Generating and Separating Twisted Light by gradient-rotation Split-Ring Antenna Metasurfaces.

PubMed

Zeng, Jinwei; Li, Ling; Yang, Xiaodong; Gao, Jie

2016-05-11

Nanoscale compact optical vortex generators promise substantially significant prospects in modern optics and photonics, leading to many advances in sensing, imaging, quantum communication, and optical manipulation. However, conventional vortex generators often suffer from bulky size, low vortex mode purity in the converted beam, or limited operation bandwidth. Here, we design and demonstrate gradient-rotation split-ring antenna metasurfaces as unique spin-to-orbital angular momentum beam converters to simultaneously generate and separate pure optical vortices in a broad wavelength range. Our proposed design has the potential for realizing miniaturized on-chip OAM-multiplexers, as well as enabling new types of metasurface devices for the manipulation of complex structured light beams.

Light Makes a Surface Banana-Bond Split: Photodesorption of Molecular Hydrogen from RuO 2 (110)

DOE Office of Scientific and Technical Information (OSTI.GOV)

Henderson, Michael A.; Mu, Rentao; Dahal, Arjun

The coordination of H2 to a metal center via polarization of its bond electron density, known as a Kubas complex, is the means by which H2 chemisorbs at Ru4+ sites on the rutile RuO2(110) surface. This distortion of electron density off an interatomic axis is often described as a ‘banana-bond.’ We show that the Ru-H2 banana-bond can be destabilized, and split, using visible light. Photodesorption of H2 (or D2) is evident by mass spectrometry and scanning tunneling microscopy. From time-dependent density functional theory, the key optical excitation splitting the Ru-H2 banana-bond involves an interband transition in RuO2 which effectively diminishesmore » its Lewis acidity, and thereby weakening the Kubas complex. Such excitations are not expected to affect adsorbates on RuO2 given its metallic properties. Therefore, this common thermal co-catalyst employed in promoting water splitting is, itself, photo-active in the visible.« less

New shape models of asteroids reconstructed from sparse-in-time photometry

NASA Astrophysics Data System (ADS)

Durech, Josef; Hanus, Josef; Vanco, Radim; Oszkiewicz, Dagmara Anna

2015-08-01

Asteroid physical parameters - the shape, the sidereal rotation period, and the spin axis orientation - can be reconstructed from the disk-integrated photometry either dense (classical lightcurves) or sparse in time by the lightcurve inversion method. We will review our recent progress in asteroid shape reconstruction from sparse photometry. The problem of finding a unique solution of the inverse problem is time consuming because the sidereal rotation period has to be found by scanning a wide interval of possible periods. This can be efficiently solved by splitting the period parameter space into small parts that are sent to computers of volunteers and processed in parallel. We will show how this approach of distributed computing works with currently available sparse photometry processed in the framework of project Asteroids@home. In particular, we will show the results based on the Lowell Photometric Database. The method produce reliable asteroid models with very low rate of false solutions and the pipelines and codes can be directly used also to other sources of sparse photometry - Gaia data, for example. We will present the distribution of spin axis of hundreds of asteroids, discuss the dependence of the spin obliquity on the size of an asteroid,and show examples of spin-axis distribution in asteroid families that confirm the Yarkovsky/YORP evolution scenario.

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

Visible near-infrared light scattering of single silver split-ring structure made by nanosphere lithography.

PubMed

Okamoto, Toshihiro; Fukuta, Tetsuya; Sato, Shuji; Haraguchi, Masanobu; Fukui, Masuo

2011-04-11

We succeeded in making a silver split-ring (SR) structure of approximately 130 nm in diameter on a glass substrate using a nanosphere lithography technique. The light scattering spectrum in visible near-infrared region of a single, isolated SR was measured using a microscope spectroscopy optical system. The electromagnetic field enhancement spectrum and distribution of the SR structure were simulated by the finite-difference time-domain method, and the excitation modes were clarified. The long wavelength peak in the light scattering spectra corresponded to a fundamental LC resonance mode excited by an incident electric field. It was shown that a single SR structure fabricated as abovementioned can operate as a resonator and generate a magnetic dipole. © 2011 Optical Society of America

Robust Cell Detection of Histopathological Brain Tumor Images Using Sparse Reconstruction and Adaptive Dictionary Selection

PubMed Central

Su, Hai; Xing, Fuyong; Yang, Lin

2016-01-01

Successful diagnostic and prognostic stratification, treatment outcome prediction, and therapy planning depend on reproducible and accurate pathology analysis. Computer aided diagnosis (CAD) is a useful tool to help doctors make better decisions in cancer diagnosis and treatment. Accurate cell detection is often an essential prerequisite for subsequent cellular analysis. The major challenge of robust brain tumor nuclei/cell detection is to handle significant variations in cell appearance and to split touching cells. In this paper, we present an automatic cell detection framework using sparse reconstruction and adaptive dictionary learning. The main contributions of our method are: 1) A sparse reconstruction based approach to split touching cells; 2) An adaptive dictionary learning method used to handle cell appearance variations. The proposed method has been extensively tested on a data set with more than 2000 cells extracted from 32 whole slide scanned images. The automatic cell detection results are compared with the manually annotated ground truth and other state-of-the-art cell detection algorithms. The proposed method achieves the best cell detection accuracy with a F1 score = 0.96. PMID:26812706

From a philanthropic idea to building of civic hospital in Split in light of new archival evidence.

PubMed

Brisky, Livia; Fatović-Ferencić, Stella

2006-02-01

We investigated the circumstances of building of the Civic Hospital in Split in the light of new archival evidence. The study necessitated a thorough review of the older historiography and previously unpublished archival sources kept in the State Archives in Venice and Zadar. The findings showed that construction of the hospital building finished in 1797, ie, five years later than officially cited. The topographical plan and the original project of the Split Civic Hospital were found, as well as the name of the project's author and the building supervisor. The data on the earliest efforts of Ergovac brothers to acquire land and building permission were corrected. The study revealed a recognizable pattern in the attitude of the authorities toward the establishment of a hospital at the end of 18th century.

111 oriented gold nanoplatelets on multilayer graphene as visible light photocatalyst for overall water splitting.

PubMed

Mateo, Diego; Esteve-Adell, Iván; Albero, Josep; Royo, Juan F Sánchez; Primo, Ana; Garcia, Hermenegildo

2016-06-06

Development of renewable fuels from solar light appears as one of the main current challenges in energy science. A plethora of photocatalysts have been investigated to obtain hydrogen and oxygen from water and solar light in the last decades. However, the photon-to-hydrogen molecule conversion is still far from allowing real implementation of solar fuels. Here we show that 111 facet-oriented gold nanoplatelets on multilayer graphene films deposited on quartz is a highly active photocatalyst for simulated sunlight overall water splitting into hydrogen and oxygen in the absence of sacrificial electron donors, achieving hydrogen production rate of 1.2 molH2 per gcomposite per h. This photocatalytic activity arises from the gold preferential orientation and the strong gold-graphene interaction occurring in the composite system.

111 oriented gold nanoplatelets on multilayer graphene as visible light photocatalyst for overall water splitting

PubMed Central

Mateo, Diego; Esteve-Adell, Iván; Albero, Josep; Royo, Juan F. Sánchez; Primo, Ana; Garcia, Hermenegildo

2016-01-01

Development of renewable fuels from solar light appears as one of the main current challenges in energy science. A plethora of photocatalysts have been investigated to obtain hydrogen and oxygen from water and solar light in the last decades. However, the photon-to-hydrogen molecule conversion is still far from allowing real implementation of solar fuels. Here we show that 111 facet-oriented gold nanoplatelets on multilayer graphene films deposited on quartz is a highly active photocatalyst for simulated sunlight overall water splitting into hydrogen and oxygen in the absence of sacrificial electron donors, achieving hydrogen production rate of 1.2 molH2 per gcomposite per h. This photocatalytic activity arises from the gold preferential orientation and the strong gold–graphene interaction occurring in the composite system. PMID:27264495

Anatomy of a Visible Light Activated Photocatalyst for Water Splitting

DOE PAGES

Phivilay, Somphonh Peter; Roberts, Charles; Gamalski, Andrew; ...

2018-06-08

The supported mixed oxide (Rh 2-yCr yO 3)/(Ga 1-xZn x)(N 1-xO x) photocatalyst, highly active for splitting of H 2O, was extensively characterized for its bulk and surface properties with the objective of developing fundamental structure-photoactivity relationships. Raman and UV-vis spectroscopy revealed that the molecular and electronic structures, respectively, of the oxynitride (Ga 1-xZn x)(N 1-xO x) support are not perturbed by the deposition of the (Rh 2-yCr yO 3) NPs. Photoluminescence (PL) spectroscopy, however, showed that the oxynitride (Ga 1-xZn x)(N 1-xO x) support is the source of excited electrons/holes and the (Rh 2-yCr yO 3) NPs greatly reducemore » the undesirable recombination of photoexcited electron/holes by acting as efficient electron traps as well as increase the lifetimes of the excitons. High Resolution-XPS and High Sensitivity-LEIS surface analyses reveal that the surfaces of the (Rh 2-yCr yO 3) NPs consist of Rh +3 and Cr +3 mixed oxide species. In Situ AP-XPS help to reveal that the Rh+3 and surface N atoms are involved in water splitting. Dispersed RhOx species on the (Ga 1-xZn x)(N 1-xO x) support and on CrO x NPs were found to be the photocatalytic active sites for H 2 generation and N and Zn sites from the (Ga 1-xZn x)(N 1-xO x) support are the photocatalytic active site for O 2 generation. The current investigation establishes the fundamental structure-photoactivity relationships of these visible light activated photocatalysts.« less

Anatomy of a Visible Light Activated Photocatalyst for Water Splitting

DOE Office of Scientific and Technical Information (OSTI.GOV)

Phivilay, Somphonh Peter; Roberts, Charles; Gamalski, Andrew

The supported mixed oxide (Rh 2-yCr yO 3)/(Ga 1-xZn x)(N 1-xO x) photocatalyst, highly active for splitting of H 2O, was extensively characterized for its bulk and surface properties with the objective of developing fundamental structure-photoactivity relationships. Raman and UV-vis spectroscopy revealed that the molecular and electronic structures, respectively, of the oxynitride (Ga 1-xZn x)(N 1-xO x) support are not perturbed by the deposition of the (Rh 2-yCr yO 3) NPs. Photoluminescence (PL) spectroscopy, however, showed that the oxynitride (Ga 1-xZn x)(N 1-xO x) support is the source of excited electrons/holes and the (Rh 2-yCr yO 3) NPs greatly reducemore » the undesirable recombination of photoexcited electron/holes by acting as efficient electron traps as well as increase the lifetimes of the excitons. High Resolution-XPS and High Sensitivity-LEIS surface analyses reveal that the surfaces of the (Rh 2-yCr yO 3) NPs consist of Rh +3 and Cr +3 mixed oxide species. In Situ AP-XPS help to reveal that the Rh+3 and surface N atoms are involved in water splitting. Dispersed RhOx species on the (Ga 1-xZn x)(N 1-xO x) support and on CrO x NPs were found to be the photocatalytic active sites for H 2 generation and N and Zn sites from the (Ga 1-xZn x)(N 1-xO x) support are the photocatalytic active site for O 2 generation. The current investigation establishes the fundamental structure-photoactivity relationships of these visible light activated photocatalysts.« less

Angularly symmetric splitting of a light beam upon reflection and refraction at an air-dielectric plane boundary.

PubMed

Azzam, R M A

2015-12-01

Conditions for achieving equal and opposite angular deflections of a light beam by reflection and refraction at an air-dielectric boundary are determined. Such angularly symmetric beam splitting (ASBS) is possible only if the angle of incidence is >60° by exactly one third of the angle of refraction. This simple law, plus Snell's law, leads to several analytical results that clarify all aspects of this phenomenon. In particular, it is shown that the intensities of the two symmetrically deflected beams can be equalized by proper choice of the prism refractive index and the azimuth of incident linearly polarized light. ASBS enables a geometrically attractive layout of optical systems that employ multiple prism beam splitters.

Controlling slow and fast light and dynamic pulse-splitting with tunable optical gain in a whispering-gallery-mode microcavity

DOE Office of Scientific and Technical Information (OSTI.GOV)

Asano, M.; Ikuta, R.; Imoto, N.

We report controllable manipulation of slow and fast light in a whispering-gallery-mode microtoroid resonator fabricated from Erbium (Er{sup 3+}) doped silica. We observe continuous transition of the coupling between the fiber-taper waveguide and the microresonator from undercoupling to critical coupling and then to overcoupling regimes by increasing the pump power even though the spatial distance between the resonator and the waveguide was kept fixed. This, in turn, enables switching from fast to slow light and vice versa just by increasing the optical gain. An enhancement of delay of two-fold over the passive silica resonator (no optical gain) was observed inmore » the slow light regime. Moreover, we show dynamic pulse splitting and its control in slow/fast light systems using optical gain.« less

Particulate photocatalysts for overall water splitting

NASA Astrophysics Data System (ADS)

Chen, Shanshan; Takata, Tsuyoshi; Domen, Kazunari

2017-10-01

The conversion of solar energy to chemical energy is a promising way of generating renewable energy. Hydrogen production by means of water splitting over semiconductor photocatalysts is a simple, cost-effective approach to large-scale solar hydrogen synthesis. Since the discovery of the Honda-Fujishima effect, considerable progress has been made in this field, and numerous photocatalytic materials and water-splitting systems have been developed. In this Review, we summarize existing water-splitting systems based on particulate photocatalysts, focusing on the main components: light-harvesting semiconductors and co-catalysts. The essential design principles of the materials employed for overall water-splitting systems based on one-step and two-step photoexcitation are also discussed, concentrating on three elementary processes: photoabsorption, charge transfer and surface catalytic reactions. Finally, we outline challenges and potential advances associated with solar water splitting by particulate photocatalysts for future commercial applications.

Grafting of burns with widely meshed autograft split skin and Langerhans cell-depressed allograft split skin overlay

DOE Office of Scientific and Technical Information (OSTI.GOV)

Alsbjoern, B.F.S.; Sorensen, B.

1986-12-01

Extensively burned patients suffer from lack of sufficient autologous donor skin. Meshing and wide expansion of the obtained split skin has met the requirement to a large degree. However, the wider the expansion, the less chance of a proper take. By covering widely expanded autografts with viable cadaver split skin, the take has been improved. If the epidermal Langerhans cells in the cadaver split skin are depressed by ultraviolet B light and glucocorticosteroids before grafting, a prolonged allograft take can be achieved and the healing of the underlying autografts is ensured for an extended period. Grafting results in 6 patientsmore » with extensive burns are reported.« less

Light-Driven Water Splitting by a Covalently Linked Ruthenium-Based Chromophore–Catalyst Assembly

DOE PAGES

Sherman, Benjamin D.; Xie, Yan; Sheridan, Matthew V.; ...

2016-12-09

The preparation and characterization of new Ru(II) polypyridyl-based chromophore–catalyst assemblies, [(4,4'-PO 3H 2-bpy) 2Ru(4-Mebpy-4'-epic)Ru(bda)(pic)] 2+ (1, bpy = 2,2'-bipyridine; 4-Mebpy-4'-epic = 4-(4-methylbipyridin-4'-yl-ethyl)-pyridine; bda = 2,2'-bipyridine-6,6'-dicarboxylate; pic = 4-picoline), and [(bpy) 2Ru(4-Mebpy-4'-epic)Ru(bda)(pic)] 2+ (1') are described, as is the application of 1 in a dye-sensitized photoelectrosynthesis cell (DSPEC) for solar water splitting. Furthermore, on SnO 2/TiO 2 core–shell electrodes in a DSPEC configuration with a Pt cathode, the chromophore–catalyst assembly undergoes light-driven water oxidation at pH 5.7 in a 0.1 M acetate buffer, 0.5 M in NaClO 4. We observed photocurrents of ~0.85 mA cm –2, with illumination by a 100more » mW cm –2 white light source, after 30 s under a 0.1 V vs Ag/AgCl applied bias with a faradaic efficiency for O 2 production of 74% measured over a 5 min illumination period.« less

Light-Driven Water Splitting by a Covalently Linked Ruthenium-Based Chromophore–Catalyst Assembly

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sherman, Benjamin D.; Xie, Yan; Sheridan, Matthew V.

The preparation and characterization of new Ru(II) polypyridyl-based chromophore–catalyst assemblies, [(4,4'-PO 3H 2-bpy) 2Ru(4-Mebpy-4'-epic)Ru(bda)(pic)] 2+ (1, bpy = 2,2'-bipyridine; 4-Mebpy-4'-epic = 4-(4-methylbipyridin-4'-yl-ethyl)-pyridine; bda = 2,2'-bipyridine-6,6'-dicarboxylate; pic = 4-picoline), and [(bpy) 2Ru(4-Mebpy-4'-epic)Ru(bda)(pic)] 2+ (1') are described, as is the application of 1 in a dye-sensitized photoelectrosynthesis cell (DSPEC) for solar water splitting. Furthermore, on SnO 2/TiO 2 core–shell electrodes in a DSPEC configuration with a Pt cathode, the chromophore–catalyst assembly undergoes light-driven water oxidation at pH 5.7 in a 0.1 M acetate buffer, 0.5 M in NaClO 4. We observed photocurrents of ~0.85 mA cm –2, with illumination by a 100more » mW cm –2 white light source, after 30 s under a 0.1 V vs Ag/AgCl applied bias with a faradaic efficiency for O 2 production of 74% measured over a 5 min illumination period.« less

Fractional-topological-charge-induced vortex birth and splitting of light fields on the submicron scale

NASA Astrophysics Data System (ADS)

Fang, Yiqi; Lu, Qinghong; Wang, Xiaolei; Zhang, Wuhong; Chen, Lixiang

2017-02-01

The study of vortex dynamics is of fundamental importance in understanding the structured light's propagation behavior in the realm of singular optics. Here, combining with the large-angle holographic lithography in photoresist, a simple experiment to trace and visualize the vortex birth and splitting of light fields induced by various fractional topological charges is reported. For a topological charge M =1.76 , the recorded microstructures reveal that although it finally leads to the formation of a pair of fork gratings, these two vortices evolve asynchronously. More interestingly, it is observed on the submicron scale that high-order topological charges M =3.48 and 3.52, respectively, give rise to three and four characteristic forks embedded in the samples with one-wavelength resolution of about 450 nm. Numerical simulations based on orbital angular momentum eigenmode decomposition support well the experimental observations. Our method could be applied effectively to study other structured matter waves, such as the electron and neutron beams.

Ray-splitting correction to the Weyl formula: Experiment versus theory

NASA Astrophysics Data System (ADS)

Blumel, Reinhold

2004-03-01

Ray splitting is a phenomenon we are all familiar with: A light ray hitting a water surface at an angle is split into a transmitted and a reflected ray. Ray splitting is not restricted to light and water, but occurs generally in all wave systems in which the properties of the propagation medium change rapidly on the scale of a wave length. It was predicted by Prange et al. [Phys. Rev. E 53, 207 (1996)] that ray splitting produces universal corrections to the Weyl formula, i.e. the average density of states. Following a brief review of Weyl's theory and the theory of ray splitting, this talk presents recent results of a first experimental confirmation of the existence of ray-splitting corrections to the Weyl formula. The experiment, a quasi two-dimensional microwave cavity loaded with two dielectric bars, has been carried out by Corrie Vaa and Peter Koch at the State University of New York at Stony Brook [C. Vaa, P. M. Koch, and R. Blumel, Phys. Rev. Lett. 90, 194102 (2003)]. This research is supported by the NSF under Grant Numbers PHY-9732443, PHY-0099398 and PHY-9984075.

Band structure engineering of TiO2 nanowires by n-p codoping for enhanced visible-light photoelectrochemical water-splitting.

PubMed

Zhang, Daoyu; Yang, Minnan

2013-11-14

The advantages of one-dimensional nanostructures, such as excellent charge separation and charge transport, low charge carrier recombination losses and so on, render them the photocatalysts of choice for many applications that exploit solar energy. In this work, based on very recently synthesized ultrathin anatase TiO2 nanowires, we explore the possibility of these wires as photocatalysts for photoelectrochemical water-splitting via the mono-doping (C, N, V, and Cr) and n-p codoping (C&V, C&Cr, N&V, and N&Cr) schemes. Our first-principles calculations predict that the C&Cr and C&V codoped ANWs may be strong candidates for photoelectrochemical water-splitting, because they have a substantially reduced band gap of 2.49 eV, appropriate band edge positions, no carrier recombination centers, and enhanced optical absorption in the visible light region.

Superresolving Black Hole Images with Full-Closure Sparse Modeling

NASA Astrophysics Data System (ADS)

Crowley, Chelsea; Akiyama, Kazunori; Fish, Vincent

2018-01-01

It is believed that almost all galaxies have black holes at their centers. Imaging a black hole is a primary objective to answer scientific questions relating to relativistic accretion and jet formation. The Event Horizon Telescope (EHT) is set to capture images of two nearby black holes, Sagittarius A* at the center of the Milky Way galaxy roughly 26,000 light years away and the other M87 which is in Virgo A, a large elliptical galaxy that is 50 million light years away. Sparse imaging techniques have shown great promise for reconstructing high-fidelity superresolved images of black holes from simulated data. Previous work has included the effects of atmospheric phase errors and thermal noise, but not systematic amplitude errors that arise due to miscalibration. We explore a full-closure imaging technique with sparse modeling that uses closure amplitudes and closure phases to improve the imaging process. This new technique can successfully handle data with systematic amplitude errors. Applying our technique to synthetic EHT data of M87, we find that full-closure sparse modeling can reconstruct images better than traditional methods and recover key structural information on the source, such as the shape and size of the predicted photon ring. These results suggest that our new approach will provide superior imaging performance for data from the EHT and other interferometric arrays.

Water Splitting with Series-Connected Polymer Solar Cells.

PubMed

Esiner, Serkan; van Eersel, Harm; van Pruissen, Gijs W P; Turbiez, Mathieu; Wienk, Martijn M; Janssen, René A J

2016-10-12

We investigate light-driven electrochemical water splitting with series-connected polymer solar cells using a combined experimental and modeling approach. The expected maximum solar-to-hydrogen conversion efficiency (η STH ) for light-driven water splitting is modeled for two, three, and four series-connected polymer solar cells. In the modeling, we assume an electrochemical water splitting potential of 1.50 V and a polymer solar cell for which the external quantum efficiency and fill factor are both 0.65. The minimum photon energy loss (E loss ), defined as the energy difference between the optical band gap (E g ) and the open-circuit voltage (V oc ), is set to 0.8 eV, which we consider a realistic value for polymer solar cells. Within these approximations, two series-connected single junction cells with E g = 1.73 eV or three series-connected cells with E g = 1.44 eV are both expected to give an η STH of 6.9%. For four series-connected cells, the maximum η STH is slightly less at 6.2% at an optimal E g = 1.33 eV. Water splitting was performed with series-connected polymer solar cells using polymers with different band gaps. PTPTIBDT-OD (E g = 1.89 eV), PTB7-Th (E g = 1.56 eV), and PDPP5T-2 (E g = 1.44 eV) were blended with [70]PCBM as absorber layer for two, three, and four series-connected configurations, respectively, and provide η STH values of 4.1, 6.1, and 4.9% when using a retroreflective foil on top of the cell to enhance light absorption. The reasons for deviations with experiments are analyzed and found to be due to differences in E g and E loss . Light-driven electrochemical water splitting was also modeled for multijunction polymer solar cells with vertically stacked photoactive layers. Under identical assumptions, an η STH of 10.0% is predicted for multijunction cells.

ASTEROID LIGHT CURVES FROM THE PALOMAR TRANSIENT FACTORY SURVEY: ROTATION PERIODS AND PHASE FUNCTIONS FROM SPARSE PHOTOMETRY

DOE Office of Scientific and Technical Information (OSTI.GOV)

Waszczak, Adam; Chang, Chan-Kao; Cheng, Yu-Chi

We fit 54,296 sparsely sampled asteroid light curves in the Palomar Transient Factory survey to a combined rotation plus phase-function model. Each light curve consists of 20 or more observations acquired in a single opposition. Using 805 asteroids in our sample that have reference periods in the literature, we find that the reliability of our fitted periods is a complicated function of the period, amplitude, apparent magnitude, and other light-curve attributes. Using the 805-asteroid ground-truth sample, we train an automated classifier to estimate (along with manual inspection) the validity of the remaining ∼53,000 fitted periods. By this method we findmore » that 9033 of our light curves (of ∼8300 unique asteroids) have “reliable” periods. Subsequent consideration of asteroids with multiple light-curve fits indicates a 4% contamination in these “reliable” periods. For 3902 light curves with sufficient phase-angle coverage and either a reliable fit period or low amplitude, we examine the distribution of several phase-function parameters, none of which are bimodal though all correlate with the bond albedo and with visible-band colors. Comparing the theoretical maximal spin rate of a fluid body with our amplitude versus spin-rate distribution suggests that, if held together only by self-gravity, most asteroids are in general less dense than ∼2 g cm{sup −3}, while C types have a lower limit of between 1 and 2 g cm{sup −3}. These results are in agreement with previous density estimates. For 5–20 km diameters, S types rotate faster and have lower amplitudes than C types. If both populations share the same angular momentum, this may indicate the two types’ differing ability to deform under rotational stress. Lastly, we compare our absolute magnitudes (and apparent-magnitude residuals) to those of the Minor Planet Center’s nominal (G = 0.15, rotation-neglecting) model; our phase-function plus Fourier-series fitting reduces asteroid photometric

An adaptive sparse deconvolution method for distinguishing the overlapping echoes of ultrasonic guided waves for pipeline crack inspection

NASA Astrophysics Data System (ADS)

Chang, Yong; Zi, Yanyang; Zhao, Jiyuan; Yang, Zhe; He, Wangpeng; Sun, Hailiang

2017-03-01

In guided wave pipeline inspection, echoes reflected from closely spaced reflectors generally overlap, meaning useful information is lost. To solve the overlapping problem, sparse deconvolution methods have been developed in the past decade. However, conventional sparse deconvolution methods have limitations in handling guided wave signals, because the input signal is directly used as the prototype of the convolution matrix, without considering the waveform change caused by the dispersion properties of the guided wave. In this paper, an adaptive sparse deconvolution (ASD) method is proposed to overcome these limitations. First, the Gaussian echo model is employed to adaptively estimate the column prototype of the convolution matrix instead of directly using the input signal as the prototype. Then, the convolution matrix is constructed upon the estimated results. Third, the split augmented Lagrangian shrinkage (SALSA) algorithm is introduced to solve the deconvolution problem with high computational efficiency. To verify the effectiveness of the proposed method, guided wave signals obtained from pipeline inspection are investigated numerically and experimentally. Compared to conventional sparse deconvolution methods, e.g. the {{l}1} -norm deconvolution method, the proposed method shows better performance in handling the echo overlap problem in the guided wave signal.

Holographic spectrum-splitting optical systems for solar photovoltaics

NASA Astrophysics Data System (ADS)

Zhang, Deming

Solar energy is the most abundant source of renewable energy available. The relatively high cost prevents solar photovoltaic (PV) from replacing fossil fuel on a larger scale. In solar PV power generation the cost is reduced with more efficient PV technologies. In this dissertation, methods to improve PV conversion efficiency with holographic optical components are discussed. The tandem multiple-junction approach has achieved very high conversion efficiency. However it is impossible to manufacture tandem PV cells at a low cost due to stringent fabrication standards and limited material types that satisfy lattice compatibility. Current produced by the tandem multi-junction PV cell is limited by the lowest junction due to series connection. Spectrum-splitting is a lateral multi-junction concept that is free of lattice and current matching constraints. Each PV cell can be optimized towards full absorption of a spectral band with tailored light-trapping schemes. Holographic optical components are designed to achieve spectrum-splitting PV energy conversion. The incident solar spectrum is separated onto multiple PV cells that are matched to the corresponding spectral band. Holographic spectrum-splitting can take advantage of existing and future low-cost technologies that produces high efficiency thin-film solar cells. Spectrum-splitting optical systems are designed and analyzed with both transmission and reflection holographic optical components. Prototype holograms are fabricated and high optical efficiency is achieved. Light-trapping in PV cells increases the effective optical path-length in the semiconductor material leading to improved absorption and conversion efficiency. It has been shown that the effective optical path length can be increased by a factor of 4n2 using diffusive surfaces. Ultra-light-trapping can be achieved with optical filters that limit the escape angle of the diffused light. Holographic reflection gratings have been shown to act as angle

Efficient solar water-splitting using a nanocrystalline CoO photocatalyst

NASA Astrophysics Data System (ADS)

Liao, Longb; Zhang, Qiuhui; Su, Zhihua; Zhao, Zhongzheng; Wang, Yanan; Li, Yang; Lu, Xiaoxiang; Wei, Dongguang; Feng, Guoying; Yu, Qingkai; Cai, Xiaojun; Zhao, Jimin; Ren, Zhifeng; Fang, Hui; Robles-Hernandez, Francisco; Baldelli, Steven; Bao, Jiming

2014-01-01

The generation of hydrogen from water using sunlight could potentially form the basis of a clean and renewable source of energy. Various water-splitting methods have been investigated previously, but the use of photocatalysts to split water into stoichiometric amounts of H2 and O2 (overall water splitting) without the use of external bias or sacrificial reagents is of particular interest because of its simplicity and potential low cost of operation. However, despite progress in the past decade, semiconductor water-splitting photocatalysts (such as (Ga1-xZnx)(N1-xOx)) do not exhibit good activity beyond 440 nm (refs 1,2,9) and water-splitting devices that can harvest visible light typically have a low solar-to-hydrogen efficiency of around 0.1%. Here we show that cobalt(II) oxide (CoO) nanoparticles can carry out overall water splitting with a solar-to-hydrogen efficiency of around 5%. The photocatalysts were synthesized from non-active CoO micropowders using two distinct methods (femtosecond laser ablation and mechanical ball milling), and the CoO nanoparticles that result can decompose pure water under visible-light irradiation without any co-catalysts or sacrificial reagents. Using electrochemical impedance spectroscopy, we show that the high photocatalytic activity of the nanoparticles arises from a significant shift in the position of the band edge of the material.

Spectral splitting for thermal management in photovoltaic cells

NASA Astrophysics Data System (ADS)

Apostoleris, Harry; Chiou, Yu-Cheng; Chiesa, Matteo; Almansouri, Ibraheem

2017-09-01

Spectral splitting is widely employed as a way to divide light between different solar cells or processes to optimize energy conversion. Well-understood but less explored is the use of spectrum splitting or filtering to combat solar cell heating. This has impacts both on cell performance and on the surrounding environment. In this manuscript we explore the design of spectral filtering systems that can improve the thermal and power-conversion performance of commercial PV modules.

Visible light induction of an electron paramagnetic resonance split signal in Photosystem II in the S(2) state reveals the importance of charges in the oxygen-evolving center during catalysis: a unifying model.

PubMed

Sjöholm, Johannes; Styring, Stenbjörn; Havelius, Kajsa G V; Ho, Felix M

2012-03-13

Cryogenic illumination of Photosystem II (PSII) can lead to the trapping of the metastable radical Y(Z)(•), the radical form of the redox-active tyrosine residue D1-Tyr161 (known as Y(Z)). Magnetic interaction between this radical and the CaMn(4) cluster of PSII gives rise to so-called split electron paramagnetic resonance (EPR) signals with characteristics that are dependent on the S state. We report here the observation and characterization of a split EPR signal that can be directly induced from PSII centers in the S(2) state through visible light illumination at 10 K. We further show that the induction of this split signal takes place via a Mn-centered mechanism, in the same way as when using near-infrared light illumination [Koulougliotis, D., et al. (2003) Biochemistry 42, 3045-3053]. On the basis of interpretations of these results, and in combination with literature data for other split signals induced under a variety of conditions (temperature and light quality), we propose a unified model for the mechanisms of split signal induction across the four S states (S(0), S(1), S(2), and S(3)). At the heart of this model is the stability or instability of the Y(Z)(•)(D1-His190)(+) pair that would be formed during cryogenic oxidation of Y(Z). Furthermore, the model is closely related to the sequence of transfers of protons and electrons from the CaMn(4) cluster during the S cycle and further demonstrates the utility of the split signals in probing the immediate environment of the oxygen-evolving center in PSII.

A multiple hold-out framework for Sparse Partial Least Squares.

PubMed

Monteiro, João M; Rao, Anil; Shawe-Taylor, John; Mourão-Miranda, Janaina

2016-09-15

Supervised classification machine learning algorithms may have limitations when studying brain diseases with heterogeneous populations, as the labels might be unreliable. More exploratory approaches, such as Sparse Partial Least Squares (SPLS), may provide insights into the brain's mechanisms by finding relationships between neuroimaging and clinical/demographic data. The identification of these relationships has the potential to improve the current understanding of disease mechanisms, refine clinical assessment tools, and stratify patients. SPLS finds multivariate associative effects in the data by computing pairs of sparse weight vectors, where each pair is used to remove its corresponding associative effect from the data by matrix deflation, before computing additional pairs. We propose a novel SPLS framework which selects the adequate number of voxels and clinical variables to describe each associative effect, and tests their reliability by fitting the model to different splits of the data. As a proof of concept, the approach was applied to find associations between grey matter probability maps and individual items of the Mini-Mental State Examination (MMSE) in a clinical sample with various degrees of dementia. The framework found two statistically significant associative effects between subsets of brain voxels and subsets of the questions/tasks. SPLS was compared with its non-sparse version (PLS). The use of projection deflation versus a classical PLS deflation was also tested in both PLS and SPLS. SPLS outperformed PLS, finding statistically significant effects and providing higher correlation values in hold-out data. Moreover, projection deflation provided better results. Copyright © 2016 The Author(s). Published by Elsevier B.V. All rights reserved.

StackSplit - a plugin for multi-event shear wave splitting analyses in SplitLab

NASA Astrophysics Data System (ADS)

Grund, Michael

2017-04-01

The SplitLab package (Wüstefeld et al., Computers and Geosciences, 2008), written in MATLAB, is a powerful and widely used tool for analysing seismological shear wave splitting of single event measurements. However, in many cases, especially temporary station deployments close to seaside or for recordings affected by strong anthropogenic noise, only multi-event approaches provide stable and reliable splitting results. In order to extend the original SplitLab environment for such analyses, I present the StackSplit plugin that can easily be implemented within the well accepted main program. StackSplit grants easy access to several different analysis approaches within SplitLab, including a new multiple waveform based inversion method as well as the most established standard stacking procedures. The possibility to switch between different analysis approaches at any time allows the user for the most flexible processing of individual multi-event splitting measurements for a single recording station. Besides the provided functions of the plugin, no other external program is needed for the multi-event analyses since StackSplit performs within the available SplitLab structure.

Extreme Sparse Multinomial Logistic Regression: A Fast and Robust Framework for Hyperspectral Image Classification

NASA Astrophysics Data System (ADS)

Cao, Faxian; Yang, Zhijing; Ren, Jinchang; Ling, Wing-Kuen; Zhao, Huimin; Marshall, Stephen

2017-12-01

Although the sparse multinomial logistic regression (SMLR) has provided a useful tool for sparse classification, it suffers from inefficacy in dealing with high dimensional features and manually set initial regressor values. This has significantly constrained its applications for hyperspectral image (HSI) classification. In order to tackle these two drawbacks, an extreme sparse multinomial logistic regression (ESMLR) is proposed for effective classification of HSI. First, the HSI dataset is projected to a new feature space with randomly generated weight and bias. Second, an optimization model is established by the Lagrange multiplier method and the dual principle to automatically determine a good initial regressor for SMLR via minimizing the training error and the regressor value. Furthermore, the extended multi-attribute profiles (EMAPs) are utilized for extracting both the spectral and spatial features. A combinational linear multiple features learning (MFL) method is proposed to further enhance the features extracted by ESMLR and EMAPs. Finally, the logistic regression via the variable splitting and the augmented Lagrangian (LORSAL) is adopted in the proposed framework for reducing the computational time. Experiments are conducted on two well-known HSI datasets, namely the Indian Pines dataset and the Pavia University dataset, which have shown the fast and robust performance of the proposed ESMLR framework.

StackSplit - a plugin for multi-event shear wave splitting analyses in SplitLab

NASA Astrophysics Data System (ADS)

Grund, Michael

2017-08-01

SplitLab is a powerful and widely used tool for analysing seismological shear wave splitting of single event measurements. However, in many cases, especially temporary station deployments close to the noisy seaside, ocean bottom or for recordings affected by strong anthropogenic noise, only multi-event approaches provide stable and reliable splitting results. In order to extend the original SplitLab environment for such analyses, I present the StackSplit plugin that can easily be implemented within the well accepted main program. StackSplit grants easy access to several different analysis approaches within SplitLab, including a new multiple waveform based inversion method as well as the most established standard stacking procedures. The possibility to switch between different analysis approaches at any time allows the user for the most flexible processing of individual multi-event splitting measurements for a single recording station. Besides the provided functions of the plugin, no other external program is needed for the multi-event analyses since StackSplit performs within the available SplitLab structure which is based on MATLAB. The effectiveness and use of this plugin is demonstrated with data examples of a long running seismological recording station in Finland.

Utilization of Metal Sulfide Material of (CuGa)(1-x)Zn(2x)S2 Solid Solution with Visible Light Response in Photocatalytic and Photoelectrochemical Solar Water Splitting Systems.

PubMed

Kato, Takaaki; Hakari, Yuichiro; Ikeda, Satoru; Jia, Qingxin; Iwase, Akihide; Kudo, Akihiko

2015-03-19

Upon forming a solid solution between CuGaS2 and ZnS, we have successfully developed a highly active (CuGa)(1-x)Zn(2x)S2 photocatalyst for H2 evolution in the presence of sacrificial reagents under visible light irradiation. The Ru-loaded (CuGa)0.8Zn0.4S2 functioned as a H2-evolving photocatalyst in a Z-scheme system with BiVO4 of an O2-evolving photocatalyst and Co complexes of an electron mediator. The Z-scheme system split water into H2 and O2 under visible light and simulated sunlight irradiation. The (CuGa)(1-x)Zn(2x)S2 possessed a p-type semiconductor character. The photoelectrochemical cell with a Ru-loaded (CuGa)0.5ZnS2 photocathode and a CoO(x)-modified BiVO4 photoanode split water even without applying an external bias. Thus, we successfully demonstrated that the metal sulfide material group can be available for Z-scheme and electrochemical systems to achieve solar water splitting into H2 and O2.

Photoelectrochemical devices for solar water splitting - materials and challenges.

PubMed

Jiang, Chaoran; Moniz, Savio J A; Wang, Aiqin; Zhang, Tao; Tang, Junwang

2017-07-31

It is widely accepted within the community that to achieve a sustainable society with an energy mix primarily based on solar energy we need an efficient strategy to convert and store sunlight into chemical fuels. A photoelectrochemical (PEC) device would therefore play a key role in offering the possibility of carbon-neutral solar fuel production through artificial photosynthesis. The past five years have seen a surge in the development of promising semiconductor materials. In addition, low-cost earth-abundant co-catalysts are ubiquitous in their employment in water splitting cells due to the sluggish kinetics of the oxygen evolution reaction (OER). This review commences with a fundamental understanding of semiconductor properties and charge transfer processes in a PEC device. We then describe various configurations of PEC devices, including single light-absorber cells and multi light-absorber devices (PEC, PV-PEC and PV/electrolyser tandem cell). Recent progress on both photoelectrode materials (light absorbers) and electrocatalysts is summarized, and important factors which dominate photoelectrode performance, including light absorption, charge separation and transport, surface chemical reaction rate and the stability of the photoanode, are discussed. Controlling semiconductor properties is the primary concern in developing materials for solar water splitting. Accordingly, strategies to address the challenges for materials development in this area, such as the adoption of smart architectures, innovative device configuration design, co-catalyst loading, and surface protection layer deposition, are outlined throughout the text, to deliver a highly efficient and stable PEC device for water splitting.

Measuring and Evaluating TCP Splitting for Cloud Services

NASA Astrophysics Data System (ADS)

Pathak, Abhinav; Wang, Y. Angela; Huang, Cheng; Greenberg, Albert; Hu, Y. Charlie; Kern, Randy; Li, Jin; Ross, Keith W.

In this paper, we examine the benefits of split-TCP proxies, deployed in an operational world-wide network, for accelerating cloud services. We consider a fraction of a network consisting of a large number of satellite datacenters, which host split-TCP proxies, and a smaller number of mega datacenters, which ultimately perform computation or provide storage. Using web search as an exemplary case study, our detailed measurements reveal that a vanilla TCP splitting solution deployed at the satellite DCs reduces the 95 th percentile of latency by as much as 43% when compared to serving queries directly from the mega DCs. Through careful dissection of the measurement results, we characterize how individual components, including proxy stacks, network protocols, packet losses and network load, can impact the latency. Finally, we shed light on further optimizations that can fully realize the potential of the TCP splitting solution.

Visible-light driven nitrogen-doped petal-morphological ceria nanosheets for water splitting

NASA Astrophysics Data System (ADS)

Qian, Junchao; Zhang, Wenya; Wang, Yaping; Chen, Zhigang; Chen, Feng; Liu, Chengbao; Lu, Xiaowang; Li, Ping; Wang, Kaiyuan; Chen, Ailian

2018-06-01

Water splitting is a promising sustainable technology for solar-to-chemical energy conversion. Herein, we successfully fabricated nitrogen-doped ultrathin CeO2 nanosheets by using field poppy petals as templates, which exhibit an efficiently catalytic activity for water splitting. Abundant oxygen vacancies and substitutional N atoms were experimentally observed in the film due to its unique biomorphic texture. In view of high efficiency and long durability of the as-prepared photocatalyst, this biotemplate method may provide an alternative technique for using biomolecules to assemble 2D nanomaterials.

Non-destructive splitter of twisted light based on modes splitting in a ring cavity.

PubMed

Li, Yan; Zhou, Zhi-Yuan; Ding, Dong-Sheng; Zhang, Wei; Shi, Shuai; Shi, Bao-Sen; Guo, Guang-Can

2016-02-08

Efficiently discriminating beams carrying different orbital angular momentum (OAM) is of fundamental importance for various applications including high capacity optical communication and quantum information processing. We design and experimentally verify a distinguished method for effectively splitting different OAM-carried beams by introducing Dove prisms in a ring cavity. Because of rotational symmetry broken of two OAM-carried beams with opposite topological charges, their transmission spectra will split. When mode and impedance matches between the cavity and one OAM-carried beam are achieved, this beam will transmit through the cavity and other beam will be reflected, both beams keep their spatial shapes. In this case, the cavity acts like a polarized beam splitter. Besides, the transmitting beam can be selected at your will, the splitting efficiency can reach unity if the cavity is lossless and it completely matches the beam. Furthermore, beams carry multi-OAMs can also be split by cascading ring cavities.

Light distribution in diffractive multifocal optics and its optimization.

PubMed

Portney, Valdemar

2011-11-01

To expand a geometrical model of diffraction efficiency and its interpretation to the multifocal optic and to introduce formulas for analysis of far and near light distribution and their application to multifocal intraocular lenses (IOLs) and to diffraction efficiency optimization. Medical device consulting firm, Newport Coast, California, USA. Experimental study. Application of a geometrical model to the kinoform (single focus diffractive optical element) was expanded to a multifocal optic to produce analytical definitions of light split between far and near images and light loss to other diffraction orders. The geometrical model gave a simple interpretation of light split in a diffractive multifocal IOL. An analytical definition of light split between far, near, and light loss was introduced as curve fitting formulas. Several examples of application to common multifocal diffractive IOLs were developed; for example, to light-split change with wavelength. The analytical definition of diffraction efficiency may assist in optimization of multifocal diffractive optics that minimize light loss. Formulas for analysis of light split between different foci of multifocal diffractive IOLs are useful in interpreting diffraction efficiency dependence on physical characteristics, such as blaze heights of the diffractive grooves and wavelength of light, as well as for optimizing multifocal diffractive optics. Disclosure is found in the footnotes. Copyright © 2011 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.

Production of arrays of chemically distinct nanolitre plugs via repeated splitting in microfluidic devices.

PubMed

Adamson, David N; Mustafi, Debarshi; Zhang, John X J; Zheng, Bo; Ismagilov, Rustem F

2006-09-01

This paper reports a method for the production of arrays of nanolitre plugs with distinct chemical compositions. One of the primary constraints on the use of plug-based microfluidics for large scale biological screening is the difficulty of fabricating arrays of chemically distinct plugs on the nanolitre scale. Here, using microfluidic devices with several T-junctions linked in series, a single input array of large (approximately 320 nL) plugs was split to produce 16 output arrays of smaller (approximately 20 nL) plugs; the composition and configuration of these arrays were identical to that of the input. This paper shows how the passive break-up of plugs in T-junction microchannel geometries can be used to produce a set of smaller-volume output arrays useful for chemical screening from a single large-volume array. A simple theoretical description is presented to describe splitting as a function of the Capillary number, the capillary pressure, the total pressure difference across the channel, and the geometric fluidic resistance. By accounting for these considerations, plug coalescence and plug-plug contamination can be eliminated from the splitting process and the symmetry of splitting can be preserved. Furthermore, single-outlet splitting devices were implemented with both valve- and volume-based methods for coordinating the release of output arrays. Arrays of plugs containing commercial sparse matrix screens were obtained from the presented splitting method and these arrays were used in protein crystallization trials. The techniques presented in this paper may facilitate the implementation of high-throughput chemical and biological screening.

Research Update: Photoelectrochemical water splitting and photocatalytic hydrogen production using ferrites (MFe2O4) under visible light irradiation

NASA Astrophysics Data System (ADS)

Dillert, Ralf; Taffa, Dereje H.; Wark, Michael; Bredow, Thomas; Bahnemann, Detlef W.

2015-10-01

The utilization of solar light for the photoelectrochemical and photocatalytic production of molecular hydrogen from water is a scientific and technical challenge. Semiconductors with suitable properties to promote solar-driven water splitting are a desideratum. A hitherto rarely investigated group of semiconductors are ferrites with the empirical formula MFe2O4 and related compounds. This contribution summarizes the published results of the experimental investigations on the photoelectrochemical and photocatalytic properties of these compounds. It will be shown that the potential of this group of compounds in regard to the production of solar hydrogen has not been fully explored yet.

On Valence-Band Splitting in Layered MoS2.

PubMed

Zhang, Youwei; Li, Hui; Wang, Haomin; Liu, Ran; Zhang, Shi-Li; Qiu, Zhi-Jun

2015-08-25

As a representative two-dimensional semiconducting transition-metal dichalcogenide (TMD), the electronic structure in layered MoS2 is a collective result of quantum confinement, interlayer interaction, and crystal symmetry. A prominent energy splitting in the valence band gives rise to many intriguing electronic, optical, and magnetic phenomena. Despite numerous studies, an experimental determination of valence-band splitting in few-layer MoS2 is still lacking. Here, we show how the valence-band maximum (VBM) splits for one to five layers of MoS2. Interlayer coupling is found to contribute significantly to phonon energy but weakly to VBM splitting in bilayers, due to a small interlayer hopping energy for holes. Hence, spin-orbit coupling is still predominant in the splitting. A temperature-independent VBM splitting, known for single-layer MoS2, is, thus, observed for bilayers. However, a Bose-Einstein type of temperature dependence of VBM splitting prevails in three to five layers of MoS2. In such few-layer MoS2, interlayer coupling is enhanced with a reduced interlayer distance, but thermal expansion upon temperature increase tends to decouple adjacent layers and therefore decreases the splitting energy. Our findings that shed light on the distinctive behaviors about VBM splitting in layered MoS2 may apply to other hexagonal TMDs as well. They will also be helpful in extending our understanding of the TMD electronic structure for potential applications in electronics and optoelectronics.

Vibrational quenching of excitonic splittings in H-bonded molecular dimers: The electronic Davydov splittings cannot match experiment

NASA Astrophysics Data System (ADS)

Ottiger, Philipp; Leutwyler, Samuel; Köppel, Horst

2012-05-01

The S1/S2 state exciton splittings of symmetric doubly hydrogen-bonded gas-phase dimers provide spectroscopic benchmarks for the excited-state electronic couplings between UV chromophores. These have important implications for electronic energy transfer in multichromophoric systems ranging from photosynthetic light-harvesting antennae to photosynthetic reaction centers, conjugated polymers, molecular crystals, and nucleic acids. We provide laser spectroscopic data on the S1/S2 excitonic splitting Δexp of the doubly H-bonded o-cyanophenol (oCP) dimer and compare to the splittings of the dimers of (2-aminopyridine)2, [(2AP)2], (2-pyridone)2, [(2PY)2], (benzoic acid)2, [(BZA)2], and (benzonitrile)2, [(BN)2]. The experimental S1/S2 excitonic splittings are Δexp = 16.4 cm-1 for (oCP)2, 11.5 cm-1 for (2AP)2, 43.5 cm-1 for (2PY)2, and <1 cm-1 for (BZA)2. In contrast, the vertical S1/S2 energy gaps Δcalc calculated by the approximate second-order coupled cluster (CC2) method for the same dimers are 10-40 times larger than the Δexp values. The qualitative failure of this and other ab initio methods to reproduce the exciton splitting Δexp arises from the Born-Oppenheimer (BO) approximation, which implicitly assumes the strong-coupling case and cannot be employed to evaluate excitonic splittings of systems that are in the weak-coupling limit. Given typical H-bond distances and oscillator strengths, the majority of H-bonded dimers lie in the weak-coupling limit. In this case, the monomer electronic-vibrational coupling upon electronic excitation must be accounted for; the excitonic splittings arise between the vibronic (and not the electronic) transitions. The discrepancy between the BO-based splittings Δcalc and the much smaller experimental Δexp values is resolved by taking into account the quenching of the BO splitting by the intramolecular vibronic coupling in the monomer S1 ← S0 excitation. The vibrational quenching factors Γ for the five dimers (oCP)2, (2AP)2

Fast Physically Correct Refocusing for Sparse Light Fields Using Block-Based Multi-Rate View Interpolation.

