic to choice of linear feature space and highly resilient to image corruption. In this work, we examined the SRC algorithm performance on the vehicle recognition application, using images from the semi-synthetic vehicle database generated by the Air Force Research Laboratory. To represent modern operating conditions, vehicle images were corrupted with noise, blurring, and occlusion, with representation of varying pose and lighting conditions. Experiments suggest that linear feature space selection is important, particularly in the cases involving corrupted images. Overall, the SRC algorithm consistently outperforms a standard k nearest neighbor classifier on the vehicle recognition task.
Sparse and stable Markowitz portfolios
Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace
2009-01-01
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. PMID:19617537
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. PMID:25955995
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.
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.
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.
Sparse Spectrotemporal Coding of Sounds
NASA Astrophysics Data System (ADS)
Klein, David J.; König, Peter; Körding, Konrad P.
2003-12-01
Recent studies of biological auditory processing have revealed that sophisticated spectrotemporal analyses are performed by central auditory systems of various animals. The analysis is typically well matched with the statistics of relevant natural sounds, suggesting that it produces an optimal representation of the animal's acoustic biotope. We address this topic using simulated neurons that learn an optimal representation of a speech corpus. As input, the neurons receive a spectrographic representation of sound produced by a peripheral auditory model. The output representation is deemed optimal when the responses of the neurons are maximally sparse. Following optimization, the simulated neurons are similar to real neurons in many respects. Most notably, a given neuron only analyzes the input over a localized region of time and frequency. In addition, multiple subregions either excite or inhibit the neuron, together producing selectivity to spectral and temporal modulation patterns. This suggests that the brain's solution is particularly well suited for coding natural sound; therefore, it may prove useful in the design of new computational methods for processing speech.
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. PMID:26963062
Sparse Bayesian infinite factor models
Bhattacharya, A.; Dunson, D. B.
2011-01-01
We focus on sparse modelling of high-dimensional covariance matrices using Bayesian latent factor models. We propose a multiplicative gamma process shrinkage prior on the factor loadings which allows introduction of infinitely many factors, with the loadings increasingly shrunk towards zero as the column index increases. We use our prior on a parameter-expanded loading matrix to avoid the order dependence typical in factor analysis models and develop an efficient Gibbs sampler that scales well as data dimensionality increases. The gain in efficiency is achieved by the joint conjugacy property of the proposed prior, which allows block updating of the loadings matrix. We propose an adaptive Gibbs sampler for automatically truncating the infinite loading matrix through selection of the number of important factors. Theoretical results are provided on the support of the prior and truncation approximation bounds. A fast algorithm is proposed to produce approximate Bayes estimates. Latent factor regression methods are developed for prediction and variable selection in applications with high-dimensional correlated predictors. Operating characteristics are assessed through simulation studies, and the approach is applied to predict survival times from gene expression data. PMID:23049129
Enhancing Scalability of Sparse Direct Methods
Li, Xiaoye S.; Demmel, James; Grigori, Laura; Gu, Ming; Xia,Jianlin; Jardin, Steve; Sovinec, Carl; Lee, Lie-Quan
2007-07-23
TOPS is providing high-performance, scalable sparse direct solvers, which have had significant impacts on the SciDAC applications, including fusion simulation (CEMM), accelerator modeling (COMPASS), as well as many other mission-critical applications in DOE and elsewhere. Our recent developments have been focusing on new techniques to overcome scalability bottleneck of direct methods, in both time and memory. These include parallelizing symbolic analysis phase and developing linear-complexity sparse factorization methods. The new techniques will make sparse direct methods more widely usable in large 3D simulations on highly-parallel petascale computers.
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
Imaging correlography with sparse collecting apertures
NASA Astrophysics Data System (ADS)
Idell, Paul S.; Fienup, J. R.
1987-01-01
This paper investigates the possibility of implementing an imaging correlography system with sparse arrays of intensity detectors. The theory underlying the image formation process for imaging correlography is reviewed, emphasizing the spatial filtering effects that sparse collecting apertures have on the reconstructed imagery. Image recovery with sparse arrays of intensity detectors through the use of computer experiments in which laser speckle measurements are digitally simulated is then demonstrated. It is shown that the quality of imagery reconstructed using this technique is visibly enhanced when appropriate filtering techniques are applied. A performance tradeoff between collecting array redundancy and the number of speckle pattern measurements is briefly discussed.
Sparse principal component analysis in cancer research
Hsu, Ying-Lin; Huang, Po-Yu; Chen, Dung-Tsa
2015-01-01
A critical challenging component in analyzing high-dimensional data in cancer research is how to reduce the dimension of data and how to extract relevant features. Sparse principal component analysis (PCA) is a powerful statistical tool that could help reduce data dimension and select important variables simultaneously. In this paper, we review several approaches for sparse PCA, including variance maximization (VM), reconstruction error minimization (REM), singular value decomposition (SVD), and probabilistic modeling (PM) approaches. A simulation study is conducted to compare PCA and the sparse PCAs. An example using a published gene signature in a lung cancer dataset is used to illustrate the potential application of sparse PCAs in cancer research. PMID:26719835
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 subspace clustering: algorithm, theory, and applications.
Elhamifar, Ehsan; Vidal, René
2013-11-01
Many real-world problems deal with collections of high-dimensional data, such as images, videos, text, and web documents, DNA microarray data, and more. Often, such high-dimensional data lie close to low-dimensional structures corresponding to several classes or categories to which the data belong. In this paper, we propose and study an algorithm, called sparse subspace clustering, to cluster data points that lie in a union of low-dimensional subspaces. The key idea is that, among the infinitely many possible representations of a data point in terms of other points, a sparse representation corresponds to selecting a few points from the same subspace. This motivates solving a sparse optimization program whose solution is used in a spectral clustering framework to infer the clustering of the data into subspaces. Since solving the sparse optimization program is in general NP-hard, we consider a convex relaxation and show that, under appropriate conditions on the arrangement of the subspaces and the distribution of the data, the proposed minimization program succeeds in recovering the desired sparse representations. The proposed algorithm is efficient and can handle data points near the intersections of subspaces. Another key advantage of the proposed algorithm with respect to the state of the art is that it can deal directly with data nuisances, such as noise, sparse outlying entries, and missing entries, by incorporating the model of the data into the sparse optimization program. We demonstrate the effectiveness of the proposed algorithm through experiments on synthetic data as well as the two real-world problems of motion segmentation and face clustering. PMID:24051734
Wavelet-based reconstruction of fossil-fuel CO2 emissions from sparse measurements
NASA Astrophysics Data System (ADS)
McKenna, S. A.; Ray, J.; Yadav, V.; Van Bloemen Waanders, B.; Michalak, A. M.
2012-12-01
We present a method to estimate spatially resolved fossil-fuel CO2 (ffCO2) emissions from sparse measurements of time-varying CO2 concentrations. It is based on the wavelet-modeling of the strongly non-stationary spatial distribution of ffCO2 emissions. The dimensionality of the wavelet model is first reduced using images of nightlights, which identify regions of human habitation. Since wavelets are a multiresolution basis set, most of the reduction is accomplished by removing fine-scale wavelets, in the regions with low nightlight radiances. The (reduced) wavelet model of emissions is propagated through an atmospheric transport model (WRF) to predict CO2 concentrations at a handful of measurement sites. The estimation of the wavelet model of emissions i.e., inferring the wavelet weights, is performed by fitting to observations at the measurement sites. This is done using Staggered Orthogonal Matching Pursuit (StOMP), which first identifies (and sets to zero) the wavelet coefficients that cannot be estimated from the observations, before estimating the remaining coefficients. This model sparsification and fitting is performed simultaneously, allowing us to explore multiple wavelet-models of differing complexity. This technique is borrowed from the field of compressive sensing, and is generally used in image and video processing. We test this approach using synthetic observations generated from emissions from the Vulcan database. 35 sensor sites are chosen over the USA. FfCO2 emissions, averaged over 8-day periods, are estimated, at a 1 degree spatial resolutions. We find that only about 40% of the wavelets in emission model can be estimated from the data; however the mix of coefficients that are estimated changes with time. Total US emission can be reconstructed with about ~5% errors. The inferred emissions, if aggregated monthly, have a correlation of 0.9 with Vulcan fluxes. We find that the estimated emissions in the Northeast US are the most accurate. Sandia
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.
Removing sparse noise from hyperspectral images with sparse and low-rank penalties
NASA Astrophysics Data System (ADS)
Tariyal, Snigdha; Aggarwal, Hemant Kumar; Majumdar, Angshul
2016-03-01
In diffraction grating, at times, there are defective pixels on the focal plane array; this results in horizontal lines of corrupted pixels in some channels. Since only a few such pixels exist, the corruption/noise is sparse. Studies on sparse noise removal from hyperspectral noise are parsimonious. To remove such sparse noise, a prior work exploited the interband spectral correlation along with intraband spatial redundancy to yield a sparse representation in transform domains. We improve upon the prior technique. The intraband spatial redundancy is modeled as a sparse set of transform coefficients and the interband spectral correlation is modeled as a rank deficient matrix. The resulting optimization problem is solved using the split Bregman technique. Comparative experimental results show that our proposed approach is better than the previous one.
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
Sparse regularization for force identification using dictionaries
NASA Astrophysics Data System (ADS)
Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng
2016-04-01
The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.
Native ultrametricity of sparse random ensembles
NASA Astrophysics Data System (ADS)
Avetisov, V.; Krapivsky, P. L.; Nechaev, S.
2016-01-01
We investigate the eigenvalue density in ensembles of large sparse Bernoulli random matrices. Analyzing in detail the spectral density of ensembles of linear subgraphs, we discuss its ultrametric nature and show that near the spectrum boundary, the tails of the spectral density exhibit a Lifshitz singularity typical for Anderson localization. We pay attention to an intriguing connection of the spectral density to the Dedekind η-function. We conjecture that ultrametricity emerges in rare-event statistics and is inherit to generic complex sparse systems.
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.
Sparse representation of complex MRI images.
Nandakumar, Hari Prasad; Ji, Jim
2008-01-01
Sparse representation of images acquired from Magnet Resonance Imaging (MRI) has several potential applications. MRI is unique in that the raw images are complex. Complex wavelet transforms (CWT) can be used to produce flexible signal representations when compared to Discrete Wavelet Transform (DWT). In this work, five different schemes using CWT or DWT are tested for sparse representation of MRI images which are in the form of complex values, separate real/imaginary, or separate magnitude/phase. The experimental results on real in-vivo MRI images show that appropriate CWT, e.g., dual-tree CWT (DTCWT), can achieve sparsity better than DWT with similar Mean Square Error. PMID:19162677
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.
Sparse representation in speech signal processing
NASA Astrophysics Data System (ADS)
Lee, Te-Won; Jang, Gil-Jin; Kwon, Oh-Wook
2003-11-01
We review the sparse representation principle for processing speech signals. A transformation for encoding the speech signals is learned such that the resulting coefficients are as independent as possible. We use independent component analysis with an exponential prior to learn a statistical representation for speech signals. This representation leads to extremely sparse priors that can be used for encoding speech signals for a variety of purposes. We review applications of this method for speech feature extraction, automatic speech recognition and speaker identification. Furthermore, this method is also suited for tackling the difficult problem of separating two sounds given only a single microphone.
Infrared image recognition based on structure sparse and atomic sparse parallel
NASA Astrophysics Data System (ADS)
Wu, Yalu; Li, Ruilong; Xu, Yi; Wang, Liping
2015-12-01
Use the redundancy of the super complete dictionary can capture the structural features of the image effectively, can achieving the effective representation of the image. However, the commonly used atomic sparse representation without regard the structure of the dictionary and the unrelated non-zero-term in the process of the computation, though structure sparse consider the structure feature of dictionary, the majority coefficients of the blocks maybe are non-zero, it may affect the identification efficiency. For the disadvantages of these two sparse expressions, a weighted parallel atomic sparse and sparse structure is proposed, and the recognition efficiency is improved by the adaptive computation of the optimal weights. The atomic sparse expression and structure sparse expression are respectively, and the optimal weights are calculated by the adaptive method. Methods are as follows: training by using the less part of the identification sample, the recognition rate is calculated by the increase of the certain step size and t the constraint between weight. The recognition rate as the Z axis, two weight values respectively as X, Y axis, the resulting points can be connected in a straight line in the 3 dimensional coordinate system, by solving the highest recognition rate, the optimal weights can be obtained. Through simulation experiments can be known, the optimal weights based on adaptive method are better in the recognition rate, weights obtained by adaptive computation of a few samples, suitable for parallel recognition calculation, can effectively improve the recognition rate of infrared images.
Facial expression recognition with facial parts based sparse representation classifier
NASA Astrophysics Data System (ADS)
Zhi, Ruicong; Ruan, Qiuqi
2009-10-01
Facial expressions play important role in human communication. The understanding of facial expression is a basic requirement in the development of next generation human computer interaction systems. Researches show that the intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse representation to select face features and classify facial expressions. The sparse solution is obtained by solving l1 -norm minimization problem with constraint of linear combination equation. Experimental results show that sparse representation is efficient for facial expression recognition and sparse representation classifier obtain much higher recognition accuracies than other compared methods.
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.
A Comparative Study of Sparse Associative Memories
NASA Astrophysics Data System (ADS)
Gripon, Vincent; Heusel, Judith; Löwe, Matthias; Vermet, Franck
2016-05-01
We study various models of associative memories with sparse information, i.e. a pattern to be stored is a random string of 0s and 1s with about log N 1s, only. We compare different synaptic weights, architectures and retrieval mechanisms to shed light on the influence of the various parameters on the storage capacity.
Self-Control in Sparsely Coded Networks
NASA Astrophysics Data System (ADS)
Dominguez, D. R. C.; Bollé, D.
1998-03-01
A complete self-control mechanism is proposed in the dynamics of neural networks through the introduction of a time-dependent threshold, determined in function of both the noise and the pattern activity in the network. Especially for sparsely coded models this mechanism is shown to considerably improve the storage capacity, the basins of attraction, and the mutual information content.
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.
A Comparative Study of Sparse Associative Memories
NASA Astrophysics Data System (ADS)
Gripon, Vincent; Heusel, Judith; Löwe, Matthias; Vermet, Franck
2016-07-01
We study various models of associative memories with sparse information, i.e. a pattern to be stored is a random string of 0s and 1s with about log N 1s, only. We compare different synaptic weights, architectures and retrieval mechanisms to shed light on the influence of the various parameters on the storage capacity.
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. PMID:24872597
Multilevel sparse functional principal component analysis
Di, Chongzhi; Crainiceanu, Ciprian M.; Jank, Wolfgang S.
2014-01-01
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. PMID:24872597
Automatic anatomy recognition of sparse objects
NASA Astrophysics Data System (ADS)
Zhao, Liming; Udupa, Jayaram K.; Odhner, Dewey; Wang, Huiqian; Tong, Yubing; Torigian, Drew A.
2015-03-01
A general body-wide automatic anatomy recognition (AAR) methodology was proposed in our previous work based on hierarchical fuzzy models of multitudes of objects which was not tied to any specific organ system, body region, or image modality. That work revealed the challenges encountered in modeling, recognizing, and delineating sparse objects throughout the body (compared to their non-sparse counterparts) if the models are based on the object's exact geometric representations. The challenges stem mainly from the variation in sparse objects in their shape, topology, geographic layout, and relationship to other objects. That led to the idea of modeling sparse objects not from the precise geometric representations of their samples but by using a properly designed optimal super form. This paper presents the underlying improved methodology which includes 5 steps: (a) Collecting image data from a specific population group G and body region Β and delineating in these images the objects in Β to be modeled; (b) Building a super form, S-form, for each object O in Β; (c) Refining the S-form of O to construct an optimal (minimal) super form, S*-form, which constitutes the (fuzzy) model of O; (d) Recognizing objects in Β using the S*-form; (e) Defining confounding and background objects in each S*-form for each object and performing optimal delineation. Our evaluations based on 50 3D computed tomography (CT) image sets in the thorax on four sparse objects indicate that substantially improved performance (FPVF~2%, FNVF~10%, and success where the previous approach failed) can be achieved using the new approach.
Technology Transfer Automated Retrieval System (TEKTRAN)
This chapter is organized into several sections. The first has information on the history of vegetable fermentation research in the US, dating back to the late 1880s. A overview of commercial cucumber and sauerkraut fermentation practices follows, focusing on the US market, although there is some me...
Technology Transfer Automated Retrieval System (TEKTRAN)
The wide variety of fermented foods of the world can be classified by the materials obtained from the fermentation, such as alcohol (beer, wine), organic acid such as lactic acid and acetic acid (vegetables, dairy), carbon dioxide (bread), and amino acids or peptides from protein (fish fermentations...
The relationship between phytomass, NDVI and vegetation communities on Svalbard
NASA Astrophysics Data System (ADS)
Johansen, Bernt; Tømmervik, Hans
2014-04-01
Several studies have shown a close relationship between vegetation fertility and different vegetation indices extracted from satellite data. The vegetation fertility in Arctic is at overall scales highly related to temperature. At lower scales surface material, snow cover, hydrology and anthropogenic effects (geese, reindeer) are determinant in constituting the different vegetation communities. The extent and occurrence of different vegetation communities are expressed in vegetation maps. On Svalbard a vegetation map covering the entire archipelago has recently been developed. The map is differentiated into 18 map units showing large areas of non- and sparsely vegetated ground. The most favorable vegetation is seen as productive marshes and moss tundra communities in the lowland. Various mathematical combinations of spectral channels in satellite images have been applied as sensitive indicators of the presence and condition of green vegetation. Today the normalized difference vegetation index (NDVI) is mostly used to display this information. NDVI is an indicator of the density of chlorophyll in leaf tissue calculated from the red and near infrared bands: NDVI = (NIR - RED)/(NIR + RED). NDVI gives values between -1 and +1 where vegetated areas in general yield high positive values, while non-vegetated ground is found on the negative side.
NASA Astrophysics Data System (ADS)
Qiu, G. Y.; Feng, Y.
2011-12-01
Separately estimation of soil evaporation (E) and vegetation transpiration (T) is important for water management. In this study, a methodology to estimate the ratio of vegetation cover, soil evaporation and vegetation transpiration in a sparse steppe is developed based on a previous published model, the three-temperatures (3T) model. The input parameters of the model includes the surface temperatures of soil and vegetation (from thermal image), net radiation (estimated from surface temperature and solar radiation), and air temperature. The approach of unsupervised classification was used to separate the bare soil and vegetation pixels from the images. The areas with higher temperature could be regarded as the bare soil and E was estimated by the evaporation sub-model in the 3T model; while the areas with lower temperature could be regarded as pure vegetation and T was estimated by the transpiration sub-model in the 3T model. Afterward, the estimated E and T were converted into daily values and compared with the measured E and T by using Bowen Ratio and micro-lysimeter methods. Results show that the proposed approach is a useful way to separately estimated E and T in sparse steppe.
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.
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 decomposition learning based dynamic MRI reconstruction
NASA Astrophysics Data System (ADS)
Zhu, Peifei; Zhang, Qieshi; Kamata, Sei-ichiro
2015-02-01
Dynamic MRI is widely used for many clinical exams but slow data acquisition becomes a serious problem. The application of Compressed Sensing (CS) demonstrated great potential to increase imaging speed. However, the performance of CS is largely depending on the sparsity of image sequence in the transform domain, where there are still a lot to be improved. In this work, the sparsity is exploited by proposed Sparse Decomposition Learning (SDL) algorithm, which is a combination of low-rank plus sparsity and Blind Compressed Sensing (BCS). With this decomposition, only sparsity component is modeled as a sparse linear combination of temporal basis functions. This enables coefficients to be sparser and remain more details of dynamic components comparing learning the whole images. A reconstruction is performed on the undersampled data where joint multicoil data consistency is enforced by combing Parallel Imaging (PI). The experimental results show the proposed methods decrease about 15~20% of Mean Square Error (MSE) compared to other existing methods.
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.
Color demosaicking via robust adaptive sparse representation
NASA Astrophysics Data System (ADS)
Huang, Lili; Xiao, Liang; Chen, Qinghua; Wang, Kai
2015-09-01
A single sensor camera can capture scenes by means of a color filter array. Each pixel samples only one of the three primary colors. We use a color demosaicking (CDM) technique to produce full color images and propose a robust adaptive sparse representation model for high quality CDM. The data fidelity term is characterized by l1 norm to suppress the heavy-tailed visual artifacts with an adaptively learned dictionary, while the regularization term is encouraged to seek sparsity by forcing sparse coding close to its nonlocal means to reduce coding errors. Based on the classical quadratic penalty function technique in optimization and an operator splitting method in convex analysis, we further present an effective iterative algorithm to solve the variational problem. The efficiency of the proposed method is demonstrated by experimental results with simulated and real camera data.
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. PMID:18229804
Dictionary Learning Algorithms for Sparse Representation
Kreutz-Delgado, Kenneth; Murray, Joseph F.; Rao, Bhaskar D.; Engan, Kjersti; Lee, Te-Won; Sejnowski, Terrence J.
2010-01-01
Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial “25 words or less”), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an over-complete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error). PMID:12590811
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.
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.
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<
Robust Fringe Projection Profilometry via Sparse Representation.
Budianto; Lun, Daniel P K
2016-04-01
In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment. PMID:26890867
Modified sparse regularization for electrical impedance tomography.
Fan, Wenru; Wang, Huaxiang; Xue, Qian; Cui, Ziqiang; Sun, Benyuan; Wang, Qi
2016-03-01
Electrical impedance tomography (EIT) aims to estimate the electrical properties at the interior of an object from current-voltage measurements on its boundary. It has been widely investigated due to its advantages of low cost, non-radiation, non-invasiveness, and high speed. Image reconstruction of EIT is a nonlinear and ill-posed inverse problem. Therefore, regularization techniques like Tikhonov regularization are used to solve the inverse problem. A sparse regularization based on L1 norm exhibits superiority in preserving boundary information at sharp changes or discontinuous areas in the image. However, the limitation of sparse regularization lies in the time consumption for solving the problem. In order to further improve the calculation speed of sparse regularization, a modified method based on separable approximation algorithm is proposed by using adaptive step-size and preconditioning technique. Both simulation and experimental results show the effectiveness of the proposed method in improving the image quality and real-time performance in the presence of different noise intensities and conductivity contrasts. PMID:27036798
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.
Learning joint intensity-depth sparse representations.
Tosic, Ivana; Drewes, Sarah
2014-05-01
This paper presents a method for learning overcomplete dictionaries of atoms composed of two modalities that describe a 3D scene: 1) image intensity and 2) scene depth. We propose a novel joint basis pursuit (JBP) algorithm that finds related sparse features in two modalities using conic programming and we integrate it into a two-step dictionary learning algorithm. The JBP differs from related convex algorithms because it finds joint sparsity models with different atoms and different coefficient values for intensity and depth. This is crucial for recovering generative models where the same sparse underlying causes (3D features) give rise to different signals (intensity and depth). We give a bound for recovery error of sparse coefficients obtained by JBP, and show numerically that JBP is superior to the group lasso algorithm. When applied to the Middlebury depth-intensity database, our learning algorithm converges to a set of related features, such as pairs of depth and intensity edges or image textures and depth slants. Finally, we show that JBP outperforms state of the art methods on depth inpainting for time-of-flight and Microsoft Kinect 3D data. PMID:24723574
Mean-field sparse optimal control
Fornasier, Massimo; Piccoli, Benedetto; Rossi, Francesco
2014-01-01
We introduce the rigorous limit process connecting finite dimensional sparse optimal control problems with ODE constraints, modelling parsimonious interventions on the dynamics of a moving population divided into leaders and followers, to an infinite dimensional optimal control problem with a constraint given by a system of ODE for the leaders coupled with a PDE of Vlasov-type, governing the dynamics of the probability distribution of the followers. In the classical mean-field theory, one studies the behaviour of a large number of small individuals freely interacting with each other, by simplifying the effect of all the other individuals on any given individual by a single averaged effect. In this paper, we address instead the situation where the leaders are actually influenced also by an external policy maker, and we propagate its effect for the number N of followers going to infinity. The technical derivation of the sparse mean-field optimal control is realized by the simultaneous development of the mean-field limit of the equations governing the followers dynamics together with the Γ-limit of the finite dimensional sparse optimal control problems. PMID:25288818
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.
SAR Image despeckling via sparse representation
NASA Astrophysics Data System (ADS)
Wang, Zhongmei; Yang, Xiaomei; Zheng, Liang
2014-11-01
SAR image despeckling is an active research area in image processing due to its importance in improving the quality of image for object detection and classification.In this paper, a new approach is proposed for multiplicative noise in SAR image removal based on nonlocal sparse representation by dictionary learning and collaborative filtering. First, a image is divided into many patches, and then a cluster is formed by clustering log-similar image patches using Fuzzy C-means (FCM). For each cluster, an over-complete dictionary is computed using the K-SVD method that iteratively updates the dictionary and the sparse coefficients. The patches belonging to the same cluster are then reconstructed by a sparse combination of the corresponding dictionary atoms. The reconstructed patches are finally collaboratively aggregated to build the denoised image. The experimental results show that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both objective evaluation index (PSNR and ENL) and subjective visual perception.
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.
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.
Galaxy redshift surveys with sparse sampling
Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro; Jee, Inh; Jeong, Donghui; Blanc, Guillermo A.; Ciardullo, Robin; Gronwall, Caryl; Hagen, Alex; Schneider, Donald P.; Drory, Niv; Fabricius, Maximilian; Landriau, Martin; Finkelstein, Steven; Jogee, Shardha; Cooper, Erin Mentuch; Tuttle, Sarah; Gebhardt, Karl; Hill, Gary J.
2013-12-01
Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., V{sub survey} ∼ 10Gpc{sup 3}) to be covered, and thus tends to be expensive. A ''sparse sampling'' method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, V{sub survey}, we observe only a fraction of the volume. The distribution of observed regions should be chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of V{sub survey} (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by V{sub survey} (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. On the other hand, we show that the two-point correlation function (pair counting) is not affected by sparse sampling. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys.
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.
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.
