Modeling human performance with low light sparse color imagers
NASA Astrophysics Data System (ADS)
Haefner, David P.; Reynolds, Joseph P.; Cha, Jae; Hodgkin, Van
2011-05-01
Reflective band sensors are often signal to noise limited in low light conditions. Any additional filtering to obtain spectral information further reduces the signal to noise, greatly affecting range performance. Modern sensors, such as the sparse color filter CCD, circumvent this additional degradation through reducing the number of pixels affected by filters and distributing the color information. As color sensors become more prevalent in the warfighter arsenal, the performance of the sensor-soldier system must be quantified. While field performance testing ultimately validates the success of a sensor, accurately modeling sensor performance greatly reduces the development time and cost, allowing the best technology to reach the soldier the fastest. Modeling of sensors requires accounting for how the signal is affected through the modulation transfer function (MTF) and noise of the system. For the modeling of these new sensors, the MTF and noise for each color band must be characterized, and the appropriate sampling and blur must be applied. We show how sparse array color filter sensors may be modeled and how a soldier's performance with such a sensor may be predicted. This general approach to modeling color sensors can be extended to incorporate all types of low light color sensors.
Symmetric splitting of very light systems
Grotowski, K.; Majka, Z.; Planeta, R.; Szczodrak, M.; Chan, Y.; Guarino, G.; Moretto, L.G.; Morrissey, D.J.; Sobotka, L.G.; Stokstad, R.G.; Tserruya, I.; Wald, S.; Wozniak, G.J.
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.
NASA Astrophysics Data System (ADS)
Kumar, U.; Milesi, C.; Nemani, R. R.; Raja, S. Kumar; Ganguly, S.; Wang, W.
2015-06-01
In this paper, we explore the possibility of sparse regression, a new direction in unmixing, for vegetation and urban area classification. SUnSAL (Sparse unmixing via variable splitting and augmented Lagrangian) in both unconstrained and constrained forms (with the abundance non-negativity and abundance sum-to-one constraints) were used with a set of global endmembers (substrate, vegetation and dark objects) to unmix a set of computer simulated noise-free and noisy data (with Gaussian noise of different signal-to-noise ratio) in order to judge the robustness of the algorithm. The error in the fractional estimate was examined for varying noise power (variance): 2, 4, 8, 16, 32, 64, 128 and 256. In the second set of experiments, a spectrally diverse collection of 11 scenes of Level 1 terrain corrected, cloud free Landsat-5 TM data representing an agricultural setup in Fresno, California, USA were used. The corresponding ground data for validation were collected on the same days of satellite overpass. Finally in the third set of experiments, a clear sky Landsat-5 TM data for an area near the Golden Gate Bridge, San Francisco (an urbanized landscape), California, USA were used to assess the algorithm. The fractional estimates of the 30 m Landsat-5 TM data were compared with the fractional estimates of a high-resolution World View-2 data (2 m spatial resolution) obtained using a fully constrained least squares algorithm. The results were evaluated using descriptive statistics, correlation coefficient, RMSE, probability of success and bivariate distribution function, which showed that constrained model was better than unconstrained form.
Water splitting on semiconductor catalysts under visible-light irradiation.
Navarro Yerga, Rufino M; Alvarez Galván, M Consuelo; del Valle, F; Villoria de la Mano, José A; Fierro, José L G
2009-01-01
Sustainable hydrogen production is a key target for the development of alternative, future energy systems that will provide a clean and affordable energy supply. The Sun is a source of silent and precious energy that is distributed fairly all over the Earth daily. However, its tremendous potential as a clean, safe, and economical energy source cannot be exploited unless the energy is accumulated or converted into more useful forms. The conversion of solar energy into hydrogen via the water-splitting process, assisted by photo-semiconductor catalysts, is one of the most promising technologies for the future because large quantities of hydrogen can potentially be generated in a clean and sustainable manner. This Minireview provides an overview of the principles, approaches, and research progress on solar hydrogen production via the water-splitting reaction on photo-semiconductor catalysts. It presents a survey of the advances made over the last decades in the development of catalysts for photochemical water splitting under visible-light irradiation. The Minireview also analyzes the energy requirements and main factors that determine the activity of photocatalysts in the conversion of water into hydrogen and oxygen using sunlight. Remarkable progress has been made since the pioneering work by Fujishima and Honda in 1972, but he development of photocatalysts with improved efficiencies for hydrogen production from water using solar energy still faces major challenges. Research strategies and approaches adopted in the search for active and efficient photocatalysts, for example through new materials and synthesis methods, are presented and analyzed. PMID:19536754
Split Hole Resonator: A Nanoscale UV Light Source.
Melentiev, Pavel N; Afanasiev, Anton E; Kuzin, Arthur A; Gusev, Valeriy M; Kompanets, Oleg N; Esenaliev, Rinat O; Balykin, Victor I
2016-02-10
Because of strong light absorption by metals, it is believed that plasmonic nanostructures cannot be used for generating intensive radiation harmonics in the ultraviolet (UV) spectral range. This work presents results of investigation of nonlinear optical interaction with a single gold nanostructure, the split-hole resonator (SHR) under the state-of-the-art experimentally realized conditions. To realize interaction with all spectral components of a 6 fs laser pulse several multipole plasmon resonances were simultaneously excited in the SHR nanostructure. To the best of our knowledge, this paper reports for the first time a strong nonlinear optical interaction at the frequencies of these resonances that leads to (i) the second harmonic generation, (ii) the third harmonic generation (THG), and (iii) the light generation at mixed frequencies. The THG near field amplitude reaches 0.6% of the fundamental frequency field amplitude, which enables the creation of UV radiation sources with a record high intensity. The UV THG may find many important applications including biomedical ones (such as cancer therapy). PMID:26797270
NASA Astrophysics Data System (ADS)
Konoshonkin, Alexander V.; Kustova, Natalia V.; Borovoi, Anatoli G.
2015-10-01
The open-source beam-splitting code is described which implements the geometric-optics approximation to light scattering by convex faceted particles. This code is written in C++ as a library which can be easy applied to a particular light scattering problem. The code uses only standard components, that makes it to be a cross-platform solution and provides its compatibility to popular Integrated Development Environments (IDE's). The included example of solving the light scattering by a randomly oriented ice crystal is written using Qt 5.1, consequently it is a cross-platform solution, too. Both physical and computational aspects of the beam-splitting algorithm are discussed. Computational speed of the beam-splitting code is obviously higher compared to the conventional ray-tracing codes. A comparison of the phase matrix as computed by our code with the ray-tracing code by A. Macke shows excellent agreement.
Optical power splitter for splitting high power light
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.
Optical power splitter for splitting high power light
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.
Semihydrogenated BN Sheet: A Promising Visible-light Driven Photocatalyst for Water Splitting
Li, Xingxing; Zhao, Jin; Yang, Jinlong
2013-01-01
Based on first principles calculations, we predict semihydrogenated graphitic BN (sh-BN) sheet is a potential metal-free visible-light driven photocatalyst for water splitting. The ground state of sh-BN is a strip-like antiferromagnetic semiconductor with a band gap suitable for visible-light absorption. The redox potentials of water splitting are all located inside the band gap and the probability densities of valence and conduction bands are distributed apart spatially leading to a well-separation of photogenerated electrons and holes. PMID:23681171
Defect-engineered GaN:Mg nanowire arrays for overall water splitting under violet light
Kibria, M. G.; Chowdhury, F. A.; Zhao, S.; Mi, Z.; Trudeau, M. L.; Guo, H.
2015-03-16
We report that by engineering the intra-gap defect related energy states in GaN nanowire arrays using Mg dopants, efficient and stable overall neutral water splitting can be achieved under violet light. Overall neutral water splitting on Rh/Cr{sub 2}O{sub 3} co-catalyst decorated Mg doped GaN nanowires is demonstrated with intra-gap excitation up to 450 nm. Through optimized Mg doping, the absorbed photon conversion efficiency of GaN nanowires reaches ∼43% at 375–450 nm, providing a viable approach to extend the solar absorption of oxide and non-oxide photocatalysts.
Defect-engineered GaN:Mg nanowire arrays for overall water splitting under violet light
NASA Astrophysics Data System (ADS)
Kibria, M. G.; Chowdhury, F. A.; Zhao, S.; Trudeau, M. L.; Guo, H.; Mi, Z.
2015-03-01
We report that by engineering the intra-gap defect related energy states in GaN nanowire arrays using Mg dopants, efficient and stable overall neutral water splitting can be achieved under violet light. Overall neutral water splitting on Rh/Cr2O3 co-catalyst decorated Mg doped GaN nanowires is demonstrated with intra-gap excitation up to 450 nm. Through optimized Mg doping, the absorbed photon conversion efficiency of GaN nanowires reaches ˜43% at 375-450 nm, providing a viable approach to extend the solar absorption of oxide and non-oxide photocatalysts.
Highly efficient and ultrastable visible-light photocatalytic water splitting over ReS2.
Liu, Huimei; Xu, Bo; Liu, J-M; Yin, Jiang; Miao, Feng; Duan, Chun-Gang; Wan, X G
2016-06-01
Two dimensional materials have many outstanding intrinsic advantages that can be utilized to enhance the photocatalytic efficiency of water splitting. Herein, based on ab initio calculations, we reveal that for monolayer and multilayer rhenium disulphide (ReS2), the band gap and band edge positions are an excellent match with the water splitting energy levels. Moreover, the effective masses of the carriers are relatively light, and the optical absorption coefficients are high under visible illumination. Due to the feature of weak interlayer coupling, these properties are independent of the layer thickness. Our results suggest that ReS2 is a stable and efficient photocatalyst with potential applications in the use of solar energy for water splitting. PMID:27167677
Zeeman splitting of light hole in quantum wells: Comparison of theory and experiments
NASA Astrophysics Data System (ADS)
Durnev, M. V.
2014-07-01
The theory for light-hole Zeeman splitting developed in [5] is compared with experimental data found in literature for GaAs/AlGaAs, InGaAs/InP, and CdTe/CdMgTe quantum wells. It is shown that the description of experiments is possible with account for excitonic effects and peculiarities of the hole energy spectrum in a quantum well including complex structure of the valence band and the interface mixing of light and heavy holes. It is demonstrated that the absolute values and the sign of the light-hole g-factor are extremely sensitive to the parametrization of the Luttinger Hamiltonian.
Cunefare, David; Cooper, Robert F.; Higgins, Brian; Katz, David F.; Dubra, Alfredo; Carroll, Joseph; Farsiu, Sina
2016-01-01
Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice’s coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice’s coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images. PMID:27231641
Cunefare, David; Cooper, Robert F; Higgins, Brian; Katz, David F; Dubra, Alfredo; Carroll, Joseph; Farsiu, Sina
2016-05-01
Quantitative analysis of the cone photoreceptor mosaic in the living retina is potentially useful for early diagnosis and prognosis of many ocular diseases. Non-confocal split detector based adaptive optics scanning light ophthalmoscope (AOSLO) imaging reveals the cone photoreceptor inner segment mosaics often not visualized on confocal AOSLO imaging. Despite recent advances in automated cone segmentation algorithms for confocal AOSLO imagery, quantitative analysis of split detector AOSLO images is currently a time-consuming manual process. In this paper, we present the fully automatic adaptive filtering and local detection (AFLD) method for detecting cones in split detector AOSLO images. We validated our algorithm on 80 images from 10 subjects, showing an overall mean Dice's coefficient of 0.95 (standard deviation 0.03), when comparing our AFLD algorithm to an expert grader. This is comparable to the inter-observer Dice's coefficient of 0.94 (standard deviation 0.04). To the best of our knowledge, this is the first validated, fully-automated segmentation method which has been applied to split detector AOSLO images. PMID:27231641
Photon-number splitting of squeezed light by a single qubit in circuit QED
NASA Astrophysics Data System (ADS)
Moon, Kyungsun
2013-10-01
We theoretically propose an efficient way to generate and detect squeezed light by a single qubit in circuit QED. By tuning the qubit energy splitting close to the fundamental frequency of the first harmonic mode (FHM) in a transmission line resonator and placing the qubit at the nodal point of the third harmonic mode, one can generate the resonantly enhanced squeezing of the FHM upon pumping with the second harmonic mode. In order to investigate the photon number splitting for the squeezed FHM, we have numerically calculated the qubit absorption spectrum, which exhibits regularly spaced peaks at frequencies separated by twice the effective dispersive shift. It is also shown that adding a small pump field for the FHM makes additional peaks develop in between the dominant ones as well.
Electric Field-Assisted Photochemical Water Splitting Should Operate with 287 nm Light.
Bachler, Vinzenz; Gärtner, Wolfgang
2016-05-01
The major photoreaction of water is the homolytic splitting of one O-H bond starting from the 1(1) B1 excited state (λmax = 167 nm). This reaction produces H• and •OH radicals. The combination of two H• atoms leads to the potential energy carrier dihydrogen. However, the energy required to obtain the photoreactive 1(1) B1 electronic state is about 7.4 eV, which cannot be effectively provided by solar radiation. The sun light spectrum on earth comprises the visible and ultraviolet region, but shows vanishing intensity near 7 eV (177.1 nm). This work provides theoretical evidence that the photoreactive 1(1) B1 state of water can be shifted into the ultraviolet (UV-B) light region (≈287 nm) by including explicitly an electric field in the calculations of the water absorption spectrum. To accomplish such bathochromic shift, a large field strength of 3.08 VÅ(-1) is required. The field-dependent excitation energies were calculated by applying the symmetry-adapted cluster configuration interaction (SAC-CI) procedure. Based on this theoretical analysis, we propose that photochemical water splitting can be accomplished by means of 287 nm light provided the water molecule is favorably oriented by an external electric field and is subsequently activated by a reversal of the field orientation. PMID:26876336
Software, Anyhere; Fernandes, Luis; Lee, Eleanor; Ward, Greg
2013-03-15
A simulation study was conducted to evaluate lighting energy savings of split-pane electrochromic (EC) windows controlled to satisfy key visual comfort parameters. Using the Radiance lighting simulation software, interior illuminance and luminance levels were computed for a south-facing private office illuminated by a window split into two independently-controlled EC panes. The transmittance of these was optimized hourly for a workplane illuminance target while meeting visual comfort constraints, using a least-squares algorithm with linear inequality constraints. Blinds were successively deployed until visual comfort criteria were satisfied. The energy performance of electrochromics proved to be highly dependent on how blinds were controlled. With hourly blind position adjustments, electrochromics showed significantly higher (62percent and 53percent, respectively without and with overhang) lighting energy consumption than clear glass. With a control algorithm designed to better approximate realistic manual control by an occupant, electrochromics achieved significant savings (48percent and 37percent, respectively without and with overhang). In all cases, energy consumption decreased when the workplace illuminance target was increased. In addition, the fraction of time during which the occupant had an unobstructed view of the outside was significantly greater with electrochromics: 10 months out of the year versus a handful of days for the reference case.
Light-splitting photovoltaic system utilizing two dual-junction solar cells
Xiong, Kanglin; Yang, Hui; Lu, Shulong; Dong, Jianrong; Zhou, Taofei; Wang, Rongxin; Jiang, Desheng
2010-12-15
There are many difficulties limiting the further development of monolithic multi-junction solar cells, such as the growth of lattice-mismatched material and the current matching constraint. As an alternative approach, the light-splitting photovoltaic system is investigated intensively in different aspects, including the energy loss mechanism and the choice of energy bandgaps of solar cells. Based on the investigation, a two-dual junction system has been implemented employing lattice-matched GaInP/GaAs and InGaAsP/InGaAs cells grown epitaxially on GaAs and InP substrates, respectively. (author)
NASA Astrophysics Data System (ADS)
Zheng, Sheng; Tian, Jinwen; Liu, Jian
2005-04-01
We propose wavelength-division-multiplexing (WDM) networks with light splitting and wavelength conversion that can efficiently support multicast routing between nodes. Our iterative algorithm analyzes the original multicast routing network by decomposing it into multicast subgroups. These subgroups have the same wavelength, and the individual subgroup is combined to build a multicast tree. From the multicast tree, we can compute efficiently to multicast for short paths. Numerical results obtained for the ARPANET show that our algorithm can greatly reduce the optical blocking probability and the number of required wavelength conversions.
Wang, Jiajun; Meng, Jie; Li, Qunxiang; Yang, Jinlong
2016-06-22
Recently, various single-layer materials have been explored as desirable photocatalyts for water splitting. In this work, based on extensive density functional theory calculations, we examine the geometric, electronic, optical, and potential photocatalytic properties of single-layer cadmium chalcogenides (CdX sheets, X = S, Se, and Te), which are cleaved from the (001) plane of the bulk wurtzite structure. The predicted formation energies have relatively low values and a suitable substrate (i.e. graphene) that can effectively stabilize CdX sheets, which imply that the fabrication and application of CdX sheets are highly possible in experiments. The calculated band gaps, band edge positions and optical absorptions clearly reveal that CdSe and CdTe sheets are promising photocatalysts for water splitting driven by visible light. Moreover, the band gaps and band edge positions of three CdX sheets can be effectively tuned by applying biaxial strain, which then can enhance their photocatalytic performance. These theoretical findings imply that CdX sheets are promising candidates for photocatalytic water splitting. PMID:27296472
NASA Astrophysics Data System (ADS)
Mateo, Diego; Esteve-Adell, Iván; Albero, Josep; Royo, Juan F. Sánchez; Primo, Ana; Garcia, Hermenegildo
2016-06-01
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.
Interface-induced heavy-hole/light-hole splitting of acceptors in silicon
NASA Astrophysics Data System (ADS)
Mol, J. A.; Salfi, J.; Rahman, R.; Hsueh, Y.; Miwa, J. A.; Klimeck, G.; Simmons, M. Y.; Rogge, S.
2015-05-01
The energy spectrum of spin-orbit coupled states of individual sub-surface boron acceptor dopants in silicon have been investigated using scanning tunneling spectroscopy at cryogenic temperatures. The spatially resolved tunnel spectra show two resonances, which we ascribe to the heavy- and light-hole Kramers doublets. This type of broken degeneracy has recently been argued to be advantageous for the lifetime of acceptor-based qubits [R. Ruskov and C. Tahan, Phys. Rev. B 88, 064308 (2013)]. The depth dependent energy splitting between the heavy- and light-hole Kramers doublets is consistent with tight binding calculations, and is in excess of 1 meV for all acceptors within the experimentally accessible depth range (<2 nm from the surface). These results will aid the development of tunable acceptor-based qubits in silicon with long coherence times and the possibility for electrical manipulation.
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
Interface-induced heavy-hole/light-hole splitting of acceptors in silicon
Mol, J. A.; Salfi, J.; Simmons, M. Y.; Rogge, S.; Rahman, R.; Hsueh, Y.; Klimeck, G.; Miwa, J. A.
2015-05-18
The energy spectrum of spin-orbit coupled states of individual sub-surface boron acceptor dopants in silicon have been investigated using scanning tunneling spectroscopy at cryogenic temperatures. The spatially resolved tunnel spectra show two resonances, which we ascribe to the heavy- and light-hole Kramers doublets. This type of broken degeneracy has recently been argued to be advantageous for the lifetime of acceptor-based qubits [R. Ruskov and C. Tahan, Phys. Rev. B 88, 064308 (2013)]. The depth dependent energy splitting between the heavy- and light-hole Kramers doublets is consistent with tight binding calculations, and is in excess of 1 meV for all acceptors within the experimentally accessible depth range (<2 nm from the surface). These results will aid the development of tunable acceptor-based qubits in silicon with long coherence times and the possibility for electrical manipulation.
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
Chen, Quanpeng; Li, Jinhua; Li, Xuejin; Huang, Ke; Zhou, Baoxue; Shangguan, Wenfeng
2013-07-01
A self-biasing photoelectrochemical (PEC) cell that could work for spontaneous overall water splitting in a neutral solution was established based on the mismatched Fermi levels between the photoelectrodes. A Pt-catalyst-decorated crystalline silicon photovoltaic cell (Pt/PVC) was prepared and employed as an effective photocathode. This was coupled with a poly(ethylene glycol)-directed WO3/W photoanode prepared by a hydrothermal process. Both of the photoelectrodes showed a response to visible light. The WO3/W photoanode had a positively located valence band edge, the energy level of which was enough for water oxidation, and the Pt/PVC photocathode possessed a negatively located conduction band edge, which was capable of water reduction. More importantly, the Fermi level of the WO3/W photoanode was more positive than that of the Pt/PVC photocathode because of the p-n junction of the PVC that decoupled the band bending and enlarged the photovoltage. Under visible-light irradiation, the WO3/W photoanode provided a negative bias for the Pt/PVC photocathode, and the Pt/PVC photocathode provided a positive bias for the WO3/W photoanode. An interior bias was generated that could relax the strict criteria of overall water splitting by cooperatively separating the hole-electron pairs at both photoelectrodes. In this system, the short-circuit current and the open-circuit voltage increased with increasing light intensity (AM 1.5 illumination) to reach 121 μA cm(-2) and 0.541 V, respectively, at a light intensity of 100 mW cm(-2). Such a combination provides a promising method for the fabrication of self-driven devices for solar-energy storage. PMID:23775929
Heavy gravitino and split SUSY in the light of BICEP2
NASA Astrophysics Data System (ADS)
Fan, JiJi; Jain, Bithika; Özsoy, Ogan
2014-07-01
High-scale supersymmetry (SUSY) with a split spectrum has become increasingly interesting given the current experimental results. A SUSY scale above the weak scale could be naturally associated with a heavy unstable gravitino, whose decays populate the dark matter (DM) particles. In the mini-split scenario with gravitino at about the PeV scale and the lightest TeV scale neutralino being (a component of) DM, the requirement that the DM relic abundance resulting from gravitino decays does not overclose the Universe and satisfies the indirect detection constraints demand the reheating temperature to be below 109 - 1010 GeV. On the other hand, the BICEP2 result prefers a heavy inflaton with mass at around 1013 GeV and a reheating temperature at or above 109 GeV with some general assumptions. The mild tension could be alleviated if SUSY scale is even higher with the gravitino mass above the PeV scale. Intriguingly, in no-scale supergravity, gravitinos could be very heavy at about 1013 GeV, the inflaton mass scale, while gauginos could still be light at the TeV scale.
Generating and Separating Twisted Light by gradient-rotation Split-Ring Antenna Metasurfaces.
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. PMID:27092965
NASA Astrophysics Data System (ADS)
Yu, Ting
2015-03-01
Two-dimensional (2D) semiconductors, such as transitional-metal-dichalcogenide monolayers (TMD 1Ls), have aroused great interest because of the underlying fundamental physics (e.g. many body effects and wealth excitonic states) and the promising optoelectronic applications such as light-emitting diodes and solar cells. Here, we report excitonic emission and valley splitting of monolayer WS2 and MoS2 under electrical, optical and magnetic manipulation. Through electrical and optical injection of charge carriers, tunable excitonic emission has been realized due to interplay of various excitonic states, and basic binding energies of trions have been extracted. At low temperature, the Zeeman shifts of excitons and trions have been determined by polarization-dependent photoluminescence measurements under perpendicular magnetic fields, which reveal the breaking of valley degeneracy. Our studies provide the fundamental understanding on large excitonic and unique valleytronic effects in TMD 1Ls. Moreover, we also develop multiple strategies for managing the light emission, which opens up many possibilities for improving the performance and creating the multifunction of 2D TMD-based light emitting applications. Also at Department of Physics, Faculty of Science, National University of Singapore 117542, Singapore; Centre for Advanced 2D Materials and Graphene Research Centre, National University of Singapore 117546, Singapore.
NASA Astrophysics Data System (ADS)
Waszczak, Adam; Chang, Chan-Kao; Ofek, Eran O.; Laher, Russ; Masci, Frank; Levitan, David; Surace, Jason; Cheng, Yu-Chi; Ip, Wing-Huen; Kinoshita, Daisuke; Helou, George; Prince, Thomas A.; Kulkarni, Shrinivas
2015-09-01
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 find 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‑3, while C types have a lower limit of between 1 and 2 g cm‑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 rms scatter by a
Mir, Mustafa; Babacan, S Derin; Bednarz, Michael; Do, Minh N; Golding, Ido; Popescu, Gabriel
2012-01-01
Studying the 3D sub-cellular structure of living cells is essential to our understanding of biological function. However, tomographic imaging of live cells is challenging mainly because they are transparent, i.e., weakly scattering structures. Therefore, this type of imaging has been implemented largely using fluorescence techniques. While confocal fluorescence imaging is a common approach to achieve sectioning, it requires fluorescence probes that are often harmful to the living specimen. On the other hand, by using the intrinsic contrast of the structures it is possible to study living cells in a non-invasive manner. One method that provides high-resolution quantitative information about nanoscale structures is a broadband interferometric technique known as Spatial Light Interference Microscopy (SLIM). In addition to rendering quantitative phase information, when combined with a high numerical aperture objective, SLIM also provides excellent depth sectioning capabilities. However, like in all linear optical systems, SLIM's resolution is limited by diffraction. Here we present a novel 3D field deconvolution algorithm that exploits the sparsity of phase images and renders images with resolution beyond the diffraction limit. We employ this label-free method, called deconvolution Spatial Light Interference Tomography (dSLIT), to visualize coiled sub-cellular structures in E. coli cells which are most likely the cytoskeletal MreB protein and the division site regulating MinCDE proteins. Previously these structures have only been observed using specialized strains and plasmids and fluorescence techniques. Our results indicate that dSLIT can be employed to study such structures in a practical and non-invasive manner. PMID:22761910
Petrov, P. V.; Ivanov, Yu. L.
2013-04-15
The paramagnetic splitting of a light-hole level in a GaAs/AlGaAs quantum well is experimentally measured by the method of measuring the polarized photoluminescence in the magnetic field. The phenomenon of giant splitting, which was previously predicted theoretically and leads to an increase in the g factor of a light hole to 9.4, is found.
MWCNT/WO{sub 3} nanocomposite photoanode for visible light induced water splitting
Yousefzadeh, Samira; Reyhani, Ali; Naseri, Naimeh; Moshfegh, Alireza Z.
2013-08-15
The Multi-walled carbon nanotube (MWCNT)/WO{sub 3} nanocomposite thin films with different MWCNT’s weight percentages were prepared by sol–gel method as visible light induced photoanode in water splitting reaction. Weight percentage of MWCNT in the all nanocomposite thin films was confirmed by TGA/DSC analysis. According to XPS analysis, oxygenated groups at the surface of the MWCNT and stoichiometric formation of WO{sub 3} thin films were determined, while the crystalline structure of the nanocomposite samples was studied by XRD indicating (0 0 2) peak of MWCNT in the monoclinic phase of WO{sub 3}. The influence of different weight percentage (wt%) of MWCNT on WO{sub 3} photoactivity showed that the electron conductivity, charge transfer and electron life time had improved as compared with the pure WO{sub 3}. Based on linear sweep voltammetry and chronoamperometry measurements, the (1 wt%) MWCNT/WO{sub 3} nanocomposite thin films photoanode has a maximum photocurrent density of ∼4.5 A/m{sup 2} and electron life time of about 57 s. - Graphical abstract: Photocurrent density versus time at constant potential (0.7 V) for the WO{sub 3} films containing different MWCNT weight percentages annealed at 400 °C under 1000 Wm{sup −2} visible photo-illumination. Display Omitted - Highlights: • MWCNT/ WO{sub 3} nanocomposite thin films were synthesized using sol–gel derived method. • TGA/DSC confirmed the weight percentage of MWCNT in the all nanocomposite thin films. • XPS analysis revealed that WO{sub 3} was attached on the oxygenated group of MWCNT surface. • The Highest Photoelectrochemical activity is achieved for (1 wt%)MWCNT/WO{sub 3} thin film.
Non-destructive splitter of twisted light based on modes splitting in a ring cavity.
Li, Yan; Zhou, Zhi-Yuan; Ding, Dong-Sheng; Zhang, Wei; Shi, Shuai; Shi, Bao-Sen; Guo, Guang-Can
2016-02-01
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. PMID:26906792
NASA Astrophysics Data System (ADS)
Peng, Rui
Hydrogen generation from photocatalytic splitting of water is an ideal scenario that possesses promise for the sustainable development of human society and the establishment of the ultimate "green," infinitely renewable energy system. This work contains a series of novel photocatalytic systems in which the photoactive chromophores and/or the co-catalysts were incorporated into highly periodically cubic-phased MCM-48 mesoporous materials to achieve significantly higher photocatalytic efficiencies compared with conventional semiconductor photocatalysts. Cubic-phased MCM-48 mesoporous materials were chosen as supports to accommodate the photoactive species throughout the entire work. Several unique and iconic properties of these materials, such as large surface area, highly uniform mesoscale pores arrayed in a long-range periodicity, and an interconnected network of three-dimensional sets of pores that were recognized as positive parameters facilitated the photogenerated charge transfer and promoted the photocatalytic performance of the encapsulated photoactive species. It was validated that in the CdS/TiO2-incorporated MCM-48 photocatalytic system, the solar hydrogen conversion efficiency was prevalently governed by the photogenerated electron injection efficiency from the CdS conduction band to that of TiO2. The use of MCM-48 mesoporous host materials enabled the high and even dispersion of both CdS and TiO 2 so that the intimate and sufficient contact between CdS and TiO 2 was realized. In addition, with the presence of both TiO2 and MCM-48 mesoporous support, the photostability of CdS species was dramatically enhanced compared with that of bare CdS or CdS-incorporated MCM-48 photocatalysts. In advance, by loading the RuO2 co-catalyst into the CdS/TiO 2-incorporated MCM-48 photocatalytic system, the photocatalytic splitting of pure water to generate both hydrogen and oxygen under visible light illumination was achieved. In the various Pd-assisted, TiO2-incorporated
NASA Astrophysics Data System (ADS)
Wu, Z. W.; Volotka, A. V.; Surzhykov, A.; Dong, C. Z.; Fritzsche, S.
2016-06-01
The angular distribution and linear polarization of the fluorescence light following the resonant photoexcitation is investigated within the framework of density matrix and second-order perturbation theory. Emphasis has been placed on "signatures" for determining the level sequence and splitting of intermediate (partially) overlapping resonances, if analyzed as a function of photon energy of incident light. Detailed computations within the multiconfiguration Dirac-Fock method have been performed, especially for the 1 s22 s22 p63 s ,Ji=1 /2 +γ1→(1s22 s 2 p63 s ) 13 p3 /2,J =1 /2 ,3 /2 →1 s22 s22 p63 s ,Jf=1 /2 +γ2 photoexcitation and subsequent fluorescence emission of atomic sodium. A remarkably strong dependence of the angular distribution and linear polarization of the γ2 fluorescence emission is found upon the level sequence and splitting of the intermediate (1s22 s 2 p63 s ) 13 p3 /2,J =1 /2 ,3 /2 overlapping resonances owing to their finite lifetime (linewidth). We therefore suggest that accurate measurements of the angular distribution and linear polarization might help identify the sequence and small splittings of closely spaced energy levels, even if they cannot be spectroscopically resolved.
From a Philanthropic Idea to Building of Civic Hospital in Split in Light of New Archival Evidence
Brisky, Livia; Fatović-Ferenčić, Stella
2006-01-01
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. PMID:16489710
Jia, Qingxin; Iwashina, Katsuya; Kudo, Akihiko
2012-07-17
An efficient BiVO(4) thin film electrode for overall water splitting was prepared by dipping an F-doped SnO(2) (FTO) substrate electrode in an aqueous nitric acid solution of Bi(NO(3))(3) and NH(4)VO(3), and subsequently calcining it. X-ray diffraction of the BiVO(4) thin film revealed that a photocatalytically active phase of scheelite-monoclinic BiVO(4) was obtained. Scanning electron microscopy images showed that the surface of an FTO substrate was uniformly coated with the BiVO(4) film with 300-400 nm of the thickness. The BiVO(4) thin film electrode gave an excellent anodic photocurrent with 73% of an IPCE at 420 nm at 1.0 V vs. Ag/AgCl. Modification with CoO on the BiVO(4) electrode improved the photoelectrochemical property. A photoelectrochemical cell consisting of the BiVO(4) thin film electrode with and without CoO, and a Pt counter electrode was constructed for water splitting under visible light irradiation and simulated sunlight irradiation. Photocurrent due to water splitting to form H(2) and O(2) was confirmed with applying an external bias smaller than 1.23 V that is a theoretical voltage for electrolysis of water. Water splitting without applying external bias under visible light irradiation was demonstrated using a SrTiO(3)Rh photocathode and the BiVO(4) photoanode. PMID:22699499
Jia, Qingxin; Iwashina, Katsuya; Kudo, Akihiko
2012-01-01
An efficient BiVO4 thin film electrode for overall water splitting was prepared by dipping an F-doped SnO2 (FTO) substrate electrode in an aqueous nitric acid solution of Bi(NO3)3 and NH4VO3, and subsequently calcining it. X-ray diffraction of the BiVO4 thin film revealed that a photocatalytically active phase of scheelite-monoclinic BiVO4 was obtained. Scanning electron microscopy images showed that the surface of an FTO substrate was uniformly coated with the BiVO4 film with 300–400 nm of the thickness. The BiVO4 thin film electrode gave an excellent anodic photocurrent with 73% of an IPCE at 420 nm at 1.0 V vs. Ag/AgCl. Modification with CoO on the BiVO4 electrode improved the photoelectrochemical property. A photoelectrochemical cell consisting of the BiVO4 thin film electrode with and without CoO, and a Pt counter electrode was constructed for water splitting under visible light irradiation and simulated sunlight irradiation. Photocurrent due to water splitting to form H2 and O2 was confirmed with applying an external bias smaller than 1.23 V that is a theoretical voltage for electrolysis of water. Water splitting without applying external bias under visible light irradiation was demonstrated using a SrTiO3∶Rh photocathode and the BiVO4 photoanode. PMID:22699499
GASOLINE/DIESEL PM SPLIT STUDY: LIGHT-DUTY VEHICLE TESTING, DATA, AND ANALYSIS
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...
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...
Isospin splittings in the light-baryon octet from lattice QCD and QED.
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. PMID:24483739
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. PMID:26831398
Hung, Wei Hsuan; Lai, Sz Nian; Lo, An Ya
2015-04-29
The enhanced water splitting photocurrent has been observed through plasmonic mesoporous composite electrode TiO2-CMK-3/Ag under visible light irradiation. Strong light absorption achieved from the integrations of ordered mesoporous carbon (CMK-3) and silver plasmonic nanoparticles (NPs) layer in the TiO2, which significantly increased the effective optical depth of TiO2-CMK-3/Ag photoelectrode. The carbon-based CMK-3 also increased the surface wetting behavior and conductivity of the photoelectrodes, which resulted in a higher ion exchange rate and faster electron transport. The synthesis of high crystalline TiO2-CMK-3/Ag composite photocatalyst was verified by X-ray diffraction (XRD) and scanning electron microscopy (SEM). Pronounced enhancement of light absorption of TiO2-CMK-3/Ag photoelectrode was confirmed by UV/vis spectrophotometers. Two orders of magnitude of the enhanced water splitting photocurrent were obtained in the TiO2-CMK-3/Ag composite photoelectrode with respect to TiO2 only. Finally, spatially resolved mapping photocurrents were also demonstrated in this study. PMID:25848834
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.
Light Makes a Surface Banana-Bond Split: Photodesorption of Molecular Hydrogen from RuO2(110).
Henderson, Michael A; Mu, Rentao; Dahal, Arjun; Lyubinetsky, Igor; Dohnálek, Zdenek; Glezakou, Vassiliki-Alexandra; Rousseau, Roger
2016-07-20
The coordination of H2 to a metal center via polarization of its σ bond electron density, known as a Kubas complex, is the means by which H2 chemisorbs at Ru(4+) 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 complex involves an interband transition in RuO2 which effectively diminishes its Lewis acidity, thereby weakening the Kubas complex. Such excitations are not expected to affect adsorbates on RuO2 given its metallic properties. Therefore, this common thermal cocatalyst employed in photocatalysis is, itself, photoactive. PMID:27390889
Wu, Yun-Ben; Yang, Wen; Wang, Tong-Biao; Deng, Xin-Hua; Liu, Jiang-Tao
2016-01-01
The light absorption of a monolayer graphene-molybdenum disulfide photovoltaic (GM-PV) cell in a wedge-shaped microcavity with a spectrum-splitting structure is investigated theoretically. The GM-PV cell, which is three times thinner than the traditional photovoltaic cell, exhibits up to 98% light absorptance in a wide wavelength range. This rate exceeds the fundamental limit of nanophotonic light trapping in solar cells. The effects of defect layer thickness, GM-PV cell position in the microcavity, incident angle, and lens aberration on the light absorptance of the GM-PV cell are explored. Despite these effects, the GM-PV cell can still achieve at least 90% light absorptance with the current technology. Our proposal provides different methods to design light-trapping structures and apply spectrum-splitting systems. PMID:26864749
Wu, Yun-Ben; Yang, Wen; Wang, Tong-Biao; Deng, Xin-Hua; Liu, Jiang-Tao
2016-01-01
The light absorption of a monolayer graphene-molybdenum disulfide photovoltaic (GM-PV) cell in a wedge-shaped microcavity with a spectrum-splitting structure is investigated theoretically. The GM-PV cell, which is three times thinner than the traditional photovoltaic cell, exhibits up to 98% light absorptance in a wide wavelength range. This rate exceeds the fundamental limit of nanophotonic light trapping in solar cells. The effects of defect layer thickness, GM-PV cell position in the microcavity, incident angle, and lens aberration on the light absorptance of the GM-PV cell are explored. Despite these effects, the GM-PV cell can still achieve at least 90% light absorptance with the current technology. Our proposal provides different methods to design light-trapping structures and apply spectrum-splitting systems. PMID:26864749
NASA Astrophysics Data System (ADS)
Wu, Yun-Ben; Yang, Wen; Wang, Tong-Biao; Deng, Xin-Hua; Liu, Jiang-Tao
2016-02-01
The light absorption of a monolayer graphene-molybdenum disulfide photovoltaic (GM-PV) cell in a wedge-shaped microcavity with a spectrum-splitting structure is investigated theoretically. The GM-PV cell, which is three times thinner than the traditional photovoltaic cell, exhibits up to 98% light absorptance in a wide wavelength range. This rate exceeds the fundamental limit of nanophotonic light trapping in solar cells. The effects of defect layer thickness, GM-PV cell position in the microcavity, incident angle, and lens aberration on the light absorptance of the GM-PV cell are explored. Despite these effects, the GM-PV cell can still achieve at least 90% light absorptance with the current technology. Our proposal provides different methods to design light-trapping structures and apply spectrum-splitting systems.