PubMed

Huang, Chao-Tsung; Wang, Yu-Wen; Huang, Li-Ren; Chin, Jui; Chen, Liang-Gee

2017-02-01

Digital refocusing has a tradeoff between complexity and quality when using sparsely sampled light fields for low-storage applications. In this paper, we propose a fast physically correct refocusing algorithm to address this issue in a twofold way. First, view interpolation is adopted to provide photorealistic quality at infocus-defocus hybrid boundaries. Regarding its conventional high complexity, we devised a fast line-scan method specifically for refocusing, and its 1D kernel can be 30× faster than the benchmark View Synthesis Reference Software (VSRS)-1D-Fast. Second, we propose a block-based multi-rate processing flow for accelerating purely infocused or defocused regions, and a further 3- 34× speedup can be achieved for high-resolution images. All candidate blocks of variable sizes can interpolate different numbers of rendered views and perform refocusing in different subsampled layers. To avoid visible aliasing and block artifacts, we determine these parameters and the simulated aperture filter through a localized filter response analysis using defocus blur statistics. The final quadtree block partitions are then optimized in terms of computation time. Extensive experimental results are provided to show superior refocusing quality and fast computation speed. In particular, the run time is comparable with the conventional single-image blurring, which causes serious boundary artifacts.

Turbulent flows over sparse canopies

NASA Astrophysics Data System (ADS)

Sharma, Akshath; García-Mayoral, Ricardo

2018-04-01

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

Dye-Sensitized Hydrobromic Acid Splitting for Hydrogen Solar Fuel Production.

PubMed

Brady, Matthew D; Sampaio, Renato N; Wang, Degao; Meyer, Thomas J; Meyer, Gerald J

2017-11-08

Hydrobromic acid (HBr) has significant potential as an inexpensive feedstock for hydrogen gas (H 2 ) solar fuel production through HBr splitting. Mesoporous thin films of anatase TiO 2 or SnO 2 /TiO 2 core-shell nanoparticles were sensitized to visible light with a new Ru II polypyridyl complex that served as a photocatalyst for bromide oxidation. These thin films were tested as photoelectrodes in dye-sensitized photoelectrosynthesis cells. In 1 N HBr (aq), the photocatalyst undergoes excited-state electron injection and light-driven Br - oxidation. The injected electrons induce proton reduction at a Pt electrode. Under 100 mW cm -2 white-light illumination, sustained photocurrents of 1.5 mA cm -2 were measured under an applied bias. Faradaic efficiencies of 71 ± 5% for Br - oxidation and 94 ± 2% for H 2 production were measured. A 12 μmol h -1 sustained rate of H 2 production was maintained during illumination. The results demonstrate a molecular approach to HBr splitting with a visible light absorbing complex capable of aqueous Br - oxidation and excited-state electron injection.

Cross-domain expression recognition based on sparse coding and transfer learning

NASA Astrophysics Data System (ADS)

Yang, Yong; Zhang, Weiyi; Huang, Yong

2017-05-01

Traditional facial expression recognition methods usually assume that the training set and the test set are independent and identically distributed. However, in actual expression recognition applications, the conditions of independent and identical distribution are hardly satisfied for the training set and test set because of the difference of light, shade, race and so on. In order to solve this problem and improve the performance of expression recognition in the actual applications, a novel method based on transfer learning and sparse coding is applied to facial expression recognition. First of all, a common primitive model, that is, the dictionary is learnt. Then, based on the idea of transfer learning, the learned primitive pattern is transferred to facial expression and the corresponding feature representation is obtained by sparse coding. The experimental results in CK +, JAFFE and NVIE database shows that the transfer learning based on sparse coding method can effectively improve the expression recognition rate in the cross-domain expression recognition task and is suitable for the practical facial expression recognition applications.

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.

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

DOEpatents

Moody, Daniela; Wohlberg, Brendt

2018-01-02

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

Discrete Sparse Coding.

PubMed

Exarchakis, Georgios; Lücke, Jörg

2017-11-01

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

Population splitting of rodlike swimmers in Couette flow.

PubMed

Nili, Hossein; Kheyri, Masoud; Abazari, Javad; Fahimniya, Ali; Naji, Ali

2017-06-28

We present a quantitative analysis on the response of a dilute active suspension of self-propelled rods (swimmers) in a planar channel subjected to an imposed shear flow. To best capture the salient features of the shear-induced effects, we consider the case of an imposed Couette flow, providing a constant shear rate across the channel. We argue that the steady-state behavior of swimmers can be understood in the light of a population splitting phenomenon, occurring as the shear rate exceeds a certain threshold, initiating the reversal of the swimming direction for a finite fraction of swimmers from down- to upstream or vice versa, depending on the swimmer position within the channel. Swimmers thus split into two distinct, statistically significant and oppositely swimming majority and minority populations. The onset of population splitting translates into a transition from a self-propulsion-dominated regime to a shear-dominated regime, corresponding to a unimodal-to-bimodal change in the probability distribution function of the swimmer orientation. We present a phase diagram in terms of the swim and flow Péclet numbers showing the separation of these two regimes by a discontinuous transition line. Our results shed further light on the behavior of swimmers in a shear flow and provide an explanation for the previously reported non-monotonic behavior of the mean, near-wall, parallel-to-flow orientation of swimmers with increasing shear strength.

Inverse lithography using sparse mask representations

NASA Astrophysics Data System (ADS)

Ionescu, Radu C.; Hurley, Paul; Apostol, Stefan

2015-03-01

We present a novel optimisation algorithm for inverse lithography, based on optimization of the mask derivative, a domain inherently sparse, and for rectilinear polygons, invertible. The method is first developed assuming a point light source, and then extended to general incoherent sources. What results is a fast algorithm, producing manufacturable masks (the search space is constrained to rectilinear polygons), and flexible (specific constraints such as minimal line widths can be imposed). One inherent trick is to treat polygons as continuous entities, thus making aerial image calculation extremely fast and accurate. Requirements for mask manufacturability can be integrated in the optimization without too much added complexity. We also explain how to extend the scheme for phase-changing mask optimization.

Multiple Sparse Representations Classification

PubMed Central

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

2015-01-01

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

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.

Isospin splittings in the light-baryon octet from lattice QCD and QED.

PubMed

Borsanyi, Sz; Dürr, S; Fodor, Z; Frison, J; Hoelbling, C; Katz, S D; Krieg, S; Kurth, Th; Lellouch, L; Lippert, Th; Portelli, A; Ramos, A; Sastre, A; Szabo, K

2013-12-20

While electromagnetic and up-down quark mass difference effects on octet baryon masses are very small, they have important consequences. The stability of the hydrogen atom against beta decay is a prominent example. Here, we include these effects by adding them to valence quarks in a lattice QCD calculation based on Nf=2+1 simulations with five lattice spacings down to 0.054 fm, lattice sizes up to 6 fm, and average up-down quark masses all the way down to their physical value. This allows us to gain control over all systematic errors, except for the one associated with neglecting electromagnetism in the sea. We compute the octet baryon isomultiplet mass splittings, as well as the individual contributions from electromagnetism and the up-down quark mass difference. Our results for the total splittings are in good agreement with experiment.

Isospin Splittings in the Light-Baryon Octet from Lattice QCD and QED

NASA Astrophysics Data System (ADS)

Borsanyi, Sz.; Dürr, S.; Fodor, Z.; Frison, J.; Hoelbling, C.; Katz, S. D.; Krieg, S.; Kurth, Th.; Lellouch, L.; Lippert, Th.; Portelli, A.; Ramos, A.; Sastre, A.; Szabo, K.; Budapest-Marseille-Wuppertal Collaboration

2013-12-01

While electromagnetic and up-down quark mass difference effects on octet baryon masses are very small, they have important consequences. The stability of the hydrogen atom against beta decay is a prominent example. Here, we include these effects by adding them to valence quarks in a lattice QCD calculation based on Nf=2+1 simulations with five lattice spacings down to 0.054 fm, lattice sizes up to 6 fm, and average up-down quark masses all the way down to their physical value. This allows us to gain control over all systematic errors, except for the one associated with neglecting electromagnetism in the sea. We compute the octet baryon isomultiplet mass splittings, as well as the individual contributions from electromagnetism and the up-down quark mass difference. Our results for the total splittings are in good agreement with experiment.

Research Update: Photoelectrochemical water splitting and photocatalytic hydrogen production using ferrites (MFe{sub 2}O{sub 4}) under visible light irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dillert, Ralf; Laboratorium für Nano- und Quantenengineering, Gottfried Wilhelm Leibniz Universität Hannover, Schneiderberg 39, 30167 Hannover; Taffa, Dereje H.

2015-10-01

The utilization of solar light for the photoelectrochemical and photocatalytic production of molecular hydrogen from water is a scientific and technical challenge. Semiconductors with suitable properties to promote solar-driven water splitting are a desideratum. A hitherto rarely investigated group of semiconductors are ferrites with the empirical formula MFe{sub 2}O{sub 4} and related compounds. This contribution summarizes the published results of the experimental investigations on the photoelectrochemical and photocatalytic properties of these compounds. It will be shown that the potential of this group of compounds in regard to the production of solar hydrogen has not been fully explored yet.

Recent Advances in Bismuth-Based Nanomaterials for Photoelectrochemical Water Splitting.

PubMed

Bhat, Swetha S M; Jang, Ho Won

2017-08-10

In recent years, bismuth-based nanomaterials have drawn considerable interest as potential candidates for photoelectrochemical (PEC) water splitting owing to their narrow band gaps, nontoxicity, and low costs. The unique electronic structure of bismuth-based materials with a well-dispersed valence band comprising Bi 6s and O 2p orbitals offers a suitable band gap to harvest visible light. This Review presents significant advancements in exploiting bismuth-based nanomaterials for solar water splitting. An overview of the different strategies employed and the new ideas adopted to improve the PEC performance of bismuth-based nanomaterials are discussed. Morphology control, the construction of heterojunctions, doping, and co-catalyst loading are several approaches that are implemented to improve the efficiency of solar water splitting. Key issues are identified and guidelines are suggested to rationalize the design of efficient bismuth-based materials for sunlight-driven water splitting. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Locality constrained joint dynamic sparse representation for local matching based face recognition.

PubMed

Wang, Jianzhong; Yi, Yugen; Zhou, Wei; Shi, Yanjiao; Qi, Miao; Zhang, Ming; Zhang, Baoxue; Kong, Jun

2014-01-01

Recently, Sparse Representation-based Classification (SRC) has attracted a lot of attention for its applications to various tasks, especially in biometric techniques such as face recognition. However, factors such as lighting, expression, pose and disguise variations in face images will decrease the performances of SRC and most other face recognition techniques. In order to overcome these limitations, we propose a robust face recognition method named Locality Constrained Joint Dynamic Sparse Representation-based Classification (LCJDSRC) in this paper. In our method, a face image is first partitioned into several smaller sub-images. Then, these sub-images are sparsely represented using the proposed locality constrained joint dynamic sparse representation algorithm. Finally, the representation results for all sub-images are aggregated to obtain the final recognition result. Compared with other algorithms which process each sub-image of a face image independently, the proposed algorithm regards the local matching-based face recognition as a multi-task learning problem. Thus, the latent relationships among the sub-images from the same face image are taken into account. Meanwhile, the locality information of the data is also considered in our algorithm. We evaluate our algorithm by comparing it with other state-of-the-art approaches. Extensive experiments on four benchmark face databases (ORL, Extended YaleB, AR and LFW) demonstrate the effectiveness of LCJDSRC.

Holographic lens spectrum splitting photovoltaic system for increased diffuse collection and annual energy yield

NASA Astrophysics Data System (ADS)

Vorndran, Shelby D.; Wu, Yuechen; Ayala, Silvana; Kostuk, Raymond K.

2015-09-01

Concentrating and spectrum splitting photovoltaic (PV) modules have a limited acceptance angle and thus suffer from optical loss under off-axis illumination. This loss manifests itself as a substantial reduction in energy yield in locations where a significant portion of insulation is diffuse. In this work, a spectrum splitting PV system is designed to efficiently collect and convert light in a range of illumination conditions. The system uses a holographic lens to concentrate shortwavelength light onto a smaller, more expensive indium gallium phosphide (InGaP) PV cell. The high efficiency PV cell near the axis is surrounded with silicon (Si), a less expensive material that collects a broader portion of the solar spectrum. Under direct illumination, the device achieves increased conversion efficiency from spectrum splitting. Under diffuse illumination, the device collects light with efficiency comparable to a flat-panel Si module. Design of the holographic lens is discussed. Optical efficiency and power output of the module under a range of illumination conditions from direct to diffuse are simulated with non-sequential raytracing software. Using direct and diffuse Typical Metrological Year (TMY3) irradiance measurements, annual energy yield of the module is calculated for several installation sites. Energy yield of the spectrum splitting module is compared to that of a full flat-panel Si reference module.

Tantalum-based semiconductors for solar water splitting.

PubMed

Zhang, Peng; Zhang, Jijie; Gong, Jinlong

2014-07-07

Solar energy utilization is one of the most promising solutions for the energy crises. Among all the possible means to make use of solar energy, solar water splitting is remarkable since it can accomplish the conversion of solar energy into chemical energy. The produced hydrogen is clean and sustainable which could be used in various areas. For the past decades, numerous efforts have been put into this research area with many important achievements. Improving the overall efficiency and stability of semiconductor photocatalysts are the research focuses for the solar water splitting. Tantalum-based semiconductors, including tantalum oxide, tantalate and tantalum (oxy)nitride, are among the most important photocatalysts. Tantalum oxide has the band gap energy that is suitable for the overall solar water splitting. The more negative conduction band minimum of tantalum oxide provides photogenerated electrons with higher potential for the hydrogen generation reaction. Tantalates, with tunable compositions, show high activities owning to their layered perovskite structure. (Oxy)nitrides, especially TaON and Ta3N5, have small band gaps to respond to visible-light, whereas they can still realize overall solar water splitting with the proper positions of conduction band minimum and valence band maximum. This review describes recent progress regarding the improvement of photocatalytic activities of tantalum-based semiconductors. Basic concepts and principles of solar water splitting will be discussed in the introduction section, followed by the three main categories regarding to the different types of tantalum-based semiconductors. In each category, synthetic methodologies, influencing factors on the photocatalytic activities, strategies to enhance the efficiencies of photocatalysts and morphology control of tantalum-based materials will be discussed in detail. Future directions to further explore the research area of tantalum-based semiconductors for solar water splitting

Colloidal nanocrystals for photoelectrochemical and photocatalytic water splitting

NASA Astrophysics Data System (ADS)

Gadiyar, Chethana; Loiudice, Anna; Buonsanti, Raffaella

2017-02-01

Colloidal nanocrystals (NCs) are among the most modular and versatile nanomaterial platforms for studying emerging phenomena in different fields thanks to their superb compositional and morphological tunability. A promising, yet challenging, application involves the use of colloidal NCs as light absorbers and electrocatalysts for water splitting. In this review we discuss how the tunability of these materials is ideal to understand the complex phenomena behind storing energy in chemical bonds and to optimize performance through structural and compositional modification. First, we describe the colloidal synthesis method as a means to achieve a high degree of control over single material NCs and NC heterostructures, including examples of the role of the ligands in modulating size and shape. Next, we focus on the use of NCs as light absorbers and catalysts to drive both the hydrogen evolution reaction (HER) and the oxygen evolution reaction (OER), together with some of the challenges related to the use of colloidal NCs as model systems and/or technological solution in water splitting. We conclude with a broader prospective on the use of colloidal chemistry for new material discovery.

GASOLINE/DIESEL PM SPLIT STUDY: LIGHT-DUTY VEHICLE TESTING, DATA, AND ANALYSIS

EPA Science Inventory

During June 2001, the EPA participated in DOE's Gasoline/Diesel PM Split Study in Riverside, California. The purpose of the study was to determine the contribution of diesel versus gasoline-powered exhaust to the particulate matter (PM) inventory in the South Coast Air Basin. T...

Language Recognition via Sparse Coding

DTIC Science & Technology

2016-09-08

a posteriori (MAP) adaptation scheme that further optimizes the discriminative quality of sparse-coded speech fea - tures. We empirically validate the...significantly improve the discriminative quality of sparse-coded speech fea - tures. In Section 4, we evaluate the proposed approaches against an i-vector

Ni-doped TiO2 nanotubes photoanode for enhanced photoelectrochemical water splitting

NASA Astrophysics Data System (ADS)

Dong, Zhenbiao; Ding, Dongyan; Li, Ting; Ning, Congqin

2018-06-01

Photoelectrochemical (PEC) water splitting hydrogen production provides a promising way for sustainable development. In this work, we prepared Ni-doped TiO2 (Ti-Ni-O) nanotubes through anodizing different Ti-Ni alloys and further annealing them at elevated temperatures, and reported their PEC water splitting performance. It was found that Ni doping could improve light absorption and facilitate separation of photo-excited electron-hole pair. The nanotubes fabricated on Ti-1 wt.% Ni alloy and annealed at 550 °C exhibited better PEC water splitting performance than those on Ti-10 wt.% Ni alloy. The photoconversion efficiency was 0.67%, which was 3.35 times the photoconversion efficiency of undoped TiO2. It demonstrated that it was feasible to fabricate high-performance Ti-Ni-O nanotubes on Ti-Ni alloys and used as photoanode for improving PEC water splitting.

Implicit kernel sparse shape representation: a sparse-neighbors-based objection segmentation framework.

PubMed

Yao, Jincao; Yu, Huimin; Hu, Roland

2017-01-01

This paper introduces a new implicit-kernel-sparse-shape-representation-based object segmentation framework. Given an input object whose shape is similar to some of the elements in the training set, the proposed model can automatically find a cluster of implicit kernel sparse neighbors to approximately represent the input shape and guide the segmentation. A distance-constrained probabilistic definition together with a dualization energy term is developed to connect high-level shape representation and low-level image information. We theoretically prove that our model not only derives from two projected convex sets but is also equivalent to a sparse-reconstruction-error-based representation in the Hilbert space. Finally, a "wake-sleep"-based segmentation framework is applied to drive the evolutionary curve to recover the original shape of the object. We test our model on two public datasets. Numerical experiments on both synthetic images and real applications show the superior capabilities of the proposed framework.

The application of low-rank and sparse decomposition method in the field of climatology

NASA Astrophysics Data System (ADS)

Gupta, Nitika; Bhaskaran, Prasad K.

2018-04-01

The present study reports a low-rank and sparse decomposition method that separates the mean and the variability of a climate data field. Until now, the application of this technique was limited only in areas such as image processing, web data ranking, and bioinformatics data analysis. In climate science, this method exactly separates the original data into a set of low-rank and sparse components, wherein the low-rank components depict the linearly correlated dataset (expected or mean behavior), and the sparse component represents the variation or perturbation in the dataset from its mean behavior. The study attempts to verify the efficacy of this proposed technique in the field of climatology with two examples of real world. The first example attempts this technique on the maximum wind-speed (MWS) data for the Indian Ocean (IO) region. The study brings to light a decadal reversal pattern in the MWS for the North Indian Ocean (NIO) during the months of June, July, and August (JJA). The second example deals with the sea surface temperature (SST) data for the Bay of Bengal region that exhibits a distinct pattern in the sparse component. The study highlights the importance of the proposed technique used for interpretation and visualization of climate data.

Efficient solar-driven water splitting by nanocone BiVO4-perovskite tandem cells

PubMed Central

Qiu, Yongcai; Liu, Wei; Chen, Wei; Chen, Wei; Zhou, Guangmin; Hsu, Po-Chun; Zhang, Rufan; Liang, Zheng; Fan, Shoushan; Zhang, Yuegang; Cui, Yi

2016-01-01

Bismuth vanadate (BiVO4) has been widely regarded as a promising photoanode material for photoelectrochemical (PEC) water splitting because of its low cost, its high stability against photocorrosion, and its relatively narrow band gap of 2.4 eV. However, the achieved performance of the BiVO4 photoanode remains unsatisfactory to date because its short carrier diffusion length restricts the total thickness of the BiVO4 film required for sufficient light absorption. We addressed the issue by deposition of nanoporous Mo-doped BiVO4 (Mo:BiVO4) on an engineered cone-shaped nanostructure, in which the Mo:BiVO4 layer with a larger effective thickness maintains highly efficient charge separation and high light absorption capability, which can be further enhanced by multiple light scattering in the nanocone structure. As a result, the nanocone/Mo:BiVO4/Fe(Ni)OOH photoanode exhibits a high water-splitting photocurrent of 5.82 ± 0.36 mA cm−2 at 1.23 V versus the reversible hydrogen electrode under 1-sun illumination. We also demonstrate that the PEC cell in tandem with a single perovskite solar cell exhibits unassisted water splitting with a solar-to-hydrogen conversion efficiency of up to 6.2%. PMID:27386565

Image super-resolution via sparse representation.

PubMed

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

2010-11-01

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

Efficient convolutional sparse coding

DOEpatents

Wohlberg, Brendt

2017-06-20

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

Atom beams split by gentle persuasion

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pool, R.

1994-02-25

Two different research teams have taken a big step toward atom interferometry. They have succeeded in splitting atomic beams by using atoms in spin states that neither absorb nor reemit laser light. By proper adjustment of experimental conditions, atoms are changed from one spin state to another, without passing through the intermediary excited state. The atoms in essence absorb momentum from the laser photons, without absorption or emission of photons. The change in momentum deflects atoms in the proper spin state.

Split-wedge antennas with sub-5 nm gaps for plasmonic nanofocusing

DOE PAGES

Chen, Xiaoshu; Lindquist, Nathan C.; Klemme, Daniel J.; ...

2016-11-22

Here, we present a novel plasmonic antenna structure, a split-wedge antenna, created by splitting an ultrasharp metallic wedge with a nanogap perpendicular to its apex. The nanogap can tightly confine gap plasmons and boost the local optical field intensity in and around these opposing metallic wedge tips. This three-dimensional split-wedge antenna integrates the key features of nanogaps and sharp tips, i.e., tight field confinement and three-dimensional nanofocusing, respectively, into a single platform. We fabricate split-wedge antennas with gaps that are as small as 1 nm in width at the wafer scale by combining silicon V-grooves with template stripping and atomicmore » layer lithography. Computer simulations show that the field enhancement and confinement are stronger at the tip–gap interface compared to what standalone tips or nanogaps produce, with electric field amplitude enhancement factors exceeding 50 when near-infrared light is focused on the tip–gap geometry. The resulting nanometric hotspot volume is on the order of λ 3/10 6. Experimentally, Raman enhancement factors exceeding 10 7 are observed from a 2 nm gap split-wedge antenna, demonstrating its potential for sensing and spectroscopy applications.« less

Split-Wedge Antennas with Sub-5 nm Gaps for Plasmonic Nanofocusing

PubMed Central

2016-01-01

We present a novel plasmonic antenna structure, a split-wedge antenna, created by splitting an ultrasharp metallic wedge with a nanogap perpendicular to its apex. The nanogap can tightly confine gap plasmons and boost the local optical field intensity in and around these opposing metallic wedge tips. This three-dimensional split-wedge antenna integrates the key features of nanogaps and sharp tips, i.e., tight field confinement and three-dimensional nanofocusing, respectively, into a single platform. We fabricate split-wedge antennas with gaps that are as small as 1 nm in width at the wafer scale by combining silicon V-grooves with template stripping and atomic layer lithography. Computer simulations show that the field enhancement and confinement are stronger at the tip–gap interface compared to what standalone tips or nanogaps produce, with electric field amplitude enhancement factors exceeding 50 when near-infrared light is focused on the tip–gap geometry. The resulting nanometric hotspot volume is on the order of λ3/106. Experimentally, Raman enhancement factors exceeding 107 are observed from a 2 nm gap split-wedge antenna, demonstrating its potential for sensing and spectroscopy applications. PMID:27960527

Image fusion using sparse overcomplete feature dictionaries

DOEpatents

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.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Deveci, Mehmet; Trott, Christian Robert; Rajamanickam, Sivasankaran

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

Sparse High Dimensional Models in Economics

PubMed Central

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

Algebraic techniques for diagonalization of a split quaternion matrix in split quaternionic mechanics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jiang, Tongsong, E-mail: jiangtongsong@sina.com; Department of Mathematics, Heze University, Heze, Shandong 274015; Jiang, Ziwu

In the study of the relation between complexified classical and non-Hermitian quantum mechanics, physicists found that there are links to quaternionic and split quaternionic mechanics, and this leads to the possibility of employing algebraic techniques of split quaternions to tackle some problems in complexified classical and quantum mechanics. This paper, by means of real representation of a split quaternion matrix, studies the problem of diagonalization of a split quaternion matrix and gives algebraic techniques for diagonalization of split quaternion matrices in split quaternionic mechanics.

Evidence for sparse synergies in grasping actions.

PubMed

Prevete, Roberto; Donnarumma, Francesco; d'Avella, Andrea; Pezzulo, Giovanni

2018-01-12

Converging evidence shows that hand-actions are controlled at the level of synergies and not single muscles. One intriguing aspect of synergy-based action-representation is that it may be intrinsically sparse and the same synergies can be shared across several distinct types of hand-actions. Here, adopting a normative angle, we consider three hypotheses for hand-action optimal-control: sparse-combination hypothesis (SC) - sparsity in the mapping between synergies and actions - i.e., actions implemented using a sparse combination of synergies; sparse-elements hypothesis (SE) - sparsity in synergy representation - i.e., the mapping between degrees-of-freedom (DoF) and synergies is sparse; double-sparsity hypothesis (DS) - a novel view combining both SC and SE - i.e., both the mapping between DoF and synergies and between synergies and actions are sparse, each action implementing a sparse combination of synergies (as in SC), each using a limited set of DoFs (as in SE). We evaluate these hypotheses using hand kinematic data from six human subjects performing nine different types of reach-to-grasp actions. Our results support DS, suggesting that the best action representation is based on a relatively large set of synergies, each involving a reduced number of degrees-of-freedom, and that distinct sets of synergies may be involved in distinct tasks.

"DNA Origami Traffic Lights" with a Split Aptamer Sensor for a Bicolor Fluorescence Readout.

PubMed

Walter, Heidi-Kristin; Bauer, Jens; Steinmeyer, Jeannine; Kuzuya, Akinori; Niemeyer, Christof M; Wagenknecht, Hans-Achim

2017-04-12

A split aptamer for adenosine triphosphate (ATP) was embedded as a recognition unit into two levers of a nanomechanical DNA origami construct by extension and modification of selected staple strands. An additional optical module in the stem of the split aptamer comprised two different cyanine-styryl dyes that underwent an energy transfer from green (donor) to red (acceptor) emission if two ATP molecules were bound as target molecule to the recognition module and thereby brought the dyes in close proximity. As a result, the ATP as a target triggered the DNA origami shape transition and yielded a fluorescence color change from green to red as readout. Conventional atomic force microscopy (AFM) images confirmed the topology change from the open form of the DNA origami in the absence of ATP into the closed form in the presence of the target molecule. The obtained closed/open ratios in the absence and presence of target molecules tracked well with the fluorescence color ratios and thereby validated the bicolor fluorescence readout. The correct positioning of the split aptamer as the functional unit farthest away from the fulcrum of the DNA origami was crucial for the aptasensing by fluorescence readout. The fluorescence color change allowed additionally to follow the topology change of the DNA origami aptasensor in real time in solution. The concepts of fluorescence energy transfer for bicolor readout in a split aptamer in solution, and AFM on surfaces, were successfully combined in a single DNA origami construct to obtain a bimodal readout. These results are important for future custom DNA devices for chemical-biological and bioanalytical purposes because they are not only working as simple aptamers but are also visible by AFM on the single-molecule level.

Polarized micro-cavity organic light-emitting devices.

PubMed

Park, Byoungchoo; Kim, Mina; Park, Chan Hyuk

2009-04-27

We present the results of a study of light emissions from a polarized micro-cavity Organic Light-Emitting Device (OLED), which consisted of a flexible, anisotropic one-dimensional (1-D) photonic crystal (PC) film substrate. It is shown that luminous Electroluminescent (EL) emissions from the polarized micro-cavity OLED were produced at relatively low operating voltages. It was also found that the peak wavelengths of the emitted EL light corresponded to the two split eigen modes of the high-energy band edges of the anisotropic PC film, with a strong dependence on the polarization state of the emitting light. For polarization along the ordinary axis of the anisotropic PC film, the optical split micro-cavity modes occurred at the longer high-energy photonic band gap (PBG) edge, while for polarization along the extraordinary axis, the split micro-cavity modes occurred at the shorter high-energy PBG edge, with narrow bandwidths. We demonstrated that the polarization and emission mode of the micro-cavity OLED may be selected by choosing the appropriate optical axis of the anisotropic 1-D PC film.

Adaptive regulation of sparseness by feedforward inhibition

PubMed Central

Assisi, Collins; Stopfer, Mark; Laurent, Gilles; Bazhenov, Maxim

2014-01-01

In the mushroom body of insects, odors are represented by very few spikes in a small number of neurons, a highly efficient strategy known as sparse coding. Physiological studies of these neurons have shown that sparseness is maintained across thousand-fold changes in odor concentration. Using a realistic computational model, we propose that sparseness in the olfactory system is regulated by adaptive feedforward inhibition. When odor concentration changes, feedforward inhibition modulates the duration of the temporal window over which the mushroom body neurons may integrate excitatory presynaptic input. This simple adaptive mechanism could maintain the sparseness of sensory representations across wide ranges of stimulus conditions. PMID:17660812

Symmetric splitting of very light systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Grotowski, K.; Majka, Z.; Planeta, R.

1984-10-01

Inclusive and coincidence measurements have been performed to study symmetric products from the reactions 74--186 MeV /sup 12/C+ /sup 40/Ca, 141 MeV /sup 9/Be+ /sup 40/Ca, and 153 MeV /sup 6/Li+ /sup 40/Ca. The binary decay of the composite system has been verified. Energy spectra, angular distributions, and fragment correlations are presented. The total kinetic energies for the symmetric products from these very light composite systems are compared to liquid drop model calculations and fission systematics.

Optical power splitter for splitting high power light

DOEpatents

English, Jr., Ronald E.; Christensen, John J.

1995-01-01

An optical power splitter for the distribution of high-power light energy has a plurality of prisms arranged about a central axis to form a central channel. The input faces of the prisms are in a common plane which is substantially perpendicular to the central axis. A beam of light which is substantially coaxial to the central axis is incident on the prisms and at least partially strikes a surface area of each prism input face. The incident beam also partially passes through the central channel.

Isospin splitting of nucleon effective mass and symmetry energy in isotopic nuclear reactions

NASA Astrophysics Data System (ADS)

Guo, Ya-Fei; Chen, Peng-Hui; Niu, Fei; Zhang, Hong-Fei; Jin, Gen-Ming; Feng, Zhao-Qing

2017-10-01

Within an isospin and momentum dependent transport model, the dynamics of isospin particles (nucleons and light clusters) in Fermi-energy heavy-ion collisions are investigated for constraining the isospin splitting of nucleon effective mass and the symmetry energy at subsaturation densities. The impacts of the isoscalar and isovector parts of the momentum dependent interaction on the emissions of isospin particles are explored, i.e., the mass splittings of and (). The single and double neutron to proton ratios of free nucleons and light particles are thoroughly investigated in the isotopic nuclear reactions of 112Sn+112Sn and 124Sn+124Sn at incident energies of 50 and 120 MeV/nucleon, respectively. It is found that both the effective mass splitting and symmetry energy impact the kinetic energy spectra of the single ratios, in particular at the high energy tail (larger than 20 MeV). The isospin splitting of nucleon effective mass slightly impacts the double ratio spectra at the energy of 50 MeV/nucleon. A soft symmetry energy with stiffness coefficient of γ s=0.5 is constrained from the experimental data with the Fermi-energy heavy-ion collisions. Supported by Major State Basic Research Development Program in China (2014CB845405, 2015CB856903), National Natural Science Foundation of China (11722546, 11675226, 11675066, U1332207) and Youth Innovation Promotion Association of Chinese Academy of Sciences

Optical power splitter for splitting high power light

DOEpatents

English, R.E. Jr.; Christensen, J.J.

1995-04-18

An optical power splitter for the distribution of high-power light energy has a plurality of prisms arranged about a central axis to form a central channel. The input faces of the prisms are in a common plane which is substantially perpendicular to the central axis. A beam of light which is substantially coaxial to the central axis is incident on the prisms and at least partially strikes a surface area of each prism input face. The incident beam also partially passes through the central channel. 5 figs.

Nano-ferrites for Water Splitting: Unprecedented High Photocatalytic Hydrogen Production under Visible Light

EPA Science Inventory

In the present investigation, hydrogen production via water splitting by nano ferrites has been studied using ethanol as the sacrificial donor. The nano ferrite has shown great potential in hydrogen generation with hydrogen yield of 8275 9moles/h/ g of photocatalyst under visible...

A Hybrid Algorithm for Period Analysis from Multiband Data with Sparse and Irregular Sampling for Arbitrary Light-curve Shapes

NASA Astrophysics Data System (ADS)

Saha, Abhijit; Vivas, A. Katherina

2017-12-01

Ongoing and future surveys with repeat imaging in multiple bands are producing (or will produce) time-spaced measurements of brightness, resulting in the identification of large numbers of variable sources in the sky. A large fraction of these are periodic variables: compilations of these are of scientific interest for a variety of purposes. Unavoidably, the data sets from many such surveys not only have sparse sampling, but also have embedded frequencies in the observing cadence that beat against the natural periodicities of any object under investigation. Such limitations can make period determination ambiguous and uncertain. For multiband data sets with asynchronous measurements in multiple passbands, we wish to maximally use the information on periodicity in a manner that is agnostic of differences in the light-curve shapes across the different channels. Given large volumes of data, computational efficiency is also at a premium. This paper develops and presents a computationally economic method for determining periodicity that combines the results from two different classes of period-determination algorithms. The underlying principles are illustrated through examples. The effectiveness of this approach for combining asynchronously sampled measurements in multiple observables that share an underlying fundamental frequency is also demonstrated.

Photocatalytic Water Splitting for O2 Production under Visible Light Irradiation Using NdVO4-V2O5 Hybrid Powders

PubMed Central

Chiang, Tzu Hsuan; Chen, Tso-Ming

2017-01-01

The study investigated photocatalytic water splitting for O2 production under visible light irradiation using neodymium vanadium oxide (NdVO4) and vanadium oxide (V2O5) hybrid powders. The results in a sacrificial agent of 0.01 M AgNO3 solution were obtained, and the highest photocatalytic O2 evolution was 2.63 μmol/h, when the hybrid powders were prepared by mixing Nd and V at a volume ratio of 1:3 at a calcination temperature of 350 °C for 1 h. The hybrid powders were synthesized by neodymium nitrate and ammonium metavanadate using the glycothermal method in ethylene glycol at 120 °C for 1 h. The hybrid powders consisted of two shapes, NdVO4 nanoparticles and the cylindrical V2O5 particles, and they possessed the ability for photocatalytic oxygen (O2) evolution during irradiation with visible light. The band gaps and structures of the hybrid powders were analyzed using UV-visible spectroscopy and transmission electron microscopy. PMID:28772692

Sparse Learning with Stochastic Composite Optimization.

PubMed

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

2017-06-01

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

Bad splits in bilateral sagittal split osteotomy: systematic review of fracture patterns.

PubMed

Steenen, S A; Becking, A G

2016-07-01

An unfavourable and unanticipated pattern of the mandibular sagittal split osteotomy is generally referred to as a 'bad split'. Few restorative techniques to manage the situation have been described. In this article, a classification of reported bad split pattern types is proposed and appropriate salvage procedures to manage the different types of undesired fracture are presented. A systematic review was undertaken, yielding a total of 33 studies published between 1971 and 2015. These reported a total of 458 cases of bad splits among 19,527 sagittal ramus osteotomies in 10,271 patients. The total reported incidence of bad split was 2.3% of sagittal splits. The most frequently encountered were buccal plate fractures of the proximal segment (types 1A-F) and lingual fractures of the distal segment (types 2A and 2B). Coronoid fractures (type 3) and condylar neck fractures (type 4) have seldom been reported. The various types of bad split may require different salvage approaches. Copyright © 2016 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Local structure preserving sparse coding for infrared target recognition

PubMed Central

Han, Jing; Yue, Jiang; Zhang, Yi; Bai, Lianfa

2017-01-01

Sparse coding performs well in image classification. However, robust target recognition requires a lot of comprehensive template images and the sparse learning process is complex. We incorporate sparsity into a template matching concept to construct a local sparse structure matching (LSSM) model for general infrared target recognition. A local structure preserving sparse coding (LSPSc) formulation is proposed to simultaneously preserve the local sparse and structural information of objects. By adding a spatial local structure constraint into the classical sparse coding algorithm, LSPSc can improve the stability of sparse representation for targets and inhibit background interference in infrared images. Furthermore, a kernel LSPSc (K-LSPSc) formulation is proposed, which extends LSPSc to the kernel space to weaken the influence of the linear structure constraint in nonlinear natural data. Because of the anti-interference and fault-tolerant capabilities, both LSPSc- and K-LSPSc-based LSSM can implement target identification based on a simple template set, which just needs several images containing enough local sparse structures to learn a sufficient sparse structure dictionary of a target class. Specifically, this LSSM approach has stable performance in the target detection with scene, shape and occlusions variations. High performance is demonstrated on several datasets, indicating robust infrared target recognition in diverse environments and imaging conditions. PMID:28323824

Multilevel sparse functional principal component analysis.

PubMed

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.

Mechanistic Understanding of the Plasmonic Enhancement for Solar Water Splitting.

PubMed

Zhang, Peng; Wang, Tuo; Gong, Jinlong

2015-09-23

H2 generation by solar water splitting is one of the most promising solutions to meet the increasing energy demands of the fast developing society. However, the efficiency of solar-water-splitting systems is still too low for practical applications, which requires further enhancement via different strategies such as doping, construction of heterojunctions, morphology control, and optimization of the crystal structure. Recently, integration of plasmonic metals to semiconductor photocatalysts has been proved to be an effective way to improve their photocatalytic activities. Thus, in-depth understanding of the enhancement mechanisms is of great importance for better utilization of the plasmonic effect. This review describes the relevant mechanisms from three aspects, including: i) light absorption and scattering; ii) hot-electron injection and iii) plasmon-induced resonance energy transfer (PIRET). Perspectives are also proposed to trigger further innovative thinking on plasmonic-enhanced solar water splitting. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Gas-Kinetic Theory Based Flux Splitting Method for Ideal Magnetohydrodynamics

NASA Technical Reports Server (NTRS)

Xu, Kun

1998-01-01

A gas-kinetic solver is developed for the ideal magnetohydrodynamics (MHD) equations. The new scheme is based on the direct splitting of the flux function of the MHD equations with the inclusion of "particle" collisions in the transport process. Consequently, the artificial dissipation in the new scheme is much reduced in comparison with the MHD Flux Vector Splitting Scheme. At the same time, the new scheme is compared with the well-developed Roe-type MHD solver. It is concluded that the kinetic MHD scheme is more robust and efficient than the Roe- type method, and the accuracy is competitive. In this paper the general principle of splitting the macroscopic flux function based on the gas-kinetic theory is presented. The flux construction strategy may shed some light on the possible modification of AUSM- and CUSP-type schemes for the compressible Euler equations, as well as to the development of new schemes for a non-strictly hyperbolic system.

Photocatalytic Water-Splitting Enhancement by Sub-Bandgap Photon Harvesting.

PubMed

Monguzzi, Angelo; Oertel, Amadeus; Braga, Daniele; Riedinger, Andreas; Kim, David K; Knüsel, Philippe N; Bianchi, Alberto; Mauri, Michele; Simonutti, Roberto; Norris, David J; Meinardi, Francesco

2017-11-22

Upconversion is a photon-management process especially suited to water-splitting cells that exploit wide-bandgap photocatalysts. Currently, such catalysts cannot utilize 95% of the available solar photons. We demonstrate here that the energy-conversion yield for a standard photocatalytic water-splitting device can be enhanced under solar irradiance by using a low-power upconversion system that recovers part of the unutilized incident sub-bandgap photons. The upconverter is based on a sensitized triplet-triplet annihilation mechanism (sTTA-UC) obtained in a dye-doped elastomer and boosted by a fluorescent nanocrystal/polymer composite that allows for broadband light harvesting. The complementary and tailored optical properties of these materials enable efficient upconversion at subsolar irradiance, allowing the realization of the first prototype water-splitting cell assisted by solid-state upconversion. In our proof-of concept device the increase of the performance is 3.5%, which grows to 6.3% if concentrated sunlight (10 sun) is used. Our experiments show how the sTTA-UC materials can be successfully implemented in technologically relevant devices while matching the strict requirements of clean-energy production.

Solar Water Splitting at λ=600 nm: A Step Closer to Sustainable Hydrogen Production.

PubMed

Zhang, Jinshui; Wang, Xinchen

2015-06-15

Overall water splitting with a semiconductor photocatalyst under visible-light irradiation is considered as a "dream reaction" in chemistry. The development of a 600 nm photocatalyst for solar water splitting highlighted here is not only an important milestone towards sustainable hydrogen production, but also a new starting point for artificial photosynthesis. STH=solar-to-hydrogen energy conversion efficiency. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Volume holographic lens spectrum-splitting photovoltaic system for high energy yield with direct and diffuse solar illumination

NASA Astrophysics Data System (ADS)

Chrysler, Benjamin D.; Wu, Yuechen; Yu, Zhengshan; Kostuk, Raymond K.

2017-08-01

In this paper a prototype spectrum-splitting photovoltaic system based on volume holographic lenses (VHL) is designed, fabricated and tested. In spectrum-splitting systems, incident sunlight is divided in spectral bands for optimal conversion by a set of single-junction PV cells that are laterally separated. The VHL spectrumsplitting system in this paper has a form factor similar to conventional silicon PV modules but with higher efficiencies (>30%). Unlike many other spectrum-splitting systems that have been proposed in the past, the system in this work converts both direct and diffuse sunlight while using inexpensive 1-axis tracking systems. The VHL system uses holographic lenses that focus light at a transition wavelength to the boundary between two PV cells. Longer wavelength light is dispersed to the narrow bandgap cell and shorter wavelength light to the wide bandgap cell. A prototype system is designed with silicon and GaAs PV cells. The holographic lenses are fabricated in Covestro Bayfol HX photopolymer by `stitching' together lens segments through sequential masked exposures. The PV cells and holographic lenses were characterized and the data was used in a raytrace simulation and predicts an improvement in total power output of 15.2% compared to a non-spectrum-splitting reference. A laboratory measurement yielded an improvement in power output of 8.5%.

Feasibility of Very Large Sparse Aperture Deployable Antennas

DTIC Science & Technology

2014-03-27

FEASIBILITY OF VERY LARGE SPARSE APERTURE DEPLOYABLE ANTENNAS THESIS Jason C. Heller, Captain...States. AFIT-ENY-14-M-24 FEASIBILITY OF VERY LARGE SPARSE APERTURE DEPLOYABLE ANTENNAS THESIS Presented to the Faculty...UNLIMITED AFIT-ENY-14-M-24 FEASIBILITY OF VERY LARGE SPARSE APERTURE DEPLOYABLE ANTENNAS Jason C. Heller, B.S., Aerospace

Sparse PCA with Oracle Property.

PubMed

Gu, Quanquan; Wang, Zhaoran; Liu, Han

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

Sparse PCA with Oracle Property

PubMed Central

Gu, Quanquan; Wang, Zhaoran; Liu, Han

2014-01-01

In this paper, we study the estimation of the k-dimensional sparse principal subspace of covariance matrix Σ in the high-dimensional setting. We aim to recover the oracle principal subspace solution, i.e., the principal subspace estimator obtained assuming the true support is known a priori. To this end, we propose a family of estimators based on the semidefinite relaxation of sparse PCA with novel regularizations. In particular, under a weak assumption on the magnitude of the population projection matrix, one estimator within this family exactly recovers the true support with high probability, has exact rank-k, and attains a s/n statistical rate of convergence with s being the subspace sparsity level and n the sample size. Compared to existing support recovery results for sparse PCA, our approach does not hinge on the spiked covariance model or the limited correlation condition. As a complement to the first estimator that enjoys the oracle property, we prove that, another estimator within the family achieves a sharper statistical rate of convergence than the standard semidefinite relaxation of sparse PCA, even when the previous assumption on the magnitude of the projection matrix is violated. We validate the theoretical results by numerical experiments on synthetic datasets. PMID:25684971

Feature Selection and Pedestrian Detection Based on Sparse Representation.

PubMed

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.

Accessing sparse arrays in parallel memories

DOE Office of Scientific and Technical Information (OSTI.GOV)

Banerjee, U.; Gajski, D.; Kuck, D.

The concept of dense and sparse execution of arrays is introduced. Arrays themselves can be stored in a dense or sparse manner in a parallel memory with m memory modules. The paper proposes hardware for speeding up the execution of array operations of the form c(c/sub 0/+ci)=a(a/sub 0/+ai) op b(b/sub 0/+bi), where a/sub 0/, a, b/sub 0/, b, c/sub 0/, c are integer constants and i is an index variable. The hardware handles 'sparse execution', in which the operation op is not executed for every value of i. The hardware also makes provision for 'sparse storage', in which memory spacemore » is not provided for every array element. It is shown how to access array elements of the above form without conflict in an efficient way. The efficiency is obtained by using some specialised units which are basically smart memories with priority detection, one's counting or associative searching. Generalisation to multidimensional arrays is shown possible under restrictions defined in the paper. 12 references.« less

Anomalous complete opaqueness in a sparse array of gold nanoparticle chains

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bai Benfeng; Tsinghua-Foxconn Nanotechnology Research Center, Tsinghua University, Beijing 100084; Department of Physics and Mathematics, University of Eastern Finland, P.O. Box 111, FI-80101 Joensuu

2011-08-22

We report on an anomalous polarization-switching extinction effect in a sparse array of gold nanoparticle chains: under normal incidence of light, the array is almost transparent for one polarization; whereas it is fully opaque (with nearly zero transmittance) for the orthogonal polarization within a narrow band, even though the nanoparticles cover only a tiny fraction (say, 3.5%) of the transparent substrate surface. We reveal that the strong polarization-dependent short-range dipolar coupling and long-range radiative coupling of gold nanoparticles in this highly asymmetric array is responsible for this extraordinary effect.

Enhanced valley splitting in monolayer WSe2 due to magnetic exchange field.

PubMed

Zhao, Chuan; Norden, Tenzin; Zhang, Peiyao; Zhao, Puqin; Cheng, Yingchun; Sun, Fan; Parry, James P; Taheri, Payam; Wang, Jieqiong; Yang, Yihang; Scrace, Thomas; Kang, Kaifei; Yang, Sen; Miao, Guo-Xing; Sabirianov, Renat; Kioseoglou, George; Huang, Wei; Petrou, Athos; Zeng, Hao

2017-08-01

Exploiting the valley degree of freedom to store and manipulate information provides a novel paradigm for future electronics. A monolayer transition-metal dichalcogenide (TMDC) with a broken inversion symmetry possesses two degenerate yet inequivalent valleys, which offers unique opportunities for valley control through the helicity of light. Lifting the valley degeneracy by Zeeman splitting has been demonstrated recently, which may enable valley control by a magnetic field. However, the realized valley splitting is modest (∼0.2 meV T -1 ). Here we show greatly enhanced valley spitting in monolayer WSe 2 , utilizing the interfacial magnetic exchange field (MEF) from a ferromagnetic EuS substrate. A valley splitting of 2.5 meV is demonstrated at 1 T by magnetoreflectance measurements and corresponds to an effective exchange field of ∼12 T. Moreover, the splitting follows the magnetization of EuS, a hallmark of the MEF. Utilizing the MEF of a magnetic insulator can induce magnetic order and valley and spin polarization in TMDCs, which may enable valleytronic and quantum-computing applications.

Two-loop mass splittings in electroweak multiplets: Winos and minimal dark matter

NASA Astrophysics Data System (ADS)

McKay, James; Scott, Pat

2018-03-01

The radiatively-induced splitting of masses in electroweak multiplets is relevant for both collider phenomenology and dark matter. Precision two-loop corrections of O (MeV ) to the triplet mass splitting in the wino limit of the minimal supersymmetric standard model can affect particle lifetimes by up to 40%. We improve on previous two-loop self-energy calculations for the wino model by obtaining consistent input parameters to the calculation via two-loop renormalization-group running, and including the effect of finite light quark masses. We also present the first two-loop calculation of the mass splitting in an electroweak fermionic quintuplet, corresponding to the viable form of minimal dark matter (MDM). We place significant constraints on the lifetimes of the charged and doubly-charged fermions in this model. We find that the two-loop mass splittings in the MDM quintuplet are not constant in the large-mass limit, as might naively be expected from the triplet calculation. This is due to the influence of the additional heavy fermions in loop corrections to the gauge boson propagators.

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.

Angular-split/temporal-delay approach to ultrafast protein dynamics at XFELs.

PubMed

Ren, Zhong; Yang, Xiaojing

2016-07-01

X-ray crystallography promises direct insights into electron-density changes that lead to and arise from structural changes such as electron and proton transfer and the formation, rupture and isomerization of chemical bonds. The ultrashort pulses of hard X-rays produced by free-electron lasers present an exciting opportunity for capturing ultrafast structural events in biological macromolecules within femtoseconds after photoexcitation. However, shot-to-shot fluctuations, which are inherent to the very process of self-amplified spontaneous emission (SASE) that generates the ultrashort X-ray pulses, are a major source of noise that may conceal signals from structural changes. Here, a new approach is proposed to angularly split a single SASE pulse and to produce a temporal delay of picoseconds between the split pulses. These split pulses will allow the probing of two distinct states before and after photoexcitation triggered by a laser pulse between the split X-ray pulses. The split pulses originate from a single SASE pulse and share many common properties; thus, noise arising from shot-to-shot fluctuations is self-canceling. The unambiguous interpretation of ultrafast structural changes would require diffraction data at atomic resolution, as these changes may or may not involve any atomic displacement. This approach, in combination with the strategy of serial crystallography, offers a solution to study ultrafast dynamics of light-initiated biochemical reactions or biological processes at atomic resolution.