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
Sparse Multivariate Regression With Covariance Estimation
Rothman, Adam J.; Levina, Elizaveta; Zhu, Ji
2014-01-01
We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. This method, which we call multivariate regression with covariance estimation (MRCE), involves penalized likelihood with simultaneous estimation of the regression coefficients and the covariance structure. An efficient optimization algorithm and a fast approximation are developed for computing MRCE. Using simulation studies, we show that the proposed method outperforms relevant competitors when the responses are highly correlated. We also apply the new method to a finance example on predicting asset returns. An R-package containing this dataset and code for computing MRCE and its approximation are available online. PMID:24963268
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.
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
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
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.
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
2014-01-01
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. PMID:24638883
Inferring sparse networks for noisy transient processes.
Tran, Hoang M; Bukkapatnam, Satish T S
2016-01-01
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 l1-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 l1-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. PMID:26916813
Iterative Sparse Approximation of the Gravitational Potential
NASA Astrophysics Data System (ADS)
Telschow, R.
2012-04-01
In recent applications in the approximation of gravitational potential fields, several new challenges arise. We are concerned with a huge quantity of data (e.g. in case of the Earth) or strongly irregularly distributed data points (e.g. in case of the Juno mission to Jupiter), where both of these problems bring the established approximation methods to their limits. Our novel method, which is a matching pursuit, however, iteratively chooses a best basis out of a large redundant family of trial functions to reconstruct the signal. It is independent of the data points which makes it possible to take into account a much higher amount of data and, furthermore, handle irregularly distributed data, since the algorithm is able to combine arbitrary spherical basis functions, i.e., global as well as local trial functions. This additionaly results in a solution, which is sparse in the sense that it features more basis functions where the signal has a higher local detail density. Summarizing, we get a method which reconstructs large quantities of data with a preferably low number of basis functions, combining global as well as several localizing functions to a sparse basis and a solution which is locally adapted to the data density and also to the detail density of the signal.
A density functional for sparse matter
NASA Astrophysics Data System (ADS)
Langreth, D. C.; Lundqvist, B. I.; Chakarova-Käck, S. D.; Cooper, V. R.; Dion, M.; Hyldgaard, P.; Kelkkanen, A.; Kleis, J.; Kong, Lingzhu; Li, Shen; Moses, P. G.; Murray, E.; Puzder, A.; Rydberg, H.; Schröder, E.; Thonhauser, T.
2009-02-01
Sparse matter is abundant and has both strong local bonds and weak nonbonding forces, in particular nonlocal van der Waals (vdW) forces between atoms separated by empty space. It encompasses a broad spectrum of systems, like soft matter, adsorption systems and biostructures. Density-functional theory (DFT), long since proven successful for dense matter, seems now to have come to a point, where useful extensions to sparse matter are available. In particular, a functional form, vdW-DF (Dion et al 2004 Phys. Rev. Lett. 92 246401; Thonhauser et al 2007 Phys. Rev. B 76 125112), has been proposed for the nonlocal correlations between electrons and applied to various relevant molecules and materials, including to those layered systems like graphite, boron nitride and molybdenum sulfide, to dimers of benzene, polycyclic aromatic hydrocarbons (PAHs), doped benzene, cytosine and DNA base pairs, to nonbonding forces in molecules, to adsorbed molecules, like benzene, naphthalene, phenol and adenine on graphite, alumina and metals, to polymer and carbon nanotube (CNT) crystals, and hydrogen storage in graphite and metal-organic frameworks (MOFs), and to the structure of DNA and of DNA with intercalators. Comparison with results from wavefunction calculations for the smaller systems and with experimental data for the extended ones show the vdW-DF path to be promising. This could have great ramifications.
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.
Inferring sparse networks for noisy transient processes
Tran, Hoang M.; Bukkapatnam, Satish T.S.
2016-01-01
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. PMID:26916813
Topological sparse learning of dynamic form patterns.
Guthier, T; Willert, V; Eggert, J
2015-01-01
Motion is a crucial source of information for a variety of tasks in social interactions. The process of how humans recognize complex articulated movements such as gestures or face expressions remains largely unclear. There is an ongoing discussion if and how explicit low-level motion information, such as optical flow, is involved in the recognition process. Motivated by this discussion, we introduce a computational model that classifies the spatial configuration of gradient and optical flow patterns. The patterns are learned with an unsupervised learning algorithm based on translation-invariant nonnegative sparse coding called VNMF that extracts prototypical optical flow patterns shaped, for example, as moving heads or limb parts. A key element of the proposed system is a lateral inhibition term that suppresses activations of competing patterns in the learning process, leading to a low number of dominant and topological sparse activations. We analyze the classification performance of the gradient and optical flow patterns on three real-world human action recognition and one face expression recognition data set. The results indicate that the recognition of human actions can be achieved by gradient patterns alone, but adding optical flow patterns increases the classification performance. The combined patterns outperform other biological-inspired models and are competitive with current computer vision approaches. PMID:25248088
Towards robust topology of sparsely sampled data.
Correa, Carlos D; Lindstrom, Peter
2011-12-01
Sparse, irregular sampling is becoming a necessity for reconstructing large and high-dimensional signals. However, the analysis of this type of data remains a challenge. One issue is the robust selection of neighborhoods--a crucial part of analytic tools such as topological decomposition, clustering and gradient estimation. When extracting the topology of sparsely sampled data, common neighborhood strategies such as k-nearest neighbors may lead to inaccurate results, either due to missing neighborhood connections, which introduce false extrema, or due to spurious connections, which conceal true extrema. Other neighborhoods, such as the Delaunay triangulation, are costly to compute and store even in relatively low dimensions. In this paper, we address these issues. We present two new types of neighborhood graphs: a variation on and a generalization of empty region graphs, which considerably improve the robustness of neighborhood-based analysis tools, such as topological decomposition. Our findings suggest that these neighborhood graphs lead to more accurate topological representations of low- and high- dimensional data sets at relatively low cost, both in terms of storage and computation time. We describe the implications of our work in the analysis and visualization of scalar functions, and provide general strategies for computing and applying our neighborhood graphs towards robust data analysis. PMID:22034302
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
Genetic apertures: an improved sparse aperture design framework.
Salvaggio, Philip S; Schott, John R; McKeown, Donald M
2016-04-20
The majority of optical sparse aperture imaging research in the remote sensing field has been confined to a small set of aperture layouts. While these layouts possess some desirable properties for imaging, they may not be ideal for all applications. This work introduces an optimization framework for sparse aperture layouts based on genetic algorithms as well as a small set of fitness functions for incoherent sparse aperture image quality. The optimization results demonstrate the merits of existing designs and the opportunity for creating new sparse aperture layouts. PMID:27140086
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
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
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.
Evolutionary induction of sparse neural trees
Zhang; Ohm; Muhlenbein
1997-01-01
This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest. PMID:10021759
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).
Multiplication method for sparse interferometric fringes.
Liu, Cong; Zhang, Xingyi; Zhou, Youhe
2016-04-01
Fringe analysis in the interferometry has been of long-standing interest to the academic community. However, the process of sparse fringe is always a headache in the measurement, especially when the specimen is very small. Through theoretical derivation and experimental measurements, our work demonstrates a new method for fringe multiplication. Theoretically, arbitrary integral-multiple fringe multiplication can be acquired by using the interferogram phase as the parameter. We simulate digital images accordingly and find that not only the skeleton lines of the multiplied fringe are very convenient to extract, but also the main frequency of which can be easily separated from the DC component. Meanwhile, the experimental results have a good agreement with the theoretic ones in a validation using the classical photoelasticity. PMID:27137055
Predicting structure in nonsymmetric sparse matrix factorizations
Gilbert, J.R.; Ng, E.
1991-12-31
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.
Predicting structure in nonsymmetric sparse matrix factorizations
Gilbert, J.R. ); Ng, E. )
1991-01-01
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.
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. PMID:23747569
Functional fixedness in a technologically sparse culture.
German, Tim P; Barrett, H Clark
2005-01-01
Problem solving can be inefficient when the solution requires subjects to generate an atypical function for an object and the object's typical function has been primed. Subjects become "fixed" on the design function of the object, and problem solving suffers relative to control conditions in which the object's function is not demonstrated. In the current study, such functional fixedness was demonstrated in a sample of adolescents (mean age of 16 years) among the Shuar of Ecuadorian Amazonia, whose technologically sparse culture provides limited access to large numbers of artifacts with highly specialized functions. This result suggests that design function may universally be the core property of artifact concepts in human semantic memory. PMID:15660843
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.
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
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
2015-01-01
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most 𝒪(n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity. PMID:26356296
Encoding Cortical Dynamics in Sparse Features
Khan, Sheraz; Lefèvre, Julien; Baillet, Sylvain; Michmizos, Konstantinos P.; Ganesan, Santosh; Kitzbichler, Manfred G.; Zetino, Manuel; Hämäläinen, Matti S.; Papadelis, Christos; Kenet, Tal
2014-01-01
Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz–Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical
Bayesian learning of sparse multiscale image representations.
Hughes, James Michael; Rockmore, Daniel N; Wang, Yang
2013-12-01
Multiscale representations of images have become a standard tool in image analysis. Such representations offer a number of advantages over fixed-scale methods, including the potential for improved performance in denoising, compression, and the ability to represent distinct but complementary information that exists at various scales. A variety of multiresolution transforms exist, including both orthogonal decompositions such as wavelets as well as nonorthogonal, overcomplete representations. Recently, techniques for finding adaptive, sparse representations have yielded state-of-the-art results when applied to traditional image processing problems. Attempts at developing multiscale versions of these so-called dictionary learning models have yielded modest but encouraging results. However, none of these techniques has sought to combine a rigorous statistical formulation of the multiscale dictionary learning problem and the ability to share atoms across scales. We present a model for multiscale dictionary learning that overcomes some of the drawbacks of previous approaches by first decomposing an input into a pyramid of distinct frequency bands using a recursive filtering scheme, after which we perform dictionary learning and sparse coding on the individual levels of the resulting pyramid. The associated image model allows us to use a single set of adapted dictionary atoms that is shared--and learned--across all scales in the model. The underlying statistical model of our proposed method is fully Bayesian and allows for efficient inference of parameters, including the level of additive noise for denoising applications. We apply the proposed model to several common image processing problems including non-Gaussian and nonstationary denoising of real-world color images. PMID:24002002
Encoding cortical dynamics in sparse features.
Khan, Sheraz; Lefèvre, Julien; Baillet, Sylvain; Michmizos, Konstantinos P; Ganesan, Santosh; Kitzbichler, Manfred G; Zetino, Manuel; Hämäläinen, Matti S; Papadelis, Christos; Kenet, Tal
2014-01-01
Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz-Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical
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.
Zhang, Di; He, Jiazhong; Zhao, Yun; Du, Minghui
2015-03-01
In magnetic resonance (MR) imaging, image spatial resolution is determined by various instrumental limitations and physical considerations. This paper presents a new algorithm for producing a high-resolution version of a low-resolution MR image. The proposed method consists of two consecutive steps: (1) reconstructs a high-resolution MR image from a given low-resolution observation via solving a joint sparse representation and nonlocal similarity L1-norm minimization problem; and (2) applies a sparse derivative prior based post-processing to suppress blurring effects. Extensive experiments on simulated brain MR images and two real clinical MR image datasets validate that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both quantitative measures and visual perception. PMID:25638262
Group-based sparse representation for image restoration.
Zhang, Jian; Zhao, Debin; Gao, Wen
2014-08-01
Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse coding, which ignores the relationship among patches, resulting in inaccurate sparse coding coefficients. In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR). The proposed GSR is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. In addition, an effective self-adaptive dictionary learning method for each group with low complexity is designed, rather than dictionary learning from natural images. To make GSR tractable and robust, a split Bregman-based technique is developed to solve the proposed GSR-driven ℓ0 minimization problem for image restoration efficiently. Extensive experiments on image inpainting, image deblurring and image compressive sensing recovery manifest that the proposed GSR modeling outperforms many current state-of-the-art schemes in both peak signal-to-noise ratio and visual perception. PMID:24835225
Cervigram image segmentation based on reconstructive sparse representations
NASA Astrophysics Data System (ADS)
Zhang, Shaoting; Huang, Junzhou; Wang, Wei; Huang, Xiaolei; Metaxas, Dimitris
2010-03-01
We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination and specular reflection, the color and texture features in optical images often overlap with each other and are not linearly separable. By leveraging sparse representations the data can be transformed to higher dimensions with sparse constraints and become more separated. K-SVD algorithm is employed to find sparse representations and corresponding dictionaries. The data can be reconstructed from its sparse representations and positive and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In the experiments we applied our method to automatically segment the biomarker AcetoWhite (AW) regions in an archive of 60,000 images of the uterine cervix. Compared with other general methods, our approach showed lower space and time complexity and higher sensitivity.
Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences.
Harandi, Mehrtash T; Hartley, Richard; Lovell, Brian; Sanderson, Conrad
2016-06-01
This paper introduces sparse coding and dictionary learning for symmetric positive definite (SPD) matrices, which are often used in machine learning, computer vision, and related areas. Unlike traditional sparse coding schemes that work in vector spaces, in this paper, we discuss how SPD matrices can be described by sparse combination of dictionary atoms, where the atoms are also SPD matrices. We propose to seek sparse coding by embedding the space of SPD matrices into the Hilbert spaces through two types of the Bregman matrix divergences. This not only leads to an efficient way of performing sparse coding but also an online and iterative scheme for dictionary learning. We apply the proposed methods to several computer vision tasks where images are represented by region covariance matrices. Our proposed algorithms outperform state-of-the-art methods on a wide range of classification tasks, including face recognition, action recognition, material classification, and texture categorization. PMID:25643414
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…
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.
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.
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.
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.
Sparse coding for layered neural networks
NASA Astrophysics Data System (ADS)
Katayama, Katsuki; Sakata, Yasuo; Horiguchi, Tsuyoshi
2002-07-01
We investigate storage capacity of two types of fully connected layered neural networks with sparse coding when binary patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered network, in which a transfer function of even layers is different from that of odd layers. The other is a layered network with intra-layer connections, in which the transfer function of inter-layer is different from that of intra-layer, and inter-layered neurons and intra-layered neurons are updated alternately. We derive recursion relations for order parameters by means of the signal-to-noise ratio method, and then apply the self-control threshold method proposed by Dominguez and Bollé to both layered networks with monotonic transfer functions. We find that a critical value αC of storage capacity is about 0.11|a ln a| -1 ( a≪1) for both layered networks, where a is a neuronal activity. It turns out that the basin of attraction is larger for both layered networks when the self-control threshold method is applied.
Anisotropic interpolation of sparse generalized image samples.
Bourquard, Aurélien; Unser, Michael
2013-02-01
Practical image-acquisition systems are often modeled as a continuous-domain prefilter followed by an ideal sampler, where generalized samples are obtained after convolution with the impulse response of the device. In this paper, our goal is to interpolate images from a given subset of such samples. We express our solution in the continuous domain, considering consistent resampling as a data-fidelity constraint. To make the problem well posed and ensure edge-preserving solutions, we develop an efficient anisotropic regularization approach that is based on an improved version of the edge-enhancing anisotropic diffusion equation. Following variational principles, our reconstruction algorithm minimizes successive quadratic cost functionals. To ensure fast convergence, we solve the corresponding sequence of linear problems by using multigrid iterations that are specifically tailored to their sparse structure. We conduct illustrative experiments and discuss the potential of our approach both in terms of algorithmic design and reconstruction quality. In particular, we present results that use as little as 2% of the image samples. PMID:22968212
Approximation and compression with sparse orthonormal transforms.
Sezer, Osman Gokhan; Guleryuz, Onur G; Altunbasak, Yucel
2015-08-01
We propose a new transform design method that targets the generation of compression-optimized transforms for next-generation multimedia applications. The fundamental idea behind transform compression is to exploit regularity within signals such that redundancy is minimized subject to a fidelity cost. Multimedia signals, in particular images and video, are well known to contain a diverse set of localized structures, leading to many different types of regularity and to nonstationary signal statistics. The proposed method designs sparse orthonormal transforms (SOTs) that automatically exploit regularity over different signal structures and provides an adaptation method that determines the best representation over localized regions. Unlike earlier work that is motivated by linear approximation constructs and model-based designs that are limited to specific types of signal regularity, our work uses general nonlinear approximation ideas and a data-driven setup to significantly broaden its reach. We show that our SOT designs provide a safe and principled extension of the Karhunen-Loeve transform (KLT) by reducing to the KLT on Gaussian processes and by automatically exploiting non-Gaussian statistics to significantly improve over the KLT on more general processes. We provide an algebraic optimization framework that generates optimized designs for any desired transform structure (multiresolution, block, lapped, and so on) with significantly better n -term approximation performance. For each structure, we propose a new prototype codec and test over a database of images. Simulation results show consistent increase in compression and approximation performance compared with conventional methods. PMID:25823033
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. PMID:27119052
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.
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.
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. PMID:25291733
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.
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
Unified inference for sparse and dense longitudinal models.
Kim, Seonjin; Zhao, Zhibiao
2013-03-01
In longitudinal data analysis, statistical inference for sparse data and dense data could be substantially different. For kernel smoothing estimate of the mean function, the convergence rates and limiting variance functions are different under the two scenarios. The latter phenomenon poses challenges for statistical inference as a subjective choice between the sparse and dense cases may lead to wrong conclusions. We develop self-normalization based methods that can adapt to the sparse and dense cases in a unified framework. Simulations show that the proposed methods outperform some existing methods. PMID:24966413
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.
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.
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.
Sparse source configurations for asteroid tomography
NASA Astrophysics Data System (ADS)
Pursiainen, S.; Kaasalainen, M.
2014-04-01
The objective of our recent research has been to develop non-invasive imaging techniques for future planetary research and mining activities involving a challenging in situ environment and tight payload limits [1]. This presentation will deal in particular with an approach in which the internal relative permittivity ∈r or the refractive index n = √ ∈r of an asteroid is to be recovered based on radio signal transmitted by a sparse set [2] of fixed or movable landers. To address important aspects of mission planning, we have analyzed different signal source configurations to find the minimal number of source positions needed for robust localization of anomalies, such as internal voids. Characteristic to this inverse problem are the large relative changes in signal speed caused by the high permittivity of typical asteroid minerals (e.g. basalt), leading to strong refractions and reflections of the signal. Finding an appropriate problemspecific signaling arrangement is an important premission goal for successful in situ measurements. This presentation will include inversion results obtained with laboratory-recorded travel time data y of the form in which n δ denotes a perturbation of a refractive index n = n δ + nbg; gi estimates the total noise due to different error sources; (ybg)i = ∫Ci nbg ds is an entry of noiseless background data ybg; and Ci is a signal path. Also simulated time-evolution data will be covered with respect to potential u satisfying the wave equation ∈rδ2/δt2+ ōδu/δt-∆u = f, where ō is a (latent) conductivity distribution and f is a source term. Special interest will be paid to inversion robustness regarding changes of the prior model and source positioning. Among other things, our analysis suggests that strongly refractive anomalies can be detected with three or four sources independently of their positioning.
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}.
Detecting novel genes with sparse arrays
Haiminen, Niina; Smit, Bart; Rautio, Jari; Vitikainen, Marika; Wiebe, Marilyn; Martinez, Diego; Chee, Christine; Kunkel, Joe; Sanchez, Charles; Nelson, Mary Anne; Pakula, Tiina; Saloheimo, Markku; Penttilä, Merja; Kivioja, Teemu
2014-01-01
Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g., to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast to tiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts that could code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties. PMID:20691772
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 inpainting based on sparse representations with a perceptual metric
NASA Astrophysics Data System (ADS)
Ogawa, Takahiro; Haseyama, Miki
2013-12-01
This paper presents an image inpainting method based on sparse representations optimized with respect to a perceptual metric. In the proposed method, the structural similarity (SSIM) index is utilized as a criterion to optimize the representation performance of image data. Specifically, the proposed method enables the formulation of two important procedures in the sparse representation problem, 'estimation of sparse representation coefficients' and 'update of the dictionary', based on the SSIM index. Then, using the generated dictionary, approximation of target patches including missing areas via the SSIM-based sparse representation becomes feasible. Consequently, image inpainting for which procedures are totally derived from the SSIM index is realized. Experimental results show that the proposed method enables successful inpainting of missing areas.
Multiple kernel learning for sparse representation-based classification.
Shrivastava, Ashish; Patel, Vishal M; Chellappa, Rama
2014-07-01
In this paper, we propose a multiple kernel learning (MKL) algorithm that is based on the sparse representation-based classification (SRC) method. Taking advantage of the nonlinear kernel SRC in efficiently representing the nonlinearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. The effectiveness of the proposed method is demonstrated using several publicly available image classification databases and it is shown that this method can perform significantly better than many competitive image classification algorithms. PMID:24835226
Marginality and needs of dietary valine for broilers fed certain all-vegetable diets
Technology Transfer Automated Retrieval System (TEKTRAN)
Valine is likely the fourth limiting amino acid in most diets based of corn and soybean meal. However, its exact needs are not well known, and information regarding it is sparse. A series of studies were conducted to validate valine’s limitation in all-vegetable diets fed to broilers, and subseque...
Retrieval of Understory NDVI in Sparse Boreal Forests By MODIS Brdf Data
NASA Astrophysics Data System (ADS)
Yang, W.; Kobayashi, H.; Suzuki, R.; Nasahara, K. N.
2014-12-01
Global products of leaf area index (LAI) usually show large uncertainties in sparsely vegetated areas. The reason is that the understory contribution is not negligible in reflectance modeling for the case of low to intermediate canopy cover. Therefore many efforts have been carried out on inclusion of understory properties in the LAI estimation algorithms. Compared with conventional data bank method, estimation of forest understory property from satellite data is superior in the studies at global or continental scale during a long periods. However, the existing remote sensing method based on multi-angular observations is very complicated to implement. Alternatively, a simple method to retrieve understory NDVI (NDVIu) for sparse boreal forests was proposed in this study. The method is based on the property that the bi-directional variation of NDVIu is much smaller than that of the canopy-level NDVI. To retrieve NDVIu for a certain pixel, linear extrapolation was applied using the pixels within a 5×5 target-pixel-centered window. The NDVI values were reconstructed from the MODIS BRDF data corresponding to eight different solar-view angles. NDVIu was estimated as the average of the NDVI values corresponding to the position where the stand NDVI has the smallest angular variation. Validation by noise-free simulation dataset yielded high agreement between estimated and true NDVIu with R2 and RMSE of 0.99 and 0.03, respectively. By the MODIS BRDF data, we got the estimate of NDVIu close to the in situ measured value (0.61 vs. 0.66 for estimate and measurement, respectively), and also reasonable seasonal patterns of NDVIu in 2010-2013. The results imply a potential application of the retrieved NDVIu to improve the estimation of overstory LAI for sparse boreal forests.
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.
Out-of-Core Solutions of Complex Sparse Linear Equations
NASA Technical Reports Server (NTRS)
Yip, E. L.
1982-01-01
ETCLIB is library of subroutines for obtaining out-of-core solutions of complex sparse linear equations. Routines apply to dense and sparse matrices too large to be stored in core. Useful for solving any set of linear equations, but particularly useful in cases where coefficient matrix has no special properties that guarantee convergence with any of interative processes. The only assumption made is that coefficient matrix is not singular.
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
Vector sparse representation of color image using quaternion matrix analysis.
Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong
2015-04-01
Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain. PMID:25643407
A sparse embedding and least variance encoding approach to hashing.
Zhu, Xiaofeng; Zhang, Lei; Huang, Zi
2014-09-01
Hashing is becoming increasingly important in large-scale image retrieval for fast approximate similarity search and efficient data storage. Many popular hashing methods aim to preserve the kNN graph of high dimensional data points in the low dimensional manifold space, which is, however, difficult to achieve when the number of samples is big. In this paper, we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary. To this end, we partition the sample space into clusters via a linear spectral clustering method, and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters. This actually embeds each sample sparsely in the sample space. The sparse embedding vector is employed as the feature of each sample for hashing. We then propose a least variance encoding model, which learns a dictionary to encode the sparse embedding feature, and consequently binarize the coding coefficients as the hash codes. The dictionary and the binarization threshold are jointly optimized in our model. Experimental results on benchmark data sets demonstrated the effectiveness of the proposed approach in comparison with state-of-the-art methods. PMID:24968174
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. PMID:26735744
Water Control on Vegetation Growth Pattern in Eurasia from GRACE
NASA Astrophysics Data System (ADS)
A, G.; Velicogna, I.; Kimball, J. S.; Kim, Y.
2014-12-01
High latitude ecosystem productivity is constrained by cold temperature and moisture limitations to plant growth, while these environmental restrictions may be changing with global warming. Satellite data driven assessments indicate that over the past three decades, rapid warming in the northern high latitudes has resulted in earlier and longer potential growing seasons and widespread greening, due to general relaxation of cold temperature constraints to vegetation productivity. However, warming may have also increased water stress limitations to growth. In this study, we use GRACE (Gravity Recovery and Climate Experiment) derived total water storage (TWS), 2-m air temperature (T) from ERA-interim reanalysis, normalized difference vegetation index (NDVI) data from MODIS (Collection 5) and satellite data driven vegetation gross primary productivity (GPP) estimates as surrogates for vegetation growth, for the period August 2002-December 2013 to evaluate terrestrial water supply controls to vegetation growth changes over the three major river basins of northern Eurasia. We find that during the analyzed period, the apparent growth response follows regional vegetation, moisture and temperature gradients and is spatially complex. In the drier southwest characterized by grassland, vegetation growth is mainly controlled by TWS availability. In the central region, dominated by cold temperature and water limited boreal forest, T is the main control on vegetation growth. In the Lena basin, where vegetation includes both boreal forest and water limited grassland, both T and TWS impact vegetation growth. We suggest that GRACE TWS estimates provide reliable observational constraints on water availability to vegetation that supplement sparse soil moisture observations and satellite precipitation estimates with unknown bias.
... the non-starchy vegetables . Starchy vegetables like potatoes, corn and peas are included in the " Grains and ... or Chinese spinach Artichoke Artichoke hearts Asparagus Baby corn Bamboo shoots Beans (green, wax, Italian) Bean sprouts ...
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, ...
Ecogeomorphology of Sand Dunes Shaped by Vegetation
NASA Astrophysics Data System (ADS)
Tsoar, H.