Phase transition-induced band edge engineering of BiVO4 to split pure water under visible light
Jo, Won Jun; Kang, Hyun Joon; Kong, Ki-Jeong; Lee, Yun Seog; Park, Hunmin; Lee, Younghye; Buonassisi, Tonio; Gleason, Karen K.; Lee, Jae Sung
2015-01-01
Through phase transition-induced band edge engineering by dual doping with In and Mo, a new greenish BiVO4 (Bi1-XInXV1-XMoXO4) is developed that has a larger band gap energy than the usual yellow scheelite monoclinic BiVO4 as well as a higher (more negative) conduction band than H+/H2 potential [0 VRHE (reversible hydrogen electrode) at pH 7]. Hence, it can extract H2 from pure water by visible light-driven overall water splitting without using any sacrificial reagents. The density functional theory calculation indicates that In3+/Mo6+ dual doping triggers partial phase transformation from pure monoclinic BiVO4 to a mixture of monoclinic BiVO4 and tetragonal BiVO4, which sequentially leads to unit cell volume growth, compressive lattice strain increase, conduction band edge uplift, and band gap widening. PMID:26508636
Pan, Hui; Qiu, Xiaofeng; Ivanov, Ilia N; Meyer III, Harry M; Wang, Wei; Zhu, Wenguang; Paranthaman, Mariappan Parans; Zhang, Zhenyu; Eres, Gyula; Gu, Baohua
2009-01-01
We report that mild oxidation of Ti foils in air results in brookite-rich titanium oxide (TiO2) films with similar spectral response to that of dye-sensitized TiO2. X-ray powder diffraction and Raman spectroscopy show that the onset of brookite formation occurs at 500 8C, and the material is characterized by a strong absorption band in the visible spectral range. The first-principle calculations show that enhanced visible light absorption correlates with the presence of Ti interstitials. Photocurrent density measurements of water splitting reveal that the brookite-rich TiO2 exhibits the highest photocatalytic performance among the different forms of TiO2 produced by oxidation of Ti foils. With increasing oxidation temperature transformation to the rutile phase accompanied by declining visible range photoactivity is observed.
NASA Astrophysics Data System (ADS)
Stumpe, Martin C.; Smith, J. C.; Van Cleve, J.; Jenkins, J. M.; Barclay, T. S.; Fanelli, M. N.; Girouard, F.; Kolodziejczak, J.; McCauliff, S.; Morris, R. L.; Twicken, J. D.
2012-05-01
Kepler photometric data contain significant systematic and stochastic errors as they come from the Kepler Spacecraft. The main cause for the systematic errors are changes in the photometer focus due to thermal changes in the instrument, and also residual spacecraft pointing errors. It is the main purpose of the Presearch-Data-Conditioning (PDC) module of the Kepler Science processing pipeline to remove these systematic errors from the light curves. While PDC has recently seen a dramatic performance improvement by means of a Bayesian approach to systematic error correction and improved discontinuity correction, there is still room for improvement. One problem of the current (Kepler 8.1) implementation of PDC is that injection of high frequency noise can be observed in some light curves. Although this high frequency noise does not negatively impact the general cotrending, an increased noise level can make detection of planet transits or other astrophysical signals more difficult. The origin of this noise-injection is that high frequency components of light curves sometimes get included into detrending basis vectors characterizing long term trends. Similarly, small scale features like edges can sometimes get included in basis vectors which otherwise describe low frequency trends. As a side effect to removing the trends, detrending with these basis vectors can then also mistakenly introduce these small scale features into the light curves. A solution to this problem is to perform a separation of scales, such that small scale features and large scale features are described by different basis vectors. We present our new multiscale approach that employs wavelet-based band splitting to decompose small scale from large scale features in the light curves. The PDC Bayesian detrending can then be performed on each band individually to correct small and large scale systematics independently. Funding for the Kepler Mission is provided by the NASA Science Mission Directorate.
Kravets, V G; Grigorenko, A N
2015-11-30
Water splitting is unanimously recognized as environment friendly, potentially low cost and renewable energy solution based on the future hydrogen economy. Especially appealing is photocatalytic water splitting whereby a suitably chosen catalyst dramatically improves efficiency of the hydrogen production driven by direct sunlight and allows it to happen even at zero driving potential. Here, we suggest a new class of stable photocatalysts and the corresponding principle for catalytic water splitting in which infrared and visible light play the main role in producing the photocurrent and hydrogen. The new class of catalysts - ionic or covalent binary metals with layered graphite-like structures - effectively absorb visible and infrared light facilitating the reaction of water splitting, suppress the inverse reaction of ion recombination by separating ions due to internal electric fields existing near alternating layers, provide the sites for ion trapping of both polarities, and finally deliver the electrons and holes required to generate hydrogen and oxygen gases. As an example, we demonstrate conversion efficiency of ~27% at bias voltage V_{bias} = 0.5V for magnesium diboride working as a catalyst for photoinduced water splitting. We discuss its advantages over some existing materials and propose the underlying mechanism of photocatalytic water splitting by binary layered metals. PMID:26698811
Light to Electrons to Bonds: Imaging Water Splitting and Collecting Photoexcited Electrons
NASA Astrophysics Data System (ADS)
Leenheer, Andrew Jay
Photoelectrochemical devices can store solar energy as chemical bonds in fuels, but more control over the materials involved is needed for economic feasibility. Both efficient capture of photon energy into electron energy and subsequent electron transfer and bond formation are necessary, and this thesis explores various steps of the process. To look at the electrochemical fuel formation step, the spatially-resolved reaction rate on a water-splitting electrode was imaged during operation at a few-micron scale using optical microscopy. One method involved localized excitation of a semiconductor photoanode and recording the growth rate of bubbles to determine the local reaction rate. A second method imaged the reactant profile with a pH-sensitive fluorophore in the electrolyte to determine the local three-dimensional pH profile at patterned electrocatalysts in a confocal microscope. These methods provide insight on surface features optimal for efficient electron transfer into fuel products. A second set of studies examined the initial process of photoexcited electron transport and collection. An independent method to measure the minority carrier diffusion length in semiconductor photoelectrodes was developed, in which a wedge geometry is back illuminated with a small scanned spot. The diffusion length can be determined from the exponential decrease of photocurrent with thickness, and the method was demonstrated on solid-state silicon wedge diodes, as well as tungsten oxide thin-film wedge photoanodes. Finally, the possibility of absorbing and collecting sub-bandgap illumination via plasmon-enhanced hot carrier internal photoemission was modeled to predict the energy conversion efficiency. The effect of photon polarization on emission yield was experimentally tested using gold nanoantennas buried in silicon, and the correlation was found to be small.
NASA Astrophysics Data System (ADS)
Yang, Jingjing; Zeng, Xiaopeng; Chen, Lijuan; Yuan, Wenxia
2013-02-01
We report a method to realize the H2 production and graphene-oxide (GO) reduction simultaneously over GO/SiC composite under visible light irradiation with KI as sacrifice reagent. The weight content of GO is regulated in the reaction system. The rate of H2 production reaches to 95 μL/h with 1% GO content in GO/SiC composite system, which is 1.3 times larger compared to the case in pure SiC NPs under visible light. The reduced-GO sheet can serve as an electron collector and transporter to efficiently separate the photo-generated electron-hole pairs, lengthening the lifetime of the charge carriers effectively.
Light trap with reactive sun tracking for high-efficiency spectrum splitting photovoltaic conversion
NASA Astrophysics Data System (ADS)
Apostoleris, H.; Chiesa, M.; Stefancich, M.
2015-05-01
We present a design for a modification of a previously proposed light-trapping solar collector that enables reactive solar tracking by the incorporation of an optically activated transparency-switching material. The material forms an entry aperture whose position reactively varies to admit sunlight, which is focused to a point on the receiving surface by a lens or set of lenses, over a wide range of solar angles. An analytic model for assessing device performance based on statistical ray optics is described and confirmed by raytrace simulations on a model system.
NASA Astrophysics Data System (ADS)
Sambandam, Balaji; Michael, Robin Jude Vimal; Manoharan, Periakaruppan T.
2015-08-01
ZnO nanorods and Mn/ZnO microflowers with nano-sized petals exhibit singly ionized oxygen vacancies, V+O. This is strongly supported by a green photoluminescence emission at 2.22 eV and an EPR g value of 1.953, both of which are suppressed greatly after annealing in an oxygen atmosphere. A strong red emission observed during exposure to X-rays reveals the presence of F+ centres as a consequence of the V+O. Mn/ZnO displayed enhanced H2 generation with visible light exposure, when compared to pure ZnO and annealed Mn/ZnO in the visible region, which directly correlated with the oxygen vacancy concentration. There is an interesting correlation between the intensities of the EPR lines at the g-value of 1.953 due to the oxygen vacancies, the intensity of light emitted from the exposure to X-rays, the intensity of the photoluminescence due to oxygen vacancies and the quantity of H2 produced by the photocatalytic effect when comparing the three different nanomaterials, viz. pure ZnO, Mn/ZnO before and after annealing, all having been made exactly by the same methodologies.ZnO nanorods and Mn/ZnO microflowers with nano-sized petals exhibit singly ionized oxygen vacancies, V+O. This is strongly supported by a green photoluminescence emission at 2.22 eV and an EPR g value of 1.953, both of which are suppressed greatly after annealing in an oxygen atmosphere. A strong red emission observed during exposure to X-rays reveals the presence of F+ centres as a consequence of the V+O. Mn/ZnO displayed enhanced H2 generation with visible light exposure, when compared to pure ZnO and annealed Mn/ZnO in the visible region, which directly correlated with the oxygen vacancy concentration. There is an interesting correlation between the intensities of the EPR lines at the g-value of 1.953 due to the oxygen vacancies, the intensity of light emitted from the exposure to X-rays, the intensity of the photoluminescence due to oxygen vacancies and the quantity of H2 produced by the
Chen, Min; Gu, Jiajun; Sun, Cheng; Zhao, Yixin; Zhang, Ruoxi; You, Xinyuan; Liu, Qinglei; Zhang, Wang; Su, Yishi; Su, Huilan; Zhang, Di
2016-07-26
Photoelectric conversion driven by sunlight has a broad range of energy/environmental applications (e.g., in solar cells and water splitting). However, difficulties are encountered in the separation of photoexcited charges. Here, we realize a long-range (∼1.5 μm period) electric polarization via asymmetric localization of surface plasmons on a three-dimensional silver structure (3D-Ag). This visible-light-responsive effect-the photo-Dember effect, can be analogous to the thermoelectric effect, in which hot carriers are thermally generated instead of being photogenerated. The induced electric field can efficiently separate photogenerated charges, enabling sunlight-driven overall water splitting on a series of dopant-free commercial semiconductor particles (i.e., ZnO, CeO2, TiO2, and WO3) once they are combined with the 3D-Ag substrate. These photocatalytic processes can last over 30 h on 3D-Ag+ZnO, 3D-Ag+CeO2, and 3D-Ag+TiO2, thus demonstrating good catalytic stability for these systems. Using commercial WO3 powder as a reference, the amount of O2 generated with 3D-Ag+CeO2 surpasses even its recently reported counterpart in which sacrificial reagents had to be involved to run half-reactions. This plasmon-mediated charge separation strategy provides an effective way to improve the efficiency of photoelectric energy conversion, which can be useful in photovoltaics and photocatalysis. PMID:27351779
Hyperfine splitting and the Zeeman effect in holographic heavy-light mesons
Herzog, Christopher P.; Stricker, Stefan A.; Vuorinen, Aleksi
2010-08-15
We inspect the mass spectrum of heavy-light mesons in deformed N=2 super Yang-Mills theory using the AdS/CFT correspondence. We demonstrate how some of the degeneracies of the supersymmetric meson spectrum can be removed upon breaking the supersymmetry, thus leading to the emergence of a hyperfine structure. The explicit SUSY breaking scenarios we consider involve on the one hand, tilting one of the two fundamental D7-branes inside the internal R{sup 6} space, and on the other hand, applying an external magnetic field on the (untilted) branes. The latter scenario leads to the well-known Zeeman effect, which we inspect for both weak and strong magnetic fields.
Hyperfine splitting and the Zeeman effect in holographic heavy-light mesons
NASA Astrophysics Data System (ADS)
Herzog, Christopher P.; Stricker, Stefan A.; Vuorinen, Aleksi
2010-08-01
We inspect the mass spectrum of heavy-light mesons in deformed N=2 super Yang-Mills theory using the AdS/CFT correspondence. We demonstrate how some of the degeneracies of the supersymmetric meson spectrum can be removed upon breaking the supersymmetry, thus leading to the emergence of a hyperfine structure. The explicit SUSY breaking scenarios we consider involve on the one hand, tilting one of the two fundamental D7-branes inside the internal R6 space, and on the other hand, applying an external magnetic field on the (untilted) branes. The latter scenario leads to the well-known Zeeman effect, which we inspect for both weak and strong magnetic fields.
Covalent Triazine-Based Frameworks as Visible Light Photocatalysts for the Splitting of Water.
Bi, Jinhong; Fang, Wei; Li, Liuyi; Wang, Jinyun; Liang, Shijing; He, Yunhui; Liu, Minghua; Wu, Ling
2015-10-01
Covalent triazine-based frameworks (CTFs) with a graphene-like layered morphology have been controllably synthesized by the trifluoromethanesulfonic acid-catalyzed nitrile trimerization reactions at room temperature via selecting different monomers. Platinum nanoparticles are well dispersed in CTF-T1, which is ascribed to the synergistic effects of the coordination of triazine moieties and the nanoscale confinement effect of CTFs. CTF-T1 exhibits excellent photocatalytic activity and stability for H2 evolution in the presence of platinum under visible light irradiation (λ ≥ 420 nm). The activity and stability of CTF-T1 are comparable to those of g-C3 N4 . Importantly, as a result of the tailorable electronic and spatial structures of CTFs that can be achieved through the judicial selection of monomers, CTFs not only show great potential as organic semiconductor for photocatalysis but also may provide a molecular-level understanding of the inherent heterogeneous photocatalysis. PMID:26292975
Zheng, Xue-Li; Song, Ji-Peng; Ling, Tao; Hu, Zhen Peng; Yin, Peng-Fei; Davey, Kenneth; Du, Xi-Wen; Qiao, Shi-Zhang
2016-06-01
T. Ling, X.-W. Du, S. Z. Qiao, and co-workers report strongly coupled Nafion molecules and ordered-porous CdS networks for visible-light water splitting. The image conceptually shows how the three-dimensional ordered structure effectively harvests incoming light. As described on page 4935, the inorganic CdS skeleton is homogeneously passivated by the organic Nafion molecules to facilitate hydrogen generation. PMID:27311095
Optical sparse aperture imaging.
Miller, Nicholas J; Dierking, Matthew P; Duncan, Bradley D
2007-08-10
The resolution of a conventional diffraction-limited imaging system is proportional to its pupil diameter. A primary goal of sparse aperture imaging is to enhance resolution while minimizing the total light collection area; the latter being desirable, in part, because of the cost of large, monolithic apertures. Performance metrics are defined and used to evaluate several sparse aperture arrays constructed from multiple, identical, circular subapertures. Subaperture piston and/or tilt effects on image quality are also considered. We selected arrays with compact nonredundant autocorrelations first described by Golay. We vary both the number of subapertures and their relative spacings to arrive at an optimized array. We report the results of an experiment in which we synthesized an image from multiple subaperture pupil fields by masking a large lens with a Golay array. For this experiment we imaged a slant edge feature of an ISO12233 resolution target in order to measure the modulation transfer function. We note the contrast reduction inherent in images formed through sparse aperture arrays and demonstrate the use of a Wiener-Helstrom filter to restore contrast in our experimental images. Finally, we describe a method to synthesize images from multiple subaperture focal plane intensity images using a phase retrieval algorithm to obtain estimates of subaperture pupil fields. Experimental results from synthesizing an image of a point object from multiple subaperture images are presented, and weaknesses of the phase retrieval method for this application are discussed. PMID:17694146
Chai, Zhigang; Zeng, Ting-Ting; Li, Qi; Lu, Liang-Qiu; Xiao, Wen-Jing; Xu, Dongsheng
2016-08-17
Splitting of alcohols into hydrogen and corresponding carbonyl compounds has potential applications in hydrogen production and chemical industry. Herein, we report that a heterogeneous photocatalyst (Ni-modified CdS nanoparticles) could efficiently split alcohols into hydrogen and corresponding aldehydes or ketones in a stoichiometric manner under visible light irradiation. Optimized apparent quantum yields of 38%, 46%, and 48% were obtained at 447 nm for dehydrogenation of methanol, ethanol, and 2-propanol, respectively. In the case of dehydrogenation of 2-propanol, a turnover number of greater than 44 000 was achieved. To our knowledge, these are unprecedented values for photocatalytic splitting of liquid alcohols under visible light to date. Besides, the current catalyst system functions well with other aliphatic and aromatic alcohols, affording the corresponding carbonyl compounds with good to excellent conversion and outstanding selectivity. Moreover, mechanistic investigations suggest that an interface between Ni nanocrystal and CdS plays a key role in the reaction mechanism of the photocatalytic splitting of alcohol. PMID:27477237
Removing sparse noise from hyperspectral images with sparse and low-rank penalties
NASA Astrophysics Data System (ADS)
Tariyal, Snigdha; Aggarwal, Hemant Kumar; Majumdar, Angshul
2016-03-01
In diffraction grating, at times, there are defective pixels on the focal plane array; this results in horizontal lines of corrupted pixels in some channels. Since only a few such pixels exist, the corruption/noise is sparse. Studies on sparse noise removal from hyperspectral noise are parsimonious. To remove such sparse noise, a prior work exploited the interband spectral correlation along with intraband spatial redundancy to yield a sparse representation in transform domains. We improve upon the prior technique. The intraband spatial redundancy is modeled as a sparse set of transform coefficients and the interband spectral correlation is modeled as a rank deficient matrix. The resulting optimization problem is solved using the split Bregman technique. Comparative experimental results show that our proposed approach is better than the previous one.
NASA Astrophysics Data System (ADS)
Asano, M.; Özdemir, Ş. K.; Chen, W.; Ikuta, R.; Yang, L.; Imoto, N.; Yamamoto, T.
2016-05-01
We report controllable manipulation of slow and fast light in a whispering-gallery-mode microtoroid resonator fabricated from Erbium (Er3+) 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 in the slow light regime. Moreover, we show dynamic pulse splitting and its control in slow/fast light systems using optical gain.
Learning Sparse Representations of Depth
NASA Astrophysics Data System (ADS)
Tosic, Ivana; Olshausen, Bruno A.; Culpepper, Benjamin J.
2011-09-01
This paper introduces a new method for learning and inferring sparse representations of depth (disparity) maps. The proposed algorithm relaxes the usual assumption of the stationary noise model in sparse coding. This enables learning from data corrupted with spatially varying noise or uncertainty, typically obtained by laser range scanners or structured light depth cameras. Sparse representations are learned from the Middlebury database disparity maps and then exploited in a two-layer graphical model for inferring depth from stereo, by including a sparsity prior on the learned features. Since they capture higher-order dependencies in the depth structure, these priors can complement smoothness priors commonly used in depth inference based on Markov Random Field (MRF) models. Inference on the proposed graph is achieved using an alternating iterative optimization technique, where the first layer is solved using an existing MRF-based stereo matching algorithm, then held fixed as the second layer is solved using the proposed non-stationary sparse coding algorithm. This leads to a general method for improving solutions of state of the art MRF-based depth estimation algorithms. Our experimental results first show that depth inference using learned representations leads to state of the art denoising of depth maps obtained from laser range scanners and a time of flight camera. Furthermore, we show that adding sparse priors improves the results of two depth estimation methods: the classical graph cut algorithm by Boykov et al. and the more recent algorithm of Woodford et al.
NASA Astrophysics Data System (ADS)
Ordóñez-Romero, César L.; Kalinikos, Boris A.; Krivosik, Pavol; Tong, Wei; Kabos, Pavel; Patton, Carl E.
2009-04-01
Brillouin light scattering (BLS) has been used to observe and confirm the existence of nonlinear three magnon splitting and confluence processes for propagating spin waves in the magnetostatic backward volume wave configuration. Wave vector and frequency selective BLS techniques were also used to provide a quantitative map of the wave vector make-up for the parametrically excited half-frequency dipole-exchange spin wave (DESW) split magnons and the confluence magnons that result from the recombination of these DESW modes. The experimental wave vector maps for the product splitting and confluence magnons matched nicely with those expected from spin-wave theory. The data were obtained with (1) a strip line excitation/detection transducer structure, (2) forward-scattering BLS optics, (3) a fixed magnetic field of 352 Oe applied along the propagation direction, (4) pumping frequencies from 2.5 down to 2.1 GHz, (5) and cw input powers from 200μW to 6 mW. The wave vector selective measurements utilized variable diameter circular diaphragms, rotatable slit apertures, and circular light blocks to access spin waves with wave numbers from about 100 to 3.6×104rad/cm and the full 360° range of propagation angles.
Wang, Meng; Ren, Feng; Zhou, Jigang; Cai, Guangxu; Cai, Li; Hu, Yongfeng; Wang, Dongniu; Liu, Yichao; Guo, Liejin; Shen, Shaohua
2015-01-01
Solution-based ZnO nanorod arrays (NRAs) were modified with controlled N doping by an advanced ion implantation method, and were subsequently utilized as photoanodes for photoelectrochemical (PEC) water splitting under visible light irradiation. A gradient distribution of N dopants along the vertical direction of ZnO nanorods was realized. N doped ZnO NRAs displayed a markedly enhanced visible-light-driven PEC photocurrent density of ~160 μA/cm2 at 1.1 V vs. saturated calomel electrode (SCE), which was about 2 orders of magnitude higher than pristine ZnO NRAs. The gradiently distributed N dopants not only extended the optical absorption edges to visible light region, but also introduced terraced band structure. As a consequence, N gradient-doped ZnO NRAs can not only utilize the visible light irradiation but also efficiently drive photo-induced electron and hole transfer via the terraced band structure. The superior potential of ion implantation technique for creating gradient dopants distribution in host semiconductors will provide novel insights into doped photoelectrode materials for solar water splitting. PMID:26262752
Wang, Meng; Ren, Feng; Zhou, Jigang; Cai, Guangxu; Cai, Li; Hu, Yongfeng; Wang, Dongniu; Liu, Yichao; Guo, Liejin; Shen, Shaohua
2015-01-01
Solution-based ZnO nanorod arrays (NRAs) were modified with controlled N doping by an advanced ion implantation method, and were subsequently utilized as photoanodes for photoelectrochemical (PEC) water splitting under visible light irradiation. A gradient distribution of N dopants along the vertical direction of ZnO nanorods was realized. N doped ZnO NRAs displayed a markedly enhanced visible-light-driven PEC photocurrent density of ~160 μA/cm(2) at 1.1 V vs. saturated calomel electrode (SCE), which was about 2 orders of magnitude higher than pristine ZnO NRAs. The gradiently distributed N dopants not only extended the optical absorption edges to visible light region, but also introduced terraced band structure. As a consequence, N gradient-doped ZnO NRAs can not only utilize the visible light irradiation but also efficiently drive photo-induced electron and hole transfer via the terraced band structure. The superior potential of ion implantation technique for creating gradient dopants distribution in host semiconductors will provide novel insights into doped photoelectrode materials for solar water splitting. PMID:26262752
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. PMID:24072357
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
Sun, Yi-Yang; Zhang, Shengbai
2016-01-28
Using density functional theory calculation we investigate the carbon doping of anatase TiO2, a technique widely studied for visible-light driven water splitting. By a detailed analysis of the thermodynamics of C defects in TiO2, we show that any significant concentration of C dopants in the TiO2 lattice must be a result of non-equilibrium doping, which emphasizes the importance of kinetics stabilized C defects. Based on the band gaps calculated using hybrid density functionals, we exclude the possibility of C occupying Ti lattice sites or interstitial sites to enhance visible-light absorption of TiO2, as extensively discussed in the literature. Also, the recently proposed defect with a CO species occupying two O sites yields a too small band gap for water splitting. Two defects that can effectively reduce the band gap for the water splitting application are identified to be: (1) the CO-VO complex, i.e., a C substituting for O (CO) paired with an O vacancy (VO) and (2) the (C2)2O complex with a C dimer (C2) occupying two neighboring O vacancies. Compared with the CO-VO complex, (C2)2O exhibits strong binding (greater than 2.5 eV) between the two C atoms, which could significantly enhance its kinetic stability to survive from high temperature annealing. With a reduced band gap of about 1.4 eV, carbon dimers could be ideal for kinetic doping of anatase TiO2 to enhance its visible-light activity in photocatalytic reactions. Molecular doping using C2H2 or C2H4 as C precursors has been proposed to introduce the carbon dimers into TiO2. PMID:26725589
Wang, Degao; Chang, Guoliang; Zhang, Yuying; Chao, Jie; Yang, Jianzhong; Su, Shao; Wang, Lihua; Fan, Chunhai; Wang, Lianhui
2016-07-01
Herein, we presented hierarchical three-dimensional (3D) branched hematite nanorod arrays (NAs) on transparent fluorine-doped tin oxide (FTO) conductive glass substrates, which exhibited high PEC water splitting performance due to the enhancement of mid-visible light harvesting as well as charge separation and transfer. The introduction of a TiO2 underlayer made the as-prepared 3D branched hematite NAs achieve a photocurrent density of 0.61 mA cm(-2) at 1.23 V vs. reversible hydrogen electrode (RHE) without high-temperature activation. PMID:27283270
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 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.
Azzam, R M A
2016-05-01
The simplified explicit expressions derived by Andersen [J. Opt. Soc. Am. A33, 984 (2016)JOAOD60740-323210.1364/JOSAA.32.000984], that relate to angularly symmetric beam splitting by reflection and refraction at an air-dielectric interface recently described by Azzam [J. Opt. Soc. Am. A32, 2436 (2015)JOAOD60740-323210.1364/JOSAA.32.002436], are welcome. A few additional remarks are also included in my reply to Andersen's comment. PMID:27140898
Finding Nonoverlapping Substructures of a Sparse Matrix
Pinar, Ali; Vassilevska, Virginia
2005-08-11
Many applications of scientific computing rely on computations on sparse matrices. The design of efficient implementations of sparse matrix kernels is crucial for the overall efficiency of these applications. Due to the high compute-to-memory ratio and irregular memory access patterns, the performance of sparse matrix kernels is often far away from the peak performance on a modern processor. Alternative data structures have been proposed, which split the original matrix A into A{sub d} and A{sub s}, so that A{sub d} contains all dense blocks of a specified size in the matrix, and A{sub s} contains the remaining entries. This enables the use of dense matrix kernels on the entries of A{sub d} producing better memory performance. In this work, we study the problem of finding a maximum number of nonoverlapping dense blocks in a sparse matrix, which is previously not studied in the sparse matrix community. We show that the maximum nonoverlapping dense blocks problem is NP-complete by using a reduction from the maximum independent set problem on cubic planar graphs. We also propose a 2/3-approximation algorithm that runs in linear time in the number of nonzeros in the matrix. This extended abstract focuses on our results for 2x2 dense blocks. However we show that our results can be generalized to arbitrary sized dense blocks, and many other oriented substructures, which can be exploited to improve the memory performance of sparse matrix operations.
NASA Astrophysics Data System (ADS)
Wang, Degao; Chang, Guoliang; Zhang, Yuying; Chao, Jie; Yang, Jianzhong; Su, Shao; Wang, Lihua; Fan, Chunhai; Wang, Lianhui
2016-06-01
Herein, we presented hierarchical three-dimensional (3D) branched hematite nanorod arrays (NAs) on transparent fluorine-doped tin oxide (FTO) conductive glass substrates, which exhibited high PEC water splitting performance due to the enhancement of mid-visible light harvesting as well as charge separation and transfer. The introduction of a TiO2 underlayer made the as-prepared 3D branched hematite NAs achieve a photocurrent density of 0.61 mA cm-2 at 1.23 V vs. reversible hydrogen electrode (RHE) without high-temperature activation.Herein, we presented hierarchical three-dimensional (3D) branched hematite nanorod arrays (NAs) on transparent fluorine-doped tin oxide (FTO) conductive glass substrates, which exhibited high PEC water splitting performance due to the enhancement of mid-visible light harvesting as well as charge separation and transfer. The introduction of a TiO2 underlayer made the as-prepared 3D branched hematite NAs achieve a photocurrent density of 0.61 mA cm-2 at 1.23 V vs. reversible hydrogen electrode (RHE) without high-temperature activation. Electronic supplementary information (ESI) available: Experimental details. See DOI: 10.1039/c6nr03855g
Discovery of earth abundant light absorbers for solar water splitting: Mn2V2O7 and beyond
NASA Astrophysics Data System (ADS)
Yan, Qimin; Newhouse, Pawl F.; Li, Guo; Yu, Jie; Chen, Wei; Persson, Kristin; Gregoire, John; Neaton, Jeffrey
2015-03-01
Utilizing a first-principles data driven discovery approach with high-throughput computations and machine learning techniques, we screen for transition metal oxide (TMO) compounds with low band gaps and optimal band edges for solar water splitting applications. Combining the computational screening with the high-throughput experimental synthesis efforts, we identify the complex oxide β-Mn2V2O7 as exhibiting a band gap and band edges that are near optimal for photocatalytic water splitting. Experiments, corroborated by theory, indicate that β-Mn2V2O7 has a near-direct band gap near 1.8 eV. Our calculations further reveal a valence band maximum composed of mixed O-p/Mn-d states, and a conduction band maximum of V d-character, leading to dipole-allowed direct transitions at the band edges. Photoelectrochemical measurements indicate appreciable photocurrent from Mn2V2O7 samples, corroborating our predictions. We further discuss design principles for guiding the discovery of more promising metal oxides with optimal band energetics for solar fuels applications. This work was supported by the DOE through the Materials Project and the Joint Center for Artificial Photosynthesis. Computational resources provided by NERSC.
High performance Au-Cu alloy for enhanced visible-light water splitting driven by coinage metals.
Liu, Mingyang; Zhou, Wei; Wang, Ting; Wang, Defa; Liu, Lequan; Ye, Jinhua
2016-03-17
A Au-Cu alloy strategy is, for the first time, demonstrated to be effective in enhancing visible-light photocatalytic H2 evolution via promoting metal interband transitions. Au3Cu/SrTiO3, in which oxidation of Cu was successfully restrained, showed the highest visible-light H2 evolution activity. PMID:26952932
NASA Astrophysics Data System (ADS)
Bergmann, Uwe
2010-02-01
Arguably the most important chemical reaction on earth is the photosynthetic splitting of water to molecular oxygen by the Mn-containing oxygen-evolving complex (Mn-OEC) in the protein known as photosystem II (PSII). It is this reaction which has, over the course of some 3.8 billion years, gradually filled our atmosphere with O2 and consequently enabled and sustained the evolution of complex aerobic life. Coupled to the reduction of carbon dioxide, biological photosynthesis contributes foodstuffs for nutrition while recycling CO2 from the atmosphere and replacing it with O2. By utilizing sunlight to power these energy-requiring reactions, photosynthesis also serves as a model for addressing societal energy needs as we enter an era of diminishing fossil hydrocarbon resources. Understanding, at the molecular level, the dynamics and mechanism of how nature has solved this problem is of fundamental importance and could be critical to aid in the design of manufactured devices to accomplish the conversion of sunlight into useful electrochemical energy and transportable fuel in the foreseeable future. In order to understand the photosynthetic splitting of water by the Mn-OEC we need to be able to follow the reaction in real time at an atomic level. A powerful probe to study the electronic and molecular structure of the Mn-OEC is x-ray spectroscopy. Here, in particular x-ray emission spectroscopy (XES) has two crucial qualities for LCLS based time-dependent pump-probe studies of the Mn-OEC: a) it directly probes the Mn oxidation state and ligation, b) it can be performed with wavelength dispersive optics to avoid the necessity of scanning in pump probe experiments. Recent results and the planned time dependent experiments at LCLS will be discussed. )
Ge, Ming-Zheng; Cao, Chun-Yan; Li, Shu-Hui; Tang, Yu-Xin; Wang, Lu-Ning; Qi, Ning; Huang, Jian-Ying; Zhang, Ke-Qin; Al-Deyab, S S; Lai, Yue-Kun
2016-03-01
An ultrasonication-assisted in situ deposition strategy was utilised to uniformly decorate plasmonic Ag nanoparticles on vertically aligned TiO2 nanotube arrays (NTAs) to construct a Ag@TiO2 NTA composite. The Ag nanoparticles act as efficient surface plasmon resonance (SPR) photosensitizers to drive photocatalytic water splitting under visible light irradiation. The Ag nanoparticles were uniformly deposited on the surface and inside the highly oriented TiO2 nanotubes. The visible-light-driven hydrogen production activities of silver nanoparticle anchored TiO2 nanotube array photocatalysts were evaluated using methanol as a sacrificial reagent in water under a 500 W Xe lamp with a UV light cutoff filter (λ ≥ 420 nm). It was found that the hydrogen production rate of the Ag@TiO2 NTAs prepared with ultrasonication-assisted deposition for 5 min was approximately 15 times higher than that of its pristine TiO2 NTAs counterpart. The highly efficient photocatalytic hydrogen evolution is attributed to the SPR effect of Ag for enhanced visible light absorption and boosting the photogenerated electron-hole separation/transfer. This strategy is promising for the design and construction of high efficiency TiO2 based photocatalysts for solar energy conversion. PMID:26878901
Finding nonoverlapping substructures of a sparse matrix
Pinar, Ali; Vassilevska, Virginia
2004-08-09
Many applications of scientific computing rely on computations on sparse matrices, thus the design of efficient implementations of sparse matrix kernels is crucial for the overall efficiency of these applications. Due to the high compute-to-memory ratio and irregular memory access patterns, the performance of sparse matrix kernels is often far away from the peak performance on a modern processor. Alternative data structures have been proposed, which split the original matrix A into A{sub d} and A{sub s}, so that A{sub d} contains all dense blocks of a specified size in the matrix, and A{sub s} contains the remaining entries. This enables the use of dense matrix kernels on the entries of A{sub d} producing better memory performance. In this work, we study the problem of finding a maximum number of non overlapping rectangular dense blocks in a sparse matrix, which has not been studied in the sparse matrix community. We show that the maximum non overlapping dense blocks problem is NP-complete by using a reduction from the maximum independent set problem on cubic planar graphs. We also propose a 2/3-approximation algorithm for 2 times 2 blocks that runs in linear time in the number of nonzeros in the matrix. We discuss alternatives to rectangular blocks such as diagonal blocks and cross blocks and present complexity analysis and approximation algorithms.
Higashi, Masanobu; Yamanaka, Yuta; Tomita, Osamu; Abe, Ryu
2015-10-01
A series of cation-doped BaTaO{sub 2}N particle was synthesized to control the donor density in the bulk for improving the performance of photoelectrochemical water splitting on porous BaTaO{sub 2}N photoanodes under visible light. Among the dopants (Mo{sup 6+}, W{sup 6+}, Zr{sup 4+}, and Ti{sup 4+}) examined, Mo{sup 6+} cations can be introduced into the Ta{sup 5+} site up to 5 mol. % without producing any impurity phases; the donor density of BaTaO{sub 2}N was indeed increased significantly by introducing higher ratio of Mo{sup 6+} dopant. The porous photoanodes of Mo-doped BaTaO{sub 2}N showed much higher photocurrent than others including undoped one and also exhibited much improved performance in photoelectrochemical water splitting into H{sub 2} and O{sub 2} after loaded with cobalt oxide cocatalyst and coupled with Pt counter electrode.
NASA Astrophysics Data System (ADS)
Harris, Thomas R.; Yeo, Yung Kee; Ryu, Mee-Yi; Beeler, Richard T.; Kouvetakis, John
2014-09-01
Temperature- (T-) and laser power-dependent photoluminescence (PL) measurements have been made for the tensile-strained, undoped GeSn (0.03% Sn) film grown on Si substrate. The PL results show not only clear strain-split direct bandgap transitions to the light-hole (LH) and heavy-hole (HH) bands at energies of 0.827 and 0.851 eV at 10 K, respectively, but also clearly show both strong direct and indirect bandgap related PL emissions at almost all temperatures, which are rarely observed. This split of PL emissions can be directly observed only at low T and moderate laser power, and the two PL peaks merge into one broad PL peak at room temperature, which is mainly due to the HH PL emission rather than LH transition. The evolution of T-dependent PL results also clearly show the competitive nature between the direct and indirect bandgap related PL transitions as T changes. The PL analysis also indicates that the energy gap reduction in Γ valley could be larger, whereas the bandgap reduction in L valley could be smaller than the theory predicted. As a result, the separation energy between Γ and L valleys (˜86 meV at 300 K) is smaller than theory predicted (125 meV) for this Ge-like sample, which is mainly due to the tensile strain. This finding strongly suggests that the indirect-to-direct bandgap transition of Ge1-ySny could be achieved at much lower Sn concentration than originally anticipated if one utilizes the tensile strain properly. Thus, Ge1-ySny alloys could be attractive materials for the fabrication of direct bandgap Si-based light emitting devices.