An Inexpensive Co-Intercalated Layered Double Hydroxide Composite with Electron Donor-Acceptor Character for Photoelectrochemical Water Splitting

PubMed Central

Zheng, Shufang; Lu, Jun; Yan, Dongpeng; Qin, Yumei; Li, Hailong; Evans, David G.; Duan, Xue

2015-01-01

In this paper, the inexpensive 4,4-diaminostilbene-2,2-disulfonate (DAS) and 4,4-dinitro-stilbene-2,2- disulfonate (DNS) anions with arbitrary molar ratios were successfully co-intercalated into Zn2Al-layered double hydroxides (LDHs). The DAS(50%)-DNS/LDHs composite exhibited the broad UV-visible light absorption and fluorescence quenching, which was a direct indication of photo-induced electron transfer (PET) process between the intercalated DAS (donor) and DNS (acceptor) anions. This was confirmed by the matched HOMO/LUMO energy levels alignment of the intercalated DAS and DNS anions, which was also compatible for water splitting. The DAS(50%)-DNS/LDHs composite was fabricated as the photoanode and Pt as the cathode. Under the UV-visible light illumination, the enhanced photo-generated current (4.67 mA/cm2 at 0.8 V vs. SCE) was generated in the external circuit, and the photoelectrochemical water split was realized. Furthermore, this photoelectrochemical water splitting performance had excellent crystalline, electrochemical and optical stability. Therefore, this novel inorganic/organic hybrid photoanode exhibited potential application prospect in photoelectrochemical water splitting. PMID:26174201

Circularly split-ring-resonator-based frequency-reconfigurable antenna

NASA Astrophysics Data System (ADS)

Rahman, M. A.; Faruque, M. R. I.; Islam, M. T.

2017-01-01

In this paper, an antenna with frequency configurability in light of a circularly split-ring resonator (CSRR) is introduced. The proposed reconfigurable monopole antenna consists of a microstrip-fed hook-shaped structure and a CSRR having single reconfigurable split only. A new band of radiation unlike the band radiated from monopole only is observed due to magnetic coupling between the CSRR and the monopole antenna. The resonance frequency of the CSRR can be arbitrarily chosen by varying the dimension and relative position of its gap with the monopole, which leads the antenna to become reconfigurable one. By using a single switch with perfect electric conductor at the gap of CSRR cell, the effect of CSRR can be deactivated and, hence, it is possible to suppress the corresponding resonance, resulting in a frequency-reconfigurable antenna. Commercially available Computer Simulation Technology microwave studio based on finite integration technique was adopted throughout the study.

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.

Universal Priors for Sparse Modeling(PREPRINT)

DTIC Science & Technology

2009-08-01

Ingenierı́a Eléctrica, Universidad de la República J. Herrera y Reissig 565, Montevideo 11300, Uruguay 2fefo@fing.edu.uy Abstract—Sparse data models, where...Aj‖0 = |Aj | as its cardinality . The goal of sparse modeling is to design a dictionary D such that X = DA with ‖Aj‖0 sufficiently small (usually below

Lateral Variations in SKS Splitting Across the MAGIC Array, Central Appalachians

NASA Astrophysics Data System (ADS)

Aragon, John C.; Long, Maureen D.; Benoit, Margaret H.

2017-11-01

The eastern margin of North America has been shaped by several cycles of supercontinent assembly. These past episodes of orogenesis and continental rifting have likely deformed the lithosphere, but the extent, style, and geometry of this deformation remain poorly known. Measurements of seismic anisotropy in the upper mantle can shed light on past lithospheric deformation, but may also reveal contributions from present-day mantle flow in the asthenosphere. Here we examine SKS waveforms and measure splitting of SKS phases recorded by the MAGIC experiment, a dense transect of seismic stations across the central Appalachians. Our measurements constrain small-scale lateral variations in azimuthal anisotropy and reveal distinct regions of upper mantle anisotropy. Stations within the present-day Appalachian Mountains exhibit fast splitting directions roughly parallel to the strike of the mountains and delay times of about 1.0 s. To the west, transverse component waveforms for individual events reveal lateral variability in anisotropic structure. Stations immediately to the east of the mountains exhibit complicated splitting patterns, more null SKS arrivals, and a distinct clockwise rotation of fast directions. The observed variability in splitting behavior argues for contributions from both the lithosphere and the asthenospheric mantle. We infer that the sharp lateral transition in splitting behavior at the eastern edge of the Appalachians is controlled by a change in anisotropy in the lithospheric mantle. We hypothesize that beneath the Appalachians, SKS splitting reflects lithospheric deformation associated with Appalachian orogenesis, while just to the east this anisotropic signature was modified by Mesozoic rifting.

Storage of sparse files using parallel log-structured file system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bent, John M.; Faibish, Sorin; Grider, Gary

A sparse file is stored without holes by storing a data portion of the sparse file using a parallel log-structured file system; and generating an index entry for the data portion, the index entry comprising a logical offset, physical offset and length of the data portion. The holes can be restored to the sparse file upon a reading of the sparse file. The data portion can be stored at a logical end of the sparse file. Additional storage efficiency can optionally be achieved by (i) detecting a write pattern for a plurality of the data portions and generating a singlemore » patterned index entry for the plurality of the patterned data portions; and/or (ii) storing the patterned index entries for a plurality of the sparse files in a single directory, wherein each entry in the single directory comprises an identifier of a corresponding sparse file.« less

A high temperature hybrid photovoltaic-thermal receiver employing spectral beam splitting for linear solar concentrators

NASA Astrophysics Data System (ADS)

Mojiri, Ahmad; Stanley, Cameron; Rosengarten, Gary

2015-09-01

Hybrid photovoltaic/thermal (PV-T) solar collectors are capable of delivering heat and electricity concurrently. Implementing such receivers in linear concentrators for high temperature applications need special considerations such as thermal decoupling of the photovoltaic (pv) cells from the thermal receiver. Spectral beam splitting of concentrated light provides an option for achieving this purpose. In this paper we introduce a relatively simple hybrid receiver configuration that spectrally splits the light between a high temperature thermal fluid and silicon pv cells using volumetric light filtering by semi-conductor doped glass and propylene glycol. We analysed the optical performance of this device theoretically using ray tracing and experimentally through the construction and testing of a full scale prototype. The receiver was mounted on a commercial parabolic trough concentrator in an outdoor experiment. The prototype receiver delivered heat and electricity at total thermal efficiency of 44% and electrical efficiency of 3.9% measured relative to the total beam energy incident on the primary mirror.

Determination of the orbital moment and crystal-field splitting in LaTiO3.

PubMed

Haverkort, M W; Hu, Z; Tanaka, A; Ghiringhelli, G; Roth, H; Cwik, M; Lorenz, T; Schüssler-Langeheine, C; Streltsov, S V; Mylnikova, A S; Anisimov, V I; de Nadai, C; Brookes, N B; Hsieh, H H; Lin, H-J; Chen, C T; Mizokawa, T; Taguchi, Y; Tokura, Y; Khomskii, D I; Tjeng, L H

2005-02-11

Utilizing a sum rule in a spin-resolved photoelectron spectroscopic experiment with circularly polarized light, we show that the orbital moment in LaTiO3 is strongly reduced from its ionic value, both below and above the Ne el temperature. Using Ti L2,3 x-ray absorption spectroscopy as a local probe, we found that the crystal-field splitting in the t2g subshell is about 0.12-0.30 eV. This large splitting does not facilitate the formation of an orbital liquid.

Tensor Sparse Coding for Positive Definite Matrices.

PubMed

Sivalingam, Ravishankar; Boley, Daniel; Morellas, Vassilios; Papanikolopoulos, Nikos

2013-08-02

In recent years, there has been extensive research on sparse representation of vector-valued signals. In the matrix case, the data points are merely vectorized and treated as vectors thereafter (for e.g., image patches). However, this approach cannot be used for all matrices, as it may destroy the inherent structure of the data. Symmetric positive definite (SPD) matrices constitute one such class of signals, where their implicit structure of positive eigenvalues is lost upon vectorization. This paper proposes a novel sparse coding technique for positive definite matrices, which respects the structure of the Riemannian manifold and preserves the positivity of their eigenvalues, without resorting to vectorization. Synthetic and real-world computer vision experiments with region covariance descriptors demonstrate the need for and the applicability of the new sparse coding model. This work serves to bridge the gap between the sparse modeling paradigm and the space of positive definite matrices.

Tensor sparse coding for positive definite matrices.

PubMed

Sivalingam, Ravishankar; Boley, Daniel; Morellas, Vassilios; Papanikolopoulos, Nikolaos

2014-03-01

In recent years, there has been extensive research on sparse representation of vector-valued signals. In the matrix case, the data points are merely vectorized and treated as vectors thereafter (for example, image patches). However, this approach cannot be used for all matrices, as it may destroy the inherent structure of the data. Symmetric positive definite (SPD) matrices constitute one such class of signals, where their implicit structure of positive eigenvalues is lost upon vectorization. This paper proposes a novel sparse coding technique for positive definite matrices, which respects the structure of the Riemannian manifold and preserves the positivity of their eigenvalues, without resorting to vectorization. Synthetic and real-world computer vision experiments with region covariance descriptors demonstrate the need for and the applicability of the new sparse coding model. This work serves to bridge the gap between the sparse modeling paradigm and the space of positive definite matrices.

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.

A heterojunction photocatalyst composed of zinc rhodium oxide, single crystal-derived bismuth vanadium oxide, and silver for overall pure-water splitting under visible light up to 740 nm.

PubMed

Kobayashi, Ryoya; Takashima, Toshihiro; Tanigawa, Satoshi; Takeuchi, Shugo; Ohtani, Bunsho; Irie, Hiroshi

2016-10-12

We recently reported the synthesis of a solid-state heterojunction photocatalyst consisting of zinc rhodium oxide (ZnRh 2 O 4 ) and bismuth vanadium oxide (Bi 4 V 2 O 11 ), which functioned as hydrogen (H 2 ) and oxygen (O 2 ) evolution photocatalysts, respectively, connected with silver (Ag). Polycrystalline Bi 4 V 2 O 11 (p-Bi 4 V 2 O 11 ) powders were utilized to form ZnRh 2 O 4 /Ag/p-Bi 4 V 2 O 11 , which was able to photocatalyze overall pure-water splitting under red-light irradiation with a wavelength of 700 nm (R. Kobayashi et al., J. Mater. Chem. A, 2016, 4, 3061). In the present study, we replaced p-Bi 4 V 2 O 11 with a powder obtained by pulverizing single crystals of Bi 4 V 2 O 11 (s-Bi 4 V 2 O 11 ) to form ZnRh 2 O 4 /Ag/s-Bi 4 V 2 O 11 , and demonstrated that this heterojunction photocatalyst had enhanced water-splitting activity. In addition, ZnRh 2 O 4 /Ag/s-Bi 4 V 2 O 11 was able to utilize nearly the entire range of visible light up to a wavelength of 740 nm. These properties were attributable to the higher O 2 evolution activity of s-Bi 4 V 2 O 11 .

Multimodal Sparse Coding for Event Detection

DTIC Science & Technology

2015-10-13

classification tasks based on single modality. We present multimodal sparse coding for learning feature representations shared across multiple modalities...The shared representa- tions are applied to multimedia event detection (MED) and evaluated in compar- ison to unimodal counterparts, as well as other...and video tracks from the same multimedia clip, we can force the two modalities to share a similar sparse representation whose benefit includes robust

Thermocouple split follower

DOEpatents

Howell, deceased, Louis J.

1980-01-01

Thermoelectric generator assembly accommodating differential thermal expansion between thermoelectric elements by means of a cylindrical split follower forming a slot and having internal spring loaded wedges that permit the split follower to open and close across the slot.

Enhanced light trapping and high charge transmission capacities of novel structures for efficient photoelectrochemical water splitting.

PubMed

Mu, Jianglong; Miao, Hui; Liu, Enzhou; Feng, Juan; Teng, Feng; Zhang, Dekai; Kou, Yumeng; Jin, Yanping; Fan, Jun; Hu, Xiaoyun

2018-06-13

Excellent PEC efficiency, good reusability and the super stability of trap-like SnS2/TiO2 nanotube arrays (NTs)-based photoanodes are reported. Specifically, the SnS2/TiO2-180 °C (ST-180) photoanode exhibited the highest photocurrent density (1.05 mA cm-2) and an optimal η (0.73%) at 0.5 V (vs. SCE) under simulated light irradiation (AM 1.5G), which are 4.6 and 3.8 times higher than those of pure TiO2 NTs (0.23 mA cm-2 and 0.19%). The IPCE values of ST-180 can reach 21.5% (365 nm) and 13.8% (420 nm), which are much higher than those of pure TiO2 NTs (10.6% at 365 nm and 0.8% at 420 nm). The APCE values of the pure TiO2 NTs photoelectrode are 12.8% (365 nm) and 1.1% (420 nm), while the ST-180 values are 22.3% and 14.2%, respectively. Furthermore, the generation rates of H2 and O2 for the ST-180 photoanode are 47.2 and 23.1 μmol cm-2 h-1 at 0.5 V under AM 1.5G, corresponding to faradaic efficiencies of around 80.1% and 78.3%, respectively. In short, the high-efficiency PEC water splitting performance of this SnS2/TiO2 photoanode results from the enhanced light harvesting ability of the trap-like SnS2 structure, accelerated carrier transportation properties of TiO2 NTs, and effective carrier separation of the type-II heterojunction structure. This work may offer a combinatorial strategy for the preparation of heterojunction structures with high PEC performance and can be a model structure for similar photoanode materials.

Optimized design and analysis of sparse-sampling FMRI experiments.

PubMed

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

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

PubMed

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

2015-01-01

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

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

PubMed Central

Tang, Xin; Feng, Guo-can; Li, Xiao-xin; Cai, Jia-xin

2015-01-01

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

Exhaustive Search for Sparse Variable Selection in Linear Regression

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

Combining the spin-separated exact two-component relativistic Hamiltonian with the equation-of-motion coupled-cluster method for the treatment of spin-orbit splittings of light and heavy elements.

PubMed

Cao, Zhanli; Li, Zhendong; Wang, Fan; Liu, Wenjian

2017-02-01

The spin-separated exact two-component (X2C) relativistic Hamiltonian [sf-X2C+so-DKHn, J. Chem. Phys., 2012, 137, 154114] is combined with the equation-of-motion coupled-cluster method with singles and doubles (EOM-CCSD) for the treatment of spin-orbit splittings of open-shell molecular systems. Scalar relativistic effects are treated to infinite order from the outset via the spin-free part of the X2C Hamiltonian (sf-X2C), whereas the spin-orbit couplings (SOC) are handled at the CC level via the first-order Douglas-Kroll-Hess (DKH) type of spin-orbit operator (so-DKH1). Since the exponential of single excitations, i.e., exp(T 1 ), introduces sufficient spin orbital relaxations, the inclusion of SOC at the CC level is essentially the same in accuracy as the inclusion of SOC from the outset in terms of the two-component spinors determined variationally by the sf-X2C+so-DKH1 Hamiltonian, but is computationally more efficient. Therefore, such an approach (denoted as sf-X2C-EOM-CCSD(SOC)) can achieve uniform accuracy for the spin-orbit splittings of both light and heavy elements. For light elements, the treatment of SOC can even be postponed until the EOM step (denoted as sf-X2C-EOM(SOC)-CCSD), so as to further reduce the computational cost. To reveal the efficacy of sf-X2C-EOM-CCSD(SOC) and sf-X2C-EOM(SOC)-CCSD, the spin-orbit splittings of the 2 Π states of monohydrides up to the sixth row of the periodic table are investigated. The results show that sf-X2C-EOM-CCSD(SOC) predicts very accurate results (within 5%) for elements up to the fifth row, whereas sf-X2C-EOM(SOC)-CCSD is useful only for light elements (up to the third row but with some exceptions). For comparison, the sf-X2C-S-TD-DFT-SOC approach [spin-adapted open-shell time-dependent density functional theory, Mol. Phys., 2013, 111, 3741] is applied to the same systems. The overall accuracy (1-10%) is satisfactory.

Facile Preparation of Porous WO3 Film for Photoelectrochemical Splitting of Natural Seawater

NASA Astrophysics Data System (ADS)

Shi, Yonghong; Li, Yuangang; Wei, Xiaoliang; Feng, Juan; Li, Huajing; Zhou, Wanyi

2017-12-01

Sunlight-driven natural seawater splitting provides a promising way for large-scale conversion and storage of solar energy. Here, we develop a facile and low-cost method via a deposition-annealing technique to fabricate porous WO3 film and demonstrate its application as a photoanode for natural seawater splitting. The WO3 film yields a photocurrent density of 1.95 mA cm-2 and possesses excellent stability at 1.23 V (versus RHE), under the illumination of 100 mW cm-2 (AM 1.5G). The photoelectrochemical performance is ascribed to the large surface area and good permeation of the electrolyte into the porous film. Furthermore, the photocurrent density remains almost the same during 3 h continuous light irradiation. The evolution of chlorine gas from seawater splitting was determined with qualitative and quantitative analyses, with a Faradic efficiency of about 56%.

GaN microcavities: Giant Rabi splitting and optical anisotropy

NASA Astrophysics Data System (ADS)

Kavokin, Alexey; Gil, Bernard

1998-06-01

Numerical simulation of light reflection from a λ/2 GaN microcavity with Ga0.8Al0.2N/Ga0.5Al0.5N Bragg mirrors grown on the A surface of Al2O3 revealed a Rabi splitting of the order of 50 meV and remarkable optical anisotropy. These effects are originated from the giant exciton oscillator strength in GaN and a pronounced uniaxial strain in the structure.

Biclustering sparse binary genomic data.

PubMed

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

2008-12-01

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

Sloped terrain segmentation for autonomous drive using sparse 3D point cloud.

PubMed

Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Jeong, Young-Sik; Um, Kyhyun; Sim, Sungdae

2014-01-01

A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.

Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud

PubMed Central

Cho, Seoungjae; Kim, Jonghyun; Ikram, Warda; Cho, Kyungeun; Sim, Sungdae

2014-01-01

A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles. An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive. This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain. A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors. For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated. We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap. The ground area is determined on the basis of the number of voxels in each voxel group. We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels. Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame. PMID:25093204

The osmotic stress response of split influenza vaccine particles in an acidic environment.

PubMed

Choi, Hyo-Jick; Kim, Min-Chul; Kang, Sang-Moo; Montemagno, Carlo D

2014-12-01

Oral influenza vaccine provides an efficient means of preventing seasonal and pandemic disease. In this work, the stability of envelope-type split influenza vaccine particles in acidic environments has been investigated. Owing to the fact that hyper-osmotic stress can significantly affect lipid assembly of vaccine, osmotic stress-induced morphological change of split vaccine particles, in conjunction with structural change of antigenic proteins, was investigated by the use of stopped-flow light scattering (SFLS), intrinsic fluorescence, transmission electron microscopy (TEM), and hemagglutination assay. Split vaccine particles were found to exhibit a step-wise morphological change in response to osmotic stress due to double-layered wall structure. The presence of hyper-osmotic stress in acidic medium (0.3 osmolarity, pH 2.0) induced a significant level of membrane perturbation as measured by SFLS and TEM, imposing more damage to antigenic proteins on vaccine envelope than can be caused by pH-induced conformational change at acidic iso-osmotic condition. Further supports were provided by the intrinsic fluorescence and hemagglutinin activity measurements. Thus, hyper-osmotic stress becomes an important factor for determining stability of split vaccine particles in acidic medium. These results are useful in better understanding the destabilizing mechanism of split influenza vaccine particles in gastric environment and in designing oral influenza vaccine formulations.

SparseCT: interrupted-beam acquisition and sparse reconstruction for radiation dose reduction

NASA Astrophysics Data System (ADS)

Koesters, Thomas; Knoll, Florian; Sodickson, Aaron; Sodickson, Daniel K.; Otazo, Ricardo

2017-03-01

State-of-the-art low-dose CT methods reduce the x-ray tube current and use iterative reconstruction methods to denoise the resulting images. However, due to compromises between denoising and image quality, only moderate dose reductions up to 30-40% are accepted in clinical practice. An alternative approach is to reduce the number of x-ray projections and use compressed sensing to reconstruct the full-tube-current undersampled data. This idea was recognized in the early days of compressed sensing and proposals for CT dose reduction appeared soon afterwards. However, no practical means of undersampling has yet been demonstrated in the challenging environment of a rapidly rotating CT gantry. In this work, we propose a moving multislit collimator as a practical incoherent undersampling scheme for compressed sensing CT and evaluate its application for radiation dose reduction. The proposed collimator is composed of narrow slits and moves linearly along the slice dimension (z), to interrupt the incident beam in different slices for each x-ray tube angle (θ). The reduced projection dataset is then reconstructed using a sparse approach, where 3D image gradients are employed to enforce sparsity. The effects of the collimator slits on the beam profile were measured and represented as a continuous slice profile. SparseCT was tested using retrospective undersampling and compared against commercial current-reduction techniques on phantoms and in vivo studies. Initial results suggest that SparseCT may enable higher performance than current-reduction, particularly for high dose reduction factors.

Adapting iterative algorithms for solving large sparse linear systems for efficient use on the CDC CYBER 205

NASA Technical Reports Server (NTRS)

Kincaid, D. R.; Young, D. M.

1984-01-01

Adapting and designing mathematical software to achieve optimum performance on the CYBER 205 is discussed. Comments and observations are made in light of recent work done on modifying the ITPACK software package and on writing new software for vector supercomputers. The goal was to develop very efficient vector algorithms and software for solving large sparse linear systems using iterative methods.

The Split-Brain Phenomenon Revisited: A Single Conscious Agent with Split Perception.

PubMed

Pinto, Yair; de Haan, Edward H F; Lamme, Victor A F

2017-11-01

The split-brain phenomenon is caused by the surgical severing of the corpus callosum, the main route of communication between the cerebral hemispheres. The classical view of this syndrome asserts that conscious unity is abolished. The left hemisphere consciously experiences and functions independently of the right hemisphere. This view is a cornerstone of current consciousness research. In this review, we first discuss the evidence for the classical view. We then propose an alternative, the 'conscious unity, split perception' model. This model asserts that a split brain produces one conscious agent who experiences two parallel, unintegrated streams of information. In addition to changing our view of the split-brain phenomenon, this new model also poses a serious challenge for current dominant theories of consciousness. Copyright © 2017 Elsevier Ltd. All rights reserved.

Triadic split-merge sampler

NASA Astrophysics Data System (ADS)

van Rossum, Anne C.; Lin, Hai Xiang; Dubbeldam, Johan; van der Herik, H. Jaap

2018-04-01

In machine vision typical heuristic methods to extract parameterized objects out of raw data points are the Hough transform and RANSAC. Bayesian models carry the promise to optimally extract such parameterized objects given a correct definition of the model and the type of noise at hand. A category of solvers for Bayesian models are Markov chain Monte Carlo methods. Naive implementations of MCMC methods suffer from slow convergence in machine vision due to the complexity of the parameter space. Towards this blocked Gibbs and split-merge samplers have been developed that assign multiple data points to clusters at once. In this paper we introduce a new split-merge sampler, the triadic split-merge sampler, that perform steps between two and three randomly chosen clusters. This has two advantages. First, it reduces the asymmetry between the split and merge steps. Second, it is able to propose a new cluster that is composed out of data points from two different clusters. Both advantages speed up convergence which we demonstrate on a line extraction problem. We show that the triadic split-merge sampler outperforms the conventional split-merge sampler. Although this new MCMC sampler is demonstrated in this machine vision context, its application extend to the very general domain of statistical inference.

Spin splitting generated in a Y-shaped semiconductor nanostructure with a quantum point contact

NASA Astrophysics Data System (ADS)

Wójcik, P.; Adamowski, J.; Wołoszyn, M.; Spisak, B. J.

2015-07-01

We have studied the spin splitting of the current in the Y-shaped semiconductor nanostructure with a quantum point contact (QPC) in a perpendicular magnetic field. Our calculations show that the appropriate tuning of the QPC potential and the external magnetic field leads to an almost perfect separation of the spin-polarized currents: electrons with opposite spins flow out through different output branches. The spin splitting results from the joint effect of the QPC, the spin Zeeman splitting, and the electron transport through the edge states formed in the nanowire at the sufficiently high magnetic field. The Y-shaped nanostructure can be used to split the unpolarized current into two spin currents with opposite spins as well as to detect the flow of the spin current. We have found that the separation of the spin currents is only slightly affected by the Rashba spin-orbit coupling. The spin-splitter device is an analogue of the optical device—the birefractive crystal that splits the unpolarized light into two beams with perpendicular polarizations. In the magnetic-field range, in which the current is carried through the edges states, the spin splitting is robust against the spin-independent scattering. This feature opens up a possibility of the application of the Y-shaped nanostructure as a non-ballistic spin-splitter device in spintronics.

Spin splitting generated in a Y-shaped semiconductor nanostructure with a quantum point contact

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wójcik, P., E-mail: pawel.wojcik@fis.agh.edu.pl; Adamowski, J., E-mail: janusz.adamowski@fis.agh.edu.pl; Wołoszyn, M.

2015-07-07

We have studied the spin splitting of the current in the Y-shaped semiconductor nanostructure with a quantum point contact (QPC) in a perpendicular magnetic field. Our calculations show that the appropriate tuning of the QPC potential and the external magnetic field leads to an almost perfect separation of the spin-polarized currents: electrons with opposite spins flow out through different output branches. The spin splitting results from the joint effect of the QPC, the spin Zeeman splitting, and the electron transport through the edge states formed in the nanowire at the sufficiently high magnetic field. The Y-shaped nanostructure can be usedmore » to split the unpolarized current into two spin currents with opposite spins as well as to detect the flow of the spin current. We have found that the separation of the spin currents is only slightly affected by the Rashba spin-orbit coupling. The spin-splitter device is an analogue of the optical device—the birefractive crystal that splits the unpolarized light into two beams with perpendicular polarizations. In the magnetic-field range, in which the current is carried through the edges states, the spin splitting is robust against the spin-independent scattering. This feature opens up a possibility of the application of the Y-shaped nanostructure as a non-ballistic spin-splitter device in spintronics.« less

Semiconductor-based photoelectrochemical water splitting at the limit of very wide depletion region

DOE PAGES

Liu, Mingzhao; Lyons, John L.; Yan, Danhua H.; ...

2015-11-23

In semiconductor-based photoelectrochemical (PEC) water splitting, carrier separation and delivery largely relies on the depletion region formed at the semiconductor/water interface. As a Schottky junction device, the trade-off between photon collection and minority carrier delivery remains a persistent obstacle for maximizing the performance of a water splitting photoelectrode. Here, it is demonstrated that the PEC water splitting efficiency for an n-SrTiO 3 (n-STO) photoanode is improved very significantly despite its weak indirect band gap optical absorption (α < 10⁴ cm⁻¹), by widening the depletion region through engineering its doping density and profile. Graded doped n-SrTiO 3 photoanodes are fabricated withmore » their bulk heavily doped with oxygen vacancies but their surface lightly doped over a tunable depth of a few hundred nanometers, through a simple low temperature re-oxidation technique. The graded doping profile widens the depletion region to over 500 nm, thus leading to very efficient charge carrier separation and high quantum efficiency (>70%) for the weak indirect transition. As a result, this simultaneous optimization of the light absorption, minority carrier (hole) delivery, and majority carrier (electron) transport by means of a graded doping architecture may be useful for other indirect band gap photocatalysts that suffer from a similar problem of weak optical absorption.« less

Position sensitivity by light splitting in scintillator arrays

NASA Astrophysics Data System (ADS)

Bisplinghoff, J.; Bollmann, R.; Cloth, P.; Dohrmann, F.; Dorner, G.; Drüke, V.; Ernst, J.; Eversheim, P. D.; Filges, D.; Gasthuber, M.; Gebel, R.; Groβ, A.; Groβ-Hardt, R.; Hinterberger, F.; Jahn, R.; Kühl, L.; Lahr, U.; Langkau, R.; Lippert, G.; Mayer-Kuckuk, T.; Maschuw, R.; Mertler, G.; Metsch, B.; Mosel, F.; Paetz gen. Schieck, H.; Petry, H. R.; Prasuhn, D.; Przewoski, B. v.; Rohdjeβ, H.; Rosendaal, D.; Rossen, P. v.; Scheid, H.; Schirm, N.; Schwandt, F.; Scobel, W.; Sprute, L.; Stein, H.; Theis, D.; Weber, J.; Wiedmann, W.; Woller, K.; Ziegler, R.; EDDA Collaboration

1993-05-01

A novel detector design of overlapping plastic scintillator elements in cylindrical geometry has been developed for detection of low multiplicity events of fast protons and other light charged particles: each particle traversing the detector from the axis outwards will produce light in several elements. The relative amounts of energy deposited in those elements allow one to interpolate on the particle trajectory beyond the resolution given by the granularity. The detector covers the angular range 10° ≤ Θlab ≤ 72° and 0° ≤ ϕ ≤ 360° with an inner layer of scintillator bars of triangular cross section and an outer layer of rings. The material is BC408. Tests with minimum ionizing electron beams show that spatial resolutions of ΔΘlab ≈ 1.5° and Δϕ12 ≈ 1.5° (FWHM) can be obtained for electrons or proton pairs with energies in the GeV range. In the EDDA experiment the ultimate spatial resolution is then determined by the size of the interaction area rather than by the intrinsic pulse height resolution of the detector.

Learning Sparse Feature Representations using Probabilistic Quadtrees and Deep Belief Nets

DTIC Science & Technology

2015-04-24

Feature Representations usingProbabilistic Quadtrees and Deep Belief Nets Learning sparse feature representations is a useful instru- ment for solving an...novel framework for the classifi cation of handwritten digits that learns sparse representations using probabilistic quadtrees and Deep Belief Nets... Learning Sparse Feature Representations usingProbabilistic Quadtrees and Deep Belief Nets Report Title Learning sparse feature representations is a useful

Institute for the Study of Sparsely Populated Areas. A Centre for Interdisciplinary Research into Sparsely Populated and Peripheral Regions.

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…

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.

1-norm support vector novelty detection and its sparseness.

PubMed

Zhang, Li; Zhou, WeiDa

2013-12-01

This paper proposes a 1-norm support vector novelty detection (SVND) method and discusses its sparseness. 1-norm SVND is formulated as a linear programming problem and uses two techniques for inducing sparseness, or the 1-norm regularization and the hinge loss function. We also find two upper bounds on the sparseness of 1-norm SVND, or exact support vector (ESV) and kernel Gram matrix rank bounds. The ESV bound indicates that 1-norm SVND has a sparser representation model than SVND. The kernel Gram matrix rank bound can loosely estimate the sparseness of 1-norm SVND. Experimental results show that 1-norm SVND is feasible and effective. Copyright © 2013 Elsevier Ltd. All rights reserved.

Incomplete Sparse Approximate Inverses for Parallel Preconditioning

DOE PAGES

Anzt, Hartwig; Huckle, Thomas K.; Bräckle, Jürgen; ...

2017-10-28

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

Improving the efficiency of water splitting in dye-sensitized solar cells by using a biomimetic electron transfer mediator

PubMed Central

Zhao, Yixin; Swierk, John R.; Megiatto, Jackson D.; Sherman, Benjamin; Youngblood, W. Justin; Qin, Dongdong; Lentz, Deanna M.; Moore, Ana L.; Moore, Thomas A.; Gust, Devens; Mallouk, Thomas E.

2012-01-01

Photoelectrochemical water splitting directly converts solar energy to chemical energy stored in hydrogen, a high energy density fuel. Although water splitting using semiconductor photoelectrodes has been studied for more than 40 years, it has only recently been demonstrated using dye-sensitized electrodes. The quantum yield for water splitting in these dye-based systems has, so far, been very low because the charge recombination reaction is faster than the catalytic four-electron oxidation of water to oxygen. We show here that the quantum yield is more than doubled by incorporating an electron transfer mediator that is mimetic of the tyrosine-histidine mediator in Photosystem II. The mediator molecule is covalently bound to the water oxidation catalyst, a colloidal iridium oxide particle, and is coadsorbed onto a porous titanium dioxide electrode with a Ruthenium polypyridyl sensitizer. As in the natural photosynthetic system, this molecule mediates electron transfer between a relatively slow metal oxide catalyst that oxidizes water on the millisecond timescale and a dye molecule that is oxidized in a fast light-induced electron transfer reaction. The presence of the mediator molecule in the system results in photoelectrochemical water splitting with an internal quantum efficiency of approximately 2.3% using blue light. PMID:22547794

DETECTION OF FLUX EMERGENCE, SPLITTING, MERGING, AND CANCELLATION OF NETWORK FIELD. I. SPLITTING AND MERGING

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iida, Y.; Yokoyama, T.; Hagenaar, H. J.

2012-06-20

Frequencies of magnetic patch processes on the supergranule boundary, namely, flux emergence, splitting, merging, and cancellation, are investigated through automatic detection. We use a set of line-of-sight magnetograms taken by the Solar Optical Telescope (SOT) on board the Hinode satellite. We found 1636 positive patches and 1637 negative patches in the data set, whose time duration is 3.5 hr and field of view is 112'' Multiplication-Sign 112''. The total numbers of magnetic processes are as follows: 493 positive and 482 negative splittings, 536 positive and 535 negative mergings, 86 cancellations, and 3 emergences. The total numbers of emergence and cancellationmore » are significantly smaller than those of splitting and merging. Further, the frequency dependence of the merging and splitting processes on the flux content are investigated. Merging has a weak dependence on the flux content with a power-law index of only 0.28. The timescale for splitting is found to be independent of the parent flux content before splitting, which corresponds to {approx}33 minutes. It is also found that patches split into any flux contents with the same probability. This splitting has a power-law distribution of the flux content with an index of -2 as a time-independent solution. These results support that the frequency distribution of the flux content in the analyzed flux range is rapidly maintained by merging and splitting, namely, surface processes. We suggest a model for frequency distributions of cancellation and emergence based on this idea.« less

Learning dictionaries of sparse codes of 3D movements of body joints for real-time human activity understanding.

PubMed

Qi, Jin; Yang, Zhiyong

2014-01-01

Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

Split Dirac Supersymmetry: An Ultraviolet Completion of Higgsino Dark Matter

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fox, Patrick J.; Kribs, Graham D.; Martin, Adam

2014-10-07

Motivated by the observation that the Higgs quartic coupling runs to zero at an intermediate scale, we propose a new framework for models of split supersymmetry, in which gauginos acquire intermediate scale Dirac masses ofmore » $$\\sim 10^{8-11}$$ GeV. Scalar masses arise from one-loop finite contributions as well as direct gravity-mediated contributions. Like split supersymmetry, one Higgs doublet is fine-tuned to be light. The scale at which the Dirac gauginos are introduced to make the Higgs quartic zero is the same as is necessary for gauge coupling unification. Thus, gauge coupling unification persists (nontrivially, due to adjoint multiplets), though with a somewhat higher unification scale $$\\gtrsim 10^{17}$$ GeV. The $$\\mu$$-term is naturally at the weak scale, and provides an opportunity for experimental verification. We present two manifestations of Split Dirac Supersymmetry. In the "Pure Dirac" model, the lightest Higgsino must decay through R-parity violating couplings, leading to an array of interesting signals in colliders. In the "Hypercharge Impure" model, the bino acquires a Majorana mass that is one-loop suppressed compared with the Dirac gluino and wino. This leads to weak scale Higgsino dark matter whose overall mass scale, as well as the mass splitting between the neutral components, is naturally generated from the same UV dynamics. We outline the challenges to discovering pseudo-Dirac Higgsino dark matter in collider and dark matter detection experiments.« less

Sparse modeling of spatial environmental variables associated with asthma

PubMed Central

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

Split-face comparison between single-band and dual-band pulsed light technology for treatment of photodamage.

PubMed

Varughese, Neal; Keller, Lauren; Goldberg, David J

2016-08-01

Intense pulsed light (IPL) has a well-recognized role in the treatment of photodamaged skin. To assess the safety and efficacy of a novel single-band IPL handpiece versus dual-band IPL handpiece in the treatment of photodamage. This was a prospective, single-center split-face study with 20 enrolled participants. Three treatments, 21 days apart, were administered to the subjects and follow-up was performed for 20 weeks. The left side of the face was treated with the single-band handpiece. The right side of the face was treated with the dual-band handpiece. Blinded investigators assessed the subjects' skin texture, pigmented components of photodamage, and presence of telangiectasia both before and after treatment, utilizing a five-point scale. Pigmented components of photodamage, skin texture, and presence of telangiectasias on the left and right side of the face were improved at the end of treatment. At 20-week follow-up, the side treated with single-band handpiece showed improvement in telangiectasia and pigmentation that was statistically superior to the contralateral side treated with the dual-band handpiece. Both devices equally improved textural changes. No adverse effects were noted with either device. Both single-band and dual-band IPL technology are safe and effective in the treatment of photodamaged facial skin. IPL treatment with a single-band handpiece yielded results comparable or superior to dual-band technology.

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.

Low-count PET image restoration using sparse representation

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

X-ray computed tomography using curvelet sparse regularization.

PubMed

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

2015-04-01

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

Optimized Design and Analysis of Sparse-Sampling fMRI Experiments

PubMed Central

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

Sparse dictionary for synthetic transmit aperture medical ultrasound imaging.

PubMed

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

2017-07-01

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

New predictions on meson decays from string splitting

NASA Astrophysics Data System (ADS)

Bigazzi, Francesco; Cotrone, Aldo L.

2006-11-01

We study certain exclusive decays of high spin mesons into mesons in models of large Nc Yang-Mills with few flavors at strong coupling using string theory. The rate of the process is calculated by studying the splitting of a macroscopic string on the relevant dual gravity backgrounds. In the leading channel for the decay of heavy quarkonium into two open-heavy quark states, one of the two produced mesons has much larger spin than the other. In this channel the decay rate is practically independent on the spin and has a mild dependence on the mass of the heavy quarks. Moreover, it is only power-like suppressed with the mass of the produced quark-anti quark pair. We also reconsider decays of high spin mesons made up of light quarks, confirming the linear dependence of the rate on the mass of the decaying meson. As a bonus of our computation, we provide a formula for the splitting rate of a macroscopic string lying on a Dp-brane in flat space.

Sparse aperture 3D passive image sensing and recognition

NASA Astrophysics Data System (ADS)

Daneshpanah, Mehdi

The way we perceive, capture, store, communicate and visualize the world has greatly changed in the past century Novel three dimensional (3D) imaging and display systems are being pursued both in academic and industrial settings. In many cases, these systems have revolutionized traditional approaches and/or enabled new technologies in other disciplines including medical imaging and diagnostics, industrial metrology, entertainment, robotics as well as defense and security. In this dissertation, we focus on novel aspects of sparse aperture multi-view imaging systems and their application in quantum-limited object recognition in two separate parts. In the first part, two concepts are proposed. First a solution is presented that involves a generalized framework for 3D imaging using randomly distributed sparse apertures. Second, a method is suggested to extract the profile of objects in the scene through statistical properties of the reconstructed light field. In both cases, experimental results are presented that demonstrate the feasibility of the techniques. In the second part, the application of 3D imaging systems in sensing and recognition of objects is addressed. In particular, we focus on the scenario in which only 10s of photons reach the sensor from the object of interest, as opposed to hundreds of billions of photons in normal imaging conditions. At this level, the quantum limited behavior of light will dominate and traditional object recognition practices may fail. We suggest a likelihood based object recognition framework that incorporates the physics of sensing at quantum-limited conditions. Sensor dark noise has been modeled and taken into account. This framework is applied to 3D sensing of thermal objects using visible spectrum detectors. Thermal objects as cold as 250K are shown to provide enough signature photons to be sensed and recognized within background and dark noise with mature, visible band, image forming optics and detector arrays. The results

Bad splits in bilateral sagittal split osteotomy: systematic review and meta-analysis of reported risk factors.

PubMed

Steenen, S A; van Wijk, A J; Becking, A G

2016-08-01

An unfavourable and unanticipated pattern of the bilateral sagittal split osteotomy (BSSO) is generally referred to as a 'bad split'. Patient factors predictive of a bad split reported in the literature are controversial. Suggested risk factors are reviewed in this article. A systematic review was undertaken, yielding a total of 30 studies published between 1971 and 2015 reporting the incidence of bad split and patient age, and/or surgical technique employed, and/or the presence of third molars. These included 22 retrospective cohort studies, six prospective cohort studies, one matched-pair analysis, and one case series. Spearman's rank correlation showed a statistically significant but weak correlation between increasing average age and increasing occurrence of bad splits in 18 studies (ρ=0.229; P<0.01). No comparative studies were found that assessed the incidence of bad split among the different splitting techniques. A meta-analysis pooling the effect sizes of seven cohort studies showed no significant difference in the incidence of bad split between cohorts of patients with third molars present and concomitantly removed during surgery, and patients in whom third molars were removed at least 6 months preoperatively (odds ratio 1.16, 95% confidence interval 0.73-1.85, Z=0.64, P=0.52). In summary, there is no robust evidence to date to show that any risk factor influences the incidence of bad split. Copyright © 2016 International Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Sparse modeling of spatial environmental variables associated with asthma.

PubMed

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

2015-02-01

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

Analog system for computing sparse codes

DOEpatents

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.

Concentric Split Flow Filter

NASA Technical Reports Server (NTRS)

Stapleton, Thomas J. (Inventor)

2015-01-01

A concentric split flow filter may be configured to remove odor and/or bacteria from pumped air used to collect urine and fecal waste products. For instance, filter may be designed to effectively fill the volume that was previously considered wasted surrounding the transport tube of a waste management system. The concentric split flow filter may be configured to split the air flow, with substantially half of the air flow to be treated traveling through a first bed of filter media and substantially the other half of the air flow to be treated traveling through the second bed of filter media. This split flow design reduces the air velocity by 50%. In this way, the pressure drop of filter may be reduced by as much as a factor of 4 as compare to the conventional design.

Solution of matrix equations using sparse techniques

NASA Technical Reports Server (NTRS)

Baddourah, Majdi

1994-01-01

The solution of large systems of matrix equations is key to the solution of a large number of scientific and engineering problems. This talk describes the sparse matrix solver developed at Langley which can routinely solve in excess of 263,000 equations in 40 seconds on one Cray C-90 processor. It appears that for large scale structural analysis applications, sparse matrix methods have a significant performance advantage over other methods.

Deep Marginalized Sparse Denoising Auto-Encoder for Image Denoising

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

Precession missile feature extraction using sparse component analysis of radar measurements

NASA Astrophysics Data System (ADS)

Liu, Lihua; Du, Xiaoyong; Ghogho, Mounir; Hu, Weidong; McLernon, Des

2012-12-01

According to the working mode of the ballistic missile warning radar (BMWR), the radar return from the BMWR is usually sparse. To recognize and identify the warhead, it is necessary to extract the precession frequency and the locations of the scattering centers of the missile. This article first analyzes the radar signal model of the precessing conical missile during flight and develops the sparse dictionary which is parameterized by the unknown precession frequency. Based on the sparse dictionary, the sparse signal model is then established. A nonlinear least square estimation is first applied to roughly extract the precession frequency in the sparse dictionary. Based on the time segmented radar signal, a sparse component analysis method using the orthogonal matching pursuit algorithm is then proposed to jointly estimate the precession frequency and the scattering centers of the missile. Simulation results illustrate the validity of the proposed method.

Strong Coupling of Localized Surface Plasmons to Excitons in Light-Harvesting Complexes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tsargorodska, Anna; Cartron, Michaël L.; Vasilev, Cvetelin

Gold nanostructure arrays exhibit surface plasmon resonances that split after attaching light harvesting complexes 1 and 2 (LH1 and LH2) from purple bacteria. The splitting is attributed to strong coupling between the localized surface plasmon resonances and excitons in the light-harvesting complexes. Wild-type and mutant LH1 and LH2 from Rhodobacter sphaeroides containing different carotenoids yield different splitting energies, demonstrating that the coupling mechanism is sensitive to the electronic states in the light harvesting complexes. Plasmon–exciton coupling models reveal different coupling strengths depending on the molecular organization and the protein coverage, consistent with strong coupling. Strong coupling was also observed formore » self-assembling polypeptide maquettes that contain only chlorins. However, it is not observed for monolayers of bacteriochlorophyll, indicating that strong plasmon–exciton coupling is sensitive to the specific presentation of the pigment molecules.« less

Strong Coupling of Localized Surface Plasmons to Excitons in Light-Harvesting Complexes

DOE PAGES

Tsargorodska, Anna; Cartron, Michaël L.; Vasilev, Cvetelin; ...

2016-09-30

Gold nanostructure arrays exhibit surface plasmon resonances that split after attaching light harvesting complexes 1 and 2 (LH1 and LH2) from purple bacteria. The splitting is attributed to strong coupling between the localized surface plasmon resonances and excitons in the light-harvesting complexes. Wild-type and mutant LH1 and LH2 from Rhodobacter sphaeroides containing different carotenoids yield different splitting energies, demonstrating that the coupling mechanism is sensitive to the electronic states in the light harvesting complexes. Plasmon–exciton coupling models reveal different coupling strengths depending on the molecular organization and the protein coverage, consistent with strong coupling. Strong coupling was also observed formore » self-assembling polypeptide maquettes that contain only chlorins. However, it is not observed for monolayers of bacteriochlorophyll, indicating that strong plasmon–exciton coupling is sensitive to the specific presentation of the pigment molecules.« less

Split off-specular reflection and surface scattering from woven materials

NASA Astrophysics Data System (ADS)

Pont, Sylvia C.; Koenderink, Jan J.

2003-03-01

We measured radiance distributions for black lining cloth and copper gauze using the convenient technique of wrapping the materials around a circular cylinder, irradiating it with a parallel light source and collecting the scattered radiance by a digital camera. One family of parallel threads (weave or weft) was parallel to the cylinder generator. The most salient features for such glossy plane weaves are a splitting up of the reflection peak due to the wavy variations in local slopes of the threads around the cylinders and a surface scattering lobe due to the threads that run along the cylinder. These scattering characteristics are quite different from the (off-)specular peaks and lobes that were found before for random rough specular surfaces. The split off-specular reflection is due to the regular structures in our samples of man-made materials. We derived simple approximations for these reflectance characteristics using geometrical optics.

Sparse representation based SAR vehicle recognition along with aspect angle.

PubMed

Xing, Xiangwei; Ji, Kefeng; Zou, Huanxin; Sun, Jixiang

2014-01-01

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

Evaluation of Mandibular Anatomy Associated With Bad Splits in Sagittal Split Ramus Osteotomy of Mandible.

PubMed

Wang, Tongyue; Han, Jeong Joon; Oh, Hee-Kyun; Park, Hong-Ju; Jung, Seunggon; Park, Yeong-Joon; Kook, Min-Suk

2016-07-01

This study aimed to identify risk factors associated with bad splits during sagittal split ramus osteotomy by using three-dimensional computed tomography. This study included 8 bad splits and 47 normal patients without bad splits. Mandibular anatomic parameters related to osteotomy line were measured. These included anteroposterior width of the ramus at level of lingula, distance between external oblique ridge and lingula, distance between sigmoid notch and inferior border of mandible, mandibular angle, distance between inferior outer surface of mandibular canal and inferior border of mandible under distal root of second molar (MCEM), buccolingual thickness of the ramus at level of lingula, and buccolingual thickness of the area just distal to first molar (BTM1) and second molar (BTM2). The incidence of bad splits in 625 sagittal split osteotomies was 1.28%. Compared with normal group, bad split group exhibited significantly thinner BTM2 and shorter sigmoid notch and inferior border of mandible (P <0.05). However, for BTM1 and buccolingual thickness of the ramus at level of lingula, there was no statistical difference between the 2 groups. Mandibular angle, anteroposterior width of the ramus at level of lingula, external oblique ridge and lingula, and MCEM were not significantly different between the groups. This study suggests that patients with shorter ramus and low thickness of the buccolingual alveolar region distal to the second molar had a higher risk of bad splits. These anatomic data may help surgeons to choose the safest surgical techniques and best osteotomy sites.

NNLO splitting and coefficient functions with time-like kinematics

NASA Astrophysics Data System (ADS)

Mitov, A.; Moch, S.; Vogt, A.

2006-10-01

We discuss recent results on the three-loop (next-to-next-to-leading order, NNLO) time-like splitting functions of QCD and the two-loop (NNLO) coefficient functions in one-particle inclusive e+e--annihilation. These results form the basis for extracting fragmentation functions for light and heavy flavors with NNLO accuracy that will be needed at the LHC and ILC. The two-loop calculations have been performed in Mellin space based on a new method, the main features of which we also describe briefly.

CO2 splitting by H2O to CO and O2 under UV light in TiMCM-41silicate sieve

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Wenyong; Han, Hongxian; Frei, Heinz

2004-04-06

The 266 nm light-induced reaction of CO{sub 2} and H{sub 2}O gas mixtures (including isotopic modifications {sup 13}CO{sub 2}, C{sup 18}O{sub 2}, and D{sub 2}O) in framework TiMCM-41 silicate sieve was monitored by in-situ FT-IR spectroscopy at room temperature. Carbon monoxide gas was observed as the sole product by infrared, and the growth was found to depend linearly on the photolysis laser power. H{sub 2}O was confirmed as stoichiometric electron donor. The work establishes CO as the single photon, 2-electron transfer product of CO{sub 2} photoreduction by H{sub 2}O at framework Ti centers for the first time. O{sub 2} wasmore » detected as co-product by mass spectrometric analysis of the photolysis gas mixture. These results are explained by single UV photon-induced splitting of CO{sub 2} by H{sub 2}O to CO and surface OH radical.« less

Entropy Splitting and Numerical Dissipation

NASA Technical Reports Server (NTRS)

Yee, H. C.; Vinokur, M.; Djomehri, M. J.