2014-12-01
Two dune types associated with vegetation are known: Parabolic and Vegetated Linear Dunes (VLDs), the latters are the dominant dune type in the world deserts. Parabolic dunes are formed in humid, sub-humid and semi-arid environments (rather than arid) where vegetation is nearby. VLDs are known today in semiarid and arid lands where the average yearly rainfall is ≥100 mm, enough to support sparse cover of vegetation. These two dune types are formed by unidirectional winds although they demonstrate a different form and have a distinct dynamics. Conceptual and mathematical models of dunes mobility and stability, based on three control parameters: wind power (DP), average annual precipitation (p), and the human impact parameter (μ) show that where human impact is negligible the effect of wind power (DP) on vegetative cover is substantial. The average yearly rainfall of 60-80 mm is the threshold of annual average rainfall for vegetation growth on dune sand. The model is shown to follow a hysteresis path, which explains the bistability of active and stabilized dunes under the same climatic conditions with respect to wind power. We have discerned formation of parabolic dunes from barchans and transverse dunes in the coastal plain of Israel where a decrease in human activity during the second half of the 20th century caused establishment of vegetation on the crest of the dunes, a process that changed the dynamics of these barchans and transverse dunes and led to a change in the shape of the windward slope from convex to concave. These dunes gradually became parabolic. It seems that VLDs in Australia or the Kalahari have always been vegetated to some degree, though the shrubs were sparser in colder periods when the aeolian erosion was sizeable. Those ancient conditions are characterized by higher wind power and lower rainfall that can reduce, but not completely destroy, the vegetation cover, leading to the formation of lee (shadow) dunes behind each shrub. Formation of
NASA Technical Reports Server (NTRS)
Schrumpf, B. J.
1973-01-01
Unique characteristics of ERTS imagery can be used to inventory natural vegetation. While satellite images can seldom be interpreted and identified directly in terms of vegetation types, such types can be inferred by interpretation of physical terrain features and through an understanding of the ecology of the vegetation.
Technology Transfer Automated Retrieval System (TEKTRAN)
The vegetative shoot apical meristem is a highly organized yet dynamic structure. It is responsible for maintaining the proliferation of a population of undifferentiated stem cells through-out vegetative development, as well as for initiating lateral organs in stereotypical patterns. The vegetative...
NASA Astrophysics Data System (ADS)
Gong, T. T.; Lei, H. M.; Yang, D. W.; Jiao, Y.; Yang, H. B.
2014-12-01
Vegetation change is a primary factor that affect evapotranspiration (ET), which is an important process in the hydrological cycle. In this study, an attempt is made to analyze the effects of vegetation change on ET using continuous observation data from eddy-covariance (EC) measurements over three periods (1 July 2011 to 30 June 2014) of a study site in a sparse shrubland study site located in the Loess Plateau of China, which is a fragile ecosystem experiencing serious soil desiccation. In our study, vegetation change includes phenological change and land use change. Phenological process of vegetation is validated to have a remarkable positive effect on ET in a rate of 1.83 ± 0.01 along with vegetation greening. Land use change at our study site was due to the native vegetation being cut-off by human activities, converting sparse shrubland to bare soil. With land use condition changing during the three years, annual total ET was observed to increase 103 mm, suggesting that soil evaporation consumes more water than canopy transpiration. In summary, the effects of vegetation change on ET suggest that both vegetation greening and increased area of exposed soil would aggravate the soil desiccation at our site in the north Loess Plateau.
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.
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.
Sparse modeling of spatial environmental variables associated with asthma
Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.
2014-01-01
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 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 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. PMID:25533437
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. PMID:25533437
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)
Sparsely sampling the sky: Regular vs. random sampling
NASA Astrophysics Data System (ADS)
Paykari, P.; Pires, S.; Starck, J.-L.; Jaffe, A. H.
2015-09-01
Aims: The next generation of galaxy surveys, aiming to observe millions of galaxies, are expensive both in time and money. This raises questions regarding the optimal investment of this time and money for future surveys. In a previous work, we have shown that a sparse sampling strategy could be a powerful substitute for the - usually favoured - contiguous observation of the sky. In our previous paper, regular sparse sampling was investigated, where the sparse observed patches were regularly distributed on the sky. The regularity of the mask introduces a periodic pattern in the window function, which induces periodic correlations at specific scales. Methods: In this paper, we use a Bayesian experimental design to investigate a "random" sparse sampling approach, where the observed patches are randomly distributed over the total sparsely sampled area. Results: We find that in this setting, the induced correlation is evenly distributed amongst all scales as there is no preferred scale in the window function. Conclusions: This is desirable when we are interested in any specific scale in the galaxy power spectrum, such as the matter-radiation equality scale. As the figure of merit shows, however, there is no preference between regular and random sampling to constrain the overall galaxy power spectrum and the cosmological parameters.
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.
Sparse approximation problem: how rapid simulated annealing succeeds and fails
NASA Astrophysics Data System (ADS)
Obuchi, Tomoyuki; Kabashima, Yoshiyuki
2016-03-01
Information processing techniques based on sparseness have been actively studied in several disciplines. Among them, a mathematical framework to approximately express a given dataset by a combination of a small number of basis vectors of an overcomplete basis is termed the sparse approximation. In this paper, we apply simulated annealing, a metaheuristic algorithm for general optimization problems, to sparse approximation in the situation where the given data have a planted sparse representation and noise is present. The result in the noiseless case shows that our simulated annealing works well in a reasonable parameter region: the planted solution is found fairly rapidly. This is true even in the case where a common relaxation of the sparse approximation problem, the G-relaxation, is ineffective. On the other hand, when the dimensionality of the data is close to the number of non-zero components, another metastable state emerges, and our algorithm fails to find the planted solution. This phenomenon is associated with a first-order phase transition. In the case of very strong noise, it is no longer meaningful to search for the planted solution. In this situation, our algorithm determines a solution with close-to-minimum distortion fairly quickly.
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.
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. PMID:26974648
[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. PMID:27209749
Human interaction recognition through two-phase sparse coding
NASA Astrophysics Data System (ADS)
Zhang, B.; Conci, N.; De Natale, Francesco G. B.
2014-03-01
In this paper, we propose a novel method to recognize two-person interactions through a two-phase sparse coding approach. In the first phase, we adopt the non-negative sparse coding on the spatio-temporal interest points (STIPs) extracted from videos, and then construct the feature vector for each video by sum-pooling and l2-normalization. At the second stage, we apply the label-consistent KSVD (LC-KSVD) algorithm on the video feature vectors to train a new dictionary. The algorithm has been validated on the TV human interaction dataset, and the experimental results show that the classification performance is considerably improved compared with the standard bag-of-words approach and the single layer non-negative sparse coding.
Analyzing Sparse Dictionaries for Online Learning With Kernels
NASA Astrophysics Data System (ADS)
Honeine, Paul
2015-12-01
Many signal processing and machine learning methods share essentially the same linear-in-the-parameter model, with as many parameters as available samples as in kernel-based machines. Sparse approximation is essential in many disciplines, with new challenges emerging in online learning with kernels. To this end, several sparsity measures have been proposed in the literature to quantify sparse dictionaries and constructing relevant ones, the most prolific ones being the distance, the approximation, the coherence and the Babel measures. In this paper, we analyze sparse dictionaries based on these measures. By conducting an eigenvalue analysis, we show that these sparsity measures share many properties, including the linear independence condition and inducing a well-posed optimization problem. Furthermore, we prove that there exists a quasi-isometry between the parameter (i.e., dual) space and the dictionary's induced feature space.
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.
Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model
NASA Astrophysics Data System (ADS)
Meng, Jia; Zhang, Jianqiu(Michelle); Qi, Yuan(Alan); Chen, Yidong; Huang, Yufei
2010-12-01
The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ([InlineEquation not available: see fulltext.]) status and Estrogen Receptor negative ([InlineEquation not available: see fulltext.]) status, respectively.
PSPIKE: A Parallel Hybrid Sparse Linear System Solver
NASA Astrophysics Data System (ADS)
Manguoglu, Murat; Sameh, Ahmed H.; Schenk, Olaf
The availability of large-scale computing platforms comprised of tens of thousands of multicore processors motivates the need for the next generation of highly scalable sparse linear system solvers. These solvers must optimize parallel performance, processor (serial) performance, as well as memory requirements, while being robust across broad classes of applications and systems. In this paper, we present a new parallel solver that combines the desirable characteristics of direct methods (robustness) and effective iterative solvers (low computational cost), while alleviating their drawbacks (memory requirements, lack of robustness). Our proposed hybrid solver is based on the general sparse solver PARDISO, and the “Spike” family of hybrid solvers. The resulting algorithm, called PSPIKE, is as robust as direct solvers, more reliable than classical preconditioned Krylov subspace methods, and much more scalable than direct sparse solvers. We support our performance and parallel scalability claims using detailed experimental studies and comparison with direct solvers, as well as classical preconditioned Krylov methods.
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.
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. PMID:26340790
Sparse representation based face recognition using weighted regions
NASA Astrophysics Data System (ADS)
Bilgazyev, Emil; Yeniaras, E.; Uyanik, I.; Unan, Mahmut; Leiss, E. L.
2013-12-01
Face recognition is a challenging research topic, especially when the training (gallery) and recognition (probe) images are acquired using different cameras under varying conditions. Even a small noise or occlusion in the images can compromise the accuracy of recognition. Lately, sparse encoding based classification algorithms gave promising results for such uncontrollable scenarios. In this paper, we introduce a novel methodology by modeling the sparse encoding with weighted patches to increase the robustness of face recognition even further. In the training phase, we define a mask (i.e., weight matrix) using a sparse representation selecting the facial regions, and in the recognition phase, we perform comparison on selected facial regions. The algorithm was evaluated both quantitatively and qualitatively using two comprehensive surveillance facial image databases, i.e., SCfaceandMFPV, with the results clearly superior to common state-of-the-art methodologies in different scenarios.
SAR target classification based on multiscale sparse representation
NASA Astrophysics Data System (ADS)
Ruan, Huaiyu; Zhang, Rong; Li, Jingge; Zhan, Yibing
2016-03-01
We propose a novel multiscale sparse representation approach for SAR target classification. It firstly extracts the dense SIFT descriptors on multiple scales, then trains a global multiscale dictionary by sparse coding algorithm. After obtaining the sparse representation, the method applies spatial pyramid matching (SPM) and max pooling to summarize the features for each image. The proposed method can provide more information and descriptive ability than single-scale ones. Moreover, it costs less extra computation than existing multiscale methods which compute a dictionary for each scale. The MSTAR database and ship database collected from TerraSAR-X images are used in classification setup. Results show that the best overall classification rate of the proposed approach can achieve 98.83% on the MSTAR database and 92.67% on the TerraSAR-X ship database.
A note on rank reduction in sparse multivariate regression
Chen, Kun; Chan, Kung-Sik
2016-01-01
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. PMID:26997938
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.
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 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 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.
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. PMID:24231870
The sparseness of neuronal responses in ferret primary visual cortex.
Tolhurst, David J; Smyth, Darragh; Thompson, Ian D
2009-02-25
Various arguments suggest that neuronal coding of natural sensory stimuli should be sparse (i.e., individual neurons should respond rarely but should respond reliably). We examined sparseness of visual cortical neurons in anesthetized ferret to flashed natural scenes. Response behavior differed widely between neurons. The median firing rate of 4.1 impulses per second was slightly higher than predicted from consideration of metabolic load. Thirteen percent of neurons (12 of 89) responded to <5% of the images, but one-half responded to >25% of images. Multivariate analysis of the range of sparseness values showed that 67% of the variance was accounted for by differing response patterns to moving gratings. Repeat presentation of images showed that response variance for natural images exaggerated sparseness measures; variance was scaled with mean response, but with a lower Fano factor than for the responses to moving gratings. This response variability and the "soft" sparse responses (Rehn and Sommer, 2007) raise the question of what constitutes a reliable neuronal response and imply parallel signaling by multiple neurons. We investigated whether the temporal structure of responses might be reliable enough to give additional information about natural scenes. Poststimulus time histogram shape was similar for "strong" and "weak" stimuli, with no systematic change in first-spike latency with stimulus strength. The variance of first-spike latency for repeat presentations of the same image was greater than the latency variance between images. In general, responses to flashed natural scenes do not seem compatible with a sparse encoding in which neurons fire rarely but reliably. PMID:19244512
Content addressable systolic array for sparse matrix computation
Wing, O.
1983-01-01
A systolic array is proposed which is specifically designed to solve a system of sparse linear equations. The array consists of a number of processing elements connected in a ring. Each processing element has its own content addressable memory where the nonzero elements of the sparse matrix are stored. Matrix elements to which elementary operations are applied are extracted from the memory by content addressing. The system of equations is solved in a systolic fashion and the solution is obtained in nz+5n-2 steps where nz is the number of nonzero elements along and below the diagonal and n is the number of equations. 13 references.
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.
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
D Super-Resolution Approach for Sparse Laser Scanner Data
NASA Astrophysics Data System (ADS)
Hosseinyalamdary, S.; Yilmaz, A.
2015-08-01
Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.
Three-dimensional sparse-aperture moving-target imaging
NASA Astrophysics Data System (ADS)
Ferrara, Matthew; Jackson, Julie; Stuff, Mark
2008-04-01
If a target's motion can be determined, the problem of reconstructing a 3D target image becomes a sparse-aperture imaging problem. That is, the data lies on a random trajectory in k-space, which constitutes a sparse data collection that yields very low-resolution images if backprojection or other standard imaging techniques are used. This paper investigates two moving-target imaging algorithms: the first is a greedy algorithm based on the CLEAN technique, and the second is a version of Basis Pursuit Denoising. The two imaging algorithms are compared for a realistic moving-target motion history applied to a Xpatch-generated backhoe data set.
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.
Learning control for robotic manipulators with sparse data
NASA Technical Reports Server (NTRS)
Morita, Atsushi; Dubowsky, Steven; Hootsmans, Norbert A. M.
1987-01-01
Learning control algorithms have been proposed for error compensation in repetitive robotic manipulator tasks. It is shown that the performance of such control algorithms can be seriously degraded when the feedback data they use is relatively sparse in time, such as might be provided by vision systems. It is also shown that learning control algorithms can be modified to compensate for the effects of sparse data and thereby yield performance which approaches that of systems without limitations on the sensory information available for control.
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.
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.
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...
Voxel selection in FMRI data analysis based on sparse representation.
Li, Yuanqing; Namburi, Praneeth; Yu, Zhuliang; Guan, Cuntai; Feng, Jianfeng; Gu, Zhenghui
2009-10-01
Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method. PMID:19567340
Global and Local Sparse Subspace Optimization for Motion Segmentation
NASA Astrophysics Data System (ADS)
Yang, M. Ying; Feng, S.; Ackermann, H.; Rosenhahn, B.
2015-08-01
In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time.
Multiple kernel sparse representations for supervised and unsupervised learning.
Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas
2014-07-01
In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods. PMID:24833593
Automatic stellar spectral classification via sparse representations and dictionary learning
NASA Astrophysics Data System (ADS)
Díaz-Hernández, R.; Peregrina-Barreto, H.; Altamirano-Robles, L.; González-Bernal, J. A.; Ortiz-Esquivel, A. E.
2014-11-01
Stellar classification is an important topic in astronomical tasks such as the study of stellar populations. However, stellar classification of a region of the sky is a time-consuming process due to the large amount of objects present in an image. Therefore, automatic techniques to speed up the process are required. In this work, we study the application of a sparse representation and a dictionary learning for automatic spectral stellar classification. Our dataset consist of 529 calibrated stellar spectra of classes B to K, belonging to the Pulkovo Spectrophotometric catalog, in the 3400-5500Å range. These stellar spectra are used for both training and testing of the proposed methodology. The sparse technique is applied by using the greedy algorithm OMP (Orthogonal Matching Pursuit) for finding an approximated solution, and the K-SVD (K-Singular Value Decomposition) for the dictionary learning step. Thus, sparse classification is based on the recognition of the common characteristics of a particular stellar type through the construction of a trained basis. In this work, we propose a classification criterion that evaluates the results of the sparse representation techniques and determines the final classification of the spectra. This methodology demonstrates its ability to achieve levels of classification comparable with automatic methodologies previously reported such as the Maximum Correlation Coefficient (MCC) and Artificial Neural Networks (ANN).
SAR imaging via iterative adaptive approach and sparse Bayesian learning
NASA Astrophysics Data System (ADS)
Xue, Ming; Santiago, Enrique; Sedehi, Matteo; Tan, Xing; Li, Jian
2009-05-01
We consider sidelobe reduction and resolution enhancement in synthetic aperture radar (SAR) imaging via an iterative adaptive approach (IAA) and a sparse Bayesian learning (SBL) method. The nonparametric weighted least squares based IAA algorithm is a robust and user parameter-free adaptive approach originally proposed for array processing. We show that it can be used to form enhanced SAR images as well. SBL has been used as a sparse signal recovery algorithm for compressed sensing. It has been shown in the literature that SBL is easy to use and can recover sparse signals more accurately than the l 1 based optimization approaches, which require delicate choice of the user parameter. We consider using a modified expectation maximization (EM) based SBL algorithm, referred to as SBL-1, which is based on a three-stage hierarchical Bayesian model. SBL-1 is not only more accurate than benchmark SBL algorithms, but also converges faster. SBL-1 is used to further enhance the resolution of the SAR images formed by IAA. Both IAA and SBL-1 are shown to be effective, requiring only a limited number of iterations, and have no need for polar-to-Cartesian interpolation of the SAR collected data. This paper characterizes the achievable performance of these two approaches by processing the complex backscatter data from both a sparse case study and a backhoe vehicle in free space with different aperture sizes.
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
Tensor methods for large sparse systems of nonlinear equations
Bouaricha, A.; Schnabel, R.B.
1996-12-31
This paper introduces censor methods for solving, large sparse systems of nonlinear equations. Tensor methods for nonlinear equations were developed in the context of solving small to medium- sized dense problems. They base each iteration on a quadratic model of the nonlinear equations. where the second-order term is selected so that the model requires no more derivative or function information per iteration than standard linear model-based methods, and hardly more storage or arithmetic operations per iteration. Computational experiments on small to medium-sized problems have shown censor methods to be considerably more efficient than standard Newton-based methods, with a particularly large advantage on singular problems. This paper considers the extension of this approach to solve large sparse problems. The key issue that must be considered is how to make efficient use of sparsity in forming and solving the censor model problem at each iteration. Accomplishing this turns out to require an entirely new way of solving the tensor model that successfully exploits the sparsity of the Jacobian, whether the Jacobian is nonsingular or singular. We develop such an approach and, based upon it, an efficient tensor method for solving large sparse systems of nonlinear equations. Test results indicate that this tensor method is significantly more efficient and robust than an efficient sparse Newton-based method. in terms of iterations, function evaluations. and execution time.
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. PMID:25647667
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.
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
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
Robust visual tracking of infrared object via sparse representation model
NASA Astrophysics Data System (ADS)
Ma, Junkai; Liu, Haibo; Chang, Zheng; Hui, Bin
2014-11-01
In this paper, we propose a robust tracking method for infrared object. We introduce the appearance model and the sparse representation in the framework of particle filter to achieve this goal. Representing every candidate image patch as a linear combination of bases in the subspace which is spanned by the target templates is the mechanism behind this method. The natural property, that if the candidate image patch is the target so the coefficient vector must be sparse, can ensure our algorithm successfully. Firstly, the target must be indicated manually in the first frame of the video, then construct the dictionary using the appearance model of the target templates. Secondly, the candidate image patches are selected in following frames and the sparse coefficient vectors of them are calculated via l1-norm minimization algorithm. According to the sparse coefficient vectors the right candidates is determined as the target. Finally, the target templates update dynamically to cope with appearance change in the tracking process. This paper also addresses the problem of scale changing and the rotation of the target occurring in tracking. Theoretic analysis and experimental results show that the proposed algorithm is effective and robust.
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.
Varying Coefficient Models for Sparse Noise-contaminated Longitudinal Data
2014-01-01
Summary In this paper we propose a varying coefficient model for highly sparse longitudinal data that allows for error-prone time-dependent variables and time-invariant covariates. We develop a new estimation procedure, based on covariance representation techniques, that enables effective borrowing of information across all subjects in sparse and irregular longitudinal data observed with measurement error, a challenge in which there is no adequate solution currently. More specifically, sparsity is addressed via a functional analysis approach that considers the observed longitudinal data as noise contaminated realizations of a random process that produces smooth trajectories. This approach allows for estimation based on pooled data, borrowing strength from all subjects, in targeting the mean functions and auto- and cross-covariances to overcome sparse noisy designs. The resulting estimators are shown to be uniformly consistent. Consistent prediction for the response trajectories are also obtained via conditional expectation under Gaussian assumptions. Asymptotic distribution of the predicted response trajectories are derived, allowing for construction of asymptotic pointwise confidence bands. Efficacy of the proposed method is investigated in simulation studies and compared to the commonly used local polynomial smoothing method. The proposed method is illustrated with a sparse longitudinal data set, examining the age-varying relationship between calcium absorption and dietary calcium. Prediction of individual calcium absorption curves as a function of age are also examined. PMID:25589822
Inference algorithms and learning theory for Bayesian sparse factor analysis
NASA Astrophysics Data System (ADS)
Rattray, Magnus; Stegle, Oliver; Sharp, Kevin; Winn, John
2009-12-01
Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.
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
Scaled norm-based Euclidean projection for sparse speaker adaptation
NASA Astrophysics Data System (ADS)
Kim, Younggwan; Kim, Myung Jong; Kim, Hoirin
2015-12-01
To reduce data storage for speaker adaptive (SA) models, in our previous work, we proposed a sparse speaker adaptation method which can efficiently reduce the number of adapted parameters by using Euclidean projection onto the L 1-ball (EPL1) while maintaining recognition performance comparable to maximum a posteriori (MAP) adaptation. In the EPL1-based sparse speaker adaptation framework, however, the adapted Gaussian mean vectors are mostly concentrated on dimensions having large variances because of assuming unit variance for all dimensions. To make EPL1 more flexible, in this paper, we propose scaled norm-based Euclidean projection (SNEP) which can consider dimension-specific variances. By using SNEP, we also propose a new sparse speaker adaptation method which can consider the variances of a speaker-independent model. Our experiments show that the adapted components of mean vectors are evenly distributed in all dimensions, and we can obtain sparsely adapted models with no loss of phone recognition performance from the proposed method compared with MAP adaptation.
Sparse methods in spectroscopy: an introduction, overview, and perspective.
Andries, Erik; Martin, Shawn
2013-06-01
Multivariate calibration methods such as partial least-squares build calibration models that are not parsimonious: all variables (either wavelengths or samples) are used to define a calibration model. In high-dimensional or large sample size settings, interpretable analysis aims to reduce model complexity by finding a small subset of variables that significantly influences the model. The term "sparsity", as used here, refers to calibration models having many zero-valued regression coefficients. Only the variables associated with non-zero coefficients influence the model. In this paper, we briefly review the regression problems associated with sparse models and discuss their spectroscopic applications. We also discuss how one can re-appropriate sparse modeling algorithms that perform wavelength selection for purposes of sample selection. In particular, we highlight specific sparse modeling algorithms that are easy to use and understand for the spectroscopist, as opposed to the overly complex "black-box" algorithms that dominate much of the statistical learning literature. We apply these sparse modeling approaches to three spectroscopic data sets. PMID:23735242
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.
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
Hypothesis Generation, Sparse Categories, and the Positive Test Strategy
ERIC Educational Resources Information Center
Navarro, Daniel J.; Perfors, Amy F.
2011-01-01
We consider the situation in which a learner must induce the rule that explains an observed set of data but the hypothesis space of possible rules is not explicitly enumerated or identified. The first part of the article demonstrates that as long as hypotheses are sparse (i.e., index less than half of the possible entities in the domain) then a…
A multithread based new sparse matrix method in bioluminescence tomography
NASA Astrophysics Data System (ADS)
Zhang, Bo; Tian, Jie; Liu, Dan; Sun, Li; Yang, Xin; Han, Dong
2010-03-01
Among many molecular imaging modalities, bioluminescence tomography (BLT) stands out as an effective approach for in vivo imaging because of its noninvasive molecular and cellular level detection ability, high sensitivity and low cost in comparison with other imaging technologies. However, there exists the case that large scale problem with large number of points and elements in the structure of mesh standing for the small animal or phantom. And the large scale problem's system matrix generated by the diffuse approximation (DA) model using finite element method (FEM) is large. So there wouldn't be enough random access memory (RAM) for the program and the related inverse problem couldn't be solved. Considering the sparse property of the BLT system matrix, we've developed a new sparse matrix (ZSM) to overcome the problem. And the related algorithms have all been speeded up by multi-thread technologies. Then the inverse problem is solved by Tikhonov regularization method in adaptive finite element (AFE) framework. Finally, the performance of this method is tested on a heterogeneous phantom and the boundary data is obtained through Monte Carlo simulation. During the process of solving the forward model, the ZSM can save more processing time and memory space than the usual way, such as those not using sparse matrix and those using Triples or Cross Linked sparse matrix. Numerical experiments have shown when more CPU cores are used, the processing speed is increased. By incorporating ZSM, BLT can be applied to large scale problems with large system matrix.
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.
Latent subspace sparse representation-based unsupervised domain adaptation
NASA Astrophysics Data System (ADS)
Shuai, Liu; Sun, Hao; Zhao, Fumin; Zhou, Shilin
2015-12-01
In this paper, we introduce and study a novel unsupervised domain adaptation (DA) algorithm, called latent subspace sparse representation based domain adaptation, based on the fact that source and target data that lie in different but related low-dimension subspaces. The key idea is that each point in a union of subspaces can be constructed by a combination of other points in the dataset. In this method, we propose to project the source and target data onto a common latent generalized subspace which is a union of subspaces of source and target domains and learn the sparse representation in the latent generalized subspace. By employing the minimum reconstruction error and maximum mean discrepancy (MMD) constraints, the structure of source and target domain are preserved and the discrepancy is reduced between the source and target domains and thus reflected in the sparse representation. We then utilize the sparse representation to build a weighted graph which reflect the relationship of points from the different domains (source-source, source- target, and target-target) to predict the labels of the target domain. We also proposed an efficient optimization method for the algorithm. Our method does not need to combine with any classifiers and therefore does not need train the test procedures. Various experiments show that the proposed method perform better than the competitive state of art subspace-based domain adaptation.