Blum, Thomas; Doi, Takumi; Hayakawa, Masashi; Izubuchi, Taku; Yamada, Norikazu
2007-12-01
We determine the light quark masses from lattice QCD simulations incorporating the electromagnetic interaction of valence quarks, using the splittings of charged and neutral pseudoscalar meson masses as inputs. The meson masses are calculated on lattice QCD configurations generated by the RBC Collaboration for two flavors of dynamical domain-wall fermions, which are combined with QED configurations generated via quenched noncompact lattice QED. The electromagnetic part of the pion mass splitting is found to be m{sub {pi}{sup +}}-m{sub {pi}{sup 0}}=4.12(21) MeV, where only the statistical error is quoted, and similarly for the kaon, 1.443(55) MeV. Our results for the light quark masses are m{sub u}{sup MS}(2 GeV)=3.02(27)(19) MeV, m{sub d}{sup MS}(2 GeV)=5.49(20)(34) MeV, and m{sub s}{sup MS}(2 GeV)=119.5(56)(74) MeV, where the first error is statistical and the second reflects the uncertainty in our nonperturbative renormalization procedure. By averaging over {+-}e to cancel O(e) noise exactly on each combined gauge field configuration, we are able to work at physical {alpha}=1/137 and obtain very small statistical errors. In our calculation, several sources of systematic error remain, including finite volume, nonzero lattice spacing, chiral extrapolation, quenched QED, and quenched strange quark, which may be more significant than the errors quoted above. We discuss these systematic errors and how to reduce or eliminate them.
Luan, Peng; Xie, Mingzheng; Fu, Xuedong; Qu, Yang; Sun, Xiaojun; Jing, Liqiang
2015-02-21
In this work, we have successfully constructed phosphate bridges in a TiO2-Fe2O3 nanocomposite using wet-chemical processes. Based on FTIR, XPS and TEM measurements it is confirmed that phosphate groups form bridges that effectively connect TiO2 and α-Fe2O3. From steady-state surface photovoltage spectra (SS-SPS) and transient-state surface photovoltage (TS-SPV) measurements in N2, it is clearly demonstrated that the separation and lifetime of the photogenerated charge carriers in the TiO2-Fe2O3 nanocomposite are greatly enhanced by the introduction of the phosphate bridges. As a consequence, the visible light photocatalytic activity in water reduction by methanol and the photoelectrochemical water oxidation were obviously improved after phosphate bridging. It is concluded mainly on the basis of ultra-low-temperature EPR signals, EIS spectra, and the normalized photocurrent action spectra that the photogenerated electrons of α-Fe2O3 under irradiation with visible light would transfer to TiO2 in the nanocomposite, and the built phosphate bridges are favorable for charge transportation, leading to the greatly-increased separation and lifetime of visible-light excited charge carriers. This work provides a feasible route to improve the photoactivity of other visible-response nanocomposites for water splitting. PMID:25598386
Nikolis, Andreas; Bernstein, Steven; Kinney, Brian; Scuderi, Nicolo; Rastogi, Shipra; Sampalis, John S
2016-01-01
Purpose Many treatment modalities exist to counteract the effects of cutaneous aging. Ablative methods have been the mainstay for nonsurgical facial rejuvenation. In recent years, nonablative techniques have been developed with the aim of achieving facial rejuvenation without epidermal damage. Light-emitting diode (LED) photorejuvenation is a novel nonablative technique that induces collagen synthesis through biophotomodulatory pathways. Materials and methods A single-center, randomized, single-blinded, placebo-controlled, split-faced clinical trial was designed. Thirty-two patients were enrolled for a 12-week study. Patients were randomized into one of four groups: Group A, treatment with KLOX-001 gel formulation and white LED (placebo) light; Group B, treatment with a placebo/base gel (no active chromophore) formulation and KLOX LED light; Group C, treatment with KLOX-001 gel formulation and KLOX LED light; and Group D, treatment with the standard skin rejuvenating treatment (0.1% retinol-based cream). Patients received treatment at weeks 0, 1, 2, and 3, and returned to the clinic at weeks 4, 8, and 12 for clinical assessments performed by an independent, blinded committee of physicians using subjective clinician assessment scales. Tolerability, adverse outcomes, and patient satisfaction were also assessed. Results Analysis demonstrated that the KLOX LED light with KLOX placebo/base gel and the KLOX LED light + KLOX-001 gel formulation groups were superior to standard of care and KLOX-001 gel formulation with placebo light on subjective clinical assessment and multiple wrinkle scales, with statistically significant results obtained for brow positioning, perioral wrinkling, and total wrinkle score. Conclusion The study results show that KLOX LED light with KLOX-001 gel formulation and KLOX LED light with KLOX placebo/base gel are effective, safe, well-tolerated, and painless treatment modalities for skin rejuvenation. PMID:27257391
NASA Astrophysics Data System (ADS)
Ge, Ming-Zheng; Cao, Chun-Yan; Li, Shu-Hui; Tang, Yu-Xin; Wang, Lu-Ning; Qi, Ning; Huang, Jian-Ying; Zhang, Ke-Qin; Al-Deyab, S. S.; Lai, Yue-Kun
2016-02-01
An ultrasonication-assisted in situ deposition strategy was utilised to uniformly decorate plasmonic Ag nanoparticles on vertically aligned TiO2 nanotube arrays (NTAs) to construct a Ag@TiO2 NTA composite. The Ag nanoparticles act as efficient surface plasmon resonance (SPR) photosensitizers to drive photocatalytic water splitting under visible light irradiation. The Ag nanoparticles were uniformly deposited on the surface and inside the highly oriented TiO2 nanotubes. The visible-light-driven hydrogen production activities of silver nanoparticle anchored TiO2 nanotube array photocatalysts were evaluated using methanol as a sacrificial reagent in water under a 500 W Xe lamp with a UV light cutoff filter (λ >= 420 nm). It was found that the hydrogen production rate of the Ag@TiO2 NTAs prepared with ultrasonication-assisted deposition for 5 min was approximately 15 times higher than that of its pristine TiO2 NTAs counterpart. The highly efficient photocatalytic hydrogen evolution is attributed to the SPR effect of Ag for enhanced visible light absorption and boosting the photogenerated electron-hole separation/transfer. This strategy is promising for the design and construction of high efficiency TiO2 based photocatalysts for solar energy conversion.An ultrasonication-assisted in situ deposition strategy was utilised to uniformly decorate plasmonic Ag nanoparticles on vertically aligned TiO2 nanotube arrays (NTAs) to construct a Ag@TiO2 NTA composite. The Ag nanoparticles act as efficient surface plasmon resonance (SPR) photosensitizers to drive photocatalytic water splitting under visible light irradiation. The Ag nanoparticles were uniformly deposited on the surface and inside the highly oriented TiO2 nanotubes. The visible-light-driven hydrogen production activities of silver nanoparticle anchored TiO2 nanotube array photocatalysts were evaluated using methanol as a sacrificial reagent in water under a 500 W Xe lamp with a UV light cutoff filter (λ >= 420 nm
Sparse representation with kernels.
Gao, Shenghua; Tsang, Ivor Wai-Hung; Chia, Liang-Tien
2013-02-01
Recent research has shown the initial success of sparse coding (Sc) in solving many computer vision tasks. Motivated by the fact that kernel trick can capture the nonlinear similarity of features, which helps in finding a sparse representation of nonlinear features, we propose kernel sparse representation (KSR). Essentially, KSR is a sparse coding technique in a high dimensional feature space mapped by an implicit mapping function. We apply KSR to feature coding in image classification, face recognition, and kernel matrix approximation. More specifically, by incorporating KSR into spatial pyramid matching (SPM), we develop KSRSPM, which achieves a good performance for image classification. Moreover, KSR-based feature coding can be shown as a generalization of efficient match kernel and an extension of Sc-based SPM. We further show that our proposed KSR using a histogram intersection kernel (HIK) can be considered a soft assignment extension of HIK-based feature quantization in the feature coding process. Besides feature coding, comparing with sparse coding, KSR can learn more discriminative sparse codes and achieve higher accuracy for face recognition. Moreover, KSR can also be applied to kernel matrix approximation in large scale learning tasks, and it demonstrates its robustness to kernel matrix approximation, especially when a small fraction of the data is used. Extensive experimental results demonstrate promising results of KSR in image classification, face recognition, and kernel matrix approximation. All these applications prove the effectiveness of KSR in computer vision and machine learning tasks. PMID:23014744
Andersen, Torben B
2016-05-01
In a recent paper, conditions for achieving equal and opposite angular deflections of a light beam by reflection and refraction at an interface between air and a dielectric were determined [J. Opt. Soc. Am. A32, 2436 (2015)JOAOD60740-323210.1364/JOSAA.32.002436]. The paper gives plots of angles of incidence and refraction as a function of the prism refractive index as well as plots of reflectances and incident linear-polarization azimuth angles as functions of the refractive index. We show here that it is possible to express these quantities as simple algebraic functions of the refractive index. PMID:27140897
NASA Astrophysics Data System (ADS)
Luo, Jie; Chen, Jiaoyan; Wang, Haiyan; Liu, Huangqing
2016-01-01
Visible-light-driven responsive Au/TiO2 nanotube arrays nanocomposites (LE-Au/TNTs) are prepared by depositing self-assembled monolayer of 3-mercaptopropionic acid (MPA) inside TNTs and then removing the ligands on the surface of Au through direct ligand-exchange method, which is beneficial for formation of intimate Au/TNTs Schottky contact after a mild annealing process. Under visible light illumination (λ > 400 nm), the photocurrent density of LE-Au/TNTs is 202 μA/cm2, which is the highest value ever reported in Au/TiO2 systems. Moreover, the incident photon to current conversion efficiency (IPCE) of LE-Au/TNTs at surface plasmonic resonance induced absorption peak (555 nm) is 7.4%. It is worth noted that the hydrogen evolution rates of the LE-Au/TNTs under simulated solar irradiation (AM1.5G, 100 mW/cm2) are 22.5 μmol/h, which is much higher than that of pristine TNTs (8.1 μmol/h). In addition, the LE-Au/TNTs show higher photoelectrochemical water splitting performance than the Au/TNTs prepared by direct impregnation-precipitation (IPAu/TNTs) strategy, which is ascribed to the close Schottky contact between Au and TNTs for better charge separation and transfer.
Petranto, J.J.
1989-09-05
A split gland having only three parts is described. The gland has substantially the same stability to the relative motion of the constituent half-gland members during the attachment process to a female fitting as have more complicated designs. Ease of manufacture and use result from the reduction in complexity of the present invention. 15 figs.
Petranto, Joseph J.
1989-01-01
A split gland having only three parts is described. The gland has substantially the same stability to the relative motion of the constituent half-gland members during the attachment process to a female fitting as have more complicated designs. Ease of manufacture and use result from the reduction in complexity of the present invention.
Kosaka, Sachiko; Yasumoto, Minako; Akilov, Oleg E; Hasan, Tayyaba; Kawana, Seiji
2010-12-01
Photodynamic therapy (PDT) with 5-aminolevulinic acid (ALA) (ALA-PDT) using intense pulsed light (IPL) as a light source (IPL-ALA-PDT) has been used for photorejuvenation, but it is unclear if this protocol can be applied to darker skin types. We performed this study to assess our IPL-ALA-PDT protocol for photorejuvenation in Asian skin. To determine an appropriate dose, ALA ointment (0-20%) was applied to the upper arm of five healthy volunteers and the fluorescence intensity (FI) was measured using a spectrofluorometer. Non-linear regression analysis of FI 2 h after ALA application with global fitting gave a typical sigmoid dose-response curve with R² = 0.9705 and saturation after 5% ALA. The entire faces of 16 Japanese women with photodamage were then treated with IPL (500-670 and 870-1400 nm, 23-30 J/cm²) 2 h after application of 5% ALA to one side of the face. Three treatments were delivered at 4-week intervals with follow-up visits. Comparative analysis of photorejuvenation showed noticeable improvements on both sides of the face, although the reduction in the photoaging score from baseline did not differ significantly between the two sides in all subjects. Despite this finding, 75% of the patients felt that the IPL-ALA-PDT-treated side of the face showed greater improvement than the IPL-treated side. However, all IPL-ALA-PDT-treated sides showed adverse effects such as erythema and pain. Therefore, we conclude that the IPL-ALA-PDT protocol requires optimization for photorejuvenation in Asians. PMID:21083701
Group-based sparse representation for image restoration.
Zhang, Jian; Zhao, Debin; Gao, Wen
2014-08-01
Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high computational complexity in dictionary learning. Second, each patch is considered independently in dictionary learning and sparse coding, which ignores the relationship among patches, resulting in inaccurate sparse coding coefficients. In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR). The proposed GSR is able to sparsely represent natural images in the domain of group, which enforces the intrinsic local sparsity and nonlocal self-similarity of images simultaneously in a unified framework. In addition, an effective self-adaptive dictionary learning method for each group with low complexity is designed, rather than dictionary learning from natural images. To make GSR tractable and robust, a split Bregman-based technique is developed to solve the proposed GSR-driven ℓ0 minimization problem for image restoration efficiently. Extensive experiments on image inpainting, image deblurring and image compressive sensing recovery manifest that the proposed GSR modeling outperforms many current state-of-the-art schemes in both peak signal-to-noise ratio and visual perception. PMID:24835225
Kraljević, L; Tomaseo, I
1990-01-01
In 1904, the first private surgical sanatorium in the Slavic South was founded in Split by Jaksa Racić, M.D., surgeon, urologist and radiologist. He was conferred degree in Insbruck "sub auspiciis imperatoris" in 1900. The sanatorium had 14 beds, operating theaters for aseptic and septic surgeries, the most modern devices, instruments, roentgenograph and electric light (17 years before Split was supplied with electricity). Doctor Jaksa Racić contributed a lot to the development of Marjan as city-park. All those achievements of Dr. Jaksa Racić were often reported in the Yugoslav and foreign newspapers. Doctor Vladimir Roić opened his own private surgical sanatorium in Split in 1929. His sanatorium had 20 beds, operating theater, delivery room and roentgenograph. In 1933 Dr. Jakov Milicić opened gynecological and obstetric sanatorium in Split. His sanatorium had 15 beds, operating theater, delivery room and roentgenograph. Those three sanatoriums, founded at the beginning of this century, with the first surgical sanatorium of Dr. Jaksa Racić, in 1904, have been of great importance for the development of health services in Split, in general. PMID:2204775
Iwase, Akihide; Yoshino, Shunya; Takayama, Tomoaki; Ng, Yun Hau; Amal, Rose; Kudo, Akihiko
2016-08-17
Metal sulfides are highly active photocatalysts for water reduction to form H2 under visible light irradiation, whereas they are unfavorable for water oxidation to form O2 because of severe self-photooxidation (i.e., photocorrosion). Construction of a Z-scheme system is a useful strategy to split water into H2 and O2 using such photocorrosive metal sulfides because the photogenerated holes in metal sulfides are efficiently transported away. Here, we demonstrate powdered Z-schematic water splitting under visible light and simulated sunlight irradiation by combining metal sulfides as an H2-evolving photocatalyst, reduced graphene oxide (RGO) as an electron mediator, and a visible-light-driven BiVO4 as an O2-evolving photocatalyst. This Z-schematic photocatalyst composite is also active in CO2 reduction using water as the sole electron donor under visible light. PMID:27459021
Color demosaicking via robust adaptive sparse representation
NASA Astrophysics Data System (ADS)
Huang, Lili; Xiao, Liang; Chen, Qinghua; Wang, Kai
2015-09-01
A single sensor camera can capture scenes by means of a color filter array. Each pixel samples only one of the three primary colors. We use a color demosaicking (CDM) technique to produce full color images and propose a robust adaptive sparse representation model for high quality CDM. The data fidelity term is characterized by l1 norm to suppress the heavy-tailed visual artifacts with an adaptively learned dictionary, while the regularization term is encouraged to seek sparsity by forcing sparse coding close to its nonlocal means to reduce coding errors. Based on the classical quadratic penalty function technique in optimization and an operator splitting method in convex analysis, we further present an effective iterative algorithm to solve the variational problem. The efficiency of the proposed method is demonstrated by experimental results with simulated and real camera data.
A Comparative Study of Sparse Associative Memories
NASA Astrophysics Data System (ADS)
Gripon, Vincent; Heusel, Judith; Löwe, Matthias; Vermet, Franck
2016-05-01
We study various models of associative memories with sparse information, i.e. a pattern to be stored is a random string of 0s and 1s with about log N 1s, only. We compare different synaptic weights, architectures and retrieval mechanisms to shed light on the influence of the various parameters on the storage capacity.
A Comparative Study of Sparse Associative Memories
NASA Astrophysics Data System (ADS)
Gripon, Vincent; Heusel, Judith; Löwe, Matthias; Vermet, Franck
2016-07-01
We study various models of associative memories with sparse information, i.e. a pattern to be stored is a random string of 0s and 1s with about log N 1s, only. We compare different synaptic weights, architectures and retrieval mechanisms to shed light on the influence of the various parameters on the storage capacity.
Grassmannian sparse representations
NASA Astrophysics Data System (ADS)
Azary, Sherif; Savakis, Andreas
2015-05-01
We present Grassmannian sparse representations (GSR), a sparse representation Grassmann learning framework for efficient classification. Sparse representation classification offers a powerful approach for recognition in a variety of contexts. However, a major drawback of sparse representation methods is their computational performance and memory utilization for high-dimensional data. A Grassmann manifold is a space that promotes smooth surfaces where points represent subspaces and the relationship between points is defined by the mapping of an orthogonal matrix. Grassmann manifolds are well suited for computer vision problems because they promote high between-class discrimination and within-class clustering, while offering computational advantages by mapping each subspace onto a single point. The GSR framework combines Grassmannian kernels and sparse representations, including regularized least squares and least angle regression, to improve high accuracy recognition while overcoming the drawbacks of performance and dependencies on high dimensional data distributions. The effectiveness of GSR is demonstrated on computationally intensive multiview action sequences, three-dimensional action sequences, and face recognition datasets.
Sparse distributed memory overview
NASA Technical Reports Server (NTRS)
Raugh, Mike
1990-01-01
The Sparse Distributed Memory (SDM) project is investigating the theory and applications of massively parallel computing architecture, called sparse distributed memory, that will support the storage and retrieval of sensory and motor patterns characteristic of autonomous systems. The immediate objectives of the project are centered in studies of the memory itself and in the use of the memory to solve problems in speech, vision, and robotics. Investigation of methods for encoding sensory data is an important part of the research. Examples of NASA missions that may benefit from this work are Space Station, planetary rovers, and solar exploration. Sparse distributed memory offers promising technology for systems that must learn through experience and be capable of adapting to new circumstances, and for operating any large complex system requiring automatic monitoring and control. Sparse distributed memory is a massively parallel architecture motivated by efforts to understand how the human brain works. Sparse distributed memory is an associative memory, able to retrieve information from cues that only partially match patterns stored in the memory. It is able to store long temporal sequences derived from the behavior of a complex system, such as progressive records of the system's sensory data and correlated records of the system's motor controls.
NASA Astrophysics Data System (ADS)
Chen, Wei; Chu, Mingchao; Gao, Li; Mao, Liqun; Yuan, Jian; Shangguan, Wenfeng
2015-01-01
The noble-metal-free Ni(OH)2 as a cocatalyst was loaded on TaON photocatalyst by a precipitation method. The characterization of XRD, SEM, HRTEM and XPS revealed that Ni species was loaded on TaON in the form of β-Ni(OH)2. The composite Ni(OH)2/TaON showed higher photocatalytic activity for hydrogen evolution (3.15 μmol/h) than that of naked TaON and even higher than 0.5 wt% Pt/TaON (1.48 μmol/h) under visible light (λ > 400 nm) in the presence of methanol as a sacrificial reagent, while the weight ratio of TaON: Ni(OH)2 reached 5. The enhancement of photocatalytic activity of Ni(OH)2/TaON is attributed to the rapid electrons migration from CB of TaON to Ni(OH)2, which restrains the recombination of charge carriers generated by TaON and facilitates the hydrogen evolution.
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. PMID:22352968
LOFAR sparse image reconstruction
NASA Astrophysics Data System (ADS)
Garsden, H.; Girard, J. N.; Starck, J. L.; Corbel, S.; Tasse, C.; Woiselle, A.; McKean, J. P.; van Amesfoort, A. S.; Anderson, J.; Avruch, I. M.; Beck, R.; Bentum, M. J.; Best, P.; Breitling, F.; Broderick, J.; Brüggen, M.; Butcher, H. R.; Ciardi, B.; de Gasperin, F.; de Geus, E.; de Vos, M.; Duscha, S.; Eislöffel, J.; Engels, D.; Falcke, H.; Fallows, R. A.; Fender, R.; Ferrari, C.; Frieswijk, W.; Garrett, M. A.; Grießmeier, J.; Gunst, A. W.; Hassall, T. E.; Heald, G.; Hoeft, M.; Hörandel, J.; van der Horst, A.; Juette, E.; Karastergiou, A.; Kondratiev, V. I.; Kramer, M.; Kuniyoshi, M.; Kuper, G.; Mann, G.; Markoff, S.; McFadden, R.; McKay-Bukowski, D.; Mulcahy, D. D.; Munk, H.; Norden, M. J.; Orru, E.; Paas, H.; Pandey-Pommier, M.; Pandey, V. N.; Pietka, G.; Pizzo, R.; Polatidis, A. G.; Renting, A.; Röttgering, H.; Rowlinson, A.; Schwarz, D.; Sluman, J.; Smirnov, O.; Stappers, B. W.; Steinmetz, M.; Stewart, A.; Swinbank, J.; Tagger, M.; Tang, Y.; Tasse, C.; Thoudam, S.; Toribio, C.; Vermeulen, R.; Vocks, C.; van Weeren, R. J.; Wijnholds, S. J.; Wise, M. W.; Wucknitz, O.; Yatawatta, S.; Zarka, P.; Zensus, A.
2015-03-01
Context. The LOw Frequency ARray (LOFAR) radio telescope is a giant digital phased array interferometer with multiple antennas distributed in Europe. It provides discrete sets of Fourier components of the sky brightness. Recovering the original brightness distribution with aperture synthesis forms an inverse problem that can be solved by various deconvolution and minimization methods. Aims: Recent papers have established a clear link between the discrete nature of radio interferometry measurement and the "compressed sensing" (CS) theory, which supports sparse reconstruction methods to form an image from the measured visibilities. Empowered by proximal theory, CS offers a sound framework for efficient global minimization and sparse data representation using fast algorithms. Combined with instrumental direction-dependent effects (DDE) in the scope of a real instrument, we developed and validated a new method based on this framework. Methods: We implemented a sparse reconstruction method in the standard LOFAR imaging tool and compared the photometric and resolution performance of this new imager with that of CLEAN-based methods (CLEAN and MS-CLEAN) with simulated and real LOFAR data. Results: We show that i) sparse reconstruction performs as well as CLEAN in recovering the flux of point sources; ii) performs much better on extended objects (the root mean square error is reduced by a factor of up to 10); and iii) provides a solution with an effective angular resolution 2-3 times better than the CLEAN images. Conclusions: Sparse recovery gives a correct photometry on high dynamic and wide-field images and improved realistic structures of extended sources (of simulated and real LOFAR datasets). This sparse reconstruction method is compatible with modern interferometric imagers that handle DDE corrections (A- and W-projections) required for current and future instruments such as LOFAR and SKA.
Structured Multifrontal Sparse Solver
Energy Science and Technology Software Center (ESTSC)
2014-05-01
StruMF is an algebraic structured preconditioner for the interative solution of large sparse linear systems. The preconditioner corresponds to a multifrontal variant of sparse LU factorization in which some dense blocks of the factors are approximated with low-rank matrices. It is algebraic in that it only requires the linear system itself, and the approximation threshold that determines the accuracy of individual low-rank approximations. Favourable rank properties are obtained using a block partitioning which is amore » refinement of the partitioning induced by nested dissection ordering.« less
Sparse representation for vehicle recognition
NASA Astrophysics Data System (ADS)
Monnig, Nathan D.; Sakla, Wesam
2014-06-01
The Sparse Representation for Classification (SRC) algorithm has been demonstrated to be a state-of-the-art algorithm for facial recognition applications. Wright et al. demonstrate that under certain conditions, the SRC algorithm classification performance is agnostic to choice of linear feature space and highly resilient to image corruption. In this work, we examined the SRC algorithm performance on the vehicle recognition application, using images from the semi-synthetic vehicle database generated by the Air Force Research Laboratory. To represent modern operating conditions, vehicle images were corrupted with noise, blurring, and occlusion, with representation of varying pose and lighting conditions. Experiments suggest that linear feature space selection is important, particularly in the cases involving corrupted images. Overall, the SRC algorithm consistently outperforms a standard k nearest neighbor classifier on the vehicle recognition task.
Sparse inpainting and isotropy
Feeney, Stephen M.; McEwen, Jason D.; Peiris, Hiranya V.; Marinucci, Domenico; Cammarota, Valentina; Wandelt, Benjamin D. E-mail: marinucc@axp.mat.uniroma2.it E-mail: h.peiris@ucl.ac.uk E-mail: cammarot@axp.mat.uniroma2.it
2014-01-01
Sparse inpainting techniques are gaining in popularity as a tool for cosmological data analysis, in particular for handling data which present masked regions and missing observations. We investigate here the relationship between sparse inpainting techniques using the spherical harmonic basis as a dictionary and the isotropy properties of cosmological maps, as for instance those arising from cosmic microwave background (CMB) experiments. In particular, we investigate the possibility that inpainted maps may exhibit anisotropies in the behaviour of higher-order angular polyspectra. We provide analytic computations and simulations of inpainted maps for a Gaussian isotropic model of CMB data, suggesting that the resulting angular trispectrum may exhibit small but non-negligible deviations from isotropy.
Sparse matrix test collections
Duff, I.
1996-12-31
This workshop will discuss plans for coordinating and developing sets of test matrices for the comparison and testing of sparse linear algebra software. We will talk of plans for the next release (Release 2) of the Harwell-Boeing Collection and recent work on improving the accessibility of this Collection and others through the World Wide Web. There will only be three talks of about 15 to 20 minutes followed by a discussion from the floor.
NASA Technical Reports Server (NTRS)
Kanerva, Pentti
1988-01-01
Theoretical models of the human brain and proposed neural-network computers are developed analytically. Chapters are devoted to the mathematical foundations, background material from computer science, the theory of idealized neurons, neurons as address decoders, and the search of memory for the best match. Consideration is given to sparse memory, distributed storage, the storage and retrieval of sequences, the construction of distributed memory, and the organization of an autonomous learning system.
Kanerva, P.
1988-01-01
Theoretical models of the human brain and proposed neural-network computers are developed analytically. Chapters are devoted to the mathematical foundations, background material from computer science, the theory of idealized neurons, neurons as address decoders, and the search of memory for the best match. Consideration is given to sparse memory, distributed storage, the storage and retrieval of sequences, the construction of distributed memory, and the organization of an autonomous learning system. 63 refs.
Artero, Vincent; Chavarot-Kerlidou, Murielle; Fontecave, Marc
2011-08-01
The future of energy supply depends on innovative breakthroughs regarding the design of cheap, sustainable, and efficient systems for the conversion and storage of renewable energy sources, such as solar energy. The production of hydrogen, a fuel with remarkable properties, through sunlight-driven water splitting appears to be a promising and appealing solution. While the active sites of enzymes involved in the overall water-splitting process in natural systems, namely hydrogenases and photosystem II, use iron, nickel, and manganese ions, cobalt has emerged in the past five years as the most versatile non-noble metal for the development of synthetic H(2)- and O(2)-evolving catalysts. Such catalysts can be further coupled with photosensitizers to generate photocatalytic systems for light-induced hydrogen evolution from water. PMID:21748828
Method and apparatus for distinguishing actual sparse events from sparse event false alarms
Spalding, Richard E.; Grotbeck, Carter L.
2000-01-01
Remote sensing method and apparatus wherein sparse optical events are distinguished from false events. "Ghost" images of actual optical phenomena are generated using an optical beam splitter and optics configured to direct split beams to a single sensor or segmented sensor. True optical signals are distinguished from false signals or noise based on whether the ghost image is presence or absent. The invention obviates the need for dual sensor systems to effect a false target detection capability, thus significantly reducing system complexity and cost.
Energy Science and Technology Software Center (ESTSC)
2007-04-12
The Sparse Image Format (SIF) is a file format for storing spare raster images. It works by breaking an image down into tiles. Space is savid by only storing non-uniform tiles, i.e. tiles with at least two different pixel values. If a tile is completely uniform, its common pixel value is stored instead of the complete tile raster. The software is a library in the C language used for manipulating files in SIF format. Itmore » supports large files (> 2GB) and is designed to build in Windows and Linux environments.« less
Eads, Damian Ryan
2007-04-12
The Sparse Image Format (SIF) is a file format for storing spare raster images. It works by breaking an image down into tiles. Space is savid by only storing non-uniform tiles, i.e. tiles with at least two different pixel values. If a tile is completely uniform, its common pixel value is stored instead of the complete tile raster. The software is a library in the C language used for manipulating files in SIF format. It supports large files (> 2GB) and is designed to build in Windows and Linux environments.
and Drayton Munster, Miroslav Stoyanov
2013-09-20
Sparse Grids are the family of methods of choice for multidimensional integration and interpolation in low to moderate number of dimensions. The method is to select extend a one dimensional set of abscissas, weights and basis functions by taking a subset of all possible tensor products. The module provides the ability to create global and local approximations based on polynomials and wavelets. The software has three components, a library, a wrapper for the library that provides a command line interface via text files ad a MATLAB interface via the command line tool.
Energy Science and Technology Software Center (ESTSC)
2013-09-20
Sparse Grids are the family of methods of choice for multidimensional integration and interpolation in low to moderate number of dimensions. The method is to select extend a one dimensional set of abscissas, weights and basis functions by taking a subset of all possible tensor products. The module provides the ability to create global and local approximations based on polynomials and wavelets. The software has three components, a library, a wrapper for the library thatmore » provides a command line interface via text files ad a MATLAB interface via the command line tool.« less
Compressed Sampling of Spectrally Sparse Signals Using Sparse Circulant Matrices
NASA Astrophysics Data System (ADS)
Xu, Guangjie; Wang, Huali; Sun, Lei; Zeng, Weijun; Wang, Qingguo
2014-11-01
Circulant measurement matrices constructed by partial cyclically shifts of one generating sequence, are easier to be implemented in hardware than widely used random measurement matrices; however, the diminishment of randomness makes it more sensitive to signal noise. Selecting a deterministic sequence with optimal periodic autocorrelation property (PACP) as generating sequence, would enhance the noise robustness of circulant measurement matrix, but this kind of deterministic circulant matrices only exists in the fixed periodic length. Actually, the selection of generating sequence doesn't affect the compressive performance of circulant measurement matrix but the subspace energy in spectrally sparse signals. Sparse circulant matrices, whose generating sequence is a sparse sequence, could keep the energy balance of subspaces and have similar noise robustness to deterministic circulant matrices. In addition, sparse circulant matrices have no restriction on length and are more suitable for the compressed sampling of spectrally sparse signals at arbitrary dimensionality.
NASA Technical Reports Server (NTRS)
Denning, Peter J.
1989-01-01
Sparse distributed memory was proposed be Pentti Kanerva as a realizable architecture that could store large patterns and retrieve them based on partial matches with patterns representing current sensory inputs. This memory exhibits behaviors, both in theory and in experiment, that resemble those previously unapproached by machines - e.g., rapid recognition of faces or odors, discovery of new connections between seemingly unrelated ideas, continuation of a sequence of events when given a cue from the middle, knowing that one doesn't know, or getting stuck with an answer on the tip of one's tongue. These behaviors are now within reach of machines that can be incorporated into the computing systems of robots capable of seeing, talking, and manipulating. Kanerva's theory is a break with the Western rationalistic tradition, allowing a new interpretation of learning and cognition that respects biology and the mysteries of individual human beings.
Percolation on Sparse Networks
NASA Astrophysics Data System (ADS)
Karrer, Brian; Newman, M. E. J.; Zdeborová, Lenka
2014-11-01
We study percolation on networks, which is used as a model of the resilience of networked systems such as the Internet to attack or failure and as a simple model of the spread of disease over human contact networks. We reformulate percolation as a message passing process and demonstrate how the resulting equations can be used to calculate, among other things, the size of the percolating cluster and the average cluster size. The calculations are exact for sparse networks when the number of short loops in the network is small, but even on networks with many short loops we find them to be highly accurate when compared with direct numerical simulations. By considering the fixed points of the message passing process, we also show that the percolation threshold on a network with few loops is given by the inverse of the leading eigenvalue of the so-called nonbacktracking matrix.
Estimating sparse precision matrices
NASA Astrophysics Data System (ADS)
Padmanabhan, Nikhil; White, Martin; Zhou, Harrison H.; O'Connell, Ross
2016-08-01
We apply a method recently introduced to the statistical literature to directly estimate the precision matrix from an ensemble of samples drawn from a corresponding Gaussian distribution. Motivated by the observation that cosmological precision matrices are often approximately sparse, the method allows one to exploit this sparsity of the precision matrix to more quickly converge to an asymptotic 1/sqrt{N_sim} rate while simultaneously providing an error model for all of the terms. Such an estimate can be used as the starting point for further regularization efforts which can improve upon the 1/sqrt{N_sim} limit above, and incorporating such additional steps is straightforward within this framework. We demonstrate the technique with toy models and with an example motivated by large-scale structure two-point analysis, showing significant improvements in the rate of convergence. For the large-scale structure example, we find errors on the precision matrix which are factors of 5 smaller than for the sample precision matrix for thousands of simulations or, alternatively, convergence to the same error level with more than an order of magnitude fewer simulations.
James, Gareth M.; Sabatti, Chiara; Zhou, Nengfeng; Zhu, Ji
2011-01-01
In many organisms the expression levels of each gene are controlled by the activation levels of known “Transcription Factors” (TF). A problem of considerable interest is that of estimating the “Transcription Regulation Networks” (TRN) relating the TFs and genes. While the expression levels of genes can be observed, the activation levels of the corresponding TFs are usually unknown, greatly increasing the difficulty of the problem. Based on previous experimental work, it is often the case that partial information about the TRN is available. For example, certain TFs may be known to regulate a given gene or in other cases a connection may be predicted with a certain probability. In general, the biology of the problem indicates there will be very few connections between TFs and genes. Several methods have been proposed for estimating TRNs. However, they all suffer from problems such as unrealistic assumptions about prior knowledge of the network structure or computational limitations. We propose a new approach that can directly utilize prior information about the network structure in conjunction with observed gene expression data to estimate the TRN. Our approach uses L1 penalties on the network to ensure a sparse structure. This has the advantage of being computationally efficient as well as making many fewer assumptions about the network structure. We use our methodology to construct the TRN for E. coli and show that the estimate is biologically sensible and compares favorably with previous estimates. PMID:21625366
Estimating sparse precision matrices
NASA Astrophysics Data System (ADS)
Padmanabhan, Nikhil; White, Martin; Zhou, Harrison H.; O'Connell, Ross
2016-05-01
We apply a method recently introduced to the statistical literature to directly estimate the precision matrix from an ensemble of samples drawn from a corresponding Gaussian distribution. Motivated by the observation that cosmological precision matrices are often approximately sparse, the method allows one to exploit this sparsity of the precision matrix to more quickly converge to an asymptotic 1/√{N_sim} rate while simultaneously providing an error model for all of the terms. Such an estimate can be used as the starting point for further regularization efforts which can improve upon the 1/√{N_sim} limit above, and incorporating such additional steps is straightforward within this framework. We demonstrate the technique with toy models and with an example motivated by large-scale structure two-point analysis, showing significant improvements in the rate of convergence. For the large-scale structure example we find errors on the precision matrix which are factors of 5 smaller than for the sample precision matrix for thousands of simulations or, alternatively, convergence to the same error level with more than an order of magnitude fewer simulations.
Estimating sparse precision matrices
NASA Astrophysics Data System (ADS)
Padmanabhan, Nikhil; White, Martin; Zhou, Harrison H.; O'Connell, Ross
2016-08-01
We apply a method recently introduced to the statistical literature to directly estimate the precision matrix from an ensemble of samples drawn from a corresponding Gaussian distribution. Motivated by the observation that cosmological precision matrices are often approximately sparse, the method allows one to exploit this sparsity of the precision matrix to more quickly converge to an asymptotic 1/√{N_sim} rate while simultaneously providing an error model for all of the terms. Such an estimate can be used as the starting point for further regularization efforts which can improve upon the 1/√{N_sim} limit above, and incorporating such additional steps is straightforward within this framework. We demonstrate the technique with toy models and with an example motivated by large-scale structure two-point analysis, showing significant improvements in the rate of convergence. For the large-scale structure example, we find errors on the precision matrix which are factors of 5 smaller than for the sample precision matrix for thousands of simulations or, alternatively, convergence to the same error level with more than an order of magnitude fewer simulations.
Completeness for sparse potential scattering
Shen, Zhongwei
2014-01-15
The present paper is devoted to the scattering theory of a class of continuum Schrödinger operators with deterministic sparse potentials. We first establish the limiting absorption principle for both modified free resolvents and modified perturbed resolvents. This actually is a weak form of the classical limiting absorption principle. We then prove the existence and completeness of local wave operators, which, in particular, imply the existence of wave operators. Under additional assumptions on the sparse potential, we prove the completeness of wave operators. In the context of continuum Schrödinger operators with sparse potentials, this paper gives the first proof of the completeness of wave operators.
Robust Fringe Projection Profilometry via Sparse Representation.
Budianto; Lun, Daniel P K
2016-04-01
In this paper, a robust fringe projection profilometry (FPP) algorithm using the sparse dictionary learning and sparse coding techniques is proposed. When reconstructing the 3D model of objects, traditional FPP systems often fail to perform if the captured fringe images have a complex scene, such as having multiple and occluded objects. It introduces great difficulty to the phase unwrapping process of an FPP system that can result in serious distortion in the final reconstructed 3D model. For the proposed algorithm, it encodes the period order information, which is essential to phase unwrapping, into some texture patterns and embeds them to the projected fringe patterns. When the encoded fringe image is captured, a modified morphological component analysis and a sparse classification procedure are performed to decode and identify the embedded period order information. It is then used to assist the phase unwrapping process to deal with the different artifacts in the fringe images. Experimental results show that the proposed algorithm can significantly improve the robustness of an FPP system. It performs equally well no matter the fringe images have a simple or complex scene, or are affected due to the ambient lighting of the working environment. PMID:26890867
Structural basis of photosynthetic water-splitting
Shen, Jian-Ren; Kawakami, Keisuke; Kamiya, Nobuo
2013-12-10
Photosynthetic water-splitting takes place in photosystem II (PSII), a membrane protein complex consisting of 20 subunits with an overall molecular mass of 350 kDa. The light-induced water-splitting reaction catalyzed by PSII not only converts light energy into biologically useful chemical energy, but also provides us with oxygen indispensible for sustaining oxygenic life on the earth. We have solved the structure of PSII at a 1.9 Å resolution, from which, the detailed structure of the Mn{sub 4}CaO{sub 5}-cluster, the catalytic center for water-splitting, became clear. Based on the structure of PSII at the atomic resolution, possible mechanism of light-induced water-splitting was discussed.