1999-01-01

A rigorous stability estimate for arbitrary order of accuracy of spatial central difference schemes for initial-boundary value problems of nonlinear symmetrizable systems of hyperbolic conservation laws was established recently by Olsson and Oliger (1994) and Olsson (1995) and was applied to the two-dimensional compressible Euler equations for a perfect gas by Gerritsen and Olsson (1996) and Gerritsen (1996). The basic building block in developing the stability estimate is a generalized energy approach based on a special splitting of the flux derivative via a convex entropy function and certain homogeneous properties. Due to some of the unique properties of the compressible Euler equations for a perfect gas, the splitting resulted in the sum of a conservative portion and a non-conservative portion of the flux derivative. hereafter referred to as the "Entropy Splitting." There are several potential desirable attributes and side benefits of the entropy splitting for the compressible Euler equations that were not fully explored in Gerritsen and Olsson. The paper has several objectives. The first is to investigate the choice of the arbitrary parameter that determines the amount of splitting and its dependence on the type of physics of current interest to computational fluid dynamics. The second is to investigate in what manner the splitting affects the nonlinear stability of the central schemes for long time integrations of unsteady flows such as in nonlinear aeroacoustics and turbulence dynamics. If numerical dissipation indeed is needed to stabilize the central scheme, can the splitting help minimize the numerical dissipation compared to its un-split cousin? Extensive numerical study on the vortex preservation capability of the splitting in conjunction with central schemes for long time integrations will be presented. The third is to study the effect of the non-conservative proportion of splitting in obtaining the correct shock location for high speed complex shock

Low light adaptation: energy transfer processes in different types of light harvesting complexes from Rhodopseudomonas palustris.

PubMed

Moulisová, Vladimíra; Luer, Larry; Hoseinkhani, Sajjad; Brotosudarmo, Tatas H P; Collins, Aaron M; Lanzani, Guglielmo; Blankenship, Robert E; Cogdell, Richard J

2009-12-02

Energy transfer processes in photosynthetic light harvesting 2 (LH2) complexes isolated from purple bacterium Rhodopseudomonas palustris grown at different light intensities were studied by ground state and transient absorption spectroscopy. The decomposition of ground state absorption spectra shows contributions from B800 and B850 bacteriochlorophyll (BChl) a rings, the latter component splitting into a low energy and a high energy band in samples grown under low light (LL) conditions. A spectral analysis reveals strong inhomogeneity of the B850 excitons in the LL samples that is well reproduced by an exponential-type distribution. Transient spectra show a bleach of both the low energy and high energy bands, together with the respective blue-shifted exciton-to-biexciton transitions. The different spectral evolutions were analyzed by a global fitting procedure. Energy transfer from B800 to B850 occurs in a mono-exponential process and the rate of this process is only slightly reduced in LL compared to high light samples. In LL samples, spectral relaxation of the B850 exciton follows strongly nonexponential kinetics that can be described by a reduction of the bleach of the high energy excitonic component and a red-shift of the low energetic one. We explain these spectral changes by picosecond exciton relaxation caused by a small coupling parameter of the excitonic splitting of the BChl a molecules to the surrounding bath. The splitting of exciton energy into two excitonic bands in LL complex is most probably caused by heterogenous composition of LH2 apoproteins that gives some of the BChls in the B850 ring B820-like site energies, and causes a disorder in LH2 structure.

Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.

PubMed

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.

Sparse SPM: Group Sparse-dictionary learning in SPM framework for resting-state functional connectivity MRI analysis.

PubMed

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. Copyright © 2015 Elsevier Inc. All rights reserved.

Sparse image reconstruction for molecular imaging.

PubMed

Ting, Michael; Raich, Raviv; Hero, Alfred O

2009-06-01

The application that motivates this paper is molecular imaging at the atomic level. When discretized at subatomic distances, the volume is inherently sparse. Noiseless measurements from an imaging technology can be modeled by convolution of the image with the system point spread function (psf). Such is the case with magnetic resonance force microscopy (MRFM), an emerging technology where imaging of an individual tobacco mosaic virus was recently demonstrated with nanometer resolution. We also consider additive white Gaussian noise (AWGN) in the measurements. Many prior works of sparse estimators have focused on the case when H has low coherence; however, the system matrix H in our application is the convolution matrix for the system psf. A typical convolution matrix has high coherence. This paper, therefore, does not assume a low coherence H. A discrete-continuous form of the Laplacian and atom at zero (LAZE) p.d.f. used by Johnstone and Silverman is formulated, and two sparse estimators derived by maximizing the joint p.d.f. of the observation and image conditioned on the hyperparameters. A thresholding rule that generalizes the hard and soft thresholding rule appears in the course of the derivation. This so-called hybrid thresholding rule, when used in the iterative thresholding framework, gives rise to the hybrid estimator, a generalization of the lasso. Estimates of the hyperparameters for the lasso and hybrid estimator are obtained via Stein's unbiased risk estimate (SURE). A numerical study with a Gaussian psf and two sparse images shows that the hybrid estimator outperforms the lasso.

Dictionary Learning Algorithms for Sparse Representation

PubMed Central

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

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.

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.

Sparsely-synchronized brain rhythm in a small-world neural network

NASA Astrophysics Data System (ADS)

Kim, Sang-Yoon; Lim, Woochang

2013-07-01

Sparsely-synchronized cortical rhythms, associated with diverse cognitive functions, have been observed in electric recordings of brain activity. At the population level, cortical rhythms exhibit small-amplitude fast oscillations while at the cellular level, individual neurons show stochastic firings sparsely at a much lower rate than the population rate. We study the effect of network architecture on sparse synchronization in an inhibitory population of subthreshold Morris-Lecar neurons (which cannot fire spontaneously without noise). Previously, sparse synchronization was found to occur for cases of both global coupling ( i.e., regular all-to-all coupling) and random coupling. However, a real neural network is known to be non-regular and non-random. Here, we consider sparse Watts-Strogatz small-world networks which interpolate between a regular lattice and a random graph via rewiring. We start from a regular lattice with only short-range connections and then investigate the emergence of sparse synchronization by increasing the rewiring probability p for the short-range connections. For p = 0, the average synaptic path length between pairs of neurons becomes long; hence, only an unsynchronized population state exists because the global efficiency of information transfer is low. However, as p is increased, long-range connections begin to appear, and global effective communication between distant neurons may be available via shorter synaptic paths. Consequently, as p passes a threshold p th (}~ 0.044), sparsely-synchronized population rhythms emerge. However, with increasing p, longer axon wirings become expensive because of their material and energy costs. At an optimal value p* DE (}~ 0.24) of the rewiring probability, the ratio of the synchrony degree to the wiring cost is found to become maximal. In this way, an optimal sparse synchronization is found to occur at a minimal wiring cost in an economic small-world network through trade-off between synchrony and

Discovering sparse transcription factor codes for cell states and state transitions during development

PubMed Central

Furchtgott, Leon A; Melton, Samuel; Menon, Vilas; Ramanathan, Sharad

2017-01-01

Computational analysis of gene expression to determine both the sequence of lineage choices made by multipotent cells and to identify the genes influencing these decisions is challenging. Here we discover a pattern in the expression levels of a sparse subset of genes among cell types in B- and T-cell developmental lineages that correlates with developmental topologies. We develop a statistical framework using this pattern to simultaneously infer lineage transitions and the genes that determine these relationships. We use this technique to reconstruct the early hematopoietic and intestinal developmental trees. We extend this framework to analyze single-cell RNA-seq data from early human cortical development, inferring a neocortical-hindbrain split in early progenitor cells and the key genes that could control this lineage decision. Our work allows us to simultaneously infer both the identity and lineage of cell types as well as a small set of key genes whose expression patterns reflect these relationships. DOI: http://dx.doi.org/10.7554/eLife.20488.001 PMID:28296636

Bayesian sparse channel estimation

NASA Astrophysics Data System (ADS)

Chen, Chulong; Zoltowski, Michael D.

2012-05-01

In Orthogonal Frequency Division Multiplexing (OFDM) systems, the technique used to estimate and track the time-varying multipath channel is critical to ensure reliable, high data rate communications. It is recognized that wireless channels often exhibit a sparse structure, especially for wideband and ultra-wideband systems. In order to exploit this sparse structure to reduce the number of pilot tones and increase the channel estimation quality, the application of compressed sensing to channel estimation is proposed. In this article, to make the compressed channel estimation more feasible for practical applications, it is investigated from a perspective of Bayesian learning. Under the Bayesian learning framework, the large-scale compressed sensing problem, as well as large time delay for the estimation of the doubly selective channel over multiple consecutive OFDM symbols, can be avoided. Simulation studies show a significant improvement in channel estimation MSE and less computing time compared to the conventional compressed channel estimation techniques.

Brief announcement: Hypergraph parititioning for parallel sparse matrix-matrix multiplication

DOE PAGES

Ballard, Grey; Druinsky, Alex; Knight, Nicholas; ...

2015-01-01

The performance of parallel algorithms for sparse matrix-matrix multiplication is typically determined by the amount of interprocessor communication performed, which in turn depends on the nonzero structure of the input matrices. In this paper, we characterize the communication cost of a sparse matrix-matrix multiplication algorithm in terms of the size of a cut of an associated hypergraph that encodes the computation for a given input nonzero structure. Obtaining an optimal algorithm corresponds to solving a hypergraph partitioning problem. Furthermore, our hypergraph model generalizes several existing models for sparse matrix-vector multiplication, and we can leverage hypergraph partitioners developed for that computationmore » to improve application-specific algorithms for multiplying sparse matrices.« less

White Light Heterodyne Interferometry SNR

DTIC Science & Technology

2015-04-09

interferometers in the visible- and near-IR, where shot - noise -limited detectors are available. In the LWIR, the advantage of a direct detection...wavebands where shot - noise -limited detection is possible with direct detection systems, the relationship changes in the mid-wave infrared (MWIR) and...flux, without either having to split the light N – 1 ways or take the extra shot - noise penalty from Fizeau beam combining light from all apertures

A new flux splitting scheme

NASA Technical Reports Server (NTRS)

Liou, Meng-Sing; Steffen, Christopher J., Jr.

1993-01-01

A new flux splitting scheme is proposed. The scheme is remarkably simple and yet its accuracy rivals and in some cases surpasses that of Roe's solver in the Euler and Navier-Stokes solutions performed in this study. The scheme is robust and converges as fast as the Roe splitting. An approximately defined cell-face advection Mach number is proposed using values from the two straddling cells via associated characteristic speeds. This interface Mach number is then used to determine the upwind extrapolation for the convective quantities. Accordingly, the name of the scheme is coined as Advection Upstream Splitting Method (AUSM). A new pressure splitting is introduced which is shown to behave successfully, yielding much smoother results than other existing pressure splittings. Of particular interest is the supersonic blunt body problem in which the Roe scheme gives anomalous solutions. The AUSM produces correct solutions without difficulty for a wide range of flow conditions as well as grids.

A new flux splitting scheme

NASA Technical Reports Server (NTRS)

Liou, Meng-Sing; Steffen, Christopher J., Jr.

1991-01-01

A new flux splitting scheme is proposed. The scheme is remarkably simple and yet its accuracy rivals and in some cases surpasses that of Roe's solver in the Euler and Navier-Stokes solutions performed in this study. The scheme is robust and converges as fast as the Roe splitting. An approximately defined cell-face advection Mach number is proposed using values from the two straddling cells via associated characteristic speeds. This interface Mach number is then used to determine the upwind extrapolation for the convective quantities. Accordingly, the name of the scheme is coined as Advection Upstream Splitting Method (AUSM). A new pressure splitting is introduced which is shown to behave successfully, yielding much smoother results than other existing pressure splittings. Of particular interest is the supersonic blunt body problem in which the Roe scheme gives anomalous solutions. The AUSM produces correct solutions without difficulty for a wide range of flow conditions as well as grids.

Visual Tracking Based on Extreme Learning Machine and Sparse Representation

PubMed Central

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

Optical coherence tomography retinal image reconstruction via nonlocal weighted sparse representation

NASA Astrophysics Data System (ADS)

Abbasi, Ashkan; Monadjemi, Amirhassan; Fang, Leyuan; Rabbani, Hossein

2018-03-01

We present a nonlocal weighted sparse representation (NWSR) method for reconstruction of retinal optical coherence tomography (OCT) images. To reconstruct a high signal-to-noise ratio and high-resolution OCT images, utilization of efficient denoising and interpolation algorithms are necessary, especially when the original data were subsampled during acquisition. However, the OCT images suffer from the presence of a high level of noise, which makes the estimation of sparse representations a difficult task. Thus, the proposed NWSR method merges sparse representations of multiple similar noisy and denoised patches to better estimate a sparse representation for each patch. First, the sparse representation of each patch is independently computed over an overcomplete dictionary, and then a nonlocal weighted sparse coefficient is computed by averaging representations of similar patches. Since the sparsity can reveal relevant information from noisy patches, combining noisy and denoised patches' representations is beneficial to obtain a more robust estimate of the unknown sparse representation. The denoised patches are obtained by applying an off-the-shelf image denoising method and our method provides an efficient way to exploit information from noisy and denoised patches' representations. The experimental results on denoising and interpolation of spectral domain OCT images demonstrated the effectiveness of the proposed NWSR method over existing state-of-the-art methods.

Optical signal splitting and chirping device modeling

NASA Astrophysics Data System (ADS)

Vinogradova, Irina L.; Andrianova, Anna V.; Meshkov, Ivan K.; Sultanov, Albert Kh.; Abdrakhmanova, Guzel I.; Grakhova, Elizaveta P.; Ishmyarov, Arsen A.; Yantilina, Liliya Z.; Kutlieva, Gulnaz R.

2017-04-01

This article examines the devices for optical signal splitting and chirping device modeling. Models with splitting and switching functions are taken into consideration. The described device for optical signal splitting and chirping represents interferential splitter with profiled mixer which provides allocation of correspondent spectral component from ultra wide band frequency diapason, and signal phase shift for aerial array (AA) directive diagram control. This paper proposes modeling for two types of devices for optical signal splitting and chirping: the interference-type optical signal splitting and chirping device and the long-distance-type optical signal splitting and chirping device.

Bad split during bilateral sagittal split osteotomy of the mandible with separators: a retrospective study of 427 patients.

PubMed

Mensink, Gertjan; Verweij, Jop P; Frank, Michael D; Eelco Bergsma, J; Richard van Merkesteyn, J P

2013-09-01

An unfavourable fracture, known as a bad split, is a common operative complication in bilateral sagittal split osteotomy (BSSO). The reported incidence ranges from 0.5 to 5.5%/site. Since 1994 we have used sagittal splitters and separators instead of chisels for BSSO in our clinic in an attempt to prevent postoperative hypoaesthesia. Theoretically an increased percentage of bad splits could be expected with this technique. In this retrospective study we aimed to find out the incidence of bad splits associated with BSSO done with splitters and separators. We also assessed the risk factors for bad splits. The study group comprised 427 consecutive patients among whom the incidence of bad splits was 2.0%/site, which is well within the reported range. The only predictive factor for a bad split was the removal of third molars at the same time as BSSO. There was no significant association between bad splits and age, sex, class of occlusion, or the experience of the surgeon. We think that doing a BSSO with splitters and separators instead of chisels does not increase the risk of a bad split, and is therefore safe with predictable results. Copyright © 2012 The British Association of Oral and Maxillofacial Surgeons. Published by Elsevier Ltd. All rights reserved.

Tuning Cu dopant of Zn 0.5 Cd 0.5 S nanocrystals enables high-performance photocatalytic H 2 evolution from water splitting under visible-light irradiation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mei, Zongwei; Zhang, Bingkai; Zheng, Jiaxin

2016-08-01

Cu-doping into Zn1-xCdxS can greatly enhance the photocatalytic H2 evolution from water splitting under visible-light irradiation. However, it is still controversial for how the Cu-dopant improves this performance. Here, we report that appropriate Cu-doped Zn0.5Cd0.5S nanocrystals reach 21.4 mmol/h/g of H2 evolution rate without cocatalyst in the visible-light region, which is also 2.8 times as high as that of the undoped counterpart, and the corresponding apparent quantum efficiency is 18.8% at 428 nm. It is firstly confirmed that the Cu2+ changes into Cu+ after being doped by soft X-ray absorption spectroscopy (sXAS). We theoretically propose that the transformation of 2Cu2+more » to 2Cu+ results in one adjacent S2- vacancy (VS) in host during the doping process, while the Cu+-dopant and VS attract the photoexcited holes and electrons, respectively. Accordingly, the photocatalytic activity is improved due to the enhanced separation of photoexcited carriers accompanied with the enhanced light absorption resulting from the Cu+-dopant and 2Cu+/VS complex as possible active site for photocatalytic H2 evolution.« less

Fully Decomposable Split Graphs

NASA Astrophysics Data System (ADS)

Broersma, Hajo; Kratsch, Dieter; Woeginger, Gerhard J.

We discuss various questions around partitioning a split graph into connected parts. Our main result is a polynomial time algorithm that decides whether a given split graph is fully decomposable, i.e., whether it can be partitioned into connected parts of order α 1,α 2,...,α k for every α 1,α 2,...,α k summing up to the order of the graph. In contrast, we show that the decision problem whether a given split graph can be partitioned into connected parts of order α 1,α 2,...,α k for a given partition α 1,α 2,...,α k of the order of the graph, is NP-hard.

Sparse Reconstruction Techniques in MRI: Methods, Applications, and Challenges to Clinical Adoption

PubMed Central

Yang, Alice Chieh-Yu; Kretzler, Madison; Sudarski, Sonja; Gulani, Vikas; Seiberlich, Nicole

2016-01-01

The family of sparse reconstruction techniques, including the recently introduced compressed sensing framework, has been extensively explored to reduce scan times in Magnetic Resonance Imaging (MRI). While there are many different methods that fall under the general umbrella of sparse reconstructions, they all rely on the idea that a priori information about the sparsity of MR images can be employed to reconstruct full images from undersampled data. This review describes the basic ideas behind sparse reconstruction techniques, how they could be applied to improve MR imaging, and the open challenges to their general adoption in a clinical setting. The fundamental principles underlying different classes of sparse reconstructions techniques are examined, and the requirements that each make on the undersampled data outlined. Applications that could potentially benefit from the accelerations that sparse reconstructions could provide are described, and clinical studies using sparse reconstructions reviewed. Lastly, technical and clinical challenges to widespread implementation of sparse reconstruction techniques, including optimization, reconstruction times, artifact appearance, and comparison with current gold-standards, are discussed. PMID:27003227

Mid-frequency MTF compensation of optical sparse aperture system.

PubMed

Zhou, Chenghao; Wang, Zhile

2018-03-19

Optical sparse aperture (OSA) can greatly improve the spatial resolution of optical system. However, because of its aperture dispersion and sparse, its mid-frequency modulation transfer function (MTF) are significantly lower than that of a single aperture system. The main focus of this paper is on the mid-frequency MTF compensation of the optical sparse aperture system. Firstly, the principle of the mid-frequency MTF decreasing and missing of optical sparse aperture are analyzed. This paper takes the filling factor as a clue. The method of processing the mid-frequency MTF decreasing with large filling factor and method of compensation mid-frequency MTF with small filling factor are given respectively. For the MTF mid-frequency decreasing, the image spatial-variant restoration method is proposed to restore the mid-frequency information in the image; for the mid-frequency MTF missing, two images obtained by two system respectively are fused to compensate the mid-frequency information in optical sparse aperture image. The feasibility of the two method are analyzed in this paper. The numerical simulation of the system and algorithm of the two cases are presented using Zemax and Matlab. The results demonstrate that by these two methods the mid-frequency MTF of OSA system can be compensated effectively.

Highly-efficient capillary photoelectrochemical water splitting using cellulose nanofiber-templated TiO 2 photoanodes

Treesearch

Zhaodong Li; Chunhua Yao; Yanhao Yu; Zhiyong Cai; Xudong Wang

2014-01-01

Among current endeavors to explore renewable energy technologies, photoelectrochemical (PEC) water splitting holds great promise for conversion of solar energy to chemical energy. [ 1–4 ] Light absorption, charge separation, and appropriate interfacial redox reactions are three key aspects that lead to highly efficient solar energy conversion. [ 5–10 ] Therefore,...

Slope angle estimation method based on sparse subspace clustering for probe safe landing

NASA Astrophysics Data System (ADS)

Li, Haibo; Cao, Yunfeng; Ding, Meng; Zhuang, Likui

2018-06-01

To avoid planetary probes landing on steep slopes where they may slip or tip over, a new method of slope angle estimation based on sparse subspace clustering is proposed to improve accuracy. First, a coordinate system is defined and established to describe the measured data of light detection and ranging (LIDAR). Second, this data is processed and expressed with a sparse representation. Third, on this basis, the data is made to cluster to determine which subspace it belongs to. Fourth, eliminating outliers in subspace, the correct data points are used for the fitting planes. Finally, the vectors normal to the planes are obtained using the plane model, and the angle between the normal vectors is obtained through calculation. Based on the geometric relationship, this angle is equal in value to the slope angle. The proposed method was tested in a series of experiments. The experimental results show that this method can effectively estimate the slope angle, can overcome the influence of noise and obtain an exact slope angle. Compared with other methods, this method can minimize the measuring errors and further improve the estimation accuracy of the slope angle.

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.

Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering.

PubMed

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.

Immunogenicity is preferentially induced in sparse dendritic cell cultures

PubMed Central

Nasi, Aikaterini; Bollampalli, Vishnu Priya; Sun, Meng; Chen, Yang; Amu, Sylvie; Nylén, Susanne; Eidsmo, Liv; Rothfuchs, Antonio Gigliotti; Réthi, Bence

2017-01-01

We have previously shown that human monocyte-derived dendritic cells (DCs) acquired different characteristics in dense or sparse cell cultures. Sparsity promoted the development of IL-12 producing migratory DCs, whereas dense cultures increased IL-10 production. Here we analysed whether the density-dependent endogenous breaks could modulate DC-based vaccines. Using murine bone marrow-derived DC models we show that sparse cultures were essential to achieve several key functions required for immunogenic DC vaccines, including mobility to draining lymph nodes, recruitment and massive proliferation of antigen-specific CD4+ T cells, in addition to their TH1 polarization. Transcription analyses confirmed higher commitment in sparse cultures towards T cell activation, whereas DCs obtained from dense cultures up-regulated immunosuppressive pathway components and genes suggesting higher differentiation plasticity towards osteoclasts. Interestingly, we detected a striking up-regulation of fatty acid and cholesterol biosynthesis pathways in sparse cultures, suggesting an important link between DC immunogenicity and lipid homeostasis regulation. PMID:28276533

Immunogenicity is preferentially induced in sparse dendritic cell cultures.

PubMed

Nasi, Aikaterini; Bollampalli, Vishnu Priya; Sun, Meng; Chen, Yang; Amu, Sylvie; Nylén, Susanne; Eidsmo, Liv; Rothfuchs, Antonio Gigliotti; Réthi, Bence

2017-03-09

We have previously shown that human monocyte-derived dendritic cells (DCs) acquired different characteristics in dense or sparse cell cultures. Sparsity promoted the development of IL-12 producing migratory DCs, whereas dense cultures increased IL-10 production. Here we analysed whether the density-dependent endogenous breaks could modulate DC-based vaccines. Using murine bone marrow-derived DC models we show that sparse cultures were essential to achieve several key functions required for immunogenic DC vaccines, including mobility to draining lymph nodes, recruitment and massive proliferation of antigen-specific CD4+ T cells, in addition to their TH1 polarization. Transcription analyses confirmed higher commitment in sparse cultures towards T cell activation, whereas DCs obtained from dense cultures up-regulated immunosuppressive pathway components and genes suggesting higher differentiation plasticity towards osteoclasts. Interestingly, we detected a striking up-regulation of fatty acid and cholesterol biosynthesis pathways in sparse cultures, suggesting an important link between DC immunogenicity and lipid homeostasis regulation.

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering.

PubMed

Sicat, Ronell; Krüger, Jens; Möller, Torsten; Hadwiger, Markus

2014-12-01

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.

Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering

PubMed Central

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

7 CFR 51.2002 - Split shell.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 7 Agriculture 2 2011-01-01 2011-01-01 false Split shell. 51.2002 Section 51.2002 Agriculture... Standards for Grades of Filberts in the Shell 1 Definitions § 51.2002 Split shell. Split shell means a shell... of the shell, measured in the direction of the crack. ...

7 CFR 51.2002 - Split shell.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Split shell. 51.2002 Section 51.2002 Agriculture... Standards for Grades of Filberts in the Shell 1 Definitions § 51.2002 Split shell. Split shell means a shell... of the shell, measured in the direction of the crack. ...

Visual saliency detection based on in-depth analysis of sparse representation

NASA Astrophysics Data System (ADS)

Wang, Xin; Shen, Siqiu; Ning, Chen

2018-03-01

Visual saliency detection has been receiving great attention in recent years since it can facilitate a wide range of applications in computer vision. A variety of saliency models have been proposed based on different assumptions within which saliency detection via sparse representation is one of the newly arisen approaches. However, most existing sparse representation-based saliency detection methods utilize partial characteristics of sparse representation, lacking of in-depth analysis. Thus, they may have limited detection performance. Motivated by this, this paper proposes an algorithm for detecting visual saliency based on in-depth analysis of sparse representation. A number of discriminative dictionaries are first learned with randomly sampled image patches by means of inner product-based dictionary atom classification. Then, the input image is partitioned into many image patches, and these patches are classified into salient and nonsalient ones based on the in-depth analysis of sparse coding coefficients. Afterward, sparse reconstruction errors are calculated for the salient and nonsalient patch sets. By investigating the sparse reconstruction errors, the most salient atoms, which tend to be from the most salient region, are screened out and taken away from the discriminative dictionaries. Finally, an effective method is exploited for saliency map generation with the reduced dictionaries. Comprehensive evaluations on publicly available datasets and comparisons with some state-of-the-art approaches demonstrate the effectiveness of the proposed algorithm.

Cool covered sky-splitting spectrum-splitting FK

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mohedano, Rubén; Chaves, Julio; Falicoff, Waqidi

2014-09-26

Placing a plane mirror between the primary lens and the receiver in a Fresnel Köhler (FK) concentrator gives birth to a quite different CPV system where all the high-tech components sit on a common plane, that of the primary lens panels. The idea enables not only a thinner device (a half of the original) but also a low cost 1-step manufacturing process for the optics, automatic alignment of primary and secondary lenses, and cell/wiring protection. The concept is also compatible with two different techniques to increase the module efficiency: spectrum splitting between a 3J and a BPC Silicon cell formore » better usage of Direct Normal Irradiance DNI, and sky splitting to harvest the energy of the diffuse radiation and higher energy production throughout the year. Simple calculations forecast the module would convert 45% of the DNI into electricity.« less

InGaN working electrodes with assisted bias generated from GaAs solar cells for efficient water splitting.

PubMed

Liu, Shu-Yen; Sheu, J K; Lin, Yu-Chuan; Chen, Yu-Tong; Tu, S J; Lee, M L; Lai, W C

2013-11-04

Hydrogen generation through water splitting by n-InGaN working electrodes with bias generated from GaAs solar cell was studied. Instead of using an external bias provided by power supply, a GaAs-based solar cell was used as the driving force to increase the rate of hydrogen production. The water-splitting system was tuned using different approaches to set the operating points to the maximum power point of the GaAs solar cell. The approaches included changing the electrolytes, varying the light intensity, and introducing the immersed ITO ohmic contacts on the working electrodes. As a result, the hybrid system comprising both InGaN-based working electrodes and GaAs solar cells operating under concentrated illumination could possibly facilitate efficient water splitting.

10 CFR 26.135 - Split specimens.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 1 2011-01-01 2011-01-01 false Split specimens. 26.135 Section 26.135 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Licensee Testing Facilities § 26.135 Split specimens. (a) If the FFD program follows split-specimen procedures, as described in § 26.113, the licensee testing...

10 CFR 26.135 - Split specimens.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 1 2010-01-01 2010-01-01 false Split specimens. 26.135 Section 26.135 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Licensee Testing Facilities § 26.135 Split specimens. (a) If the FFD program follows split-specimen procedures, as described in § 26.113, the licensee testing...

10 CFR 26.135 - Split specimens.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 1 2013-01-01 2013-01-01 false Split specimens. 26.135 Section 26.135 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Licensee Testing Facilities § 26.135 Split specimens. (a) If the FFD program follows split-specimen procedures, as described in § 26.113, the licensee testing...

10 CFR 26.135 - Split specimens.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 1 2014-01-01 2014-01-01 false Split specimens. 26.135 Section 26.135 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Licensee Testing Facilities § 26.135 Split specimens. (a) If the FFD program follows split-specimen procedures, as described in § 26.113, the licensee testing...

10 CFR 26.135 - Split specimens.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 1 2012-01-01 2012-01-01 false Split specimens. 26.135 Section 26.135 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Licensee Testing Facilities § 26.135 Split specimens. (a) If the FFD program follows split-specimen procedures, as described in § 26.113, the licensee testing...

Sunspot splitting triggering an eruptive flare

NASA Astrophysics Data System (ADS)

Louis, Rohan E.; Puschmann, Klaus G.; Kliem, Bernhard; Balthasar, Horst; Denker, Carsten

2014-02-01

Aims: We investigate how the splitting of the leading sunspot and associated flux emergence and cancellation in active region NOAA 11515 caused an eruptive M5.6 flare on 2012 July 2. Methods: Continuum intensity, line-of-sight magnetogram, and dopplergram data of the Helioseismic and Magnetic Imager were employed to analyse the photospheric evolution. Filtergrams in Hα and He I 10830 Å of the Chromospheric Telescope at the Observatorio del Teide, Tenerife, track the evolution of the flare. The corresponding coronal conditions were derived from 171 Å and 304 Å images of the Atmospheric Imaging Assembly. Local correlation tracking was utilized to determine shear flows. Results: Emerging flux formed a neutral line ahead of the leading sunspot and new satellite spots. The sunspot splitting caused a long-lasting flow towards this neutral line, where a filament formed. Further flux emergence, partly of mixed polarity, as well as episodes of flux cancellation occurred repeatedly at the neutral line. Following a nearby C-class precursor flare with signs of interaction with the filament, the filament erupted nearly simultaneously with the onset of the M5.6 flare and evolved into a coronal mass ejection. The sunspot stretched without forming a light bridge, splitting unusually fast (within about a day, complete ≈6 h after the eruption) in two nearly equal parts. The front part separated strongly from the active region to approach the neighbouring active region where all its coronal magnetic connections were rooted. It also rotated rapidly (by 4.9° h-1) and caused significant shear flows at its edge. Conclusions: The eruption resulted from a complex sequence of processes in the (sub-)photosphere and corona. The persistent flows towards the neutral line likely caused the formation of a flux rope that held the filament. These flows, their associated flux cancellation, the emerging flux, and the precursor flare all contributed to the destabilization of the flux rope. We

Sparse nonnegative matrix factorization with ℓ0-constraints

PubMed Central

Peharz, Robert; Pernkopf, Franz

2012-01-01

Although nonnegative matrix factorization (NMF) favors a sparse and part-based representation of nonnegative data, there is no guarantee for this behavior. Several authors proposed NMF methods which enforce sparseness by constraining or penalizing the ℓ1-norm of the factor matrices. On the other hand, little work has been done using a more natural sparseness measure, the ℓ0-pseudo-norm. In this paper, we propose a framework for approximate NMF which constrains the ℓ0-norm of the basis matrix, or the coefficient matrix, respectively. For this purpose, techniques for unconstrained NMF can be easily incorporated, such as multiplicative update rules, or the alternating nonnegative least-squares scheme. In experiments we demonstrate the benefits of our methods, which compare to, or outperform existing approaches. PMID:22505792

Convolutional Sparse Coding for RGB+NIR Imaging.

PubMed

Hu, Xuemei; Heide, Felix; Dai, Qionghai; Wetzstein, Gordon

2018-04-01

Emerging sensor designs increasingly rely on novel color filter arrays (CFAs) to sample the incident spectrum in unconventional ways. In particular, capturing a near-infrared (NIR) channel along with conventional RGB color is an exciting new imaging modality. RGB+NIR sensing has broad applications in computational photography, such as low-light denoising, it has applications in computer vision, such as facial recognition and tracking, and it paves the way toward low-cost single-sensor RGB and depth imaging using structured illumination. However, cost-effective commercial CFAs suffer from severe spectral cross talk. This cross talk represents a major challenge in high-quality RGB+NIR imaging, rendering existing spatially multiplexed sensor designs impractical. In this work, we introduce a new approach to RGB+NIR image reconstruction using learned convolutional sparse priors. We demonstrate high-quality color and NIR imaging for challenging scenes, even including high-frequency structured NIR illumination. The effectiveness of the proposed method is validated on a large data set of experimental captures, and simulated benchmark results which demonstrate that this work achieves unprecedented reconstruction quality.

Split spline screw

NASA Technical Reports Server (NTRS)

Vranish, John M. (Inventor)

1993-01-01

A split spline screw type payload fastener assembly, including three identical male and female type split spline sections, is discussed. The male spline sections are formed on the head of a male type spline driver. Each of the split male type spline sections has an outwardly projecting load baring segment including a convex upper surface which is adapted to engage a complementary concave surface of a female spline receptor in the form of a hollow bolt head. Additionally, the male spline section also includes a horizontal spline releasing segment and a spline tightening segment below each load bearing segment. The spline tightening segment consists of a vertical web of constant thickness. The web has at least one flat vertical wall surface which is designed to contact a generally flat vertically extending wall surface tab of the bolt head. Mutual interlocking and unlocking of the male and female splines results upon clockwise and counter clockwise turning of the driver element.

Metal-free organic sensitizers for use in water-splitting dye-sensitized photoelectrochemical cells

PubMed Central

Swierk, John R.; Méndez-Hernández, Dalvin D.; McCool, Nicholas S.; Liddell, Paul; Terazono, Yuichi; Pahk, Ian; Tomlin, John J.; Oster, Nolan V.; Moore, Thomas A.; Moore, Ana L.; Gust, Devens; Mallouk, Thomas E.

2015-01-01

Solar fuel generation requires the efficient capture and conversion of visible light. In both natural and artificial systems, molecular sensitizers can be tuned to capture, convert, and transfer visible light energy. We demonstrate that a series of metal-free porphyrins can drive photoelectrochemical water splitting under broadband and red light (λ > 590 nm) illumination in a dye-sensitized TiO2 solar cell. We report the synthesis, spectral, and electrochemical properties of the sensitizers. Despite slow recombination of photoinjected electrons with oxidized porphyrins, photocurrents are low because of low injection yields and slow electron self-exchange between oxidized porphyrins. The free-base porphyrins are stable under conditions of water photoelectrolysis and in some cases photovoltages in excess of 1 V are observed. PMID:25583488

Sensitivity analyses for sparse-data problems-using weakly informative bayesian priors.

PubMed

Hamra, Ghassan B; MacLehose, Richard F; Cole, Stephen R

2013-03-01

Sparse-data problems are common, and approaches are needed to evaluate the sensitivity of parameter estimates based on sparse data. We propose a Bayesian approach that uses weakly informative priors to quantify sensitivity of parameters to sparse data. The weakly informative prior is based on accumulated evidence regarding the expected magnitude of relationships using relative measures of disease association. We illustrate the use of weakly informative priors with an example of the association of lifetime alcohol consumption and head and neck cancer. When data are sparse and the observed information is weak, a weakly informative prior will shrink parameter estimates toward the prior mean. Additionally, the example shows that when data are not sparse and the observed information is not weak, a weakly informative prior is not influential. Advancements in implementation of Markov Chain Monte Carlo simulation make this sensitivity analysis easily accessible to the practicing epidemiologist.

Sensitivity Analyses for Sparse-Data Problems—Using Weakly Informative Bayesian Priors

PubMed Central

Hamra, Ghassan B.; MacLehose, Richard F.; Cole, Stephen R.

2013-01-01

Sparse-data problems are common, and approaches are needed to evaluate the sensitivity of parameter estimates based on sparse data. We propose a Bayesian approach that uses weakly informative priors to quantify sensitivity of parameters to sparse data. The weakly informative prior is based on accumulated evidence regarding the expected magnitude of relationships using relative measures of disease association. We illustrate the use of weakly informative priors with an example of the association of lifetime alcohol consumption and head and neck cancer. When data are sparse and the observed information is weak, a weakly informative prior will shrink parameter estimates toward the prior mean. Additionally, the example shows that when data are not sparse and the observed information is not weak, a weakly informative prior is not influential. Advancements in implementation of Markov Chain Monte Carlo simulation make this sensitivity analysis easily accessible to the practicing epidemiologist. PMID:23337241

Photocatalytic generation of hydrogen by core-shell WO3/BiVO4 nanorods with ultimate water splitting efficiency

PubMed Central

Pihosh, Yuriy; Turkevych, Ivan; Mawatari, Kazuma; Uemura, Jin; Kazoe, Yutaka; Kosar, Sonya; Makita, Kikuo; Sugaya, Takeyoshi; Matsui, Takuya; Fujita, Daisuke; Tosa, Masahiro; Kondo, Michio; Kitamori, Takehiko

2015-01-01

Efficient photocatalytic water splitting requires effective generation, separation and transfer of photo-induced charge carriers that can hardly be achieved simultaneously in a single material. Here we show that the effectiveness of each process can be separately maximized in a nanostructured heterojunction with extremely thin absorber layer. We demonstrate this concept on WO3/BiVO4+CoPi core-shell nanostructured photoanode that achieves near theoretical water splitting efficiency. BiVO4 is characterized by a high recombination rate of photogenerated carriers that have much shorter diffusion length than the thickness required for sufficient light absorption. This issue can be resolved by the combination of BiVO4 with more conductive WO3 nanorods in a form of core-shell heterojunction, where the BiVO4 absorber layer is thinner than the carrier diffusion length while it’s optical thickness is reestablished by light trapping in high aspect ratio nanostructures. Our photoanode demonstrates ultimate water splitting photocurrent of 6.72 mA cm−2 under 1 sun illumination at 1.23 VRHE that corresponds to ~90% of the theoretically possible value for BiVO4. We also demonstrate a self-biased operation of the photoanode in tandem with a double-junction GaAs/InGaAsP photovoltaic cell with stable water splitting photocurrent of 6.56 mA cm−2 that corresponds to the solar to hydrogen generation efficiency of 8.1%. PMID:26053164

Photocatalytic generation of hydrogen by core-shell WO3/BiVO4 nanorods with ultimate water splitting efficiency

NASA Astrophysics Data System (ADS)

Pihosh, Yuriy; Turkevych, Ivan; Mawatari, Kazuma; Uemura, Jin; Kazoe, Yutaka; Kosar, Sonya; Makita, Kikuo; Sugaya, Takeyoshi; Matsui, Takuya; Fujita, Daisuke; Tosa, Masahiro; Kondo, Michio; Kitamori, Takehiko

2015-06-01

Efficient photocatalytic water splitting requires effective generation, separation and transfer of photo-induced charge carriers that can hardly be achieved simultaneously in a single material. Here we show that the effectiveness of each process can be separately maximized in a nanostructured heterojunction with extremely thin absorber layer. We demonstrate this concept on WO3/BiVO4+CoPi core-shell nanostructured photoanode that achieves near theoretical water splitting efficiency. BiVO4 is characterized by a high recombination rate of photogenerated carriers that have much shorter diffusion length than the thickness required for sufficient light absorption. This issue can be resolved by the combination of BiVO4 with more conductive WO3 nanorods in a form of core-shell heterojunction, where the BiVO4 absorber layer is thinner than the carrier diffusion length while it’s optical thickness is reestablished by light trapping in high aspect ratio nanostructures. Our photoanode demonstrates ultimate water splitting photocurrent of 6.72 mA cm-2 under 1 sun illumination at 1.23 VRHE that corresponds to ~90% of the theoretically possible value for BiVO4. We also demonstrate a self-biased operation of the photoanode in tandem with a double-junction GaAs/InGaAsP photovoltaic cell with stable water splitting photocurrent of 6.56 mA cm-2 that corresponds to the solar to hydrogen generation efficiency of 8.1%.

Collaborative sparse priors for multi-view ATR

NASA Astrophysics Data System (ADS)

Li, Xuelu; Monga, Vishal

2018-04-01

Recent work has seen a surge of sparse representation based classification (SRC) methods applied to automatic target recognition problems. While traditional SRC approaches used l0 or l1 norm to quantify sparsity, spike and slab priors have established themselves as the gold standard for providing general tunable sparse structures on vectors. In this work, we employ collaborative spike and slab priors that can be applied to matrices to encourage sparsity for the problem of multi-view ATR. That is, target images captured from multiple views are expanded in terms of a training dictionary multiplied with a coefficient matrix. Ideally, for a test image set comprising of multiple views of a target, coefficients corresponding to its identifying class are expected to be active, while others should be zero, i.e. the coefficient matrix is naturally sparse. We develop a new approach to solve the optimization problem that estimates the sparse coefficient matrix jointly with the sparsity inducing parameters in the collaborative prior. ATR problems are investigated on the mid-wave infrared (MWIR) database made available by the US Army Night Vision and Electronic Sensors Directorate, which has a rich collection of views. Experimental results show that the proposed joint prior and coefficient estimation method (JPCEM) can: 1.) enable improved accuracy when multiple views vs. a single one are invoked, and 2.) outperform state of the art alternatives particularly when training imagery is limited.

Structured sparse linear graph embedding.

PubMed

Wang, Haixian

2012-03-01

Subspace learning is a core issue in pattern recognition and machine learning. Linear graph embedding (LGE) is a general framework for subspace learning. In this paper, we propose a structured sparse extension to LGE (SSLGE) by introducing a structured sparsity-inducing norm into LGE. Specifically, SSLGE casts the projection bases learning into a regression-type optimization problem, and then the structured sparsity regularization is applied to the regression coefficients. The regularization selects a subset of features and meanwhile encodes high-order information reflecting a priori structure information of the data. The SSLGE technique provides a unified framework for discovering structured sparse subspace. Computationally, by using a variational equality and the Procrustes transformation, SSLGE is efficiently solved with closed-form updates. Experimental results on face image show the effectiveness of the proposed method. Copyright © 2011 Elsevier Ltd. All rights reserved.

Sparse, decorrelated odor coding in the mushroom body enhances learned odor discrimination.

PubMed

Lin, Andrew C; Bygrave, Alexei M; de Calignon, Alix; Lee, Tzumin; Miesenböck, Gero

2014-04-01

Sparse coding may be a general strategy of neural systems for augmenting memory capacity. In Drosophila melanogaster, 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 showed that Kenyon cells activated APL and APL inhibited Kenyon cells. Disrupting the Kenyon cell-APL feedback loop decreased the sparseness of Kenyon cell odor responses, increased inter-odor correlations and prevented 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.

Discussion of CoSA: Clustering of Sparse Approximations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Armstrong, Derek Elswick

2017-03-07

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

Relationship between mandibular anatomy and the occurrence of a bad split upon sagittal split osteotomy.

PubMed

Aarabi, Mohammadali; Tabrizi, Reza; Hekmat, Mina; Shahidi, Shoaleh; Puzesh, Ayatollah

2014-12-01

A bad split is a troublesome complication of the sagittal split osteotomy (SSO). The aim of this study was to evaluate the relation between the occurrence of a bad split and mandibular anatomy in SSO using cone-beam computed tomography. The authors designed a cohort retrospective study. Forty-eight patients (96 SSO sites) were studied. The buccolingual thickness of the retromandibular area (BLR), the buccolingual thickness of the ramus at the level of the lingula (BLTR), the height of the mandible from the alveolar crest to the inferior border of the mandible, (ACIB), the distance between the sigmoid notch and the inferior border of the mandible (SIBM), and the anteroposterior width of the ramus (APWR) were measured. The independent t test was applied to compare anatomic measurements between the group with and the group without bad splits. The receiver operating characteristic (ROC) test was used to find a cutoff point in anatomic size for various parts of the mandible related to the occurrence of bad splits. The mean SIBM was 47.05±6.33 mm in group 1 (with bad splits) versus 40.66±2.44 mm in group 2 (without bad splits; P=.01). The mean BLTR was 5.74±1.11 mm in group 1 versus 3.19±0.55 mm in group 2 (P=.04). The mean BLR was 14.98±2.78 mm in group 1 versus 11.21±1.29 mm in group 2 (P=.001). No statistically significant difference was found for APWR and ACIB between the 2 groups. The ROC test showed cutoff points of 10.17 mm for BLR, 36.69 mm for SIBM, and 4.06 mm for BLTR. This study showed that certain mandibular anatomic differences can increase the risk of a bad split during SSO surgery. Copyright © 2014 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

SplitRacer - a new Semi-Automatic Tool to Quantify And Interpret Teleseismic Shear-Wave Splitting

NASA Astrophysics Data System (ADS)

Reiss, M. C.; Rumpker, G.

2017-12-01

We have developed a semi-automatic, MATLAB-based GUI to combine standard seismological tasks such as the analysis and interpretation of teleseismic shear-wave splitting. Shear-wave splitting analysis is widely used to infer seismic anisotropy, which can be interpreted in terms of lattice-preferred orientation of mantle minerals, shape-preferred orientation caused by fluid-filled cracks or alternating layers. Seismic anisotropy provides a unique link between directly observable surface structures and the more elusive dynamic processes in the mantle below. Thus, resolving the seismic anisotropy of the lithosphere/asthenosphere is of particular importance for geodynamic modeling and interpretations. The increasing number of seismic stations from temporary experiments and permanent installations creates a new basis for comprehensive studies of seismic anisotropy world-wide. However, the increasingly large data sets pose new challenges for the rapid and reliably analysis of teleseismic waveforms and for the interpretation of the measurements. Well-established routines and programs are available but are often impractical for analyzing large data sets from hundreds of stations. Additionally, shear wave splitting results are seldom evaluated using the same well-defined quality criteria which may complicate comparison with results from different studies. SplitRacer has been designed to overcome these challenges by incorporation of the following processing steps: i) downloading of waveform data from multiple stations in mseed-format using FDSNWS tools; ii) automated initial screening and categorizing of XKS-waveforms using a pre-set SNR-threshold; iii) particle-motion analysis of selected phases at longer periods to detect and correct for sensor misalignment; iv) splitting analysis of selected phases based on transverse-energy minimization for multiple, randomly-selected, relevant time windows; v) one and two-layer joint-splitting analysis for all phases at one station by

Light-activated disinfection using a light-emitting diode lamp in the red spectrum: clinical and microbiological short-term findings on periodontitis patients in maintenance. A randomized controlled split-mouth clinical trial.

PubMed

Mongardini, Claudio; Di Tanna, Gian Luca; Pilloni, Andrea

2014-01-01

Eradication or suppression of pathogens is a major goal in periodontal therapy. Due to the increase in antibiotic resistance, the need of new disinfection therapies is raising. Photodynamic therapy (PDT) has demonstrated anti-infective potential. No data are available on the use of light-emitting diode (LED) lights as the light source in PDT. The aim of this study was to investigate the microbiological and clinical adjunctive outcome of a new photodynamic LED device, compared to scaling and root planing in periodontitis patients in maintenance [supportive periodontal therapy (SPT)]. In this masked, split-mouth design study, 30 treated chronic periodontitis subjects (mean age, 46.2 years; 13 males) in SPT were included. Two residual interdental sites with probing pocket depth (PPD) ≥ 5 mm in two opposite quadrants, with positive bleeding on probing (BOP) and comparable periodontal breakdown, were selected. PPD, BOP and subgingival microbiological samples for real-time PCR analysis (Carpegen® PerioDiagnostics, Carpegen GmbH, Münster, Germany) were recorded at baseline and 1 week after treatment. Scaling and root planing was performed under local anesthesia. Randomly one of the sites was selected to receive adjunctive photodynamic therapy by inserting a photosensitizer (toluidine blue O solution) and exposing it to a LED light in the red spectrum (Fotosan, CMS Dental, Copenhagen, Denmark), according to the manufacturer's instructions. After 1 week, 73 % of the control sites and 27 % of the test sites were still BOP+. These differences compared to baseline values and in-between groups were statistically significantly different (p < 0.001). Mean PPD decreased from 5.47 mm (±0.68) to 4.73 mm (±0.74, p < 0.001) in control sites and from 5.63 mm (±0.85) to 4.43 mm (±1.25, p < 0.001, test vs control p = 0.01) in the test group. Microbiologically, higher reductions of relative proportions of red complex bacteria were observed in test sites (68.1 vs. 4.1 %; p = 0

Tunable absorption enhancement in electric split-ring resonators-shaped graphene arrays

NASA Astrophysics Data System (ADS)

Liu, Lin; Chen, Jiajia; Zhou, Zigang; Yi, Zao; Ye, Xin

2018-04-01

In this paper, we propose a wavelength-tunable absorber consisting of electric split-ring resonators (eSRRs)-shaped graphene arrays deposited on a SiO2/Si substrate in the far-infrared and terahertz regions. The simulation results exhibit that two resonance modes are supported by the structure. In terms of the resonance at longer wavelength, the light absorption declines while the period a or length L increases. However, absorption contrarily improves with enlargement of incident angle under the transverse magnetic (TM) polarization. And in terms of resonance at shorter wavelengths, absorption enhances with increasing length L and incident angle θ. Generally, the light absorption enhances with Fermi level E F of graphene, accompanied by blue shift. The aforementioned results unquestionably provide a distinctive source of inspiration for how to design and manufacture devices related to absorption such as filters, spatial light modulator and sensors.

Unravelling Photocarrier Dynamics beyond the Space Charge Region for Photoelectrochemical Water Splitting

DOE PAGES

Zhang, Wenrui; Yan, Danhua; Appavoo, Kannatassen; ...