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…
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.
Technology Transfer Automated Retrieval System (TEKTRAN)
Consumption of fruit and vegetable products has dramatically increased by more than 30% over the last few decades in the U.S. It is also estimated that about 20% of all fruit and vegetables produced is lost each year due to spoilage. The focus of this chapter is to provide a general background on mi...
POTENTIAL NATURAL VEGETATION COVERAGE
"Potential natural vegetation is defined as the vegetation that would exist today if humans were removed from the scene and if the plant succession after their removal were telescoped into a single moment. The time compression eliminates the effects of future climatic fluc...
Towards robust and effective shape modeling: sparse shape composition.
Zhang, Shaoting; Zhan, Yiqiang; Dewan, Maneesh; Huang, Junzhou; Metaxas, Dimitris N; Zhou, Xiang Sean
2012-01-01
Organ shape plays an important role in various clinical practices, e.g., diagnosis, surgical planning and treatment evaluation. It is usually derived from low level appearance cues in medical images. However, due to diseases and imaging artifacts, low level appearance cues might be weak or misleading. In this situation, shape priors become critical to infer and refine the shape derived by image appearances. Effective modeling of shape priors is challenging because: (1) shape variation is complex and cannot always be modeled by a parametric probability distribution; (2) a shape instance derived from image appearance cues (input shape) may have gross errors; and (3) local details of the input shape are difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to deal with these three challenges in a unified framework. In our method, a sparse set of shapes in the shape repository is selected and composed together to infer/refine an input shape. The a priori information is thus implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: (1) the input shape can be approximately represented by a sparse linear combination of shapes in the shape repository; (2) parts of the input shape may contain gross errors but such errors are sparse. Our model is formulated as a sparse learning problem. Using L1 norm relaxation, it can be solved by an efficient expectation-maximization (EM) type of framework. Our method is extensively validated on two medical applications, 2D lung localization in X-ray images and 3D liver segmentation in low-dose CT scans. Compared to state-of-the-art methods, our model exhibits better performance in both studies. PMID:21963296
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. PMID:22959839
lp-lq penalty for sparse linear and sparse multiple kernel multitask learning.
Rakotomamonjy, Alain; Flamary, Rémi; Gasso, Gilles; Canu, Stéphane
2011-08-01
Recently, there has been much interest around multitask learning (MTL) problem with the constraints that tasks should share a common sparsity profile. Such a problem can be addressed through a regularization framework where the regularizer induces a joint-sparsity pattern between task decision functions. We follow this principled framework and focus on l(p)-l(q) (with 0 ≤ p ≤ 1 and 1 ≤ q ≤ 2) mixed norms as sparsity-inducing penalties. Our motivation for addressing such a larger class of penalty is to adapt the penalty to a problem at hand leading thus to better performances and better sparsity pattern. For solving the problem in the general multiple kernel case, we first derive a variational formulation of the l(1)-l(q) penalty which helps us in proposing an alternate optimization algorithm. Although very simple, the latter algorithm provably converges to the global minimum of the l(1)-l(q) penalized problem. For the linear case, we extend existing works considering accelerated proximal gradient to this penalty. Our contribution in this context is to provide an efficient scheme for computing the l(1)-l(q) proximal operator. Then, for the more general case, when , we solve the resulting nonconvex problem through a majorization-minimization approach. The resulting algorithm is an iterative scheme which, at each iteration, solves a weighted l(1)-l(q) sparse MTL problem. Empirical evidences from toy dataset and real-word datasets dealing with brain-computer interface single-trial electroencephalogram classification and protein subcellular localization show the benefit of the proposed approaches and algorithms. PMID:21813358
Hillslope soils and vegetation
NASA Astrophysics Data System (ADS)
Amundson, Ronald; Heimsath, Arjun; Owen, Justine; Yoo, Kyungsoo; Dietrich, William E.
2015-04-01
Assessing how vegetation controls hillslope soil processes is a challenging problem, as few abiotic landscapes exist as observational controls. Here we identify five avenues to examine how actively eroding hillslope soils and processes would differ without vegetation, and we explore some potential feedbacks that may result in landscape resilience on vegetated hillslopes. The various approaches suggest that a plant-free world would be characterized by largely soil-free hillslopes, that plants may control the maximum thickness of soils on slopes, that vegetated landforms erode at rates about one order of magnitude faster than plant-free outcrops in comparable settings, and that vegetated hillslope soils generally maintain long residence times such that both N and P sufficiency for ecosystems is the norm. We conclude that quantitatively parameterizing biota within process-based hillslope models needs to be a priority in order to project how human activity may further impact the soil mantle.
NASA Astrophysics Data System (ADS)
Kim, J.
2015-12-01
Fractional vegetation cover (FVC) is a useful index of monitoring land cover dynamics and land surface energy partitioning into sensible and latent heats from satellite because it can be estimated by using satellite-based spectral vegetation indices (VI), such as NDVI and EVI. The relationship between FVC and vegetation indices is however variable depending on regional vegetation types and background soil types across different regions. In particular, arid and semi-arid region shows substantial uncertainty in the VI-FVC relations because of sparse vegetation cover and hence, important roles of local soil type in land-surface spectral reflectance. In this study, VI-FVC relations were investigated for arid and semi-arid regions of Mongolia. The FVC data was prepared from digital-camera image interpretation sheets taken at 160 sites in our field excursions from 2012 to 2014. In comparisons with visual inspections, the camera-based FVC showed good linear relations (r = 0.97, p < 0.001) with 6.83% of RMSE. Three satellite-based VIs were prepared, i.e. MODIS NDVI, EVI, and SAVI, which applied to produce linear regression models of FVC. The model parameters (i.e. slope and intercept) was obtained through iterated calculation process. Among the 160 sites, 120 sites were arbitrarily extracted to produce regression model and the remaining 40 sites were used for model validation. This iterated process were repeated 1,000,000 times and then, statistics were derived as averages of model (slope and interception) and validation (Pearson correlation coefficient and RMSE). In results, the regression models generally showed good agreements in model validations over r = 0.8 (p < 0.001). This study discussed problems in long-term FVC retrieval for the arid and semi-arid regions of Mongolia.
Masuda, Y; Aguilar, I; Tsuruta, S; Misztal, I
2015-10-01
The objective of this study was to remove bottlenecks generally found in a computer program for average-information REML. The refinements included improvements to setting-up mixed-model equations on a hash table with a faster hash function as sparse matrix storage, changing sparse structures in calculation of traces, and replacing a sparse matrix package using traditional methods (FSPAK) with a new package using supernodal methods (YAMS); the latter package quickly processed sparse matrices containing large, dense blocks. Comparisons included 23 models with data sets from broiler, swine, beef, and dairy cattle. Models included single-trait, multiple-trait, maternal, and random regression models with phenotypic data; selected models used genomic information in a single-step approach. Setting-up mixed model equations was completed without abnormal termination in all analyses. Calculations in traces were accelerated with a hash format, especially for models with a genomic relationship matrix, and the maximum speed was 67 times faster. Computations with YAMS were, on average, more than 10 times faster than with FSPAK and had greater advantages for large data and more complicated models including multiple traits, random regressions, and genomic effects. These refinements can be applied to general average-information REML programs. PMID:26523559
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
Neogene biomarker record of vegetation change in eastern Africa.
Uno, Kevin T; Polissar, Pratigya J; Jackson, Kevin E; deMenocal, Peter B
2016-06-01
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
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.
Vegetation against dune mobility.
Durán, Orencio; Herrmann, Hans J
2006-11-01
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. PMID:17155579
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.
Disentangling giant component and finite cluster contributions in sparse random matrix spectra
NASA Astrophysics Data System (ADS)
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.
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. PMID:27176257
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,…
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.
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 logistic regression with Lp penalty for biomarker identification.
Liu, Zhenqiu; Jiang, Feng; Tian, Guoliang; Wang, Suna; Sato, Fumiaki; Meltzer, Stephen J; Tan, Ming
2007-01-01
In this paper, we propose a novel method for sparse logistic regression with non-convex regularization Lp (p <1). Based on smooth approximation, we develop several fast algorithms for learning the classifier that is applicable to high dimensional dataset such as gene expression. To the best of our knowledge, these are the first algorithms to perform sparse logistic regression with an Lp and elastic net (Le) penalty. The regularization parameters are decided through maximizing the area under the ROC curve (AUC) of the test data. Experimental results on methylation and microarray data attest the accuracy, sparsity, and efficiency of the proposed algorithms. Biomarkers identified with our methods are compared with that in the literature. Our computational results show that Lp Logistic regression (p <1) outperforms the L1 logistic regression and SCAD SVM. Software is available upon request from the first author. PMID:17402921
Sparse cortical source localization using spatio-temporal atoms.
Korats, Gundars; Ranta, Radu; Le Cam, Steven; Louis-Dorr, Valérie
2015-08-01
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. PMID:26737185
Scene-based nonuniformity correction using sparse prior
NASA Astrophysics Data System (ADS)
Mou, Xingang; Zhang, Guilin; Hu, Ruolan; Zhou, Xiao
2011-11-01
The performance of infrared focal plane array (IRFPA) is known to be affected by the presence of spatial fixed pattern noise (FPN) that is superimposed on the true image. Scene-based nonuniformity correction (NUC) algorithms are widely concerned since they only need the readout infrared data captured by the imaging system during its normal operation. A novel adaptive NUC algorithm is proposed using the sparse prior that when derivative filters are applied to infrared images, the filter outputs tends to be sparse. A change detection module based on results of derivative filters is introduced to avoid stationary object being learned into the background, so the ghosting artifact is eliminated effectively. The performance of the new algorithm is evaluated with both real and simulated imagery.
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.
Sparse Canonical Correlation Analysis: New Formulation and Algorithm.
Chu, Delin; Liao, Li-Zhi; Ng, Michael K; Zhang, Xiaowei
2013-05-24
In this paper, we study canonical correlation analysis (CCA), which has become a powerful tool in multivariate data analysis for finding the correlations between two sets of multidimensional variables. The main contributions of the paper are: (i) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem; (ii) to obtain the explicit characterization of all solutions for the multiple CCA problem even the covariance matrices are singular; (iii) to develop a new sparse CCA algorithm; and (iv) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real world data sets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms. PMID:23712996
Sparse canonical correlation analysis: new formulation and algorithm.
Chu, Delin; Liao, Li-Zhi; Ng, Michael K; Zhang, Xiaowei
2013-12-01
In this paper, we study canonical correlation analysis (CCA), which is a powerful tool in multivariate data analysis for finding the correlation between two sets of multidimensional variables. The main contributions of the paper are: 1) to reveal the equivalent relationship between a recursive formula and a trace formula for the multiple CCA problem, 2) to obtain the explicit characterization for all solutions of the multiple CCA problem even when the corresponding covariance matrices are singular, 3) to develop a new sparse CCA algorithm, and 4) to establish the equivalent relationship between the uncorrelated linear discriminant analysis and the CCA problem. We test several simulated and real-world datasets in gene classification and cross-language document retrieval to demonstrate the effectiveness of the proposed algorithm. The performance of the proposed method is competitive with the state-of-the-art sparse CCA algorithms. PMID:24136440
A parallel sparse algorithm targeting arterial fluid mechanics computations
NASA Astrophysics Data System (ADS)
Manguoglu, Murat; Takizawa, Kenji; Sameh, Ahmed H.; Tezduyar, Tayfun E.
2011-09-01
Iterative solution of large sparse nonsymmetric linear equation systems is one of the numerical challenges in arterial fluid-structure interaction computations. This is because the fluid mechanics parts of the fluid + structure block of the equation system that needs to be solved at every nonlinear iteration of each time step corresponds to incompressible flow, the computational domains include slender parts, and accurate wall shear stress calculations require boundary layer mesh refinement near the arterial walls. We propose a hybrid parallel sparse algorithm, domain-decomposing parallel solver (DDPS), to address this challenge. As the test case, we use a fluid mechanics equation system generated by starting with an arterial shape and flow field coming from an FSI computation and performing two time steps of fluid mechanics computation with a prescribed arterial shape change, also coming from the FSI computation. We show how the DDPS algorithm performs in solving the equation system and demonstrate the scalability of the algorithm.
Accurate Sparse-Projection Image Reconstruction via Nonlocal TV Regularization
Zhang, Yi; Zhang, Weihua; Zhou, Jiliu
2014-01-01
Sparse-projection image reconstruction is a useful approach to lower the radiation dose; however, the incompleteness of projection data will cause degeneration of imaging quality. As a typical compressive sensing method, total variation has obtained great attention on this problem. Suffering from the theoretical imperfection, total variation will produce blocky effect on smooth regions and blur edges. To overcome this problem, in this paper, we introduce the nonlocal total variation into sparse-projection image reconstruction and formulate the minimization problem with new nonlocal total variation norm. The qualitative and quantitative analyses of numerical as well as clinical results demonstrate the validity of the proposed method. Comparing to other existing methods, our method more efficiently suppresses artifacts caused by low-rank reconstruction and reserves structure information better. PMID:24592168
Dictionary Learning for Sparse Coding: Algorithms and Convergence Analysis.
Bao, Chenglong; Ji, Hui; Quan, Yuhui; Shen, Zuowei
2016-07-01
In recent years, sparse coding has been widely used in many applications ranging from image processing to pattern recognition. Most existing sparse coding based applications require solving a class of challenging non-smooth and non-convex optimization problems. Despite the fact that many numerical methods have been developed for solving these problems, it remains an open problem to find a numerical method which is not only empirically fast, but also has mathematically guaranteed strong convergence. In this paper, we propose an alternating iteration scheme for solving such problems. A rigorous convergence analysis shows that the proposed method satisfies the global convergence property: the whole sequence of iterates is convergent and converges to a critical point. Besides the theoretical soundness, the practical benefit of the proposed method is validated in applications including image restoration and recognition. Experiments show that the proposed method achieves similar results with less computation when compared to widely used methods such as K-SVD. PMID:26452248
Spitzer IRAC Sparsely Sampled Phase Curve of WASP-14b
NASA Astrophysics Data System (ADS)
Krick, Jessica; Ingalls, James G.; Carey, Sean; von Braun, Kaspar; Ciardi, David; Kane, Stephen
2015-08-01
We present a new technique of sparsely sampling phase curves of hot jupiters with Spitzer IRAC. Snapshot phase curves are enabled by technical advances of precision pointing as well as careful characterization of a portion of the central pixel on the array. This method allows for observations which are a factor of ~2 more efficient than full phase curve observations, and are easy to schedule. We present the first results from this program using the exoplanet WASP-14b. As our pilot study, this planet has data taken both as a sparsely sampled phase curve as well as a staring mode phase curve as proof of technique. We successfully recover physical parameters for the transit and eclipse depths as well as phase curve shape of this slightly eccentric hot jupiter.
Recent History Functional Linear Models for Sparse Longitudinal Data
Kim, Kion; Şentürk, Damla; Li, Runze
2011-01-01
We consider the recent history functional linear models, relating a longitudinal response to a longitudinal predictor where the predictor process only in a sliding window into the recent past has an effect on the response value at the current time. We propose an estimation procedure for recent history functional linear models that is geared towards sparse longitudinal data, where the observation times across subjects are irregular and total number of measurements per subject is small. The proposed estimation procedure builds upon recent developments in literature for estimation of functional linear models with sparse data and utilizes connections between the recent history functional linear models and varying coefficient models. We establish uniform consistency of the proposed estimators, propose prediction of the response trajectories and derive their asymptotic distribution leading to asymptotic point-wise confidence bands. We include a real data application and simulation studies to demonstrate the efficacy of the proposed methodology. PMID:21691421
An improved sparse LS-SVR for estimating illumination
NASA Astrophysics Data System (ADS)
Zhu, Zhenmin; Lv, Zhaokang; Liu, Baifen
2015-07-01
Support Vector Regression performs well on estimating illumination chromaticity in a scene. Then the concept of Least Squares Support Vector Regression has been put forward as an effective, statistical and learning prediction model. Although it is successful to solve some problems of estimation, it also has obvious defects. Due to a large amount of support vectors which are chosen in the process of training LS-SVR , the calculation become very complex and it lost the sparsity of SVR. In this paper, we get inspiration from WLS-SVM(Weighted Least Squares Support Vector Machines) and a new method for sparse model. A Density Weighted Pruning algorithm is used to improve the sparsity of LS-SVR and named SLS-SVR(Sparse Least Squares Support Vector Regression).The simulation indicates that only need to select 30 percent of support vectors, the prediction can reach to 75 percent of the original one.
Constructing Bayesian formulations of sparse kernel learning methods.
Cawley, Gavin C; Talbot, Nicola L C
2005-01-01
We present here a simple technique that simplifies the construction of Bayesian treatments of a variety of sparse kernel learning algorithms. An incomplete Cholesky factorisation is employed to modify the dual parameter space, such that the Gaussian prior over the dual model parameters is whitened. The regularisation term then corresponds to the usual weight-decay regulariser, allowing the Bayesian analysis to proceed via the evidence framework of MacKay. There is in addition a useful by-product associated with the incomplete Cholesky factorisation algorithm, it also identifies a subset of the training data forming an approximate basis for the entire dataset in the kernel-induced feature space, resulting in a sparse model. Bayesian treatments of the kernel ridge regression (KRR) algorithm, with both constant and heteroscedastic (input dependent) variance structures, and kernel logistic regression (KLR) are provided as illustrative examples of the proposed method, which we hope will be more widely applicable. PMID:16085387
EPR Oximetry in Three Spatial Dimensions using Sparse Spin Distribution
Som, Subhojit; Potter, Lee C.; Ahmad, Rizwan; Vikram, Deepti S.; Kuppusamy, Periannan
2008-01-01
A method is presented to use continuous wave electron paramagnetic resonance imaging for rapid measurement of oxygen partial pressure in three spatial dimensions. A particulate paramagnetic probe is employed to create a sparse distribution of spins in a volume of interest. Information encoding location and spectral linewidth is collected by varying the spatial orientation and strength of an applied magnetic gradient field. Data processing exploits the spatial sparseness of spins to detect voxels with nonzero spin and to estimate the spectral linewidth for those voxels. The parsimonious representation of spin locations and linewidths permits an order of magnitude reduction in data acquisition time, compared to four-dimensional tomographic reconstruction using traditional spectral-spatial imaging. The proposed oximetry method is experimentally demonstrated for a lithium octa-n-butoxy naphthalocyanine (LiNc-BuO) probe using an L-band EPR spectrometer. PMID:18538600
Supervised nonparametric sparse discriminant analysis for hyperspectral imagery classification
NASA Astrophysics Data System (ADS)
Wu, Longfei; Sun, Hao; Ji, Kefeng
2016-03-01
Owing to the high spectral sampling, the spectral information in hyperspectral imagery (HSI) is often highly correlated and contains redundancy. Motivated by the recent success of sparsity preserving based dimensionality reduction (DR) techniques in both computer vision and remote sensing image analysis community, a novel supervised nonparametric sparse discriminant analysis (NSDA) algorithm is presented for HSI classification. The objective function of NSDA aims at preserving the within-class sparse reconstructive relationship for within-class compactness characterization and maximizing the nonparametric between-class scatter simultaneously to enhance discriminative ability of the features in the projected space. Essentially, it seeks for the optimal projection matrix to identify the underlying discriminative manifold structure of a multiclass dataset. Experimental results on one visualization dataset and three recorded HSI dataset demonstrate NSDA outperforms several state-of-the-art feature extraction methods for HSI classification.
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.
Fast and Adaptive Sparse Precision Matrix Estimation in High Dimensions
Liu, Weidong; Luo, Xi
2014-01-01
This paper proposes a new method for estimating sparse precision matrices in the high dimensional setting. It has been popular to study fast computation and adaptive procedures for this problem. We propose a novel approach, called Sparse Column-wise Inverse Operator, to address these two issues. We analyze an adaptive procedure based on cross validation, and establish its convergence rate under the Frobenius norm. The convergence rates under other matrix norms are also established. This method also enjoys the advantage of fast computation for large-scale problems, via a coordinate descent algorithm. Numerical merits are illustrated using both simulated and real datasets. In particular, it performs favorably on an HIV brain tissue dataset and an ADHD resting-state fMRI dataset. PMID:25750463
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.
Optimal Estimation and Rank Detection for Sparse Spiked Covariance Matrices
Cai, Tony; Ma, Zongming; Wu, Yihong
2014-01-01
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. PMID:26257453
Minimum complexity FIR filters and sparse systolic arrays
Ferrari, L.A.; Sankar, P.V.
1988-06-01
The properties of BETA-spline approximation and the integral/derivative properties of convolution lead to efficient algorithms for the implementation of multidimensional FIR filters. The implementations are of minimum time complexity under the Nyquist criterion. The algorithm can easily be implemented using a sparse systolic array architecture. The resulting BETA-spline convolvers have much lower circuit complexity than systolic architectures based on conventional convolution algorithms. A two-dimensional hardware implementation based on simplifications of current architectures is presented.
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.
Inverse sparse tracker with a locally weighted distance metric.
Wang, Dong; Lu, Huchuan; Xiao, Ziyang; Yang, Ming-Hsuan
2015-09-01
Sparse representation has been recently extensively studied for visual tracking and generally facilitates more accurate tracking results than classic methods. In this paper, we propose a sparsity-based tracking algorithm that is featured with two components: 1) an inverse sparse representation formulation and 2) a locally weighted distance metric. In the inverse sparse representation formulation, the target template is reconstructed with particles, which enables the tracker to compute the weights of all particles by solving only one l1 optimization problem and thereby provides a quite efficient model. This is in direct contrast to most previous sparse trackers that entail solving one optimization problem for each particle. However, we notice that this formulation with normal Euclidean distance metric is sensitive to partial noise like occlusion and illumination changes. To this end, we design a locally weighted distance metric to replace the Euclidean one. Similar ideas of using local features appear in other works, but only being supported by popular assumptions like local models could handle partial noise better than holistic models, without any solid theoretical analysis. In this paper, we attempt to explicitly explain it from a mathematical view. On that basis, we further propose a method to assign local weights by exploiting the temporal and spatial continuity. In the proposed method, appearance changes caused by partial occlusion and shape deformation are carefully considered, thereby facilitating accurate similarity measurement and model update. The experimental validation is conducted from two aspects: 1) self validation on key components and 2) comparison with other state-of-the-art algorithms. Results over 15 challenging sequences show that the proposed tracking algorithm performs favorably against the existing sparsity-based trackers and the other state-of-the-art methods. PMID:25935033
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.
Shape prior modeling using sparse representation and online dictionary learning.
Zhang, Shaoting; Zhan, Yiqiang; Zhou, Yan; Uzunbas, Mustafa; Metaxas, Dimitris N
2012-01-01
The recently proposed sparse shape composition (SSC) opens a new avenue for shape prior modeling. Instead of assuming any parametric model of shape statistics, SSC incorporates shape priors on-the-fly by approximating a shape instance (usually derived from appearance cues) by a sparse combination of shapes in a training repository. Theoretically, one can increase the modeling capability of SSC by including as many training shapes in the repository. However, this strategy confronts two limitations in practice. First, since SSC involves an iterative sparse optimization at run-time, the more shape instances contained in the repository, the less run-time efficiency SSC has. Therefore, a compact and informative shape dictionary is preferred to a large shape repository. Second, in medical imaging applications, training shapes seldom come in one batch. It is very time consuming and sometimes infeasible to reconstruct the shape dictionary every time new training shapes appear. In this paper, we propose an online learning method to address these two limitations. Our method starts from constructing an initial shape dictionary using the K-SVD algorithm. When new training shapes come, instead of re-constructing the dictionary from the ground up, we update the existing one using a block-coordinates descent approach. Using the dynamically updated dictionary, sparse shape composition can be gracefully scaled up to model shape priors from a large number of training shapes without sacrificing run-time efficiency. Our method is validated on lung localization in X-Ray and cardiac segmentation in MRI time series. Compared to the original SSC, it shows comparable performance while being significantly more efficient. PMID:23286160
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. PMID:17279973
Sparsely Sampled Phase-Insensitive Ultrasonic Transducer Arrays
NASA Technical Reports Server (NTRS)
Johnston, Patrick H.
1992-01-01
Three methods of interpretation of outputs from sparsely sampled two-dimensional array of receiving ultrasonic transducers used in transmission experiments investigated. Methods are: description of sampled beam in terms of first few spatial moments of sampled distribution of energy; use of signal-dependent cutoff to limit extent of effective receiver aperture; and use of spatial interpolation to increase apparent density of sampling during computation. Methods reduce errors in computations of shapes of ultrasonic beams.
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.
Parallel iterative methods for sparse linear and nonlinear equations
NASA Technical Reports Server (NTRS)
Saad, Youcef
1989-01-01
As three-dimensional models are gaining importance, iterative methods will become almost mandatory. Among these, preconditioned Krylov subspace methods have been viewed as the most efficient and reliable, when solving linear as well as nonlinear systems of equations. There has been several different approaches taken to adapt iterative methods for supercomputers. Some of these approaches are discussed and the methods that deal more specifically with general unstructured sparse matrices, such as those arising from finite element methods, are emphasized.
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.
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.
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.
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.
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.
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.
Estimating Wind Turbine Inflow Using Sparse Wind Data
NASA Astrophysics Data System (ADS)
Rai, Raj; Naughton, Jonathan
2011-11-01
An accurate spatially and temporally resolved estimation of the wind inflow under various atmospheric boundary layer stability conditions is useful for several applications relevant to wind turbines. Estimations of a wind inflow plane in a neutrally stable boundary layer using sparse data (temporally resolved but spatially sparse, and spatially resolved but temporally sparse) has shown good agreement with the original data provided by a Large Eddy Simulation. A complementary Proper Orthogonal Decomposition-Linear Stochastic Estimation (POD-LSE) approach has been used for the estimation in which the POD identifies the energetic modes of the flow that are then used in estimating the time dependent flow-field using LSE. The applicability of such an approach is considered by simulating the estimation of the wind inflow using data collected in the field. Modern remote measurement approaches, such as Lidar (Light detection and ranging), can sample the wind at the multiple locations, but cannot sufficiently resolve the inflow in space in time that is required for many wind turbine applications. Since inflow estimations using the POD-LSE approach can simultaneously provide spatial and temporal behavior, the use of the approach with field data for better understanding the characteristics of the wind inflow at a particular site under different atmospheric conditions is demonstrated. Support from a gift from BP is acknowledged.