Threaded Operations on Sparse Matrices
Sneed, Brett
2015-09-01
We investigate the use of sparse matrices and OpenMP multi-threading on linear algebra operations involving them. Several sparse matrix data structures are presented. Implementation of the multi- threading primarily occurs in the level one and two BLAS functions used within the four algorithms investigated{the Power Method, Conjugate Gradient, Biconjugate Gradient, and Jacobi's Method. The bene ts of launching threads once per high level algorithm are explored.
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.
NASA Astrophysics Data System (ADS)
Inoue, Tomoyuki; Toprasertpong, Kasidit; Delamarre, Amaury; Watanabe, Kentaroh; Paire, Myriam; Lombez, Laurent; Guillemoles, Jean-François; Sugiyama, Masakazu; Nakano, Yoshiaki
2016-03-01
Insertion of InGaAs/GaAsP strain-balanced multiple quantum wells (MQWs) into i-regions of GaAs p-i-n solar cells show several advantages against GaAs bulk p-i-n solar cells. Particularly under high-concentration sunlight condition, enhancement of the open-circuit voltage with increasing concentration ratio in thin-barrier MQW cells has been reported to be more apparent than that in GaAs bulk cells. However, investigation of the MQW cell mechanisms in terms of I-V characteristics under high-concentration sunlight suffers from the increase in cell temperature and series resistance. In order to investigate the mechanism of the steep enhancement of open-circuit voltage in MQW cells under high-concentration sunlight without affected by temperature, the quasi-Fermi level splitting was evaluated by analyzing electroluminescence (EL) from a cell. Since a cell under current injection with a density Jinjhas similar excess carrier density to a cell under concentrated sunlight with an equivalent short-circuit current Jsc = Jinj, EL measurement with varied Jinj can approximately evaluate a cell performance under a variety of concentration ratio. In addition to the evaluation of quasi-Fermi level splitting, the external luminescence efficiency was also investigated with the EL measurement. The MQW cells showed higher external luminescence efficiency than the GaAs reference cells especially under high-concentration condition. The results suggest that since the MQW region can trap and confine carriers, the localized excess carriers inside the cells make radiative recombination more dominant.
United States. Bonneville Power Administration.
1992-09-01
Since lighting accounts for about one-third of the energy used in commercial buildings, there is opportunity to conserve. There are two ways to reduce lighting energy use: modify lighting systems so that they used less electricity and/or reduce the number of hours the lights are used. This booklet presents a number of ways to do both. Topics covered include: reassessing lighting levels, reducing lighting levels, increasing bulb & fixture efficiency, using controls to regulate lighting, and taking advantage of daylight.
Wavelet Sparse Approximate Inverse Preconditioners
NASA Technical Reports Server (NTRS)
Chan, Tony F.; Tang, W.-P.; Wan, W. L.
1996-01-01
There is an increasing interest in using sparse approximate inverses as preconditioners for Krylov subspace iterative methods. Recent studies of Grote and Huckle and Chow and Saad also show that sparse approximate inverse preconditioner can be effective for a variety of matrices, e.g. Harwell-Boeing collections. Nonetheless a drawback is that it requires rapid decay of the inverse entries so that sparse approximate inverse is possible. However, for the class of matrices that, come from elliptic PDE problems, this assumption may not necessarily hold. Our main idea is to look for a basis, other than the standard one, such that a sparse representation of the inverse is feasible. A crucial observation is that the kind of matrices we are interested in typically have a piecewise smooth inverse. We exploit this fact, by applying wavelet techniques to construct a better sparse approximate inverse in the wavelet basis. We shall justify theoretically and numerically that our approach is effective for matrices with smooth inverse. We emphasize that in this paper we have only presented the idea of wavelet approximate inverses and demonstrated its potential but have not yet developed a highly refined and efficient algorithm.
Qian, Ling; Chen, Jian Fu; Li, Yu Hang; Wu, Long; Wang, Hai Feng; Chen, Ai Ping; Hu, P; Zheng, Li Rong; Yang, Hua Gui
2015-09-21
The efficiency of solar-energy-conversion devices depends on the absorption region and intensity of the photon collectors. Organic chromophores, which have been widely stabilized on inorganic semiconductors for light trapping, are limited by the interface between the chromophore and semiconductor. Herein we report a novel orange zinc germanate (Zn-Ge-O) with a chromophore-like structure, by which the absorption region can be dramatically expanded. Structural characterizations and theoretical calculations together reveal that the origin of visible-light response can be attributed to the unusual metallic Ge-Ge bonds which act in a similar way to organic chromophores. Benefiting from the enhanced light harvest, the orange Zn-Ge-O demonstrates superior capacity for solar-driven hydrogen production. PMID:26291331
Moniz, Savio J A; Blackman, Christopher S; Southern, Paul; Weaver, Paul M; Tang, Junwang; Carmalt, Claire J
2015-10-21
Phase-pure BiFeO3 films were grown directly via dual-source low-pressure CVD (LPCVD) from the ligand-matched precursors [Bi(O(t)Bu)3] and [Fe(O(t)Bu)3]2, without the requirement for oxidising gas or post deposition annealing. Photocatalytic testing for water oxidation revealed extremely high activity for PEC water splitting and photocatalytic water oxidation under visible light irradiation (λ > 420 nm) with a benchmark IPCE for BiFeO3 of 23% at 400 nm. The high activity is ascribed to the ultrafine morphology achieved via the LPCVD process. The performance was enhanced by over four times when the BiFeO3 photoanode is coupled to a Ni-B surface OEC. PMID:26383028
Zhang, Jian; Zhu, Wenfeng; Liu, Xiaoheng
2014-06-28
CdS quantum dot/vermiculite (CdS/VMT) nanocomposites have been synthesized via a facile one-step method and characterized by X-ray diffraction, UV-vis diffuse reflection spectroscopy, X-ray photoelectron spectroscopy, and transmission electron microscopy. The photocatalytic hydrogen generation activities of these samples were evaluated using Na2S and Na2SO3 as sacrificial reagents in water under visible-light illumination (λ ≥ 420 nm). The most important aspect of this work is the use of natural products (VMT) as host photocatalysts. The effect of CdS content on the rate of visible light photocatalytic hydrogen generation was investigated for different CdS loadings. The synergistic effect of VMT and CdS quantum dots (QDs) leads to efficient separation of the photogenerated charge carriers and, consequently, enhances the visible light photocatalytic hydrogen production activity of the photocatalyst. The CdS/VMT composite with an optimal ratio of 5% exhibits the highest hydrogen evolution rate of 92 μmol h(-1) under visible light irradiation and the highest apparent quantum efficiency of 17.7% at 420 nm. A possible photocatalytic mechanism of the CdS/VMT nanocomposite is proposed and corroborated by photoelectrochemical curves. PMID:24819860
NASA Astrophysics Data System (ADS)
Pilipchuk, L. A.; Pilipchuk, A. S.
2015-11-01
In this paper we propose the theory of decomposition, methods, technologies, applications and implementation in Wol-fram Mathematica for the constructing the solutions of the sparse linear systems. One of the applications is the Sensor Location Problem for the symmetric graph in the case when split ratios of some arc flows can be zeros. The objective of that application is to minimize the number of sensors that are assigned to the nodes. We obtain a sparse system of linear algebraic equations and research its matrix rank. Sparse systems of these types appear in generalized network flow programming problems in the form of restrictions and can be characterized as systems with a large sparse sub-matrix representing the embedded network structure.
Pilipchuk, L. A.; Pilipchuk, A. S.
2015-11-30
In this paper we propose the theory of decomposition, methods, technologies, applications and implementation in Wol-fram Mathematica for the constructing the solutions of the sparse linear systems. One of the applications is the Sensor Location Problem for the symmetric graph in the case when split ratios of some arc flows can be zeros. The objective of that application is to minimize the number of sensors that are assigned to the nodes. We obtain a sparse system of linear algebraic equations and research its matrix rank. Sparse systems of these types appear in generalized network flow programming problems in the form of restrictions and can be characterized as systems with a large sparse sub-matrix representing the embedded network structure.
A scalable 2-D parallel sparse solver
Kothari, S.C.; Mitra, S.
1995-12-01
Scalability beyond a small number of processors, typically 32 or less, is known to be a problem for existing parallel general sparse (PGS) direct solvers. This paper presents a parallel general sparse PGS direct solver for general sparse linear systems on distributed memory machines. The algorithm is based on the well-known sequential sparse algorithm Y12M. To achieve efficient parallelization, a 2-D scattered decomposition of the sparse matrix is used. The proposed algorithm is more scalable than existing parallel sparse direct solvers. Its scalability is evaluated on a 256 processor nCUBE2s machine using Boeing/Harwell benchmark matrices.
Fitch, Joseph P.
1999-07-06
An endoscope which reduces the volume needed by the imaging part thereof, maintains resolution of a wide diameter optical system, while increasing tool access, and allows stereographic or interferometric processing for depth and perspective information/visualization. Because the endoscope decreases the volume consumed by imaging optics such allows a larger fraction of the volume to be used for non-imaging tools, which allows smaller incisions in surgical and diagnostic medical applications thus produces less trauma to the patient or allows access to smaller volumes than is possible with larger instruments. The endoscope utilizes fiber optic light pipes in an outer layer for illumination, a multi-pupil imaging system in an inner annulus, and an access channel for other tools in the center. The endoscope is amenable to implementation as a flexible scope, and thus increases the utility thereof. Because the endoscope uses a multi-aperture pupil, it can also be utilized as an optical array, allowing stereographic and interferometric processing.
Fitch, J.P.
1999-07-06
An endoscope is disclosed which reduces the volume needed by the imaging part, maintains resolution of a wide diameter optical system, while increasing tool access, and allows stereographic or interferometric processing for depth and perspective information/visualization. Because the endoscope decreases the volume consumed by imaging optics such allows a larger fraction of the volume to be used for non-imaging tools, which allows smaller incisions in surgical and diagnostic medical applications thus produces less trauma to the patient or allows access to smaller volumes than is possible with larger instruments. The endoscope utilizes fiber optic light pipes in an outer layer for illumination, a multi-pupil imaging system in an inner annulus, and an access channel for other tools in the center. The endoscope is amenable to implementation as a flexible scope, and thus increases it's utility. Because the endoscope uses a multi-aperture pupil, it can also be utilized as an optical array, allowing stereographic and interferometric processing. 7 figs.
Jegerschoeld, C.V.; Virgin, I.; Styring, S. )
1990-07-03
Strong illumination of oxygen-evolving organisms inhibits the electron transport through photosystem II (photoinhibition). In addition the illumination leads to a rapid turnover of the D1 protein in the reaction center of photosystem II. In this study the light-dependent degradation of the D1 reaction center protein and the light-dependent inhibition of electron-transport reactions have been studied in thylakoid membranes in which the oxygen evolution has been reversibly inhibited by Cl- depletion. The results show that Cl(-)-depleted thylakoid membranes are very vulnerable to damage induced by illumination. Both the D1 protein and the inhibition of the oxygen evolution are 15-20 times more sensitive to illumination than in control thylakoid membranes. The presence, during the illumination, of the herbicide 3-(3,4-dichlorophenyl)-1,1-dimethylurea (DCMU) prevented both the light-dependent degradation of the D1 protein and the inhibition of the electron transport. The protection exerted by DCMU is seen only in Cl(-)-depleted thylakoid membranes. These observations lead to the proposal that continuous illumination of Cl(-)-depleted thylakoid membranes generates anomalously long-lived, highly oxidizing radicals on the oxidizing side of photosystem II, which are responsible for the light-induced protein damage and inhibition. The presence of DCMU during the illumination prevents the formation of these radicals, which explains the protective effects of the herbicide. It is also observed that in Cl(-)-depleted thylakoid membranes, oxygen evolution (measured after the readdition of Cl-) is inhibited before electron transfer from diphenylcarbazide to dichlorophenolindophenol.
Photocatalytic water splitting
NASA Astrophysics Data System (ADS)
Kuo, Yenting
New photocatalystic materials Ti-In oxy(nitride) and nanosized Ru-loaded strontium titanate doped with Rh (Ru/SrTiO3:Rh) have been synthesized. The textural and surface characteristic properties were studied by nitrogen BET analysis, diffuse reflectance UV-vis spectroscopy, X-ray photoelectron spectroscopy, transmission electron microscopy, scanning electron microscopy and powder XRD. The photocatalytic properties were enhanced by the binary metal oxides of titanium dioxide and indium oxide. The XRD patterns confirmed the oxygen exchange between two metal oxides during the synthesis. Moreover, the presence of titanium dioxide can help the stabilization of InN during hot NH3(g) treatment. On the other hand, the particle sizes of aerogel prepared Ru/SrTiO3:Rh varied from 12 to 25 nm depended on different Rh doping. A mixture of ethanol and toluene was found to be the best binary solvent for supercritical drying, which yielded a SrTiO3 sample with a surface area of 130 m2/g and an average crystallite size of 6 nm. Enhanced photocatalytic hydrogen production under UV-vis light irradiation was achieved by ammonolysis of intimately mixed titanium dioxide and indium oxide at high temperatures. Gas chromatography monitored steadily the formation of hydrogen when sacrificial (methanol or ethanol) were present. XRD patterns confirmed that the photocatalysts maintain crystalline integrity before and after water splitting experiments. Moreover, the presence of InN may be crucial for the increase of hydrogen production activities. These Ru/SrTiO3:Rh photocatalysts have been studied for photocatalytic hydrogen production under visible light. The band gap of the bulk SrTiO 3 (3.2 eV) does not allow response to visible light. However, after doping with rhodium and loaded with ruthenium, the modified strontium titanates can utilize light above 400 nm due to the formation of valence band or electron donor levels inside of the band gap. Moreover, the surface areas of these
NASA Astrophysics Data System (ADS)
Bhirud, Ashwini P.; Sathaye, Shivaram D.; Waichal, Rupali P.; Ambekar, Jalindar D.; Park, Chan-J.; Kale, Bharat B.
2015-03-01
Highly monodispersed nitrogen doped TiO2 nanoparticles were successfully deposited on graphene (N-TiO2/Gr) by a facile in-situ wet chemical method for the first time. N-TiO2/Gr has been further used for photocatalytic hydrogen production using a naturally occurring abundant source of energy i.e. solar light. The N-TiO2/Gr nanocomposite composition was optimized by varying the concentrations of dopant nitrogen and graphene (using various concentrations of graphene) for utmost hydrogen production. The structural, optical and morphological aspects of nanocomposites were studied using XRD, UV-DRS, Raman, XPS, FESEM, and TEM. The structural study of the nanocomposite shows existence of anatase N-TiO2. Further, the details of the components present in the composition were confirmed with Raman and XPS. The morphological study shows that very tiny, 7-10 nm sized, N-TiO2 nanoparticles are deposited on the graphene sheet. The optical study reveals a drastic change in absorption edge and consequent total absorption due to nitrogen doping and presence of graphene. Considering the extended absorption edge to the visible region, these nanocomposites were further used as a photocatalyst to transform hazardous H2S waste into eco-friendly hydrogen using solar light. The N-TiO2/Gr nanocomposite with 2% graphene exhibits enhanced photocatalytic stable hydrogen production i.e. ~5941 μmol h-1 under solar light irradiation using just 0.2 gm nanocomposite, which is much higher as compared to P25, undoped TiO2 and TiO2/Gr nanocomposite. The enhancement in the photocatalytic activity is attributed to `N' doping as well as high specific surface area and charge carrier ability of graphene. The recycling of the photocatalyst shows a good stability of the nanocomposites. This work may provide new insights to design other semiconductor deposited graphene novel nanocomposites as a visible light active photocatalyst.Highly monodispersed nitrogen doped TiO2 nanoparticles were successfully
Amesos2 Templated Direct Sparse Solver Package
Energy Science and Technology Software Center (ESTSC)
2011-05-24
Amesos2 is a templated direct sparse solver package. Amesos2 provides interfaces to direct sparse solvers, rather than providing native solver capabilities. Amesos2 is a derivative work of the Trilinos package Amesos.
Wang, Ruosong; Xu, Xiaoxue; Zhang, Yi; Chang, Zhimin; Sun, Zaicheng; Dong, Wen-Fei
2015-07-01
We have designed a novel semiconductor core/layer nanostructure of a uniform ZnO@TiO2 nanorod array modified with a ZnIn0.25Cu0.02S1.395 solid-solution on the surface via a facile hydrothermal synthesis. This novel nanostructure combines the merits of all components and meets the requirements of photovoltaic system application. An intimate PN heterojunction is formed from the ZnO@TiO2 nanorod and polymetallic sulphide solid-solution, which is remarkably beneficial for the effective visible light absorption and rapid charge carrier separation. The nanostructures exhibit higher photocurrent and incident photon to electron conversion efficiency (IPCE) under no bias potential versus the Ag/AgCl electrode. We also analyzed the interface and photoelectrochemical characteristics of the nanostructure and revealed the kinetic process of the electron and hole transmission. In addition, the photoanode test shows the hydrogen production capability of the nanostructures from solar water splitting. These results verified that the ZnO and TiO2 can be sensitized by the polymetallic sulfide for UV-Vis light driven energy conversion. Importantly, the approach we used to design the photoanode enables the development of micro-nano electronic devices with enhanced performance. PMID:26055666
SparsePZ: Sparse Representation of Photometric Redshift PDFs
NASA Astrophysics Data System (ADS)
Carrasco Kind, Matias; Brunner, R. J.
2015-11-01
SparsePZ uses sparse basis representation to fully represent individual photometric redshift probability density functions (PDFs). This approach requires approximately half the parameters for the same multi-Gaussian fitting accuracy, and has the additional advantage that an entire PDF can be stored by using a 4-byte integer per basis function. Only 10-20 points per galaxy are needed to reconstruct both the individual PDFs and the ensemble redshift distribution, N(z), to an accuracy of 99.9 per cent when compared to the one built using the original PDFs computed with a resolution of δz = 0.01, reducing the required storage of 200 original values by a factor of 10-20. This basis representation can be directly extended to a cosmological analysis, thereby increasing computational performance without losing resolution or accuracy.
McDevitt, J T; Gurst, A H; Chen, Y
1998-01-01
We attempted to determine the accuracy of manually splitting hydrochlorothiazide tablets. Ninety-four healthy volunteers each split ten 25-mg hydrochlorothiazide tablets, which were then weighed using an analytical balance. Demographics, grip and pinch strength, digit circumference, and tablet-splitting experience were documented. Subjects were also surveyed regarding their willingness to pay a premium for commercially available, lower-dose tablets. Of 1752 manually split tablet portions, 41.3% deviated from ideal weight by more than 10% and 12.4% deviated by more than 20%. Gender, age, education, and tablet-splitting experience were not predictive of variability. Most subjects (96.8%) stated a preference for commercially produced, lower-dose tablets, and 77.2% were willing to pay more for them. For drugs with steep dose-response curves or narrow therapeutic windows, the differences we recorded could be clinically relevant. PMID:9469693
Linear birefringence in split-ring resonators.
Iyer, Srinivasan; Popov, Sergei; Friberg, Ari T
2012-06-01
We study polarization-dependent transmission of light through arrays of single-slit split-ring resonator (SSRR) based systems at normal incidence using finite integration time domain (FITD) and finite element methods (FEM). It is found that a conventional planar array of SSRRs acts as an effective optical wave plate at certain polarizations of incident light. The effect is attributed to the intrinsic linear birefringence of individual SSRRs. A comparison is made with other split-ring resonator-based systems exhibiting wave-plate-like properties due to inter-SSRR coupling. PMID:22660115
Sparse Biclustering of Transposable Data
Tan, Kean Ming
2013-01-01
We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ1 penalty to the means of the biclusters in order to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of k-means clustering. We show that k-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for bi-clustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression data set. This article has supplementary material online. PMID:25364221
Sparse Biclustering of Transposable Data.
Tan, Kean Ming; Witten, Daniela M
2014-01-01
We consider the task of simultaneously clustering the rows and columns of a large transposable data matrix. We assume that the matrix elements are normally distributed with a bicluster-specific mean term and a common variance, and perform biclustering by maximizing the corresponding log likelihood. We apply an ℓ1 penalty to the means of the biclusters in order to obtain sparse and interpretable biclusters. Our proposal amounts to a sparse, symmetrized version of k-means clustering. We show that k-means clustering of the rows and of the columns of a data matrix can be seen as special cases of our proposal, and that a relaxation of our proposal yields the singular value decomposition. In addition, we propose a framework for bi-clustering based on the matrix-variate normal distribution. The performances of our proposals are demonstrated in a simulation study and on a gene expression data set. This article has supplementary material online. PMID:25364221
Moftah, Nayera Hassan; Ibrahim, Shady Mahmoud; Wahba, Nadine Hassan
2016-05-01
Acne vulgaris is an extremely common skin condition. It often leads to negative psychological consequences. Photodynamic therapy (PDT) using intense pulsed light has been introduced for effective treatment of acne. The objective was to study the effect of PDT in truncal acne vulgaris using liposomal methylene blue (LMB) versus IPL alone. Thirty-five patients with varying degrees of acne were treated with topical 0.1 % LMB hydrogel applied on the randomly selected one side of the back, and after 60 min the entire back was exposed to IPL. The procedure was done once weekly for three sessions and patients were re-evaluated 1 month after the third session by two independent dermatologists. Acne severity was graded using the Burton scale. Patient satisfaction using Cardiff Acne Disability Index (CADI) was recorded before and after treatment. On LMB-pretreated side, inflammatory acne lesion counts were significantly decreased by 56.40 % compared with 34.06 % on IPL alone. Marked improvement was seen on LMB-pretreated side in 11.5 % of patients compared with 2.8 % on IPL alone. There was a correlation between CADI score and overall improvement. Our study concluded that LMB-IPL is more effective than IPL alone, safe with tolerable pain in the treatment of acne vulgaris on the back. LMB-IPL is more effective than IPL alone, safe with tolerable pain in the treatment of acne vulgaris on the back. PMID:26993345
Fast sparse matrix-vector multiplication by exploiting variable block structure
Vuduc, R W; Moon, H
2005-07-07
We improve the performance of sparse matrix-vector multiply (SpMV) on modern cache-based superscalar machines when the matrix structure consists of multiple, irregularly aligned rectangular blocks. Matrices from finite element modeling applications often have this kind of structure. Our technique splits the matrix, A, into a sum, A{sub 1} + A{sub 2} + ... + A{sub s}, where each term is stored in a new data structure, unaligned block compressed sparse row (UBCSR) format . The classical alternative approach of storing A in a block compressed sparse row (BCSR) format yields limited performance gains because it imposes a particular alignment of the matrix non-zero structure, leading to extra work from explicitly padded zeros. Combining splitting and UBCSR reduces this extra work while retaining the generally lower memory bandwidth requirements and register-level tiling opportunities of BCSR. Using application test matrices, we show empirically that speedups can be as high as 2.1x over not blocking at all, and as high as 1.8x over the standard BCSR implementation used in prior work. When performance does not improve, split UBCSR can still significantly reduce matrix storage. Through extensive experiments, we further show that the empirically optimal number of splittings s and the block size for each matrix term A{sub i} will in practice depend on the matrix and hardware platform. Our data lay a foundation for future development of fully automated methods for tuning these parameters.
Finding communities in sparse networks
Singh, Abhinav; Humphries, Mark D.
2015-01-01
Spectral algorithms based on matrix representations of networks are often used to detect communities, but classic spectral methods based on the adjacency matrix and its variants fail in sparse networks. New spectral methods based on non-backtracking random walks have recently been introduced that successfully detect communities in many sparse networks. However, the spectrum of non-backtracking random walks ignores hanging trees in networks that can contain information about their community structure. We introduce the reluctant backtracking operators that explicitly account for hanging trees as they admit a small probability of returning to the immediately previous node, unlike the non-backtracking operators that forbid an immediate return. We show that the reluctant backtracking operators can detect communities in certain sparse networks where the non-backtracking operators cannot, while performing comparably on benchmark stochastic block model networks and real world networks. We also show that the spectrum of the reluctant backtracking operator approximately optimises the standard modularity function. Interestingly, for this family of non- and reluctant-backtracking operators the main determinant of performance on real-world networks is whether or not they are normalised to conserve probability at each node. PMID:25742951
Highly parallel sparse Cholesky factorization
NASA Technical Reports Server (NTRS)
Gilbert, John R.; Schreiber, Robert
1990-01-01
Several fine grained parallel algorithms were developed and compared to compute the Cholesky factorization of a sparse matrix. The experimental implementations are on the Connection Machine, a distributed memory SIMD machine whose programming model conceptually supplies one processor per data element. In contrast to special purpose algorithms in which the matrix structure conforms to the connection structure of the machine, the focus is on matrices with arbitrary sparsity structure. The most promising algorithm is one whose inner loop performs several dense factorizations simultaneously on a 2-D grid of processors. Virtually any massively parallel dense factorization algorithm can be used as the key subroutine. The sparse code attains execution rates comparable to those of the dense subroutine. Although at present architectural limitations prevent the dense factorization from realizing its potential efficiency, it is concluded that a regular data parallel architecture can be used efficiently to solve arbitrarily structured sparse problems. A performance model is also presented and it is used to analyze the algorithms.
Sparse Matrices in MATLAB: Design and Implementation
NASA Technical Reports Server (NTRS)
Gilbert, John R.; Moler, Cleve; Schreiber, Robert
1992-01-01
The matrix computation language and environment MATLAB is extended to include sparse matrix storage and operations. The only change to the outward appearance of the MATLAB language is a pair of commands to create full or sparse matrices. Nearly all the operations of MATLAB now apply equally to full or sparse matrices, without any explicit action by the user. The sparse data structure represents a matrix in space proportional to the number of nonzero entries, and most of the operations compute sparse results in time proportional to the number of arithmetic operations on nonzeros.
Turner, H.
1996-12-31
This paper presents mathematical results that can sometimes be used to simplify the task of reasoning about a default theory, by {open_quotes}splitting it into parts.{close_quotes} These so-called Splitting Theorems for default logic are related in spirit to {open_quotes}partial evaluation{close_quotes} in logic programming, in which results obtained from one part of a program are used to simplify the remainder of the program. In this paper we focus primarily on the statement and proof of the Splitting Theorems for default logic. We illustrate the usefulness of the results by applying them to an example default theory for commonsense reasoning about action.
Tablet Splitting: A Risky Practice
... and splitting tablets in an effort to save money. Regarding the practice of splitting tablets, the Food ... tablet. So a patient may try to save money by buying the 30 mg tablets and splitting ...
Phase-image-based sparse-gray-level data pages for holographic data storage.
Das, Bhargab; Joseph, Joby; Singh, Kehar
2009-10-01
We propose a method for implementation of gray-scale sparse block modulation codes with a single spatial light modulator in phase mode for holographic data storage. Sparse data pages promise higher recording densities with reduced consumption of the dynamic range of the recording material and reduced interpixel cross talk. A balanced sparse-gray-level phase data page gives a homogenized Fourier spectrum that improves the interference efficiency between the signal and the reference beams. Construction rules for sparse three-gray-level phase data pages, readout methods, and interpixel cross talk are discussed extensively. We also explore theoretically the potential storage density improvement while using low-pass filtering and sparse-gray-level phase data pages for holographic storage, and demonstrate the trade-off between code rate, block length, and estimated capacity gain. PMID:19798361
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.
Polarized Antenna Splitting Functions
Larkoski, Andrew J.; Peskin, Michael E.; /SLAC
2009-10-17
We consider parton showers based on radiation from QCD dipoles or 'antennae'. These showers are built from 2 {yields} 3 parton splitting processes. The question then arises of what functions replace the Altarelli-Parisi splitting functions in this approach. We give a detailed answer to this question, applicable to antenna showers in which partons carry definite helicity, and to both initial- and final-state emissions.
NASA Astrophysics Data System (ADS)
Wang, Ruosong; Xu, Xiaoxue; Zhang, Yi; Chang, Zhimin; Sun, Zaicheng; Dong, Wen-Fei
2015-06-01
We have designed a novel semiconductor core/layer nanostructure of a uniform ZnO@TiO2 nanorod array modified with a ZnIn0.25Cu0.02S1.395 solid-solution on the surface via a facile hydrothermal synthesis. This novel nanostructure combines the merits of all components and meets the requirements of photovoltaic system application. An intimate PN heterojunction is formed from the ZnO@TiO2 nanorod and polymetallic sulphide solid-solution, which is remarkably beneficial for the effective visible light absorption and rapid charge carrier separation. The nanostructures exhibit higher photocurrent and incident photon to electron conversion efficiency (IPCE) under no bias potential versus the Ag/AgCl electrode. We also analyzed the interface and photoelectrochemical characteristics of the nanostructure and revealed the kinetic process of the electron and hole transmission. In addition, the photoanode test shows the hydrogen production capability of the nanostructures from solar water splitting. These results verified that the ZnO and TiO2 can be sensitized by the polymetallic sulfide for UV-Vis light driven energy conversion. Importantly, the approach we used to design the photoanode enables the development of micro-nano electronic devices with enhanced performance.We have designed a novel semiconductor core/layer nanostructure of a uniform ZnO@TiO2 nanorod array modified with a ZnIn0.25Cu0.02S1.395 solid-solution on the surface via a facile hydrothermal synthesis. This novel nanostructure combines the merits of all components and meets the requirements of photovoltaic system application. An intimate PN heterojunction is formed from the ZnO@TiO2 nanorod and polymetallic sulphide solid-solution, which is remarkably beneficial for the effective visible light absorption and rapid charge carrier separation. The nanostructures exhibit higher photocurrent and incident photon to electron conversion efficiency (IPCE) under no bias potential versus the Ag/AgCl electrode. We also
Hisatomi, Takashi; Maeda, Kazuhiko; Lu, Daling; Domen, Kazunari
2009-01-01
The influence of starting materials on the physicochemical and photocatalytic properties of (Ga(1-x)Zn(x))(N(1-x)O(x)) were investigated in an attempt to optimize the preparation conditions. The catalyst was successfully prepared by nitriding a starting mixture of ZnO and Ga2O3. A mixture of metallic zinc and GaN, however, did not afford the desired compound. The crystallinity, surface area, composition, and absorption characteristics of the resultant (Ga(1-x)Zn(x))(N(1-x)O(x)) solid solution are found to be dependent on the morphology of ZnO but largely insensitive to the choice of Ga2O3 polymorph. The use of coarser-grained ZnO results in a coarser-grained catalyst with elevated zinc and oxygen content and reduced uniformity in composition and crystallinity. The results demonstrate the importance of selecting appropriate ZnO and Ga2O3 starting materials for maximizing the photocatalytic activity of (Ga(1-x)Zn(x))(N(1-x)O(x)) for overall water splitting under visible light. PMID:19107886
Sparse Coding for Alpha Matting
NASA Astrophysics Data System (ADS)
Johnson, Jubin; Varnousfaderani, Ehsan Shahrian; Cholakkal, Hisham; Rajan, Deepu
2016-07-01
Existing color sampling based alpha matting methods use the compositing equation to estimate alpha at a pixel from pairs of foreground (F) and background (B) samples. The quality of the matte depends on the selected (F,B) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples. A non-parametric probabilistic segmentation provides a certainty measure on the pixel belonging to foreground or background, based on which a dictionary is formed for use in sparse coding. By removing the restriction to conform to (F,B) pairs, this method allows for better alpha estimation from multiple F and B samples. The same framework is extended to videos, where the requirement of temporal coherence is handled effectively. Here, the dictionary is formed by samples from multiple frames. A multi-frame graph model, as opposed to a single image as for image matting, is proposed that can be solved efficiently in closed form. Quantitative and qualitative evaluations on a benchmark dataset are provided to show that the proposed method outperforms current state-of-the-art in image and video matting.
Gene Golub; Kwok Ko
2009-03-30
The solutions of sparse eigenvalue problems and linear systems constitute one of the key computational kernels in the discretization of partial differential equations for the modeling of linear accelerators. The computational challenges faced by existing techniques for solving those sparse eigenvalue problems and linear systems call for continuing research to improve on the algorithms so that ever increasing problem size as required by the physics application can be tackled. Under the support of this award, the filter algorithm for solving large sparse eigenvalue problems was developed at Stanford to address the computational difficulties in the previous methods with the goal to enable accelerator simulations on then the world largest unclassified supercomputer at NERSC for this class of problems. Specifically, a new method, the Hemitian skew-Hemitian splitting method, was proposed and researched as an improved method for solving linear systems with non-Hermitian positive definite and semidefinite matrices.
Image fusion using sparse overcomplete feature dictionaries
Brumby, Steven P.; Bettencourt, Luis; Kenyon, Garrett T.; Chartrand, Rick; Wohlberg, Brendt
2015-10-06
Approaches for deciding what individuals in a population of visual system "neurons" are looking for using sparse overcomplete feature dictionaries are provided. A sparse overcomplete feature dictionary may be learned for an image dataset and a local sparse representation of the image dataset may be built using the learned feature dictionary. A local maximum pooling operation may be applied on the local sparse representation to produce a translation-tolerant representation of the image dataset. An object may then be classified and/or clustered within the translation-tolerant representation of the image dataset using a supervised classification algorithm and/or an unsupervised clustering algorithm.
Isospin Splittings of Doubly Heavy Baryons
Brodsky, Stanley J.; Guo, Feng-Kun; Hanhart, Christoph; Meissner, Ulf-G.; /Julich, Forschungszentrum /JCHP, Julich /IAS, Julich /Bonn U., HISKP /Bonn U.
2011-08-18
The SELEX Collaboration has reported a very large isospin splitting of doubly charmed baryons. We show that this effect would imply that the doubly charmed baryons are very compact. One intriguing possibility is that such baryons have a linear geometry Q-q-Q where the light quark q oscillates between the two heavy quarks Q, analogous to a linear molecule such as carbon dioxide. However, using conventional arguments, the size of a heavy-light hadron is expected to be around 0.5 fm, much larger than the size needed to explain the observed large isospin splitting. Assuming the distance between two heavy quarks is much smaller than that between the light quark and a heavy one, the doubly heavy baryons are related to the heavy mesons via heavy quark-diquark symmetry. Based on this symmetry, we predict the isospin splittings for doubly heavy baryons including {Xi}{sub cc}, {Xi}{sub bb} and {Xi}{sub bc}. The prediction for the {Xi}{sub cc} is much smaller than the SELEX value. On the other hand, the {Xi}{sub bb} baryons are predicted to have an isospin splitting as large as (6.3 {+-} 1.7) MeV. An experimental study of doubly bottomed baryons is therefore very important to better understand the structure of baryons with heavy quarks.
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.
NASA Technical Reports Server (NTRS)
1981-01-01
The development of a stablized Zeeman split laser for use in a polarization profilometer is discussed. A Hewlett-Packard laser was modified to stabilize the Zeeman split beat frequency thereby increasing the phase measurement accuracy from the Hewlett-Packard 3 degrees to an accuracy of .01 degrees. The addition of a two layered inductive winding converts the laser to a current controlled oscillator whose frequency is linearly related to coil current. This linear relationship between coil current and laser frequency permits phase locking the laser frequency to a stable crystal controlled reference frequency. The stability of the system is examined and the equipment operation procedures are outlined.
Ultrasensitive detection of mode splitting in active optical microcavities
He, Lina; Oezdemir, Sahin Kaya; Zhu Jiangang; Yang Lan
2010-11-15
Scattering-induced mode splitting in active microcavities is demonstrated. Below the lasing threshold, quality factor enhancement by optical gain allows resolving, in the wavelength-scanning transmission spectrum, of resonance dips of the split modes which otherwise would not be detected in a passive resonator. In the lasing regime, mode splitting manifests itself as two lasing modes with extremely narrow linewidths. Mixing these lasing modes in a detector leads to a heterodyne beat signal whose frequency corresponds to the mode-splitting amount. Lasing regime not only allows ultra-high sensitivity for mode-splitting measurements but also provides an easily accessible scheme by eliminating the need for wavelength scanning around resonant modes. Mode splitting in active microcavities has an immediate impact in enhancing the sensitivity of subwavelength scatterer detection and in studying light-matter interactions in a strong-coupling regime.
Sparse and stable Markowitz portfolios
Brodie, Joshua; Daubechies, Ingrid; De Mol, Christine; Giannone, Domenico; Loris, Ignace
2009-01-01
We consider the problem of portfolio selection within the classical Markowitz mean-variance framework, reformulated as a constrained least-squares regression problem. We propose to add to the objective function a penalty proportional to the sum of the absolute values of the portfolio weights. This penalty regularizes (stabilizes) the optimization problem, encourages sparse portfolios (i.e., portfolios with only few active positions), and allows accounting for transaction costs. Our approach recovers as special cases the no-short-positions portfolios, but does allow for short positions in limited number. We implement this methodology on two benchmark data sets constructed by Fama and French. Using only a modest amount of training data, we construct portfolios whose out-of-sample performance, as measured by Sharpe ratio, is consistently and significantly better than that of the naïve evenly weighted portfolio. PMID:19617537
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…
Splitting of asphaltene species
Galimov, R.A.; Yusupova, T.N.; Abushaeva, V.V.
1994-05-10
The extent of splitting of asphaltene species under the action of solvents correlates with their nature, and primarily with their electron- and proton-donor properties. According to the data of thermal analysis asphaltene species being retained after the action of solvents differ in the weight ratio of peripheral substituents to condensed part and in the fraction of labile bonds. 12 refs., 4 tabs.