2017-04-18

Semiconductor photoelectrodes for photoelectrochemical (PEC) water splitting require efficient carrier generation, separation, and transport at and beyond the space charge region (SCR) formed at the aqueous interface. The trade-off between photon collection and minority carrier delivery governs the photoelectrode design and implies maximum water splitting efficiency at an electrode thickness equivalent to the light absorption depth. Here, using planar ZnO thin films as a model system, we identify the photocarriers beyond the SCR as another significant source to substantially enhance the PEC performance. The high-quality ZnO films synthesized by pulsed laser deposition feature very few deep trap states and supportmore » a long photocarrier lifetime. Combined with photoelectrochemical characterization, ultrafast spectroscopy, and numerical calculations, it is revealed that engineering the exciton concentration gradient by film thickness facilitates the inward diffusion of photocarriers from the neighboring illuminated region to the SCR and, therefore, achieves a record high quantum efficiency over 80% at a thickness far beyond its light absorption depth and the SCR width. Furthermore, these results elucidate the important role of the photocarriers beyond SCR for the PEC process and provide new insight into exploring the full potential for efficient photoelectrode materials with large exciton diffusivity.« less

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

PubMed Central

Qu, Shiru

2016-01-01

Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness. PMID:27630710

HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION

PubMed Central

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

Joint sparse representation for robust multimodal biometrics recognition.

PubMed

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.

Reconstruction of finite-valued sparse signals

NASA Astrophysics Data System (ADS)

Keiper, Sandra; Kutyniok, Gitta; Lee, Dae Gwan; Pfander, Götz

2017-08-01

The need of reconstructing discrete-valued sparse signals from few measurements, that is solving an undetermined system of linear equations, appears frequently in science and engineering. Those signals appear, for example, in error correcting codes as well as massive Multiple-Input Multiple-Output (MIMO) channel and wideband spectrum sensing. A particular example is given by wireless communications, where the transmitted signals are sequences of bits, i.e., with entries in f0; 1g. Whereas classical compressed sensing algorithms do not incorporate the additional knowledge of the discrete nature of the signal, classical lattice decoding approaches do not utilize sparsity constraints. In this talk, we present an approach that incorporates a discrete values prior into basis pursuit. In particular, we address finite-valued sparse signals, i.e., sparse signals with entries in a finite alphabet. We will introduce an equivalent null space characterization and show that phase transition takes place earlier than when using the classical basis pursuit approach. We will further discuss robustness of the algorithm and show that the nonnegative case is very different from the bipolar one. One of our findings is that the positioning of the zero in the alphabet - i.e., whether it is a boundary element or not - is crucial.

Surface Passivation of GaN Nanowires for Enhanced Photoelectrochemical Water-Splitting.

PubMed

Varadhan, Purushothaman; Fu, Hui-Chun; Priante, Davide; Retamal, Jose Ramon Duran; Zhao, Chao; Ebaid, Mohamed; Ng, Tien Khee; Ajia, Idirs; Mitra, Somak; Roqan, Iman S; Ooi, Boon S; He, Jr-Hau

2017-03-08

Hydrogen production via photoelectrochemical water-splitting is a key source of clean and sustainable energy. The use of one-dimensional nanostructures as photoelectrodes is desirable for photoelectrochemical water-splitting applications due to the ultralarge surface areas, lateral carrier extraction schemes, and superior light-harvesting capabilities. However, the unavoidable surface states of nanostructured materials create additional charge carrier trapping centers and energy barriers at the semiconductor-electrolyte interface, which severely reduce the solar-to-hydrogen conversion efficiency. In this work, we address the issue of surface states in GaN nanowire photoelectrodes by employing a simple and low-cost surface treatment method, which utilizes an organic thiol compound (i.e., 1,2-ethanedithiol). The surface-treated photocathode showed an enhanced photocurrent density of -31 mA/cm 2 at -0.2 V versus RHE with an incident photon-to-current conversion efficiency of 18.3%, whereas untreated nanowires yielded only 8.1% efficiency. Furthermore, the surface passivation provides enhanced photoelectrochemical stability as surface-treated nanowires retained ∼80% of their initial photocurrent value and produced 8000 μmol of gas molecules over 55 h at acidic conditions (pH ∼ 0), whereas the untreated nanowires demonstrated only <4 h of photoelectrochemical stability. These findings shed new light on the importance of surface passivation of nanostructured photoelectrodes for photoelectrochemical applications.

Hyperspherical Sparse Approximation Techniques for High-Dimensional Discontinuity Detection

DOE PAGES

Zhang, Guannan; Webster, Clayton G.; Gunzburger, Max; ...

2016-08-04

This work proposes a hyperspherical sparse approximation framework for detecting jump discontinuities in functions in high-dimensional spaces. The need for a novel approach results from the theoretical and computational inefficiencies of well-known approaches, such as adaptive sparse grids, for discontinuity detection. Our approach constructs the hyperspherical coordinate representation of the discontinuity surface of a function. Then sparse approximations of the transformed function are built in the hyperspherical coordinate system, with values at each point estimated by solving a one-dimensional discontinuity detection problem. Due to the smoothness of the hypersurface, the new technique can identify jump discontinuities with significantly reduced computationalmore » cost, compared to existing methods. Several approaches are used to approximate the transformed discontinuity surface in the hyperspherical system, including adaptive sparse grid and radial basis function interpolation, discrete least squares projection, and compressed sensing approximation. Moreover, hierarchical acceleration techniques are also incorporated to further reduce the overall complexity. In conclusion, rigorous complexity analyses of the new methods are provided, as are several numerical examples that illustrate the effectiveness of our approach.« less

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.

Sparse dictionary learning of resting state fMRI networks.

PubMed

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

2012-07-02

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

Plasmonic enhancement of visible-light water splitting with Au-TiO2 composite aerogels

NASA Astrophysics Data System (ADS)

Desario, Paul A.; Pietron, Jeremy J.; Devantier, Devyn E.; Brintlinger, Todd H.; Stroud, Rhonda M.; Rolison, Debra R.

2013-08-01

We demonstrate plasmonic enhancement of visible-light-driven splitting of water at three-dimensionally (3D) networked gold-titania (Au-TiO2) aerogels. The sol-gel-derived ultraporous composite nanoarchitecture, which contains 1 to 8.5 wt% Au nanoparticles and titania in the anatase form, retains the high surface area and mesoporosity of unmodified TiO2 aerogels and maintains stable dispersion of the ~5 nm Au guests. A broad surface plasmon resonance (SPR) feature centered at ~550 nm is present for the Au-TiO2 aerogels, but not Au-free TiO2 aerogels, and spans a wide range of the visible spectrum. Gold-derived SPR in Au-TiO2 aerogels cast as films on transparent electrodes drives photoelectrochemical oxidation of aqueous hydroxide and extends the photocatalytic activity of TiO2 from the ultraviolet region to visible wavelengths exceeding 700 nm. Films of Au-TiO2 aerogels in which Au nanoparticles are deposited on pre-formed TiO2 aerogels by a deposition-precipitation method (DP Au/TiO2) also photoelectrochemically oxidize aqueous hydroxide, but less efficiently than 3D Au-TiO2, despite having an essentially identical Au nanoparticle weight fraction and size distribution. For example, 3D Au-TiO2 containing 1 wt% Au is as active as DP Au/TiO2 with 4 wt% Au. The higher photocatalytic activity of 3D Au-TiO2 derives only in part from its ability to retain the surface area and porosity of unmodified TiO2 aerogel. The magnitude of improvement indicates that in the 3D arrangement either a more accessible photoelectrochemical reaction interphase (three-phase boundary) exists or more efficient conversion of excited surface plasmons into charge carriers occurs, thereby amplifying reactivity over DP Au/TiO2. The difference in photocatalytic efficiency between the two forms of Au-TiO2 demonstrates the importance of defining the structure of Au||TiO2 interfaces within catalytic Au-TiO2 nanoarchitectures.We demonstrate plasmonic enhancement of visible-light-driven splitting of

Optical fringe-reflection deflectometry with sparse representation

NASA Astrophysics Data System (ADS)

Xiao, Yong-Liang; Li, Sikun; Zhang, Qican; Zhong, Jianxin; Su, Xianyu; You, Zhisheng

2018-05-01

Optical fringe-reflection deflectometry is a surprisingly attractive scratch detection technique for specular surfaces owing to its unparalleled local sensibility. Full-field surface topography is obtained from a measured normal field using gradient integration. However, there may not be an ideal measured gradient field for deflectometry reconstruction in practice. Both the non-integrability condition and various kinds of image noise distributions, which are present in the indirect measured gradient field, may lead to ambiguity about the scratches on specular surfaces. In order to reduce misjudgment of scratches, sparse representation is introduced into the Southwell curl equation for deflectometry. The curl can be represented as a linear combination of the given redundant dictionary for curl and the sparsest solution for gradient refinement. The non-integrability condition and noise permutation can be overcome with sparse representation for gradient refinement. Numerical simulations demonstrate that the accuracy rate of judgment of scratches can be enhanced with sparse representation compared to the standard least-squares integration. Preliminary experiments are performed with the application of practical measured deflectometric data to verify the validity of the algorithm.

Ground-state splitting of ultrashallow thermal donors with negative central-cell corrections in silicon

NASA Astrophysics Data System (ADS)

Hara, Akito; Awano, Teruyoshi

2017-06-01

Ultrashallow thermal donors (USTDs), which consist of light element impurities such as carbon, hydrogen, and oxygen, have been found in Czochralski silicon (CZ Si) crystals. To the best of our knowledge, these are the shallowest hydrogen-like donors with negative central-cell corrections in Si. We observed the ground-state splitting of USTDs by far-infrared optical absorption at different temperatures. The upper ground-state levels are approximately 4 meV higher than the ground-state levels. This energy level splitting is also consistent with that obtained by thermal excitation from the ground state to the upper ground state. This is direct evidence that the wave function of the USTD ground state is made up of a linear combination of conduction band minimums.

A performance study of sparse Cholesky factorization on INTEL iPSC/860

NASA Technical Reports Server (NTRS)

Zubair, M.; Ghose, M.

1992-01-01

The problem of Cholesky factorization of a sparse matrix has been very well investigated on sequential machines. A number of efficient codes exist for factorizing large unstructured sparse matrices. However, there is a lack of such efficient codes on parallel machines in general, and distributed machines in particular. Some of the issues that are critical to the implementation of sparse Cholesky factorization on a distributed memory parallel machine are ordering, partitioning and mapping, load balancing, and ordering of various tasks within a processor. Here, we focus on the effect of various partitioning schemes on the performance of sparse Cholesky factorization on the Intel iPSC/860. Also, a new partitioning heuristic for structured as well as unstructured sparse matrices is proposed, and its performance is compared with other schemes.

Enhanced Photoelectrochemical Water Splitting Behaviour of Tuned Band Gap CdSe QDs Sensitized LaB₆.

PubMed

Babu, M Soban; Sivanantham, A; Chakravarthi, B Barath; Kannan, R Sujith; Panda, Subhendu K; Berchmans, L John; Arya, S B; Sreedhar, Gosipathala

2017-01-01

We report the fabrication of tuned band gap quantum dots sensitized LaB₆ hybrid nanostructures and their application as a photoanode for photoelectrochemical water splitting. The lanthanum hexaboride (LaB₆) obtained by molten salt electrolysis method is sensitized with different sized CdSe quantum dots, which form a multiple-level hierarchical heterostructure and such design enhance the light absorption and charge carrier separation, which in turn showed higher photocurrent density compared to that of pristine LaB₆. When LaB₆ is sensitized with CdSe quantum dots of different band gaps, which have the absorption in the green and red (530 and 605 nm) regions in visible light, developed a ten times higher photocurrent density (11.0 mA cm(−2)) compared to that of pristine LaB6 (0.5 mA cm(−2) at 0.75 V vs. Ag/AgCl) in 1 M Na₂S electrolyte under illumination. These results prove that the tuned band gap quantum dots sensitized LaB₆ heterostructures are an ideal candidate for a photoanode in solar water splitting applications.

Are Ducted Mini-Splits Worth It?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Winkler, Jonathan M; Maguire, Jeffrey B; Metzger, Cheryn E.

Ducted mini-split heat pumps are gaining popularity in some regions of the country due to their energy-efficient specifications and their ability to be hidden from sight. Although product and install costs are typically higher than the ductless mini-split heat pumps, this technology is well worth the premium for some homeowners who do not like to see an indoor unit in their living area. Due to the interest in this technology by local utilities and homeowners, the Bonneville Power Administration (BPA) has funded the Pacific Northwest National Laboratory (PNNL) and the National Renewable Energy Laboratory (NREL) to develop capabilities within themore » Building Energy Optimization (BEopt) tool to model ducted mini-split heat pumps. After the fundamental capabilities were added, energy-use results could be compared to other technologies that were already in BEopt, such as zonal electric resistance heat, central air source heat pumps, and ductless mini-split heat pumps. Each of these technologies was then compared using five prototype configurations in three different BPA heating zones to determine how the ducted mini-split technology would perform under different scenarios. The result of this project was a set of EnergyPlus models representing the various prototype configurations in each climate zone. Overall, the ducted mini-split heat pumps saved about 33-60% compared to zonal electric resistance heat (with window AC systems modeled in the summer). The results also showed that the ducted mini-split systems used about 4% more energy than the ductless mini-split systems, which saved about 37-64% compared to electric zonal heat (depending on the prototype and climate).« less

TMD splitting functions in [Formula: see text] factorization: the real contribution to the gluon-to-gluon splitting.

PubMed

Hentschinski, M; Kusina, A; Kutak, K; Serino, M

2018-01-01

We calculate the transverse momentum dependent gluon-to-gluon splitting function within [Formula: see text]-factorization, generalizing the framework employed in the calculation of the quark splitting functions in Hautmann et al. (Nucl Phys B 865:54-66, arXiv:1205.1759, 2012), Gituliar et al. (JHEP 01:181, arXiv:1511.08439, 2016), Hentschinski et al. (Phys Rev D 94(11):114013, arXiv:1607.01507, 2016) and demonstrate at the same time the consistency of the extended formalism with previous results. While existing versions of [Formula: see text] factorized evolution equations contain already a gluon-to-gluon splitting function i.e. the leading order Balitsky-Fadin-Kuraev-Lipatov (BFKL) kernel or the Ciafaloni-Catani-Fiorani-Marchesini (CCFM) kernel, the obtained splitting function has the important property that it reduces both to the leading order BFKL kernel in the high energy limit, to the Dokshitzer-Gribov-Lipatov-Altarelli-Parisi (DGLAP) gluon-to-gluon splitting function in the collinear limit as well as to the CCFM kernel in the soft limit. At the same time we demonstrate that this splitting kernel can be obtained from a direct calculation of the QCD Feynman diagrams, based on a combined implementation of the Curci-Furmanski-Petronzio formalism for the calculation of the collinear splitting functions and the framework of high energy factorization.

Fast sparsely synchronized brain rhythms in a scale-free neural network

NASA Astrophysics Data System (ADS)

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D . For small D , full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp> ( : ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For D sparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of statistically

Fast sparsely synchronized brain rhythms in a scale-free neural network.

PubMed

Kim, Sang-Yoon; Lim, Woochang

2015-08-01

We consider a directed version of the Barabási-Albert scale-free network model with symmetric preferential attachment with the same in- and out-degrees and study the emergence of sparsely synchronized rhythms for a fixed attachment degree in an inhibitory population of fast-spiking Izhikevich interneurons. Fast sparsely synchronized rhythms with stochastic and intermittent neuronal discharges are found to appear for large values of J (synaptic inhibition strength) and D (noise intensity). For an intensive study we fix J at a sufficiently large value and investigate the population states by increasing D. For small D, full synchronization with the same population-rhythm frequency fp and mean firing rate (MFR) fi of individual neurons occurs, while for large D partial synchronization with fp>〈fi〉 (〈fi〉: ensemble-averaged MFR) appears due to intermittent discharge of individual neurons; in particular, the case of fp>4〈fi〉 is referred to as sparse synchronization. For the case of partial and sparse synchronization, MFRs of individual neurons vary depending on their degrees. As D passes a critical value D* (which is determined by employing an order parameter), a transition to unsynchronization occurs due to the destructive role of noise to spoil the pacing between sparse spikes. For Dsparse synchronization do contributions of individual neuronal dynamics to population synchronization change depending on their degrees, unlike in the case of full synchronization. Consequently, dynamics of individual neurons reveal the inhomogeneous network structure for the case of partial and sparse synchronization, which is in contrast to the case of

On split regular Hom-Lie superalgebras

NASA Astrophysics Data System (ADS)

Albuquerque, Helena; Barreiro, Elisabete; Calderón, A. J.; Sánchez, José M.

2018-06-01

We introduce the class of split regular Hom-Lie superalgebras as the natural extension of the one of split Hom-Lie algebras and Lie superalgebras, and study its structure by showing that an arbitrary split regular Hom-Lie superalgebra L is of the form L = U +∑jIj with U a linear subspace of a maximal abelian graded subalgebra H and any Ij a well described (split) ideal of L satisfying [Ij ,Ik ] = 0 if j ≠ k. Under certain conditions, the simplicity of L is characterized and it is shown that L is the direct sum of the family of its simple ideals.

Kurtosis based weighted sparse model with convex optimization technique for bearing fault diagnosis

NASA Astrophysics Data System (ADS)

Zhang, Han; Chen, Xuefeng; Du, Zhaohui; Yan, Ruqiang

2016-12-01

The bearing failure, generating harmful vibrations, is one of the most frequent reasons for machine breakdowns. Thus, performing bearing fault diagnosis is an essential procedure to improve the reliability of the mechanical system and reduce its operating expenses. Most of the previous studies focused on rolling bearing fault diagnosis could be categorized into two main families, kurtosis-based filter method and wavelet-based shrinkage method. Although tremendous progresses have been made, their effectiveness suffers from three potential drawbacks: firstly, fault information is often decomposed into proximal frequency bands and results in impulsive feature frequency band splitting (IFFBS) phenomenon, which significantly degrades the performance of capturing the optimal information band; secondly, noise energy spreads throughout all frequency bins and contaminates fault information in the information band, especially under the heavy noisy circumstance; thirdly, wavelet coefficients are shrunk equally to satisfy the sparsity constraints and most of the feature information energy are thus eliminated unreasonably. Therefore, exploiting two pieces of prior information (i.e., one is that the coefficient sequences of fault information in the wavelet basis is sparse, and the other is that the kurtosis of the envelope spectrum could evaluate accurately the information capacity of rolling bearing faults), a novel weighted sparse model and its corresponding framework for bearing fault diagnosis is proposed in this paper, coined KurWSD. KurWSD formulates the prior information into weighted sparse regularization terms and then obtains a nonsmooth convex optimization problem. The alternating direction method of multipliers (ADMM) is sequentially employed to solve this problem and the fault information is extracted through the estimated wavelet coefficients. Compared with state-of-the-art methods, KurWSD overcomes the three drawbacks and utilizes the advantages of both family

Deep ensemble learning of sparse regression models for brain disease diagnosis

PubMed Central

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

2018-01-01

Recent studies on brain imaging analysis witnessed the core roles of machine learning techniques in computer-assisted intervention for brain disease diagnosis. Of various machine-learning techniques, sparse regression models have proved their effectiveness in handling high-dimensional data but with a small number of training samples, especially in medical problems. In the meantime, deep learning methods have been making great successes by outperforming the state-of-the-art performances in various applications. In this paper, we propose a novel framework that combines the two conceptually different methods of sparse regression and deep learning for Alzheimer’s disease/mild cognitive impairment diagnosis and prognosis. Specifically, we first train multiple sparse regression models, each of which is trained with different values of a regularization control parameter. Thus, our multiple sparse regression models potentially select different feature subsets from the original feature set; thereby they have different powers to predict the response values, i.e., clinical label and clinical scores in our work. By regarding the response values from our sparse regression models as target-level representations, we then build a deep convolutional neural network for clinical decision making, which thus we call ‘ Deep Ensemble Sparse Regression Network.’ To our best knowledge, this is the first work that combines sparse regression models with deep neural network. In our experiments with the ADNI cohort, we validated the effectiveness of the proposed method by achieving the highest diagnostic accuracies in three classification tasks. We also rigorously analyzed our results and compared with the previous studies on the ADNI cohort in the literature. PMID:28167394

Solar Water Splitting with a Hydrogenase Integrated in Photoelectrochemical Tandem Cells.

PubMed

Nam, Dong Heon; Zhang, Jenny; Andrei, Virgil; Kornienko, Nikolay; Heidary, Nina; Wagner, Andreas; Nakanishi, Kenichi; Sokol, Katarzyna; Slater, Barnaby; Zebger, Ingo; Hofmann, Stephan; Fontecilla-Camps, Juan; Park, Chan Beum; Reisner, Erwin

2018-06-11

Hydrogenases (H2ases) are benchmark electrocatalysts in H2 production, both in biology and (photo)catalysis in vitro. We report the tailoring of a p-type Si photocathode for optimal loading and wiring of H2ase by the introduction of a hierarchical inverse opal (IO) TiO2 interlayer. This proton reducing Si|IO-TiO2|H2ase photocathode is capable of driving overall water splitting in combination with a complementary photoanode. We demonstrate unassisted (bias-free) water-splitting by wiring Si|IO-TiO2|H2ase to a modified BiVO4 photoanode in a photoelectrochemical (PEC) cell during several hours of irradiation. Connecting the Si|IO-TiO2|H2ase to a photosystem II (PSII) photoanode provides proof-of-concept for an engineered Z-scheme that replaces the non-complementary, natural light absorber photosystem I with a complementary abiotic silicon photocathode. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

FPGA implementation of sparse matrix algorithm for information retrieval

NASA Astrophysics Data System (ADS)

Bojanic, Slobodan; Jevtic, Ruzica; Nieto-Taladriz, Octavio

2005-06-01

Information text data retrieval requires a tremendous amount of processing time because of the size of the data and the complexity of information retrieval algorithms. In this paper the solution to this problem is proposed via hardware supported information retrieval algorithms. Reconfigurable computing may adopt frequent hardware modifications through its tailorable hardware and exploits parallelism for a given application through reconfigurable and flexible hardware units. The degree of the parallelism can be tuned for data. In this work we implemented standard BLAS (basic linear algebra subprogram) sparse matrix algorithm named Compressed Sparse Row (CSR) that is showed to be more efficient in terms of storage space requirement and query-processing timing over the other sparse matrix algorithms for information retrieval application. Although inverted index algorithm is treated as the de facto standard for information retrieval for years, an alternative approach to store the index of text collection in a sparse matrix structure gains more attention. This approach performs query processing using sparse matrix-vector multiplication and due to parallelization achieves a substantial efficiency over the sequential inverted index. The parallel implementations of information retrieval kernel are presented in this work targeting the Virtex II Field Programmable Gate Arrays (FPGAs) board from Xilinx. A recent development in scientific applications is the use of FPGA to achieve high performance results. Computational results are compared to implementations on other platforms. The design achieves a high level of parallelism for the overall function while retaining highly optimised hardware within processing unit.

Image fusion via nonlocal sparse K-SVD dictionary learning.

PubMed

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.

k - dependent Jeff=1/2 band splitting and the electron-hole asymmetry in SrIrO3

NASA Astrophysics Data System (ADS)

Singh, Vijeta; Pulikkotil, J. J.

2017-02-01

The Ir ion in Srn+1 IrnO 3 n + 1 series of compounds is octahedrally coordinated. However, unlike Sr2IrO4 (n=1) and Sr3Ir2O7 (n=2) which are insulating due to spin-orbit induced Jeff splitting of the t2g bands, SrIrO3 (n= ∞) is conducting. To explore whether such a splitting is relevant in SrIrO3, and if so to what extent, we investigate the electronic structure of orthorhombic SrIrO3 using density functional theory. Calculations reveal that the crystal field split Ir t2 g bands in SrIrO3 are indeed split into Jeff=3/2 and and Jeff=1/2 states. However, the splitting is found to be strongly k - dependent with its magnitude determined by the Ir - O orbital hybridization. Besides, we find that the spin-orbit induced pseudo-gap, into which the Fermi energy is positioned, is composed of both light electron-like and heavy hole-like bands. These features in the band structure of SrIrO3 suggest that variations in the carrier concentration control the electronic transport properties in SrIrO3, which is consistent with the experiments.

Massively parallel sparse matrix function calculations with NTPoly

NASA Astrophysics Data System (ADS)

Dawson, William; Nakajima, Takahito

2018-04-01

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

Deformable segmentation via sparse representation and dictionary learning.

PubMed

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

2012-10-01

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

An algorithm for the split-feasibility problems with application to the split-equality problem.

PubMed

Chuang, Chih-Sheng; Chen, Chi-Ming

2017-01-01

In this paper, we study the split-feasibility problem in Hilbert spaces by using the projected reflected gradient algorithm. As applications, we study the convex linear inverse problem and the split-equality problem in Hilbert spaces, and we give new algorithms for these problems. Finally, numerical results are given for our main results.

Metal-free organic sensitizers for use in water-splitting dye-sensitized photoelectrochemical cells

DOE PAGES

Swierk, John R.; Méndez-Hernández, Dalvin D.; McCool, Nicholas S.; ...

2015-01-12

Solar fuel generation requires the efficient capture and conversion of visible light. In both natural and artificial systems, molecular sensitizers can be tuned to capture, convert, and transfer visible light energy. We demonstrate that a series of metal-free porphyrins can drive photoelectrochemical water splitting under broadband and red light (λ > 590 nm) illumination in a dye-sensitized TiO 2 solar cell. Here, we report the synthesis, spectral, and electrochemical properties of the sensitizers. Despite slow recombination of photoinjected electrons with oxidized porphyrins, photocurrents are low because of low injection yields and slow electron self-exchange between oxidized porphyrins. As a result,more » the free-base porphyrins are stable under conditions of water photoelectrolysis and in some cases photovoltages in excess of 1 V are observed.« less

Sparse principal component analysis in medical shape modeling

NASA Astrophysics Data System (ADS)

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

2006-03-01

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

M-Split: A Graphical User Interface to Analyze Multilayered Anisotropy from Shear Wave Splitting

NASA Astrophysics Data System (ADS)

Abgarmi, Bizhan; Ozacar, A. Arda

2017-04-01

Shear wave splitting analysis are commonly used to infer deep anisotropic structure. For simple cases, obtained delay times and fast-axis orientations are averaged from reliable results to define anisotropy beneath recording seismic stations. However, splitting parameters show systematic variations with back azimuth in the presence of complex anisotropy and cannot be represented by average time delay and fast axis orientation. Previous researchers had identified anisotropic complexities at different tectonic settings and applied various approaches to model them. Most commonly, such complexities are modeled by using multiple anisotropic layers with priori constraints from geologic data. In this study, a graphical user interface called M-Split is developed to easily process and model multilayered anisotropy with capabilities to properly address the inherited non-uniqueness. M-Split program runs user defined grid searches through the model parameter space for two-layer anisotropy using formulation of Silver and Savage (1994) and creates sensitivity contour plots to locate local maximas and analyze all possible models with parameter tradeoffs. In order to minimize model ambiguity and identify the robust model parameters, various misfit calculation procedures are also developed and embedded to M-Split which can be used depending on the quality of the observations and their back-azimuthal coverage. Case studies carried out to evaluate the reliability of the program using real noisy data and for this purpose stations from two different networks are utilized. First seismic network is the Kandilli Observatory and Earthquake research institute (KOERI) which includes long term running permanent stations and second network comprises seismic stations deployed temporary as part of the "Continental Dynamics-Central Anatolian Tectonics (CD-CAT)" project funded by NSF. It is also worth to note that M-Split is designed as open source program which can be modified by users for

Disentangling giant component and finite cluster contributions in sparse random matrix spectra.

PubMed

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.

Machine-learned Identification of RR Lyrae Stars from Sparse, Multi-band Data: The PS1 Sample

NASA Astrophysics Data System (ADS)

Sesar, Branimir; Hernitschek, Nina; Mitrović, Sandra; Ivezić, Željko; Rix, Hans-Walter; Cohen, Judith G.; Bernard, Edouard J.; Grebel, Eva K.; Martin, Nicolas F.; Schlafly, Edward F.; Burgett, William S.; Draper, Peter W.; Flewelling, Heather; Kaiser, Nick; Kudritzki, Rolf P.; Magnier, Eugene A.; Metcalfe, Nigel; Tonry, John L.; Waters, Christopher

2017-05-01

RR Lyrae stars may be the best practical tracers of Galactic halo (sub-)structure and kinematics. The PanSTARRS1 (PS1) 3π survey offers multi-band, multi-epoch, precise photometry across much of the sky, but a robust identification of RR Lyrae stars in this data set poses a challenge, given PS1's sparse, asynchronous multi-band light curves (≲ 12 epochs in each of five bands, taken over a 4.5 year period). We present a novel template fitting technique that uses well-defined and physically motivated multi-band light curves of RR Lyrae stars, and demonstrate that we get accurate period estimates, precise to 2 s in > 80 % of cases. We augment these light-curve fits with other features from photometric time-series and provide them to progressively more detailed machine-learned classification models. From these models, we are able to select the widest (three-fourths of the sky) and deepest (reaching 120 kpc) sample of RR Lyrae stars to date. The PS1 sample of ˜45,000 RRab stars is pure (90%) and complete (80% at 80 kpc) at high galactic latitudes. It also provides distances that are precise to 3%, measured with newly derived period-luminosity relations for optical/near-infrared PS1 bands. With the addition of proper motions from Gaia and radial velocity measurements from multi-object spectroscopic surveys, we expect the PS1 sample of RR Lyrae stars to become the premier source for studying the structure, kinematics, and the gravitational potential of the Galactic halo. The techniques presented in this study should translate well to other sparse, multi-band data sets, such as those produced by the Dark Energy Survey and the upcoming Large Synoptic Survey Telescope Galactic plane sub-survey.

Deep ensemble learning of sparse regression models for brain disease diagnosis.

PubMed

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

2017-04-01

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

Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules

PubMed Central

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

Hierarchical Bayesian sparse image reconstruction with application to MRFM.

PubMed

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

2009-09-01

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

12 CFR 7.2023 - Reverse stock splits.

Code of Federal Regulations, 2010 CFR

2010-01-01

... Corporate Practices § 7.2023 Reverse stock splits. (a) Authority to engage in reverse stock splits. A national bank may engage in a reverse stock split if the transaction serves a legitimate corporate purpose and provides adequate dissenting shareholders' rights. (b) Legitimate corporate purpose. Examples of...

Algorithms and Application of Sparse Matrix Assembly and Equation Solvers for Aeroacoustics

NASA Technical Reports Server (NTRS)

Watson, W. R.; Nguyen, D. T.; Reddy, C. J.; Vatsa, V. N.; Tang, W. H.

2001-01-01

An algorithm for symmetric sparse equation solutions on an unstructured grid is described. Efficient, sequential sparse algorithms for degree-of-freedom reordering, supernodes, symbolic/numerical factorization, and forward backward solution phases are reviewed. Three sparse algorithms for the generation and assembly of symmetric systems of matrix equations are presented. The accuracy and numerical performance of the sequential version of the sparse algorithms are evaluated over the frequency range of interest in a three-dimensional aeroacoustics application. Results show that the solver solutions are accurate using a discretization of 12 points per wavelength. Results also show that the first assembly algorithm is impractical for high-frequency noise calculations. The second and third assembly algorithms have nearly equal performance at low values of source frequencies, but at higher values of source frequencies the third algorithm saves CPU time and RAM. The CPU time and the RAM required by the second and third assembly algorithms are two orders of magnitude smaller than that required by the sparse equation solver. A sequential version of these sparse algorithms can, therefore, be conveniently incorporated into a substructuring for domain decomposition formulation to achieve parallel computation, where different substructures are handles by different parallel processors.

Finger vein verification system based on sparse representation.

PubMed

Xin, Yang; Liu, Zhi; Zhang, Haixia; Zhang, Hong

2012-09-01

Finger vein verification is a promising biometric pattern for personal identification in terms of security and convenience. The recognition performance of this technology heavily relies on the quality of finger vein images and on the recognition algorithm. To achieve efficient recognition performance, a special finger vein imaging device is developed, and a finger vein recognition method based on sparse representation is proposed. The motivation for the proposed method is that finger vein images exhibit a sparse property. In the proposed system, the regions of interest (ROIs) in the finger vein images are segmented and enhanced. Sparse representation and sparsity preserving projection on ROIs are performed to obtain the features. Finally, the features are measured for recognition. An equal error rate of 0.017% was achieved based on the finger vein image database, which contains images that were captured by using the near-IR imaging device that was developed in this study. The experimental results demonstrate that the proposed method is faster and more robust than previous methods.

Copper Oxide Nanograss for Efficient and Stable Photoelectrochemical Hydrogen Production by Water Splitting

NASA Astrophysics Data System (ADS)

Borkar, Rajnikant; Dahake, Rashmi; Rayalu, Sadhana; Bansiwal, Amit

2018-03-01

A biphasic copper oxide thin film of grass-like appendage morphology is synthesized by two-step electro-deposition method and later investigated for photoelectrochemical (PEC) water splitting for hydrogen production. Further, the thin film was characterized by UV-Visible spectroscopy, x-ray diffraction (XRD), Scanning electron microscopy (SEM) and PEC techniques. The XRD analysis confirms formation of biphasic copper oxide phases, and SEM reveals high surface area grass appendage-like morphology. These grass appendage structures exhibit a high cathodic photocurrent of - 1.44 mAcm-2 at an applied bias of - 0.7 (versus Ag/AgCl) resulting in incident to photon current efficiency (IPCE) of ˜ 10% at 400 nm. The improved light harvesting and charge transport properties of grass appendage structured biphasic copper oxides makes it a potential candidate for PEC water splitting for hydrogen production.

Periodically Ordered Nanoporous Perovskite Photoelectrode for Efficient Photoelectrochemical Water Splitting.

PubMed

Shi, Li; Zhou, Wei; Li, Zhao; Koul, Supriya; Kushima, Akihiro; Yang, Yang

2018-06-18

Nonmetallic materials with localized surface plasmon resonance (LSPR) have a great potential for solar energy harvesting applications. Exploring nonmetallic plasmonic materials is desirable yet challenging. Herein, an efficient nonmetallic plasmonic perovskite photoelectrode, namely, SrTiO 3 , with a periodically ordered nanoporous structure showing an intense LSPR in the visible light region is reported. The crystalline-core@amorphous-shell structure of the SrTiO 3 photoelectrode enables a strong LSPR due to the high charge carrier density induced by oxygen vacancies in the amorphous shell. The reversible tunability in LSPR of the SrTiO 3 photoelectrode was observed by oxidation/reduction treatment and incident angle adjusting. Such a nonmetallic plasmonic SrTiO 3 photoelectrode displays a dramatic plasmon-enhanced photoelectrochemical water splitting performance with a photocurrent density of 170.0 μA cm -2 under visible light illumination and a maximum incident photon-to-current-conversion efficiency of 4.0% in the visible light region, which are comparable to the state-of-the-art plasmonic noble metal sensitized photoelectrodes.

Sparse Matrix Software Catalog, Sparse Matrix Symposium 1982, Fairfield Glade, Tennessee, October 24-27, 1982,

DTIC Science & Technology

1982-10-27

are buried within * a much larger, special purpose package. We regret such omissions, but to have reached the practi- tioners in each of the diverse...sparse matrix (form PAQ ) 4. Method of solution: Distribution count sort 5. Programming language: FORTRAN g Precision: Single and double precision 7

Split-liver transplantation. The Paul Brousse policy.

PubMed Central

Azoulay, D; Astarcioglu, I; Bismuth, H; Castaing, D; Majno, P; Adam, R; Johann, M

1996-01-01

OBJECTIVE: The authors objective is to report their recent experience with split-liver transplantation, focusing on the results and the impact on organ shortage. SUMMARY BACKGROUND DATA: There is an insufficient number of organs for liver transplantation. Split-liver transplantation is a method to increase the number of grafts, but the procedure is slow to gain wide acceptance because of its complexity and the poor results reported in previous series. METHODS: During the year 1995, the authors split 20 of 83 transplantable livers allocated to the authors' center, generating 40 grafts: 23 were transplanted locally and 17 were given to partner centers. During the same period, the authors accepted four split-liver grafts proposed to them by other centers. Overall, 27 split-liver transplantations were done in the authors' unit, accounting for 30% of the 90 transplants performed in 1995. RESULTS: One-year patient and graft survival rates for split-liver transplantation were 79.4% and 78.5%, respectively. Arterial and biliary complications rates were 15% and 22%, respectively, with none leading to graft loss. Primary nonfunction occurred in one case (4%). By splitting 24 of 87 transplantable livers (4 of which were in partner units), a total of 111 transplantations were performed, increasing graft availability by 28%. CONCLUSIONS: Split-liver transplantation is achieving graft and patient survival rates similar to that of whole liver transplantation despite a higher incidence of complications, which could become less frequent as experience is gained with this procedure. A wider acceptance of split-liver transplantation could markedly increase the supply of liver grafts. Images Figure 1. PMID:8968228

Pseudospin-orbit splitting and its consequences for the central depression in nuclear density

NASA Astrophysics Data System (ADS)

Li, Jia Jie; Long, Wen Hui; Song, Jun Ling; Zhao, Qiang

2016-05-01

The occurrence of the bubble-like structure has been studied, in the light of pseudospin degeneracy, within the relativistic Hartree-Fock-Bogoliubov (RHFB) theory. It is concluded that the charge/neutron bubble-like structure is predicted to occur in the mirror system of {34Si,34Ca } commonly by the selected Lagrangians, due to the persistence of Z (N )=14 subshell gaps above which the π (ν ) 2 s1 /2 states are not occupied. However, for the popular candidate 46Ar, the RHFB Lagrangian PKA1 does not support the occurrence of the bubble-like structure in the charge (proton) density profiles, due to the almost degenerate pseudospin doublet {π 2 s1 /2,π 1 d3 /2} and coherent pairing effects. The formation of a semibubble in heavy nuclei is less possible as a result of small pseudospin-orbit (PSO) splitting, while it tends to appear at Z =120 superheavy systems which coincides with large PSO splitting of the doublet {π 3 p3 /2,π 2 f5 /2} and couples with significant shell effects. Pairing correlations, which can work against bubble formation, significantly affect the PSO splitting. Furthermore, we found that the influence on semibubble formation due to different types of pairing interactions is negligible. The quenching of the spin-orbit splitting in the p orbit has been also stressed, and it may be considered the hallmark for semibubble nuclei.

Precision aligned split V-block

DOEpatents

George, Irwin S.

1984-01-01

A precision aligned split V-block for holding a workpiece during a milling operation having an expandable frame for allowing various sized workpieces to be accommodated, is easily secured directly to the mill table and having key lugs in one base of the split V-block that assures constant alignment.

Mixed chimerism and split tolerance

PubMed Central

Al-Adra, David P.

2011-01-01

Establishing hematopoietic mixed chimerism can lead to donor-specific tolerance to transplanted organs and may eliminate the need for long-term immunosuppressive therapy, while also preventing chronic rejection. In this review, we discuss central and peripheral mechanisms of chimerism induced tolerance. However, even in the long-lasting presence of a donor organ or donor hematopoietic cells, some allogeneic tissues from the same donor can be rejected; a phenomenon known as split tolerance. With the current goal of creating mixed chimeras using clinically feasible amounts of donor bone marrow and with minimal conditioning, split tolerance may become more prevalent and its mechanisms need to be explored. Some predisposing factors that may increase the likelihood of split tolerance are immunogenicity of the graft, certain donor-recipient combinations, prior sensitization, location and type of graft and minimal conditioning chimerism induction protocols. Additionally, split tolerance may occur due to a differential susceptibility of various types of tissues to rejection. The mechanisms involved in a tissue’s differential susceptibility to rejection include the presence of polymorphic tissue-specific antigens and variable sensitivity to indirect pathway effector mechanisms. Finally, we review the clinical attempts at allograft tolerance through the induction of chimerism; studies that are revealing the complex relationship between chimerism and tolerance. This relationship often displays split tolerance, and further research into its mechanisms is warranted. PMID:22509425

Surface properties of lead-free halide double perovskites: Possible visible-light photo-catalysts for water splitting

NASA Astrophysics Data System (ADS)

Volonakis, George; Giustino, Feliciano

2018-06-01

Halide double perovskites based on combinations of monovalent and trivalent cations have been proposed as promising lead-free alternatives to lead halide perovskites. Among the newly synthesized compounds Cs2BiAgCl6, Cs2BiAgBr6, Cs2SbAgCl6, and Cs2InAgCl6, some exhibit bandgaps in the visible range and all have low carrier effective masses; therefore, these materials constitute potential candidates for various opto-electronic applications. Here, we use first-principles calculations to investigate the electronic properties of the surfaces of these four compounds and determine, for the first time, their ionization potential and electron affinity. We find that the double perovskites Cs2BiAgCl6 and Cs2BiAgBr6 are potentially promising materials for photo-catalytic water splitting, while Cs2InAgCl6 and Cs2SbAgCl6 would require controlling their surface termination to obtain energy levels appropriate for water splitting. The energy of the halogen p orbitals is found to control the conduction band level; therefore, we propose that mixed halides could be used to fine-tune the electronic affinity.

Tunneling splitting in double-proton transfer: direct diagonalization results for porphycene.

PubMed

Smedarchina, Zorka; Siebrand, Willem; Fernández-Ramos, Antonio

2014-11-07

Zero-point and excited level splittings due to double-proton tunneling are calculated for porphycene and the results are compared with experiment. The calculation makes use of a multidimensional imaginary-mode Hamiltonian, diagonalized directly by an effective reduction of its dimensionality. Porphycene has a complex potential energy surface with nine stationary configurations that allow a variety of tunneling paths, many of which include classically accessible regions. A symmetry-based approach is used to show that the zero-point level, although located above the cis minimum, corresponds to concerted tunneling along a direct trans - trans path; a corresponding cis - cis path is predicted at higher energy. This supports the conclusion of a previous paper [Z. Smedarchina, W. Siebrand, and A. Fernández-Ramos, J. Chem. Phys. 127, 174513 (2007)] based on the instanton approach to a model Hamiltonian of correlated double-proton transfer. A multidimensional tunneling Hamiltonian is then generated, based on a double-minimum potential along the coordinate of concerted proton motion, which is newly evaluated at the RI-CC2/cc-pVTZ level of theory. To make it suitable for diagonalization, its dimensionality is reduced by treating fast weakly coupled modes in the adiabatic approximation. This results in a coordinate-dependent mass of tunneling, which is included in a unique Hermitian form into the kinetic energy operator. The reduced Hamiltonian contains three symmetric and one antisymmetric mode coupled to the tunneling mode and is diagonalized by a modified Jacobi-Davidson algorithm implemented in the Jadamilu software for sparse matrices. The results are in satisfactory agreement with the observed splitting of the zero-point level and several vibrational fundamentals after a partial reassignment, imposed by recently derived selection rules. They also agree well with instanton calculations based on the same Hamiltonian.

Mimicking Natural Photosynthesis: Solar to Renewable H2 Fuel Synthesis by Z-Scheme Water Splitting Systems

PubMed Central

2018-01-01

Visible light-driven water splitting using cheap and robust photocatalysts is one of the most exciting ways to produce clean and renewable energy for future generations. Cutting edge research within the field focuses on so-called “Z-scheme” systems, which are inspired by the photosystem II–photosystem I (PSII/PSI) coupling from natural photosynthesis. A Z-scheme system comprises two photocatalysts and generates two sets of charge carriers, splitting water into its constituent parts, hydrogen and oxygen, at separate locations. This is not only more efficient than using a single photocatalyst, but practically it could also be safer. Researchers within the field are constantly aiming to bring systems toward industrial level efficiencies by maximizing light absorption of the materials, engineering more stable redox couples, and also searching for new hydrogen and oxygen evolution cocatalysts. This review provides an in-depth survey of relevant Z-schemes from past to present, with particular focus on mechanistic breakthroughs, and highlights current state of the art systems which are at the forefront of the field. PMID:29676566

Mimicking Natural Photosynthesis: Solar to Renewable H2 Fuel Synthesis by Z-Scheme Water Splitting Systems.

PubMed

Wang, Yiou; Suzuki, Hajime; Xie, Jijia; Tomita, Osamu; Martin, David James; Higashi, Masanobu; Kong, Dan; Abe, Ryu; Tang, Junwang

2018-05-23

Visible light-driven water splitting using cheap and robust photocatalysts is one of the most exciting ways to produce clean and renewable energy for future generations. Cutting edge research within the field focuses on so-called "Z-scheme" systems, which are inspired by the photosystem II-photosystem I (PSII/PSI) coupling from natural photosynthesis. A Z-scheme system comprises two photocatalysts and generates two sets of charge carriers, splitting water into its constituent parts, hydrogen and oxygen, at separate locations. This is not only more efficient than using a single photocatalyst, but practically it could also be safer. Researchers within the field are constantly aiming to bring systems toward industrial level efficiencies by maximizing light absorption of the materials, engineering more stable redox couples, and also searching for new hydrogen and oxygen evolution cocatalysts. This review provides an in-depth survey of relevant Z-schemes from past to present, with particular focus on mechanistic breakthroughs, and highlights current state of the art systems which are at the forefront of the field.

Single and Multiple Object Tracking Using a Multi-Feature Joint Sparse Representation.

PubMed

Hu, Weiming; Li, Wei; Zhang, Xiaoqin; Maybank, Stephen

2015-04-01

In this paper, we propose a tracking algorithm based on a multi-feature joint sparse representation. The templates for the sparse representation can include pixel values, textures, and edges. In the multi-feature joint optimization, noise or occlusion is dealt with using a set of trivial templates. A sparse weight constraint is introduced to dynamically select the relevant templates from the full set of templates. A variance ratio measure is adopted to adaptively adjust the weights of different features. The multi-feature template set is updated adaptively. We further propose an algorithm for tracking multi-objects with occlusion handling based on the multi-feature joint sparse reconstruction. The observation model based on sparse reconstruction automatically focuses on the visible parts of an occluded object by using the information in the trivial templates. The multi-object tracking is simplified into a joint Bayesian inference. The experimental results show the superiority of our algorithm over several state-of-the-art tracking algorithms.

Status of air-shower measurements with sparse radio arrays

NASA Astrophysics Data System (ADS)

Schröder, Frank G.

2017-03-01

This proceeding gives a summary of the current status and open questions of the radio technique for cosmic-ray air showers, assuming that the reader is already familiar with the principles. It includes recent results of selected experiments not present at this conference, e.g., LOPES and TREND. Current radio arrays like AERA or Tunka-Rex have demonstrated that areas of several km2 can be instrumented for reasonable costs with antenna spacings of the order of 200m. For the energy of the primary particle such sparse antenna arrays can already compete in absolute accuracy with other precise techniques, like the detection of air-fluorescence or air-Cherenkov light. With further improvements in the antenna calibration, the radio detection might become even more accurate. For the atmospheric depth of the shower maximum, Xmax, currently only the dense array LOFAR features a precision similar to the fluorescence technique, but analysis methods for the radio measurement of Xmax are still under development. Moreover, the combination of radio and muon measurements is expected to increase the accuracy of the mass composition, and this around-the-clock recording is not limited to clear nights as are the light-detection methods. Consequently, radio antennas will be a valuable add-on for any air shower array targeting the energy range above 100 PeV.

A critical analysis of computational protein design with sparse residue interaction graphs

PubMed Central

Georgiev, Ivelin S.

2017-01-01

Protein design algorithms enumerate a combinatorial number of candidate structures to compute the Global Minimum Energy Conformation (GMEC). To efficiently find the GMEC, protein design algorithms must methodically reduce the conformational search space. By applying distance and energy cutoffs, the protein system to be designed can thus be represented using a sparse residue interaction graph, where the number of interacting residue pairs is less than all pairs of mutable residues, and the corresponding GMEC is called the sparse GMEC. However, ignoring some pairwise residue interactions can lead to a change in the energy, conformation, or sequence of the sparse GMEC vs. the original or the full GMEC. Despite the widespread use of sparse residue interaction graphs in protein design, the above mentioned effects of their use have not been previously analyzed. To analyze the costs and benefits of designing with sparse residue interaction graphs, we computed the GMECs for 136 different protein design problems both with and without distance and energy cutoffs, and compared their energies, conformations, and sequences. Our analysis shows that the differences between the GMECs depend critically on whether or not the design includes core, boundary, or surface residues. Moreover, neglecting long-range interactions can alter local interactions and introduce large sequence differences, both of which can result in significant structural and functional changes. Designs on proteins with experimentally measured thermostability show it is beneficial to compute both the full and the sparse GMEC accurately and efficiently. To this end, we show that a provable, ensemble-based algorithm can efficiently compute both GMECs by enumerating a small number of conformations, usually fewer than 1000. This provides a novel way to combine sparse residue interaction graphs with provable, ensemble-based algorithms to reap the benefits of sparse residue interaction graphs while avoiding their

Reconstructing cortical current density by exploring sparseness in the transform domain

NASA Astrophysics Data System (ADS)

Ding, Lei

2009-05-01

In the present study, we have developed a novel electromagnetic source imaging approach to reconstruct extended cortical sources by means of cortical current density (CCD) modeling and a novel EEG imaging algorithm which explores sparseness in cortical source representations through the use of L1-norm in objective functions. The new sparse cortical current density (SCCD) imaging algorithm is unique since it reconstructs cortical sources by attaining sparseness in a transform domain (the variation map of cortical source distributions). While large variations are expected to occur along boundaries (sparseness) between active and inactive cortical regions, cortical sources can be reconstructed and their spatial extents can be estimated by locating these boundaries. We studied the SCCD algorithm using numerous simulations to investigate its capability in reconstructing cortical sources with different extents and in reconstructing multiple cortical sources with different extent contrasts. The SCCD algorithm was compared with two L2-norm solutions, i.e. weighted minimum norm estimate (wMNE) and cortical LORETA. Our simulation data from the comparison study show that the proposed sparse source imaging algorithm is able to accurately and efficiently recover extended cortical sources and is promising to provide high-accuracy estimation of cortical source extents.

Pharmaceutical counselling about different types of tablet-splitting methods based on the results of weighing tests and mechanical development of splitting devices.