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.
Compressive Sensing Based Design of Sparse Tripole Arrays
Hawes, Matthew; Liu, Wei; Mihaylova, Lyudmila
2015-01-01
This paper considers the problem of designing sparse linear tripole arrays. In such arrays at each antenna location there are three orthogonal dipoles, allowing full measurement of both the horizontal and vertical components of the received waveform. We formulate this problem from the viewpoint of Compressive Sensing (CS). However, unlike for isotropic array elements (single antenna), we now have three complex valued weight coefficients associated with each potential location (due to the three dipoles), which have to be simultaneously minimised. If this is not done, we may only set the weight coefficients of individual dipoles to be zero valued, rather than complete tripoles, meaning some dipoles may remain at each location. Therefore, the contributions of this paper are to formulate the design of sparse tripole arrays as an optimisation problem, and then we obtain a solution based on the minimisation of a modified l1 norm or a series of iteratively solved reweighted minimisations, which ensure a truly sparse solution. Design examples are provided to verify the effectiveness of the proposed methods and show that a good approximation of a reference pattern can be achieved using fewer tripoles than a Uniform Linear Array (ULA) of equivalent length. PMID:26690436
Sparse principal component analysis by choice of norm
Luo, Ruiyan; Zhao, Hongyu
2012-01-01
Recent years have seen the developments of several methods for sparse principal component analysis due to its importance in the analysis of high dimensional data. Despite the demonstration of their usefulness in practical applications, they are limited in terms of lack of orthogonality in the loadings (coefficients) of different principal components, the existence of correlation in the principal components, the expensive computation needed, and the lack of theoretical results such as consistency in high-dimensional situations. In this paper, we propose a new sparse principal component analysis method by introducing a new norm to replace the usual norm in traditional eigenvalue problems, and propose an efficient iterative algorithm to solve the optimization problems. With this method, we can efficiently obtain uncorrelated principal components or orthogonal loadings, and achieve the goal of explaining a high percentage of variations with sparse linear combinations. Due to the strict convexity of the new norm, we can prove the convergence of the iterative method and provide the detailed characterization of the limits. We also prove that the obtained principal component is consistent for a single component model in high dimensional situations. As illustration, we apply this method to real gene expression data with competitive results. PMID:23524453
Compressive Sensing Based Design of Sparse Tripole Arrays.
Hawes, Matthew; Liu, Wei; Mihaylova, Lyudmila
2015-01-01
This paper considers the problem of designing sparse linear tripole arrays. In such arrays at each antenna location there are three orthogonal dipoles, allowing full measurement of both the horizontal and vertical components of the received waveform. We formulate this problem from the viewpoint of Compressive Sensing (CS). However, unlike for isotropic array elements (single antenna), we now have three complex valued weight coefficients associated with each potential location (due to the three dipoles), which have to be simultaneously minimised. If this is not done, we may only set the weight coefficients of individual dipoles to be zero valued, rather than complete tripoles, meaning some dipoles may remain at each location. Therefore, the contributions of this paper are to formulate the design of sparse tripole arrays as an optimisation problem, and then we obtain a solution based on the minimisation of a modified l1 norm or a series of iteratively solved reweighted minimisations, which ensure a truly sparse solution. Design examples are provided to verify the effectiveness of the proposed methods and show that a good approximation of a reference pattern can be achieved using fewer tripoles than a Uniform Linear Array (ULA) of equivalent length. PMID:26690436
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
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.
Inversion of magnetotelluric data in a sparse model domain
NASA Astrophysics Data System (ADS)
Nittinger, Christian G.; Becken, Michael
2016-06-01
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 a ℓ1 sense (ℓ2-ℓ1 minimization). The ℓ1 coefficient norm renders the solution sparse in a suitable representation such as the multi-resolution 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 non-linear 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.
Cluster-enhanced sparse approximation of overlapping ultrasonic echoes.
Mor, Etai; Aladjem, Mayer; Azoulay, Amnon
2015-02-01
Ultrasonic pulse-echo methods have been used extensively in non-destructive testing of layered structures. In acoustic measurements on thin layers, the resulting echoes from two successive interfaces overlap in time, making it difficult to assess the individual echo parameters. Over the last decade sparse approximation methods have been extensively used to address this issue. These methods employ a large dictionary of elementary functions (atoms) and attempt to select the smallest subset of atoms (sparsest approximation) that represent the ultrasonic signal accurately. In this paper we propose the cluster-enhanced sparse approximation (CESA) method for estimating overlapping ultrasonic echoes. CESA is specifically adapted to deal with a large number of signals acquired during an ultrasonic scan. It incorporates two principal algorithms. The first is a clustering algorithm, which divides a set of signals comprising an ultrasonic scan into groups of signals that can be approximated by the same set of atoms. The second is a two-stage iterative algorithm, which alternates between update of the atoms associated with each cluster, and re-clustering of the signals according to the updated atoms. Because CESA operates on clusters of signals, it achieves improved results in terms of approximation error and computation time compared with conventional sparse methods, which operate on each signal separately. The superior ability of CESA to approximate highly overlapping ultrasonic echoes is demonstrated through simulation and experiments on adhesively bonded structures. PMID:25643086
Visual Tracking via Sparse and Local Linear Coding.
Wang, Guofeng; Qin, Xueying; Zhong, Fan; Liu, Yue; Li, Hongbo; Peng, Qunsheng; Yang, Ming-Hsuan
2015-11-01
The state search is an important component of any object tracking algorithm. Numerous algorithms have been proposed, but stochastic sampling methods (e.g., particle filters) are arguably one of the most effective approaches. However, the discretization of the state space complicates the search for the precise object location. In this paper, we propose a novel tracking algorithm that extends the state space of particle observations from discrete to continuous. The solution is determined accurately via iterative linear coding between two convex hulls. The algorithm is modeled by an optimal function, which can be efficiently solved by either convex sparse coding or locality constrained linear coding. The algorithm is also very flexible and can be combined with many generic object representations. Thus, we first use sparse representation to achieve an efficient searching mechanism of the algorithm and demonstrate its accuracy. Next, two other object representation models, i.e., least soft-threshold squares and adaptive structural local sparse appearance, are implemented with improved accuracy to demonstrate the flexibility of our algorithm. Qualitative and quantitative experimental results demonstrate that the proposed tracking algorithm performs favorably against the state-of-the-art methods in dynamic scenes. PMID:26353352
Sparse encoding of automatic visual association in hippocampal networks.
Hulme, Oliver J; Skov, Martin; Chadwick, Martin J; Siebner, Hartwig R; Ramsøy, Thomas Z
2014-11-15
Intelligent action entails exploiting predictions about associations between elements of ones environment. The hippocampus and mediotemporal cortex are endowed with the network topology, physiology, and neurochemistry to automatically and sparsely code sensori-cognitive associations that can be reconstructed from single or partial inputs. Whilst acquiring fMRI data and performing an attentional task, participants were incidentally presented with a sequence of cartoon images. By assigning subjects a post-scan free-association task on the same images we assayed the density of associations triggered by these stimuli. Using multivariate Bayesian decoding, we show that human hippocampal and temporal neocortical structures host sparse associative representations that are automatically triggered by visual input. Furthermore, as predicted theoretically, there was a significant increase in sparsity in the Cornu Ammonis subfields, relative to the entorhinal cortex. Remarkably, the sparsity of CA encoding correlated significantly with associative memory performance over subjects; elsewhere within the temporal lobe, entorhinal, parahippocampal, perirhinal and fusiform cortices showed the highest model evidence for the sparse encoding of associative density. In the absence of reportability or attentional confounds, this charts a distribution of visual associative representations within hippocampal populations and their temporal lobe afferent fields, and demonstrates the viability of retrospective associative sampling techniques for assessing the form of reflexive associative encoding. PMID:25038440
Dentate Gyrus Circuitry Features Improve Performance of Sparse Approximation Algorithms
Petrantonakis, Panagiotis C.; Poirazi, Panayiota
2015-01-01
Memory-related activity in the Dentate Gyrus (DG) is characterized by sparsity. Memory representations are seen as activated neuronal populations of granule cells, the main encoding cells in DG, which are estimated to engage 2–4% of the total population. This sparsity is assumed to enhance the ability of DG to perform pattern separation, one of the most valuable contributions of DG during memory formation. In this work, we investigate how features of the DG such as its excitatory and inhibitory connectivity diagram can be used to develop theoretical algorithms performing Sparse Approximation, a widely used strategy in the Signal Processing field. Sparse approximation stands for the algorithmic identification of few components from a dictionary that approximate a certain signal. The ability of DG to achieve pattern separation by sparsifing its representations is exploited here to improve the performance of the state of the art sparse approximation algorithm “Iterative Soft Thresholding” (IST) by adding new algorithmic features inspired by the DG circuitry. Lateral inhibition of granule cells, either direct or indirect, via mossy cells, is shown to enhance the performance of the IST. Apart from revealing the potential of DG-inspired theoretical algorithms, this work presents new insights regarding the function of particular cell types in the pattern separation task of the DG. PMID:25635776
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. PMID:27085311
Crack growth sparse pursuit for wind turbine blade
NASA Astrophysics Data System (ADS)
Li, Xiang; Yang, Zhibo; Zhang, Han; Du, Zhaohui; Chen, Xuefeng
2015-01-01
One critical challenge to achieving reliable wind turbine blade structural health monitoring (SHM) is mainly caused by composite laminates with an anisotropy nature and a hard-to-access property. The typical pitch-catch PZTs approach generally detects structural damage with both measured and baseline signals. However, the accuracy of imaging or tomography by delay-and-sum approaches based on these signals requires improvement in practice. Via the model of Lamb wave propagation and the establishment of a dictionary that corresponds to scatters, a robust sparse reconstruction approach for structural health monitoring comes into view for its promising performance. This paper proposes a neighbor dictionary that identifies the first crack location through sparse reconstruction and then presents a growth sparse pursuit algorithm that can precisely pursue the extension of the crack. An experiment with the goal of diagnosing a composite wind turbine blade with an artificial crack is performed, and it validates the proposed approach. The results give competitively accurate crack detection with the correct locations and extension length.
Robust Reconstruction of Complex Networks from Sparse Data
NASA Astrophysics Data System (ADS)
Han, Xiao; Shen, Zhesi; Wang, Wen-Xu; Di, Zengru
2015-01-01
Reconstructing complex networks from measurable data is a fundamental problem for understanding and controlling collective dynamics of complex networked systems. However, a significant challenge arises when we attempt to decode structural information hidden in limited amounts of data accompanied by noise and in the presence of inaccessible nodes. Here, we develop a general framework for robust reconstruction of complex networks from sparse and noisy data. Specifically, we decompose the task of reconstructing the whole network into recovering local structures centered at each node. Thus, the natural sparsity of complex networks ensures a conversion from the local structure reconstruction into a sparse signal reconstruction problem that can be addressed by using the lasso, a convex optimization method. We apply our method to evolutionary games, transportation, and communication processes taking place in a variety of model and real complex networks, finding that universal high reconstruction accuracy can be achieved from sparse data in spite of noise in time series and missing data of partial nodes. Our approach opens new routes to the network reconstruction problem and has potential applications in a wide range of fields.
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.
An Equivalence Between Sparse Approximation and Support Vector Machines.
Girosi
1998-07-28
This article shows a relationship between two different approximation techniques: the support vector machines (SVM), proposed by V. Vapnik (1995) and a sparse approximation scheme that resembles the basis pursuit denoising algorithm (Chen, 1995; Chen, Donoho, and Saunders, 1995). SVM is a technique that can be derived from the structural risk minimization principle (Vapnik, 1982) and can be used to estimate the parameters of several different approximation schemes, including radial basis functions, algebraic and trigonometric polynomials, B-splines, and some forms of multilayer perceptrons. Basis pursuit denoising is a sparse approximation technique in which a function is reconstructed by using a small number of basis functions chosen from a large set (the dictionary). We show that if the data are noiseless, the modified version of basis pursuit denoising proposed in this article is equivalent to SVM in the following sense: if applied to the same data set, the two techniques give the same solution, which is obtained by solving the same quadratic programming problem. In the appendix, we present a derivation of the SVM technique in one framework of regularization theory, rather than statistical learning theory, establishing a connection between SVM, sparse approximation, and regularization theory. PMID:9698353
Accelerating sparse linear algebra using graphics processing units
NASA Astrophysics Data System (ADS)
Spagnoli, Kyle E.; Humphrey, John R.; Price, Daniel K.; Kelmelis, Eric J.
2011-06-01
The modern graphics processing unit (GPU) found in many standard personal computers is a highly parallel math processor capable of over 1 TFLOPS of peak computational throughput at a cost similar to a high-end CPU with excellent FLOPS-to-watt ratio. High-level sparse linear algebra operations are computationally intense, often requiring large amounts of parallel operations and would seem a natural fit for the processing power of the GPU. Our work is on a GPU accelerated implementation of sparse linear algebra routines. We present results from both direct and iterative sparse system solvers. The GPU execution model featured by NVIDIA GPUs based on CUDA demands very strong parallelism, requiring between hundreds and thousands of simultaneous operations to achieve high performance. Some constructs from linear algebra map extremely well to the GPU and others map poorly. CPUs, on the other hand, do well at smaller order parallelism and perform acceptably during low-parallelism code segments. Our work addresses this via hybrid a processing model, in which the CPU and GPU work simultaneously to produce results. In many cases, this is accomplished by allowing each platform to do the work it performs most naturally. For example, the CPU is responsible for graph theory portion of the direct solvers while the GPU simultaneously performs the low level linear algebra routines.
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.
On A Nonlinear Generalization of Sparse Coding and Dictionary Learning
Xie, Yuchen; Ho, Jeffrey; Vemuri, Baba
2013-01-01
Existing dictionary learning algorithms are based on the assumption that the data are vectors in an Euclidean vector space ℝd, and the dictionary is learned from the training data using the vector space structure of ℝd and its Euclidean L2-metric. However, in many applications, features and data often originated from a Riemannian manifold that does not support a global linear (vector space) structure. Furthermore, the extrinsic viewpoint of existing dictionary learning algorithms becomes inappropriate for modeling and incorporating the intrinsic geometry of the manifold that is potentially important and critical to the application. This paper proposes a novel framework for sparse coding and dictionary learning for data on a Riemannian manifold, and it shows that the existing sparse coding and dictionary learning methods can be considered as special (Euclidean) cases of the more general framework proposed here. We show that both the dictionary and sparse coding can be effectively computed for several important classes of Riemannian manifolds, and we validate the proposed method using two well-known classification problems in computer vision and medical imaging analysis. PMID:24129583
Scalable Library for the Parallel Solution of Sparse Linear Systems
Energy Science and Technology Software Center (ESTSC)
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, eachmore » 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.« less
Investigation of wall-bounded turbulence over sparsely distributed roughness
NASA Astrophysics Data System (ADS)
Placidi, Marco; Ganapathisubramani, Bharath
2011-11-01
The effects of sparsely distributed roughness elements on the structure of a turbulent boundary layer are examined by performing a series of Particle Image Velocimetry (PIV) experiments in a wind tunnel. From the literature, the best way to characterise a rough wall, especially one where the density of roughness elements is sparse, is unclear. In this study, rough surfaces consisting of sparsely and uniformly distributed LEGO® blocks are used. Five different patterns are adopted in order to examine the effects of frontal solidity (λf, frontal area of the roughness elements per unit wall-parallel area), plan solidity (λp, plan area of roughness elements per unit wall-parallel area) and the geometry of the roughness element (square and cylindrical elements), on the turbulence structure. The Karman number, Reτ , has been matched, at the value of approximately 2300, in order to compare across the different cases. In the talk, we will present detailed analysis of mean and rms velocity profiles, Reynolds stresses and quadrant decomposition.
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
Sparse and redundant representations for inverse problems and recognition
NASA Astrophysics Data System (ADS)
Patel, Vishal M.
Sparse and redundant representation of data enables the description of signals as linear combinations of a few atoms from a dictionary. In this dissertation, we study applications of sparse and redundant representations in inverse problems and object recognition. Furthermore, we propose two novel imaging modalities based on the recently introduced theory of Compressed Sensing (CS). This dissertation consists of four major parts. In the first part of the dissertation, we study a new type of deconvolution algorithm that is based on estimating the image from a shearlet decomposition. Shearlets provide a multi-directional and multi-scale decomposition that has been mathematically shown to represent distributed discontinuities such as edges better than traditional wavelets. We develop a deconvolution algorithm that allows for the approximation inversion operator to be controlled on a multi-scale and multi-directional basis. Furthermore, we develop a method for the automatic determination of the threshold values for the noise shrinkage for each scale and direction without explicit knowledge of the noise variance using a generalized cross validation method. In the second part of the dissertation, we study a reconstruction method that recovers highly undersampled images assumed to have a sparse representation in a gradient domain by using partial measurement samples that are collected in the Fourier domain. Our method makes use of a robust generalized Poisson solver that greatly aids in achieving a significantly improved performance over similar proposed methods. We will demonstrate by experiments that this new technique is more flexible to work with either random or restricted sampling scenarios better than its competitors. In the third part of the dissertation, we introduce a novel Synthetic Aperture Radar (SAR) imaging modality which can provide a high resolution map of the spatial distribution of targets and terrain using a significantly reduced number of needed
Lee, Young-Beom; Lee, Jeonghyeon; Tak, Sungho; Lee, Kangjoo; Na, Duk L; Seo, Sang Won; Jeong, Yong; Ye, Jong Chul
2016-01-15
Recent studies of functional connectivity MR imaging have revealed that the default-mode network activity is disrupted in diseases such as Alzheimer's disease (AD). However, there is not yet a consensus on the preferred method for resting-state analysis. Because the brain is reported to have complex interconnected networks according to graph theoretical analysis, the independency assumption, as in the popular independent component analysis (ICA) approach, often does not hold. Here, rather than using the independency assumption, we present a new statistical parameter mapping (SPM)-type analysis method based on a sparse graph model where temporal dynamics at each voxel position are described as a sparse combination of global brain dynamics. In particular, a new concept of a spatially adaptive design matrix has been proposed to represent local connectivity that shares the same temporal dynamics. If we further assume that local network structures within a group are similar, the estimation problem of global and local dynamics can be solved using sparse dictionary learning for the concatenated temporal data across subjects. Moreover, under the homoscedasticity variance assumption across subjects and groups that is often used in SPM analysis, the aforementioned individual and group analyses using sparse dictionary learning can be accurately modeled by a mixed-effect model, which also facilitates a standard SPM-type group-level inference using summary statistics. Using an extensive resting fMRI data set obtained from normal, mild cognitive impairment (MCI), and Alzheimer's disease patient groups, we demonstrated that the changes in the default mode network extracted by the proposed method are more closely correlated with the progression of Alzheimer's disease. PMID:26524138
Role of vegetation in modulating denudation and topography across the Himalaya
NASA Astrophysics Data System (ADS)
Olen, Stephanie; Bookhagen, Bodo; Strecker, Manfred
2015-04-01
Studies of Himalayan denudation, to date, have primarily focused on the effects of lithology, tectonic activity, and climate in shaping landscape and controlling denudation rates. Climate can impact denudation not only through increased precipitation, runoff, or glaciation, but also via its role in controlling vegetation cover. Since the classical study of Langbein and Schumm [1958] emphasizing the role of vegetation cover in determining erosional efficiency, theoretical and plot-scale studies have highlighted the role of vegetation on surface processes [Collins et al., 2004; Istanbulluoglu and Bras, 2005; Collins and Bras, 2010; Carretier et al., 2013; Jeffery et al., 2014]. Vegetation cover and density vary considerably in the Himalaya, both across and along strike. Across strike, vegetation transitions from dense forest and agriculturally-used plots in the Lesser Himalaya to sparse alpine and arid, virtually non-vegetated regions at high elevation and in the rain shadow north of the Higher Himalaya peaks. Along-strike vegetation densities also differ significantly and show a pronounced E-W gradient. To quantify the along-strike vegetation gradient, we use 14 years of MODIS 13C1 enhanced vegetation index (EVI) data to calculate mean annual, summer (MJJASO), and winter (NDJFMA) for the entire Himalaya. Additionally, we calculate a differential EVI that compares summer versus winter vegetation density (MJJASO/NDJFMA). A decrease in vegetation density is observed from east to west, with the greatest difference in winter vegetation cover (225% higher in the eastern than western Himalaya). In contrast, differential EVI is higher in the western Himalaya, increasing 170% from east to west. To evaluate the effect of vegetation on denudation and landscape evolution, we combine the 14-year EVI data, topographic analysis, and a compilation of >100 published and unpublished 10-Be terrestrial cosmogenic nuclide (TCN) catchment-mean denudation rates from across the Himalaya
Pothen, A.
1999-02-01
Gary Kumfert and Alex Pothen have improved the quality and run time of two ordering algorithms for minimizing the wavefront and envelope size of sparse matrices and graphs. These algorithms compute orderings for irregular data structures (e.g., unstructured meshes) that reduce the number of cache misses on modern workstation architectures. They have completed the implementation of a parallel solver for sparse, symmetric indefinite systems for distributed memory computers such as the IBM SP-2. The indefiniteness requires one to incorporate block pivoting (2 by 2 blocks) in the algorithm, thus demanding dynamic, parallel data structures. This is the first reported parallel solver for the indefinite problem. Direct methods for solving systems of linear equations employ sophisticated combinatorial and algebraic algorithms that contribute to software complexity, and hence it is natural to consider object-oriented design (OOD) in this context. The authors have continued to create software for solving sparse systems of linear equations by direct methods employing OOD. Fast computation of robust preconditioners is a priority for solving large systems of equations on unstructured grids and in other applications. They have developed new algorithms and software that can compute incomplete factorization preconditioners for high level fill in time proportional to the number of floating point operations and memory accesses.
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 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.; 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 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.
2014-01-01
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. 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.
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.
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
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
Sparse EEG Source Localization Using Bernoulli Laplacian Priors.
Costa, Facundo; Batatia, Hadj; Chaari, Lotfi; Tourneret, Jean-Yves
2015-12-01
Source localization in electroencephalography has received an increasing amount of interest in the last decade. Solving the underlying ill-posed inverse problem usually requires choosing an appropriate regularization. The usual l2 norm has been considered and provides solutions with low computational complexity. However, in several situations, realistic brain activity is believed to be focused in a few focal areas. In these cases, the l2 norm is known to overestimate the activated spatial areas. One solution to this problem is to promote sparse solutions for instance based on the l1 norm that are easy to handle with optimization techniques. In this paper, we consider the use of an l0 + l1 norm to enforce sparse source activity (by ensuring the solution has few nonzero elements) while regularizing the nonzero amplitudes of the solution. More precisely, the l0 pseudonorm handles the position of the nonzero elements while the l1 norm constrains the values of their amplitudes. We use a Bernoulli-Laplace prior to introduce this combined l0 + l1 norm in a Bayesian framework. The proposed Bayesian model is shown to favor sparsity while jointly estimating the model hyperparameters using a Markov chain Monte Carlo sampling technique. We apply the model to both simulated and real EEG data, showing that the proposed method provides better results than the l2 and l1 norms regularizations in the presence of pointwise sources. A comparison with a recent method based on multiple sparse priors is also conducted. PMID:26126270
Prediction of siRNA potency using sparse logistic regression.
Hu, Wei; Hu, John
2014-06-01
RNA interference (RNAi) can modulate gene expression at post-transcriptional as well as transcriptional levels. Short interfering RNA (siRNA) serves as a trigger for the RNAi gene inhibition mechanism, and therefore is a crucial intermediate step in RNAi. There have been extensive studies to identify the sequence characteristics of potent siRNAs. One such study built a linear model using LASSO (Least Absolute Shrinkage and Selection Operator) to measure the contribution of each siRNA sequence feature. This model is simple and interpretable, but it requires a large number of nonzero weights. We have introduced a novel technique, sparse logistic regression, to build a linear model using single-position specific nucleotide compositions which has the same prediction accuracy of the linear model based on LASSO. The weights in our new model share the same general trend as those in the previous model, but have only 25 nonzero weights out of a total 84 weights, a 54% reduction compared to the previous model. Contrary to the linear model based on LASSO, our model suggests that only a few positions are influential on the efficacy of the siRNA, which are the 5' and 3' ends and the seed region of siRNA sequences. We also employed sparse logistic regression to build a linear model using dual-position specific nucleotide compositions, a task LASSO is not able to accomplish well due to its high dimensional nature. Our results demonstrate the superiority of sparse logistic regression as a technique for both feature selection and regression over LASSO in the context of siRNA design. PMID:21091052
Mono- and multistatic polarimetric sparse aperture 3D SAR imaging
NASA Astrophysics Data System (ADS)
DeGraaf, Stuart; Twigg, Charles; Phillips, Louis
2008-04-01
SAR imaging at low center frequencies (UHF and L-band) offers advantages over imaging at more conventional (X-band) frequencies, including foliage penetration for target detection and scene segmentation based on polarimetric coherency. However, bandwidths typically available at these center frequencies are small, affording poor resolution. By exploiting extreme spatial diversity (partial hemispheric k-space coverage) and nonlinear bandwidth extrapolation/interpolation methods such as Least-Squares SuperResolution (LSSR) and Least-Squares CLEAN (LSCLEAN), one can achieve resolutions that are commensurate with the carrier frequency (λ/4) rather than the bandwidth (c/2B). Furthermore, extreme angle diversity affords complete coverage of a target's backscatter, and a correspondingly more literal image. To realize these benefits, however, one must image the scene in 3-D; otherwise layover-induced misregistration compromises the coherent summation that yields improved resolution. Practically, one is limited to very sparse elevation apertures, i.e. a small number of circular passes. Here we demonstrate that both LSSR and LSCLEAN can reduce considerably the sidelobe and alias artifacts caused by these sparse elevation apertures. Further, we illustrate how a hypothetical multi-static geometry consisting of six vertical real-aperture receive apertures, combined with a single circular transmit aperture provide effective, though sparse and unusual, 3-D k-space support. Forward scattering captured by this geometry reveals horizontal scattering surfaces that are missed in monostatic backscattering geometries. This paper illustrates results based on LucernHammer UHF and L-band mono- and multi-static simulations of a backhoe.