Veligdan, James T.
2007-05-29
A video image is displayed from an optical panel by splitting the image into a plurality of image components, and then projecting the image components through corresponding portions of the panel to collectively form the image. Depth of the display is correspondingly reduced.
Veligdan, James T.
2005-05-31
A video image is displayed from an optical panel by splitting the image into a plurality of image components, and then projecting the image components through corresponding portions of the panel to collectively form the image. Depth of the display is correspondingly reduced.
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…
NASA Astrophysics Data System (ADS)
Benakli, Karim; Darmé, Luc; Goodsell, Mark D.
2015-11-01
We study two realisations of the Fake Split Supersymmetry Model (FSSM), the simplest model that can easily reproduce the experimental value of the Higgs mass for an arbitrarily high supersymmetry scale M S , as a consequence of swapping higgsinos for equivalent states, fake higgsinos, with suppressed Yukawa couplings. If the LSP is identified as the main Dark matter component, then a standard thermal history of the Universe implies upper bounds on M S , which we derive. On the other hand, we show that renormalisation group running of soft masses above M S barely constrains the model — in stark contrast to Split Supersymmetry — and hence we can have a "Mega Split" spectrum even with all of these assumptions and constraints, which include the requirements of a correct relic abundance, a gluino life-time compatible with Big Bang Nucleosynthesis and absence of signals in present direct detection experiments of inelastic dark matter. In an appendix we describe a related scenario, Fake Split Extended Supersymmetry, which enjoys similar properties.
Approximate Orthogonal Sparse Embedding for Dimensionality Reduction.
Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Yang, Jian; Zhang, David
2016-04-01
Locally linear embedding (LLE) is one of the most well-known manifold learning methods. As the representative linear extension of LLE, orthogonal neighborhood preserving projection (ONPP) has attracted widespread attention in the field of dimensionality reduction. In this paper, a unified sparse learning framework is proposed by introducing the sparsity or L1-norm learning, which further extends the LLE-based methods to sparse cases. Theoretical connections between the ONPP and the proposed sparse linear embedding are discovered. The optimal sparse embeddings derived from the proposed framework can be computed by iterating the modified elastic net and singular value decomposition. We also show that the proposed model can be viewed as a general model for sparse linear and nonlinear (kernel) subspace learning. Based on this general model, sparse kernel embedding is also proposed for nonlinear sparse feature extraction. Extensive experiments on five databases demonstrate that the proposed sparse learning framework performs better than the existing subspace learning algorithm, particularly in the cases of small sample sizes. PMID:25955995
Sparse representation for the ISAR image reconstruction
NASA Astrophysics Data System (ADS)
Hu, Mengqi; Montalbo, John; Li, Shuxia; Sun, Ligang; Qiao, Zhijun G.
2016-05-01
In this paper, a sparse representation of the data for an inverse synthetic aperture radar (ISAR) system is provided in two dimensions. The proposed sparse representation motivates the use a of a Convex Optimization that recovers the image with far less samples, which is required by Nyquist-Shannon sampling theorem to increases the efficiency and decrease the cost of calculation in radar imaging.
Pan, Chengsi; Takata, Tsuyoshi; Domen, Kazunari
2016-01-26
One of the main targets of studies on water splitting photocatalysts is to develop semiconductor materials with narrower bandgaps capable of overall water splitting for efficient harvesting of solar energy. A series of transition-metal oxynitrides, LaMgx Ta1-xO1+3xN2-3x(x ≥ 1/3), with a complex perovskite structure was reported as the first example of overall water splitting operable at up to 600 nm. The photocatalytic behavior of LaMg1/3Ta2/3O2N was investigated in detail in order to optimize photocatalyst preparation and water-splitting activity. Various attempts exploring photocatalyst preparation steps, that is, cocatalyst selection, coating material and method, and synthesis method for the oxide precursor, revealed photocatalyst structures necessary for achieving overall water splitting. Careful examination of photocatalyst preparation procedures likely enhanced the quality of the produced photocatalyst, leading to a more homogeneous coating quality and semiconductor particles with fewer defects. Thus, the photocatalytic activity for water splitting on LaMg1/3Ta2/3O2N was largely enhanced. PMID:26680470
Approximate inverse preconditioners for general sparse matrices
Chow, E.; Saad, Y.
1994-12-31
Preconditioned Krylov subspace methods are often very efficient in solving sparse linear matrices that arise from the discretization of elliptic partial differential equations. However, for general sparse indifinite matrices, the usual ILU preconditioners fail, often because of the fact that the resulting factors L and U give rise to unstable forward and backward sweeps. In such cases, alternative preconditioners based on approximate inverses may be attractive. We are currently developing a number of such preconditioners based on iterating on each column to get the approximate inverse. For this approach to be efficient, the iteration must be done in sparse mode, i.e., we must use sparse-matrix by sparse-vector type operatoins. We will discuss a few options and compare their performance on standard problems from the Harwell-Boeing collection.
Large-scale sparse singular value computations
NASA Technical Reports Server (NTRS)
Berry, Michael W.
1992-01-01
Four numerical methods for computing the singular value decomposition (SVD) of large sparse matrices on a multiprocessor architecture are presented. Lanczos and subspace iteration-based methods for determining several of the largest singular triplets (singular values and corresponding left and right-singular vectors) for sparse matrices arising from two practical applications: information retrieval and seismic reflection tomography are emphasized. The target architectures for implementations are the CRAY-2S/4-128 and Alliant FX/80. The sparse SVD problem is well motivated by recent information-retrieval techniques in which dominant singular values and their corresponding singular vectors of large sparse term-document matrices are desired, and by nonlinear inverse problems from seismic tomography applications which require approximate pseudo-inverses of large sparse Jacobian matrices.
Framelet-Based Sparse Unmixing of Hyperspectral Images.
Zhang, Guixu; Xu, Yingying; Fang, Faming
2016-04-01
Spectral unmixing aims at estimating the proportions (abundances) of pure spectrums (endmembers) in each mixed pixel of hyperspectral data. Recently, a semi-supervised approach, which takes the spectral library as prior knowledge, has been attracting much attention in unmixing. In this paper, we propose a new semi-supervised unmixing model, termed framelet-based sparse unmixing (FSU), which promotes the abundance sparsity in framelet domain and discriminates the approximation and detail components of hyperspectral data after framelet decomposition. Due to the advantages of the framelet representations, e.g., images have good sparse approximations in framelet domain, and most of the additive noises are included in the detail coefficients, the FSU model has a better antinoise capability, and accordingly leads to more desirable unmixing performance. The existence and uniqueness of the minimizer of the FSU model are then discussed, and the split Bregman algorithm and its convergence property are presented to obtain the minimal solution. Experimental results on both simulated data and real data demonstrate that the FSU model generally performs better than the compared methods. PMID:26849863
Audin, L.
1994-12-31
EPAct covers a vast territory beyond lighting and, like all legislation, also contains numerous {open_quotes}favors,{close_quotes} compromises, and even some sleight-of-hand. Tucked away under Title XIX, for example, is an increase from 20% to 28% tax on gambling winnings, effective January 1, 1993 - apparently as a way to help pay for new spending listed elsewhere in the bill. Overall, it is a landmark piece of legislation, about a decade overdue. It remains to be seen how the Federal Government will enforce upgrading of state (or even their own) energy codes. There is no mention of funding for {open_quotes}energy police{close_quotes} in EPAct. Merely creating such a national standard, however, provides a target for those who sincerely wish to create an energy-efficient future.
Split Bregman's optimization method for image construction in compressive sensing
NASA Astrophysics Data System (ADS)
Skinner, D.; Foo, S.; Meyer-Bäse, A.
2014-05-01
The theory of compressive sampling (CS) was reintroduced by Candes, Romberg and Tao, and D. Donoho in 2006. Using a priori knowledge that a signal is sparse, it has been mathematically proven that CS can defY Nyquist sampling theorem. Theoretically, reconstruction of a CS image relies on the minimization and optimization techniques to solve this complex almost NP-complete problem. There are many paths to consider when compressing and reconstructing an image but these methods have remained untested and unclear on natural images, such as underwater sonar images. The goal of this research is to perfectly reconstruct the original sonar image from a sparse signal while maintaining pertinent information, such as mine-like object, in Side-scan sonar (SSS) images. Goldstein and Osher have shown how to use an iterative method to reconstruct the original image through a method called Split Bregman's iteration. This method "decouples" the energies using portions of the energy from both the !1 and !2 norm. Once the energies are split, Bregman iteration is used to solve the unconstrained optimization problem by recursively solving the problems simultaneously. The faster these two steps or energies can be solved then the faster the overall method becomes. While the majority of CS research is still focused on the medical field, this paper will demonstrate the effectiveness of the Split Bregman's methods on sonar images.
Bilateral Sagittal Split Osteotomy
Monson, Laura A.
2013-01-01
The bilateral sagittal split osteotomy is an indispensable tool in the correction of dentofacial abnormalities. The technique has been in practice since the late 1800s, but did not reach widespread acceptance and use until several modifications were described in the 1960s and 1970s. Those modifications came from a desire to make the procedure safer, more reliable, and more predictable with less relapse. Those goals continue to stimulate innovation in the field today and have helped the procedure evolve to be a very dependable, consistent method of correction of many types of malocclusion. The operative surgeon should be well versed in the history, anatomy, technical aspects, and complications of the bilateral sagittal split osteotomy to fully understand the procedure and to counsel the patient. PMID:24872760
Fee splitting in ophthalmology.
Levin, Alex V; Ganesh, Anuradha; Al-Busaidi, Ahmed
2011-02-01
Fee splitting and co-management are common practices in ophthalmology. These arrangements may conflict with the ethical principles governing the doctor-patient relationship, may constitute professional misconduct, and at times, may be illegal. Implications and perceptions of these practices may vary between different cultures. Full disclosure to the patient may minimize the adverse effects of conflicts of interest that arise from these practices, and may thereby allow these practices to be deemed acceptable by some cultural morays, professional guidelines, or by law. Disclosure does not necessarily relieve the physician from a potential ethical compromise. This review examines the practice of fee splitting in ophthalmology, its legal implications, the policies or guidelines governing such arrangements, and the possible ethical ramifications. A comparative view between 3 countries, Canada, the United States, and Oman, was conducted; illustrating that even in disparate cultures, there may be some universality to the application of ethical principles. PMID:21283153
Frantz, C.E.; Cawley, W.E.
1961-05-01
A tool is described for cutting a coolant tube adapted to contain fuel elements to enable the tube to be removed from a graphite moderator mass. The tool splits the tube longitudinally into halves and curls the longitudinal edges of the halves inwardly so that they occupy less space and can be moved radially inwardly away from the walls of the hole in the graphite for easy removal from the graphite.
Sparse Spectrotemporal Coding of Sounds
NASA Astrophysics Data System (ADS)
Klein, David J.; König, Peter; Körding, Konrad P.
2003-12-01
Recent studies of biological auditory processing have revealed that sophisticated spectrotemporal analyses are performed by central auditory systems of various animals. The analysis is typically well matched with the statistics of relevant natural sounds, suggesting that it produces an optimal representation of the animal's acoustic biotope. We address this topic using simulated neurons that learn an optimal representation of a speech corpus. As input, the neurons receive a spectrographic representation of sound produced by a peripheral auditory model. The output representation is deemed optimal when the responses of the neurons are maximally sparse. Following optimization, the simulated neurons are similar to real neurons in many respects. Most notably, a given neuron only analyzes the input over a localized region of time and frequency. In addition, multiple subregions either excite or inhibit the neuron, together producing selectivity to spectral and temporal modulation patterns. This suggests that the brain's solution is particularly well suited for coding natural sound; therefore, it may prove useful in the design of new computational methods for processing speech.
Resistant multiple sparse canonical correlation.
Coleman, Jacob; Replogle, Joseph; Chandler, Gabriel; Hardin, Johanna
2016-04-01
Canonical correlation analysis (CCA) is a multivariate technique that takes two datasets and forms the most highly correlated possible pairs of linear combinations between them. Each subsequent pair of linear combinations is orthogonal to the preceding pair, meaning that new information is gleaned from each pair. By looking at the magnitude of coefficient values, we can find out which variables can be grouped together, thus better understanding multiple interactions that are otherwise difficult to compute or grasp intuitively. CCA appears to have quite powerful applications to high-throughput data, as we can use it to discover, for example, relationships between gene expression and gene copy number variation. One of the biggest problems of CCA is that the number of variables (often upwards of 10,000) makes biological interpretation of linear combinations nearly impossible. To limit variable output, we have employed a method known as sparse canonical correlation analysis (SCCA), while adding estimation which is resistant to extreme observations or other types of deviant data. In this paper, we have demonstrated the success of resistant estimation in variable selection using SCCA. Additionally, we have used SCCA to find multiple canonical pairs for extended knowledge about the datasets at hand. Again, using resistant estimators provided more accurate estimates than standard estimators in the multiple canonical correlation setting. R code is available and documented at https://github.com/hardin47/rmscca. PMID:26963062
Sparse Bayesian infinite factor models
Bhattacharya, A.; Dunson, D. B.
2011-01-01
We focus on sparse modelling of high-dimensional covariance matrices using Bayesian latent factor models. We propose a multiplicative gamma process shrinkage prior on the factor loadings which allows introduction of infinitely many factors, with the loadings increasingly shrunk towards zero as the column index increases. We use our prior on a parameter-expanded loading matrix to avoid the order dependence typical in factor analysis models and develop an efficient Gibbs sampler that scales well as data dimensionality increases. The gain in efficiency is achieved by the joint conjugacy property of the proposed prior, which allows block updating of the loadings matrix. We propose an adaptive Gibbs sampler for automatically truncating the infinite loading matrix through selection of the number of important factors. Theoretical results are provided on the support of the prior and truncation approximation bounds. A fast algorithm is proposed to produce approximate Bayes estimates. Latent factor regression methods are developed for prediction and variable selection in applications with high-dimensional correlated predictors. Operating characteristics are assessed through simulation studies, and the approach is applied to predict survival times from gene expression data. PMID:23049129
Fast estimation of sparse doubly spread acoustic channels.
Zeng, Wen-Jun; Xu, Wen
2012-01-01
The estimation of doubly spread underwater acoustic channels is addressed. By exploiting the sparsity in the delay-Doppler domain, this paper proposes a fast projected gradient method (FPGM) that can handle complex-valued data for estimating the delay-Doppler spread function of a time-varying channel. The proposed FPGM formulates the sparse channel estimation as a complex-valued convex optimization using an [script-l](1)-norm constraint. Conventional approaches to complex-valued optimization split the complex variables into their real and imaginary parts; this doubles the dimension compared with the original problem and may break the special data structure. Unlike the conventional methods, the proposed method directly handles the complex variables as a whole without splitting them into real numbers; hence the dimension will not increase. By exploiting the block Toeplitz-like structure of the coefficient matrix, the computational complexity of the FPGM is reduced to O(LNlogN), where L is the dimension of the Doppler shift and N is the signal length. Simulation results verify the accuracy and efficiency of the FPGM, indicating that is robust to parameter selection and is orders-of-magnitude faster than standard convex optimization algorithms. The Kauai experimental data processing results are also provided to demonstrate the performance of the proposed algorithm. PMID:22280593
Atom beams split by gentle persuasion
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.
Photosynthetic water splitting for hydrogen fuel synthesis
NASA Astrophysics Data System (ADS)
Greenbaum, E.
Three key advances in photosynthesis research are reported. A significant advance in microalgal water splitting has been made. In the linear, low-intensity region of the light saturation curves, equivalent solar conversion efficiencies of 10% have been measured. A technological advance in the ability to genetically screen individual algal colonies has been made. Successive subcultures of anaerobiosis-stressed Chlamydomonas reinhardtii exhibited enhanced capacity for photoproduction of hydrogen and oxygen.
Enhancing Scalability of Sparse Direct Methods
Li, Xiaoye S.; Demmel, James; Grigori, Laura; Gu, Ming; Xia,Jianlin; Jardin, Steve; Sovinec, Carl; Lee, Lie-Quan
2007-07-23
TOPS is providing high-performance, scalable sparse direct solvers, which have had significant impacts on the SciDAC applications, including fusion simulation (CEMM), accelerator modeling (COMPASS), as well as many other mission-critical applications in DOE and elsewhere. Our recent developments have been focusing on new techniques to overcome scalability bottleneck of direct methods, in both time and memory. These include parallelizing symbolic analysis phase and developing linear-complexity sparse factorization methods. The new techniques will make sparse direct methods more widely usable in large 3D simulations on highly-parallel petascale computers.
Sparse High Dimensional Models in Economics
Fan, Jianqing; Lv, Jinchi; Qi, Lei
2010-01-01
This paper reviews the literature on sparse high dimensional models and discusses some applications in economics and finance. Recent developments of theory, methods, and implementations in penalized least squares and penalized likelihood methods are highlighted. These variable selection methods are proved to be effective in high dimensional sparse modeling. The limits of dimensionality that regularization methods can handle, the role of penalty functions, and their statistical properties are detailed. Some recent advances in ultra-high dimensional sparse modeling are also briefly discussed. PMID:22022635
Imaging correlography with sparse collecting apertures
NASA Astrophysics Data System (ADS)
Idell, Paul S.; Fienup, J. R.
1987-01-01
This paper investigates the possibility of implementing an imaging correlography system with sparse arrays of intensity detectors. The theory underlying the image formation process for imaging correlography is reviewed, emphasizing the spatial filtering effects that sparse collecting apertures have on the reconstructed imagery. Image recovery with sparse arrays of intensity detectors through the use of computer experiments in which laser speckle measurements are digitally simulated is then demonstrated. It is shown that the quality of imagery reconstructed using this technique is visibly enhanced when appropriate filtering techniques are applied. A performance tradeoff between collecting array redundancy and the number of speckle pattern measurements is briefly discussed.
Estimating Wind Turbine Inflow Using Sparse Wind Data
NASA Astrophysics Data System (ADS)
Rai, Raj; Naughton, Jonathan
2011-11-01
An accurate spatially and temporally resolved estimation of the wind inflow under various atmospheric boundary layer stability conditions is useful for several applications relevant to wind turbines. Estimations of a wind inflow plane in a neutrally stable boundary layer using sparse data (temporally resolved but spatially sparse, and spatially resolved but temporally sparse) has shown good agreement with the original data provided by a Large Eddy Simulation. A complementary Proper Orthogonal Decomposition-Linear Stochastic Estimation (POD-LSE) approach has been used for the estimation in which the POD identifies the energetic modes of the flow that are then used in estimating the time dependent flow-field using LSE. The applicability of such an approach is considered by simulating the estimation of the wind inflow using data collected in the field. Modern remote measurement approaches, such as Lidar (Light detection and ranging), can sample the wind at the multiple locations, but cannot sufficiently resolve the inflow in space in time that is required for many wind turbine applications. Since inflow estimations using the POD-LSE approach can simultaneously provide spatial and temporal behavior, the use of the approach with field data for better understanding the characteristics of the wind inflow at a particular site under different atmospheric conditions is demonstrated. Support from a gift from BP is acknowledged.
ROTATIONAL SPLITTING OF PULSATION MODES
Deupree, Robert G.; Beslin, Wilfried
2010-10-01
Mode splittings produced by uniform rotation and a particular form of differential rotation are computed for two-dimensional rotating 10 M{sub sun} zero-age main sequence stellar models. The change in the character of the mode splitting is traced as a function of uniform rotation rate, and it is found that only relatively slow rotation rates are required before the mode splitting becomes asymmetric about the azimuthally symmetric (m = 0) mode. Increased rotation produces a progressively altered pattern of the individual modes with respect to each other. Large mode splittings begin to overlap with the mode splittings produced by different radial and latitudinal modes at relatively low rotation rates. The mode-splitting pattern for the differentially rotating stars we model is different than that for uniformly rotating stars, making the mode splitting a possible discriminant of the internal angular momentum distribution if one assumes that the formidable challenge of mode identification can be overcome.
Sparse principal component analysis in cancer research
Hsu, Ying-Lin; Huang, Po-Yu; Chen, Dung-Tsa
2015-01-01
A critical challenging component in analyzing high-dimensional data in cancer research is how to reduce the dimension of data and how to extract relevant features. Sparse principal component analysis (PCA) is a powerful statistical tool that could help reduce data dimension and select important variables simultaneously. In this paper, we review several approaches for sparse PCA, including variance maximization (VM), reconstruction error minimization (REM), singular value decomposition (SVD), and probabilistic modeling (PM) approaches. A simulation study is conducted to compare PCA and the sparse PCAs. An example using a published gene signature in a lung cancer dataset is used to illustrate the potential application of sparse PCAs in cancer research. PMID:26719835
Sparse reconstruction of visual appearance for computer graphics and vision
NASA Astrophysics Data System (ADS)
Ramamoorthi, Ravi
2011-09-01
A broad range of problems in computer graphics rendering, appearance acquisition for graphics and vision, and imaging, involve sampling, reconstruction, and integration of high-dimensional (4D-8D) signals. For example, precomputation-based real-time rendering of glossy materials and intricate lighting effects like caustics, can involve (pre)-computing the response of the scene to different light and viewing directions, which is often a 6D dataset. Similarly, image-based appearance acquisition of facial details, car paint, or glazed wood, requires us to take images from different light and view directions. Even offline rendering of visual effects like motion blur from a fast-moving car, or depth of field, involves high-dimensional sampling across time and lens aperture. The same problems are also common in computational imaging applications such as light field cameras. In the past few years, computer graphics and computer vision researchers have made significant progress in subsequent analysis and compact factored or multiresolution representations for some of these problems. However, the initial full dataset must almost always still be acquired or computed by brute force. This is often prohibitively expensive, taking hours to days of computation and acquisition time, as well as being a challenge for memory usage and storage. For example, on the order of 10,000 megapixel images are needed for a 1 degree sampling of lights and views for high-frequency materials. We argue that dramatically sparser sampling and reconstruction of these signals is possible, before the full dataset is acquired or simulated. Our key idea is to exploit the structure of the data that often lies in lower-frequency, sparse, or low-dimensional spaces. Our framework will apply to a diverse set of problems such as sparse reconstruction of light transport matrices for relighting, sheared sampling and denoising for offline shadow rendering, time-coherent compressive sampling for appearance
Blodwell, J.F.
1987-10-01
It is argued that the point structure of space and time must be constructed from the primitive extensional character of space and time. A procedure for doing this is laid down and applied to one-dimensional and two-dimensional systems of abstract extensions. Topological and metrical properties of the constructed point systems, which differ nontrivially from the usual R and R/sup 2/ models, are examined. Briefly, constructed points are associated with directions and the Cartesian point is split. In one-dimension each point splits into a point pair compatible with the linear ordering. An application to one-dimensional particle motion is given, with the result that natural topological assumptions force the number of left point, right point transitions to remain locally finite in a continuous motion. In general, Cartesian points are seen to correspond to certain filters on a suitable Boolean algebra. Constructed points correspond to ultrafilters. Thus, point construction gives a natural refinement of the Cartesian systems.
Recent advances in semiconductors for photocatalytic and photoelectrochemical water splitting.
Hisatomi, Takashi; Kubota, Jun; Domen, Kazunari
2014-11-21
Photocatalytic and photoelectrochemical water splitting under irradiation by sunlight has received much attention for production of renewable hydrogen from water on a large scale. Many challenges still remain in improving energy conversion efficiency, such as utilizing longer-wavelength photons for hydrogen production, enhancing the reaction efficiency at any given wavelength, and increasing the lifetime of the semiconductor materials. This introductory review covers the fundamental aspects of photocatalytic and photoelectrochemical water splitting. Controlling the semiconducting properties of photocatalysts and photoelectrode materials is the primary concern in developing materials for solar water splitting, because they determine how much photoexcitation occurs in a semiconductor under solar illumination and how many photoexcited carriers reach the surface where water splitting takes place. Given a specific semiconductor material, surface modifications are important not only to activate the semiconductor for water splitting but also to facilitate charge separation and to upgrade the stability of the material under photoexcitation. In addition, reducing resistance loss and forming p-n junction have a significant impact on the efficiency of photoelectrochemical water splitting. Correct evaluation of the photocatalytic and photoelectrochemical activity for water splitting is becoming more important in enabling an accurate comparison of a number of studies based on different systems. In the latter part, recent advances in the water splitting reaction under visible light will be presented with a focus on non-oxide semiconductor materials to give an overview of the various problems and solutions. PMID:24413305
Ueno, Kosei; Oshikiri, Tomoya; Misawa, Hiroaki
2016-01-18
Visible- and near-infrared-light-driven water splitting, which splits water molecules to generate hydrogen and oxygen gases, is a significant subject in artificial photosynthesis with the goal of achieving a low-carbon society. In recent years, considerable attention has been paid to studies on the development of a plasmon-induced water-splitting system responding to visible light. In this review, we categorized water-splitting systems as gold-nanoparticle-loaded semiconductor photocatalytic particles system and metallic-nanoparticles-loaded semiconductor photoelectrode systems, and introduce the latest studies according to these categories. Especially, we describe the studies that optimize a material or a structural design of metallic-nanoparticle-loaded semiconductor photoelectrodes and consider a whole water-splitting system, including a cathode design. Furthermore, we discuss important points when studying plasmon-induced water splitting, and we describe a methodology that enhances plasmon-induced water-splitting efficiency. PMID:26593450
Sparse subspace clustering: algorithm, theory, and applications.
Elhamifar, Ehsan; Vidal, René
2013-11-01
Many real-world problems deal with collections of high-dimensional data, such as images, videos, text, and web documents, DNA microarray data, and more. Often, such high-dimensional data lie close to low-dimensional structures corresponding to several classes or categories to which the data belong. In this paper, we propose and study an algorithm, called sparse subspace clustering, to cluster data points that lie in a union of low-dimensional subspaces. The key idea is that, among the infinitely many possible representations of a data point in terms of other points, a sparse representation corresponds to selecting a few points from the same subspace. This motivates solving a sparse optimization program whose solution is used in a spectral clustering framework to infer the clustering of the data into subspaces. Since solving the sparse optimization program is in general NP-hard, we consider a convex relaxation and show that, under appropriate conditions on the arrangement of the subspaces and the distribution of the data, the proposed minimization program succeeds in recovering the desired sparse representations. The proposed algorithm is efficient and can handle data points near the intersections of subspaces. Another key advantage of the proposed algorithm with respect to the state of the art is that it can deal directly with data nuisances, such as noise, sparse outlying entries, and missing entries, by incorporating the model of the data into the sparse optimization program. We demonstrate the effectiveness of the proposed algorithm through experiments on synthetic data as well as the two real-world problems of motion segmentation and face clustering. PMID:24051734
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.
Balanced Sparse Model for Tight Frames in Compressed Sensing Magnetic Resonance Imaging
Liu, Yunsong; Cai, Jian-Feng; Zhan, Zhifang; Guo, Di; Ye, Jing; Chen, Zhong; Qu, Xiaobo
2015-01-01
Compressed sensing has shown to be promising to accelerate magnetic resonance imaging. In this new technology, magnetic resonance images are usually reconstructed by enforcing its sparsity in sparse image reconstruction models, including both synthesis and analysis models. The synthesis model assumes that an image is a sparse combination of atom signals while the analysis model assumes that an image is sparse after the application of an analysis operator. Balanced model is a new sparse model that bridges analysis and synthesis models by introducing a penalty term on the distance of frame coefficients to the range of the analysis operator. In this paper, we study the performance of the balanced model in tight frame based compressed sensing magnetic resonance imaging and propose a new efficient numerical algorithm to solve the optimization problem. By tuning the balancing parameter, the new model achieves solutions of three models. It is found that the balanced model has a comparable performance with the analysis model. Besides, both of them achieve better results than the synthesis model no matter what value the balancing parameter is. Experiment shows that our proposed numerical algorithm constrained split augmented Lagrangian shrinkage algorithm for balanced model (C-SALSA-B) converges faster than previously proposed algorithms accelerated proximal algorithm (APG) and alternating directional method of multipliers for balanced model (ADMM-B). PMID:25849209
Grossman, Yuval; Harnik, Roni; Perez, Gilad; Schwartz, MatthewD.; Surujon, Ze'ev
2004-07-30
The observed flavor structure of the standard model arises naturally in ''split fermion'' models which localize fermions at different places in an extra dimension. It has, until now, been assumed that the bulk masses for such fermions can be chosen to be flavor diagonal simultaneously at every point in the extra dimension, with all the flavor violation coming from the Yukawa couplings to the Higgs. We consider the more natural possibility in which the bulk masses cannot be simultaneously diagonalized, that is, that they are twisted in flavor space. We show that, in general, this does not disturb the natural generation of hierarchies in the flavor parameters. Moreover, it is conceivable that all the flavor mixing and CP-violation in the standard model may come only from twisting, with the five-dimensional Yukawa couplings taken to be universal.
Grossman, Y
2004-07-24
The observed flavor structure of the standard model arises naturally in ''split fermion'' models which localize fermions at different places in an extra dimension. It has, until now, been assumed that the bulk masses for such fermions can be chosen to be flavor diagonal simultaneously at every point in the extra dimension, with all the flavor violation coming from the Yukawa couplings to the Higgs. We consider the more natural possibility in which the bulk masses cannot be simultaneously diagonalized, that is, that they are twisted in flavor space. We show that, in general, this does not disturb the natural generation of hierarchies in the flavor parameters. Moreover, it is conceivable that all the flavor mixing and CP-violation in the standard model may come only from twisting, with the five-dimensional Yukawa couplings taken to be universal.
Chen, Shanshan; Qi, Yu; Hisatomi, Takashi; Ding, Qian; Asai, Tomohiro; Li, Zheng; Ma, Su Su Khine; Zhang, Fuxiang; Domen, Kazunari; Li, Can
2015-07-13
An (oxy)nitride-based heterostructure for powdered Z-scheme overall water splitting is presented. Compared with the single MgTa2O(6-x)N(y) or TaON photocatalyst, a MgTa2O(6-x)N(y)/TaON heterostructure fabricated by a simple one-pot nitridation route was demonstrated to effectively suppress the recombination of carriers by efficient spatial charge separation and decreased defect density. By employing Pt-loaded MgTa2O(6-x)N(y)/TaON as a H2-evolving photocatalyst, a Z-scheme overall water splitting system with an apparent quantum efficiency (AQE) of 6.8% at 420 nm was constructed (PtO(x)-WO3 and IO3(-)/I(-) pairs were used as an O2-evolving photocatalyst and a redox mediator, respectively), the activity of which is circa 7 or 360 times of that using Pt-TaON or Pt-MgTa2O(6-x)N)y) as a H2-evolving photocatalyst, respectively. To the best of our knowledge, this is the highest AQE among the powdered Z-scheme overall water splitting systems ever reported. PMID:26037473
Process for photosynthetically splitting water
Greenbaum, E.
1982-01-28
In one form of the invention, hydrogen is produced by providing a reactor containing a body of water. The water contains photolytic material, i.e., photoactive material containing a hydrogen-catalyst. The interior of the reactor is isolated from atmosphere and includes a volume for receiving gases evolved from the body of water. The photolytic material is exposed to light to effect photosynthetic splitting of the water into gaseous hydrogen and oxygen. The gas-receiving volume is continuously evacuated by pumping to promote evolution of gaseous hydrogen and oxygen into that volume and to withdraw them therefrom. In another form of the invention, separation of the hydrogen and oxygen is effected by selectively diffusing the hydrogen through a heated semipermeable membrane in a separation zone while maintaining across the zone a magnetic field gradient biasing the oxygen away from the membrane. In a third form of the invention, the withdrawn gas is contacted with a membrane blocking flow of water vapor to the region for effecting recovery of the hydrogen. In a fourth embodiment, the invention comprises a process for selectively recovering hydrogen from a gas mixture comprising hydrogen and oxygen. The process is conducted in a separation zone and comprises contacting the mixture with a semipermeable membrane effecting selective diffusion of hydrogen while maintaining across the zone a magnetic field gradient effecting movement of oxygen in a direction away from the membrane.
Fast wavelet based sparse approximate inverse preconditioner
Wan, W.L.
1996-12-31
Incomplete LU factorization is a robust preconditioner for both general and PDE problems but unfortunately not easy to parallelize. Recent study of Huckle and Grote and Chow and Saad showed that sparse approximate inverse could be a potential alternative while readily parallelizable. However, for special class of matrix A that comes from elliptic PDE problems, their preconditioners are not optimal in the sense that independent of mesh size. A reason may be that no good sparse approximate inverse exists for the dense inverse matrix. Our observation is that for this kind of matrices, its inverse entries typically have piecewise smooth changes. We can take advantage of this fact and use wavelet compression techniques to construct a better sparse approximate inverse preconditioner. We shall show numerically that our approach is effective for this kind of matrices.
A unified approach to sparse signal processing
NASA Astrophysics Data System (ADS)
Marvasti, Farokh; Amini, Arash; Haddadi, Farzan; Soltanolkotabi, Mahdi; Khalaj, Babak Hossein; Aldroubi, Akram; Sanei, Saeid; Chambers, Janathon
2012-12-01
A unified view of the area of sparse signal processing is presented in tutorial form by bringing together various fields in which the property of sparsity has been successfully exploited. For each of these fields, various algorithms and techniques, which have been developed to leverage sparsity, are described succinctly. The common potential benefits of significant reduction in sampling rate and processing manipulations through sparse signal processing are revealed. The key application domains of sparse signal processing are sampling, coding, spectral estimation, array processing, component analysis, and multipath channel estimation. In terms of the sampling process and reconstruction algorithms, linkages are made with random sampling, compressed sensing, and rate of innovation. The redundancy introduced by channel coding in finite and real Galois fields is then related to over-sampling with similar reconstruction algorithms. The error locator polynomial (ELP) and iterative methods are shown to work quite effectively for both sampling and coding applications. The methods of Prony, Pisarenko, and MUltiple SIgnal Classification (MUSIC) are next shown to be targeted at analyzing signals with sparse frequency domain representations. Specifically, the relations of the approach of Prony to an annihilating filter in rate of innovation and ELP in coding are emphasized; the Pisarenko and MUSIC methods are further improvements of the Prony method under noisy environments. The iterative methods developed for sampling and coding applications are shown to be powerful tools in spectral estimation. Such narrowband spectral estimation is then related to multi-source location and direction of arrival estimation in array processing. Sparsity in unobservable source signals is also shown to facilitate source separation in sparse component analysis; the algorithms developed in this area such as linear programming and matching pursuit are also widely used in compressed sensing. Finally
Sparse regularization for force identification using dictionaries
NASA Astrophysics Data System (ADS)
Qiao, Baijie; Zhang, Xingwu; Wang, Chenxi; Zhang, Hang; Chen, Xuefeng
2016-04-01
The classical function expansion method based on minimizing l2-norm of the response residual employs various basis functions to represent the unknown force. Its difficulty lies in determining the optimum number of basis functions. Considering the sparsity of force in the time domain or in other basis space, we develop a general sparse regularization method based on minimizing l1-norm of the coefficient vector of basis functions. The number of basis functions is adaptively determined by minimizing the number of nonzero components in the coefficient vector during the sparse regularization process. First, according to the profile of the unknown force, the dictionary composed of basis functions is determined. Second, a sparsity convex optimization model for force identification is constructed. Third, given the transfer function and the operational response, Sparse reconstruction by separable approximation (SpaRSA) is developed to solve the sparse regularization problem of force identification. Finally, experiments including identification of impact and harmonic forces are conducted on a cantilever thin plate structure to illustrate the effectiveness and applicability of SpaRSA. Besides the Dirac dictionary, other three sparse dictionaries including Db6 wavelets, Sym4 wavelets and cubic B-spline functions can also accurately identify both the single and double impact forces from highly noisy responses in a sparse representation frame. The discrete cosine functions can also successfully reconstruct the harmonic forces including the sinusoidal, square and triangular forces. Conversely, the traditional Tikhonov regularization method with the L-curve criterion fails to identify both the impact and harmonic forces in these cases.
Blueberry Fruit Development and Splitting
Technology Transfer Automated Retrieval System (TEKTRAN)
A problem facing blueberry growers in the southeastern United States is rain-related fruit splitting. Splitting refers to a break in the skin and/or pulp of the berry, prevalent in some cultivars, that occurs after a period of drought followed by intense rain. We hypothesize that blueberry cultiv...
Native ultrametricity of sparse random ensembles
NASA Astrophysics Data System (ADS)
Avetisov, V.; Krapivsky, P. L.; Nechaev, S.
2016-01-01
We investigate the eigenvalue density in ensembles of large sparse Bernoulli random matrices. Analyzing in detail the spectral density of ensembles of linear subgraphs, we discuss its ultrametric nature and show that near the spectrum boundary, the tails of the spectral density exhibit a Lifshitz singularity typical for Anderson localization. We pay attention to an intriguing connection of the spectral density to the Dedekind η-function. We conjecture that ultrametricity emerges in rare-event statistics and is inherit to generic complex sparse systems.
Analog system for computing sparse codes
Rozell, Christopher John; Johnson, Don Herrick; Baraniuk, Richard Gordon; Olshausen, Bruno A.; Ortman, Robert Lowell
2010-08-24
A parallel dynamical system for computing sparse representations of data, i.e., where the data can be fully represented in terms of a small number of non-zero code elements, and for reconstructing compressively sensed images. The system is based on the principles of thresholding and local competition that solves a family of sparse approximation problems corresponding to various sparsity metrics. The system utilizes Locally Competitive Algorithms (LCAs), nodes in a population continually compete with neighboring units using (usually one-way) lateral inhibition to calculate coefficients representing an input in an over complete dictionary.
Sparse representation of complex MRI images.
Nandakumar, Hari Prasad; Ji, Jim
2008-01-01
Sparse representation of images acquired from Magnet Resonance Imaging (MRI) has several potential applications. MRI is unique in that the raw images are complex. Complex wavelet transforms (CWT) can be used to produce flexible signal representations when compared to Discrete Wavelet Transform (DWT). In this work, five different schemes using CWT or DWT are tested for sparse representation of MRI images which are in the form of complex values, separate real/imaginary, or separate magnitude/phase. The experimental results on real in-vivo MRI images show that appropriate CWT, e.g., dual-tree CWT (DTCWT), can achieve sparsity better than DWT with similar Mean Square Error. PMID:19162677
Tensor methods for large, sparse unconstrained optimization
Bouaricha, A.