PubMed

Somogyi, O; Meskó, A; Csorba, L; Szabó, P; Zelkó, R

2017-08-30

The division of tablets and adequate methods of splitting them are a complex problem in all sectors of health care. Although tablet-splitting is often required, this procedure can be difficult for patients. Four tablets were investigated with different external features (shape, score-line, film-coat and size). The influencing effect of these features and the splitting methods was investigated according to the precision and "weight loss" of splitting techniques. All four types of tablets were halved by four methods: by hand, with a kitchen knife, with an original manufactured splitting device and with a modified tablet splitter based on a self-developed mechanical model. The mechanical parameters (harness and friability) of the products were measured during the study. The "weight loss" and precision of splitting methods were determined and compared by statistical analysis. On the basis of the results, the external features (geometry), the mechanical parameters of tablets and the mechanical structure of splitting devices can influence the "weight loss" and precision of tablet-splitting. Accordingly, a new decision-making scheme was developed for the selection of splitting methods. In addition, the skills of patients and the specialties of therapy should be considered so that pharmaceutical counselling can be more effective regarding tablet-splitting. Copyright © 2017 Elsevier B.V. All rights reserved.

Liver segmentation from CT images using a sparse priori statistical shape model (SP-SSM).

PubMed

Wang, Xuehu; Zheng, Yongchang; Gan, Lan; Wang, Xuan; Sang, Xinting; Kong, Xiangfeng; Zhao, Jie

2017-01-01

This study proposes a new liver segmentation method based on a sparse a priori statistical shape model (SP-SSM). First, mark points are selected in the liver a priori model and the original image. Then, the a priori shape and its mark points are used to obtain a dictionary for the liver boundary information. Second, the sparse coefficient is calculated based on the correspondence between mark points in the original image and those in the a priori model, and then the sparse statistical model is established by combining the sparse coefficients and the dictionary. Finally, the intensity energy and boundary energy models are built based on the intensity information and the specific boundary information of the original image. Then, the sparse matching constraint model is established based on the sparse coding theory. These models jointly drive the iterative deformation of the sparse statistical model to approximate and accurately extract the liver boundaries. This method can solve the problems of deformation model initialization and a priori method accuracy using the sparse dictionary. The SP-SSM can achieve a mean overlap error of 4.8% and a mean volume difference of 1.8%, whereas the average symmetric surface distance and the root mean square symmetric surface distance can reach 0.8 mm and 1.4 mm, respectively.

Group-sparse representation with dictionary learning for medical image denoising and fusion.

PubMed

Li, Shutao; Yin, Haitao; Fang, Leyuan

2012-12-01

Recently, sparse representation has attracted a lot of interest in various areas. However, the standard sparse representation does not consider the intrinsic structure, i.e., the nonzero elements occur in clusters, called group sparsity. Furthermore, there is no dictionary learning method for group sparse representation considering the geometrical structure of space spanned by atoms. In this paper, we propose a novel dictionary learning method, called Dictionary Learning with Group Sparsity and Graph Regularization (DL-GSGR). First, the geometrical structure of atoms is modeled as the graph regularization. Then, combining group sparsity and graph regularization, the DL-GSGR is presented, which is solved by alternating the group sparse coding and dictionary updating. In this way, the group coherence of learned dictionary can be enforced small enough such that any signal can be group sparse coded effectively. Finally, group sparse representation with DL-GSGR is applied to 3-D medical image denoising and image fusion. Specifically, in 3-D medical image denoising, a 3-D processing mechanism (using the similarity among nearby slices) and temporal regularization (to perverse the correlations across nearby slices) are exploited. The experimental results on 3-D image denoising and image fusion demonstrate the superiority of our proposed denoising and fusion approaches.

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.

On the sparseness of 1-norm support vector machines.

PubMed

Zhang, Li; Zhou, Weida

2010-04-01

There is some empirical evidence available showing that 1-norm Support Vector Machines (1-norm SVMs) have good sparseness; however, both how good sparseness 1-norm SVMs can reach and whether they have a sparser representation than that of standard SVMs are not clear. In this paper we take into account the sparseness of 1-norm SVMs. Two upper bounds on the number of nonzero coefficients in the decision function of 1-norm SVMs are presented. First, the number of nonzero coefficients in 1-norm SVMs is at most equal to the number of only the exact support vectors lying on the +1 and -1 discriminating surfaces, while that in standard SVMs is equal to the number of support vectors, which implies that 1-norm SVMs have better sparseness than that of standard SVMs. Second, the number of nonzero coefficients is at most equal to the rank of the sample matrix. A brief review of the geometry of linear programming and the primal steepest edge pricing simplex method are given, which allows us to provide the proof of the two upper bounds and evaluate their tightness by experiments. Experimental results on toy data sets and the UCI data sets illustrate our analysis. Copyright 2009 Elsevier Ltd. All rights reserved.

Integrating a Semitransparent, Fullerene-Free Organic Solar Cell in Tandem with a BiVO4 Photoanode for Unassisted Solar Water Splitting.

PubMed

Peng, Yuelin; Govindaraju, Gokul V; Lee, Dong Ki; Choi, Kyoung-Shin; Andrew, Trisha L

2017-07-12

We report an unassisted solar water splitting system powered by a diketopyrrolopyrrole (DPP)-containing semitransparent organic solar cell. Two major merits of this fullerene-free solar cell enable its integration with a BiVO 4 photoanode. First is the high open circuit voltage and high fill factor displayed by this single junction solar cell, which yields sufficient power to effect water splitting when serially connected to an appropriate electrode/catalyst. Second, the wavelength-resolved photoaction spectrum of the DPP-based solar cell has minimal overlap with that of the BiVO 4 photoanode, thus ensuring that light collection across these two components can be optimized. The latter feature enables a new water splitting device configuration wherein the solar cell is placed first in the path of incident light, before the BiVO 4 photoanode, although BiVO 4 has a wider bandgap. This configuration is accessed by replacing the reflective top electrode of the standard DPP-based solar cell with a thin metal film and an antireflection layer, thus rendering the solar cell semitransparent. In this configuration, incident light does not travel through the aqueous electrolyte to reach the solar cell or photoanode, and therefore, photon losses due to the scattering of water are reduced. Moreover, this new configuration allows the BiVO 4 photoanode to be back-illuminated, i.e., through the BiVO 4 /back contact interface, which leads to higher photocurrents compared to front illumination. The combination of a semitransparent single-junction solar cell and a BiVO 4 photoanode coated with oxygen evolution catalysts in a new device configuration yielded an unassisted solar water splitting system with a solar-to-hydrogen conversion efficiency of 2.2% in water.

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.

A General Sparse Tensor Framework for Electronic Structure Theory

DOE PAGES

Manzer, Samuel; Epifanovsky, Evgeny; Krylov, Anna I.; ...

2017-01-24

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

Spectroscopic studies of two spectral variants of light-harvesting complex 2 (LH2) from the photosynthetic purple sulfur bacterium Allochromatium vinosum.

PubMed

Niedzwiedzki, Dariusz M; Bina, David; Picken, Nichola; Honkanen, Suvi; Blankenship, Robert E; Holten, Dewey; Cogdell, Richard J

2012-09-01

Two spectral forms of the peripheral light-harvesting complex (LH2) from the purple sulfur photosynthetic bacterium Allochromatium vinosum were purified and their photophysical properties characterized. The complexes contain bacteriochlorophyll a (BChl a) and multiple species of carotenoids. The composition of carotenoids depends on the light conditions applied during growth of the cultures. In addition, LH2 grown under high light has a noticeable split of the B800 absorption band. The influence of the change of carotenoid distribution as well as the spectral change of the excitonic absorption of the bacteriochlorophylls on the light-harvesting ability was studied using steady-state absorption, fluorescence and femtosecond time-resolved absorption at 77K. The results demonstrate that the change of the distribution of the carotenoids when cells were grown at low light adapts the absorptive properties of the complex to the light conditions and maintains maximum photon-capture performance. In addition, an explanation for the origin of the enigmatic split of the B800 absorption band is provided. This spectral splitting is also observed in LH2 complexes from other photosynthetic sulfur purple bacterial species. According to results obtained from transient absorption spectroscopy, the B800 band split originates from two spectral forms of the associated BChl a monomeric molecules bound within the same complex. Copyright © 2012 Elsevier B.V. All rights reserved.

Shear Wave Splitting Underneath Northwest Canada and Eastern Alaska from Transportable Array and Mackenzie Mountains Data

NASA Astrophysics Data System (ADS)

Schutt, D.; Witt, D. R.; Aster, R. C.; Freymueller, J.; Cubley, J. F.

2017-12-01

Shear wave splitting results from the Northern Cordillera and surroundings will be presented. This complex tectonic setting contains a subduction zone responding to the Yakutat Indenter, an oceanic plateau fragment, a slab window under the Yukon Territory, and the actively uplifting Mackenzie Mountains. A particular goal of this project is to understand whether asthenospheric tractions play a significant role in Mackenzie Mountain uplift. Using a new method for calculating station-averaged splitting parameters, we have analyzed stations that span a large part of the region and therefore can see the variation in splitting parameters from the dynamic NA-PA subduction zone to the stable Slave Craton. Like other shear wave splitting studies in the Northern Cordillera, we find abrupt changes in fast axis direction along the continental margin, while the continental interior displays more coherent splitting parameters. This study is also the first to look at data from a recent deployment through center of the Mackenzie Mountains. Northeast of the Tintina Fault, we find average fast axes directions that are very close to the absolute NA plate motion but our large deviations from event to event suggest that there is some crustal anisotropy and/or dipping structure present. This observation appears to support the idea of a lower crustal décollement that has been put forth by Mazzoti and Hyndman [2002]. These results serve as a broad regional overview of mantle anisotropy and may also shed light on frozen lithospheric deformation.

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.

Mesoscopic in vivo 3-D tracking of sparse cell populations using angular multiplexed optical projection tomography

PubMed Central

Chen, Lingling; Alexandrov, Yuriy; Kumar, Sunil; Andrews, Natalie; Dallman, Margaret J.; French, Paul M. W.; McGinty, James

2015-01-01

We describe an angular multiplexed imaging technique for 3-D in vivo cell tracking of sparse cell distributions and optical projection tomography (OPT) with superior time-lapse resolution and a significantly reduced light dose compared to volumetric time-lapse techniques. We demonstrate that using dual axis OPT, where two images are acquired simultaneously at different projection angles, can enable localization and tracking of features in 3-D with a time resolution equal to the camera frame rate. This is achieved with a 200x reduction in light dose compared to an equivalent volumetric time-lapse single camera OPT acquisition with 200 projection angles. We demonstrate the application of this technique to mapping the 3-D neutrophil migration pattern observed over ~25.5 minutes in a live 2 day post-fertilisation transgenic LysC:GFP zebrafish embryo following a tail wound. PMID:25909009

Mesoscopic in vivo 3-D tracking of sparse cell populations using angular multiplexed optical projection tomography.

PubMed

Chen, Lingling; Alexandrov, Yuriy; Kumar, Sunil; Andrews, Natalie; Dallman, Margaret J; French, Paul M W; McGinty, James

2015-04-01

We describe an angular multiplexed imaging technique for 3-D in vivo cell tracking of sparse cell distributions and optical projection tomography (OPT) with superior time-lapse resolution and a significantly reduced light dose compared to volumetric time-lapse techniques. We demonstrate that using dual axis OPT, where two images are acquired simultaneously at different projection angles, can enable localization and tracking of features in 3-D with a time resolution equal to the camera frame rate. This is achieved with a 200x reduction in light dose compared to an equivalent volumetric time-lapse single camera OPT acquisition with 200 projection angles. We demonstrate the application of this technique to mapping the 3-D neutrophil migration pattern observed over ~25.5 minutes in a live 2 day post-fertilisation transgenic LysC:GFP zebrafish embryo following a tail wound.

Magnetic Resonance Super-resolution Imaging Measurement with Dictionary-optimized Sparse Learning

NASA Astrophysics Data System (ADS)

Li, Jun-Bao; Liu, Jing; Pan, Jeng-Shyang; Yao, Hongxun

2017-06-01

Magnetic Resonance Super-resolution Imaging Measurement (MRIM) is an effective way of measuring materials. MRIM has wide applications in physics, chemistry, biology, geology, medical and material science, especially in medical diagnosis. It is feasible to improve the resolution of MR imaging through increasing radiation intensity, but the high radiation intensity and the longtime of magnetic field harm the human body. Thus, in the practical applications the resolution of hardware imaging reaches the limitation of resolution. Software-based super-resolution technology is effective to improve the resolution of image. This work proposes a framework of dictionary-optimized sparse learning based MR super-resolution method. The framework is to solve the problem of sample selection for dictionary learning of sparse reconstruction. The textural complexity-based image quality representation is proposed to choose the optimal samples for dictionary learning. Comprehensive experiments show that the dictionary-optimized sparse learning improves the performance of sparse representation.

Development of a new flux splitting scheme

NASA Technical Reports Server (NTRS)

Liou, Meng-Sing; Steffen, Christopher J., Jr.

1991-01-01

The use of a new splitting scheme, the advection upstream splitting method, for model aerodynamic problems where Van Leer and Roe schemes had failed previously is discussed. The present scheme is based on splitting in which the convective and pressure terms are separated and treated differently depending on the underlying physical conditions. The present method is found to be both simple and accurate.

Optimal Couple Projections for Domain Adaptive Sparse Representation-based Classification.

PubMed

Zhang, Guoqing; Sun, Huaijiang; Porikli, Fatih; Liu, Yazhou; Sun, Quansen

2017-08-29

In recent years, sparse representation based classification (SRC) is one of the most successful methods and has been shown impressive performance in various classification tasks. However, when the training data has a different distribution than the testing data, the learned sparse representation may not be optimal, and the performance of SRC will be degraded significantly. To address this problem, in this paper, we propose an optimal couple projections for domain-adaptive sparse representation-based classification (OCPD-SRC) method, in which the discriminative features of data in the two domains are simultaneously learned with the dictionary that can succinctly represent the training and testing data in the projected space. OCPD-SRC is designed based on the decision rule of SRC, with the objective to learn coupled projection matrices and a common discriminative dictionary such that the between-class sparse reconstruction residuals of data from both domains are maximized, and the within-class sparse reconstruction residuals of data are minimized in the projected low-dimensional space. Thus, the resulting representations can well fit SRC and simultaneously have a better discriminant ability. In addition, our method can be easily extended to multiple domains and can be kernelized to deal with the nonlinear structure of data. The optimal solution for the proposed method can be efficiently obtained following the alternative optimization method. Extensive experimental results on a series of benchmark databases show that our method is better or comparable to many state-of-the-art methods.

Transforming White Light into Rainbows: Segmentation Strategies for Successful School Tax Elections

ERIC Educational Resources Information Center

Senden, J. Bradford; Lifto, Don E.

2009-01-01

In the late 1600s, British physicist Sir Isaac Newton first demonstrated refraction and dispersion in a triangular prism. He discovered that a prism could decompose white light into a spectrum. Hold a prism up to the light at the correct angle and white light magically splits into vivid colors of the rainbow! So what do prisms and rainbows have to…

Scenario generation for stochastic optimization problems via the sparse grid method

DOE PAGES

Chen, Michael; Mehrotra, Sanjay; Papp, David

2015-04-19

We study the use of sparse grids in the scenario generation (or discretization) problem in stochastic programming problems where the uncertainty is modeled using a continuous multivariate distribution. We show that, under a regularity assumption on the random function involved, the sequence of optimal objective function values of the sparse grid approximations converges to the true optimal objective function values as the number of scenarios increases. The rate of convergence is also established. We treat separately the special case when the underlying distribution is an affine transform of a product of univariate distributions, and show how the sparse grid methodmore » can be adapted to the distribution by the use of quadrature formulas tailored to the distribution. We numerically compare the performance of the sparse grid method using different quadrature rules with classic quasi-Monte Carlo (QMC) methods, optimal rank-one lattice rules, and Monte Carlo (MC) scenario generation, using a series of utility maximization problems with up to 160 random variables. The results show that the sparse grid method is very efficient, especially if the integrand is sufficiently smooth. In such problems the sparse grid scenario generation method is found to need several orders of magnitude fewer scenarios than MC and QMC scenario generation to achieve the same accuracy. As a result, it is indicated that the method scales well with the dimension of the distribution--especially when the underlying distribution is an affine transform of a product of univariate distributions, in which case the method appears scalable to thousands of random variables.« less

Solar activity and oscillation frequency splittings

NASA Technical Reports Server (NTRS)

Woodard, M. F.; Libbrecht, K. G.

1993-01-01

Solar p-mode frequency splittings, parameterized by the coefficients through order N = 12 of a Legendre polynomial expansion of the mode frequencies as a function of m/L, were obtained from an analysis of helioseismology data taken at Big Bear Solar Observatory during the 4 years 1986 and 1988-1990 (approximately solar minimum to maximum). Inversion of the even-index splitting coefficients confirms that there is a significant contribution to the frequency splittings originating near the solar poles. The strength of the polar contribution is anti correlated with the overall level or solar activity in the active latitudes, suggesting a relation to polar faculae. From an analysis of the odd-index splitting coefficients we infer an uppor limit to changes in the solar equatorial near-surface rotatinal velocity of less than 1.9 m/s (3 sigma limit) between solar minimum and maximum.

Decorating CoP and Pt Nanoparticles on Graphitic Carbon Nitride Nanosheets to Promote Overall Water Splitting by Conjugated Polymers.

PubMed

Pan, Zhiming; Zheng, Yun; Guo, Fangsong; Niu, Pingping; Wang, Xinchen

2017-01-10

The splitting of water into H 2 and O 2 using solar energy is one of the key steps in artificial photosynthesis for the future production of renewable energy. Here, we show the first use of CoP and Pt nanoparticles as dual co-catalysts to modify graphitic carbon nitride (g-C 3 N 4 ) polymer to achieve overall water splitting under visible light irradiation. Our findings demonstrate that loading dual co-catalysts on delaminated g-C 3 N 4 imparts surface redox sites on the g-C 3 N 4 nanosheets that can not only promote catalytic kinetics but also promote charge separation and migration in the soft interface, thus improving the photocatalytic efficiency for overall water splitting. This robust, abundant, and stable photocatalyst based on covalent organic frameworks is demonstrated to hold great promise by forming heterojunctions with CoP and Pt for catalyzing the direct splitting of water into stoichiometric H 2 and O 2 using energy from photons. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimal sparse approximation with integrate and fire neurons.

PubMed

Shapero, Samuel; Zhu, Mengchen; Hasler, Jennifer; Rozell, Christopher

2014-08-01

Sparse approximation is a hypothesized coding strategy where a population of sensory neurons (e.g. V1) encodes a stimulus using as few active neurons as possible. We present the Spiking LCA (locally competitive algorithm), a rate encoded Spiking Neural Network (SNN) of integrate and fire neurons that calculate sparse approximations. The Spiking LCA is designed to be equivalent to the nonspiking LCA, an analog dynamical system that converges on a ℓ(1)-norm sparse approximations exponentially. We show that the firing rate of the Spiking LCA converges on the same solution as the analog LCA, with an error inversely proportional to the sampling time. We simulate in NEURON a network of 128 neuron pairs that encode 8 × 8 pixel image patches, demonstrating that the network converges to nearly optimal encodings within 20 ms of biological time. We also show that when using more biophysically realistic parameters in the neurons, the gain function encourages additional ℓ(0)-norm sparsity in the encoding, relative both to ideal neurons and digital solvers.

Nonconvex Sparse Logistic Regression With Weakly Convex Regularization

NASA Astrophysics Data System (ADS)

Shen, Xinyue; Gu, Yuantao

2018-06-01

In this work we propose to fit a sparse logistic regression model by a weakly convex regularized nonconvex optimization problem. The idea is based on the finding that a weakly convex function as an approximation of the $\\ell_0$ pseudo norm is able to better induce sparsity than the commonly used $\\ell_1$ norm. For a class of weakly convex sparsity inducing functions, we prove the nonconvexity of the corresponding sparse logistic regression problem, and study its local optimality conditions and the choice of the regularization parameter to exclude trivial solutions. Despite the nonconvexity, a method based on proximal gradient descent is used to solve the general weakly convex sparse logistic regression, and its convergence behavior is studied theoretically. Then the general framework is applied to a specific weakly convex function, and a necessary and sufficient local optimality condition is provided. The solution method is instantiated in this case as an iterative firm-shrinkage algorithm, and its effectiveness is demonstrated in numerical experiments by both randomly generated and real datasets.

Exploring Deep Learning and Sparse Matrix Format Selection

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zhao, Y.; Liao, C.; Shen, X.

We proposed to explore the use of Deep Neural Networks (DNN) for addressing the longstanding barriers. The recent rapid progress of DNN technology has created a large impact in many fields, which has significantly improved the prediction accuracy over traditional machine learning techniques in image classifications, speech recognitions, machine translations, and so on. To some degree, these tasks resemble the decision makings in many HPC tasks, including the aforementioned format selection for SpMV and linear solver selection. For instance, sparse matrix format selection is akin to image classification—such as, to tell whether an image contains a dog or a cat;more » in both problems, the right decisions are primarily determined by the spatial patterns of the elements in an input. For image classification, the patterns are of pixels, and for sparse matrix format selection, they are of non-zero elements. DNN could be naturally applied if we regard a sparse matrix as an image and the format selection or solver selection as classification problems.« less

Two-dimensional sparse wavenumber recovery for guided wavefields

NASA Astrophysics Data System (ADS)

Sabeti, Soroosh; Harley, Joel B.

2018-04-01

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

Kanerva's sparse distributed memory: An associative memory algorithm well-suited to the Connection Machine

NASA Technical Reports Server (NTRS)

Rogers, David

1988-01-01

The advent of the Connection Machine profoundly changes the world of supercomputers. The highly nontraditional architecture makes possible the exploration of algorithms that were impractical for standard Von Neumann architectures. Sparse distributed memory (SDM) is an example of such an algorithm. Sparse distributed memory is a particularly simple and elegant formulation for an associative memory. The foundations for sparse distributed memory are described, and some simple examples of using the memory are presented. The relationship of sparse distributed memory to three important computational systems is shown: random-access memory, neural networks, and the cerebellum of the brain. Finally, the implementation of the algorithm for sparse distributed memory on the Connection Machine is discussed.

Functional split brain in a driving/listening paradigm.

PubMed

Sasai, Shuntaro; Boly, Melanie; Mensen, Armand; Tononi, Giulio

2016-12-13

We often engage in two concurrent but unrelated activities, such as driving on a quiet road while listening to the radio. When we do so, does our brain split into functionally distinct entities? To address this question, we imaged brain activity with fMRI in experienced drivers engaged in a driving simulator while listening either to global positioning system instructions (integrated task) or to a radio show (split task). We found that, compared with the integrated task, the split task was characterized by reduced multivariate functional connectivity between the driving and listening networks. Furthermore, the integrated information content of the two networks, predicting their joint dynamics above and beyond their independent dynamics, was high in the integrated task and zero in the split task. Finally, individual subjects' ability to switch between high and low information integration predicted their driving performance across integrated and split tasks. This study raises the possibility that under certain conditions of daily life, a single brain may support two independent functional streams, a "functional split brain" similar to what is observed in patients with an anatomical split.

Approximate method of variational Bayesian matrix factorization/completion with sparse prior

NASA Astrophysics Data System (ADS)

Kawasumi, Ryota; Takeda, Koujin

2018-05-01

We derive the analytical expression of a matrix factorization/completion solution by the variational Bayes method, under the assumption that the observed matrix is originally the product of low-rank, dense and sparse matrices with additive noise. We assume the prior of a sparse matrix is a Laplace distribution by taking matrix sparsity into consideration. Then we use several approximations for the derivation of a matrix factorization/completion solution. By our solution, we also numerically evaluate the performance of a sparse matrix reconstruction in matrix factorization, and completion of a missing matrix element in matrix completion.

Overview of Sparse Graph for Multiple Access in Future Mobile Networks

NASA Astrophysics Data System (ADS)

Lei, Jing; Li, Baoguo; Li, Erbao; Gong, Zhenghui

2017-10-01

Multiple access via sparse graph, such as low density signature (LDS) and sparse code multiple access (SCMA), is a promising technique for future wireless communications. This survey presents an overview of the developments in this burgeoning field, including transmitter structures, extrinsic information transform (EXIT) chart analysis and comparisons with existing multiple access techniques. Such technique enables multiple access under overloaded conditions to achieve a satisfactory performance. Message passing algorithm is utilized for multi-user detection in the receiver, and structures of the sparse graph are illustrated in detail. Outlooks and challenges of this technique are also presented.

Biological components and bioelectronic interfaces of water splitting photoelectrodes for solar hydrogen production.

PubMed

Braun, Artur; Boudoire, Florent; Bora, Debajeet K; Faccio, Greta; Hu, Yelin; Kroll, Alexandra; Mun, Bongjin S; Wilson, Samuel T

2015-03-09

Artificial photosynthesis (AP) is inspired by photosynthesis in nature. In AP, solar hydrogen can be produced by water splitting in photoelectrochemical cells (PEC). The necessary photoelectrodes are inorganic semiconductors. Light-harvesting proteins and biocatalysts can be coupled with these photoelectrodes and thus form bioelectronic interfaces. We expand this concept toward PEC devices with vital bio-organic components and interfaces, and their integration into the built environment. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The hierarchically organized splitting of chromosome bands into sub-bands analyzed by multicolor banding (MCB).

PubMed

Lehrer, H; Weise, A; Michel, S; Starke, H; Mrasek, K; Heller, A; Kuechler, A; Claussen, U; Liehr, T

2004-01-01

To clarify the nature of chromosome sub-bands in more detail, the multicolor banding (MCB) probe-set for chromosome 5 was hybridized to normal metaphase spreads of GTG band levels at approximately 850, approximately 550, approximately 400 and approximately 300. It could be observed that as the chromosomes became shorter, more of the initial 39 MCB pseudo-colors disappeared, ending with 18 MCB pseudo-colored bands at the approximately 300-band level. The hierarchically organized splitting of bands into sub-bands was analyzed by comparing the disappearance or appearance of pseudo-color bands of the four different band levels. The regions to split first are telomere-near, centromere-near and in 5q23-->q31, followed by 5p15, 5p14, and all GTG dark bands in 5q apart from 5q12 and 5q32 and finalized by sub-band building in 5p15.2, 5q21.2-->q21.3, 5q23.1 and 5q34. The direction of band splitting towards the centromere or the telomere could be assigned to each band separately. Pseudo-colors assigned to GTG-light bands were resistant to band splitting. These observations are in concordance with the recently proposed concept of chromosome region-specific protein swelling. Copyright 2003 S. Karger AG, Basel

Enhanced photoelectrochemical water splitting by oxides heterojunction photocathode coupled with Ag.

PubMed

Lu, Xue; Liu, Zhifeng

2017-08-14

A novel one-dimensional Co 3 O 4 /CuO/Ag composite structure film was directly grown on indium tin oxide glass substrate by a simple hydrothermal method and electrodeposition method. The film was employed for the first time as a photocathode for photoelectrochemical (PEC) water splitting to generate hydrogen. The photocurrent density of the Co 3 O 4 /CuO/Ag composite structure achieved -5.13 mA cm -2 at -0.2 V vs. RHE, which is roughly 12.8 times that of 1D Co 3 O 4 nanowires and 3.31 times Co 3 O 4 /CuO heterojunction photocathodes. The enhanced PEC performance of this Co 3 O 4 /CuO/Ag composite structure ascribes increased light-harvesting and light-absorption, distensible photoresponse range, decreased interface charge transfer resistance, and improved photogenerated electron-hole pairs transfer and separation.

JiTTree: A Just-in-Time Compiled Sparse GPU Volume Data Structure.

PubMed

Labschütz, Matthias; Bruckner, Stefan; Gröller, M Eduard; Hadwiger, Markus; Rautek, Peter

2016-01-01

Sparse volume data structures enable the efficient representation of large but sparse volumes in GPU memory for computation and visualization. However, the choice of a specific data structure for a given data set depends on several factors, such as the memory budget, the sparsity of the data, and data access patterns. In general, there is no single optimal sparse data structure, but a set of several candidates with individual strengths and drawbacks. One solution to this problem are hybrid data structures which locally adapt themselves to the sparsity. However, they typically suffer from increased traversal overhead which limits their utility in many applications. This paper presents JiTTree, a novel sparse hybrid volume data structure that uses just-in-time compilation to overcome these problems. By combining multiple sparse data structures and reducing traversal overhead we leverage their individual advantages. We demonstrate that hybrid data structures adapt well to a large range of data sets. They are especially superior to other sparse data structures for data sets that locally vary in sparsity. Possible optimization criteria are memory, performance and a combination thereof. Through just-in-time (JIT) compilation, JiTTree reduces the traversal overhead of the resulting optimal data structure. As a result, our hybrid volume data structure enables efficient computations on the GPU, while being superior in terms of memory usage when compared to non-hybrid data structures.

Development of a new flux splitting scheme

NASA Technical Reports Server (NTRS)

Liou, Meng-Sing; Steffen, Christopher J., Jr.

1991-01-01

The successful use of a novel splitting scheme, the advection upstream splitting method, for model aerodynamic problems where Van Leer and Roe schemes had failed previously is discussed. The present scheme is based on splitting in which the convective and pressure terms are separated and treated differently depending on the underlying physical conditions. The present method is found to be both simple and accurate.

Sparse Partial Equilibrium Tables in Chemically Resolved Reactive Flow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Vitello, P; Fried, L E; Pudliner, B

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 hydrodynamicmore » 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.« less

Sparse magnetic resonance imaging reconstruction using the bregman iteration

NASA Astrophysics Data System (ADS)

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

2013-01-01

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

Relativistic kinematics for motion faster than light

NASA Technical Reports Server (NTRS)

Jones, R. T.

1982-01-01

The use of conformal coordinates in relativistic kinematics is illustrated and a simple extension of the theory of motions faster than light is provided. An object traveling at a speed greater than light discloses its presence by appearing suddenly at a point, splitting into two apparent objects which then recede from each other at sublight velocities. According to the present theory motion at speeds faster than light would not benefit a space traveler, since the twin paradox becomes inverted at such speeds. In Einstein's theory travel at the velocity of light in an intertial system is equivalent to infinite velocity for the traveler. In the present theory the converse is also true; travel at infinite velocity is equivalent to the velocity of light for the traveler.

Sparse gammatone signal model optimized for English speech does not match the human auditory filters.

PubMed

Strahl, Stefan; Mertins, Alfred

2008-07-18

Evidence that neurosensory systems use sparse signal representations as well as improved performance of signal processing algorithms using sparse signal models raised interest in sparse signal coding in the last years. For natural audio signals like speech and environmental sounds, gammatone atoms have been derived as expansion functions that generate a nearly optimal sparse signal model (Smith, E., Lewicki, M., 2006. Efficient auditory coding. Nature 439, 978-982). Furthermore, gammatone functions are established models for the human auditory filters. Thus far, a practical application of a sparse gammatone signal model has been prevented by the fact that deriving the sparsest representation is, in general, computationally intractable. In this paper, we applied an accelerated version of the matching pursuit algorithm for gammatone dictionaries allowing real-time and large data set applications. We show that a sparse signal model in general has advantages in audio coding and that a sparse gammatone signal model encodes speech more efficiently in terms of sparseness than a sparse modified discrete cosine transform (MDCT) signal model. We also show that the optimal gammatone parameters derived for English speech do not match the human auditory filters, suggesting for signal processing applications to derive the parameters individually for each applied signal class instead of using psychometrically derived parameters. For brain research, it means that care should be taken with directly transferring findings of optimality for technical to biological systems.

Incorporating biological information in sparse principal component analysis with application to genomic data.

PubMed

Li, Ziyi; Safo, Sandra E; Long, Qi

2017-07-11

Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection. Our simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma. The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases.

Generalized field-splitting algorithms for optimal IMRT delivery efficiency.

PubMed

Kamath, Srijit; Sahni, Sartaj; Li, Jonathan; Ranka, Sanjay; Palta, Jatinder

2007-09-21

Intensity-modulated radiation therapy (IMRT) uses radiation beams of varying intensities to deliver varying doses of radiation to different areas of the tissue. The use of IMRT has allowed the delivery of higher doses of radiation to the tumor and lower doses to the surrounding healthy tissue. It is not uncommon for head and neck tumors, for example, to have large treatment widths that are not deliverable using a single field. In such cases, the intensity matrix generated by the optimizer needs to be split into two or three matrices, each of which may be delivered using a single field. Existing field-splitting algorithms used the pre-specified arbitrary split line or region where the intensity matrix is split along a column, i.e., all rows of the matrix are split along the same column (with or without the overlapping of split fields, i.e., feathering). If three fields result, then the two splits are along the same two columns for all rows. In this paper we study the problem of splitting a large field into two or three subfields with the field width as the only constraint, allowing for an arbitrary overlap of the split fields, so that the total MU efficiency of delivering the split fields is maximized. Proof of optimality is provided for the proposed algorithm. An average decrease of 18.8% is found in the total MUs when compared to the split generated by a commercial treatment planning system and that of 10% is found in the total MUs when compared to the split generated by our previously published algorithm.

Image fusion based on Bandelet and sparse representation

NASA Astrophysics Data System (ADS)

Zhang, Jiuxing; Zhang, Wei; Li, Xuzhi

2018-04-01

Bandelet transform could acquire geometric regular direction and geometric flow, sparse representation could represent signals with as little as possible atoms on over-complete dictionary, both of which could be used to image fusion. Therefore, a new fusion method is proposed based on Bandelet and Sparse Representation, to fuse Bandelet coefficients of multi-source images and obtain high quality fusion effects. The test are performed on remote sensing images and simulated multi-focus images, experimental results show that the performance of new method is better than tested methods according to objective evaluation indexes and subjective visual effects.

LiDAR point classification based on sparse representation

NASA Astrophysics Data System (ADS)

Li, Nan; Pfeifer, Norbert; Liu, Chun

2017-04-01

In order to combine the initial spatial structure and features of LiDAR data for accurate classification. The LiDAR data is represented as a 4-order tensor. Sparse representation for classification(SRC) method is used for LiDAR tensor classification. It turns out SRC need only a few of training samples from each class, meanwhile can achieve good classification result. Multiple features are extracted from raw LiDAR points to generate a high-dimensional vector at each point. Then the LiDAR tensor is built by the spatial distribution and feature vectors of the point neighborhood. The entries of LiDAR tensor are accessed via four indexes. Each index is called mode: three spatial modes in direction X ,Y ,Z and one feature mode. Sparse representation for classification(SRC) method is proposed in this paper. The sparsity algorithm is to find the best represent the test sample by sparse linear combination of training samples from a dictionary. To explore the sparsity of LiDAR tensor, the tucker decomposition is used. It decomposes a tensor into a core tensor multiplied by a matrix along each mode. Those matrices could be considered as the principal components in each mode. The entries of core tensor show the level of interaction between the different components. Therefore, the LiDAR tensor can be approximately represented by a sparse tensor multiplied by a matrix selected from a dictionary along each mode. The matrices decomposed from training samples are arranged as initial elements in the dictionary. By dictionary learning, a reconstructive and discriminative structure dictionary along each mode is built. The overall structure dictionary composes of class-specified sub-dictionaries. Then the sparse core tensor is calculated by tensor OMP(Orthogonal Matching Pursuit) method based on dictionaries along each mode. It is expected that original tensor should be well recovered by sub-dictionary associated with relevant class, while entries in the sparse tensor associated with

Sparse learning of stochastic dynamical equations

NASA Astrophysics Data System (ADS)

Boninsegna, Lorenzo; Nüske, Feliks; Clementi, Cecilia

2018-06-01

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

Shape models of asteroids reconstructed from WISE data and sparse photometry

NASA Astrophysics Data System (ADS)

Durech, Josef; Hanus, Josef; Ali-Lagoa, Victor

2017-10-01

By combining sparse-in-time photometry from the Lowell Observatory photometry database with WISE observations, we reconstructed convex shape models for about 700 new asteroids and for other ~850 we derived 'partial' models with unconstrained ecliptic longitude of the spin axis direction. In our approach, the WISE data were treated as reflected light, which enabled us to directly join them with sparse photometry into one dataset that was processed by the lightcurve inversion method. This simplified treatment of thermal infrared data turned out to provide correct results, because in most cases the phase offset between optical and thermal lightcurves was small and the correct sidereal rotation period was determined. The spin and shape parameters derived from only optical data and from a combination of optical and WISE data were very similar. The new models together with those already available in the Database of Asteroid Models from Inversion Techniques (DAMIT) represent a sample of ~1650 asteroids. When including also partial models, the total sample is about 2500 asteroids, which significantly increases the number of models with respect to those that have been available so far. We will show the distribution of spin axes for different size groups and also for several collisional families. These observed distributions in general agree with theoretical expectations proving that smaller asteroids are more affected by YORP/Yarkovsky evolution. In asteroid families, we see a clear bimodal distribution of prograde/retrograde rotation that correlates with the position to the right/left from the center of the family measured by the semimajor axis.

Sparse decomposition of seismic data and migration using Gaussian beams with nonzero initial curvature

NASA Astrophysics Data System (ADS)

Liu, Peng; Wang, Yanfei

2018-04-01

We study problems associated with seismic data decomposition and migration imaging. We first represent the seismic data utilizing Gaussian beam basis functions, which have nonzero curvature, and then consider the sparse decomposition technique. The sparse decomposition problem is an l0-norm constrained minimization problem. In solving the l0-norm minimization, a polynomial Radon transform is performed to achieve sparsity, and a fast gradient descent method is used to calculate the waveform functions. The waveform functions can subsequently be used for sparse Gaussian beam migration. Compared with traditional sparse Gaussian beam methods, the seismic data can be properly reconstructed employing fewer Gaussian beams with nonzero initial curvature. The migration approach described in this paper is more efficient than the traditional sparse Gaussian beam migration.

Enhanced Photocarrier Separation in Hierarchical Graphitic-C3N4-Supported CuInS2 for Noble-Metal-Free Z-Scheme Photocatalytic Water Splitting.

PubMed

Li, Xiaoxue; Xie, Keyu; Song, Long; Zhao, Mengjia; Zhang, Zhipan

2017-07-26

The effective separation of photogenerated electrons and holes in photocatalysts is a prerequisite for efficient photocatalytic water splitting. CuInS 2 (CIS) is a widely used light absorber that works properly in photovoltaics but only shows limited performance in solar-driven hydrogen evolution due to its intrinsically severe charge recombination. Here, we prepare hierarchical graphitic C 3 N 4 -supported CuInS 2 (denoted as GsC) by an in situ growth of CIS directly on exfoliated thin graphitic C 3 N 4 nanosheets (g-C 3 N 4 NS) and demonstrate efficient separation of photoinduced charge carriers in the GsC by forming the Z-scheme system for the first time in CIS-catalyzed water splitting. Under visible light illumination, the GsC features an enhanced hydrogen evolution rate up to 1290 μmol g -1 h -1 , which is 3.3 and 6.1 times higher than that of g-C 3 N 4 NS and bare-CIS, respectively, thus setting a new performance benchmark for CIS-based water-splitting photocatalysts.

Full-wave effects on shear wave splitting

NASA Astrophysics Data System (ADS)

Lin, Yu-Pin; Zhao, Li; Hung, Shu-Huei

2014-02-01

Seismic anisotropy in the mantle plays an important role in our understanding of the Earth's internal dynamics, and shear wave splitting has always been a key observable in the investigation of seismic anisotropy. To date the interpretation of shear wave splitting in terms of anisotropy has been largely based on ray-theoretical modeling of a single vertically incident plane SKS or SKKS wave. In this study, we use sensitivity kernels of shear wave splitting to anisotropic parameters calculated by the normal-mode theory to demonstrate that the interference of SKS with other phases of similar arrival times, near-field effect, and multiple reflections in the crust lead to significant variations of SKS splitting with epicentral distance. The full-wave kernels not only widen the possibilities in the source-receiver geometry in making shear wave splitting measurements but also provide the capability for tomographic inversion to resolve vertical and lateral variations in the anisotropic structures.

Elastic-Waveform Inversion with Compressive Sensing for Sparse Seismic Data

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lin, Youzuo; Huang, Lianjie

2015-01-28

Accurate velocity models of compressional- and shear-waves are essential for geothermal reservoir characterization and microseismic imaging. Elastic-waveform inversion of multi-component seismic data can provide high-resolution inversion results of subsurface geophysical properties. However, the method requires seismic data acquired using dense source and receiver arrays. In practice, seismic sources and/or geophones are often sparsely distributed on the surface and/or in a borehole, such as 3D vertical seismic profiling (VSP) surveys. We develop a novel elastic-waveform inversion method with compressive sensing for inversion of sparse seismic data. We employ an alternating-minimization algorithm to solve the optimization problem of our new waveform inversionmore » method. We validate our new method using synthetic VSP data for a geophysical model built using geologic features found at the Raft River enhanced-geothermal-system (EGS) field. We apply our method to synthetic VSP data with a sparse source array and compare the results with those obtained with a dense source array. Our numerical results demonstrate that the velocity models produced with our new method using a sparse source array are almost as accurate as those obtained using a dense source array.« less

Discriminative object tracking via sparse representation and online dictionary learning.

PubMed

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

2014-04-01

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

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.

Geometric metasurface enabling polarization independent beam splitting.

PubMed

Yoon, Gwanho; Lee, Dasol; Nam, Ki Tae; Rho, Junsuk

2018-06-21

A polarization independent holographic beam splitter that generates equal-intensity beams based on geometric metasurface is demonstrated. Although conventional geometric metasurfaces have the advantages of working over a broad frequency range and having intuitive design principles, geometric metasurfaces have the limitation that they only work for circular polarization. In this work, Fourier holography is used to overcome this limitation. A perfect overlap resulting from the origin-symmetry of the encoded image enables polarization independent operation of geometric metasurfaces. The designed metasurface beam splitter is experimentally demonstrated by using hydrogenated amorphous silicon, and the device performs consistent beam splitting regardless of incident polarizations as well as wavelengths. Our device can be applied to generate equal-intensity beams for entangled photon light sources in quantum optics, and the design approach provides a way to develop ultra-thin broadband polarization independent components for modern optics.

Ultrathin planar hematite film for solar photoelectrochemical water splitting

DOE PAGES

Liu, Dong; Bierman, David M.; Lenert, Andrej; ...

2015-10-08

Hematite holds promise for photoelectrochemical (PEC) water splitting due to its stability, low-cost, abundance and appropriate bandgap. However, it suffers from a mismatch between the hole diffusion length and light penetration length. We have theoretically designed and characterized an ultrathin planar hematite/silver nanohole array/silver substrate photoanode. Due to the supported destructive interference and surface plasmon resonance, photons are efficiently absorbed in an ultrathin hematite film. In conclusion, compared with ultrathin hematite photoanodes with nanophotonic structures, this photoanode has comparable photon absorption but with intrinsically lower recombination losses due to its planar structure and promises to exceed the state-of-the-art photocurrent ofmore » hematite photoanodes.« less

Magnetic photon splitting and gamma ray burst spectra

NASA Technical Reports Server (NTRS)

Baring, Matthew G.

1992-01-01

The splitting of photons into two photons becomes both possible and significant in magnetic fields in excess of 10(exp 12) Gauss. Below the threshold energy, 2m sub e c(exp 2) for single photon pair production, splitting can be an astronomically observable phenomenon evident in gamma ray burst spectra. In such circumstances, it was found that magnetic photon splitting reprocesses the gamma ray burst continuum by degrading the photon energy, with a net effect that is quite similar to pair cascade reprocessing of the spectrum. Results are presented for the spectral modifications due to splitting, taking into account the different probabilities for splitting for different polarization modes. Unpolarized and polarized pair cascade photon spectra form the input spectra for the model, which calculates the resulting splitting reprocessed spectra numerically by solving the photon kinetic equations for each polarization mode. This inclusion of photon polarizations is found to not alter previous predictions that splitting produce a significant flattening of the hard X ray continuum and a bump at MeV energies below a pair production turnover. The spectrum near the bump is always strongly polarized.

Functional split brain in a driving/listening paradigm

PubMed Central

Boly, Melanie; Mensen, Armand; Tononi, Giulio

2016-01-01

We often engage in two concurrent but unrelated activities, such as driving on a quiet road while listening to the radio. When we do so, does our brain split into functionally distinct entities? To address this question, we imaged brain activity with fMRI in experienced drivers engaged in a driving simulator while listening either to global positioning system instructions (integrated task) or to a radio show (split task). We found that, compared with the integrated task, the split task was characterized by reduced multivariate functional connectivity between the driving and listening networks. Furthermore, the integrated information content of the two networks, predicting their joint dynamics above and beyond their independent dynamics, was high in the integrated task and zero in the split task. Finally, individual subjects’ ability to switch between high and low information integration predicted their driving performance across integrated and split tasks. This study raises the possibility that under certain conditions of daily life, a single brain may support two independent functional streams, a “functional split brain” similar to what is observed in patients with an anatomical split. PMID:27911805

Subject-based discriminative sparse representation model for detection of concealed information.

PubMed

Akhavan, Amir; Moradi, Mohammad Hassan; Vand, Safa Rafiei

2017-05-01

The use of machine learning approaches in concealed information test (CIT) plays a key role in the progress of this neurophysiological field. In this paper, we presented a new machine learning method for CIT in which each subject is considered independent of the others. The main goal of this study is to adapt the discriminative sparse models to be applicable for subject-based concealed information test. In order to provide sufficient discriminability between guilty and innocent subjects, we introduced a novel discriminative sparse representation model and its appropriate learning methods. For evaluation of the method forty-four subjects participated in a mock crime scenario and their EEG data were recorded. As the model input, in this study the recurrence plot features were extracted from single trial data of different stimuli. Then the extracted feature vectors were reduced using statistical dependency method. The reduced feature vector went through the proposed subject-based sparse model in which the discrimination power of sparse code and reconstruction error were applied simultaneously. Experimental results showed that the proposed approach achieved better performance than other competing discriminative sparse models. The classification accuracy, sensitivity and specificity of the presented sparsity-based method were about 93%, 91% and 95% respectively. Using the EEG data of a single subject in response to different stimuli types and with the aid of the proposed discriminative sparse representation model, one can distinguish guilty subjects from innocent ones. Indeed, this property eliminates the necessity of several subject EEG data in model learning and decision making for a specific subject. Copyright © 2017 Elsevier B.V. All rights reserved.

Towards sparse characterisation of on-body ultra-wideband wireless channels.

PubMed

Yang, Xiaodong; Ren, Aifeng; Zhang, Zhiya; Ur Rehman, Masood; Abbasi, Qammer Hussain; Alomainy, Akram

2015-06-01

With the aim of reducing cost and power consumption of the receiving terminal, compressive sensing (CS) framework is applied to on-body ultra-wideband (UWB) channel estimation. It is demonstrated in this Letter that the sparse on-body UWB channel impulse response recovered by the CS framework fits the original sparse channel well; thus, on-body channel estimation can be achieved using low-speed sampling devices.

Composition-Dependent Energy Splitting between Bright and Dark Excitons in Lead Halide Perovskite Nanocrystals.

PubMed

Chen, Lan; Li, Bin; Zhang, Chunfeng; Huang, Xinyu; Wang, Xiaoyong; Xiao, Min

2018-03-14

Perovskite semiconductor nanocrystals with different compositions have shown promise for applications in light-emitting devices. Dark excitonic states may suppress light emission from such nanocrystals by providing an additional nonradiative recombination channel. Here, we study the composition dependence of dark exciton dynamics in nanocrystals of lead halides by time-resolved photoluminescence spectroscopy at cryogenic temperatures. The presence of a spin-related dark state is revealed by magneto-optical spectroscopy. The energy splitting between bright and dark states is found to be highly sensitive to both halide elements and organic cations, which is explained by considering the effects of size confinement and charge screening, respectively, on the exchange interaction. These findings suggest the possibility of manipulating dark exciton dynamics in perovskite semiconductor nanocrystals by composition engineering, which will be instrumental in the design of highly efficient light-emitting devices.

Normalization for sparse encoding of odors by a wide-field interneuron.

PubMed

Papadopoulou, Maria; Cassenaer, Stijn; Nowotny, Thomas; Laurent, Gilles

2011-05-06

Sparse coding presents practical advantages for sensory representations and memory storage. In the insect olfactory system, the representation of general odors is dense in the antennal lobes but sparse in the mushroom bodies, only one synapse downstream. In locusts, this transformation relies on the oscillatory structure of antennal lobe output, feed-forward inhibitory circuits, intrinsic properties of mushroom body neurons, and connectivity between antennal lobe and mushroom bodies. Here we show the existence of a normalizing negative-feedback loop within the mushroom body to maintain sparse output over a wide range of input conditions. This loop consists of an identifiable "giant" nonspiking inhibitory interneuron with ubiquitous connectivity and graded release properties.

Efficiency limits for photoelectrochemical water-splitting

DOE PAGES

Fountaine, Katherine T.; Lewerenz, Hans Joachim; Atwater, Harry A.

2016-12-02

Theoretical limiting efficiencies have a critical role in determining technological viability and expectations for device prototypes, as evidenced by the photovoltaics community’s focus on detailed balance. However, due to their multicomponent nature, photoelectrochemical devices do not have an equivalent analogue to detailed balance, and reported theoretical efficiency limits vary depending on the assumptions made. Here we introduce a unified framework for photoelectrochemical device performance through which all previous limiting efficiencies can be understood and contextualized. Ideal and experimentally realistic limiting efficiencies are presented, and then generalized using five representative parameters—semiconductor absorption fraction, external radiative efficiency, series resistance, shunt resistance andmore » catalytic exchange current density—to account for imperfect light absorption, charge transport and catalysis. Finally, we discuss the origin of deviations between the limits discussed herein and reported water-splitting efficiencies. This analysis provides insight into the primary factors that determine device performance and a powerful handle to improve device efficiency.« less

Sparse representation-based image restoration via nonlocal supervised coding

NASA Astrophysics Data System (ADS)

Li, Ao; Chen, Deyun; Sun, Guanglu; Lin, Kezheng

2016-10-01

Sparse representation (SR) and nonlocal technique (NLT) have shown great potential in low-level image processing. However, due to the degradation of the observed image, SR and NLT may not be accurate enough to obtain a faithful restoration results when they are used independently. To improve the performance, in this paper, a nonlocal supervised coding strategy-based NLT for image restoration is proposed. The novel method has three main contributions. First, to exploit the useful nonlocal patches, a nonnegative sparse representation is introduced, whose coefficients can be utilized as the supervised weights among patches. Second, a novel objective function is proposed, which integrated the supervised weights learning and the nonlocal sparse coding to guarantee a more promising solution. Finally, to make the minimization tractable and convergence, a numerical scheme based on iterative shrinkage thresholding is developed to solve the above underdetermined inverse problem. The extensive experiments validate the effectiveness of the proposed method.