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
Sparse group composition for robust left ventricular epicardium segmentation.
Wang, Bing; Gu, Xiaomeng; Fan, Chonghao; Xie, Hongzhi; Zhang, Shuyang; Tian, Xuedong; Gu, Lixu
2015-12-01
Left ventricular (LV) epicardium segmentation in cardiac magnetic resonance images (MRIs) is still a challenging task, where the a-priori knowledge like those that incorporate the heart shape model is usually used to derive reasonable segmentation results. In this paper, we propose a sparse group composition (SGC) approach to model multiple shapes simultaneously, which extends conventional sparsity-based single shape prior modeling to incorporate a-priori spatial constraint information among multiple shapes on-the-fly. Multiple interrelated shapes (shapes of epi- and endo-cardium of myocardium in the case of LV epicardium segmentation) are regarded as a group, and sparse linear composition of training groups is computed to approximate the input group. A framework of iterative procedure of refinement based on SGC and segmentation based on deformation model is utilized for LV epicardium segmentation, in which an improved shape-constraint gradient Chan-Vese model (GCV) acted as deformation model. Compared with the standard sparsity-based single shape prior modeling, the refinement procedure has strong robust for relative gross and not much sparse errors in the input shape and the initial epicardium location can be estimated without complicated landmark detection due to modeling spatial constraint information among multiple shapes effectively. Proposed method was validated on 45 cardiac cine-MR clinical datasets and the results were compared with expert contours. The average perpendicular distance (APD) error of contours is 1.50±0.29mm, and the dice metric (DM) is 0.96±0.01. Compared to the state-of-the-art methods, our proposed approach appealed competitive segmentation performance and improved robustness. PMID:26198360
Social biases determine spatiotemporal sparseness of ciliate mating heuristics.
Clark, Kevin B
2012-01-01
Ciliates become highly social, even displaying animal-like qualities, in the joint presence of aroused conspecifics and nonself mating pheromones. Pheromone detection putatively helps trigger instinctual and learned courtship and dominance displays from which social judgments are made about the availability, compatibility, and fitness representativeness or likelihood of prospective mates and rivals. In earlier studies, I demonstrated the heterotrich Spirostomum ambiguum improves mating competence by effecting preconjugal strategies and inferences in mock social trials via behavioral heuristics built from Hebbian-like associative learning. Heuristics embody serial patterns of socially relevant action that evolve into ordered, topologically invariant computational networks supporting intra- and intermate selection. S. ambiguum employs heuristics to acquire, store, plan, compare, modify, select, and execute sets of mating propaganda. One major adaptive constraint over formation and use of heuristics involves a ciliate's initial subjective bias, responsiveness, or preparedness, as defined by Stevens' Law of subjective stimulus intensity, for perceiving the meaningfulness of mechanical pressures accompanying cell-cell contacts and additional perimating events. This bias controls durations and valences of nonassociative learning, search rates for appropriate mating strategies, potential net reproductive payoffs, levels of social honesty and deception, successful error diagnosis and correction of mating signals, use of insight or analysis to solve mating dilemmas, bioenergetics expenditures, and governance of mating decisions by classical or quantum statistical mechanics. I now report this same social bias also differentially affects the spatiotemporal sparseness, as measured with metric entropy, of ciliate heuristics. Sparseness plays an important role in neural systems through optimizing the specificity, efficiency, and capacity of memory representations. The present
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.
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
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. PMID:25879946
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.
Reconstructing spatially extended brain sources via enforcing multiple transform sparseness.
Zhu, Min; Zhang, Wenbo; Dickens, Deanna L; Ding, Lei
2014-02-01
Accurate estimation of location and extent of neuronal sources from EEG/MEG remain challenging. In the present study, a new source imaging method, i.e. variation and wavelet based sparse source imaging (VW-SSI), is proposed to better estimate cortical source locations and extents. VW-SSI utilizes the L1-norm regularization method with the enforcement of transform sparseness in both variation and wavelet domains. The performance of the proposed method is assessed by both simulated and experimental MEG data, obtained from a language task and a motor task. Compared to L2-norm regularizations, VW-SSI demonstrates significantly improved capability in reconstructing multiple extended cortical sources with less spatial blurredness and less localization error. With the use of transform sparseness, VW-SSI overcomes the over-focused problem in classic SSI methods. With the use of two transformations, VW-SSI further indicates significantly better performance in estimating MEG source locations and extents than other SSI methods with single transformations. The present experimental results indicate that VW-SSI can successfully estimate neural sources (and their spatial coverage) located in close areas while responsible for different functions, i.e. temporal cortical sources for auditory and language processing, and sources on the pre-bank and post-bank of the central sulcus. Meantime, all other methods investigated in the present study fail to recover these phenomena. Precise estimation of cortical source locations and extents from EEG/MEG is of significance for applications in neuroscience and neurology. PMID:24103850
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
Classification of transient signals using sparse representations over adaptive dictionaries
NASA Astrophysics Data System (ADS)
Moody, Daniela I.; Brumby, Steven P.; Myers, Kary L.; Pawley, Norma H.
2011-06-01
Automatic classification of broadband transient radio frequency (RF) signals is of particular interest in persistent surveillance applications. Because such transients are often acquired in noisy, cluttered environments, and are characterized by complex or unknown analytical models, feature extraction and classification can be difficult. We propose a fast, adaptive classification approach based on non-analytical dictionaries learned from data. Conventional representations using fixed (or analytical) orthogonal dictionaries, e.g., Short Time Fourier and Wavelet Transforms, can be suboptimal for classification of transients, as they provide a rigid tiling of the time-frequency space, and are not specifically designed for a particular signal class. They do not usually lead to sparse decompositions, and require separate feature selection algorithms, creating additional computational overhead. Pursuit-type decompositions over analytical, redundant dictionaries yield sparse representations by design, and work well for target signals in the same function class as the dictionary atoms. The pursuit search however has a high computational cost, and the method can perform poorly in the presence of realistic noise and clutter. Our approach builds on the image analysis work of Mairal et al. (2008) to learn a discriminative dictionary for RF transients directly from data without relying on analytical constraints or additional knowledge about the signal characteristics. We then use a pursuit search over this dictionary to generate sparse classification features. We demonstrate that our learned dictionary is robust to unexpected changes in background content and noise levels. The target classification decision is obtained in almost real-time via a parallel, vectorized implementation.
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
Object-Oriented Design for Sparse Direct Solvers
NASA Technical Reports Server (NTRS)
Dobrian, Florin; Kumfert, Gary; Pothen, Alex
1999-01-01
We discuss the object-oriented design of a software package for solving sparse, symmetric systems of equations (positive definite and indefinite) by direct methods. At the highest layers, we decouple data structure classes from algorithmic classes for flexibility. We describe the important structural and algorithmic classes in our design, and discuss the trade-offs we made for high performance. The kernels at the lower layers were optimized by hand. Our results show no performance loss from our object-oriented design, while providing flexibility, case of use, and extensibility over solvers using procedural design.
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.
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.
An Effective Mobile Sensor Control Method for Sparse Sensor Networks
Treeprapin, Kriengsak; Kanzaki, Akimitsu; Hara, Takahiro; Nishio, Shojiro
2009-01-01
In this paper, we propose an effective mobile sensor control method, named DATFM (Data Acquisition and Transmission with Fixed and Mobile node) for sparse sensor networks. DATFM uses two types of sensor nodes, fixed node and mobile node. The data acquired by nodes are accumulated on a fixed node before being transferred to the sink node. In addition, DATFM transfers the accumulated data efficiently by constructing a communication route of multiple mobile nodes between fixed nodes. We also conduct simulation experiments to evaluate the performance of DATFM. PMID:22389602
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.
On time delay estimation from a sparse linear prediction perspective.
He, Hongsen; Yang, Tao; Chen, Jingdong
2015-02-01
This paper proposes a sparse linear prediction based algorithm to estimate time difference of arrival. This algorithm unifies the cross correlation method without prewhitening and that with prewhitening via an ℓ2/ℓ1 optimization process, which is solved by an augmented Lagrangian alternating direction method. It also forms a set of time delay estimators that make a tradeoff between prewhitening and non-prewhitening through adjusting a regularization parameter. The effectiveness of the proposed algorithm is demonstrated in noisy and reverberant environments. PMID:25698037
An information theoretic approach of designing sparse kernel adaptive filters.
Liu, Weifeng; Park, Il; Principe, José C
2009-12-01
This paper discusses an information theoretic approach of designing sparse kernel adaptive filters. To determine useful data to be learned and remove redundant ones, a subjective information measure called surprise is introduced. Surprise captures the amount of information a datum contains which is transferable to a learning system. Based on this concept, we propose a systematic sparsification scheme, which can drastically reduce the time and space complexity without harming the performance of kernel adaptive filters. Nonlinear regression, short term chaotic time-series prediction, and long term time-series forecasting examples are presented. PMID:19923047
Estimating smoothness term for BP stereo matching with sparse disparities
NASA Astrophysics Data System (ADS)
He, Junxue; Li, Zhanming
2011-06-01
The belief propagation stereo matching algorithm has high computational cost and varied parameters. To address these problems and perform stereo matching in practical applications, we implement BP algorithm on cuda and propose an approach to estimate parameters of smoothness term in the energy function automatically. We use Harris corner detector and Pyramidal KLT feature tracker algorithm to extract a sparse disparity map. The rate of increase in the discontinuity cost can be compute out approximately. Experiments demonstrate that our approach is feasible and the approximate parameters of discontinuity cost can be computed out quickly.
Flexible Multilayer Sparse Approximations of Matrices and Applications
NASA Astrophysics Data System (ADS)
Le Magoarou, Luc; Gribonval, Remi
2016-06-01
The computational cost of many signal processing and machine learning techniques is often dominated by the cost of applying certain linear operators to high-dimensional vectors. This paper introduces an algorithm aimed at reducing the complexity of applying linear operators in high dimension by approximately factorizing the corresponding matrix into few sparse factors. The approach relies on recent advances in non-convex optimization. It is first explained and analyzed in details and then demonstrated experimentally on various problems including dictionary learning for image denoising, and the approximation of large matrices arising in inverse problems.
Replica analysis of Franz-Parisi potential for sparse systems
NASA Astrophysics Data System (ADS)
Ueda, Masahiko; Kabashima, Yoshiyuki
2015-04-01
We propose a method for calculating the Franz-Parisi potential for spin glass models on sparse random graphs using the replica method under the replica symmetric ansatz. The resulting self-consistent equations have the solution with the characteristic structure of multi-body overlaps, and the self-consistent equations under this solution are equivalent to the one-step replica symmetry breaking (1RSB) cavity equation with Parisi parameter x = 1. This method is useful for the evaluation of transition temperatures of the p-spin model on regular random graphs under a uniform magnetic field.
Dynamic Stochastic Superresolution of sparsely observed turbulent systems
Branicki, M.; Majda, A.J.
2013-05-15
Real-time capture of the relevant features of the unresolved turbulent dynamics of complex natural systems from sparse noisy observations and imperfect models is a notoriously difficult problem. The resulting lack of observational resolution and statistical accuracy in estimating the important turbulent processes, which intermittently send significant energy to the large-scale fluctuations, hinders efficient parameterization and real-time prediction using discretized PDE models. This issue is particularly subtle and important when dealing with turbulent geophysical systems with an vast range of interacting spatio-temporal scales and rough energy spectra near the mesh scale of numerical models. Here, we introduce and study a suite of general Dynamic Stochastic Superresolution (DSS) algorithms and show that, by appropriately filtering sparse regular observations with the help of cheap stochastic exactly solvable models, one can derive stochastically ‘superresolved’ velocity fields and gain insight into the important characteristics of the unresolved dynamics, including the detection of the so-called black swans. The DSS algorithms operate in Fourier domain and exploit the fact that the coarse observation network aliases high-wavenumber information into the resolved waveband. It is shown that these cheap algorithms are robust and have significant skill on a test bed of turbulent solutions from realistic nonlinear turbulent spatially extended systems in the presence of a significant model error. In particular, the DSS algorithms are capable of successfully capturing time-localized extreme events in the unresolved modes, and they provide good and robust skill for recovery of the unresolved processes in terms of pattern correlation. Moreover, we show that DSS improves the skill for recovering the primary modes associated with the sparse observation mesh which is equally important in applications. The skill of the various DSS algorithms depends on the energy spectrum
Sparse Substring Pattern Set Discovery Using Linear Programming Boosting
NASA Astrophysics Data System (ADS)
Kashihara, Kazuaki; Hatano, Kohei; Bannai, Hideo; Takeda, Masayuki
In this paper, we consider finding a small set of substring patterns which classifies the given documents well. We formulate the problem as 1 norm soft margin optimization problem where each dimension corresponds to a substring pattern. Then we solve this problem by using LPBoost and an optimal substring discovery algorithm. Since the problem is a linear program, the resulting solution is likely to be sparse, which is useful for feature selection. We evaluate the proposed method for real data such as movie reviews.
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.
Precipitation intensity and vegetation controls on geomorphology of the central Andes
NASA Astrophysics Data System (ADS)
Jeffery, M. L.; Poulsen, C. J.; Ehlers, T. A.; Yanites, B. J.
2012-12-01
Field observations and landscape evolution models indicate that landscape processes in active mountain belts are strongly dependent on vegetation and climate. In fluvial landscapes, erosional efficiency is commonly thought to depend on the intensity, frequency, and duration of precipitation events. We use Tropical Rainfall Measuring Mission (TRMM) observations to test the importance of precipitation intensity in determining geomorphology at the mountain belt scale. Precipitation metrics, including mean annual precipitation, and the mean intensity, duration, and frequency of precipitation events, are derived from the TRMM 3B42v7 product. The new precipitation datasets are then compared with different topographic metrics of the central Andes. Statistical analyses, including multiple linear regression, are used to quantify the importance of different precipitation metrics in controlling the regional topographic characteristics. In addition to climate properties, spatial variations in tectonic regime, bedrock lithology, and the amount and type of vegetation cover are accounted for in the statistical analyses. Our analysis indicates that in regions with high vegetation cover (>80%), mean precipitation intensity and mean interval correlate most strongly with mean hillslope (r = -0.51 and r = -0.66 respectively). In these regions, mean hillslope decreases from ~25° to ~ 10° with increasing mean event precipitation intensity (from 10 to 40 mm/day). In contrast, in sparsely vegetated (<40%) or shrub-dominated landscapes, precipitation intensity does not correlate with mean hillslope (r < 0.1). In regions with high vegetation cover, mean annual precipitation is weakly correlated with mean hillslope (r = 0.24). However, mean hillslope increases with increasing mean annual precipitation (r = 0.52) when all vegetation cover is considered. We interpret the results as evidence that vegetation is a key control on critical erosion thresholds at the landscape scale. Furthermore
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 ...
ERIC Educational Resources Information Center
Shepard, Mary; Shepard, Ray
Vegetable Soup is a new children's television series whose purpose is to counter the negative and destructive effects of racial isolation. This manual gives detailed instructions for discussion of activities that are presented during the television series such as: crafts, games, recipes, language activities, and children's questions. A list of…
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.
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.
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 Detector Imaging Sensor with Two-Class Silhouette Classification
Russomanno, David; Chari, Srikant; Halford, Carl
2008-01-01
This paper presents the design and test of a simple active near-infrared sparse detector imaging sensor. The prototype of the sensor is novel in that it can capture remarkable silhouettes or profiles of a wide-variety of moving objects, including humans, animals, and vehicles using a sparse detector array comprised of only sixteen sensing elements deployed in a vertical configuration. The prototype sensor was built to collect silhouettes for a variety of objects and to evaluate several algorithms for classifying the data obtained from the sensor into two classes: human versus non-human. Initial tests show that the classification of individually sensed objects into two classes can be achieved with accuracy greater than ninety-nine percent (99%) with a subset of the sixteen detectors using a representative dataset consisting of 512 signatures. The prototype also includes a Webservice interface such that the sensor can be tasked in a network-centric environment. The sensor appears to be a low-cost alternative to traditional, high-resolution focal plane array imaging sensors for some applications. After a power optimization study, appropriate packaging, and testing with more extensive datasets, the sensor may be a good candidate for deployment in vast geographic regions for a myriad of intelligent electronic fence and persistent surveillance applications, including perimeter security scenarios.
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.
Sparse kernel learning with LASSO and Bayesian inference algorithm.
Gao, Junbin; Kwan, Paul W; Shi, Daming
2010-03-01
Kernelized LASSO (Least Absolute Selection and Shrinkage Operator) has been investigated in two separate recent papers [Gao, J., Antolovich, M., & Kwan, P. H. (2008). L1 LASSO and its Bayesian inference. In W. Wobcke, & M. Zhang (Eds.), Lecture notes in computer science: Vol. 5360 (pp. 318-324); Wang, G., Yeung, D. Y., & Lochovsky, F. (2007). The kernel path in kernelized LASSO. In International conference on artificial intelligence and statistics (pp. 580-587). San Juan, Puerto Rico: MIT Press]. This paper is concerned with learning kernels under the LASSO formulation via adopting a generative Bayesian learning and inference approach. A new robust learning algorithm is proposed which produces a sparse kernel model with the capability of learning regularized parameters and kernel hyperparameters. A comparison with state-of-the-art methods for constructing sparse regression models such as the relevance vector machine (RVM) and the local regularization assisted orthogonal least squares regression (LROLS) is given. The new algorithm is also demonstrated to possess considerable computational advantages. PMID:19604671
Fast Forward Maximum entropy reconstruction of sparsely sampled data
NASA Astrophysics Data System (ADS)
Balsgart, Nicholas M.; Vosegaard, Thomas
2012-10-01
We present an analytical algorithm using fast Fourier transformations (FTs) for deriving the gradient needed as part of the iterative reconstruction of sparsely sampled datasets using the forward maximum entropy reconstruction (FM) procedure by Hyberts and Wagner [J. Am. Chem. Soc. 129 (2007) 5108]. The major drawback of the original algorithm is that it required one FT and one evaluation of the entropy per missing datapoint to establish the gradient. In the present study, we demonstrate that the entire gradient may be obtained using only two FT's and one evaluation of the entropy derivative, thus achieving impressive time savings compared to the original procedure. An example: A 2D dataset with sparse sampling of the indirect dimension, with sampling of only 75 out of 512 complex points (15% sampling) would lack (512 - 75) × 2 = 874 points per ν2 slice. The original FM algorithm would require 874 FT's and entropy function evaluations to setup the gradient, while the present algorithm is ˜450 times faster in this case, since it requires only two FT's. This allows reduction of the computational time from several hours to less than a minute. Even more impressive time savings may be achieved with 2D reconstructions of 3D datasets, where the original algorithm required days of CPU time on high-performance computing clusters only require few minutes of calculation on regular laptop computers with the new algorithm.
Sparse approximation of currents for statistics on curves and surfaces.
Durrleman, Stanley; Pennec, Xavier; Trouvé, Alain; Ayache, Nicholas
2008-01-01
Computing, processing, visualizing statistics on shapes like curves or surfaces is a real challenge with many applications ranging from medical image analysis to computational geometry. Modelling such geometrical primitives with currents avoids feature-based approach as well as point-correspondence method. This framework has been proved to be powerful to register brain surfaces or to measure geometrical invariants. However, if the state-of-the-art methods perform efficiently pairwise registrations, new numerical schemes are required to process groupwise statistics due to an increasing complexity when the size of the database is growing. Statistics such as mean and principal modes of a set of shapes often have a heavy and highly redundant representation. We propose therefore to find an adapted basis on which mean and principal modes have a sparse decomposition. Besides the computational improvement, this sparse representation offers a way to visualize and interpret statistics on currents. Experiments show the relevance of the approach on 34 sets of 70 sulcal lines and on 50 sets of 10 meshes of deep brain structures. PMID:18982629
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.
Compressive sensing of sparse radio frequency signals using optical mixing.
Valley, George C; Sefler, George A; Shaw, T Justin
2012-11-15
We demonstrate an optical mixing system for measuring properties of sparse radio frequency (RF) signals using compressive sensing (CS). Two types of sparse RF signals are investigated: (1) a signal that consists of a few 0.4 ns pulses in a 26.8 ns window and (2) a signal that consists of a few sinusoids at different frequencies. The RF is modulated onto the intensity of a repetitively pulsed, wavelength-chirped optical field, and time-wavelength-space mapping is used to map the optical field onto a 118-pixel, one-dimensional spatial light modulator (SLM). The SLM pixels are programmed with a pseudo-random bit sequence (PRBS) to form one row of the CS measurement matrix, and the optical throughput is integrated with a photodiode to obtain one value of the CS measurement vector. Then the PRBS is changed to form the second row of the mixing matrix and a second value of the measurement vector is obtained. This process is performed 118 times so that we can vary the dimensions of the CS measurement matrix from 1×118 to 118×118 (square). We use the penalized ℓ(1) norm method with stopping parameter λ (also called basis pursuit denoising) to recover pulsed or sinusoidal RF signals as a function of the small dimension of the measurement matrix and stopping parameter. For a square matrix, we also find that penalized ℓ(1) norm recovery performs better than conventional recovery using matrix inversion. PMID:23164876
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.
iSAP: Interactive Sparse Astronomical Data Analysis Packages
NASA Astrophysics Data System (ADS)
Fourt, O.; Starck, J.-L.; Sureau, F.; Bobin, J.; Moudden, Y.; Abrial, P.; Schmitt, J.
2013-03-01
iSAP consists of three programs, written in IDL, which together are useful for spherical data analysis. MR/S (MultiResolution on the Sphere) contains routines for wavelet, ridgelet and curvelet transform on the sphere, and applications such denoising on the sphere using wavelets and/or curvelets, Gaussianity tests and Independent Component Analysis on the Sphere. MR/S has been designed for the PLANCK project, but can be used for many other applications. SparsePol (Polarized Spherical Wavelets and Curvelets) has routines for polarized wavelet, polarized ridgelet and polarized curvelet transform on the sphere, and applications such denoising on the sphere using wavelets and/or curvelets, Gaussianity tests and blind source separation on the Sphere. SparsePol has been designed for the PLANCK project. MS-VSTS (Multi-Scale Variance Stabilizing Transform on the Sphere), designed initially for the FERMI project, is useful for spherical mono-channel and multi-channel data analysis when the data are contaminated by a Poisson noise. It contains routines for wavelet/curvelet denoising, wavelet deconvolution, multichannel wavelet denoising and deconvolution.
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. PMID:26614384
The efficient parallel iterative solution of large sparse linear systems
Jones, M.T.; Plassmann, P.E.
1992-06-01
The development of efficient, general-purpose software for the iterative solution of sparse linear systems on a parallel MIMD computer requires an interesting combination of expertise. Parallel graph heuristics, convergence analysis, and basic linear algebra implementation issues must all be considered. In this paper, we discuss how we have incorporated recent results in these areas into a general-purpose iterative solver. First, we consider two recently developed parallel graph coloring heuristics. We show how the method proposed by Luby, based on determining maximal independent sets, can be modified to run in an asynchronous manner and give aa expected running time bound for this modified heuristic. In addition, a number of graph reduction heuristics are described that are used in our implementation to improve the individual processor performance. The effect of these various graph reductions on the solution of sparse triangular systems is categorized. Finally, we discuss the performance of this solver from the perspective of two large-scale applications: a piezoelectric crystal finite-element modeling problem, and a nonlinear optimization problem to determine the minimum energy configuration of a three-dimensional, layered superconductor model.
Sparse graph-based transduction for image classification
NASA Astrophysics Data System (ADS)
Huang, Sheng; Yang, Dan; Zhou, Jia; Huangfu, Lunwen; Zhang, Xiaohong
2015-03-01
Motivated by the remarkable successes of graph-based transduction (GT) and sparse representation (SR), we present a classifier named sparse graph-based classifier (SGC) for image classification. In SGC, SR is leveraged to measure the correlation (similarity) of every two samples and a graph is constructed for encoding these correlations. Then the Laplacian eigenmapping is adopted for deriving the graph Laplacian of the graph. Finally, SGC can be obtained by plugging the graph Laplacian into the conventional GT framework. In the image classification procedure, SGC utilizes the correlations which are encoded in the learned graph Laplacian, to infer the labels of unlabeled images. SGC inherits the merits of both GT and SR. Compared to SR, SGC improves the robustness and the discriminating power of GT. Compared to GT, SGC sufficiently exploits the whole data. Therefore, it alleviates the undercomplete dictionary issue suffered by SR. Four popular image databases are employed for evaluation. The results demonstrate that SGC can achieve a promising performance in comparison with the state-of-the-art classifiers, particularly in the small training sample size case and the noisy sample case.
Blind deconvolution subject to sparse representation for fluorescence microscopy
NASA Astrophysics Data System (ADS)
Wang, Yu; Dai, Qionghai; Cai, Qiang; Guo, Peiyuan; Liu, Zaiwen
2013-01-01
Blind deconvolution is an effective fluorescence microscopic image processing technique to improve the quality of degraded digital images resulting from photon counting noise and out-of-focus blur. For solving the severely ill-posed problem of deconvolution, in this paper we propose an alternate minimized blind deconvolution method which considers sparse representation as constraint condition to confine the solution space of traditional Richardson-Lucy method and Gaussian model as initial value of point spread function (PSF). We assume that Poisson noise is dominating during the course of imaging. The maximum-likelihood estimation on a fluorescence image and corresponding point spread function (PSF) is developed. By solving the Euler-Lagrange equation of the total cost function, including the data term obtained by the hypothetical Poisson noise distribution model and the regularized term corresponding to the sparse representation constraint, and using gradient descent method we can get the iterative equations of the original fluorescence image and PSF respectively. Compared with the related blind deconvolution methods, our model shows superior performance in terms of both objective criteria and subjective human vision via processing simulated and real fluorescence microscopic degraded images.
Framelet-Based Sparse Unmixing of Hyperspectral Images.