1996-11-01
Tensor methods for unconstrained optimization were first introduced by Schnabel and Chow [SIAM J. Optimization, 1 (1991), pp. 293-315], who describe these methods for small to moderate size problems. This paper extends these methods to large, sparse unconstrained optimization problems. This requires an entirely new way of solving the tensor model that makes the methods suitable for solving large, sparse optimization problems efficiently. We present test results for sets of problems where the Hessian at the minimizer is nonsingular and where it is singular. These results show that tensor methods are significantly more efficient and more reliable than standard methods based on Newton`s method.
Sparse representation in speech signal processing
NASA Astrophysics Data System (ADS)
Lee, Te-Won; Jang, Gil-Jin; Kwon, Oh-Wook
2003-11-01
We review the sparse representation principle for processing speech signals. A transformation for encoding the speech signals is learned such that the resulting coefficients are as independent as possible. We use independent component analysis with an exponential prior to learn a statistical representation for speech signals. This representation leads to extremely sparse priors that can be used for encoding speech signals for a variety of purposes. We review applications of this method for speech feature extraction, automatic speech recognition and speaker identification. Furthermore, this method is also suited for tackling the difficult problem of separating two sounds given only a single microphone.
Infrared image recognition based on structure sparse and atomic sparse parallel
NASA Astrophysics Data System (ADS)
Wu, Yalu; Li, Ruilong; Xu, Yi; Wang, Liping
2015-12-01
Use the redundancy of the super complete dictionary can capture the structural features of the image effectively, can achieving the effective representation of the image. However, the commonly used atomic sparse representation without regard the structure of the dictionary and the unrelated non-zero-term in the process of the computation, though structure sparse consider the structure feature of dictionary, the majority coefficients of the blocks maybe are non-zero, it may affect the identification efficiency. For the disadvantages of these two sparse expressions, a weighted parallel atomic sparse and sparse structure is proposed, and the recognition efficiency is improved by the adaptive computation of the optimal weights. The atomic sparse expression and structure sparse expression are respectively, and the optimal weights are calculated by the adaptive method. Methods are as follows: training by using the less part of the identification sample, the recognition rate is calculated by the increase of the certain step size and t the constraint between weight. The recognition rate as the Z axis, two weight values respectively as X, Y axis, the resulting points can be connected in a straight line in the 3 dimensional coordinate system, by solving the highest recognition rate, the optimal weights can be obtained. Through simulation experiments can be known, the optimal weights based on adaptive method are better in the recognition rate, weights obtained by adaptive computation of a few samples, suitable for parallel recognition calculation, can effectively improve the recognition rate of infrared images.
Photosynthetic water splitting
NASA Astrophysics Data System (ADS)
Greenbaum, E.
It has been demonstrated that eukaryotic green algae (as represented by Chlamydomonas) are inherently rugged algae with respect to the biophotolysis of water. There also exists a potential for selecting subpropulations of wild-type algae with enhanced properties for hydrogen and oxygen production. Second, hydrogenase activity in macroscopic marine algae does not conform to the conventional dogma of the catalog of reactions that this enzyme is supposed to catalyze. A kinetic argument has been presented which suggests that, with respect to light activated reactions, hydrogenase in these organisms operates primarily in a hydrogen uptake mode. Third, the light saturation curves for the simultaneous photoproduction of hydrogen and oxygen do not have the same analytical shape. It is suggested that a Photosystem I-like hydrogen producing light reaction may be present in anaerobically adapted Scenedesmus which is uncoupled from the Z scheme.
Renewable hydrogen production by photosynthetic water splitting
Greenbaum, E.; Lee, J.W.
1998-06-01
This mission-oriented research project is focused on the production of renewable hydrogen. The authors have demonstrated that certain unicellular green algae are capable of sustained simultaneous photoproduction of hydrogen and oxygen by light-activated photosynthetic water splitting. It is the goal of this project to develop a practical chemical engineering system for the development of an economic process that can be used to produce renewable hydrogen. There are several fundamental problems that need to be solved before the application of this scientific knowledge can be applied to the development a practical process: (I) maximizing net thermodynamic conversion efficiency of light energy into hydrogen energy, (2) development of oxygen-sensitive hydrogenase-containing mutants, and (3) development of bioreactors that can be used in a real-world chemical engineering process. The authors are addressing each of these problems here at ORNL and in collaboration with their research colleagues at the National Renewable Energy Laboratory, the University of California, Berkeley, and the University of Hawaii. This year the authors have focused on item 1 above. In particular, they have focused on the question of how many light reactions are required to split water to molecular hydrogen and oxygen.
Split supersymmetry radiates flavor
NASA Astrophysics Data System (ADS)
Baumgart, Matthew; Stolarski, Daniel; Zorawski, Thomas
2014-09-01
Radiative flavor models where the hierarchies of Standard Model (SM) fermion masses and mixings are explained via loop corrections are elegant ways to solve the SM flavor puzzle. Here we build such a model in the context of mini-split supersymmetry (SUSY) where both flavor and SUSY breaking occur at a scale of 1000 TeV. This model is consistent with the observed Higgs mass, unification, and dark matter as a weakly interacting massive particle. The high scale allows large flavor mixing among the sfermions, which provides part of the mechanism for radiative flavor generation. In the deep UV, all flavors are treated democratically, but at the SUSY-breaking scale, the third, second, and first generation Yukawa couplings are generated at tree level, one loop, and two loops, respectively. Save for one, all the dimensionless parameters in the theory are O(1), with the exception being a modest and technically natural tuning that explains both the smallness of the bottom Yukawa coupling and the largeness of the Cabibbo angle.
Resonant Nanophotonic Spectrum Splitting for Ultrathin Multijunction Solar Cells
2015-01-01
We present an approach to spectrum splitting for photovoltaics that utilizes the resonant optical properties of nanostructures for simultaneous voltage enhancement and spatial separation of different colors of light. Using metal–insulator–metal resonators commonly used in broadband metamaterial absorbers we show theoretically that output voltages can be enhanced significantly compared to single-junction devices. However, the approach is general and works for any type of resonator with a large absorption cross section. Due to its resonant nature, the spectrum splitting occurs within only a fraction of the wavelength, as opposed to traditional spectrum splitting methods, where many wavelengths are required. Combining nanophotonic spectrum splitting with other nanophotonic approaches to voltage enhancements, such as angle restriction and concentration, may lead to highly efficient but deeply subwavelength photovoltaic devices. PMID:26322319
Natural split mechanism for sfermions: N = 2 supersymmetry in phenomenology
NASA Astrophysics Data System (ADS)
Shimizu, Yasuhiro; Yin, Wen
2016-03-01
We suggest a natural split mechanism for sfermions based on N = 2 supersymmetry (SUSY). N = 2 SUSY protects a sfermion in an N = 2 multiplet from gaining weight by SUSY breaking. Therefore, if partly N = 2 SUSY is effectively obtained, a split spectrum can be realized naturally. As an example of the natural split mechanism, we build a gauge-mediated SUSY breaking-like model assuming N = 2 SUSY is partly broken in an underlying theory. The model explains the Higgs boson mass and muon anomalous magnetic dipole moment within 1 σ level with a splitting sfermion spectrum. The model has seven light sparticles described by three free parameters and predicts a new chiral multiplet, sb: the N = 2 partner of the N = 1 U(1)Y vector multiplet. The bini, the fermion component of the sb, weighs MeVs. We mention the experimental and cosmological aspects of the model.
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.
Facial expression recognition with facial parts based sparse representation classifier
NASA Astrophysics Data System (ADS)
Zhi, Ruicong; Ruan, Qiuqi
2009-10-01
Facial expressions play important role in human communication. The understanding of facial expression is a basic requirement in the development of next generation human computer interaction systems. Researches show that the intrinsic facial features always hide in low dimensional facial subspaces. This paper presents facial parts based facial expression recognition system with sparse representation classifier. Sparse representation classifier exploits sparse representation to select face features and classify facial expressions. The sparse solution is obtained by solving l1 -norm minimization problem with constraint of linear combination equation. Experimental results show that sparse representation is efficient for facial expression recognition and sparse representation classifier obtain much higher recognition accuracies than other compared methods.
Second SIAM conference on sparse matrices: Abstracts. Final technical report
1996-12-31
This report contains abstracts on the following topics: invited and long presentations (IP1 & LP1); sparse matrix reordering & graph theory I; sparse matrix tools & environments I; eigenvalue computations I; iterative methods & acceleration techniques I; applications I; parallel algorithms I; sparse matrix reordering & graphy theory II; sparse matrix tool & environments II; least squares & optimization I; iterative methods & acceleration techniques II; applications II; eigenvalue computations II; least squares & optimization II; parallel algorithms II; sparse direct methods; iterative methods & acceleration techniques III; eigenvalue computations III; and sparse matrix reordering & graph theory III.
Self-Control in Sparsely Coded Networks
NASA Astrophysics Data System (ADS)
Dominguez, D. R. C.; Bollé, D.
1998-03-01
A complete self-control mechanism is proposed in the dynamics of neural networks through the introduction of a time-dependent threshold, determined in function of both the noise and the pattern activity in the network. Especially for sparsely coded models this mechanism is shown to considerably improve the storage capacity, the basins of attraction, and the mutual information content.
Structured Sparse Method for Hyperspectral Unmixing
NASA Astrophysics Data System (ADS)
Zhu, Feiyun; Wang, Ying; Xiang, Shiming; Fan, Bin; Pan, Chunhong
2014-02-01
Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method based on the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.
Sparse matrix orderings for factorized inverse preconditioners
Benzi, M.; Tuama, M.
1998-09-01
The effect of reorderings on the performance of factorized sparse approximate inverse preconditioners is considered. It is shown that certain reorderings can be very beneficial both in the preconditioner construction phase and in terms of the rate of convergence of the preconditioned iteration.
Multilevel sparse functional principal component analysis.
Di, Chongzhi; Crainiceanu, Ciprian M; Jank, Wolfgang S
2014-01-29
We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions. PMID:24872597
Multilevel sparse functional principal component analysis
Di, Chongzhi; Crainiceanu, Ciprian M.; Jank, Wolfgang S.
2014-01-01
We consider analysis of sparsely sampled multilevel functional data, where the basic observational unit is a function and data have a natural hierarchy of basic units. An example is when functions are recorded at multiple visits for each subject. Multilevel functional principal component analysis (MFPCA; Di et al. 2009) was proposed for such data when functions are densely recorded. Here we consider the case when functions are sparsely sampled and may contain only a few observations per function. We exploit the multilevel structure of covariance operators and achieve data reduction by principal component decompositions at both between and within subject levels. We address inherent methodological differences in the sparse sampling context to: 1) estimate the covariance operators; 2) estimate the functional principal component scores; 3) predict the underlying curves. Through simulations the proposed method is able to discover dominating modes of variations and reconstruct underlying curves well even in sparse settings. Our approach is illustrated by two applications, the Sleep Heart Health Study and eBay auctions. PMID:24872597
Perfectly correlated phase screen realization using sparse spectrum harmonic augmentation.
Naeh, Itay; Katzir, Abraham
2014-09-20
The split-step Fourier method is commonly used to simulate the propagation of radiation in a turbulent atmosphere using two-dimensional phase screens that have the desired spatial spectral content given by the atmospheric power spectrum. Using existing methodologies, isotropy of the structure function can never be achieved, mainly along the axis of propagation, for several reasons. In this paper, we introduce the sparse spectrum harmonic augmentation method that will address the lack of isotropy along the propagation axis, the limited achievable frequencies, and the limited time development possible using known approaches. Following the methodology described will produce phase screens that are transversely endless, perfectly correlated along the propagation axis, and contain the desired spectral content, including the low frequencies that even though they contain most of the energy, are usually neglected. The methodology presented can be used for many aspects of wave propagation in random media, such as atmospheric propagation, underwater acoustics, radio wave propagation in the ionosphere, and more. PMID:25322093
Automatic anatomy recognition of sparse objects
NASA Astrophysics Data System (ADS)
Zhao, Liming; Udupa, Jayaram K.; Odhner, Dewey; Wang, Huiqian; Tong, Yubing; Torigian, Drew A.
2015-03-01
A general body-wide automatic anatomy recognition (AAR) methodology was proposed in our previous work based on hierarchical fuzzy models of multitudes of objects which was not tied to any specific organ system, body region, or image modality. That work revealed the challenges encountered in modeling, recognizing, and delineating sparse objects throughout the body (compared to their non-sparse counterparts) if the models are based on the object's exact geometric representations. The challenges stem mainly from the variation in sparse objects in their shape, topology, geographic layout, and relationship to other objects. That led to the idea of modeling sparse objects not from the precise geometric representations of their samples but by using a properly designed optimal super form. This paper presents the underlying improved methodology which includes 5 steps: (a) Collecting image data from a specific population group G and body region Β and delineating in these images the objects in Β to be modeled; (b) Building a super form, S-form, for each object O in Β; (c) Refining the S-form of O to construct an optimal (minimal) super form, S*-form, which constitutes the (fuzzy) model of O; (d) Recognizing objects in Β using the S*-form; (e) Defining confounding and background objects in each S*-form for each object and performing optimal delineation. Our evaluations based on 50 3D computed tomography (CT) image sets in the thorax on four sparse objects indicate that substantially improved performance (FPVF~2%, FNVF~10%, and success where the previous approach failed) can be achieved using the new approach.
Compressive sensing of sparse radio frequency signals using optical mixing.
Valley, George C; Sefler, George A; Shaw, T Justin
2012-11-15
We demonstrate an optical mixing system for measuring properties of sparse radio frequency (RF) signals using compressive sensing (CS). Two types of sparse RF signals are investigated: (1) a signal that consists of a few 0.4 ns pulses in a 26.8 ns window and (2) a signal that consists of a few sinusoids at different frequencies. The RF is modulated onto the intensity of a repetitively pulsed, wavelength-chirped optical field, and time-wavelength-space mapping is used to map the optical field onto a 118-pixel, one-dimensional spatial light modulator (SLM). The SLM pixels are programmed with a pseudo-random bit sequence (PRBS) to form one row of the CS measurement matrix, and the optical throughput is integrated with a photodiode to obtain one value of the CS measurement vector. Then the PRBS is changed to form the second row of the mixing matrix and a second value of the measurement vector is obtained. This process is performed 118 times so that we can vary the dimensions of the CS measurement matrix from 1×118 to 118×118 (square). We use the penalized ℓ(1) norm method with stopping parameter λ (also called basis pursuit denoising) to recover pulsed or sinusoidal RF signals as a function of the small dimension of the measurement matrix and stopping parameter. For a square matrix, we also find that penalized ℓ(1) norm recovery performs better than conventional recovery using matrix inversion. PMID:23164876
Design and implementation of sparse aperture imaging systems
NASA Astrophysics Data System (ADS)
Chung, Soon-Jo; Miller, David W.; de Weck, Olivier L.
2002-12-01
In order to better understand the technological difficulties involved in designing and building a sparse aperture array, the challenge of building a white light Golay-3 telescope was undertaken. The MIT Adaptive Reconnaissance Golay-3 Optical Satellite (ARGOS) project exploits wide-angle Fizeau interferometer technology with an emphasis on modularity in the optics and spacecraft subsystems. Unique design procedures encompassing the nature of coherent wavefront sensing, control and combining as well as various system engineering aspects to achieve cost effectiveness, are developed. To demonstrate a complete spacecraft in a 1-g environment, the ARGOS system is mounted on a frictionless air-bearing, and has the ability to track fast orbiting satellites like the ISS or the planets. Wavefront sensing techniques are explored to mitigate initial misalignment and to feed back real-time aberrations into the optical control loop. This paper presents the results and the lessons learned from the conceive, design and implementation phases of ARGOS. A preliminary assess-ment shows that the beam combining problem is the most challenging aspect of sparse optical arrays. The need for optical control is paramount due to tight beam combining tolerances. The wavefront sensing/control requirements appear to be a major technology and cost driver.
Split-illumination electron holography
Tanigaki, Toshiaki; Aizawa, Shinji; Suzuki, Takahiro; Park, Hyun Soon; Inada, Yoshikatsu; Matsuda, Tsuyoshi; Taniyama, Akira; Shindo, Daisuke; Tonomura, Akira
2012-07-23
We developed a split-illumination electron holography that uses an electron biprism in the illuminating system and two biprisms (applicable to one biprism) in the imaging system, enabling holographic interference micrographs of regions far from the sample edge to be obtained. Using a condenser biprism, we split an electron wave into two coherent electron waves: one wave is to illuminate an observation area far from the sample edge in the sample plane and the other wave to pass through a vacuum space outside the sample. The split-illumination holography has the potential to greatly expand the breadth of applications of electron holography.
Chiral Extrapolation of Lattice Data for Heavy Meson Hyperfine Splittings
X.-H. Guo; P.C. Tandy; A.W. Thomas
2006-03-01
We investigate the chiral extrapolation of the lattice data for the light-heavy meson hyperfine splittings D*-D and B*-B to the physical region for the light quark mass. The chiral loop corrections providing non-analytic behavior in m{sub {pi}} are consistent with chiral perturbation theory for heavy mesons. Since chiral loop corrections tend to decrease the already too low splittings obtained from linear extrapolation, we investigate two models to guide the form of the analytic background behavior: the constituent quark potential model, and the covariant model of QCD based on the ladder-rainbow truncation of the Dyson-Schwinger equations. The extrapolated hyperfine splittings remain clearly below the experimental values even allowing for the model dependence in the description of the analytic background.
Peter, Laurence M; Upul Wijayantha, K G
2014-07-21
Some fundamental aspects of light-driven water splitting at semiconductor electrodes are reviewed along with recent experimental and theoretical progress. The roles of thermodynamics and kinetics in defining criteria for successful water-splitting systems are examined. An overview of recent research is given that places emphasis on new electrode materials, theoretical advances and the development of semi-quantitative experimental methods to study the dynamics of light-driven water-splitting reactions. Key areas are identified that will need particular attention as the search continues for stable, efficient and cost-effective light-driven photoelectrolysis systems that exploit electron/hole separation in semiconductor/electrolyte junctions. PMID:24819303
Compressed sensing sparse reconstruction for coherent field imaging
NASA Astrophysics Data System (ADS)
Bei, Cao; Xiu-Juan, Luo; Yu, Zhang; Hui, Liu; Ming-Lai, Chen
2016-04-01
Return signal processing and reconstruction plays a pivotal role in coherent field imaging, having a significant influence on the quality of the reconstructed image. To reduce the required samples and accelerate the sampling process, we propose a genuine sparse reconstruction scheme based on compressed sensing theory. By analyzing the sparsity of the received signal in the Fourier spectrum domain, we accomplish an effective random projection and then reconstruct the return signal from as little as 10% of traditional samples, finally acquiring the target image precisely. The results of the numerical simulations and practical experiments verify the correctness of the proposed method, providing an efficient processing approach for imaging fast-moving targets in the future. Project supported by the National Natural Science Foundation of China (Grant No. 61505248) and the Fund from Chinese Academy of Sciences, the Light of “Western” Talent Cultivation Plan “Dr. Western Fund Project” (Grant No. Y429621213).
Su, Hai; Xing, Fuyong; Yang, Lin
2016-06-01
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
Sanitary locking lip split well seal
Jozwiak, T.H.; Hunley, E.C. Jr.
1992-05-12
This patent describes a well seal for cooperating with a casing of a wall. It comprises a first split plate; a second split plate having a size and shape to allow insertion within the well casing; a split packer, which is provided with at least one tapered through hole, positioned between first split plate, and second split plate, the split packer having a size and shape approximately the same as an inner dimension of the well casing to allow insertion therein, split packer having at least two sections with interlocking lips to provide an effective sanitary seal by providing a leakproof labyrinth path to avoid a straight-through leak path; and clamp means for compressing split packer between first split plate and second split plates to expand the split packer into sealing engagement with an inner wall of the well casing.
Sparse representations and convex optimization as tools for LOFAR radio interferometric imaging
NASA Astrophysics Data System (ADS)
Girard, J. N.; Garsden, H.; Starck, J. L.; Corbel, S.; Woiselle, A.; Tasse, C.; McKean, J. P.; Bobin, J.
2015-08-01
Compressed sensing theory is slowly making its way to solve more and more astronomical inverse problems. We address here the application of sparse representations, convex optimization and proximal theory to radio interferometric imaging. First, we expose the theory behind interferometric imaging, sparse representations and convex optimization, and second, we illustrate their application with numerical tests with SASIR, an implementation of the FISTA, a Forward-Backward splitting algorithm hosted in a LOFAR imager. Various tests have been conducted in Garsden et al., 2015. The main results are: i) an improved angular resolution (super resolution of a factor ≈ 2) with point sources as compared to CLEAN on the same data, ii) correct photometry measurements on a field of point sources at high dynamic range and iii) the imaging of extended sources with improved fidelity. SASIR provides better reconstructions (five time less residuals) of the extended emission as compared to CLEAN. With the advent of large radiotelescopes, there is scope for improving classical imaging methods with convex optimization methods combined with sparse representations.
Statistical prediction with Kanerva's sparse distributed memory
NASA Technical Reports Server (NTRS)
Rogers, David
1989-01-01
A new viewpoint of the processing performed by Kanerva's sparse distributed memory (SDM) is presented. In conditions of near- or over-capacity, where the associative-memory behavior of the model breaks down, the processing performed by the model can be interpreted as that of a statistical predictor. Mathematical results are presented which serve as the framework for a new statistical viewpoint of sparse distributed memory and for which the standard formulation of SDM is a special case. This viewpoint suggests possible enhancements to the SDM model, including a procedure for improving the predictiveness of the system based on Holland's work with genetic algorithms, and a method for improving the capacity of SDM even when used as an associative memory.
Solving large sparse eigenvalue problems on supercomputers
NASA Technical Reports Server (NTRS)
Philippe, Bernard; Saad, Youcef
1988-01-01
An important problem in scientific computing consists in finding a few eigenvalues and corresponding eigenvectors of a very large and sparse matrix. The most popular methods to solve these problems are based on projection techniques on appropriate subspaces. The main attraction of these methods is that they only require the use of the matrix in the form of matrix by vector multiplications. The implementations on supercomputers of two such methods for symmetric matrices, namely Lanczos' method and Davidson's method are compared. Since one of the most important operations in these two methods is the multiplication of vectors by the sparse matrix, methods of performing this operation efficiently are discussed. The advantages and the disadvantages of each method are compared and implementation aspects are discussed. Numerical experiments on a one processor CRAY 2 and CRAY X-MP are reported. Possible parallel implementations are also discussed.
Sparse decomposition learning based dynamic MRI reconstruction
NASA Astrophysics Data System (ADS)
Zhu, Peifei; Zhang, Qieshi; Kamata, Sei-ichiro
2015-02-01
Dynamic MRI is widely used for many clinical exams but slow data acquisition becomes a serious problem. The application of Compressed Sensing (CS) demonstrated great potential to increase imaging speed. However, the performance of CS is largely depending on the sparsity of image sequence in the transform domain, where there are still a lot to be improved. In this work, the sparsity is exploited by proposed Sparse Decomposition Learning (SDL) algorithm, which is a combination of low-rank plus sparsity and Blind Compressed Sensing (BCS). With this decomposition, only sparsity component is modeled as a sparse linear combination of temporal basis functions. This enables coefficients to be sparser and remain more details of dynamic components comparing learning the whole images. A reconstruction is performed on the undersampled data where joint multicoil data consistency is enforced by combing Parallel Imaging (PI). The experimental results show the proposed methods decrease about 15~20% of Mean Square Error (MSE) compared to other existing methods.
Sparse brain network using penalized linear regression
NASA Astrophysics Data System (ADS)
Lee, Hyekyoung; Lee, Dong Soo; Kang, Hyejin; Kim, Boong-Nyun; Chung, Moo K.
2011-03-01
Sparse partial correlation is a useful connectivity measure for brain networks when it is difficult to compute the exact partial correlation in the small-n large-p setting. In this paper, we formulate the problem of estimating partial correlation as a sparse linear regression with a l1-norm penalty. The method is applied to brain network consisting of parcellated regions of interest (ROIs), which are obtained from FDG-PET images of the autism spectrum disorder (ASD) children and the pediatric control (PedCon) subjects. To validate the results, we check their reproducibilities of the obtained brain networks by the leave-one-out cross validation and compare the clustered structures derived from the brain networks of ASD and PedCon.
Sparse representation for color image restoration.
Mairal, Julien; Elad, Michael; Sapiro, Guillermo
2008-01-01
Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in. This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper. PMID:18229804
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
Dictionary Learning Algorithms for Sparse Representation
Kreutz-Delgado, Kenneth; Murray, Joseph F.; Rao, Bhaskar D.; Engan, Kjersti; Lee, Te-Won; Sejnowski, Terrence J.
2010-01-01
Algorithms for data-driven learning of domain-specific overcomplete dictionaries are developed to obtain maximum likelihood and maximum a posteriori dictionary estimates based on the use of Bayesian models with concave/Schur-concave (CSC) negative log priors. Such priors are appropriate for obtaining sparse representations of environmental signals within an appropriately chosen (environmentally matched) dictionary. The elements of the dictionary can be interpreted as concepts, features, or words capable of succinct expression of events encountered in the environment (the source of the measured signals). This is a generalization of vector quantization in that one is interested in a description involving a few dictionary entries (the proverbial “25 words or less”), but not necessarily as succinct as one entry. To learn an environmentally adapted dictionary capable of concise expression of signals generated by the environment, we develop algorithms that iterate between a representative set of sparse representations found by variants of FOCUSS and an update of the dictionary using these sparse representations. Experiments were performed using synthetic data and natural images. For complete dictionaries, we demonstrate that our algorithms have improved performance over other independent component analysis (ICA) methods, measured in terms of signal-to-noise ratios of separated sources. In the overcomplete case, we show that the true underlying dictionary and sparse sources can be accurately recovered. In tests with natural images, learned overcomplete dictionaries are shown to have higher coding efficiency than complete dictionaries; that is, images encoded with an over-complete dictionary have both higher compression (fewer bits per pixel) and higher accuracy (lower mean square error). PMID:12590811
Inpainting with sparse linear combinations of exemplars
Wohlberg, Brendt
2008-01-01
We introduce a new exemplar-based inpainting algorithm based on representing the region to be inpainted as a sparse linear combination of blocks extracted from similar parts of the image being inpainted. This method is conceptually simple, being computed by functional minimization, and avoids the complexity of correctly ordering the filling in of missing regions of other exemplar-based methods. Initial performance comparisons on small inpainting regions indicate that this method provides similar or better performance than other recent methods.
Notes on implementation of sparsely distributed memory
NASA Technical Reports Server (NTRS)
Keeler, J. D.; Denning, P. J.
1986-01-01
The Sparsely Distributed Memory (SDM) developed by Kanerva is an unconventional memory design with very interesting and desirable properties. The memory works in a manner that is closely related to modern theories of human memory. The SDM model is discussed in terms of its implementation in hardware. Two appendices discuss the unconventional approaches of the SDM: Appendix A treats a resistive circuit for fast, parallel address decoding; and Appendix B treats a systolic array for high throughput read and write operations.
Automatic target recognition via sparse representations
NASA Astrophysics Data System (ADS)
Estabridis, Katia
2010-04-01
Automatic target recognition (ATR) based on the emerging technology of Compressed Sensing (CS) can considerably improve accuracy, speed and cost associated with these types of systems. An image based ATR algorithm has been built upon this new theory, which can perform target detection and recognition in a low dimensional space. Compressed dictionaries (A) are formed to include rotational information for a scale of interest. The algorithm seeks to identify y(test sample) as a linear combination of the dictionary elements : y=Ax, where A ∈ Rnxm(n<
Modified sparse regularization for electrical impedance tomography.
Fan, Wenru; Wang, Huaxiang; Xue, Qian; Cui, Ziqiang; Sun, Benyuan; Wang, Qi
2016-03-01
Electrical impedance tomography (EIT) aims to estimate the electrical properties at the interior of an object from current-voltage measurements on its boundary. It has been widely investigated due to its advantages of low cost, non-radiation, non-invasiveness, and high speed. Image reconstruction of EIT is a nonlinear and ill-posed inverse problem. Therefore, regularization techniques like Tikhonov regularization are used to solve the inverse problem. A sparse regularization based on L1 norm exhibits superiority in preserving boundary information at sharp changes or discontinuous areas in the image. However, the limitation of sparse regularization lies in the time consumption for solving the problem. In order to further improve the calculation speed of sparse regularization, a modified method based on separable approximation algorithm is proposed by using adaptive step-size and preconditioning technique. Both simulation and experimental results show the effectiveness of the proposed method in improving the image quality and real-time performance in the presence of different noise intensities and conductivity contrasts. PMID:27036798
Aerial Scene Recognition using Efficient Sparse Representation
Cheriyadat, Anil M
2012-01-01
Advanced scene recognition systems for processing large volumes of high-resolution aerial image data are in great demand today. However, automated scene recognition remains a challenging problem. Efficient encoding and representation of spatial and structural patterns in the imagery are key in developing automated scene recognition algorithms. We describe an image representation approach that uses simple and computationally efficient sparse code computation to generate accurate features capable of producing excellent classification performance using linear SVM kernels. Our method exploits unlabeled low-level image feature measurements to learn a set of basis vectors. We project the low-level features onto the basis vectors and use simple soft threshold activation function to derive the sparse features. The proposed technique generates sparse features at a significantly lower computational cost than other methods~\\cite{Yang10, newsam11}, yet it produces comparable or better classification accuracy. We apply our technique to high-resolution aerial image datasets to quantify the aerial scene classification performance. We demonstrate that the dense feature extraction and representation methods are highly effective for automatic large-facility detection on wide area high-resolution aerial imagery.
Learning joint intensity-depth sparse representations.
Tosic, Ivana; Drewes, Sarah
2014-05-01
This paper presents a method for learning overcomplete dictionaries of atoms composed of two modalities that describe a 3D scene: 1) image intensity and 2) scene depth. We propose a novel joint basis pursuit (JBP) algorithm that finds related sparse features in two modalities using conic programming and we integrate it into a two-step dictionary learning algorithm. The JBP differs from related convex algorithms because it finds joint sparsity models with different atoms and different coefficient values for intensity and depth. This is crucial for recovering generative models where the same sparse underlying causes (3D features) give rise to different signals (intensity and depth). We give a bound for recovery error of sparse coefficients obtained by JBP, and show numerically that JBP is superior to the group lasso algorithm. When applied to the Middlebury depth-intensity database, our learning algorithm converges to a set of related features, such as pairs of depth and intensity edges or image textures and depth slants. Finally, we show that JBP outperforms state of the art methods on depth inpainting for time-of-flight and Microsoft Kinect 3D data. PMID:24723574
Mean-field sparse optimal control
Fornasier, Massimo; Piccoli, Benedetto; Rossi, Francesco
2014-01-01
We introduce the rigorous limit process connecting finite dimensional sparse optimal control problems with ODE constraints, modelling parsimonious interventions on the dynamics of a moving population divided into leaders and followers, to an infinite dimensional optimal control problem with a constraint given by a system of ODE for the leaders coupled with a PDE of Vlasov-type, governing the dynamics of the probability distribution of the followers. In the classical mean-field theory, one studies the behaviour of a large number of small individuals freely interacting with each other, by simplifying the effect of all the other individuals on any given individual by a single averaged effect. In this paper, we address instead the situation where the leaders are actually influenced also by an external policy maker, and we propagate its effect for the number N of followers going to infinity. The technical derivation of the sparse mean-field optimal control is realized by the simultaneous development of the mean-field limit of the equations governing the followers dynamics together with the Γ-limit of the finite dimensional sparse optimal control problems. PMID:25288818
Sparseness- and continuity-constrained seismic imaging
NASA Astrophysics Data System (ADS)
Herrmann, Felix J.
2005-04-01
Non-linear solution strategies to the least-squares seismic inverse-scattering problem with sparseness and continuity constraints are proposed. Our approach is designed to (i) deal with substantial amounts of additive noise (SNR < 0 dB); (ii) use the sparseness and locality (both in position and angle) of directional basis functions (such as curvelets and contourlets) on the model: the reflectivity; and (iii) exploit the near invariance of these basis functions under the normal operator, i.e., the scattering-followed-by-imaging operator. Signal-to-noise ratio and the continuity along the imaged reflectors are significantly enhanced by formulating the solution of the seismic inverse problem in terms of an optimization problem. During the optimization, sparseness on the basis and continuity along the reflectors are imposed by jointly minimizing the l1- and anisotropic diffusion/total-variation norms on the coefficients and reflectivity, respectively. [Joint work with Peyman P. Moghaddam was carried out as part of the SINBAD project, with financial support secured through ITF (the Industry Technology Facilitator) from the following organizations: BG Group, BP, ExxonMobil, and SHELL. Additional funding came from the NSERC Discovery Grants 22R81254.
SAR Image despeckling via sparse representation
NASA Astrophysics Data System (ADS)
Wang, Zhongmei; Yang, Xiaomei; Zheng, Liang
2014-11-01
SAR image despeckling is an active research area in image processing due to its importance in improving the quality of image for object detection and classification.In this paper, a new approach is proposed for multiplicative noise in SAR image removal based on nonlocal sparse representation by dictionary learning and collaborative filtering. First, a image is divided into many patches, and then a cluster is formed by clustering log-similar image patches using Fuzzy C-means (FCM). For each cluster, an over-complete dictionary is computed using the K-SVD method that iteratively updates the dictionary and the sparse coefficients. The patches belonging to the same cluster are then reconstructed by a sparse combination of the corresponding dictionary atoms. The reconstructed patches are finally collaboratively aggregated to build the denoised image. The experimental results show that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both objective evaluation index (PSNR and ENL) and subjective visual perception.
Imaging black holes with sparse modeling
NASA Astrophysics Data System (ADS)
Honma, Mareki; Akiyama, Kazunori; Tazaki, Fumie; Kuramochi, Kazuki; Ikeda, Shiro; Hada, Kazuhiro; Uemura, Makoto
2016-03-01
We introduce a new imaging method for radio interferometry based on sparse- modeling. The direct observables in radio interferometry are visibilities, which are Fourier transformation of an astronomical image on the sky-plane, and incomplete sampling of visibilities in the spatial frequency domain results in an under-determined problem, which has been usually solved with 0 filling to un-sampled grids. In this paper we propose to directly solve this under-determined problem using sparse modeling without 0 filling, which realizes super resolution, i.e., resolution higher than the standard refraction limit. We show simulation results of sparse modeling for the Event Horizon Telescope (EHT) observations of super-massive black holes and demonstrate that our approach has significant merit in observations of black hole shadows expected to be realized in near future. We also present some results with the method applied to real data, and also discuss more advanced techniques for practical observations such as imaging with closure phase as well as treating the effect of interstellar scattering effect.
Efficient visual tracking via low-complexity sparse representation
NASA Astrophysics Data System (ADS)
Lu, Weizhi; Zhang, Jinglin; Kpalma, Kidiyo; Ronsin, Joseph
2015-12-01
Thanks to its good performance on object recognition, sparse representation has recently been widely studied in the area of visual object tracking. Up to now, little attention has been paid to the complexity of sparse representation, while most works are focused on the performance improvement. By reducing the computation load related to sparse representation hundreds of times, this paper proposes by far the most computationally efficient tracking approach based on sparse representation. The proposal simply consists of two stages of sparse representation, one is for object detection and the other for object validation. Experimentally, it achieves better performance than some state-of-the-art methods in both accuracy and speed.
Nanostructure-based WO3 photoanodes for photoelectrochemical water splitting.
Liu, Xien; Wang, Fengying; Wang, Qing
2012-06-14
Nanostructured WO(3) has been developed as a promising water-splitting material due to its ability of capturing parts of the visible light and high stability in aqueous solutions under acidic conditions. In this review, the fabrication, photocatalytic performance and operating principles of photoelectrochemical cells (PECs) for water splitting based on WO(3) photoanodes, with an emphasis on the last decade, are discussed. The morphology, dimension, crystallinity, grain boundaries, defect and separation, transport of photogenerated charges will also be mentioned as the impact factors on photocatalytic performance. PMID:22534756
Semiconductor Nanowires for Photoelectrochemical Water Splitting
NASA Astrophysics Data System (ADS)
Hwang, Yun Jeong
the higher surface potential on the n-TiO 2 (photoanode) side relative to the p-Si (photocathode) side under UV illumination as the result of hole accumulation on the TiO2 side and electron accumulation on the Si side which are desirable charge separation for solar water splitting. In chapter five, TiO2 is replaced with single phase InGaN nanowire in a dual bandgap photoanode to show the potential for solar water splitting with high surface area Si/InGaN hierarchical nanowire arrays and InGaN as a possible candidate for visible light absorber. An enhancement of 5 times in photocurrent was observed when the surface area increased from InGaN nanowires on planar Si to InGaN nanowires on Si wires. Chapter six demonstrates a self-driven water splitting device with the p/n PEC cell which consists of a photocathode and a photoanode. The operating photocurrent (Iop) with the p/n PEC cell is enhanced when n-Si/p-Si photovoltage cell was embedded under an n-TiO2 photoanode by utilizing the photovoltage generated by a Si PV cell. Also, the Si nanowire photocathode surface is modified with Pt and TiO2 to increase hydrogen reducing activity and stability which enhances Iop of the p/n PEC cell as well. When Si/TiO 2 nanowire photocathode is linked with n-Si/p-Si photovoltage cell embedded TiO2 nanowire photoanode, the p/n PEC cell shows water splitting without bias voltage confirmed with 2:1 ratio of hydrogen:oxygen gas evolution and a 92 % Faradic efficiency. These studies represent a significant step towards realizing the benefit of the advanced 1D nanowire configuration for efficient solar to energy conversion.