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.

Sparse dictionary learning for resting-state fMRI analysis

NASA Astrophysics Data System (ADS)

Lee, Kangjoo; Han, Paul Kyu; Ye, Jong Chul

2011-09-01

Recently, there has been increased interest in the usage of neuroimaging techniques to investigate what happens in the brain at rest. Functional imaging studies have revealed that the default-mode network activity is disrupted in Alzheimer's disease (AD). However, there is no consensus, as yet, on the choice of analysis method for the application of resting-state analysis for disease classification. This paper proposes a novel compressed sensing based resting-state fMRI analysis tool called Sparse-SPM. As the brain's functional systems has shown to have features of complex networks according to graph theoretical analysis, we apply a graph model to represent a sparse combination of information flows in complex network perspectives. In particular, a new concept of spatially adaptive design matrix has been proposed by implementing sparse dictionary learning based on sparsity. The proposed approach shows better performance compared to other conventional methods, such as independent component analysis (ICA) and seed-based approach, in classifying the AD patients from normal using resting-state analysis.

Photocatalytic water splitting: Materials design and high-throughput screening of molecular compositions

NASA Astrophysics Data System (ADS)

Khnayzer, Rony S.

Due to the expected increases on energy demand in the near future, the development of new catalytic molecular compositions and materials capable of directly converting water, with the aid of solar photons, into hydrogen becomes obviated. Hydrogen is a combustible fuel and precious high-energy feedstock chemical. However, for the water-splitting reaction to proceed efficiently and economically enough for large-scale application, efficient light-absorbing sensitizers and water splitting catalysts are required. To study the kinetics of the water reduction reaction, we have used titania (TiO2) nanoparticles as a robust scaffold to photochemically grow platinum (Pt) nanoparticles from a unique surface-anchored molecular precursor Pt(dcbpy)Cl2 [dcbpy = 4,4'-dicarboxylic acid-2,2'-bipyridine]. The hybrid Pt/TiO 2 nanomaterials obtained were shown to be a superior water reduction catalyst (WRC) in aqueous suspensions when compared with the benchmark platinized TiO2. In addition, cobalt phosphate (CoPi) water oxidation catalyst (WOC) was photochemically assembled on the surface of TiO2, and its structure and mechanism of activity showed resemblance to the established electrochemically grown CoPi material. Both WRC and WOC described above possessed near unity Faradaic efficiency for hydrogen and oxygen production respectively, and were fully characterized by electron microscopy, x-ray absorption spectroscopy, electrochemistry and photochemistry. While there are established materials and molecules that are able to drive water splitting catalysis, some of these efficient semiconductors, including titanium dioxide (TiO2) and tungsten trioxide (WO3), are only able to absorb high-energy (ultraviolet or blue) photons. This high-energy light represents merely a fraction of the solar spectrum that strikes the earth and the energy content of those remaining photons is simply wasted. A strategy to mitigate this problem has been developed over the years in our laboratory. Briefly

View-interpolation of sparsely sampled sinogram using convolutional neural network

NASA Astrophysics Data System (ADS)

Lee, Hoyeon; Lee, Jongha; Cho, Suengryong

2017-02-01

Spare-view sampling and its associated iterative image reconstruction in computed tomography have actively investigated. Sparse-view CT technique is a viable option to low-dose CT, particularly in cone-beam CT (CBCT) applications, with advanced iterative image reconstructions with varying degrees of image artifacts. One of the artifacts that may occur in sparse-view CT is the streak artifact in the reconstructed images. Another approach has been investigated for sparse-view CT imaging by use of the interpolation methods to fill in the missing view data and that reconstructs the image by an analytic reconstruction algorithm. In this study, we developed an interpolation method using convolutional neural network (CNN), which is one of the widely used deep-learning methods, to find missing projection data and compared its performances with the other interpolation techniques.

Sparse-view photoacoustic tomography using virtual parallel-projections and spatially adaptive filtering

NASA Astrophysics Data System (ADS)

Wang, Yihan; Lu, Tong; Wan, Wenbo; Liu, Lingling; Zhang, Songhe; Li, Jiao; Zhao, Huijuan; Gao, Feng

2018-02-01

To fully realize the potential of photoacoustic tomography (PAT) in preclinical and clinical applications, rapid measurements and robust reconstructions are needed. Sparse-view measurements have been adopted effectively to accelerate the data acquisition. However, since the reconstruction from the sparse-view sampling data is challenging, both of the effective measurement and the appropriate reconstruction should be taken into account. In this study, we present an iterative sparse-view PAT reconstruction scheme where a virtual parallel-projection concept matching for the proposed measurement condition is introduced to help to achieve the "compressive sensing" procedure of the reconstruction, and meanwhile the spatially adaptive filtering fully considering the a priori information of the mutually similar blocks existing in natural images is introduced to effectively recover the partial unknown coefficients in the transformed domain. Therefore, the sparse-view PAT images can be reconstructed with higher quality compared with the results obtained by the universal back-projection (UBP) algorithm in the same sparse-view cases. The proposed approach has been validated by simulation experiments, which exhibits desirable performances in image fidelity even from a small number of measuring positions.

A modified sparse reconstruction method for three-dimensional synthetic aperture radar image

NASA Astrophysics Data System (ADS)

Zhang, Ziqiang; Ji, Kefeng; Song, Haibo; Zou, Huanxin

2018-03-01

There is an increasing interest in three-dimensional Synthetic Aperture Radar (3-D SAR) imaging from observed sparse scattering data. However, the existing 3-D sparse imaging method requires large computing times and storage capacity. In this paper, we propose a modified method for the sparse 3-D SAR imaging. The method processes the collection of noisy SAR measurements, usually collected over nonlinear flight paths, and outputs 3-D SAR imagery. Firstly, the 3-D sparse reconstruction problem is transformed into a series of 2-D slices reconstruction problem by range compression. Then the slices are reconstructed by the modified SL0 (smoothed l0 norm) reconstruction algorithm. The improved algorithm uses hyperbolic tangent function instead of the Gaussian function to approximate the l0 norm and uses the Newton direction instead of the steepest descent direction, which can speed up the convergence rate of the SL0 algorithm. Finally, numerical simulation results are given to demonstrate the effectiveness of the proposed algorithm. It is shown that our method, compared with existing 3-D sparse imaging method, performs better in reconstruction quality and the reconstruction time.

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

High quantum-yield phosphors via quantum splitting and upconversion

NASA Astrophysics Data System (ADS)

Jeong, Joayoung

The Gd3+ ion has been used to induce quantum splitting in luminescent materials by using cross-relaxation energy transfer (CRET). In Nd:LiGdF4, quantum splitting results from a two-step CRET between Gd3+ and Nd3+, first involving a transition 6G→6I on Gd3+ and an excitation within the 4f3 configuration of Nd3+ followed by a second CRET that brings Gd3+ to 6P7/2. The excited Nd3+ ion rapidly relaxes nonradiatively to the emitting 4F3/2. The excited Gd3+ ion then transfers its energy back to Nd3+, which gives rise to the second photon. The result is a quantum yield of 1.05 +/- 0.35 with emission in the NIR following excitation at 175 nm. GdF3:Pr3+, Eu 3+ also exhibits quantum splitting, but only at very low concentration of Pr3+ (0.3%) and Eu3+ (0.2%), resulting in a quantum yield of approximately 20% under 160-nm excitation. Host intrinsic emission via a self-trapped exciton (STE) was also examined as a means to sensitize Gd3+ emission. The material ScPO4:Gd 3+ exhibits a high absolute quantum yield of 0.9 +/- 0.2 under 170-nm excitation, demonstrating a potentially new and efficient pathway for exciting quantum splitting phosphors. Single crystals of the material GdZrF7 were grown, and its structure was established via single-crystal X-ray diffraction methods. Doped samples of GdZrF7:Yb3+, Er3+ exhibit bright up-conversion luminescence with light output that is up to twice that of a commercial material based on the host Gd2O2S. When doped with Eu3+, the fluoride also emits a nearly white color under vacuum ultraviolet excitation with an absolute quantum yield near 0.9. The new compound Gd4.67(SiO4)3S was synthesized and studied. The structure was established via single-crystal X-ray methods, and the luminescence of Tb3+ samples was investigated.

Solar Water Splitting and Nitrogen Fixation with Layered Bismuth Oxyhalides.

PubMed

Li, Jie; Li, Hao; Zhan, Guangming; Zhang, Lizhi

2017-01-17

precisely channeling their migration from the bulk to the surface along the different directions, thus enabling more electrons to reach the surface for water splitting and nitrogen fixation. Simultaneously, their oxygen termination feature and the strain differences between interlayers and intralayers render the easy generation of surface oxygen vacancies (OVs) that afford Lewis-base and unsaturated-unsaturated sites for nitrogen activation. With these rationales as the guideline, we can obtain striking visible-light hydrogen- and ammonia-evolving rates without using any noble-metal cocatalysts. Then we show how to utilize IEF and OV based strategies to improve the solar water splitting and nitrogen fixation performances of bismuth oxyhalide photocatalysts. Finally, we highlight the challenges remaining in using bismuth oxyhalides for solar hydrogen and ammonia syntheses, and the prospect of further development of this research field. We believe that our mechanistic insights could serve as a blueprint for the design of more efficient solar water splitting and nitrogen fixation systems, and layered bismuth oxyhalides might open up new photocatalyst paradigm for such two solar chemical syntheses.

10 CFR 26.113 - Splitting the urine specimen.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 10 Energy 1 2011-01-01 2011-01-01 false Splitting the urine specimen. 26.113 Section 26.113 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Collecting Specimens for Testing § 26.113 Splitting the urine specimen. (a) Licensees and other entities may, but are not required to, use split...

10 CFR 26.113 - Splitting the urine specimen.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 1 2010-01-01 2010-01-01 false Splitting the urine specimen. 26.113 Section 26.113 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Collecting Specimens for Testing § 26.113 Splitting the urine specimen. (a) Licensees and other entities may, but are not required to, use split...

10 CFR 26.113 - Splitting the urine specimen.

Code of Federal Regulations, 2013 CFR

2013-01-01

... 10 Energy 1 2013-01-01 2013-01-01 false Splitting the urine specimen. 26.113 Section 26.113 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Collecting Specimens for Testing § 26.113 Splitting the urine specimen. (a) Licensees and other entities may, but are not required to, use split...

10 CFR 26.113 - Splitting the urine specimen.

Code of Federal Regulations, 2012 CFR

2012-01-01

... 10 Energy 1 2012-01-01 2012-01-01 false Splitting the urine specimen. 26.113 Section 26.113 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Collecting Specimens for Testing § 26.113 Splitting the urine specimen. (a) Licensees and other entities may, but are not required to, use split...

10 CFR 26.113 - Splitting the urine specimen.

Code of Federal Regulations, 2014 CFR

2014-01-01

... 10 Energy 1 2014-01-01 2014-01-01 false Splitting the urine specimen. 26.113 Section 26.113 Energy NUCLEAR REGULATORY COMMISSION FITNESS FOR DUTY PROGRAMS Collecting Specimens for Testing § 26.113 Splitting the urine specimen. (a) Licensees and other entities may, but are not required to, use split...

Estimating the size of an open population using sparse capture-recapture data.

PubMed

Huggins, Richard; Stoklosa, Jakub; Roach, Cameron; Yip, Paul

2018-03-01

Sparse capture-recapture data from open populations are difficult to analyze using currently available frequentist statistical methods. However, in closed capture-recapture experiments, the Chao sparse estimator (Chao, 1989, Biometrics 45, 427-438) may be used to estimate population sizes when there are few recaptures. Here, we extend the Chao (1989) closed population size estimator to the open population setting by using linear regression and extrapolation techniques. We conduct a small simulation study and apply the models to several sparse capture-recapture data sets. © 2017, The International Biometric Society.

Variable is better than invariable: sparse VSS-NLMS algorithms with application to adaptive MIMO channel estimation.

PubMed

Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki

2014-01-01

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics.

Variable Is Better Than Invariable: Sparse VSS-NLMS Algorithms with Application to Adaptive MIMO Channel Estimation

PubMed Central

Gui, Guan; Chen, Zhang-xin; Xu, Li; Wan, Qun; Huang, Jiyan; Adachi, Fumiyuki

2014-01-01

Channel estimation problem is one of the key technical issues in sparse frequency-selective fading multiple-input multiple-output (MIMO) communication systems using orthogonal frequency division multiplexing (OFDM) scheme. To estimate sparse MIMO channels, sparse invariable step-size normalized least mean square (ISS-NLMS) algorithms were applied to adaptive sparse channel estimation (ACSE). It is well known that step-size is a critical parameter which controls three aspects: algorithm stability, estimation performance, and computational cost. However, traditional methods are vulnerable to cause estimation performance loss because ISS cannot balance the three aspects simultaneously. In this paper, we propose two stable sparse variable step-size NLMS (VSS-NLMS) algorithms to improve the accuracy of MIMO channel estimators. First, ASCE is formulated in MIMO-OFDM systems. Second, different sparse penalties are introduced to VSS-NLMS algorithm for ASCE. In addition, difference between sparse ISS-NLMS algorithms and sparse VSS-NLMS ones is explained and their lower bounds are also derived. At last, to verify the effectiveness of the proposed algorithms for ASCE, several selected simulation results are shown to prove that the proposed sparse VSS-NLMS algorithms can achieve better estimation performance than the conventional methods via mean square error (MSE) and bit error rate (BER) metrics. PMID:25089286

Visual Tracking via Sparse and Local Linear Coding.

PubMed

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.

Clutter Mitigation in Echocardiography Using Sparse Signal Separation

PubMed Central

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

Robust visual tracking via multiscale deep sparse networks

NASA Astrophysics Data System (ADS)

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

2017-04-01

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

Shear-wave splitting and moonquakes

NASA Astrophysics Data System (ADS)

Dimech, J. L.; Weber, R. C.; Savage, M. K.

2017-12-01

Shear-wave splitting is a powerful tool for measuring anisotropy in the Earth's crust and mantle, and is sensitive to geological features such as fluid filled cracks, thin alternating layers of rock with different elastic properties, and preferred mineral orientations caused by strain. Since a shear wave splitting measurement requires only a single 3-component seismic station, it has potential applications for future single-station planetary seismic missions, such as the InSight geophysical mission to Mars, as well as possible future missions to Europa and the Moon. Here we present a preliminary shear-wave splitting analysis of moonquakes detected by the Apollo Passive Seismic Experiment. Lunar seismic data suffers from several drawbacks compared to modern terrestrial data, including severe seismic scattering, low intrinsic attenuation, 10-bit data resolution, thermal spikes, and timing errors. Despite these drawbacks, we show that it is in principle possible to make a shear wave splitting measurement using the S-phase arrival of a relatively high-quality moonquake, as determined by several agreeing measurement criteria. Encouraged by this finding, we further extend our analysis to clusters of "deep moonquake" events by stacking multiple events from the same cluster together to further enhance the quality of the S-phase arrivals that the measurement is based on.

Sparse grid techniques for particle-in-cell schemes

NASA Astrophysics Data System (ADS)

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

2017-02-01

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

Towards sparse characterisation of on-body ultra-wideband wireless channels

PubMed Central

Ren, Aifeng; Zhang, Zhiya; Ur Rehman, Masood; Abbasi, Qammer Hussain; Alomainy, Akram

2015-01-01

With the aim of reducing cost and power consumption of the receiving terminal, compressive sensing (CS) framework is applied to on-body ultra-wideband (UWB) channel estimation. It is demonstrated in this Letter that the sparse on-body UWB channel impulse response recovered by the CS framework fits the original sparse channel well; thus, on-body channel estimation can be achieved using low-speed sampling devices. PMID:26609409

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks.

PubMed

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R; Nguyen, Tuan N; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively.

Improving EEG-Based Driver Fatigue Classification Using Sparse-Deep Belief Networks

PubMed Central

Chai, Rifai; Ling, Sai Ho; San, Phyo Phyo; Naik, Ganesh R.; Nguyen, Tuan N.; Tran, Yvonne; Craig, Ashley; Nguyen, Hung T.

2017-01-01

This paper presents an improvement of classification performance for electroencephalography (EEG)-based driver fatigue classification between fatigue and alert states with the data collected from 43 participants. The system employs autoregressive (AR) modeling as the features extraction algorithm, and sparse-deep belief networks (sparse-DBN) as the classification algorithm. Compared to other classifiers, sparse-DBN is a semi supervised learning method which combines unsupervised learning for modeling features in the pre-training layer and supervised learning for classification in the following layer. The sparsity in sparse-DBN is achieved with a regularization term that penalizes a deviation of the expected activation of hidden units from a fixed low-level prevents the network from overfitting and is able to learn low-level structures as well as high-level structures. For comparison, the artificial neural networks (ANN), Bayesian neural networks (BNN), and original deep belief networks (DBN) classifiers are used. The classification results show that using AR feature extractor and DBN classifiers, the classification performance achieves an improved classification performance with a of sensitivity of 90.8%, a specificity of 90.4%, an accuracy of 90.6%, and an area under the receiver operating curve (AUROC) of 0.94 compared to ANN (sensitivity at 80.8%, specificity at 77.8%, accuracy at 79.3% with AUC-ROC of 0.83) and BNN classifiers (sensitivity at 84.3%, specificity at 83%, accuracy at 83.6% with AUROC of 0.87). Using the sparse-DBN classifier, the classification performance improved further with sensitivity of 93.9%, a specificity of 92.3%, and an accuracy of 93.1% with AUROC of 0.96. Overall, the sparse-DBN classifier improved accuracy by 13.8, 9.5, and 2.5% over ANN, BNN, and DBN classifiers, respectively. PMID:28326009

Split-plot designs for robotic serial dilution assays.

PubMed

Buzas, Jeffrey S; Wager, Carrie G; Lansky, David M

2011-12-01

This article explores effective implementation of split-plot designs in serial dilution bioassay using robots. We show that the shortest path for a robot to fill plate wells for a split-plot design is equivalent to the shortest common supersequence problem in combinatorics. We develop an algorithm for finding the shortest common supersequence, provide an R implementation, and explore the distribution of the number of steps required to implement split-plot designs for bioassay through simulation. We also show how to construct collections of split plots that can be filled in a minimal number of steps, thereby demonstrating that split-plot designs can be implemented with nearly the same effort as strip-plot designs. Finally, we provide guidelines for modeling data that result from these designs. © 2011, The International Biometric Society.

7 CFR 51.2731 - U.S. Spanish Splits.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false U.S. Spanish Splits. 51.2731 Section 51.2731... STANDARDS) United States Standards for Grades of Shelled Spanish Type Peanuts Grades § 51.2731 U.S. Spanish Splits. “U.S. Spanish Splits” consists of shelled Spanish type peanut kernels which are split or broken...

High-SNR spectrum measurement based on Hadamard encoding and sparse reconstruction

NASA Astrophysics Data System (ADS)

Wang, Zhaoxin; Yue, Jiang; Han, Jing; Li, Long; Jin, Yong; Gao, Yuan; Li, Baoming

2017-12-01

The denoising capabilities of the H-matrix and cyclic S-matrix based on the sparse reconstruction, employed in the Pixel of Focal Plane Coded Visible Spectrometer for spectrum measurement are investigated, where the spectrum is sparse in a known basis. In the measurement process, the digital micromirror device plays an important role, which implements the Hadamard coding. In contrast with Hadamard transform spectrometry, based on the shift invariability, this spectrometer may have the advantage of a high efficiency. Simulations and experiments show that the nonlinear solution with a sparse reconstruction has a better signal-to-noise ratio than the linear solution and the H-matrix outperforms the cyclic S-matrix whether the reconstruction method is nonlinear or linear.

Medical Image Fusion Based on Feature Extraction and Sparse Representation

PubMed Central

Wei, Gao; Zongxi, Song

2017-01-01

As a novel multiscale geometric analysis tool, sparse representation has shown many advantages over the conventional image representation methods. However, the standard sparse representation does not take intrinsic structure and its time complexity into consideration. In this paper, a new fusion mechanism for multimodal medical images based on sparse representation and decision map is proposed to deal with these problems simultaneously. Three decision maps are designed including structure information map (SM) and energy information map (EM) as well as structure and energy map (SEM) to make the results reserve more energy and edge information. SM contains the local structure feature captured by the Laplacian of a Gaussian (LOG) and EM contains the energy and energy distribution feature detected by the mean square deviation. The decision map is added to the normal sparse representation based method to improve the speed of the algorithm. Proposed approach also improves the quality of the fused results by enhancing the contrast and reserving more structure and energy information from the source images. The experiment results of 36 groups of CT/MR, MR-T1/MR-T2, and CT/PET images demonstrate that the method based on SR and SEM outperforms five state-of-the-art methods. PMID:28321246

Magnetic field induced mixing of light hole excitonic states in (Cd, Mn)Te/(Cd, Mg)Te core/shell nanowires

NASA Astrophysics Data System (ADS)

Płachta, Jakub; Grodzicka, Emma; Kaleta, Anna; Kret, Sławomir; Baczewski, Lech T.; Pietruczik, Aleksiej; Wiater, Maciej; Goryca, Mateusz; Kazimierczuk, Tomasz; Kossacki, Piotr; Karczewski, Grzegorz; Wojtowicz, Tomasz; Wojnar, Piotr

2018-05-01

A detailed magneto-photoluminescence study of individual (Cd, Mn)Te/(Cd, Mg)Te core/shell nanowires grown by molecular beam epitaxy is performed. First of all, an enhancement of the Zeeman splitting due to sp-d exchange interaction between band carriers and Mn-spins is evidenced in these nanostructures. Then, it is found that the value of this splitting depends strongly on the magnetic field direction with respect to the nanowire axis. The largest splitting is observed when the magnetic field is applied perpendicular and the smallest when it is applied parallel to the nanowire axis. This effect is explained in terms of magnetic field induced valence band mixing and evidences the light hole character of the excitonic emission. The values of the light and heavy hole splitting are determined for several individual nanowires based on the comparison of experimental results to theoretical calculations.

Magnetic field induced mixing of light hole excitonic states in (Cd, Mn)Te/(Cd, Mg)Te core/shell nanowires.

PubMed

Płachta, Jakub; Grodzicka, Emma; Kaleta, Anna; Kret, Sławomir; Baczewski, Lech T; Pietruczik, Aleksiej; Wiater, Maciej; Goryca, Mateusz; Kazimierczuk, Tomasz; Kossacki, Piotr; Karczewski, Grzegorz; Wojtowicz, Tomasz; Wojnar, Piotr

2018-05-18

A detailed magneto-photoluminescence study of individual (Cd, Mn)Te/(Cd, Mg)Te core/shell nanowires grown by molecular beam epitaxy is performed. First of all, an enhancement of the Zeeman splitting due to sp-d exchange interaction between band carriers and Mn-spins is evidenced in these nanostructures. Then, it is found that the value of this splitting depends strongly on the magnetic field direction with respect to the nanowire axis. The largest splitting is observed when the magnetic field is applied perpendicular and the smallest when it is applied parallel to the nanowire axis. This effect is explained in terms of magnetic field induced valence band mixing and evidences the light hole character of the excitonic emission. The values of the light and heavy hole splitting are determined for several individual nanowires based on the comparison of experimental results to theoretical calculations.

A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.

PubMed

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.

Numerical simulation and experiment on multilayer stagger-split die.

PubMed

Liu, Zhiwei; Li, Mingzhe; Han, Qigang; Yang, Yunfei; Wang, Bolong; Sui, Zhou

2013-05-01

A novel ultra-high pressure device, multilayer stagger-split die, has been constructed based on the principle of "dividing dies before cracking." Multilayer stagger-split die includes an encircling ring and multilayer assemblages, and the mating surfaces of the multilayer assemblages are mutually staggered between adjacent layers. In this paper, we investigated the stressing features of this structure through finite element techniques, and the results were compared with those of the belt type die and single split die. The contrast experiments were also carried out to test the bearing pressure performance of multilayer stagger-split die. It is concluded that the stress distributions are reasonable and the materials are utilized effectively for multilayer stagger-split die. And experiments indicate that the multilayer stagger-split die can bear the greatest pressure.

Uniform sparse bounds for discrete quadratic phase Hilbert transforms

NASA Astrophysics Data System (ADS)

Kesler, Robert; Arias, Darío Mena

2017-09-01

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

Sparse Matrix for ECG Identification with Two-Lead Features.

PubMed

Tseng, Kuo-Kun; Luo, Jiao; Hegarty, Robert; 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.

The origin of the split B800 absorption peak in the LH2 complexes from Allochromatium vinosum.

PubMed

Löhner, Alexander; Carey, Anne-Marie; Hacking, Kirsty; Picken, Nichola; Kelly, Sharon; Cogdell, Richard; Köhler, Jürgen

2015-01-01

The absorption spectrum of the high-light peripheral light-harvesting (LH) complex from the photosynthetic purple bacterium Allochromatium vinosum features two strong absorptions around 800 and 850 nm. For the LH2 complexes from the species Rhodopseudomonas acidophila and Rhodospirillum molischianum, where high-resolution X-ray structures are available, similar bands have been observed and were assigned to two pigment pools of BChl a molecules that are arranged in two concentric rings (B800 and B850) with nine (acidophila) or eight (molischianum) repeat units, respectively. However, for the high-light peripheral LH complex from Alc. vinosum, the intruiging feature is that the B800 band is split into two components. We have studied this pigment-protein complex by ensemble CD spectroscopy and polarisation-resolved single-molecule spectroscopy. Assuming that the high-light peripheral LH complex in Alc. vinosum is constructed on the same modular principle as described for LH2 from Rps. acidophila and Rsp. molischianum, we used those repeat units as a starting point for simulating the spectra. We find the best agreement between simulation and experiment for a ring-like oligomer of 12 repeat units, where the mutual arrangement of the B800 and B850 rings resembles those from Rsp. molischianum. The splitting of the B800 band can be reproduced if both an excitonic coupling between dimers of B800 molecules and their interaction with the B850 manifold are taken into account. Such dimers predict an interesting apoprotein organisation as discussed below.

Low-rank matrix decomposition and spatio-temporal sparse recovery for STAP radar

DOE PAGES

Sen, Satyabrata

2015-08-04

We develop space-time adaptive processing (STAP) methods by leveraging the advantages of sparse signal processing techniques in order to detect a slowly-moving target. We observe that the inherent sparse characteristics of a STAP problem can be formulated as the low-rankness of clutter covariance matrix when compared to the total adaptive degrees-of-freedom, and also as the sparse interference spectrum on the spatio-temporal domain. By exploiting these sparse properties, we propose two approaches for estimating the interference covariance matrix. In the first approach, we consider a constrained matrix rank minimization problem (RMP) to decompose the sample covariance matrix into a low-rank positivemore » semidefinite and a diagonal matrix. The solution of RMP is obtained by applying the trace minimization technique and the singular value decomposition with matrix shrinkage operator. Our second approach deals with the atomic norm minimization problem to recover the clutter response-vector that has a sparse support on the spatio-temporal plane. We use convex relaxation based standard sparse-recovery techniques to find the solutions. With extensive numerical examples, we demonstrate the performances of proposed STAP approaches with respect to both the ideal and practical scenarios, involving Doppler-ambiguous clutter ridges, spatial and temporal decorrelation effects. As a result, the low-rank matrix decomposition based solution requires secondary measurements as many as twice the clutter rank to attain a near-ideal STAP performance; whereas the spatio-temporal sparsity based approach needs a considerably small number of secondary data.« less

On split regular BiHom-Lie superalgebras

NASA Astrophysics Data System (ADS)

Zhang, Jian; Chen, Liangyun; Zhang, Chiping

2018-06-01

We introduce the class of split regular BiHom-Lie superalgebras as the natural extension of the one of split Hom-Lie superalgebras and the one of split Lie superalgebras. By developing techniques of connections of roots for this kind of algebras, we show that such a split regular BiHom-Lie superalgebra L is of the form L = U +∑ [ α ] ∈ Λ / ∼I[α] with U a subspace of the Abelian (graded) subalgebra H and any I[α], a well described (graded) ideal of L, satisfying [I[α] ,I[β] ] = 0 if [ α ] ≠ [ β ] . Under certain conditions, in the case of L being of maximal length, the simplicity of the algebra is characterized and it is shown that L is the direct sum of the family of its simple (graded) ideals.

Irrational beliefs, attitudes about competition, and splitting.

PubMed

Watson, P J; Morris, R J; Miller, L

2001-03-01

Rational-Emotive Behavior Therapy (REBT) theoretically promotes actualization of both individualistic and social-oriented potentials. In a test of this assumption, the Belief Scale and subscales from the Survey of Personal Beliefs served as measures of what REBT presumes to be pathogenic irrationalities. These measures were correlated with the Hypercompetitive Attitude Scale (HCAS), the Personal Development Competitive Attitude Scale (PDCAS), factors from the Splitting Index, and self-esteem. Results for the HCAS and Self-Splitting supported the REBT claim about individualistic self-actualization. Mostly nonsignificant and a few counterintuitive linkages were observed for irrational beliefs with the PDCAS, Family-Splitting, and Other-Splitting, and these data suggested that REBT may be less successful in capturing the "rationality" of a social-oriented self-actualization. Copyright 2001 John Wiley & Sons, Inc.

Amesos2 and Belos: Direct and Iterative Solvers for Large Sparse Linear Systems

DOE PAGES

Bavier, Eric; Hoemmen, Mark; Rajamanickam, Sivasankaran; ...

2012-01-01

Solvers for large sparse linear systems come in two categories: direct and iterative. Amesos2, a package in the Trilinos software project, provides direct methods, and Belos, another Trilinos package, provides iterative methods. Amesos2 offers a common interface to many different sparse matrix factorization codes, and can handle any implementation of sparse matrices and vectors, via an easy-to-extend C++ traits interface. It can also factor matrices whose entries have arbitrary “Scalar” type, enabling extended-precision and mixed-precision algorithms. Belos includes many different iterative methods for solving large sparse linear systems and least-squares problems. Unlike competing iterative solver libraries, Belos completely decouples themore » algorithms from the implementations of the underlying linear algebra objects. This lets Belos exploit the latest hardware without changes to the code. Belos favors algorithms that solve higher-level problems, such as multiple simultaneous linear systems and sequences of related linear systems, faster than standard algorithms. The package also supports extended-precision and mixed-precision algorithms. Together, Amesos2 and Belos form a complete suite of sparse linear solvers.« less

WWSSF - a worldwide study on radioisotopic renal split function: reproducibility of renal split function assessment in children.

PubMed

Geist, Barbara Katharina; Dobrozemsky, Georg; Samal, Martin; Schaffarich, Michael P; Sinzinger, Helmut; Staudenherz, Anton

2015-12-01

The split or differential renal function is the most widely accepted quantitative parameter derived from radionuclide renography. To examine the intercenter variance of this parameter, we designed a worldwide round robin test. Five selected dynamic renal studies have been distributed all over the world by e-mail. Three of these studies are anonymized patient data acquired using the EANM standardized protocol and two studies are phantom studies. In a simple form, individual participants were asked to measure renal split function as well as to provide additional information such as data analysis software, positioning of background region of interest, or the method of calculation. We received the evaluation forms from 34 centers located in 21 countries. The analysis of the round robin test yielded an overall z-score of 0.3 (a z-score below 1 reflecting a good result). However, the z-scores from several centers were unacceptably high, with values greater than 3. In particular, the studies with impaired renal function showed a wide variance. A wide variance in the split renal function was found in patients with impaired kidney function. This study indicates the ultimate importance of quality control and standardization of the measurement of the split renal function. It is especially important with respect to the commonly accepted threshold for significant change in split renal function by 10%.

Polynomial meta-models with canonical low-rank approximations: Numerical insights and comparison to sparse polynomial chaos expansions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Konakli, Katerina, E-mail: konakli@ibk.baug.ethz.ch; Sudret, Bruno

2016-09-15

The growing need for uncertainty analysis of complex computational models has led to an expanding use of meta-models across engineering and sciences. The efficiency of meta-modeling techniques relies on their ability to provide statistically-equivalent analytical representations based on relatively few evaluations of the original model. Polynomial chaos expansions (PCE) have proven a powerful tool for developing meta-models in a wide range of applications; the key idea thereof is to expand the model response onto a basis made of multivariate polynomials obtained as tensor products of appropriate univariate polynomials. The classical PCE approach nevertheless faces the “curse of dimensionality”, namely themore » exponential increase of the basis size with increasing input dimension. To address this limitation, the sparse PCE technique has been proposed, in which the expansion is carried out on only a few relevant basis terms that are automatically selected by a suitable algorithm. An alternative for developing meta-models with polynomial functions in high-dimensional problems is offered by the newly emerged low-rank approximations (LRA) approach. By exploiting the tensor–product structure of the multivariate basis, LRA can provide polynomial representations in highly compressed formats. Through extensive numerical investigations, we herein first shed light on issues relating to the construction of canonical LRA with a particular greedy algorithm involving a sequential updating of the polynomial coefficients along separate dimensions. Specifically, we examine the selection of optimal rank, stopping criteria in the updating of the polynomial coefficients and error estimation. In the sequel, we confront canonical LRA to sparse PCE in structural-mechanics and heat-conduction applications based on finite-element solutions. Canonical LRA exhibit smaller errors than sparse PCE in cases when the number of available model evaluations is small with respect to the

Conditional Toxin Splicing Using a Split Intein System.

PubMed

Alford, Spencer C; O'Sullivan, Connor; Howard, Perry L

2017-01-01

Protein toxin splicing mediated by split inteins can be used as a strategy for conditional cell ablation. The approach requires artificial fragmentation of a potent protein toxin and tethering each toxin fragment to a split intein fragment. The toxin-intein fragments are, in turn, fused to dimerization domains, such that addition of a dimerizing agent reconstitutes the split intein. These chimeric toxin-intein fusions remain nontoxic until the dimerizer is added, resulting in activation of intein splicing and ligation of toxin fragments to form an active toxin. Considerations for the engineering and implementation of conditional toxin splicing (CTS) systems include: choice of toxin split site, split site (extein) chemistry, and temperature sensitivity. The following method outlines design criteria and implementation notes for CTS using a previously engineered system for splicing a toxin called sarcin, as well as for developing alternative CTS systems.

Distributed fiber sparse-wideband vibration sensing by sub-Nyquist additive random sampling

NASA Astrophysics Data System (ADS)

Zhang, Jingdong; Zheng, Hua; Zhu, Tao; Yin, Guolu; Liu, Min; Bai, Yongzhong; Qu, Dingrong; Qiu, Feng; Huang, Xianbing

2018-05-01

The round trip time of the light pulse limits the maximum detectable vibration frequency response range of phase-sensitive optical time domain reflectometry ({\\phi}-OTDR). Unlike the uniform laser pulse interval in conventional {\\phi}-OTDR, we randomly modulate the pulse interval, so that an equivalent sub-Nyquist additive random sampling (sNARS) is realized for every sensing point of the long interrogation fiber. For an {\\phi}-OTDR system with 10 km sensing length, the sNARS method is optimized by theoretical analysis and Monte Carlo simulation, and the experimental results verify that a wide-band spars signal can be identified and reconstructed. Such a method can broaden the vibration frequency response range of {\\phi}-OTDR, which is of great significance in sparse-wideband-frequency vibration signal detection, such as rail track monitoring and metal defect detection.

Layer Splitting in a Complex Plasma

NASA Astrophysics Data System (ADS)

Smith, Bernard; Hyde, Truell; Matthews, Lorin; Johnson, Megan; Cook, Mike; Schmoke, Jimmy

2009-11-01

Dust particle clouds are found in most plasma processing environments and many astrophysical environments. Dust particles suspended within such plasmas often acquire an electric charge from collisions with free electrons in the plasma. Depending upon the ratio of interparticle potential energy to average kinetic energy, charged dust particles can form a gaseous, liquid or crystalline structure with short to longer range ordering. An interesting facet of complex plasma behavior is that particle layers appear to split as the DC bias is increased. This splitting of layers points to a phase transition differing from the normal phase transitions found in two-dimensional solids. In 1993, Dubin noted that as the charged particle density of an initially two-dimensional Coulomb crystal increases the system's layers split at specific charge densities. This work modeled ions in a Paul or Penning trap, but may be applicable to dusty plasma systems as well. This work will discuss this possibility along with splitting observed in the CASPER GEC rf Reference Cell at specific pressures and powers.

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

NASA Astrophysics Data System (ADS)

Vishnukumar, S.; Wilscy, M.

2017-12-01

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

Faster than the Brighter-Light Beacon

ERIC Educational Resources Information Center

Baune, S.

2009-01-01

We analyse the motion of a spot of light projected onto a flat screen by a rotating source. We find that the motion of the spot has many interesting features such as spot splitting and superluminal effects. Our discussion is well suited for undergraduates and can be an interesting add-on in their curriculum, giving them new insights into the…

SPARSE: quadratic time simultaneous alignment and folding of RNAs without sequence-based heuristics

PubMed Central

Will, Sebastian; Otto, Christina; Miladi, Milad; Möhl, Mathias; Backofen, Rolf

2015-01-01

Motivation: RNA-Seq experiments have revealed a multitude of novel ncRNAs. The gold standard for their analysis based on simultaneous alignment and folding suffers from extreme time complexity of O(n6). Subsequently, numerous faster ‘Sankoff-style’ approaches have been suggested. Commonly, the performance of such methods relies on sequence-based heuristics that restrict the search space to optimal or near-optimal sequence alignments; however, the accuracy of sequence-based methods breaks down for RNAs with sequence identities below 60%. Alignment approaches like LocARNA that do not require sequence-based heuristics, have been limited to high complexity (≥ quartic time). Results: Breaking this barrier, we introduce the novel Sankoff-style algorithm ‘sparsified prediction and alignment of RNAs based on their structure ensembles (SPARSE)’, which runs in quadratic time without sequence-based heuristics. To achieve this low complexity, on par with sequence alignment algorithms, SPARSE features strong sparsification based on structural properties of the RNA ensembles. Following PMcomp, SPARSE gains further speed-up from lightweight energy computation. Although all existing lightweight Sankoff-style methods restrict Sankoff’s original model by disallowing loop deletions and insertions, SPARSE transfers the Sankoff algorithm to the lightweight energy model completely for the first time. Compared with LocARNA, SPARSE achieves similar alignment and better folding quality in significantly less time (speedup: 3.7). At similar run-time, it aligns low sequence identity instances substantially more accurate than RAF, which uses sequence-based heuristics. Availability and implementation: SPARSE is freely available at http://www.bioinf.uni-freiburg.de/Software/SPARSE. Contact: backofen@informatik.uni-freiburg.de Supplementary information: Supplementary data are available at Bioinformatics online. PMID:25838465

Enzyme-linked small-molecule detection using split aptamer ligation.

PubMed

Sharma, Ashwani K; Kent, Alexandra D; Heemstra, Jennifer M

2012-07-17

Here we report an aptamer-based analogue of the widely used sandwich enzyme-linked immunosorbent assay (ELISA). This assay utilizes the cocaine split aptamer, which is comprised of two DNA strands that only assemble in the presence of the target small molecule. One split aptamer fragment is immobilized on a microplate, then a test sample is added containing the second split aptamer fragment. If cocaine is present in the test sample, it directs assembly of the split aptamer and promotes a chemical ligation between azide and cyclooctyne functional groups appended to the termini of the split aptamer fragments. Ligation results in covalent attachment of biotin to the microplate and provides a colorimetric output upon conjugation to streptavidin-horseradish peroxidase. Using this assay, we demonstrate detection of cocaine at concentrations of 100 nM-100 μM in buffer and 1-100 μM human blood serum. The detection limit of 1 μM in serum represents an improvement of two orders of magnitude over previously reported split aptamer-based sensors and highlights the utility of covalently trapping split aptamer assembly events.

Greedy Algorithms for Nonnegativity-Constrained Simultaneous Sparse Recovery

PubMed Central

Kim, Daeun; Haldar, Justin P.

2016-01-01

This work proposes a family of greedy algorithms to jointly reconstruct a set of vectors that are (i) nonnegative and (ii) simultaneously sparse with a shared support set. The proposed algorithms generalize previous approaches that were designed to impose these constraints individually. Similar to previous greedy algorithms for sparse recovery, the proposed algorithms iteratively identify promising support indices. In contrast to previous approaches, the support index selection procedure has been adapted to prioritize indices that are consistent with both the nonnegativity and shared support constraints. Empirical results demonstrate for the first time that the combined use of simultaneous sparsity and nonnegativity constraints can substantially improve recovery performance relative to existing greedy algorithms that impose less signal structure. PMID:26973368

Sparse partial least squares regression for simultaneous dimension reduction and variable selection

PubMed Central

Chun, Hyonho; Keleş, Sündüz

2010-01-01

Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. PMID:20107611

Multiuser TOA Estimation Algorithm in DS-CDMA Sparse Channel for Radiolocation

NASA Astrophysics Data System (ADS)

Kim, Sunwoo

This letter considers multiuser time delay estimation in a sparse channel environment for radiolocation. The generalized successive interference cancellation (GSIC) algorithm is used to eliminate the multiple access interference (MAI). To adapt GSIC to sparse channels the alternating maximization (AM) algorithm is considered, and the continuous time delay of each path is estimated without requiring a priori known data sequences.

Al decorated ZnO thin-film photoanode for SPR-enhanced photoelectrochemical water splitting

NASA Astrophysics Data System (ADS)

Li, Hongxia; Li, Xin; Dong, Wei; Xi, Junhua; Wu, Xin

2018-06-01

Photoelectrochemical (PEC) water splitting has been considered to be a promising approach to ease the energy and environmental crisis. Herein, Al decorated ZnO thin films are successfully achieved through a facile dc magnetron-sputtering method followed with Al evaporation for further enhanced PEC performance. The Al/ZnO thin film with 60 s Al evaporating time exhibits the highest photocurrent density under AM1.5G and visible light irradiation, which are more than 5 and 3 times as the pure ZnO film, respectively. Such surface modification by Al not only enlarges the visible light absorption based on surface plasmonic resonance effect, but facilitates the charge separation and transportation at the electrode/electrolyte interface. Finally, a possible mechanism is proposed for the photocatalytic activity enhancement of Al/ZnO thin film photoanode.

A structure-preserving split finite element discretization of the split 1D linear shallow-water equations

NASA Astrophysics Data System (ADS)

Bauer, Werner; Behrens, Jörn

2017-04-01

We present a locally conservative, low-order finite element (FE) discretization of the covariant 1D linear shallow-water equations written in split form (cf. tet{[1]}). The introduction of additional differential forms (DF) that build pairs with the original ones permits a splitting of these equations into topological momentum and continuity equations and metric-dependent closure equations that apply the Hodge-star. Our novel discretization framework conserves this geometrical structure, in particular it provides for all DFs proper FE spaces such that the differential operators (here gradient and divergence) hold in strong form. The discrete topological equations simply follow by trivial projections onto piecewise constant FE spaces without need to partially integrate. The discrete Hodge-stars operators, representing the discretized metric equations, are realized by nontrivial Galerkin projections (GP). Here they follow by projections onto either a piecewise constant (GP0) or a piecewise linear (GP1) space. Our framework thus provides essentially three different schemes with significantly different behavior. The split scheme using twice GP1 is unstable and shares the same discrete dispersion relation and similar second-order convergence rates as the conventional P1-P1 FE scheme that approximates both velocity and height variables by piecewise linear spaces. The split scheme that applies both GP1 and GP0 is stable and shares the dispersion relation of the conventional P1-P0 FE scheme that approximates the velocity by a piecewise linear and the height by a piecewise constant space with corresponding second- and first-order convergence rates. Exhibiting for both velocity and height fields second-order convergence rates, we might consider the split GP1-GP0 scheme though as stable versions of the conventional P1-P1 FE scheme. For the split scheme applying twice GP0, we are not aware of a corresponding conventional formulation to compare with. Though exhibiting larger

Recent Progress in Energy-Driven Water Splitting.

PubMed

Tee, Si Yin; Win, Khin Yin; Teo, Wee Siang; Koh, Leng-Duei; Liu, Shuhua; Teng, Choon Peng; Han, Ming-Yong

2017-05-01

Hydrogen is readily obtained from renewable and non-renewable resources via water splitting by using thermal, electrical, photonic and biochemical energy. The major hydrogen production is generated from thermal energy through steam reforming/gasification of fossil fuel. As the commonly used non-renewable resources will be depleted in the long run, there is great demand to utilize renewable energy resources for hydrogen production. Most of the renewable resources may be used to produce electricity for driving water splitting while challenges remain to improve cost-effectiveness. As the most abundant energy resource, the direct conversion of solar energy to hydrogen is considered the most sustainable energy production method without causing pollutions to the environment. In overall, this review briefly summarizes thermolytic, electrolytic, photolytic and biolytic water splitting. It highlights photonic and electrical driven water splitting together with photovoltaic-integrated solar-driven water electrolysis.

Sparse kernel methods for high-dimensional survival data.

PubMed

Evers, Ludger; Messow, Claudia-Martina

2008-07-15

Sparse kernel methods like support vector machines (SVM) have been applied with great success to classification and (standard) regression settings. Existing support vector classification and regression techniques however are not suitable for partly censored survival data, which are typically analysed using Cox's proportional hazards model. As the partial likelihood of the proportional hazards model only depends on the covariates through inner products, it can be 'kernelized'. The kernelized proportional hazards model however yields a solution that is dense, i.e. the solution depends on all observations. One of the key features of an SVM is that it yields a sparse solution, depending only on a small fraction of the training data. We propose two methods. One is based on a geometric idea, where-akin to support vector classification-the margin between the failed observation and the observations currently at risk is maximised. The other approach is based on obtaining a sparse model by adding observations one after another akin to the Import Vector Machine (IVM). Data examples studied suggest that both methods can outperform competing approaches. Software is available under the GNU Public License as an R package and can be obtained from the first author's website http://www.maths.bris.ac.uk/~maxle/software.html.

Nonlinear spike-and-slab sparse coding for interpretable image encoding.

PubMed

Shelton, Jacquelyn A; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process.

Nonlinear Spike-And-Slab Sparse Coding for Interpretable Image Encoding

PubMed Central

Shelton, Jacquelyn A.; Sheikh, Abdul-Saboor; Bornschein, Jörg; Sterne, Philip; Lücke, Jörg

2015-01-01

Sparse coding is a popular approach to model natural images but has faced two main challenges: modelling low-level image components (such as edge-like structures and their occlusions) and modelling varying pixel intensities. Traditionally, images are modelled as a sparse linear superposition of dictionary elements, where the probabilistic view of this problem is that the coefficients follow a Laplace or Cauchy prior distribution. We propose a novel model that instead uses a spike-and-slab prior and nonlinear combination of components. With the prior, our model can easily represent exact zeros for e.g. the absence of an image component, such as an edge, and a distribution over non-zero pixel intensities. With the nonlinearity (the nonlinear max combination rule), the idea is to target occlusions; dictionary elements correspond to image components that can occlude each other. There are major consequences of the model assumptions made by both (non)linear approaches, thus the main goal of this paper is to isolate and highlight differences between them. Parameter optimization is analytically and computationally intractable in our model, thus as a main contribution we design an exact Gibbs sampler for efficient inference which we can apply to higher dimensional data using latent variable preselection. Results on natural and artificial occlusion-rich data with controlled forms of sparse structure show that our model can extract a sparse set of edge-like components that closely match the generating process, which we refer to as interpretable components. Furthermore, the sparseness of the solution closely follows the ground-truth number of components/edges in the images. The linear model did not learn such edge-like components with any level of sparsity. This suggests that our model can adaptively well-approximate and characterize the meaningful generation process. PMID:25954947

Photosynthetic water splitting by the Mn4Ca2+OX catalyst of photosystem II: its structure, robustness and mechanism.

PubMed

Barber, James

2017-01-01

The biological energy cycle of our planet is driven by photosynthesis whereby sunlight is absorbed by chlorophyll and other accessory pigments. The excitation energy is then efficiently transferred to a reaction centre where charge separation occurs in a few picoseconds. In the case of photosystem II (PSII), the energy of the charge transfer state is used to split water into oxygen and reducing equivalents. This is accomplished by the relatively low energy content of four photons of visible light. PSII is a large multi-subunit membrane protein complex embedded in the lipid environment of the thylakoid membranes of plants, algae and cyanobacteria. Four high energy electrons, together with four protons (4H+), are used to reduce plastoquinone (PQ), the terminal electron acceptor of PSII, to plastoquinol (PQH2). PQH2 passes its reducing equivalents to an electron transfer chain which feeds into photosystem I (PSI) where they gain additional reducing potential from a second light reaction which is necessary to drive CO2 reduction. The catalytic centre of PSII consists of a cluster of four Mn ions and a Ca2+ linked by oxo bonds. In addition, there are seven amino acid ligands. In this Article, I discuss the structure of this metal cluster, its stability and the probability that an acid-base (nucleophilic-electrophilic) mechanism catalyses the water splitting reaction on the surface of the metal-cluster. Evidence for this mechanism is presented from studies on water splitting catalysts consisting of organo-complexes of ruthenium and manganese and also by comparison with the enzymology of carbon monoxide dehydrogenase (CODH). Finally the relevance of our understanding of PSII is discussed in terms of artificial photosynthesis with emphasis on inorganic water splitting catalysts as oxygen generating photoelectrodes.

Efficient sparse matrix-matrix multiplication for computing periodic responses by shooting method on Intel Xeon Phi

NASA Astrophysics Data System (ADS)

Stoykov, S.; Atanassov, E.; Margenov, S.