Zhang, Guixu; Xu, Yingying; Fang, Faming
2016-04-01
Spectral unmixing aims at estimating the proportions (abundances) of pure spectrums (endmembers) in each mixed pixel of hyperspectral data. Recently, a semi-supervised approach, which takes the spectral library as prior knowledge, has been attracting much attention in unmixing. In this paper, we propose a new semi-supervised unmixing model, termed framelet-based sparse unmixing (FSU), which promotes the abundance sparsity in framelet domain and discriminates the approximation and detail components of hyperspectral data after framelet decomposition. Due to the advantages of the framelet representations, e.g., images have good sparse approximations in framelet domain, and most of the additive noises are included in the detail coefficients, the FSU model has a better antinoise capability, and accordingly leads to more desirable unmixing performance. The existence and uniqueness of the minimizer of the FSU model are then discussed, and the split Bregman algorithm and its convergence property are presented to obtain the minimal solution. Experimental results on both simulated data and real data demonstrate that the FSU model generally performs better than the compared methods. PMID:26849863
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. PMID:22040650
Improved beamforming using curved sparse 2D arrays in ultrasound.
Kirkebø, Jan Egil; Austeng, Andreas
2007-05-01
In this work we have investigated the effect of curving phase-steered sparse periodic two-dimensional arrays in one direction, and relate this effect to the geometry of the arrays. We have shown that curving is equivalent to removing some of the element periodicity, thus adding some "randomness" to the layout. Compared to flat phase-steered periodically sparse two-dimensional arrays, curving offers an even greater suppression of grating lobes located at directions along the curvature. The class of arrays yielding improved performance due to this suppression of grating lobes has been characterized. The point spread functions of some previously proposed array layouts, shown to be promising for ultrasonic imaging, have been simulated. The arrays have been simulated with various number of elements as well as various focal points, with array and field parameters typical to those in volumetric cardiac imaging. On a 48 x 48 element grid with a transducer center frequency of 3 MHz and the target at 40 mm, reductions in the peak sidelobe level of up to 12dB were recorded for some critical steering directions, without significant differences in the beamwidth. The integrated sidelobe ratio was also examined, showing an almost equivalent performance as the flat array. This study shows that, without adding any complexity to the system, the overall image quality of a volumetric imaging system can be improved significantly by curving the array in one direction. PMID:17313966
Modeling human performance with low light sparse color imagers
NASA Astrophysics Data System (ADS)
Haefner, David P.; Reynolds, Joseph P.; Cha, Jae; Hodgkin, Van
2011-05-01
Reflective band sensors are often signal to noise limited in low light conditions. Any additional filtering to obtain spectral information further reduces the signal to noise, greatly affecting range performance. Modern sensors, such as the sparse color filter CCD, circumvent this additional degradation through reducing the number of pixels affected by filters and distributing the color information. As color sensors become more prevalent in the warfighter arsenal, the performance of the sensor-soldier system must be quantified. While field performance testing ultimately validates the success of a sensor, accurately modeling sensor performance greatly reduces the development time and cost, allowing the best technology to reach the soldier the fastest. Modeling of sensors requires accounting for how the signal is affected through the modulation transfer function (MTF) and noise of the system. For the modeling of these new sensors, the MTF and noise for each color band must be characterized, and the appropriate sampling and blur must be applied. We show how sparse array color filter sensors may be modeled and how a soldier's performance with such a sensor may be predicted. This general approach to modeling color sensors can be extended to incorporate all types of low light color sensors.
Inpainting of historical seismograms using sparse representation method
NASA Astrophysics Data System (ADS)
Wang, Lifu; Sun, Yi; Cai, Xiaogang
2015-01-01
This paper presents a method of inpainting historical seismograms recorded by a pen and paper drum-type seismograph. In the seismogram, some portions of the wave may be lost or distorted owing to time marks or violent shaking. In this study, the seismic waveform is divided into several frames of equal length, and the lost or distorted portions are restored frame by frame. Because a seismogram contains several repetitive patterns in the entire waveform, each frame can be sparsely represented on the basis of these patterns. Therefore, the sparse representation model is employed to represent historical seismograms. In addition, an inpainting model that employs sparsity as a prior is formulated, and it is used to restore the lost portions by solving a L0-norm minimization problem. However, this minimization problem may be ill posed and result in an incorrect outcome if the missing interval duration of the wave is very large. Therefore, to solve this ill-posed problem, a prior based on the Fourier spectrum of the waveform is added to the inpainting method. Simulation results prove that the proposed inpainting method can restore the missing wave well.
A sparse sampling planner for sensor resource management
NASA Astrophysics Data System (ADS)
Rudary, Matthew; Khosla, Deepak; Guillochon, James; Dow, P. Alex; Blyth, Barbara
2006-05-01
The goal of sensor resource management (SRM) is to allocate resources appropriately in order to gain as much information as possible about a system. We introduce a centralized non-myopic planning algorithm, C-SPLAN, that uses sparse sampling to estimate the value of resource assignments. Sparse sampling is related to Monte Carlo simulation. In the SRM problem we consider, our network of sensors observes a set of tracks; each sensor can be set to operate in one of several modes and/or viewing geometries. Each mode incurs a different cost and provides different information about the tracks. Each track has a kinematic state and is of a certain class; the sensors can observe either or both of these, depending on their mode of operation. The goal in this problem is to maximize the overall rate of information gain, i.e. rate of improvement in kinematic tracking and classification accuracy of all tracks in the Area of Interest. The rate is defined by several metrics with the cost of the sensor mode being the primary factor. We compare C-SPLAN's performance on several tracking and target identification problems to that of other algorithms.
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.
Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging
Yu, Xingjian; Chen, Shuhang; Hu, Zhenghui; Liu, Meng; Chen, Yunmei; Shi, Pengcheng; Liu, Huafeng
2015-01-01
In dynamic Positron Emission Tomography (PET), an estimate of the radio activity concentration is obtained from a series of frames of sinogram data taken at ranging in duration from 10 seconds to minutes under some criteria. So far, all the well-known reconstruction algorithms require known data statistical properties. It limits the speed of data acquisition, besides, it is unable to afford the separated information about the structure and the variation of shape and rate of metabolism which play a major role in improving the visualization of contrast for some requirement of the diagnosing in application. This paper presents a novel low rank-based activity map reconstruction scheme from emission sinograms of dynamic PET, termed as SLCR representing Sparse/Low Rank Constrained Reconstruction for Dynamic PET Imaging. In this method, the stationary background is formulated as a low rank component while variations between successive frames are abstracted to the sparse. The resulting nuclear norm and l1 norm related minimization problem can also be efficiently solved by many recently developed numerical methods. In this paper, the linearized alternating direction method is applied. The effectiveness of the proposed scheme is illustrated on three data sets. PMID:26540274
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).
Classification of Histology Sections via Multispectral Convolutional Sparse Coding*
Zhou, Yin; Barner, Kenneth; Spellman, Paul
2014-01-01
Image-based classification of histology sections plays an important role in predicting clinical outcomes. However this task is very challenging due to the presence of large technical variations (e.g., fixation, staining) and biological heterogeneities (e.g., cell type, cell state). In the field of biomedical imaging, for the purposes of visualization and/or quantification, different stains are typically used for different targets of interest (e.g., cellular/subcellular events), which generates multi-spectrum data (images) through various types of microscopes and, as a result, provides the possibility of learning biological-component-specific features by exploiting multispectral information. We propose a multispectral feature learning model that automatically learns a set of convolution filter banks from separate spectra to efficiently discover the intrinsic tissue morphometric signatures, based on convolutional sparse coding (CSC). The learned feature representations are then aggregated through the spatial pyramid matching framework (SPM) and finally classified using a linear SVM. The proposed system has been evaluated using two large-scale tumor cohorts, collected from The Cancer Genome Atlas (TCGA). Experimental results show that the proposed model 1) outperforms systems utilizing sparse coding for unsupervised feature learning (e.g., PSD-SPM [5]); 2) is competitive with systems built upon features with biological prior knowledge (e.g., SMLSPM [4]). PMID:25554749
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.
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. PMID:24729748
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
Beyond Low Rank + Sparse: Multiscale Low Rank Matrix Decomposition
NASA Astrophysics Data System (ADS)
Ong, Frank; Lustig, Michael
2016-06-01
Low rank methods allow us to capture globally correlated components within matrices. The recent low rank + sparse decomposition further enables us to extract sparse entries along with the globally correlated components. In this paper, we present a natural generalization and consider the decomposition of matrices into components of multiple scales. Such decomposition is well motivated in practice as data matrices often exhibit local correlations in multiple scales. Concretely, we propose a multi-scale low rank modeling that represents a data matrix as a sum of block-wise low rank matrices with increasing scales of block sizes. We then consider the inverse problem of decomposing the data matrix into its multi-scale low rank components and approach the problem via a convex formulation. Theoretically, we show that under an incoherence condition, the convex program recovers the multi-scale low rank components exactly. Practically, we provide guidance on selecting the regularization parameters and incorporate cycle spinning to reduce blocking artifacts. Experimentally, we show that the multi-scale low rank decomposition provides a more intuitive decomposition than conventional low rank methods and demonstrate its effectiveness in four applications, including illumination normalization for face images, motion separation for surveillance videos, multi-scale modeling of the dynamic contrast enhanced magnetic resonance imaging and collaborative filtering exploiting age information.
Compact Structure Hashing via Sparse and Similarity Preserving Embedding.
Ye, Renzhen; Li, Xuelong
2016-03-01
Over the past few years, fast approximate nearest neighbor (ANN) search is desirable or essential, e.g., in huge databases, and therefore many hashing-based ANN techniques have been presented to return the nearest neighbors of a given query from huge databases. Hashing-based ANN techniques have become popular due to its low memory cost and good computational complexity. Recently, most of hashing methods have realized the importance of the relationship of the data and exploited the different structure of data to improve retrieval performance. However, a limitation of the aforementioned methods is that the sparse reconstructive relationship of the data is neglected. In this case, few methods can find the discriminating power and own the local properties of the data for learning compact and effective hash codes. To take this crucial issue into account, this paper proposes a method named special structure-based hashing (SSBH). SSBH can preserve the underlying geometric information among the data, and exploit the prior information that there exists sparse reconstructive relationship of the data, for learning compact and effective hash codes. Upon extensive experimental results, SSBH is demonstrated to be more robust and more effective than state-of-the-art hashing methods. PMID:25910267
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.
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.
Fault feature extraction of rolling element bearings using sparse representation
NASA Astrophysics Data System (ADS)
He, Guolin; Ding, Kang; Lin, Huibin
2016-03-01
Influenced by factors such as speed fluctuation, rolling element sliding and periodical variation of load distribution and impact force on the measuring direction of sensor, the impulse response signals caused by defective rolling bearing are non-stationary, and the amplitudes of the impulse may even drop to zero when the fault is out of load zone. The non-stationary characteristic and impulse missing phenomenon reduce the effectiveness of the commonly used demodulation method on rolling element bearing fault diagnosis. Based on sparse representation theories, a new approach for fault diagnosis of rolling element bearing is proposed. The over-complete dictionary is constructed by the unit impulse response function of damped second-order system, whose natural frequencies and relative damping ratios are directly identified from the fault signal by correlation filtering method. It leads to a high similarity between atoms and defect induced impulse, and also a sharply reduction of the redundancy of the dictionary. To improve the matching accuracy and calculation speed of sparse coefficient solving, the fault signal is divided into segments and the matching pursuit algorithm is carried out by segments. After splicing together all the reconstructed signals, the fault feature is extracted successfully. The simulation and experimental results show that the proposed method is effective for the fault diagnosis of rolling element bearing in large rolling element sliding and low signal to noise ratio circumstances.
Visual recognition and inference using dynamic overcomplete sparse learning.
Murray, Joseph F; Kreutz-Delgado, Kenneth
2007-09-01
We present a hierarchical architecture and learning algorithm for visual recognition and other visual inference tasks such as imagination, reconstruction of occluded images, and expectation-driven segmentation. Using properties of biological vision for guidance, we posit a stochastic generative world model and from it develop a simplified world model (SWM) based on a tractable variational approximation that is designed to enforce sparse coding. Recent developments in computational methods for learning overcomplete representations (Lewicki & Sejnowski, 2000; Teh, Welling, Osindero, & Hinton, 2003) suggest that overcompleteness can be useful for visual tasks, and we use an overcomplete dictionary learning algorithm (Kreutz-Delgado, et al., 2003) as a preprocessing stage to produce accurate, sparse codings of images. Inference is performed by constructing a dynamic multilayer network with feedforward, feedback, and lateral connections, which is trained to approximate the SWM. Learning is done with a variant of the back-propagation-through-time algorithm, which encourages convergence to desired states within a fixed number of iterations. Vision tasks require large networks, and to make learning efficient, we take advantage of the sparsity of each layer to update only a small subset of elements in a large weight matrix at each iteration. Experiments on a set of rotated objects demonstrate various types of visual inference and show that increasing the degree of overcompleteness improves recognition performance in difficult scenes with occluded objects in clutter. PMID:17650062
Design and implementation of sparse aperture imaging systems
NASA Astrophysics Data System (ADS)
Chung, Soon-Jo; Miller, David W.; de Weck, Olivier L.
2002-12-01
In order to better understand the technological difficulties involved in designing and building a sparse aperture array, the challenge of building a white light Golay-3 telescope was undertaken. The MIT Adaptive Reconnaissance Golay-3 Optical Satellite (ARGOS) project exploits wide-angle Fizeau interferometer technology with an emphasis on modularity in the optics and spacecraft subsystems. Unique design procedures encompassing the nature of coherent wavefront sensing, control and combining as well as various system engineering aspects to achieve cost effectiveness, are developed. To demonstrate a complete spacecraft in a 1-g environment, the ARGOS system is mounted on a frictionless air-bearing, and has the ability to track fast orbiting satellites like the ISS or the planets. Wavefront sensing techniques are explored to mitigate initial misalignment and to feed back real-time aberrations into the optical control loop. This paper presents the results and the lessons learned from the conceive, design and implementation phases of ARGOS. A preliminary assess-ment shows that the beam combining problem is the most challenging aspect of sparse optical arrays. The need for optical control is paramount due to tight beam combining tolerances. The wavefront sensing/control requirements appear to be a major technology and cost driver.
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.
Coronagraph-integrated wavefront sensing with a sparse aperture mask
NASA Astrophysics Data System (ADS)
Subedi, Hari; Zimmerman, Neil T.; Kasdin, N. Jeremy; Cavanagh, Kathleen; Riggs, A. J. Eldorado
2015-07-01
Stellar coronagraph performance is highly sensitive to optical aberrations. In order to effectively suppress starlight for exoplanet imaging applications, low-order wavefront aberrations entering a coronagraph, such as tip-tilt, defocus, and coma, must be determined and compensated. Previous authors have established the utility of pupil-plane masks (both nonredundant/sparse-aperture and generally asymmetric aperture masks) for wavefront sensing (WFS). Here, we show how a sparse aperture mask (SAM) can be integrated with a coronagraph to measure low-order differential phase aberrations. Starlight rejected by the coronagraph's focal plane stop is collimated to a relay pupil, where the mask forms an interference fringe pattern on a subsequent detector. Our numerical Fourier propagation models show that the information encoded in the fringe intensity distortions is sufficient to accurately discriminate and estimate Zernike phase modes extending from tip-tilt up to radial degree n=5, with amplitude up to λ/20 RMS. The SAM sensor can be integrated with both Lyot and shaped pupil coronagraphs at no detriment to the science beam quality. We characterize the reconstruction accuracy and the performance under low flux/short exposure time conditions, and place it in context of other coronagraph WFS schemes.
Dynamic Facial Expression Recognition With Atlas Construction and Sparse Representation.
Guo, Yimo; Zhao, Guoying; Pietikainen, Matti
2016-05-01
In this paper, a new dynamic facial expression recognition method is proposed. Dynamic facial expression recognition is formulated as a longitudinal groupwise registration problem. The main contributions of this method lie in the following aspects: 1) subject-specific facial feature movements of different expressions are described by a diffeomorphic growth model; 2) salient longitudinal facial expression atlas is built for each expression by a sparse groupwise image registration method, which can describe the overall facial feature changes among the whole population and can suppress the bias due to large intersubject facial variations; and 3) both the image appearance information in spatial domain and topological evolution information in temporal domain are used to guide recognition by a sparse representation method. The proposed framework has been extensively evaluated on five databases for different applications: the extended Cohn-Kanade, MMI, FERA, and AFEW databases for dynamic facial expression recognition, and UNBC-McMaster database for spontaneous pain expression monitoring. This framework is also compared with several state-of-the-art dynamic facial expression recognition methods. The experimental results demonstrate that the recognition rates of the new method are consistently higher than other methods under comparison. PMID:26955032
Depth reconstruction from sparse samples: representation, algorithm, and sampling.
Liu, Lee-Kang; Chan, Stanley H; Nguyen, Truong Q
2015-06-01
The rapid development of 3D technology and computer vision applications has motivated a thrust of methodologies for depth acquisition and estimation. However, existing hardware and software acquisition methods have limited performance due to poor depth precision, low resolution, and high computational cost. In this paper, we present a computationally efficient method to estimate dense depth maps from sparse measurements. There are three main contributions. First, we provide empirical evidence that depth maps can be encoded much more sparsely than natural images using common dictionaries, such as wavelets and contourlets. We also show that a combined wavelet-contourlet dictionary achieves better performance than using either dictionary alone. Second, we propose an alternating direction method of multipliers (ADMM) for depth map reconstruction. A multiscale warm start procedure is proposed to speed up the convergence. Third, we propose a two-stage randomized sampling scheme to optimally choose the sampling locations, thus maximizing the reconstruction performance for a given sampling budget. Experimental results show that the proposed method produces high-quality dense depth estimates, and is robust to noisy measurements. Applications to real data in stereo matching are demonstrated. PMID:25769151
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
Sparse aperture mask for low order wavefront sensing
NASA Astrophysics Data System (ADS)
Subedi, Hari; Zimmerman, Neil T.; Kasdin, N. Jeremy; Cavanagh, Kathleen; Riggs, A. J. E.
2015-09-01
A high contrast is required for direct imaging of exoplanets. Ideally, the level of contrast required for direct imaging of exoplanets can be achieved by coronagraphic imaging, but in practice, the contrast is degraded by wavefront aberrations. To achieve the required contrast, low-order wavefront aberrations such as tip-tilt, defocus and coma must be determined and corrected. In this paper, we present a technique that integrates a sparse- aperture mask (SAM) with a shaped pupil coronagraph (SPC) to make precise estimates of these low-order aberrations. Starlight rejected by the coronagraph's focal plane stop is collimated to a relay pupil, where the mask forms an interference fringe pattern on a detector. Using numerical simulations, we show that the SAM can estimate rapidly varying tip-tilt errors in space telescopes arising from line-of-sight pointing oscillations as well as other higher-order modes. We also show that a Kalman filter can be used with the SAM to improve the estimation. At Princetons High Contrast Imaging Laboratory, we have recently created a testbed devoted to low-order wavefront sensing experiments. The testbed incorporates custom-fabricated masks (shaped pupil, focal plane, and sparse aperture) with a deformable mirror and a CCD camera to demonstrate the estimation and correction of low-order aberrations. Our first experiments aim to replicate the results of the SAM wavefront sensor (SAM WFS) Fourier propagation models.
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.
When vegetation change alters ecosystem water availability.
Scott, Russell L; Huxman, Travis E; Barron-Gafford, Greg A; Darrel Jenerette, G; Young, Jessica M; Hamerlynck, Erik P
2014-07-01
The combined effects of vegetation and climate change on biosphere-atmosphere water vapor (H2 O) and carbon dioxide (CO2 ) exchanges are expected to vary depending, in part, on how biotic activity is controlled by and alters water availability. This is particularly important when a change in ecosystem composition alters the fractional covers of bare soil, grass, and woody plants so as to influence the accessibility of shallower vs. deeper soil water pools. To study this, we compared 5 years of eddy covariance measurements of H2 O and CO2 fluxes over a riparian grassland, shrubland, and woodland. In comparison with the surrounding upland region, groundwater access at the riparian sites increased net carbon uptake (NEP) and evapotranspiration (ET), which were sustained over more of the year. Among the sites, the grassland used less of the stable groundwater resource, and increasing woody plant density decoupled NEP and ET from incident precipitation (P), resulting in greater exchange rates that were less variable year to year. Despite similar gross patterns, how groundwater accessibility affected NEP was more complex than ET. The grassland had higher respiration (Reco ) costs. Thus, while it had similar ET and gross carbon uptake (GEP) to the shrubland, grassland NEP was substantially less. Also, grassland carbon fluxes were more variable due to occasional flooding at the site, which both stimulated and inhibited NEP depending upon phenology. Woodland NEP was large, but surprisingly similar to the less mature, sparse shrubland, even while having much greater GEP. Woodland Reco was greater than the shrubland and responded strongly and positively to P, which resulted in a surprising negative NEP response to P. This is likely due to the large accumulation of carbon aboveground and in the surface soil. These long-term observations support the strong role that water accessibility can play when determining the consequences of ecosystem vegetation change. PMID:24777485
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...
Cruciferous Vegetables and Cancer Prevention
... vegetables are part of the Brassica genus of plants. They include the following vegetables, among others: Arugula ... Jain MG, Hislop GT, Howe GR, Ghadirian P. Plant foods, antioxidants, and prostate cancer risk: findings from ...
Refinement of microwave vegetation indices
Technology Transfer Automated Retrieval System (TEKTRAN)
Previous investigations have established the basis for a new type of vegetation index based on passive microwave satellite observations. These microwave vegetation indices (MVIs) have been qualitatively evaluated by examining global spatial and seasonal temporal features. Limited quantitative studie...
Vegetable oil based grease formulations
Technology Transfer Automated Retrieval System (TEKTRAN)
Environmental concerns have brought forward vegetable oils as alternatives to more expensive synthetic lubricant base oils and less environmental friendly petroleum base stocks, in moderate operating conditions. Vegetable oils are becoming an obvious choice for potential replacement of petroleum ba...
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.
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.
NASA Astrophysics Data System (ADS)
Boulet, G.; Mougenot, B.; Lhomme, J.-P.; Fanise, P.; Lili-Chabaane, Z.; Olioso, A.; Bahir, M.; Rivalland, V.; Jarlan, L.; Merlin, O.; Coudert, B.; Er-Raki, S.; Lagouarde, J.-P.
2015-11-01
Evapotranspiration is an important component of the water cycle, especially in semi-arid lands. A way to quantify the spatial distribution of evapotranspiration and water stress from remote-sensing data is to exploit the available surface temperature as a signature of the surface energy balance. Remotely sensed energy balance models enable one to estimate stress levels and, in turn, the water status of continental surfaces. Dual-source models are particularly useful since they allow derivation of a rough estimate of the water stress of the vegetation instead of that of a soil-vegetation composite. They either assume that the soil and the vegetation interact almost independently with the atmosphere (patch approach corresponding to a parallel resistance scheme) or are tightly coupled (layer approach corresponding to a series resistance scheme). The water status of both sources is solved simultaneously from a single surface temperature observation based on a realistic underlying assumption which states that, in most cases, the vegetation is unstressed, and that if the vegetation is stressed, evaporation is negligible. In the latter case, if the vegetation stress is not properly accounted for, the resulting evaporation will decrease to unrealistic levels (negative fluxes) in order to maintain the same total surface temperature. This work assesses the retrieval performances of total and component evapotranspiration as well as surface and plant water stress levels by (1) proposing a new dual-source model named Soil Plant Atmosphere and Remote Sensing Evapotranspiration (SPARSE) in two versions (parallel and series resistance networks) based on the TSEB (Two-Source Energy Balance model, Norman et al., 1995) model rationale as well as state-of-the-art formulations of turbulent and radiative exchange, (2) challenging the limits of the underlying hypothesis for those two versions through a synthetic retrieval test and (3) testing the water stress retrievals (vegetation water
Seasonal variation of carbon fluxes in a sparse savanna in semi arid Sudan
Ardö, Jonas; Mölder, Meelis; El-Tahir, Bashir Awad; Elkhidir, Hatim Abdalla Mohammed
2008-01-01
Background Large spatial, seasonal and annual variability of major drivers of the carbon cycle (precipitation, temperature, fire regime and nutrient availability) are common in the Sahel region. This causes large variability in net ecosystem exchange and in vegetation productivity, the subsistence basis for a major part of the rural population in Sahel. This study compares the 2005 dry and wet season fluxes of CO2 for a grass land/sparse savanna site in semi arid Sudan and relates these fluxes to water availability and incoming photosynthetic photon flux density (PPFD). Data from this site could complement the current sparse observation network in Africa, a continent where climatic change could significantly impact the future and which constitute a weak link in our understanding of the global carbon cycle. Results The dry season (represented by Julian day 35–46, February 2005) was characterized by low soil moisture availability, low evapotranspiration and a high vapor pressure deficit. The mean daily NEE (net ecosystem exchange, Eq. 1) was -14.7 mmol d-1 for the 12 day period (negative numbers denote sinks, i.e. flux from the atmosphere to the biosphere). The water use efficiency (WUE) was 1.6 mmol CO2 mol H2O-1 and the light use efficiency (LUE) was 0.95 mmol CO2 mol PPFD-1. Photosynthesis is a weak, but linear function of PPFD. The wet season (represented by Julian day 266–273, September 2005) was, compared to the dry season, characterized by slightly higher soil moisture availability, higher evapotranspiration and a slightly lower vapor pressure deficit. The mean daily NEE was -152 mmol d-1 for the 8 day period. The WUE was lower, 0.97 mmol CO2 mol H2O-1 and the LUE was higher, 7.2 μmol CO2 mmol PPFD-1 during the wet season compared to the dry season. During the wet season photosynthesis increases with PPFD to about 1600 μmol m-2s-1 and then levels off. Conclusion Based on data collected during two short periods, the studied ecosystem was a sink of carbon
Sparse and optimal acquisition design for diffusion MRI and beyond
Koay, Cheng Guan; Özarslan, Evren; Johnson, Kevin M.; Meyerand, M. Elizabeth
2012-01-01
Purpose: Diffusion magnetic resonance imaging (MRI) in combination with functional MRI promises a whole new vista for scientists to investigate noninvasively the structural and functional connectivity of the human brain—the human connectome, which had heretofore been out of reach. As with other imaging modalities, diffusion MRI data are inherently noisy and its acquisition time-consuming. Further, a faithful representation of the human connectome that can serve as a predictive model requires a robust and accurate data-analytic pipeline. The focus of this paper is on one of the key segments of this pipeline—in particular, the development of a sparse and optimal acquisition (SOA) design for diffusion MRI multiple-shell acquisition and beyond. Methods: The authors propose a novel optimality criterion for sparse multiple-shell acquisition and quasimultiple-shell designs in diffusion MRI and a novel and effective semistochastic and moderately greedy combinatorial search strategy with simulated annealing to locate the optimum design or configuration. The goal of the optimality criteria is threefold: first, to maximize uniformity of the diffusion measurements in each shell, which is equivalent to maximal incoherence in angular measurements; second, to maximize coverage of the diffusion measurements around each radial line to achieve maximal incoherence in radial measurements for multiple-shell acquisition; and finally, to ensure maximum uniformity of diffusion measurement directions in the limiting case when all the shells are coincidental as in the case of a single-shell acquisition. The approach taken in evaluating the stability of various acquisition designs is based on the condition number and the A-optimal measure of the design matrix. Results: Even though the number of distinct configurations for a given set of diffusion gradient directions is very large in general—e.g., in the order of 10232 for a set of 144 diffusion gradient directions, the proposed search
Improved Image Registration by Sparse Patch-Based Deformation Estimation
Kim, Minjeong; Wu, Guorong; Wang, Qian; Shen, Dinggang
2014-01-01
Despite of intensive efforts for decades, deformable image registration is still a challenging problem due to the potential large anatomical differences across individual images, which limits the registration performance. Fortunately, this issue could be alleviated if a good initial deformation can be provided for the two images under registration, which are often termed as the moving subject and the fixed template, respectively. In this work, we present a novel patch-based initial deformation prediction framework for improving the performance of existing registration algorithms. Our main idea is to estimate the initial deformation between subject and template in a patch-wise fashion by using the sparse representation technique. We argue that two image patches should follow the same deformation towards the template image if their patch-wise appearance patterns are similar. To this end, our framework consists of two stages, i.e., the training stage and the application stage. In the training stage, we register all training images to the pre-selected template, such that the deformation of each training image with respect to the template is known. In the application stage, we apply the following four steps to efficiently calculate the initial deformation field for the new test subject: (1) We pick a small number of key points in the distinctive regions of the test subject; (2) For each key point, we extract a local patch and form a coupled appearance-deformation dictionary from training images where each dictionary atom consists of the image intensity patch as well as their respective local deformations; (3) A small set of training image patches in the coupled dictionary are selected to represent the image patch of each subject key point by sparse representation. Then, we can predict the initial deformation for each subject key point by propagating the pre-estimated deformations on the selected training patches with the same sparse representation coefficients. (4) We
Reliable positioning in a sparse GPS network, eastern Ontario
NASA Astrophysics Data System (ADS)
Samadi Alinia, H.; Tiampo, K.; Atkinson, G. M.