Lattice splitting under intermittent flows
NASA Astrophysics Data System (ADS)
Schläpfer, Markus; Trantopoulos, Konstantinos
2010-05-01
We study the splitting of regular square lattices subject to stochastic intermittent flows. Various flow patterns are produced by different groupings of the nodes, based on their random alternation between two possible states. The resulting flows on the lattices decrease with the number of groups according to a power law. By Monte Carlo simulations we reveal how the time span until the occurrence of a splitting depends on the flow patterns. Increasing the flow fluctuation frequency shortens this time span, which reaches a minimum before rising again due to inertia effects incorporated in the model. The size of the largest connected component after the splitting is rather independent of the flow fluctuation frequency but slightly decreases with the link capacities. Our findings carry important implications for real-world networks, such as electric power grids with a large share of renewable intermittent energy sources.
Lattice splitting under intermittent flows.
Schläpfer, Markus; Trantopoulos, Konstantinos
2010-05-01
We study the splitting of regular square lattices subject to stochastic intermittent flows. Various flow patterns are produced by different groupings of the nodes, based on their random alternation between two possible states. The resulting flows on the lattices decrease with the number of groups according to a power law. By Monte Carlo simulations we reveal how the time span until the occurrence of a splitting depends on the flow patterns. Increasing the flow fluctuation frequency shortens this time span, which reaches a minimum before rising again due to inertia effects incorporated in the model. The size of the largest connected component after the splitting is rather independent of the flow fluctuation frequency but slightly decreases with the link capacities. Our findings carry important implications for real-world networks, such as electric power grids with a large share of renewable intermittent energy sources. PMID:20866296
Split ring containment attachment device
Sammel, Alfred G.
1996-01-01
A containment attachment device 10 for operatively connecting a glovebag 200 to plastic sheeting 100 covering hazardous material. The device 10 includes an inner split ring member 20 connected on one end 22 to a middle ring member 30 wherein the free end 21 of the split ring member 20 is inserted through a slit 101 in the plastic sheeting 100 to captively engage a generally circular portion of the plastic sheeting 100. A collar potion 41 having an outer ring portion 42 is provided with fastening means 51 for securing the device 10 together wherein the glovebag 200 is operatively connected to the collar portion 41.
Baryon asymmetry and split SUSY
Kasuya, Shinta
2005-12-02
It is one of the greatest mysteries that the baryon asymmetry in our universe is so small. It is argued that it may originate from some profound physics beyond the standard model. We investigate the Affleck-Dine baryogenesis in split supersymmetry, and find that the smallness of the baryon asymmetry is directly related to the hierarchy between the supersymmetry breaking squark/slepton masses and the weak scale. Put simply, the baryon asymmetry is small because of the split mass spectrum. LHC may prove or falsify our scenario.
Galaxy redshift surveys with sparse sampling
Chiang, Chi-Ting; Wullstein, Philipp; Komatsu, Eiichiro; Jee, Inh; Jeong, Donghui; Blanc, Guillermo A.; Ciardullo, Robin; Gronwall, Caryl; Hagen, Alex; Schneider, Donald P.; Drory, Niv; Fabricius, Maximilian; Landriau, Martin; Finkelstein, Steven; Jogee, Shardha; Cooper, Erin Mentuch; Tuttle, Sarah; Gebhardt, Karl; Hill, Gary J.
2013-12-01
Survey observations of the three-dimensional locations of galaxies are a powerful approach to measure the distribution of matter in the universe, which can be used to learn about the nature of dark energy, physics of inflation, neutrino masses, etc. A competitive survey, however, requires a large volume (e.g., V{sub survey} ∼ 10Gpc{sup 3}) to be covered, and thus tends to be expensive. A ''sparse sampling'' method offers a more affordable solution to this problem: within a survey footprint covering a given survey volume, V{sub survey}, we observe only a fraction of the volume. The distribution of observed regions should be chosen such that their separation is smaller than the length scale corresponding to the wavenumber of interest. Then one can recover the power spectrum of galaxies with precision expected for a survey covering a volume of V{sub survey} (rather than the volume of the sum of observed regions) with the number density of galaxies given by the total number of observed galaxies divided by V{sub survey} (rather than the number density of galaxies within an observed region). We find that regularly-spaced sampling yields an unbiased power spectrum with no window function effect, and deviations from regularly-spaced sampling, which are unavoidable in realistic surveys, introduce calculable window function effects and increase the uncertainties of the recovered power spectrum. On the other hand, we show that the two-point correlation function (pair counting) is not affected by sparse sampling. While we discuss the sparse sampling method within the context of the forthcoming Hobby-Eberly Telescope Dark Energy Experiment, the method is general and can be applied to other galaxy surveys.
Dimensionality and geological implications of a sparse magnetotelluric dataset
NASA Astrophysics Data System (ADS)
Derosier, B.; Dennis, K. N.; Plata Martinez, R. O.; Montahaei, M.; Bedrosian, P.; Pellerin, L.
2014-12-01
High-quality broadband magnetotelluric (MT) data (0.01-1000 s period) were acquired at four stations in Borrego Canyon within the Santo Domingo Basin of the Rio Grande Rift, New Mexico, during the 2014 Summer of Applied Geophysical Experience (SAGE) field program. MT response functions along the 10-km long NW trending profile are nearly identical with all stations showing a distinct mode split at 10 s period, suggesting a significant conductivity contrast is located roughly 10-20 km away from the profile based on skin depth estimates. Audiomagnetotelluric, polar diagrams, impedance skew, induction vectors and phase tensor analysis all indicate one-dimensional (1-D) structure at periods <10 sec, a predominantly two-dimensional (2-D) structure at intermediate depths (10-100 s) with a 60° geoelectric strike, and three-dimensional (3-D) structure at the longest periods. Inverse modeling of the data from 0.01-10 s reveals a three-layer electrical structure: a moderately resistive sediments from the surface to ~750 m depth, a conductive layer (weathered volcanoclastics) to 4 km depth, and below 4 km a highly resistive basement of Mesozoic and Precambrian rocks. A 2-D inverse model converged, but resulted in physically unrealistic structure. Hence a 3-D forward model study was performed using the 2014 data together with three additional MT stations acquired further to the east during SAGE 2010. Models that include a NE-trending conductive structure to the north of the profile show broad consistency between the measured and synthetic phase tensors and impedances. We infer our MT data to be on the conductive side of this contact, with the resistive material to the NW attributed to a heavily intruded crust beneath the Jemez lineament, and possibly the edge of the thick lithosphere beneath the Colorado Plateau. 3-D inversion of this sparse data set is being carried out to determine whether this conceptual model is consistent with the full impedance tensor and tipper data.
High efficiency beam splitting for H/sup -/ accelerators
Kramer, S.L.; Stipp, V.; Krieger, C.; Madsen, J.
1985-01-01
Beam splitting for high energy accelerators has typically involved a significant loss of beam and radiation. This paper reports on a new method of splitting beams for H/sup -/ accelerators. This technique uses a high intensity flash of light to strip a fraction of the H/sup -/ beam to H/sup 0/ which are then easily separated by a small bending magnet. A system using a 900-watt (average electrical power) flashlamp and a highly efficient collector will provide 10/sup -3/ to 10/sup -2/ splitting of a 50 MeV H/sup -/ beam. Results on the operation and comparisons with stripping cross sections are presented. Also discussed is the possibility for developing this system to yield a higher stripping fraction.
Manipulating the spin-dependent splitting by geometric Doppler effect.
Liu, Yachao; Ke, Yougang; Zhou, Junxiao; Luo, Hailu; Wen, Shuangchun
2015-06-29
We report the manipulation of spin-dependent splitting by geometric Doppler effect based on dielectric metasurfaces. The extrapolation of rotational Doppler effect from temporal to spatial coordinate gives the phase change when the local optical axes of dielectric metasurfaces are rotating in space. Therefore, the continuous variation of local optical axes in a certain direction will introduce a phase gradient in the same direction at the beam cross section. This is additive to the phase gradient appeared when breaking the rotational symmetry of linearly polarized cylindrical vector beams, which leads to the deflections of different spin components of light, i.e., photonic spin Hall effect. Hence, it is possible to manipulate the spin-dependent splitting by introducing the geometric Doppler effect. Theoretically and experimentally, we show that the magnitude and orientation of the spin-dependent splitting are both tunable when changing the spatial rotation rate of local optical axes and incident polarization. PMID:26191680
Parallel preconditioning techniques for sparse CG solvers
Basermann, A.; Reichel, B.; Schelthoff, C.
1996-12-31
Conjugate gradient (CG) methods to solve sparse systems of linear equations play an important role in numerical methods for solving discretized partial differential equations. The large size and the condition of many technical or physical applications in this area result in the need for efficient parallelization and preconditioning techniques of the CG method. In particular for very ill-conditioned matrices, sophisticated preconditioner are necessary to obtain both acceptable convergence and accuracy of CG. Here, we investigate variants of polynomial and incomplete Cholesky preconditioners that markedly reduce the iterations of the simply diagonally scaled CG and are shown to be well suited for massively parallel machines.
Distributed memory compiler design for sparse problems
NASA Technical Reports Server (NTRS)
Wu, Janet; Saltz, Joel; Berryman, Harry; Hiranandani, Seema
1991-01-01
A compiler and runtime support mechanism is described and demonstrated. The methods presented are capable of solving a wide range of sparse and unstructured problems in scientific computing. The compiler takes as input a FORTRAN 77 program enhanced with specifications for distributing data, and the compiler outputs a message passing program that runs on a distributed memory computer. The runtime support for this compiler is a library of primitives designed to efficiently support irregular patterns of distributed array accesses and irregular distributed array partitions. A variety of Intel iPSC/860 performance results obtained through the use of this compiler are presented.
Guided wavefield reconstruction from sparse measurements
NASA Astrophysics Data System (ADS)
Mesnil, Olivier; Ruzzene, Massimo
2016-02-01
Guided wave measurements are at the basis of several Non-Destructive Evaluation (NDE) techniques. Although sparse measurements of guided wave obtained using piezoelectric sensors can efficiently detect and locate defects, extensive informa-tion on the shape and subsurface location of defects can be extracted from full-field measurements acquired by Laser Doppler Vibrometers (LDV). Wavefield acquisition from LDVs is generally a slow operation due to the fact that the wave propagation to record must be repeated for each point measurement and the initial conditions must be reached between each measurement. In this research, a Sparse Wavefield Reconstruction (SWR) process using Compressed Sensing is developed. The goal of this technique is to reduce the number of point measurements needed to apply NDE techniques by at least one order of magnitude by extrapolating the knowledge of a few randomly chosen measured pixels over an over-sampled grid. To achieve this, the Lamb wave propagation equation is used to formulate a basis of shape functions in which the wavefield has a sparse representation, in order to comply with the Compressed Sensing requirements and use l1-minimization solvers. The main assumption of this reconstruction process is that every material point of the studied area is a potential source. The Compressed Sensing matrix is defined as being the contribution that would have been received at a measurement location from each possible source, using the dispersion relations of the specimen computed using a Semi-Analytical Finite Element technique. The measurements are then processed through an l1-minimizer to find a minimum corresponding to the set of active sources and their corresponding excitation functions. This minimum represents the best combination of the parameters of the model matching the sparse measurements. Wavefields are then reconstructed using the propagation equation. The set of active sources found by minimization contains all the wave
Sparse Multivariate Regression With Covariance Estimation
Rothman, Adam J.; Levina, Elizaveta; Zhu, Ji
2014-01-01
We propose a procedure for constructing a sparse estimator of a multivariate regression coefficient matrix that accounts for correlation of the response variables. This method, which we call multivariate regression with covariance estimation (MRCE), involves penalized likelihood with simultaneous estimation of the regression coefficients and the covariance structure. An efficient optimization algorithm and a fast approximation are developed for computing MRCE. Using simulation studies, we show that the proposed method outperforms relevant competitors when the responses are highly correlated. We also apply the new method to a finance example on predicting asset returns. An R-package containing this dataset and code for computing MRCE and its approximation are available online. PMID:24963268
A view of Kanerva's sparse distributed memory
NASA Technical Reports Server (NTRS)
Denning, P. J.
1986-01-01
Pentti Kanerva is working on a new class of computers, which are called pattern computers. Pattern computers may close the gap between capabilities of biological organisms to recognize and act on patterns (visual, auditory, tactile, or olfactory) and capabilities of modern computers. Combinations of numeric, symbolic, and pattern computers may one day be capable of sustaining robots. The overview of the requirements for a pattern computer, a summary of Kanerva's Sparse Distributed Memory (SDM), and examples of tasks this computer can be expected to perform well are given.
Sparse dynamics for partial differential equations
Schaeffer, Hayden; Caflisch, Russel; Hauck, Cory D.; Osher, Stanley
2013-01-01
We investigate the approximate dynamics of several differential equations when the solutions are restricted to a sparse subset of a given basis. The restriction is enforced at every time step by simply applying soft thresholding to the coefficients of the basis approximation. By reducing or compressing the information needed to represent the solution at every step, only the essential dynamics are represented. In many cases, there are natural bases derived from the differential equations, which promote sparsity. We find that our method successfully reduces the dynamics of convection equations, diffusion equations, weak shocks, and vorticity equations with high-frequency source terms. PMID:23533273
Effective dimension reduction for sparse functional data
YAO, F.; LEI, E.; WU, Y.
2015-01-01
Summary We propose a method of effective dimension reduction for functional data, emphasizing the sparse design where one observes only a few noisy and irregular measurements for some or all of the subjects. The proposed method borrows strength across the entire sample and provides a way to characterize the effective dimension reduction space, via functional cumulative slicing. Our theoretical study reveals a bias-variance trade-off associated with the regularizing truncation and decaying structures of the predictor process and the effective dimension reduction space. A simulation study and an application illustrate the superior finite-sample performance of the method. PMID:26566293
Photosynthetic water splitting: 1987 annual report
Greenbaum, E.
1988-01-01
This document is an annual report of photosynthetic water splitting for the production of hydrogen and oxygen. Unicellular green algae are capable of evolving molecular hydrogen in the presence of carbon dioxide. Controlling factors that determine hydrogen evolution are either temperature or light intensity. Also, mutants of the green alga Chlamydomonas are capable of evolving hydrogen in the presence of carbon dioxide. The significance of these discoveries is that the presence of carbon dioxide (or bicarbonate) is a key factor in determining the activity of the Photosystem II water splitting complex. Second, a new advance in oxygen sensor technology has been made that, for the first time, allows the absolute measurement of photosynthetically evolved oxygen from a single colony of microalgae growing on a solidified agar medium. The key aspect of this electrochemical sensor is the utilization of ultra-pure potassium hydroxide as the electrolyte and a recognition of the role that electrolyte impurities play in contributing to base line noise. 9 refs., 8 figs., 2 tabs.
Locality Constrained Joint Dynamic Sparse Representation for Local Matching Based Face Recognition
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. PMID:25419662
ERIC Educational Resources Information Center
Carter, Roy A.
1972-01-01
Describes a procedure useful for investigating the effects of substances on plant growth and development. A bean seedling's stem is partially split, and each half is placed in a different nutrient solution. Suggestions for the instructional use of the technique are made. (AL)
Iterative support detection-based split Bregman method for wavelet frame-based image inpainting.
He, Liangtian; Wang, Yilun
2014-12-01
The wavelet frame systems have been extensively studied due to their capability of sparsely approximating piece-wise smooth functions, such as images, and the corresponding wavelet frame-based image restoration models are mostly based on the penalization of the l1 norm of wavelet frame coefficients for sparsity enforcement. In this paper, we focus on the image inpainting problem based on the wavelet frame, propose a weighted sparse restoration model, and develop a corresponding efficient algorithm. The new algorithm combines the idea of iterative support detection method, first proposed by Wang and Yin for sparse signal reconstruction, and the split Bregman method for wavelet frame l1 model of image inpainting, and more important, naturally makes use of the specific multilevel structure of the wavelet frame coefficients to enhance the recovery quality. This new algorithm can be considered as the incorporation of prior structural information of the wavelet frame coefficients into the traditional l1 model. Our numerical experiments show that the proposed method is superior to the original split Bregman method for wavelet frame-based l1 norm image inpainting model as well as some typical l(p) (0 ≤ p < 1) norm-based nonconvex algorithms such as mean doubly augmented Lagrangian method, in terms of better preservation of sharp edges, due to their failing to make use of the structure of the wavelet frame coefficients. PMID:25312924
OSKI: A Library of Automatically Tuned Sparse Matrix Kernels
Vuduc, R; Demmel, J W; Yelick, K A
2005-07-19
The Optimized Sparse Kernel Interface (OSKI) is a collection of low-level primitives that provide automatically tuned computational kernels on sparse matrices, for use by solver libraries and applications. These kernels include sparse matrix-vector multiply and sparse triangular solve, among others. The primary aim of this interface is to hide the complex decision-making process needed to tune the performance of a kernel implementation for a particular user's sparse matrix and machine, while also exposing the steps and potentially non-trivial costs of tuning at run-time. This paper provides an overview of OSKI, which is based on our research on automatically tuned sparse kernels for modern cache-based superscalar machines.
Neonatal Atlas Construction Using Sparse Representation
Shi, Feng; Wang, Li; Wu, Guorong; Li, Gang; Gilmore, John H.; Lin, Weili; Shen, Dinggang
2014-01-01
Atlas construction generally includes first an image registration step to normalize all images into a common space and then an atlas building step to fuse the information from all the aligned images. Although numerous atlas construction studies have been performed to improve the accuracy of the image registration step, unweighted or simply weighted average is often used in the atlas building step. In this article, we propose a novel patch-based sparse representation method for atlas construction after all images have been registered into the common space. By taking advantage of local sparse representation, more anatomical details can be recovered in the built atlas. To make the anatomical structures spatially smooth in the atlas, the anatomical feature constraints on group structure of representations and also the overlapping of neighboring patches are imposed to ensure the anatomical consistency between neighboring patches. The proposed method has been applied to 73 neonatal MR images with poor spatial resolution and low tissue contrast, for constructing a neonatal brain atlas with sharp anatomical details. Experimental results demonstrate that the proposed method can significantly enhance the quality of the constructed atlas by discovering more anatomical details especially in the highly convoluted cortical regions. The resulting atlas demonstrates superior performance of our atlas when applied to spatially normalizing three different neonatal datasets, compared with other start-of-the-art neonatal brain atlases. PMID:24638883
Inferring sparse networks for noisy transient processes.
Tran, Hoang M; Bukkapatnam, Satish T S
2016-01-01
Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the l1-min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of l1-min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues. PMID:26916813
Iterative Sparse Approximation of the Gravitational Potential
NASA Astrophysics Data System (ADS)
Telschow, R.
2012-04-01
In recent applications in the approximation of gravitational potential fields, several new challenges arise. We are concerned with a huge quantity of data (e.g. in case of the Earth) or strongly irregularly distributed data points (e.g. in case of the Juno mission to Jupiter), where both of these problems bring the established approximation methods to their limits. Our novel method, which is a matching pursuit, however, iteratively chooses a best basis out of a large redundant family of trial functions to reconstruct the signal. It is independent of the data points which makes it possible to take into account a much higher amount of data and, furthermore, handle irregularly distributed data, since the algorithm is able to combine arbitrary spherical basis functions, i.e., global as well as local trial functions. This additionaly results in a solution, which is sparse in the sense that it features more basis functions where the signal has a higher local detail density. Summarizing, we get a method which reconstructs large quantities of data with a preferably low number of basis functions, combining global as well as several localizing functions to a sparse basis and a solution which is locally adapted to the data density and also to the detail density of the signal.
A density functional for sparse matter
NASA Astrophysics Data System (ADS)
Langreth, D. C.; Lundqvist, B. I.; Chakarova-Käck, S. D.; Cooper, V. R.; Dion, M.; Hyldgaard, P.; Kelkkanen, A.; Kleis, J.; Kong, Lingzhu; Li, Shen; Moses, P. G.; Murray, E.; Puzder, A.; Rydberg, H.; Schröder, E.; Thonhauser, T.
2009-02-01
Sparse matter is abundant and has both strong local bonds and weak nonbonding forces, in particular nonlocal van der Waals (vdW) forces between atoms separated by empty space. It encompasses a broad spectrum of systems, like soft matter, adsorption systems and biostructures. Density-functional theory (DFT), long since proven successful for dense matter, seems now to have come to a point, where useful extensions to sparse matter are available. In particular, a functional form, vdW-DF (Dion et al 2004 Phys. Rev. Lett. 92 246401; Thonhauser et al 2007 Phys. Rev. B 76 125112), has been proposed for the nonlocal correlations between electrons and applied to various relevant molecules and materials, including to those layered systems like graphite, boron nitride and molybdenum sulfide, to dimers of benzene, polycyclic aromatic hydrocarbons (PAHs), doped benzene, cytosine and DNA base pairs, to nonbonding forces in molecules, to adsorbed molecules, like benzene, naphthalene, phenol and adenine on graphite, alumina and metals, to polymer and carbon nanotube (CNT) crystals, and hydrogen storage in graphite and metal-organic frameworks (MOFs), and to the structure of DNA and of DNA with intercalators. Comparison with results from wavefunction calculations for the smaller systems and with experimental data for the extended ones show the vdW-DF path to be promising. This could have great ramifications.
Inferring sparse networks for noisy transient processes
NASA Astrophysics Data System (ADS)
Tran, Hoang M.; Bukkapatnam, Satish T. S.
2016-02-01
Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the -min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of -min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues.
Inferring sparse networks for noisy transient processes
Tran, Hoang M.; Bukkapatnam, Satish T.S.
2016-01-01
Inferring causal structures of real world complex networks from measured time series signals remains an open issue. The current approaches are inadequate to discern between direct versus indirect influences (i.e., the presence or absence of a directed arc connecting two nodes) in the presence of noise, sparse interactions, as well as nonlinear and transient dynamics of real world processes. We report a sparse regression (referred to as the -min) approach with theoretical bounds on the constraints on the allowable perturbation to recover the network structure that guarantees sparsity and robustness to noise. We also introduce averaging and perturbation procedures to further enhance prediction scores (i.e., reduce inference errors), and the numerical stability of -min approach. Extensive investigations have been conducted with multiple benchmark simulated genetic regulatory network and Michaelis-Menten dynamics, as well as real world data sets from DREAM5 challenge. These investigations suggest that our approach can significantly improve, oftentimes by 5 orders of magnitude over the methods reported previously for inferring the structure of dynamic networks, such as Bayesian network, network deconvolution, silencing and modular response analysis methods based on optimizing for sparsity, transients, noise and high dimensionality issues. PMID:26916813
Topological sparse learning of dynamic form patterns.
Guthier, T; Willert, V; Eggert, J
2015-01-01
Motion is a crucial source of information for a variety of tasks in social interactions. The process of how humans recognize complex articulated movements such as gestures or face expressions remains largely unclear. There is an ongoing discussion if and how explicit low-level motion information, such as optical flow, is involved in the recognition process. Motivated by this discussion, we introduce a computational model that classifies the spatial configuration of gradient and optical flow patterns. The patterns are learned with an unsupervised learning algorithm based on translation-invariant nonnegative sparse coding called VNMF that extracts prototypical optical flow patterns shaped, for example, as moving heads or limb parts. A key element of the proposed system is a lateral inhibition term that suppresses activations of competing patterns in the learning process, leading to a low number of dominant and topological sparse activations. We analyze the classification performance of the gradient and optical flow patterns on three real-world human action recognition and one face expression recognition data set. The results indicate that the recognition of human actions can be achieved by gradient patterns alone, but adding optical flow patterns increases the classification performance. The combined patterns outperform other biological-inspired models and are competitive with current computer vision approaches. PMID:25248088
Towards robust topology of sparsely sampled data.
Correa, Carlos D; Lindstrom, Peter
2011-12-01
Sparse, irregular sampling is becoming a necessity for reconstructing large and high-dimensional signals. However, the analysis of this type of data remains a challenge. One issue is the robust selection of neighborhoods--a crucial part of analytic tools such as topological decomposition, clustering and gradient estimation. When extracting the topology of sparsely sampled data, common neighborhood strategies such as k-nearest neighbors may lead to inaccurate results, either due to missing neighborhood connections, which introduce false extrema, or due to spurious connections, which conceal true extrema. Other neighborhoods, such as the Delaunay triangulation, are costly to compute and store even in relatively low dimensions. In this paper, we address these issues. We present two new types of neighborhood graphs: a variation on and a generalization of empty region graphs, which considerably improve the robustness of neighborhood-based analysis tools, such as topological decomposition. Our findings suggest that these neighborhood graphs lead to more accurate topological representations of low- and high- dimensional data sets at relatively low cost, both in terms of storage and computation time. We describe the implications of our work in the analysis and visualization of scalar functions, and provide general strategies for computing and applying our neighborhood graphs towards robust data analysis. PMID:22034302
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.
SKS splitting beneath the eastern United States from Transportable Array data
NASA Astrophysics Data System (ADS)
McNamara, J.; Jackson, K.; Long, M. D.
2014-12-01
Previous studies of SKS splitting beneath the eastern United States have produced evidence for complex and laterally variable anisotropy, including regions that are dominated by null SKS arrivals and those that include multiple layers of anisotropy. One key question is to what extent SKS splitting reflects anisotropy due to present-day flow in the asthenospheric mantle, fossilized anisotropy in the mantle lithosphere due to past deformation processes, or a combination of the two. The broadband station coverage in the eastern US has historically been sparse, but the arrival of the EarthScope USArray Transportable Array (TA) in the eastern US provides an unprecedented opportunity to probe upper mantle anisotropy in detail. Here we present a set of ~3800 SKS splitting measurements at ~380 TA stations east of ~85°W. We identify predominantly null (that is, non-split) SKS arrivals at eastern US stations, with many stations (particularly in the southeastern US) exhibiting clear null arrivals over a wide range of backazimuths. Stations located in the Appalachian Mountains tend to exhibit well-resolved splitting, with typical delay times of ~1 sec and fast directions that are generally parallel to the strike of the mountain range. In the northern part of the study area, we observe more split arrivals, with laterally variable fast directions. Splitting patterns at many individual stations exhibit backazimuthal variations that are consistent with multiple layers of anisotropy, suggesting contributions from both the lithosphere and asthenosphere. Comparisons between fast splitting directions and indicators such as topography, magnetic and gravity anomalies, upper mantle structure derived from tomography, and absolute plate motions reveal that the relative contributions from lithospheric and asthenospheric anisotropy likely vary laterally beneath the eastern US.
Cool covered sky-splitting spectrum-splitting FK
Mohedano, Rubén; Chaves, Julio; Falicoff, Waqidi; Hernandez, Maikel; Sorgato, Simone; Miñano, Juan C.; Benitez, Pablo; Buljan, Marina
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 for 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.
Feature Selection and Pedestrian Detection Based on Sparse Representation.
Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei
2015-01-01
Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony. PMID:26295480
Genetic apertures: an improved sparse aperture design framework.
Salvaggio, Philip S; Schott, John R; McKeown, Donald M
2016-04-20
The majority of optical sparse aperture imaging research in the remote sensing field has been confined to a small set of aperture layouts. While these layouts possess some desirable properties for imaging, they may not be ideal for all applications. This work introduces an optimization framework for sparse aperture layouts based on genetic algorithms as well as a small set of fitness functions for incoherent sparse aperture image quality. The optimization results demonstrate the merits of existing designs and the opportunity for creating new sparse aperture layouts. PMID:27140086
Feature Selection and Pedestrian Detection Based on Sparse Representation
Yao, Shihong; Wang, Tao; Shen, Weiming; Pan, Shaoming; Chong, Yanwen; Ding, Fei
2015-01-01
Pedestrian detection have been currently devoted to the extraction of effective pedestrian features, which has become one of the obstacles in pedestrian detection application according to the variety of pedestrian features and their large dimension. Based on the theoretical analysis of six frequently-used features, SIFT, SURF, Haar, HOG, LBP and LSS, and their comparison with experimental results, this paper screens out the sparse feature subsets via sparse representation to investigate whether the sparse subsets have the same description abilities and the most stable features. When any two of the six features are fused, the fusion feature is sparsely represented to obtain its important components. Sparse subsets of the fusion features can be rapidly generated by avoiding calculation of the corresponding index of dimension numbers of these feature descriptors; thus, the calculation speed of the feature dimension reduction is improved and the pedestrian detection time is reduced. Experimental results show that sparse feature subsets are capable of keeping the important components of these six feature descriptors. The sparse features of HOG and LSS possess the same description ability and consume less time compared with their full features. The ratios of the sparse feature subsets of HOG and LSS to their full sets are the highest among the six, and thus these two features can be used to best describe the characteristics of the pedestrian and the sparse feature subsets of the combination of HOG-LSS show better distinguishing ability and parsimony. PMID:26295480
QIAN,S.TAKACS,P.
2003-08-03
A beam splitter to create two separated parallel beams is a critical unit of a pencil beam interferometer, for example the long trace profiler (LTP). The operating principle of the beam splitter can be based upon either amplitude-splitting (AS) or wavefront-splitting (WS). For precision measurements with the LTP, an equal optical path system with two parallel beams is desired. Frequency drift of the light source in a non-equal optical path system will cause the interference fringes to drift. An equal optical path prism beam splitter with an amplitude-splitting (AS-EBS) beam splitter and a phase shift beam splitter with a wavefront-splitting (WS-PSBS) are introduced. These beam splitters are well suited to the stability requirement for a pencil beam interferometer due to the characteristics of monolithic structure and equal optical path. Several techniques to produce WS-PSBS by hand are presented. In addition, the WS-PSBS using double thin plates, made from microscope cover plates, has great advantages of economy, convenience, availability and ease of adjustment over other beam splitting methods. Comparison of stability measurements made with the AS-EBS, WS-PSBS, and other beam splitters is presented.
Dose-shaping using targeted sparse optimization
Sayre, George A.; Ruan, Dan
2013-07-15
Purpose: Dose volume histograms (DVHs) are common tools in radiation therapy treatment planning to characterize plan quality. As statistical metrics, DVHs provide a compact summary of the underlying plan at the cost of losing spatial information: the same or similar dose-volume histograms can arise from substantially different spatial dose maps. This is exactly the reason why physicians and physicists scrutinize dose maps even after they satisfy all DVH endpoints numerically. However, up to this point, little has been done to control spatial phenomena, such as the spatial distribution of hot spots, which has significant clinical implications. To this end, the authors propose a novel objective function that enables a more direct tradeoff between target coverage, organ-sparing, and planning target volume (PTV) homogeneity, and presents our findings from four prostate cases, a pancreas case, and a head-and-neck case to illustrate the advantages and general applicability of our method.Methods: In designing the energy minimization objective (E{sub tot}{sup sparse}), the authors utilized the following robust cost functions: (1) an asymmetric linear well function to allow differential penalties for underdose, relaxation of prescription dose, and overdose in the PTV; (2) a two-piece linear function to heavily penalize high dose and mildly penalize low and intermediate dose in organs-at risk (OARs); and (3) a total variation energy, i.e., the L{sub 1} norm applied to the first-order approximation of the dose gradient in the PTV. By minimizing a weighted sum of these robust costs, general conformity to dose prescription and dose-gradient prescription is achieved while encouraging prescription violations to follow a Laplace distribution. In contrast, conventional quadratic objectives are associated with a Gaussian distribution of violations, which is less forgiving to large violations of prescription than the Laplace distribution. As a result, the proposed objective E{sub tot
Code of Federal Regulations, 2013 CFR
2013-01-01
....2002 Split shell. Split shell means a shell having any crack which is open and conspicuous for a distance of more than one-fourth the circumference of the shell, measured in the direction of the crack....
Stability of split Stirling refrigerators
NASA Astrophysics Data System (ADS)
de Waele, A. T. A. M.; Liang, W.
2009-02-01
In many thermal systems spontaneous mechanical oscillations are generated under the influence of large temperature gradients. Well-known examples are Taconis oscillations in liquid-helium cryostats and oscillations in thermoacoustic systems. In split Stirling refrigerators the compressor and the cold finger are connected by a flexible tube. The displacer in the cold head is suspended by a spring. Its motion is pneumatically driven by the pressure oscillations generated by the compressor. In this paper we give the basic dynamic equations of split Stirling refrigerators and investigate the possibility of spontaneous mechanical oscillations if a large temperature gradient develops in the cold finger, e.g. during or after cool down. These oscillations would be superimposed on the pressure oscillations of the compressor and could ruin the cooler performance.
Backscattering analysis in optical micro-resonators with mode splitting based on COMSOL
NASA Astrophysics Data System (ADS)
Yang, Zhaohua; Huo, Jiayan; Yang, Xu
2015-10-01
Rayleigh backscattering noise, which is one of the reasons that limit the sensitivity, has been deemed as noise in traditional resonant optic gyroscopes. However Rayleigh backscattering noise is one of the incentives of mode splitting phenomenon in high-Q resonators. Regarding the change of the resonance frequency of the resonator caused by the scattering signal as a measurement, we can use mode splitting to measure temperature, size of nanoparticle, etc. Light is confined by total internal reflection in whispering gallery mode (WGM) optical resonators, which is characterized by high-Q factors and small mode volumes. With regards to this, we propose a sensing mechanism based on mode splitting in high-Q WGM optical resonators. It is possible for us to measure the angular velocity of carrier according to the changes in the resonant frequencies of the two splitting modes. We propose the Miniature resonant optic gyroscope based on mode splitting (MROG-MS) with WGM resonators in the paper. Considering the Sagnac effect, mode splitting in high quality optical micro-resonators, and the rotation-induced impact on backscattering process, we modify the equations of motion that describe mode splitting, derive the explicit expression of angular rate versus the splitting amount, and verify the sensing mechanism by the simulation based on COMSOL. Furthermore, after monitoring the transmission spectra at different number of scattering particles, the simulation shows that mode splitting phenomenon resulted by single particle is more suitable for angular velocity measurement.
Sparse distributed memory and related models
NASA Technical Reports Server (NTRS)
Kanerva, Pentti
1992-01-01
Described here is sparse distributed memory (SDM) as a neural-net associative memory. It is characterized by two weight matrices and by a large internal dimension - the number of hidden units is much larger than the number of input or output units. The first matrix, A, is fixed and possibly random, and the second matrix, C, is modifiable. The SDM is compared and contrasted to (1) computer memory, (2) correlation-matrix memory, (3) feet-forward artificial neural network, (4) cortex of the cerebellum, (5) Marr and Albus models of the cerebellum, and (6) Albus' cerebellar model arithmetic computer (CMAC). Several variations of the basic SDM design are discussed: the selected-coordinate and hyperplane designs of Jaeckel, the pseudorandom associative neural memory of Hassoun, and SDM with real-valued input variables by Prager and Fallside. SDM research conducted mainly at the Research Institute for Advanced Computer Science (RIACS) in 1986-1991 is highlighted.
Evolutionary induction of sparse neural trees
Zhang; Ohm; Muhlenbein
1997-01-01
This paper is concerned with the automatic induction of parsimonious neural networks. In contrast to other program induction situations, network induction entails parametric learning as well as structural adaptation. We present a novel representation scheme called neural trees that allows efficient learning of both network architectures and parameters by genetic search. A hybrid evolutionary method is developed for neural tree induction that combines genetic programming and the breeder genetic algorithm under the unified framework of the minimum description length principle. The method is successfully applied to the induction of higher order neural trees while still keeping the resulting structures sparse to ensure good generalization performance. Empirical results are provided on two chaotic time series prediction problems of practical interest. PMID:10021759
ESTIMATION OF FUNCTIONALS OF SPARSE COVARIANCE MATRICES
Fan, Jianqing; Rigollet, Philippe; Wang, Weichen
2016-01-01
High-dimensional statistical tests often ignore correlations to gain simplicity and stability leading to null distributions that depend on functionals of correlation matrices such as their Frobenius norm and other ℓr norms. Motivated by the computation of critical values of such tests, we investigate the difficulty of estimation the functionals of sparse correlation matrices. Specifically, we show that simple plug-in procedures based on thresholded estimators of correlation matrices are sparsity-adaptive and minimax optimal over a large class of correlation matrices. Akin to previous results on functional estimation, the minimax rates exhibit an elbow phenomenon. Our results are further illustrated in simulated data as well as an empirical study of data arising in financial econometrics. PMID:26806986
Eigensolver for a Sparse, Large Hermitian Matrix
NASA Technical Reports Server (NTRS)
Tisdale, E. Robert; Oyafuso, Fabiano; Klimeck, Gerhard; Brown, R. Chris
2003-01-01
A parallel-processing computer program finds a few eigenvalues in a sparse Hermitian matrix that contains as many as 100 million diagonal elements. This program finds the eigenvalues faster, using less memory, than do other, comparable eigensolver programs. This program implements a Lanczos algorithm in the American National Standards Institute/ International Organization for Standardization (ANSI/ISO) C computing language, using the Message Passing Interface (MPI) standard to complement an eigensolver in PARPACK. [PARPACK (Parallel Arnoldi Package) is an extension, to parallel-processing computer architectures, of ARPACK (Arnoldi Package), which is a collection of Fortran 77 subroutines that solve large-scale eigenvalue problems.] The eigensolver runs on Beowulf clusters of computers at the Jet Propulsion Laboratory (JPL).
Multiplication method for sparse interferometric fringes.
Liu, Cong; Zhang, Xingyi; Zhou, Youhe
2016-04-01
Fringe analysis in the interferometry has been of long-standing interest to the academic community. However, the process of sparse fringe is always a headache in the measurement, especially when the specimen is very small. Through theoretical derivation and experimental measurements, our work demonstrates a new method for fringe multiplication. Theoretically, arbitrary integral-multiple fringe multiplication can be acquired by using the interferogram phase as the parameter. We simulate digital images accordingly and find that not only the skeleton lines of the multiplied fringe are very convenient to extract, but also the main frequency of which can be easily separated from the DC component. Meanwhile, the experimental results have a good agreement with the theoretic ones in a validation using the classical photoelasticity. PMID:27137055
Predicting structure in nonsymmetric sparse matrix factorizations
Gilbert, J.R.; Ng, E.
1991-12-31
Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.
Predicting structure in nonsymmetric sparse matrix factorizations
Gilbert, J.R. ); Ng, E. )
1991-01-01
Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.