2016-10-01

Many of the scientific applications involve sparse or dense matrix operations, such as solving linear systems, matrix-matrix products, eigensolvers, etc. In what concerns structural nonlinear dynamics, the computations of periodic responses and the determination of stability of the solution are of primary interest. Shooting method iswidely used for obtaining periodic responses of nonlinear systems. The method involves simultaneously operations with sparse and dense matrices. One of the computationally expensive operations in the method is multiplication of sparse by dense matrices. In the current work, a new algorithm for sparse matrix by dense matrix products is presented. The algorithm takes into account the structure of the sparse matrix, which is obtained by space discretization of the nonlinear Mindlin's plate equation of motion by the finite element method. The algorithm is developed to use the vector engine of Intel Xeon Phi coprocessors. It is compared with the standard sparse matrix by dense matrix algorithm and the one developed by Intel MKL and it is shown that by considering the properties of the sparse matrix better algorithms can be developed.

Communication: Tunnelling splitting in the phosphine molecule

NASA Astrophysics Data System (ADS)

Sousa-Silva, Clara; Tennyson, Jonathan; Yurchenko, Sergey N.

2016-09-01

Splitting due to tunnelling via the potential energy barrier has played a significant role in the study of molecular spectra since the early days of spectroscopy. The observation of the ammonia doublet led to attempts to find a phosphine analogous, but these have so far failed due to its considerably higher barrier. Full dimensional, variational nuclear motion calculations are used to predict splittings as a function of excitation energy. Simulated spectra suggest that such splittings should be observable in the near infrared via overtones of the ν2 bending mode starting with 4ν2.

Field by field hybrid upwind splitting methods

NASA Technical Reports Server (NTRS)

Coquel, Frederic; Liou, Meng-Sing

1993-01-01

A new and general approach to upwind splitting is presented. The design principle combines the robustness of flux vector splitting schemes in the capture of nonlinear waves and the accuracy of some flux difference splitting schemes in the resolution of linear waves. The new schemes are derived following a general hybridization technique performed directly at the basic level of the field by field decomposition involved in FDS methods. The scheme does not use a spatial switch to be tuned up according to the local smoothness of the approximate solution.

Enhancing adaptive sparse grid approximations and improving refinement strategies using adjoint-based a posteriori error estimates

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jakeman, J.D., E-mail: jdjakem@sandia.gov; Wildey, T.

2015-01-01

In this paper we present an algorithm for adaptive sparse grid approximations of quantities of interest computed from discretized partial differential equations. We use adjoint-based a posteriori error estimates of the physical discretization error and the interpolation error in the sparse grid to enhance the sparse grid approximation and to drive adaptivity of the sparse grid. Utilizing these error estimates provides significantly more accurate functional values for random samples of the sparse grid approximation. We also demonstrate that alternative refinement strategies based upon a posteriori error estimates can lead to further increases in accuracy in the approximation over traditional hierarchicalmore » surplus based strategies. Throughout this paper we also provide and test a framework for balancing the physical discretization error with the stochastic interpolation error of the enhanced sparse grid approximation.« less

Example-Based Image Colorization Using Locality Consistent Sparse Representation.

PubMed

Bo Li; Fuchen Zhao; Zhuo Su; Xiangguo Liang; Yu-Kun Lai; Rosin, Paul L

2017-11-01

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the state-of-the-art methods, both visually and quantitatively using a user study.

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

Signal Sampling for Efficient Sparse Representation of Resting State FMRI Data

PubMed Central

Ge, Bao; Makkie, Milad; Wang, Jin; Zhao, Shijie; Jiang, Xi; Li, Xiang; Lv, Jinglei; Zhang, Shu; Zhang, Wei; Han, Junwei; Guo, Lei; Liu, Tianming

2015-01-01

As the size of brain imaging data such as fMRI grows explosively, it provides us with unprecedented and abundant information about the brain. How to reduce the size of fMRI data but not lose much information becomes a more and more pressing issue. Recent literature studies tried to deal with it by dictionary learning and sparse representation methods, however, their computation complexities are still high, which hampers the wider application of sparse representation method to large scale fMRI datasets. To effectively address this problem, this work proposes to represent resting state fMRI (rs-fMRI) signals of a whole brain via a statistical sampling based sparse representation. First we sampled the whole brain’s signals via different sampling methods, then the sampled signals were aggregate into an input data matrix to learn a dictionary, finally this dictionary was used to sparsely represent the whole brain’s signals and identify the resting state networks. Comparative experiments demonstrate that the proposed signal sampling framework can speed-up by ten times in reconstructing concurrent brain networks without losing much information. The experiments on the 1000 Functional Connectomes Project further demonstrate its effectiveness and superiority. PMID:26646924

Medical image classification based on multi-scale non-negative sparse coding.

PubMed

Zhang, Ruijie; Shen, Jian; Wei, Fushan; Li, Xiong; Sangaiah, Arun Kumar

2017-11-01

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Copyright © 2017 Elsevier B.V. All rights reserved.

From the Rendering Equation to Stratified Light Transport Inversion

DTIC Science & Technology

2010-12-09

iteratively. These approaches relate closely to the radiosity method for diffuse global illumination in forward rendering (Hanrahan et al, 1991; Gortler et...currently simply use sparse matrices to represent T, we are also interested in exploring connections with hierar- chical and wavelet radiosity as in...Seidel iterative methods used in radiosity . 2.4 Inverse Light Transport Previous work on inverse rendering has considered inversion of the direct

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation

PubMed Central

Grossi, Giuliano; Lin, Jianyi

2017-01-01

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD’s robustness and wide applicability. PMID:28103283

Orthogonal Procrustes Analysis for Dictionary Learning in Sparse Linear Representation.

PubMed

Grossi, Giuliano; Lanzarotti, Raffaella; Lin, Jianyi

2017-01-01

In the sparse representation model, the design of overcomplete dictionaries plays a key role for the effectiveness and applicability in different domains. Recent research has produced several dictionary learning approaches, being proven that dictionaries learnt by data examples significantly outperform structured ones, e.g. wavelet transforms. In this context, learning consists in adapting the dictionary atoms to a set of training signals in order to promote a sparse representation that minimizes the reconstruction error. Finding the best fitting dictionary remains a very difficult task, leaving the question still open. A well-established heuristic method for tackling this problem is an iterative alternating scheme, adopted for instance in the well-known K-SVD algorithm. Essentially, it consists in repeating two stages; the former promotes sparse coding of the training set and the latter adapts the dictionary to reduce the error. In this paper we present R-SVD, a new method that, while maintaining the alternating scheme, adopts the Orthogonal Procrustes analysis to update the dictionary atoms suitably arranged into groups. Comparative experiments on synthetic data prove the effectiveness of R-SVD with respect to well known dictionary learning algorithms such as K-SVD, ILS-DLA and the online method OSDL. Moreover, experiments on natural data such as ECG compression, EEG sparse representation, and image modeling confirm R-SVD's robustness and wide applicability.

Galaxy redshift surveys with sparse sampling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro

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 bemore » 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.« less

New methods for sampling sparse populations

Treesearch

Anna Ringvall

2007-01-01

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

Two conditions for equivalence of 0-norm solution and 1-norm solution in sparse representation.

PubMed

Li, Yuanqing; Amari, Shun-Ichi

2010-07-01

In sparse representation, two important sparse solutions, the 0-norm and 1-norm solutions, have been receiving much of attention. The 0-norm solution is the sparsest, however it is not easy to obtain. Although the 1-norm solution may not be the sparsest, it can be easily obtained by the linear programming method. In many cases, the 0-norm solution can be obtained through finding the 1-norm solution. Many discussions exist on the equivalence of the two sparse solutions. This paper analyzes two conditions for the equivalence of the two sparse solutions. The first condition is necessary and sufficient, however, difficult to verify. Although the second is necessary but is not sufficient, it is easy to verify. In this paper, we analyze the second condition within the stochastic framework and propose a variant. We then prove that the equivalence of the two sparse solutions holds with high probability under the variant of the second condition. Furthermore, in the limit case where the 0-norm solution is extremely sparse, the second condition is also a sufficient condition with probability 1.

Sparse imaging for fast electron microscopy

NASA Astrophysics Data System (ADS)

Anderson, Hyrum S.; Ilic-Helms, Jovana; Rohrer, Brandon; Wheeler, Jason; Larson, Kurt

2013-02-01

Scanning electron microscopes (SEMs) are used in neuroscience and materials science to image centimeters of sample area at nanometer scales. Since imaging rates are in large part SNR-limited, large collections can lead to weeks of around-the-clock imaging time. To increase data collection speed, we propose and demonstrate on an operational SEM a fast method to sparsely sample and reconstruct smooth images. To accurately localize the electron probe position at fast scan rates, we model the dynamics of the scan coils, and use the model to rapidly and accurately visit a randomly selected subset of pixel locations. Images are reconstructed from the undersampled data by compressed sensing inversion using image smoothness as a prior. We report image fidelity as a function of acquisition speed by comparing traditional raster to sparse imaging modes. Our approach is equally applicable to other domains of nanometer microscopy in which the time to position a probe is a limiting factor (e.g., atomic force microscopy), or in which excessive electron doses might otherwise alter the sample being observed (e.g., scanning transmission electron microscopy).

The Splitting Loope

ERIC Educational Resources Information Center

Wilkins, Jesse L. M.; Norton, Anderson

2011-01-01

Teaching experiments have generated several hypotheses concerning the construction of fraction schemes and operations and relationships among them. In particular, researchers have hypothesized that children's construction of splitting operations is crucial to their construction of more advanced fractions concepts (Steffe, 2002). The authors…

Signal processing using sparse derivatives with applications to chromatograms and ECG

NASA Astrophysics Data System (ADS)

Ning, Xiaoran

In this thesis, we investigate the sparsity exist in the derivative domain. Particularly, we focus on the type of signals which posses up to Mth (M > 0) order sparse derivatives. Efforts are put on formulating proper penalty functions and optimization problems to capture properties related to sparse derivatives, searching for fast, computationally efficient solvers. Also the effectiveness of these algorithms are applied to two real world applications. In the first application, we provide an algorithm which jointly addresses the problems of chromatogram baseline correction and noise reduction. The series of chromatogram peaks are modeled as sparse with sparse derivatives, and the baseline is modeled as a low-pass signal. A convex optimization problem is formulated so as to encapsulate these non-parametric models. To account for the positivity of chromatogram peaks, an asymmetric penalty function is also utilized with symmetric penalty functions. A robust, computationally efficient, iterative algorithm is developed that is guaranteed to converge to the unique optimal solution. The approach, termed Baseline Estimation And Denoising with Sparsity (BEADS), is evaluated and compared with two state-of-the-art methods using both simulated and real chromatogram data. Promising result is obtained. In the second application, a novel Electrocardiography (ECG) enhancement algorithm is designed also based on sparse derivatives. In the real medical environment, ECG signals are often contaminated by various kinds of noise or artifacts, for example, morphological changes due to motion artifact, non-stationary noise due to muscular contraction (EMG), etc. Some of these contaminations severely affect the usefulness of ECG signals, especially when computer aided algorithms are utilized. By solving the proposed convex l1 optimization problem, artifacts are reduced by modeling the clean ECG signal as a sum of two signals whose second and third-order derivatives (differences) are sparse

Natural image sequences constrain dynamic receptive fields and imply a sparse code.

PubMed

Häusler, Chris; Susemihl, Alex; Nawrot, Martin P

2013-11-06

In their natural environment, animals experience a complex and dynamic visual scenery. Under such natural stimulus conditions, neurons in the visual cortex employ a spatially and temporally sparse code. For the input scenario of natural still images, previous work demonstrated that unsupervised feature learning combined with the constraint of sparse coding can predict physiologically measured receptive fields of simple cells in the primary visual cortex. This convincingly indicated that the mammalian visual system is adapted to the natural spatial input statistics. Here, we extend this approach to the time domain in order to predict dynamic receptive fields that can account for both spatial and temporal sparse activation in biological neurons. We rely on temporal restricted Boltzmann machines and suggest a novel temporal autoencoding training procedure. When tested on a dynamic multi-variate benchmark dataset this method outperformed existing models of this class. Learning features on a large dataset of natural movies allowed us to model spatio-temporal receptive fields for single neurons. They resemble temporally smooth transformations of previously obtained static receptive fields and are thus consistent with existing theories. A neuronal spike response model demonstrates how the dynamic receptive field facilitates temporal and population sparseness. We discuss the potential mechanisms and benefits of a spatially and temporally sparse representation of natural visual input. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Recent Progress in Energy‐Driven Water Splitting

PubMed Central

Tee, Si Yin; Win, Khin Yin; Teo, Wee Siang; Koh, Leng‐Duei; Liu, Shuhua; Teng, Choon Peng

2017-01-01

Hydrogen is readily obtained from renewable and non‐renewable resources via water splitting by using thermal, electrical, photonic and biochemical energy. The major hydrogen production is generated from thermal energy through steam reforming/gasification of fossil fuel. As the commonly used non‐renewable resources will be depleted in the long run, there is great demand to utilize renewable energy resources for hydrogen production. Most of the renewable resources may be used to produce electricity for driving water splitting while challenges remain to improve cost‐effectiveness. As the most abundant energy resource, the direct conversion of solar energy to hydrogen is considered the most sustainable energy production method without causing pollutions to the environment. In overall, this review briefly summarizes thermolytic, electrolytic, photolytic and biolytic water splitting. It highlights photonic and electrical driven water splitting together with photovoltaic‐integrated solar‐driven water electrolysis. PMID:28546906

Performance Models for Split-execution Computing Systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Humble, Travis S; McCaskey, Alex; Schrock, Jonathan

Split-execution computing leverages the capabilities of multiple computational models to solve problems, but splitting program execution across different computational models incurs costs associated with the translation between domains. We analyze the performance of a split-execution computing system developed from conventional and quantum processing units (QPUs) by using behavioral models that track resource usage. We focus on asymmetric processing models built using conventional CPUs and a family of special-purpose QPUs that employ quantum computing principles. Our performance models account for the translation of a classical optimization problem into the physical representation required by the quantum processor while also accounting for hardwaremore » limitations and conventional processor speed and memory. We conclude that the bottleneck in this split-execution computing system lies at the quantum-classical interface and that the primary time cost is independent of quantum processor behavior.« less

Sparse non-negative matrix factorizations via alternating non-negativity-constrained least squares for microarray data analysis.

PubMed

Kim, Hyunsoo; Park, Haesun

2007-06-15

Many practical pattern recognition problems require non-negativity constraints. For example, pixels in digital images and chemical concentrations in bioinformatics are non-negative. Sparse non-negative matrix factorizations (NMFs) are useful when the degree of sparseness in the non-negative basis matrix or the non-negative coefficient matrix in an NMF needs to be controlled in approximating high-dimensional data in a lower dimensional space. In this article, we introduce a novel formulation of sparse NMF and show how the new formulation leads to a convergent sparse NMF algorithm via alternating non-negativity-constrained least squares. We apply our sparse NMF algorithm to cancer-class discovery and gene expression data analysis and offer biological analysis of the results obtained. Our experimental results illustrate that the proposed sparse NMF algorithm often achieves better clustering performance with shorter computing time compared to other existing NMF algorithms. The software is available as supplementary material.

Generation of dense statistical connectomes from sparse morphological data

PubMed Central

Egger, Robert; Dercksen, Vincent J.; Udvary, Daniel; Hege, Hans-Christian; Oberlaender, Marcel

2014-01-01

Sensory-evoked signal flow, at cellular and network levels, is primarily determined by the synaptic wiring of the underlying neuronal circuitry. Measurements of synaptic innervation, connection probabilities and subcellular organization of synaptic inputs are thus among the most active fields of research in contemporary neuroscience. Methods to measure these quantities range from electrophysiological recordings over reconstructions of dendrite-axon overlap at light-microscopic levels to dense circuit reconstructions of small volumes at electron-microscopic resolution. However, quantitative and complete measurements at subcellular resolution and mesoscopic scales to obtain all local and long-range synaptic in/outputs for any neuron within an entire brain region are beyond present methodological limits. Here, we present a novel concept, implemented within an interactive software environment called NeuroNet, which allows (i) integration of sparsely sampled (sub)cellular morphological data into an accurate anatomical reference frame of the brain region(s) of interest, (ii) up-scaling to generate an average dense model of the neuronal circuitry within the respective brain region(s) and (iii) statistical measurements of synaptic innervation between all neurons within the model. We illustrate our approach by generating a dense average model of the entire rat vibrissal cortex, providing the required anatomical data, and illustrate how to measure synaptic innervation statistically. Comparing our results with data from paired recordings in vitro and in vivo, as well as with reconstructions of synaptic contact sites at light- and electron-microscopic levels, we find that our in silico measurements are in line with previous results. PMID:25426033

Fee Splitting among General Practitioners: A Cross-Sectional Study in Iran.

PubMed

Parsa, Mojtaba; Larijani, Bagher; Aramesh, Kiarash; Nedjat, Saharnaz; Fotouhi, Akbar; Yekaninejad, Mir Saeed; Ebrahimian, Nejatollah; Kandi, Mohamad Jafar

2016-12-01

Fee splitting is a process whereby a physician refers a patient to another physician or a healthcare facility and receives a portion of the charge in return. This survey was conducted to study general practitioners' (GPs) attitudes toward fee splitting as well as the prevalence, causes, and consequences of this process. This is a cross-sectional study on 223 general practitioners in 2013. Concerning the causes and consequences of fee splitting, an unpublished qualitative study was conducted by interviewing a number of GPs and specialists and the questionnaire options were the results of the information obtained from this study. Of the total 320 GPs, 247 returned the questionnaires. The response rate was 77.18%. Of the 247 returned questionnaires, 223 fulfilled the inclusion criteria. Among the participants, 69.1% considered fee splitting completely wrong and 23.2% (frequently or rarely) practiced fee splitting. The present study showed that the prevalence of fee splitting among physicians who had positive attitudes toward fee splitting was 4.63 times higher than those who had negative attitudes. In addition, this study showed that, compared to private hospitals, fee splitting is less practiced in public hospitals. The major cause of fee splitting was found to be unrealistic/unfair tariffs and the main consequence of fee splitting was thought to be an increase in the number of unnecessary patient referrals. Fee splitting is an unethical act, contradicts the goals of the medical profession, and undermines patient's best interest. In Iran, there is no code of ethics on fee splitting, but in this study, it was found that the majority of GPs considered it unethical. However, among those who had negative attitudes toward fee splitting, there were physicians who did practice fee splitting. The results of the study showed that physicians who had a positive attitude toward fee splitting practiced it more than others. Therefore, if physicians consider fee splitting unethical

Mini-Split Heat Pumps Multifamily Retrofit Feasibility Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dentz, Jordan; Podorson, David; Varshney, Kapil

Mini-split heat pumps can provide space heating and cooling in many climates and are relatively affordable. These and other features make them potentially suitable for retrofitting into multifamily buildings in cold climates to replace electric resistance heating or other outmoded heating systems. This report investigates the suitability of mini-split heat pumps for multifamily retrofits. Various technical and regulatory barriers are discussed and modeling was performed to compare long-term costs of substituting mini-splits for a variety of other heating and cooling options. A number of utility programs have retrofit mini-splits in both single family and multifamily residences. Two such multifamily programsmore » are discussed in detail.« less

Mini-Split Heat Pumps Multifamily Retrofit Feasibility Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dentz, J.; Podorson, D.; Varshney, K.

2014-05-01

Mini-split heat pumps can provide space heating and cooling in many climates and are relatively affordable. These and other features make them potentially suitable for retrofitting into multifamily buildings in cold climates to replace electric resistance heating or other outmoded heating systems. This report investigates the suitability of mini-split heat pumps for multifamily retrofits. Various technical and regulatory barriers are discussed and modeling was performed to compare long-term costs of substituting mini-splits for a variety of other heating and cooling options. A number of utility programs have retrofit mini-splits in both single family and multifamily residences. Two such multifamily programsmore » are discussed in detail.« less

An efficient classification method based on principal component and sparse representation.

PubMed

Zhai, Lin; Fu, Shujun; Zhang, Caiming; Liu, Yunxian; Wang, Lu; Liu, Guohua; Yang, Mingqiang

2016-01-01

As an important application in optical imaging, palmprint recognition is interfered by many unfavorable factors. An effective fusion of blockwise bi-directional two-dimensional principal component analysis and grouping sparse classification is presented. The dimension reduction and normalizing are implemented by the blockwise bi-directional two-dimensional principal component analysis for palmprint images to extract feature matrixes, which are assembled into an overcomplete dictionary in sparse classification. A subspace orthogonal matching pursuit algorithm is designed to solve the grouping sparse representation. Finally, the classification result is gained by comparing the residual between testing and reconstructed images. Experiments are carried out on a palmprint database, and the results show that this method has better robustness against position and illumination changes of palmprint images, and can get higher rate of palmprint recognition.

Ontology Sparse Vector Learning Algorithm for Ontology Similarity Measuring and Ontology Mapping via ADAL Technology

NASA Astrophysics Data System (ADS)

Gao, Wei; Zhu, Linli; Wang, Kaiyun

2015-12-01

Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

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

PubMed Central

Zhang, Jie; Fan, Shangang; Xiong, Jian; Cheng, Xiefeng; Sari, Hikmet; Adachi, Fumiyuki

2017-01-01

Both L1/2 and L2/3 are two typical non-convex regularizations of Lp (0sparse transformation strategy has been developed to exploit the sparsity in L1 regularization for sparse signal recovery, which combines the iterative reweighted algorithms. To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases p∈{1/2, 2/3} based on an iterative Lp thresholding algorithm and then proposes a sparse adaptive iterative-weighted Lp thresholding algorithm (SAITA). Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based Lp regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L1 algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based Lp case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work. PMID:29244777

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

PubMed

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

2017-12-15

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

The Splitting Group

ERIC Educational Resources Information Center

Norton, Anderson; Wilkins, Jesse L. M.

2012-01-01

Piagetian theory describes mathematical development as the construction and organization of mental operations within psychological structures. Research on student learning has identified the vital roles of two particular operations--splitting and units coordination--play in students' development of advanced fractions knowledge. Whereas Steffe and…

Stable and low diffusive hybrid upwind splitting methods

NASA Technical Reports Server (NTRS)

Coquel, Frederic; Liou, Meng-Sing

1992-01-01

A new concept for upwinding is introduced, named the hybrid upwind splitting (HUS), which is achieved by combining the basically distinct flux vector splitting (FVS) and the flux difference splitting (FDS) approaches. The HUS approach yields upwind methods which share the robustness of the FVS schemes in the capture of nonlinear waves and the accuracy of some of the FDS schemes. Numerical illustrations are presented proving the relevance of the HUS methods for viscous calculations.

Bunch Splitting Simulations for the JLEIC Ion Collider Ring

DOE Office of Scientific and Technical Information (OSTI.GOV)

Satogata, Todd J.; Gamage, Randika

2016-05-01

We describe the bunch splitting strategies for the proposed JLEIC ion collider ring at Jefferson Lab. This complex requires an unprecedented 9:6832 bunch splitting, performed in several stages. We outline the problem and current results, optimized with ESME including general parameterization of 1:2 bunch splitting for JLEIC parameters.

Splitting of turbulent spot in transitional pipe flow

NASA Astrophysics Data System (ADS)

Wu, Xiaohua; Moin, Parviz; Adrian, Ronald J.

2017-11-01

Recent study (Wu et al., PNAS, 1509451112, 2015) demonstrated the feasibility and accuracy of direct computation of the Osborne Reynolds' pipe transition problem without the unphysical, axially periodic boundary condition. Here we use this approach to study the splitting of turbulent spot in transitional pipe flow, a feature first discovered by E.R. Lindgren (Arkiv Fysik 15, 1959). It has been widely believed that spot splitting is a mysterious stochastic process that has general implications on the lifetime and sustainability of wall turbulence. We address the following two questions: (1) What is the dynamics of turbulent spot splitting in pipe transition? Specifically, we look into any possible connection between the instantaneous strain rate field and the spot splitting. (2) How does the passive scalar field behave during the process of pipe spot splitting. In this study, the turbulent spot is introduced at the inlet plane through a sixty degree wide numerical wedge within which fully-developed turbulent profiles are assigned over a short time interval; and the simulation Reynolds numbers are 2400 for a 500 radii long pipe, and 2300 for a 1000 radii long pipe, respectively. Numerical dye is tagged on the imposed turbulent spot at the inlet. Splitting of the imposed turbulent spot is detected very easily. Preliminary analysis of the DNS results seems to suggest that turbulent spot slitting can be easily understood based on instantaneous strain rate field, and such spot splitting may not be relevant in external flows such as the flat-plate boundary layer.

Arrangement for multiplexing and intensity splitting light beams for interface into fiber optic cables

DOEpatents

Johnson, Steve A.

1990-01-01

An arrangement especially suitable for use in a laser apparatus for converting a plurality of different input light beams, for example copper vapor laser beams, into a plurality of substantially identical light beams is disclosed herein. This arrangement utilizes an optical mixing bar which is preferably integrally formed as a single unit and which includes a main body for mixing light therein, a flat input surface on one end of the main body, and a multi-faceted output face on the opposite end of the main body. This arrangement also includes means for directing the plurality of different input light beams onto the input face of the mixing base, whereby to cause the different beams to mix within the main body of the mixing bar and exit the latter from its multi-faceted output face as the desired plurality of substantially identical output beams.

Single and multi-band electromagnetic induced transparency-like metamaterials with coupled split ring resonators

NASA Astrophysics Data System (ADS)

Bagci, Fulya; Akaoglu, Baris

2017-08-01

We present a metamaterial configuration exhibiting single and multi-band electromagnetic induced transparency (EIT)-like properties. The unit cell of the single band EIT-like metamaterial consists of a multi-split ring resonator surrounded by a split ring resonator. The multi-split ring resonator acts as a quasi-dark or dark resonator, depending on the polarization of the incident wave, and the split ring resonator serves as the bright resonator. Combination of these two resonators results in a single band EIT-like transmission inside the stop band. EIT-like transmission phenomenon is also clearly observed in the measured transmission spectrum at almost the same frequencies for vertical and horizontal polarized waves, and the numerical results are verified for normal incidence. Moreover, multi-band transmission windows are created within a wide band by combining the two slightly different single band EIT-like metamaterial unit cells that exhibit two different coupling strengths inside a supercell configuration. Group indices as high as 123 for single band and 488 for tri-band transmission, accompanying with high transmission rates (over 80%), are achieved, rendering the metamaterial very suitable for multi-band slow light applications. It is shown that the group delay of the propagating wave can be increased and dynamically controlled by changing the polarization angle. Multi-band EIT-like transmission is also verified experimentally, and a good agreement with simulations is obtained. The proposed novel methodology for obtaining multi-band EIT, which takes advantage of a supercell configuration by hosting slightly different configured unit cells, can be utilized for easily formation and manipulation of multi-band transmission windows inside a stop band.

Near Infrared Photoimmunotherapy Targeting EGFR Positive Triple Negative Breast Cancer: Optimizing the Conjugate-Light Regimen.

PubMed

Nagaya, Tadanobu; Sato, Kazuhide; Harada, Toshiko; Nakamura, Yuko; Choyke, Peter L; Kobayashi, Hisataka

2015-01-01

Triple-negative breast cancer (TNBC) is considered one of the most aggressive subtypes of breast cancer. Near infrared photoimmunotherapy (NIR-PIT) is a cancer treatment that employs an antibody-photosensitizer conjugate (APC) followed by exposure of NIR light for activating selective cytotoxicity on targeted cancer cells and may have application to TNBC. In order to minimize the dose of APC while maximizing the therapeutic effects, dosing of the APC and NIR light need to be optimized. In this study, we investigate in vitro and in vivo efficacy of cetuximab (cet)-IR700 NIR-PIT on two breast cancer models MDAMB231 (TNBC, EGFR moderate) and MDAMB468 (TNBC, EGFR high) cell lines, and demonstrate a method to optimize the dosing APC and NIR light. After validating in vitro cell-specific cytotoxicity, NIR-PIT therapeutic effects were investigated in mouse models using cell lines derived from TNBC tumors. Tumor-bearing mice were separated into 4 groups for the following treatments: (1) no treatment (control); (2) 300 μg of cet-IR700 i.v., (APC i.v. only); (3) NIR light exposure only, NIR light was administered at 50 J/cm2 on day 1 and 100 J/cm2 on day 2 (NIR light only); (4) 300 μg of cet-IR700 i.v., NIR light was administered at 50 J/cm2 on day 1 after injection and 100 J/cm2 of light on day 2 after injection (one shot NIR-PIT). To compare different treatment regimens with a fixed dose of APC, we added the following treatments (5) 100 μg of cet-IR700 i.v., NIR light administered at 50 J/cm2 on day 1 and 50 μg of cet-IR700 i.v. immediately after NIR-PIT, then NIR light was administered at 100 J/cm2 on day 2, which were performed two times every week ("two split" NIR-PIT) and (6) 100 μg of cet-IR700 i.v., NIR light was administered at 50 J/cm2 on day 1 and 100 J/cm2 on day 2, which were performed three times per week ("three split" NIR-PIT). Both specific binding and NIR-PIT effects were greater with MDAMB468 than MDAMB231 cells in vitro. Tumor accumulation of cet

Multi-layer sparse representation for weighted LBP-patches based facial expression recognition.

PubMed

Jia, Qi; Gao, Xinkai; Guo, He; Luo, Zhongxuan; Wang, Yi

2015-03-19

In this paper, a novel facial expression recognition method based on sparse representation is proposed. Most contemporary facial expression recognition systems suffer from limited ability to handle image nuisances such as low resolution and noise. Especially for low intensity expression, most of the existing training methods have quite low recognition rates. Motivated by sparse representation, the problem can be solved by finding sparse coefficients of the test image by the whole training set. Deriving an effective facial representation from original face images is a vital step for successful facial expression recognition. We evaluate facial representation based on weighted local binary patterns, and Fisher separation criterion is used to calculate the weighs of patches. A multi-layer sparse representation framework is proposed for multi-intensity facial expression recognition, especially for low-intensity expressions and noisy expressions in reality, which is a critical problem but seldom addressed in the existing works. To this end, several experiments based on low-resolution and multi-intensity expressions are carried out. Promising results on publicly available databases demonstrate the potential of the proposed approach.

Face recognition via sparse representation of SIFT feature on hexagonal-sampling image

NASA Astrophysics Data System (ADS)

Zhang, Daming; Zhang, Xueyong; Li, Lu; Liu, Huayong

2018-04-01

This paper investigates a face recognition approach based on Scale Invariant Feature Transform (SIFT) feature and sparse representation. The approach takes advantage of SIFT which is local feature other than holistic feature in classical Sparse Representation based Classification (SRC) algorithm and possesses strong robustness to expression, pose and illumination variations. Since hexagonal image has more inherit merits than square image to make recognition process more efficient, we extract SIFT keypoint in hexagonal-sampling image. Instead of matching SIFT feature, firstly the sparse representation of each SIFT keypoint is given according the constructed dictionary; secondly these sparse vectors are quantized according dictionary; finally each face image is represented by a histogram and these so-called Bag-of-Words vectors are classified by SVM. Due to use of local feature, the proposed method achieves better result even when the number of training sample is small. In the experiments, the proposed method gave higher face recognition rather than other methods in ORL and Yale B face databases; also, the effectiveness of the hexagonal-sampling in the proposed method is verified.

Robust Period Estimation Using Mutual Information for Multiband Light Curves in the Synoptic Survey Era

NASA Astrophysics Data System (ADS)

Huijse, Pablo; Estévez, Pablo A.; Förster, Francisco; Daniel, Scott F.; Connolly, Andrew J.; Protopapas, Pavlos; Carrasco, Rodrigo; Príncipe, José C.

2018-05-01

The Large Synoptic Survey Telescope (LSST) will produce an unprecedented amount of light curves using six optical bands. Robust and efficient methods that can aggregate data from multidimensional sparsely sampled time-series are needed. In this paper we present a new method for light curve period estimation based on quadratic mutual information (QMI). The proposed method does not assume a particular model for the light curve nor its underlying probability density and it is robust to non-Gaussian noise and outliers. By combining the QMI from several bands the true period can be estimated even when no single-band QMI yields the period. Period recovery performance as a function of average magnitude and sample size is measured using 30,000 synthetic multiband light curves of RR Lyrae and Cepheid variables generated by the LSST Operations and Catalog simulators. The results show that aggregating information from several bands is highly beneficial in LSST sparsely sampled time-series, obtaining an absolute increase in period recovery rate up to 50%. We also show that the QMI is more robust to noise and light curve length (sample size) than the multiband generalizations of the Lomb–Scargle and AoV periodograms, recovering the true period in 10%–30% more cases than its competitors. A python package containing efficient Cython implementations of the QMI and other methods is provided.

Temporally-Constrained Group Sparse Learning for Longitudinal Data Analysis in Alzheimer’s Disease

PubMed Central

Jie, Biao; Liu, Mingxia; Liu, Jun

2016-01-01

Sparse learning has been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing sparse learning-based studies only adopt cross-sectional analysis methods, where the sparse model is learned using data from a single time-point. Actually, multiple time-points of data are often available in brain imaging applications, which can be used in some longitudinal analysis methods to better uncover the disease progression patterns. Accordingly, in this paper we propose a novel temporally-constrained group sparse learning method aiming for longitudinal analysis with multiple time-points of data. Specifically, we learn a sparse linear regression model by using the imaging data from multiple time-points, where a group regularization term is first employed to group the weights for the same brain region across different time-points together. Furthermore, to reflect the smooth changes between data derived from adjacent time-points, we incorporate two smoothness regularization terms into the objective function, i.e., one fused smoothness term which requires that the differences between two successive weight vectors from adjacent time-points should be small, and another output smoothness term which requires the differences between outputs of two successive models from adjacent time-points should also be small. We develop an efficient optimization algorithm to solve the proposed objective function. Experimental results on ADNI database demonstrate that, compared with conventional sparse learning-based methods, our proposed method can achieve improved regression performance and also help in discovering disease-related biomarkers. PMID:27093313

Exposing the QCD Splitting Function with CMS Open Data.

PubMed

Larkoski, Andrew; Marzani, Simone; Thaler, Jesse; Tripathee, Aashish; Xue, Wei

2017-09-29

The splitting function is a universal property of quantum chromodynamics (QCD) which describes how energy is shared between partons. Despite its ubiquitous appearance in many QCD calculations, the splitting function cannot be measured directly, since it always appears multiplied by a collinear singularity factor. Recently, however, a new jet substructure observable was introduced which asymptotes to the splitting function for sufficiently high jet energies. This provides a way to expose the splitting function through jet substructure measurements at the Large Hadron Collider. In this Letter, we use public data released by the CMS experiment to study the two-prong substructure of jets and test the 1→2 splitting function of QCD. To our knowledge, this is the first ever physics analysis based on the CMS Open Data.

BIRD: A general interface for sparse distributed memory simulators

NASA Technical Reports Server (NTRS)

Rogers, David

1990-01-01

Kanerva's sparse distributed memory (SDM) has now been implemented for at least six different computers, including SUN3 workstations, the Apple Macintosh, and the Connection Machine. A common interface for input of commands would both aid testing of programs on a broad range of computer architectures and assist users in transferring results from research environments to applications. A common interface also allows secondary programs to generate command sequences for a sparse distributed memory, which may then be executed on the appropriate hardware. The BIRD program is an attempt to create such an interface. Simplifying access to different simulators should assist developers in finding appropriate uses for SDM.

Sparse modeling applied to patient identification for safety in medical physics applications

NASA Astrophysics Data System (ADS)

Lewkowitz, Stephanie

Every scheduled treatment at a radiation therapy clinic involves a series of safety protocol to ensure the utmost patient care. Despite safety protocol, on a rare occasion an entirely preventable medical event, an accident, may occur. Delivering a treatment plan to the wrong patient is preventable, yet still is a clinically documented error. This research describes a computational method to identify patients with a novel machine learning technique to combat misadministration. The patient identification program stores face and fingerprint data for each patient. New, unlabeled data from those patients are categorized according to the library. The categorization of data by this face-fingerprint detector is accomplished with new machine learning algorithms based on Sparse Modeling that have already begun transforming the foundation of Computer Vision. Previous patient recognition software required special subroutines for faces and different tailored subroutines for fingerprints. In this research, the same exact model is used for both fingerprints and faces, without any additional subroutines and even without adjusting the two hyperparameters. Sparse modeling is a powerful tool, already shown utility in the areas of super-resolution, denoising, inpainting, demosaicing, and sub-nyquist sampling, i.e. compressed sensing. Sparse Modeling is possible because natural images are inherently sparse in some bases, due to their inherent structure. This research chooses datasets of face and fingerprint images to test the patient identification model. The model stores the images of each dataset as a basis (library). One image at a time is removed from the library, and is classified by a sparse code in terms of the remaining library. The Locally Competitive Algorithm, a truly neural inspired Artificial Neural Network, solves the computationally difficult task of finding the sparse code for the test image. The components of the sparse representation vector are summed by ℓ1 pooling

Improving M-SBL for Joint Sparse Recovery Using a Subspace Penalty

NASA Astrophysics Data System (ADS)

Ye, Jong Chul; Kim, Jong Min; Bresler, Yoram

2015-12-01

The multiple measurement vector problem (MMV) is a generalization of the compressed sensing problem that addresses the recovery of a set of jointly sparse signal vectors. One of the important contributions of this paper is to reveal that the seemingly least related state-of-art MMV joint sparse recovery algorithms - M-SBL (multiple sparse Bayesian learning) and subspace-based hybrid greedy algorithms - have a very important link. More specifically, we show that replacing the $\\log\\det(\\cdot)$ term in M-SBL by a rank proxy that exploits the spark reduction property discovered in subspace-based joint sparse recovery algorithms, provides significant improvements. In particular, if we use the Schatten-$p$ quasi-norm as the corresponding rank proxy, the global minimiser of the proposed algorithm becomes identical to the true solution as $p \\rightarrow 0$. Furthermore, under the same regularity conditions, we show that the convergence to a local minimiser is guaranteed using an alternating minimization algorithm that has closed form expressions for each of the minimization steps, which are convex. Numerical simulations under a variety of scenarios in terms of SNR, and condition number of the signal amplitude matrix demonstrate that the proposed algorithm consistently outperforms M-SBL and other state-of-the art algorithms.

Improving recovery of ECG signal with deterministic guarantees using split signal for multiple supports of matching pursuit (SS-MSMP) algorithm.

PubMed

Tawfic, Israa Shaker; Kayhan, Sema Koc

2017-02-01

Compressed sensing (CS) is a new field used for signal acquisition and design of sensor that made a large drooping in the cost of acquiring sparse signals. In this paper, new algorithms are developed to improve the performance of the greedy algorithms. In this paper, a new greedy pursuit algorithm, SS-MSMP (Split Signal for Multiple Support of Matching Pursuit), is introduced and theoretical analyses are given. The SS-MSMP is suggested for sparse data acquisition, in order to reconstruct analog and efficient signals via a small set of general measurements. This paper proposes a new fast method which depends on a study of the behavior of the support indices through picking the best estimation of the corrosion between residual and measurement matrix. The term multiple supports originates from an algorithm; in each iteration, the best support indices are picked based on maximum quality created by discovering correlation for a particular length of support. We depend on this new algorithm upon our previous derivative of halting condition that we produce for Least Support Orthogonal Matching Pursuit (LS-OMP) for clear and noisy signal. For better reconstructed results, SS-MSMP algorithm provides the recovery of support set for long signals such as signals used in WBAN. Numerical experiments demonstrate that the new suggested algorithm performs well compared to existing algorithms in terms of many factors used for reconstruction performance. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Locating multiple diffusion sources in time varying networks from sparse observations.

PubMed

Hu, Zhao-Long; Shen, Zhesi; Cao, Shinan; Podobnik, Boris; Yang, Huijie; Wang, Wen-Xu; Lai, Ying-Cheng

2018-02-08

Data based source localization in complex networks has a broad range of applications. Despite recent progress, locating multiple diffusion sources in time varying networks remains to be an outstanding problem. Bridging structural observability and sparse signal reconstruction theories, we develop a general framework to locate diffusion sources in time varying networks based solely on sparse data from a small set of messenger nodes. A general finding is that large degree nodes produce more valuable information than small degree nodes, a result that contrasts that for static networks. Choosing large degree nodes as the messengers, we find that sparse observations from a few such nodes are often sufficient for any number of diffusion sources to be located for a variety of model and empirical networks. Counterintuitively, sources in more rapidly varying networks can be identified more readily with fewer required messenger nodes.

Sparse-View Ultrasound Diffraction Tomography Using Compressed Sensing with Nonuniform FFT

PubMed Central

2014-01-01

Accurate reconstruction of the object from sparse-view sampling data is an appealing issue for ultrasound diffraction tomography (UDT). In this paper, we present a reconstruction method based on compressed sensing framework for sparse-view UDT. Due to the piecewise uniform characteristics of anatomy structures, the total variation is introduced into the cost function to find a more faithful sparse representation of the object. The inverse problem of UDT is iteratively resolved by conjugate gradient with nonuniform fast Fourier transform. Simulation results show the effectiveness of the proposed method that the main characteristics of the object can be properly presented with only 16 views. Compared to interpolation and multiband method, the proposed method can provide higher resolution and lower artifacts with the same view number. The robustness to noise and the computation complexity are also discussed. PMID:24868241

Catalysts Based on Earth-Abundant Metals for Visible Light-Driven Water Oxidation Reaction.

PubMed

Lin, Junqi; Han, Qing; Ding, Yong

2018-06-04

Exploration of water oxidation catalyst (WOC) with excellent performance is the key for the overall water splitting reaction, which is a feasible strategy to convert solar energy to chemical energy. Although some compounds composed of noble metals, mainly Ru and Ir, have been reported to catalyze water oxidation with high efficiency, catalysts based on low-cost and earth-abundant transition metals are essential for realizing economical and large-scale light-driven water splitting. Various WOCs containing earth-abundant metals (mainly Mn, Fe, Co, Ni, Cu) have been utilized for visible light-driven water oxidation in recent years. In this Personal Account, we summarize our recent developments in WOCs based on earth-abundant transition metals including polyoxometalates (POMs), metal oxides or bimetal oxides, and metal complexes containing multidentate ligand scaffolds for visible light-driven water oxidation reaction. © 2018 The Chemical Society of Japan & Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Universal exchange-driven phonon splitting

NASA Astrophysics Data System (ADS)

Deisenhofer, Joachim; Kant, Christian; Schmidt, Michael; Wang, Zhe; Mayr, Franz; Tsurkan, Vladimir; Loidl, Alois

2012-02-01

We report on a linear dependence of the phonon splitting on the non-dominant exchange coupling Jnd in the antiferromagnetic monoxides MnO, Fe0.92O, CoO and NiO, and in the highly frustrated antiferromagnetic spinels CdCr2O4, MgCr2O4 and ZnCr2O4. For the monoxides our results directly confirm the theoretical prediction of a predominantly exchange induced splitting of the zone-centre optical phonon [1,2]. We find the linear relation δφ= βJndS^2 with slope β = 3.7. This relation also holds for a very different class of systems, namely the highly frustrated chromium spinels. Our finding suggests a universal dependence of the exchange-induced phonon splitting at the antiferromagnetic transition on the non-dominant exchange coupling [3].[4pt] [1] S. Massidda et al., Phys. Rev. Lett. 82, 430 (1999).[0pt] [2] W. Luo et al., Solid State Commun. 142, 504 (2007).[0pt] [3] Ch. Kant et al., arxiv:1109.4809.

Split torque transmission load sharing

NASA Technical Reports Server (NTRS)

Krantz, T. L.; Rashidi, M.; Kish, J. G.

1992-01-01

Split torque transmissions are attractive alternatives to conventional planetary designs for helicopter transmissions. The split torque designs can offer lighter weight and fewer parts but have not been used extensively for lack of experience, especially with obtaining proper load sharing. Two split torque designs that use different load sharing methods have been studied. Precise indexing and alignment of the geartrain to produce acceptable load sharing has been demonstrated. An elastomeric torque splitter that has large torsional compliance and damping produces even better load sharing while reducing dynamic transmission error and noise. However, the elastomeric torque splitter as now configured is not capable over the full range of operating conditions of a fielded system. A thrust balancing load sharing device was evaluated. Friction forces that oppose the motion of the balance mechanism are significant. A static analysis suggests increasing the helix angle of the input pinion of the thrust balancing design. Also, dynamic analysis of this design predicts good load sharing and significant torsional response to accumulative pitch errors of the gears.

Simulation and analysis of the interactions between split gradient coils and a split magnet cryostat in an MRI-PET system.

PubMed

Liu, Limei; Sanchez-Lopez, Hector; Poole, Michael; Liu, Feng; Crozier, Stuart

2012-09-01

Splitting a magnetic resonance imaging (MRI) magnet into two halves can provide a central region to accommodate other modalities, such as positron emission tomography (PET). This approach, however, produces challenges in the design of the gradient coils in terms of gradient performance and fabrication. In this paper, the impact of a central gap in a split MRI system was theoretically studied by analysing the performance of split, actively-shielded transverse gradient coils. In addition, the effects of the eddy currents induced in the cryostat on power loss, mechanical vibration and magnetic field harmonics were also investigated. It was found, as expected, that the gradient performance tended to decrease as the central gap increased. Furthermore, the effects of the eddy currents were heightened as a consequence of splitting the gradient assembly into two halves. An optimal central gap size was found, such that the split gradient coils designed with this central gap size could produce an engineering solution with an acceptable trade-off between gradient performance and eddy current effects. These investigations provide useful information on the inherent trade-offs in hybrid MRI imaging systems. Copyright © 2012 Elsevier Inc. All rights reserved.

Semi-implicit integration factor methods on sparse grids for high-dimensional systems

NASA Astrophysics Data System (ADS)

Wang, Dongyong; Chen, Weitao; Nie, Qing

2015-07-01

Numerical methods for partial differential equations in high-dimensional spaces are often limited by the curse of dimensionality. Though the sparse grid technique, based on a one-dimensional hierarchical basis through tensor products, is popular for handling challenges such as those associated with spatial discretization, the stability conditions on time step size due to temporal discretization, such as those associated with high-order derivatives in space and stiff reactions, remain. Here, we incorporate the sparse grids with the implicit integration factor method (IIF) that is advantageous in terms of stability conditions for systems containing stiff reactions and diffusions. We combine IIF, in which the reaction is treated implicitly and the diffusion is treated explicitly and exactly, with various sparse grid techniques based on the finite element and finite difference methods and a multi-level combination approach. The overall method is found to be efficient in terms of both storage and computational time for solving a wide range of PDEs in high dimensions. In particular, the IIF with the sparse grid combination technique is flexible and effective in solving systems that may include cross-derivatives and non-constant diffusion coefficients. Extensive numerical simulations in both linear and nonlinear systems in high dimensions, along with applications of diffusive logistic equations and Fokker-Planck equations, demonstrate the accuracy, efficiency, and robustness of the new methods, indicating potential broad applications of the sparse grid-based integration factor method.

Greedy Sparse Approaches for Homological Coverage in Location Unaware Sensor Networks

DTIC Science & Technology

2017-12-08

GlobalSIP); 2013 Dec; Austin , TX . p. 595– 598. 33. Farah C, Schwaner F, Abedi A, Worboys M. Distributed homology algorithm to detect topological events...ARL-TR-8235•DEC 2017 US Army Research Laboratory Greedy Sparse Approaches for Homological Coverage in Location-Unaware Sensor Net- works by Terrence...8235•DEC 2017 US Army Research Laboratory Greedy Sparse Approaches for Homological Coverage in Location-Unaware Sensor Net- works by Terrence J Moore

Transferring Goods or Splitting a Resource Pool

ERIC Educational Resources Information Center

Dijkstra, Jacob; Van Assen, Marcel A. L. M.

2008-01-01

We investigated the consequences for exchange outcomes of the violation of an assumption underlying most social psychological research on exchange. This assumption is that the negotiated direct exchange of commodities between two actors (pure exchange) can be validly represented as two actors splitting a fixed pool of resources (split pool…

Splitting and Projection at Work in Schools

ERIC Educational Resources Information Center

Dunning, Gerald; James, Chris; Jones, Nicola

2005-01-01

Purpose: The purpose of this paper is to report research into the social defence of splitting and projection in schools. In splitting and projection, organisational members separate their unbearable feelings from the more acceptable ones and project them, typically towards other individuals and groups. Design/methodology/approach: The research was…

Sparse ice: Geophysical, biological and Indigenous knowledge perspectives on a habitat for ice-associated fauna

NASA Astrophysics Data System (ADS)

Lee, O. A.; Eicken, H.; Weyapuk, W., Jr.; Adams, B.; Mohoney, A. R.

2015-12-01

The significance of highly dispersed, remnant Arctic sea ice as a platform for marine mammals and indigenous hunters in spring and summer may have increased disproportionately with changes in the ice cover. As dispersed remnant ice becomes more common in the future it will be increasingly important to understand its ecological role for upper trophic levels such as marine mammals and its role for supporting primary productivity of ice-associated algae. Potential sparse ice habitat at sea ice concentrations below 15% is difficult to detect using remote sensing data alone. A combination of high resolution satellite imagery (including Synthetic Aperture Radar), data from the Barrow sea ice radar, and local observations from indigenous sea ice experts was used to detect sparse sea ice in the Alaska Arctic. Traditional knowledge on sea ice use by marine mammals was used to delimit the scales where sparse ice could still be used as habitat for seals and walrus. Potential sparse ice habitat was quantified with respect to overall spatial extent, size of ice floes, and density of floes. Sparse ice persistence offshore did not prevent the occurrence of large coastal walrus haul outs, but the lack of sparse ice and early sea ice retreat coincided with local observations of ringed seal pup mortality. Observations from indigenous hunters will continue to be an important source of information for validating remote sensing detections of sparse ice, and improving understanding of marine mammal adaptations to sea ice change.