2013-12-01
Canada hosts two regions that are prone to large earthquakes: western British Columbia, and the St. Lawrence River region in eastern Canada. Although eastern Ontario is not as seismically active as other areas of eastern Canada, such as the Charlevoix/Ottawa Valley seismic zone, it experiences ongoing moderate seismicity. In historic times, potentially damaging events have occurred in New York State (Attica, 1929, M=5.7; Plattsburg, 2002, M=5.0), north-central Ontario (Temiskaming, 1935, M=6.2; North Bay, 2000, M=5.0), eastern Ontario (Cornwall, 1944, M=5.8), Georgian Bay (2005, MN=4.3), and western Quebec (Val-Des-Bois,2010, M=5.0, MN=5.8). In eastern Canada, the analysis of detailed, high-precision measurements of surface deformation is a key component in our efforts to better characterize the associated seismic hazard. The data from precise, continuous GPS stations is necessary to adequately characterize surface velocities from which patterns and rates of stress accumulation on faults can be estimated (Mazzotti and Adams, 2005; Mazzotti et al., 2005). Monitoring of these displacements requires employing high accuracy GPS positioning techniques. Detailed strain measurements can determine whether the regional strain everywhere is commensurate with a large event occurring every few hundred years anywhere within this general area or whether large earthquakes are limited to specific areas (Adams and Halchuck, 2003; Mazzotti and Adams, 2005). In many parts of southeastern Ontario and western Québec, GPS stations are distributed quite sparsely, with spacings of approximately 100 km or more. The challenge is to provide accurate solutions for these sparse networks with an approach that is capable of achieving high-accuracy positioning. Here, various reduction techniques are applied to a sparse network installed with the Southern Ontario Seismic Network in eastern Ontario. Recent developments include the implementation of precise point positioning processing on acquired
Object-oriented algorithmic laboratory for ordering sparse matrices
Kumfert, G K
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
Interpolating Sparse Scattered Oceanographic Data Using Flow Information
NASA Astrophysics Data System (ADS)
Streletz, G. J.; Gebbie, G.; Spero, H. J.; Kreylos, O.; Kellogg, L. H.; Hamann, B.
2012-12-01
We present a novel approach for interpolating sparse scattered data in the presence of a flow field. In order to visualize a scalar field representing a physical quantity such as salinity, temperature, or nutrient concentration in the ocean, the individual measured values of the quantity of interest typically are first converted into a representation of the scalar field on a regular grid. If the measured values are located at a number of scattered sites, then the reconstruction process will be scattered data interpolation. Scattered data interpolation itself is a well-known problem space for which many methods exist, including methods involving radial basis functions, statistical approaches such as optimal interpolation, and grid-based methods such as Laplace interpolation. However, the quality of the reconstruction result obtained using such methods depends upon having a sufficient density of sample points as input. For cases involving sparse scattered data - such as is the case when using measurements from benthic foraminifera in deep sea sedimentary cores as a proxy for the physical properties of the past ocean - the standard methods may not produce acceptable results. However, if the scalar field is associated with a known (or partially known) flow field, then the flow field information can be used to enhance the interpolation method in order to compensate for the sparsity of the available scalar field samples. Our hypothesis is that scalar field values should be more highly correlated along streamlines of the flow field than across such streamlines. We have investigated and tested such augmented, flow-field-aware scattered data interpolation methods. In particular, we have modified standard scattered data interpolation methods to use non-Euclidean distance pseudometrics, which we have constructed by employing various relative weightings of "distance-along-streamlines" versus "distance-from-streamlines." We have tested the resulting methods by applying them to
Fernández-Rivas, Montserrat
2015-01-01
Fruit and vegetable allergies are the most prevalent food allergies in adolescents and adults. The identification of the allergens involved and the elucidation of their intrinsic properties and cross-reactivity patterns has helped in the understanding of the mechanisms of sensitisation and how the allergen profiles determine the different phenotypes. The most frequent yet contrasting fruit and vegetable allergies are pollen-food syndrome (PFS) and lipid transfer protein (LTP) syndrome. In PFS, fruit and vegetable allergies result from a primary sensitisation to labile pollen allergens, such as Bet v 1 or profilin, and the resulting phenotype is mainly mild, consisting of local oropharyngeal reactions. In contrast, LTP syndrome results from a primary sensitisation to LTPs, which are stable plant food allergens, inducing frequent systemic reactions and even anaphylaxis. Although much less prevalent, severe fruit allergies may be associated with latex (latex-fruit syndrome). Molecular diagnosis is essential in guiding the management and risk assessment of these patients. Current management strategies comprise avoidance and rescue medication, including adrenaline, for severe LTP allergies. Specific immunotherapy with pollen is not indicated to treat pollen-food syndrome, but sublingual immunotherapy with LTPs seems to be a promising therapy for LTP syndrome. PMID:26022876
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
Subspace segmentation by dense block and sparse representation.
Tang, Kewei; Dunson, David B; Su, Zhixun; Liu, Risheng; Zhang, Jie; Dong, Jiangxin
2016-03-01
Subspace segmentation is a fundamental topic in computer vision and machine learning. However, the success of many popular methods is about independent subspace segmentation instead of the more flexible and realistic disjoint subspace segmentation. Focusing on the disjoint subspaces, we provide theoretical and empirical evidence of inferior performance for popular algorithms such as LRR. To solve these problems, we propose a novel dense block and sparse representation (DBSR) for subspace segmentation and provide related theoretical results. DBSR minimizes a combination of the 1,1-norm and maximum singular value of the representation matrix, leading to a combination of dense block and sparsity. We provide experimental results for synthetic and benchmark data showing that our method can outperform the state-of-the-art. PMID:26720247
A Population Shift between Sparsely Populated Folding Intermediates Determines Amyloidogenicity.
Karamanos, Theodoros K; Pashley, Clare L; Kalverda, Arnout P; Thompson, Gary S; Mayzel, Maxim; Orekhov, Vladislav Y; Radford, Sheena E
2016-05-18
The balance between protein folding and misfolding is a crucial determinant of amyloid assembly. Transient intermediates that are sparsely populated during protein folding have been identified as key players in amyloid aggregation. However, due to their ephemeral nature, structural characterization of these species remains challenging. Here, using the power of nonuniformly sampled NMR methods we investigate the folding pathway of amyloidogenic and nonamyloidogenic variants of β2-microglobulin (β2m) in atomic detail. Despite folding via common intermediate states, we show that the decreased population of the aggregation-prone ITrans state and population of a less stable, more dynamic species ablate amyloid formation by increasing the energy barrier for amyloid assembly. The results show that subtle changes in conformational dynamics can have a dramatic effect in determining whether a protein is amyloidogenic, without perturbation of the mechanism of protein folding. PMID:27117876
An efficient optical architecture for sparsely connected neural networks
NASA Technical Reports Server (NTRS)
Hine, Butler P., III; Downie, John D.; Reid, Max B.
1990-01-01
An architecture for general-purpose optical neural network processor is presented in which the interconnections and weights are formed by directing coherent beams holographically, thereby making use of the space-bandwidth products of the recording medium for sparsely interconnected networks more efficiently that the commonly used vector-matrix multiplier, since all of the hologram area is in use. An investigation is made of the use of computer-generated holograms recorded on such updatable media as thermoplastic materials, in order to define the interconnections and weights of a neural network processor; attention is given to limits on interconnection densities, diffraction efficiencies, and weighing accuracies possible with such an updatable thin film holographic device.
Improved load distribution in parallel sparse Cholesky factorization
NASA Technical Reports Server (NTRS)
Rothberg, Edward; Schreiber, Robert
1994-01-01
Compared to the customary column-oriented approaches, block-oriented, distributed-memory sparse Cholesky factorization benefits from an asymptotic reduction in interprocessor communication volume and an asymptotic increase in the amount of concurrency that is exposed in the problem. Unfortunately, block-oriented approaches (specifically, the block fan-out method) have suffered from poor balance of the computational load. As a result, achieved performance can be quite low. This paper investigates the reasons for this load imbalance and proposes simple block mapping heuristics that dramatically improve it. The result is a roughly 20% increase in realized parallel factorization performance, as demonstrated by performance results from an Intel Paragon system. We have achieved performance of nearly 3.2 billion floating point operations per second with this technique on a 196-node Paragon system.
Constraints on Fluctuations in Sparsely Characterized Biological Systems
NASA Astrophysics Data System (ADS)
Hilfinger, Andreas; Norman, Thomas M.; Vinnicombe, Glenn; Paulsson, Johan
2016-02-01
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.
Robust Simultaneous Registration and Segmentation with sparse error reconstruction.
Ghosh, Pratim; Manjunath, B S
2013-02-01
We introduce a fast and efficient variational framework for Simultaneous Registration and Segmentation (SRS) applicable to a wide variety of image sequences. We demonstrate that a dense correspondence map (between consecutive frames) can be reconstructed correctly even in the presence of partial occlusion, shading, and reflections. The errors are efficiently handled by exploiting their sparse nature. In addition, the segmentation functional is reformulated using a dual Rudin-Osher-Fatemi (ROF) model for fast implementation. Moreover, nonparametric shape prior terms that are suited for this dual-ROF model are proposed. The efficacy of the proposed method is validated with extensive experiments on both indoor, outdoor natural and biological image sequences, demonstrating the higher accuracy and efficiency compared to various state-of-the-art methods. PMID:22547427
Doubly Sparse Relevance Vector Machine for Continuous Facial Behavior Estimation.
Kaltwang, Sebastian; Todorovic, Sinisa; Pantic, Maja
2016-09-01
Certain inner feelings and physiological states like pain are subjective states that cannot be directly measured, but can be estimated from spontaneous facial expressions. Since they are typically characterized by subtle movements of facial parts, analysis of the facial details is required. To this end, we formulate a new regression method for continuous estimation of the intensity of facial behavior interpretation, called Doubly Sparse Relevance Vector Machine (DSRVM). DSRVM enforces double sparsity by jointly selecting the most relevant training examples (a.k.a. relevance vectors) and the most important kernels associated with facial parts relevant for interpretation of observed facial expressions. This advances prior work on multi-kernel learning, where sparsity of relevant kernels is typically ignored. Empirical evaluation on challenging Shoulder Pain videos, and the benchmark DISFA and SEMAINE datasets demonstrate that DSRVM outperforms competing approaches with a multi-fold reduction of running times in training and testing. PMID:26595911
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.
W-band sparse synthetic aperture for computational imaging.
Venkatesh, S; Viswanathan, N; Schurig, D
2016-04-18
We present a sparse synthetic-aperture, active imaging system at W-band (75 - 110 GHz), which uses sub-harmonic mixer modules. The system employs mechanical scanning of the receiver module position, and a fixed transmitter module. A vector network analyzer provides the back end detection. A full-wave forward model allows accurate construction of the image transfer matrix. We solve the inverse problem to reconstruct scenes using the least squares technique. We demonstrate far-field, diffraction limited imaging of 2D and 3D objects and achieve a cross-range resolution of 3 mm and a depth-range resolution of 4 mm, respectively. Furthermore, we develop an information-based metric to evaluate the performance of a given image transfer matrix for noise-limited, computational imaging systems. We use this metric to find the optimal gain of the radiating element for a given range, both theoretically and experimentally in our system. PMID:27137270
Profile analysis: the effects of rove on sparse spectra.
Lentz, Jennifer J
2005-11-01
Profile-analysis thresholds were measured in the presence and absence of overall level variation at different stimulus levels to determine whether nonlinear changes in the shape of the edges of excitation pattern peaks contribute to poorer spectral-shape sensitivity observed under roving levels. Roving levels decreased sensitivity for stimuli having few components separated widely in frequency to a greater extent than for stimuli having more densely spaced components. The stimulus level did not influence sensitivity when overall level variation was absent, suggesting that listeners rely on peaks in the excitation patterns and not the edges of the peaks (as would have been predicted by the near miss to Weber's law). Because the edges of the peaks were not used in the absence of roving levels, it follows that the larger rove effects for sparse stimuli were not likely due to excitation pattern inconstancy. PMID:16334895
Compressed sensing sparse reconstruction for coherent field imaging
NASA Astrophysics Data System (ADS)
Bei, Cao; Xiu-Juan, Luo; Yu, Zhang; Hui, Liu; Ming-Lai, Chen
2016-04-01
Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant influence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process, we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future. Project supported by the National Natural Science Foundation of China (Grant No. 61505248) and the Fund from Chinese Academy of Sciences, the Light of “Western” Talent Cultivation Plan “Dr. Western Fund Project” (Grant No. Y429621213).
Sparse Zero-Sum Games as Stable Functional Feature Selection
Sokolovska, Nataliya; Teytaud, Olivier; Rizkalla, Salwa; Clément, Karine; Zucker, Jean-Daniel
2015-01-01
In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints. PMID:26325268
Sparse Zero-Sum Games as Stable Functional Feature Selection.
Sokolovska, Nataliya; Teytaud, Olivier; Rizkalla, Salwa; Clément, Karine; Zucker, Jean-Daniel
2015-01-01
In large-scale systems biology applications, features are structured in hidden functional categories whose predictive power is identical. Feature selection, therefore, can lead not only to a problem with a reduced dimensionality, but also reveal some knowledge on functional classes of variables. In this contribution, we propose a framework based on a sparse zero-sum game which performs a stable functional feature selection. In particular, the approach is based on feature subsets ranking by a thresholding stochastic bandit. We provide a theoretical analysis of the introduced algorithm. We illustrate by experiments on both synthetic and real complex data that the proposed method is competitive from the predictive and stability viewpoints. PMID:26325268
Sparse Labeling of Proteins: Structural Characterization from Long Range Constraints
Prestegard, James H.; Agard, David A.; Moremen, Kelley W.; Lavery, Laura A.; Morris, Laura C.; Pederson, Kari
2014-01-01
Structural characterization of biologically important proteins faces many challenges associated with degradation of resolution as molecular size increases and loss of resolution improving tools such as perdeuteration when non-bacterial hosts must be used for expression. In these cases, sparse isotopic labeling (single or small subsets of amino acids) combined with long range paramagnetic constraints and improved computational modeling offer an alternative. This perspective provides a brief overview of this approach and two discussions of potential applications; one involving a very large system (an Hsp90 homolog) in which perdeuteration is possible and methyl-TROSY sequences can potentially be used to improve resolution, and one involving ligand placement in a glycosylated protein where resolution is achieved by single amino acid labeling (the sialyltransferase, ST6Gal1). This is not intended as a comprehensive review, but as a discussion of future prospects that promise impact on important questions in the structural biology area. PMID:24656078
Automated identification of crystallographic ligands using sparse-density representations
Carolan, C. G.; Lamzin, V. S.
2014-07-01
A novel procedure for identifying ligands in macromolecular crystallographic electron-density maps is introduced. Density clusters in such maps can be rapidly attributed to one of 82 different ligands in an automated manner. 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.
Sparse labeling of proteins: Structural characterization from long range constraints
NASA Astrophysics Data System (ADS)
Prestegard, James H.; Agard, David A.; Moremen, Kelley W.; Lavery, Laura A.; Morris, Laura C.; Pederson, Kari
2014-04-01
Structural characterization of biologically important proteins faces many challenges associated with degradation of resolution as molecular size increases and loss of resolution improving tools such as perdeuteration when non-bacterial hosts must be used for expression. In these cases, sparse isotopic labeling (single or small subsets of amino acids) combined with long range paramagnetic constraints and improved computational modeling offer an alternative. This perspective provides a brief overview of this approach and two discussions of potential applications; one involving a very large system (an Hsp90 homolog) in which perdeuteration is possible and methyl-TROSY sequences can potentially be used to improve resolution, and one involving ligand placement in a glycosylated protein where resolution is achieved by single amino acid labeling (the sialyltransferase, ST6Gal1). This is not intended as a comprehensive review, but as a discussion of future prospects that promise impact on important questions in the structural biology area.
Sparse-Coding-Based Computed Tomography Image Reconstruction
Yoon, Gang-Joon
2013-01-01
Computed tomography (CT) is a popular type of medical imaging that generates images of the internal structure of an object based on projection scans of the object from several angles. There are numerous methods to reconstruct the original shape of the target object from scans, but they are still dependent on the number of angles and iterations. To overcome the drawbacks of iterative reconstruction approaches like the algebraic reconstruction technique (ART), while the recovery is slightly impacted from a random noise (small amount of ℓ2 norm error) and projection scans (small amount of ℓ1 norm error) as well, we propose a medical image reconstruction methodology using the properties of sparse coding. It is a very powerful matrix factorization method which each pixel point is represented as a linear combination of a small number of basis vectors. PMID:23576898
Multiple peaks of species abundance distributions induced by sparse interactions.
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. PMID:27627322
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.
Mandala networks: ultra-small-world and highly sparse graphs.
Sampaio Filho, Cesar I N; Moreira, André A; Andrade, Roberto F S; Herrmann, Hans J; Andrade, José S
2015-01-01
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. PMID:25765450
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.
Ensemble of sparse classifiers for high-dimensional biological data.
Kim, Sunghan; Scalzo, Fabien; Telesca, Donatello; Hu, Xiao
2015-01-01
Biological data are often high in dimension while the number of samples is small. In such cases, the performance of classification can be improved by reducing the dimension of data, which is referred to as feature selection. Recently, a novel feature selection method has been proposed utilising the sparsity of high-dimensional biological data where a small subset of features accounts for most variance of the dataset. In this study we propose a new classification method for high-dimensional biological data, which performs both feature selection and classification within a single framework. Our proposed method utilises a sparse linear solution technique and the bootstrap aggregating algorithm. We tested its performance on four public mass spectrometry cancer datasets along with two other conventional classification techniques such as Support Vector Machines and Adaptive Boosting. The results demonstrate that our proposed method performs more accurate classification across various cancer datasets than those conventional classification techniques. PMID:26510301
A Population Shift between Sparsely Populated Folding Intermediates Determines Amyloidogenicity
2016-01-01
The balance between protein folding and misfolding is a crucial determinant of amyloid assembly. Transient intermediates that are sparsely populated during protein folding have been identified as key players in amyloid aggregation. However, due to their ephemeral nature, structural characterization of these species remains challenging. Here, using the power of nonuniformly sampled NMR methods we investigate the folding pathway of amyloidogenic and nonamyloidogenic variants of β2-microglobulin (β2m) in atomic detail. Despite folding via common intermediate states, we show that the decreased population of the aggregation-prone ITrans state and population of a less stable, more dynamic species ablate amyloid formation by increasing the energy barrier for amyloid assembly. The results show that subtle changes in conformational dynamics can have a dramatic effect in determining whether a protein is amyloidogenic, without perturbation of the mechanism of protein folding. PMID:27117876
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.
Predicting Homogeneous Pilus Structure from Monomeric Data and Sparse Constraints.
Xiao, Ke; Shu, Chuanjun; Yan, Qin; Sun, Xiao
2015-01-01
Type IV pili (T4P) and T2SS (Type II Secretion System) pseudopili are filaments extending beyond microbial surfaces, comprising homologous subunits called "pilins." In this paper, we presented a new approach to predict pseudo atomic models of pili combining ambiguous symmetric constraints with sparse distance information obtained from experiments and based neither on electronic microscope (EM) maps nor on accurate a priori symmetric details. The approach was validated by the reconstruction of the gonococcal (GC) pilus from Neisseria gonorrhoeae, the type IVb toxin-coregulated pilus (TCP) from Vibrio cholerae, and pseudopilus of the pullulanase T2SS (the PulG pilus) from Klebsiella oxytoca. In addition, analyses of computational errors showed that subunits should be treated cautiously, as they are slightly flexible and not strictly rigid bodies. A global sampling in a wider range was also implemented and implied that a pilus might have more than one but fewer than many possible intact conformations. PMID:26064954
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
Parallel, iterative solution of sparse linear systems: Models and architectures
NASA Technical Reports Server (NTRS)
Reed, D. A.; Patrick, M. L.
1984-01-01
A model of a general class of asynchronous, iterative solution methods for linear systems is developed. In the model, the system is solved by creating several cooperating tasks that each compute a portion of the solution vector. A data transfer model predicting both the probability that data must be transferred between two tasks and the amount of data to be transferred is presented. This model is used to derive an execution time model for predicting parallel execution time and an optimal number of tasks given the dimension and sparsity of the coefficient matrix and the costs of computation, synchronization, and communication. The suitability of different parallel architectures for solving randomly sparse linear systems is discussed. Based on the complexity of task scheduling, one parallel architecture, based on a broadcast bus, is presented and analyzed.
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 Recovery Analysis of High-Resolution Climate Data
NASA Astrophysics Data System (ADS)
Archibald, R.
2013-12-01
The field of compressed sensing is vast and currently very active, with new results, methods, and algorithms appearing almost daily. The first notions of compressed sensing began with Prony's method, which was designed by the French mathematician Gaspard Riche de Prony to extract signal information from a limited number of measurements. Since then, sparsity has been used empirically in a variety of applications, including geology and geophysics, spectroscopy, signal processing, radio astronomy, and medical ultrasound. High-resolution climate studies performed on large scale high performance computing have been producing large amounts of data that can benefit from unique mathematical methods for analysis. This work demonstrates how sparse recovery and L1 regularization can be used effectively on large datasets from high-resolution climate studies.
Sparse Reconstruction for Bioluminescence Tomography Based on the Semigreedy Method
Guo, Wei; Jia, Kebin; Zhang, Qian; Liu, Xueyan; Feng, Jinchao; Qin, Chenghu; Ma, Xibo; Yang, Xin; Tian, Jie
2012-01-01
Bioluminescence tomography (BLT) is a molecular imaging modality which can three-dimensionally resolve the molecular processes in small animals in vivo. The ill-posedness nature of BLT problem makes its reconstruction bears nonunique solution and is sensitive to noise. In this paper, we proposed a sparse BLT reconstruction algorithm based on semigreedy method. To reduce the ill-posedness and computational cost, the optimal permissible source region was automatically chosen by using an iterative search tree. The proposed method obtained fast and stable source reconstruction from the whole body and imposed constraint without using a regularization penalty term. Numerical simulations on a mouse atlas, and in vivo mouse experiments were conducted to validate the effectiveness and potential of the method. PMID:22927887
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. PMID:25515941
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
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.
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.
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
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
NASA Astrophysics Data System (ADS)
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
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
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
Hippocampal Remapping Is Constrained by Sparseness rather than Capacity
Kammerer, Axel; Leibold, Christian
2014-01-01
Grid cells in the medial entorhinal cortex encode space with firing fields that are arranged on the nodes of spatial hexagonal lattices. Potential candidates to read out the space information of this grid code and to combine it with other sensory cues are hippocampal place cells. In this paper, we investigate a population of grid cells providing feed-forward input to place cells. The capacity of the underlying synaptic transformation is determined by both spatial acuity and the number of different spatial environments that can be represented. The codes for different environments arise from phase shifts of the periodical entorhinal cortex patterns that induce a global remapping of hippocampal place fields, i.e., a new random assignment of place fields for each environment. If only a single environment is encoded, the grid code can be read out at high acuity with only few place cells. A surplus in place cells can be used to store a space code for more environments via remapping. The number of stored environments can be increased even more efficiently by stronger recurrent inhibition and by partitioning the place cell population such that learning affects only a small fraction of them in each environment. We find that the spatial decoding acuity is much more resilient to multiple remappings than the sparseness of the place code. Since the hippocampal place code is sparse, we thus conclude that the projection from grid cells to the place cells is not using its full capacity to transfer space information. Both populations may encode different aspects of space. PMID:25474570
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.