Integer sparse distributed memory: analysis and results.
Snaider, Javier; Franklin, Stan; Strain, Steve; George, E Olusegun
2013-10-01
Sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean vectors. Here we present an extension of the original SDM, the Integer SDM that uses modular arithmetic integer vectors rather than binary vectors. This extension preserves many of the desirable properties of the original SDM: auto-associativity, content addressability, distributed storage, and robustness over noisy inputs. In addition, it improves the representation capabilities of the memory and is more robust over normalization. It can also be extended to support forgetting and reliable sequence storage. We performed several simulations that test the noise robustness property and capacity of the memory. Theoretical analyses of the memory's fidelity and capacity are also presented. PMID:23747569
Functional fixedness in a technologically sparse culture.
German, Tim P; Barrett, H Clark
2005-01-01
Problem solving can be inefficient when the solution requires subjects to generate an atypical function for an object and the object's typical function has been primed. Subjects become "fixed" on the design function of the object, and problem solving suffers relative to control conditions in which the object's function is not demonstrated. In the current study, such functional fixedness was demonstrated in a sample of adolescents (mean age of 16 years) among the Shuar of Ecuadorian Amazonia, whose technologically sparse culture provides limited access to large numbers of artifacts with highly specialized functions. This result suggests that design function may universally be the core property of artifact concepts in human semantic memory. PMID:15660843
Predicting structure in nonsymmetric sparse matrix factorizations
Gilbert, J.R. ); Ng, E.G. )
1992-10-01
Many computations on sparse matrices have a phase that predicts the nonzero structure of the output, followed by a phase that actually performs the numerical computation. We study structure prediction for computations that involve nonsymmetric row and column permutations and nonsymmetric or non-square matrices. Our tools are bipartite graphs, matchings, and alternating paths. Our main new result concerns LU factorization with partial pivoting. We show that if a square matrix A has the strong Hall property (i.e., is fully indecomposable) then an upper bound due to George and Ng on the nonzero structure of L + U is as tight as possible. To show this, we prove a crucial result about alternating paths in strong Hall graphs. The alternating-paths theorem seems to be of independent interest: it can also be used to prove related results about structure prediction for QR factorization that are due to Coleman, Edenbrandt, Gilbert, Hare, Johnson, Olesky, Pothen, and van den Driessche.
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less
Fast generation of sparse random kernel graphs
Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo
2015-09-10
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.
Fast Generation of Sparse Random Kernel Graphs
2015-01-01
The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most 𝒪(n(logn)2). As a practical example we show how to generate samples of power-law degree distribution graphs with tunable assortativity. PMID:26356296
Testing split supersymmetry with inflation
NASA Astrophysics Data System (ADS)
Craig, Nathaniel; Green, Daniel
2014-07-01
Split supersymmetry (SUSY) — in which SUSY is relevant to our universe but largely inaccessible at current accelerators — has become increasingly plausible given the absence of new physics at the LHC, the success of gauge coupling unification, and the observed Higgs mass. Indirect probes of split SUSY such as electric dipole moments (EDMs) and flavor violation offer hope for further evidence but are ultimately limited in their reach. Inflation offers an alternate window into SUSY through the direct production of superpartners during inflation. These particles are capable of leaving imprints in future cosmological probes of primordial non-gaussianity. Given the recent observations of BICEP2, the scale of inflation is likely high enough to probe the full range of split SUSY scenarios and therefore offers a unique advantage over low energy probes. The key observable for future experiments is equilateral non-gaussianity, which will be probed by both cosmic microwave background (CMB) and large scale structure (LSS) surveys. In the event of a detection, we forecast our ability to find evidence for superpartners through the scaling behavior in the squeezed limit of the bispectrum.
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.
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...
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...
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...
Encoding Cortical Dynamics in Sparse Features
Khan, Sheraz; Lefèvre, Julien; Baillet, Sylvain; Michmizos, Konstantinos P.; Ganesan, Santosh; Kitzbichler, Manfred G.; Zetino, Manuel; Hämäläinen, Matti S.; Papadelis, Christos; Kenet, Tal
2014-01-01
Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz–Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical
Bayesian learning of sparse multiscale image representations.
Hughes, James Michael; Rockmore, Daniel N; Wang, Yang
2013-12-01
Multiscale representations of images have become a standard tool in image analysis. Such representations offer a number of advantages over fixed-scale methods, including the potential for improved performance in denoising, compression, and the ability to represent distinct but complementary information that exists at various scales. A variety of multiresolution transforms exist, including both orthogonal decompositions such as wavelets as well as nonorthogonal, overcomplete representations. Recently, techniques for finding adaptive, sparse representations have yielded state-of-the-art results when applied to traditional image processing problems. Attempts at developing multiscale versions of these so-called dictionary learning models have yielded modest but encouraging results. However, none of these techniques has sought to combine a rigorous statistical formulation of the multiscale dictionary learning problem and the ability to share atoms across scales. We present a model for multiscale dictionary learning that overcomes some of the drawbacks of previous approaches by first decomposing an input into a pyramid of distinct frequency bands using a recursive filtering scheme, after which we perform dictionary learning and sparse coding on the individual levels of the resulting pyramid. The associated image model allows us to use a single set of adapted dictionary atoms that is shared--and learned--across all scales in the model. The underlying statistical model of our proposed method is fully Bayesian and allows for efficient inference of parameters, including the level of additive noise for denoising applications. We apply the proposed model to several common image processing problems including non-Gaussian and nonstationary denoising of real-world color images. PMID:24002002
Encoding cortical dynamics in sparse features.
Khan, Sheraz; Lefèvre, Julien; Baillet, Sylvain; Michmizos, Konstantinos P; Ganesan, Santosh; Kitzbichler, Manfred G; Zetino, Manuel; Hämäläinen, Matti S; Papadelis, Christos; Kenet, Tal
2014-01-01
Distributed cortical solutions of magnetoencephalography (MEG) and electroencephalography (EEG) exhibit complex spatial and temporal dynamics. The extraction of patterns of interest and dynamic features from these cortical signals has so far relied on the expertise of investigators. There is a definite need in both clinical and neuroscience research for a method that will extract critical features from high-dimensional neuroimaging data in an automatic fashion. We have previously demonstrated the use of optical flow techniques for evaluating the kinematic properties of motion field projected on non-flat manifolds like in a cortical surface. We have further extended this framework to automatically detect features in the optical flow vector field by using the modified and extended 2-Riemannian Helmholtz-Hodge decomposition (HHD). Here, we applied these mathematical models on simulation and MEG data recorded from a healthy individual during a somatosensory experiment and an epilepsy pediatric patient during sleep. We tested whether our technique can automatically extract salient dynamical features of cortical activity. Simulation results indicated that we can precisely reproduce the simulated cortical dynamics with HHD; encode them in sparse features and represent the propagation of brain activity between distinct cortical areas. Using HHD, we decoded the somatosensory N20 component into two HHD features and represented the dynamics of brain activity as a traveling source between two primary somatosensory regions. In the epilepsy patient, we displayed the propagation of the epileptic activity around the margins of a brain lesion. Our findings indicate that HHD measures computed from cortical dynamics can: (i) quantitatively access the cortical dynamics in both healthy and disease brain in terms of sparse features and dynamic brain activity propagation between distinct cortical areas, and (ii) facilitate a reproducible, automated analysis of experimental and clinical
Miniature Laboratory for Detecting Sparse Biomolecules
NASA Technical Reports Server (NTRS)
Lin, Ying; Yu, Nan
2005-01-01
A miniature laboratory system has been proposed for use in the field to detect sparsely distributed biomolecules. By emphasizing concentration and sorting of specimens prior to detection, the underlying system concept would make it possible to attain high detection sensitivities without the need to develop ever more sensitive biosensors. The original purpose of the proposal is to aid the search for signs of life on a remote planet by enabling the detection of specimens as sparse as a few molecules or microbes in a large amount of soil, dust, rocks, water/ice, or other raw sample material. Some version of the system could prove useful on Earth for remote sensing of biological contamination, including agents of biological warfare. Processing in this system would begin with dissolution of the raw sample material in a sample-separation vessel. The solution in the vessel would contain floating microscopic magnetic beads coated with substances that could engage in chemical reactions with various target functional groups that are parts of target molecules. The chemical reactions would cause the targeted molecules to be captured on the surfaces of the beads. By use of a controlled magnetic field, the beads would be concentrated in a specified location in the vessel. Once the beads were thus concentrated, the rest of the solution would be discarded. This procedure would obviate the filtration steps and thereby also eliminate the filter-clogging difficulties of typical prior sample-concentration schemes. For ferrous dust/soil samples, the dissolution would be done first in a separate vessel before the solution is transferred to the microbead-containing vessel.
Split spectrum technique as a preprocessor for ultrasonic nondestructive evaluation
NASA Astrophysics Data System (ADS)
Karpur, Prasanna
Split-spectrum preprocessing (SSP) is shown in light of the present results to process A-scans in a way that allows its incorporation into artificial neural networks and other AI methods, as well as into feature-analysis techniques. SSP is consistently nonlinear in A-scan processing. Attention is given to the results obtained from the use of the magnitude spectra of the SSP-processed A-scans as inputs to a back-propagation artificial neural network.
Zhang, Di; He, Jiazhong; Zhao, Yun; Du, Minghui
2015-03-01
In magnetic resonance (MR) imaging, image spatial resolution is determined by various instrumental limitations and physical considerations. This paper presents a new algorithm for producing a high-resolution version of a low-resolution MR image. The proposed method consists of two consecutive steps: (1) reconstructs a high-resolution MR image from a given low-resolution observation via solving a joint sparse representation and nonlocal similarity L1-norm minimization problem; and (2) applies a sparse derivative prior based post-processing to suppress blurring effects. Extensive experiments on simulated brain MR images and two real clinical MR image datasets validate that the proposed method achieves much better results than many state-of-the-art algorithms in terms of both quantitative measures and visual perception. PMID:25638262
WISE data and sparse photometry used for shape reconstruction of asteroids
NASA Astrophysics Data System (ADS)
Ďurech, Josef; Hanuš, Josef; Alí-Lagoa, Victor M.; Delbo, Marco; Oszkiewicz, Dagmara A.
2016-01-01
Asteroid disk-integrated sparse-in-time photometry can be used for determination of shapes and spin states of asteroids by the lightcurve inversion method. To clearly distinguish the correct solution of the rotation period from other minima in the parameter space, data with good photometric accuracy are needed. We show that if the low-quality sparse photometry obtained from ground-based astrometric surveys is combined with data from the Wide-field Infrared Survey Explorer (WISE) satellite, the correct rotation period can be successfully derived. Although WISE observed in mid-IR wavelengths, we show that for the period and spin determination, these data can be modelled as reflected light. The absolute fluxes are not required since only relative variation of the flux over the rotation is sufficient to determine the period. We also discuss the potential of combining all WISE data with the Lowell photometric database to create physical models of thousands of asteroids.
Split-inteins and their bioapplications.
Li, Yifeng
2015-11-01
Split-inteins are a subset of inteins that are expressed in two separate halves and catalyze splicing in trans upon association of the two domains. They occur naturally and have also been artificially generated by splitting of contiguous ones. With their unique properties, split-inteins offer improved controllability, flexibility and capability to existing tools based on contiguous inteins. In addition, split-inteins have proven useful in several new applications. This review gives a general introduction to split-inteins with a focus on their role in expanding the applications of intein-based technologies. PMID:26153348
Cervigram image segmentation based on reconstructive sparse representations
NASA Astrophysics Data System (ADS)
Zhang, Shaoting; Huang, Junzhou; Wang, Wei; Huang, Xiaolei; Metaxas, Dimitris
2010-03-01
We proposed an approach based on reconstructive sparse representations to segment tissues in optical images of the uterine cervix. Because of large variations in image appearance caused by the changing of the illumination and specular reflection, the color and texture features in optical images often overlap with each other and are not linearly separable. By leveraging sparse representations the data can be transformed to higher dimensions with sparse constraints and become more separated. K-SVD algorithm is employed to find sparse representations and corresponding dictionaries. The data can be reconstructed from its sparse representations and positive and/or negative dictionaries. Classification can be achieved based on comparing the reconstructive errors. In the experiments we applied our method to automatically segment the biomarker AcetoWhite (AW) regions in an archive of 60,000 images of the uterine cervix. Compared with other general methods, our approach showed lower space and time complexity and higher sensitivity.
Sparse Coding on Symmetric Positive Definite Manifolds Using Bregman Divergences.
Harandi, Mehrtash T; Hartley, Richard; Lovell, Brian; Sanderson, Conrad
2016-06-01
This paper introduces sparse coding and dictionary learning for symmetric positive definite (SPD) matrices, which are often used in machine learning, computer vision, and related areas. Unlike traditional sparse coding schemes that work in vector spaces, in this paper, we discuss how SPD matrices can be described by sparse combination of dictionary atoms, where the atoms are also SPD matrices. We propose to seek sparse coding by embedding the space of SPD matrices into the Hilbert spaces through two types of the Bregman matrix divergences. This not only leads to an efficient way of performing sparse coding but also an online and iterative scheme for dictionary learning. We apply the proposed methods to several computer vision tasks where images are represented by region covariance matrices. Our proposed algorithms outperform state-of-the-art methods on a wide range of classification tasks, including face recognition, action recognition, material classification, and texture categorization. PMID:25643414
Weighted sparse representation for human ear recognition based on local descriptor
NASA Astrophysics Data System (ADS)
Mawloud, Guermoui; Djamel, Melaab
2016-01-01
A two-stage ear recognition framework is presented where two local descriptors and a sparse representation algorithm are combined. In a first stage, the algorithm proceeds by deducing a subset of the closest training neighbors to the test ear sample. The selection is based on the K-nearest neighbors classifier in the pattern of oriented edge magnitude feature space. In a second phase, the co-occurrence of adjacent local binary pattern features are extracted from the preselected subset and combined to form a dictionary. Afterward, sparse representation classifier is employed on the developed dictionary in order to infer the closest element to the test sample. Thus, by splitting up the ear image into a number of segments and applying the described recognition routine on each of them, the algorithm finalizes by attributing a final class label based on majority voting over the individual labels pointed out by each segment. Experimental results demonstrate the effectiveness as well as the robustness of the proposed scheme over leading state-of-the-art methods. Especially when the ear image is occluded, the proposed algorithm exhibits a great robustness and reaches the recognition performances outlined in the state of the art.
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…
Generation of dense statistical connectomes from sparse morphological data
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
NASA Astrophysics Data System (ADS)
Zahertar, S.; Yalcinkaya, A. D.; Torun, H.
2015-11-01
In this work, transmission characteristics of rectangular split-ring resonators with single-split and two-splits are analyzed at microwave frequencies. The resonators are coupled with monopole antennas for excitation. The scattering parameters of the devices are investigated under different polarizations of E and H fields. The magnetic resonances induced by E and H fields are identified and the differences in the behavior of the resonators due to orientations of the fields are explained based on simulation and experimental results. The addition of the second split of the device is investigated considering different configurations of the excitation vectors. It is demonstrated that the single-split and the two-splits resonators exhibit identical transmission characteristics for a certain excitation configuration as verified with simulations and experiments. The presented resonators can effectively function as frequency selective media for varying excitation conditions.
Sparse distributed memory: Principles and operation
NASA Technical Reports Server (NTRS)
Flynn, M. J.; Kanerva, P.; Bhadkamkar, N.
1989-01-01
Sparse distributed memory is a generalized random access memory (RAM) for long (1000 bit) binary words. Such words can be written into and read from the memory, and they can also be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original write address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech recognition and scene analysis, in signal detection and verification, and in adaptive control of automated equipment, in general, in dealing with real world information in real time. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. Major design issues were resolved which were faced in building the memories. The design is described of a prototype memory with 256 bit addresses and from 8 to 128 K locations for 256 bit words. A key aspect of the design is extensive use of dynamic RAM and other standard components.
Sparse distributed memory prototype: Principles of operation
NASA Technical Reports Server (NTRS)
Flynn, Michael J.; Kanerva, Pentti; Ahanin, Bahram; Bhadkamkar, Neal; Flaherty, Paul; Hickey, Philip
1988-01-01
Sparse distributed memory is a generalized random access memory (RAM) for long binary words. Such words can be written into and read from the memory, and they can be used to address the memory. The main attribute of the memory is sensitivity to similarity, meaning that a word can be read back not only by giving the original right address but also by giving one close to it as measured by the Hamming distance between addresses. Large memories of this kind are expected to have wide use in speech and scene analysis, in signal detection and verification, and in adaptive control of automated equipment. The memory can be realized as a simple, massively parallel computer. Digital technology has reached a point where building large memories is becoming practical. The research is aimed at resolving major design issues that have to be faced in building the memories. The design of a prototype memory with 256-bit addresses and from 8K to 128K locations for 256-bit words is described. A key aspect of the design is extensive use of dynamic RAM and other standard components.
Partially sparse imaging of stationary indoor scenes
NASA Astrophysics Data System (ADS)
Ahmad, Fauzia; Amin, Moeness G.; Dogaru, Traian
2014-12-01
In this paper, we exploit the notion of partial sparsity for scene reconstruction associated with through-the-wall radar imaging of stationary targets under reduced data volume. Partial sparsity implies that the scene being imaged consists of a sparse part and a dense part, with the support of the latter assumed to be known. For the problem at hand, sparsity is represented by a few stationary indoor targets, whereas the high scene density is defined by exterior and interior walls. Prior knowledge of wall positions and extent may be available either through building blueprints or from prior surveillance operations. The contributions of the exterior and interior walls are removed from the data through the use of projection matrices, which are determined from wall- and corner-specific dictionaries. The projected data, with enhanced sparsity, is then processed using l 1 norm reconstruction techniques. Numerical electromagnetic data is used to demonstrate the effectiveness of the proposed approach for imaging stationary indoor scenes using a reduced set of measurements.
Sparse coding for layered neural networks
NASA Astrophysics Data System (ADS)
Katayama, Katsuki; Sakata, Yasuo; Horiguchi, Tsuyoshi
2002-07-01
We investigate storage capacity of two types of fully connected layered neural networks with sparse coding when binary patterns are embedded into the networks by a Hebbian learning rule. One of them is a layered network, in which a transfer function of even layers is different from that of odd layers. The other is a layered network with intra-layer connections, in which the transfer function of inter-layer is different from that of intra-layer, and inter-layered neurons and intra-layered neurons are updated alternately. We derive recursion relations for order parameters by means of the signal-to-noise ratio method, and then apply the self-control threshold method proposed by Dominguez and Bollé to both layered networks with monotonic transfer functions. We find that a critical value αC of storage capacity is about 0.11|a ln a| -1 ( a≪1) for both layered networks, where a is a neuronal activity. It turns out that the basin of attraction is larger for both layered networks when the self-control threshold method is applied.
Anisotropic interpolation of sparse generalized image samples.
Bourquard, Aurélien; Unser, Michael
2013-02-01
Practical image-acquisition systems are often modeled as a continuous-domain prefilter followed by an ideal sampler, where generalized samples are obtained after convolution with the impulse response of the device. In this paper, our goal is to interpolate images from a given subset of such samples. We express our solution in the continuous domain, considering consistent resampling as a data-fidelity constraint. To make the problem well posed and ensure edge-preserving solutions, we develop an efficient anisotropic regularization approach that is based on an improved version of the edge-enhancing anisotropic diffusion equation. Following variational principles, our reconstruction algorithm minimizes successive quadratic cost functionals. To ensure fast convergence, we solve the corresponding sequence of linear problems by using multigrid iterations that are specifically tailored to their sparse structure. We conduct illustrative experiments and discuss the potential of our approach both in terms of algorithmic design and reconstruction quality. In particular, we present results that use as little as 2% of the image samples. PMID:22968212
Approximation and compression with sparse orthonormal transforms.
Sezer, Osman Gokhan; Guleryuz, Onur G; Altunbasak, Yucel
2015-08-01
We propose a new transform design method that targets the generation of compression-optimized transforms for next-generation multimedia applications. The fundamental idea behind transform compression is to exploit regularity within signals such that redundancy is minimized subject to a fidelity cost. Multimedia signals, in particular images and video, are well known to contain a diverse set of localized structures, leading to many different types of regularity and to nonstationary signal statistics. The proposed method designs sparse orthonormal transforms (SOTs) that automatically exploit regularity over different signal structures and provides an adaptation method that determines the best representation over localized regions. Unlike earlier work that is motivated by linear approximation constructs and model-based designs that are limited to specific types of signal regularity, our work uses general nonlinear approximation ideas and a data-driven setup to significantly broaden its reach. We show that our SOT designs provide a safe and principled extension of the Karhunen-Loeve transform (KLT) by reducing to the KLT on Gaussian processes and by automatically exploiting non-Gaussian statistics to significantly improve over the KLT on more general processes. We provide an algebraic optimization framework that generates optimized designs for any desired transform structure (multiresolution, block, lapped, and so on) with significantly better n -term approximation performance. For each structure, we propose a new prototype codec and test over a database of images. Simulation results show consistent increase in compression and approximation performance compared with conventional methods. PMID:25823033
Transformer fault diagnosis using continuous sparse autoencoder.
Wang, Lukun; Zhao, Xiaoying; Pei, Jiangnan; Tang, Gongyou
2016-01-01
This paper proposes a novel continuous sparse autoencoder (CSAE) which can be used in unsupervised feature learning. The CSAE adds Gaussian stochastic unit into activation function to extract features of nonlinear data. In this paper, CSAE is applied to solve the problem of transformer fault recognition. Firstly, based on dissolved gas analysis method, IEC three ratios are calculated by the concentrations of dissolved gases. Then IEC three ratios data is normalized to reduce data singularity and improve training speed. Secondly, deep belief network is established by two layers of CSAE and one layer of back propagation (BP) network. Thirdly, CSAE is adopted to unsupervised training and getting features. Then BP network is used for supervised training and getting transformer fault. Finally, the experimental data from IEC TC 10 dataset aims to illustrate the effectiveness of the presented approach. Comparative experiments clearly show that CSAE can extract features from the original data, and achieve a superior correct differentiation rate on transformer fault diagnosis. PMID:27119052
Partitioning sparse matrices with eigenvectors of graphs
NASA Technical Reports Server (NTRS)
Pothen, Alex; Simon, Horst D.; Liou, Kang-Pu
1990-01-01
The problem of computing a small vertex separator in a graph arises in the context of computing a good ordering for the parallel factorization of sparse, symmetric matrices. An algebraic approach for computing vertex separators is considered in this paper. It is shown that lower bounds on separator sizes can be obtained in terms of the eigenvalues of the Laplacian matrix associated with a graph. The Laplacian eigenvectors of grid graphs can be computed from Kronecker products involving the eigenvectors of path graphs, and these eigenvectors can be used to compute good separators in grid graphs. A heuristic algorithm is designed to compute a vertex separator in a general graph by first computing an edge separator in the graph from an eigenvector of the Laplacian matrix, and then using a maximum matching in a subgraph to compute the vertex separator. Results on the quality of the separators computed by the spectral algorithm are presented, and these are compared with separators obtained from other algorithms for computing separators. Finally, the time required to compute the Laplacian eigenvector is reported, and the accuracy with which the eigenvector must be computed to obtain good separators is considered. The spectral algorithm has the advantage that it can be implemented on a medium-size multiprocessor in a straightforward manner.
Optimal parallel solution of sparse triangular systems
NASA Technical Reports Server (NTRS)
Alvarado, Fernando L.; Schreiber, Robert
1990-01-01
A method for the parallel solution of triangular sets of equations is described that is appropriate when there are many right-handed sides. By preprocessing, the method can reduce the number of parallel steps required to solve Lx = b compared to parallel forward or backsolve. Applications are to iterative solvers with triangular preconditioners, to structural analysis, or to power systems applications, where there may be many right-handed sides (not all available a priori). The inverse of L is represented as a product of sparse triangular factors. The problem is to find a factored representation of this inverse of L with the smallest number of factors (or partitions), subject to the requirement that no new nonzero elements be created in the formation of these inverse factors. A method from an earlier reference is shown to solve this problem. This method is improved upon by constructing a permutation of the rows and columns of L that preserves triangularity and allow for the best possible such partition. A number of practical examples and algorithmic details are presented. The parallelism attainable is illustrated by means of elimination trees and clique trees.
Sparse alignment for robust tensor learning.
Lai, Zhihui; Wong, Wai Keung; Xu, Yong; Zhao, Cairong; Sun, Mingming
2014-10-01
Multilinear/tensor extensions of manifold learning based algorithms have been widely used in computer vision and pattern recognition. This paper first provides a systematic analysis of the multilinear extensions for the most popular methods by using alignment techniques, thereby obtaining a general tensor alignment framework. From this framework, it is easy to show that the manifold learning based tensor learning methods are intrinsically different from the alignment techniques. Based on the alignment framework, a robust tensor learning method called sparse tensor alignment (STA) is then proposed for unsupervised tensor feature extraction. Different from the existing tensor learning methods, L1- and L2-norms are introduced to enhance the robustness in the alignment step of the STA. The advantage of the proposed technique is that the difficulty in selecting the size of the local neighborhood can be avoided in the manifold learning based tensor feature extraction algorithms. Although STA is an unsupervised learning method, the sparsity encodes the discriminative information in the alignment step and provides the robustness of STA. Extensive experiments on the well-known image databases as well as action and hand gesture databases by encoding object images as tensors demonstrate that the proposed STA algorithm gives the most competitive performance when compared with the tensor-based unsupervised learning methods. PMID:25291733
Index statistical properties of sparse random graphs
NASA Astrophysics Data System (ADS)
Metz, F. L.; Stariolo, Daniel A.
2015-10-01
Using the replica method, we develop an analytical approach to compute the characteristic function for the probability PN(K ,λ ) that a large N ×N adjacency matrix of sparse random graphs has K eigenvalues below a threshold λ . The method allows to determine, in principle, all moments of PN(K ,λ ) , from which the typical sample-to-sample fluctuations can be fully characterized. For random graph models with localized eigenvectors, we show that the index variance scales linearly with N ≫1 for |λ |>0 , with a model-dependent prefactor that can be exactly calculated. Explicit results are discussed for Erdös-Rényi and regular random graphs, both exhibiting a prefactor with a nonmonotonic behavior as a function of λ . These results contrast with rotationally invariant random matrices, where the index variance scales only as lnN , with an universal prefactor that is independent of λ . Numerical diagonalization results confirm the exactness of our approach and, in addition, strongly support the Gaussian nature of the index fluctuations.
Sparsely-Bonded CMOS Hybrid Imager
NASA Technical Reports Server (NTRS)
Cunningham, Thomas J. (Inventor); Hancock, Bruce R. (Inventor); Sun, Chao (Inventor); Jones, Todd J. (Inventor); Dickie, Matthew R. (Inventor); Nikzad, Shouleh (Inventor); Hoenk, Michael E. (Inventor); Wrigley, Christopher J. (Inventor); Newton, Kenneth W. (Inventor); Pain, Bedabrata (Inventor)
2015-01-01
A method and device for imaging or detecting electromagnetic radiation is provided. A device structure includes a first chip interconnected with a second chip. The first chip includes a detector array, wherein the detector array comprises a plurality of light sensors and one or more transistors. The second chip includes a Read Out Integrated Circuit (ROIC) that reads out, via the transistors, a signal produced by the light sensors. A number of interconnects between the ROIC and the detector array can be less than one per light sensor or pixel.
Split-mode ultrasonic transducer.
Ostrovskii, Igor; Cremaldi, Lucien
2013-08-01
A split-mode ultrasonic transducer is investigated in both theory and experiment. This transducer is a two-dimensional structure of periodically poled domains in a ferroelectric wafer with free surfaces. The acoustic vibrations are excited by a radio frequency electric current applied along the length of the wafer, which allows the basal-plane surfaces to be free of metal coatings and thus ready for further biomedical applications. A specific physical property of this transducer consists of the multiple acousto-electric resonances, which occur due to an acoustic mode split when the acoustic half-wavelength is equal to the domain length. Possible applications include ultrasonic generation and detection at the micro-scale, intravascular sonification and visualization, ultrasound therapy of localized small areas such as the eye, biomedical applications for cell cultures, and traditional nondestructive testing including bones and tissues. A potential use of a non-metallized wafer is a therapeutic application with double action that is both ultrasound itself and an electric field over the wafer. The experimental measurements and theoretical calculations are in good agreement. PMID:23927212
Optimized design and analysis of sparse-sampling FMRI experiments.
Perrachione, Tyler K; Ghosh, Satrajit S
2013-01-01
Sparse-sampling is an important methodological advance in functional magnetic resonance imaging (fMRI), in which silent delays are introduced between MR volume acquisitions, allowing for the presentation of auditory stimuli without contamination by acoustic scanner noise and for overt vocal responses without motion-induced artifacts in the functional time series. As such, the sparse-sampling technique has become a mainstay of principled fMRI research into the cognitive and systems neuroscience of speech, language, hearing, and music. Despite being in use for over a decade, there has been little systematic investigation of the acquisition parameters, experimental design considerations, and statistical analysis approaches that bear on the results and interpretation of sparse-sampling fMRI experiments. In this report, we examined how design and analysis choices related to the duration of repetition time (TR) delay (an acquisition parameter), stimulation rate (an experimental design parameter), and model basis function (an analysis parameter) act independently and interactively to affect the neural activation profiles observed in fMRI. First, we conducted a series of computational simulations to explore the parameter space of sparse design and analysis with respect to these variables; second, we validated the results of these simulations in a series of sparse-sampling fMRI experiments. Overall, these experiments suggest the employment of three methodological approaches that can, in many situations, substantially improve the detection of neurophysiological response in sparse fMRI: (1) Sparse analyses should utilize a physiologically informed model that incorporates hemodynamic response convolution to reduce model error. (2) The design of sparse fMRI experiments should maintain a high rate of stimulus presentation to maximize effect size. (3) TR delays of short to intermediate length can be used between acquisitions of sparse-sampled functional image volumes to increase
Unified inference for sparse and dense longitudinal models.
Kim, Seonjin; Zhao, Zhibiao
2013-03-01
In longitudinal data analysis, statistical inference for sparse data and dense data could be substantially different. For kernel smoothing estimate of the mean function, the convergence rates and limiting variance functions are different under the two scenarios. The latter phenomenon poses challenges for statistical inference as a subjective choice between the sparse and dense cases may lead to wrong conclusions. We develop self-normalization based methods that can adapt to the sparse and dense cases in a unified framework. Simulations show that the proposed methods outperform some existing methods. PMID:24966413
SPARSKIT: A basic tool kit for sparse matrix computations
NASA Technical Reports Server (NTRS)
Saad, Youcef
1990-01-01
Presented here are the main features of a tool package for manipulating and working with sparse matrices. One of the goals of the package is to provide basic tools to facilitate the exchange of software and data between researchers in sparse matrix computations. The starting point is the Harwell/Boeing collection of matrices for which the authors provide a number of tools. Among other things, the package provides programs for converting data structures, printing simple statistics on a matrix, plotting a matrix profile, and performing linear algebra operations with sparse matrices.
Electromagnetic Formation Flight (EMFF) for Sparse Aperture Arrays
NASA Technical Reports Server (NTRS)
Kwon, Daniel W.; Miller, David W.; Sedwick, Raymond J.
2004-01-01
Traditional methods of actuating spacecraft in sparse aperture arrays use propellant as a reaction mass. For formation flying systems, propellant becomes a critical consumable which can be quickly exhausted while maintaining relative orientation. Additional problems posed by propellant include optical contamination, plume impingement, thermal emission, and vibration excitation. For these missions where control of relative degrees of freedom is important, we consider using a system of electromagnets, in concert with reaction wheels, to replace the consumables. Electromagnetic Formation Flight sparse apertures, powered by solar energy, are designed differently from traditional propulsion systems, which are based on V. This paper investigates the design of sparse apertures both inside and outside the Earth's gravity field.
NASA Astrophysics Data System (ADS)
Shao, Xiaopeng; Li, Huijuan; Wu, Tengfei; Dai, Weijia; Bi, Xiangli
2015-05-01
The incident light will be scattered away due to the inhomogeneity of the refractive index in many materials which will greatly reduce the imaging depth and degrade the imaging quality. Many exciting methods have been presented in recent years for solving this problem and realizing imaging through a highly scattering medium, such as the wavefront modulation technique and reconstruction technique. The imaging method based on compressed sensing (CS) theory can decrease the computational complexity because it doesn't require the whole speckle pattern to realize reconstruction. One of the key premises of this method is that the object is sparse or can be sparse representation. However, choosing a proper projection matrix is very important to the imaging quality. In this paper, we analyzed that the transmission matrix (TM) of a scattering medium obeys circular Gaussian distribution, which makes it possible that a scattering medium can be used as the measurement matrix in the CS theory. In order to verify the performance of this method, a whole optical system is simulated. Various projection matrices are introduced to make the object sparse, including the fast Fourier transform (FFT) basis, the discrete cosine transform (DCT) basis and the discrete wavelet transform (DWT) basis, the imaging performances of each of which are compared comprehensively. Simulation results show that for most targets, applying the discrete wavelet transform basis will obtain an image in good quality. This work can be applied to biomedical imaging and used to develop real-time imaging through highly scattering media.
Biogenesis of water splitting by photosystem II during de-etiolation of barley (Hordeum vulgare L.).
Shevela, Dmitriy; Arnold, Janine; Reisinger, Veronika; Berends, Hans-Martin; Kmiec, Karol; Koroidov, Sergey; Bue, Ann Kristin; Messinger, Johannes; Eichacker, Lutz A
2016-07-01
Etioplasts lack thylakoid membranes and photosystem complexes. Light triggers differentiation of etioplasts into mature chloroplasts, and photosystem complexes assemble in parallel with thylakoid membrane development. Plastids isolated at various time points of de-etiolation are ideal to study the kinetic biogenesis of photosystem complexes during chloroplast development. Here, we investigated the chronology of photosystem II (PSII) biogenesis by monitoring assembly status of chlorophyll-binding protein complexes and development of water splitting via O2 production in plastids (etiochloroplasts) isolated during de-etiolation of barley (Hordeum vulgare L.). Assembly of PSII monomers, dimers and complexes binding outer light-harvesting antenna [PSII-light-harvesting complex II (LHCII) supercomplexes] was identified after 1, 2 and 4 h of de-etiolation, respectively. Water splitting was detected in parallel with assembly of PSII monomers, and its development correlated with an increase of bound Mn in the samples. After 4 h of de-etiolation, etiochloroplasts revealed the same water-splitting efficiency as mature chloroplasts. We conclude that the capability of PSII to split water during de-etiolation precedes assembly of the PSII-LHCII supercomplexes. Taken together, data show a rapid establishment of water-splitting activity during etioplast-to-chloroplast transition and emphasize that assembly of the functional water-splitting site of PSII is not the rate-limiting step in the formation of photoactive thylakoid membranes. PMID:26836813
Jiang, Tongsong; Jiang, Ziwu; Zhang, Zhaozhong
2015-08-15
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.
NASA Astrophysics Data System (ADS)
Jiang, Tongsong; Jiang, Ziwu; Zhang, Zhaozhong
2015-08-01
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.
Silicon and tungsten oxide nanostructures for water splitting
NASA Astrophysics Data System (ADS)
Reyes Gil, Karla R.; Spurgeon, Joshua M.; Lewis, Nathan S.
2009-08-01
Inorganic semiconductors are promising materials for driving photoelectrochemical water-splitting reactions. However, there is not a single semiconductor material that can sustain the unassisted splitting of water into H2 and O2. Instead, we are developing a three part cell design where individual catalysts for water reduction and oxidation will be attached to the ends of a membrane. The job of splitting water is therefore divided into separate reduction and oxidation reactions, and each catalyst can be optimized independently for a single reaction. Silicon might be suitable to drive the water reduction. Inexpensive highly ordered Si wire arrays were grown on a single crystal wafer and transferred into a transparent, flexible polymer matrix. In this array, light would be absorbed along the longer axial dimension while the resulting electrons or holes would be collected along the much shorter radial dimension in a massively parallel array resembling carpet fibers on a microscale, hence the term "solar carpet". Tungsten oxide is a good candidate to drive the water oxidation. Self-organized porous tungsten oxide was successfully synthesized on the tungsten foil by anodization. This sponge-like structure absorbs light efficiently due to its high surface area; hence we called it "solar sponge".
Sparse source configurations for asteroid tomography
NASA Astrophysics Data System (ADS)
Pursiainen, S.; Kaasalainen, M.
2014-04-01
The objective of our recent research has been to develop non-invasive imaging techniques for future planetary research and mining activities involving a challenging in situ environment and tight payload limits [1]. This presentation will deal in particular with an approach in which the internal relative permittivity ∈r or the refractive index n = √ ∈r of an asteroid is to be recovered based on radio signal transmitted by a sparse set [2] of fixed or movable landers. To address important aspects of mission planning, we have analyzed different signal source configurations to find the minimal number of source positions needed for robust localization of anomalies, such as internal voids. Characteristic to this inverse problem are the large relative changes in signal speed caused by the high permittivity of typical asteroid minerals (e.g. basalt), leading to strong refractions and reflections of the signal. Finding an appropriate problemspecific signaling arrangement is an important premission goal for successful in situ measurements. This presentation will include inversion results obtained with laboratory-recorded travel time data y of the form in which n δ denotes a perturbation of a refractive index n = n δ + nbg; gi estimates the total noise due to different error sources; (ybg)i = ∫Ci nbg ds is an entry of noiseless background data ybg; and Ci is a signal path. Also simulated time-evolution data will be covered with respect to potential u satisfying the wave equation ∈rδ2/δt2+ ōδu/δt-∆u = f, where ō is a (latent) conductivity distribution and f is a source term. Special interest will be paid to inversion robustness regarding changes of the prior model and source positioning. Among other things, our analysis suggests that strongly refractive anomalies can be detected with three or four sources independently of their positioning.
Finding One Community in a Sparse Graph
NASA Astrophysics Data System (ADS)
Montanari, Andrea
2015-10-01
We consider a random sparse graph with bounded average degree, in which a subset of vertices has higher connectivity than the background. In particular, the average degree inside this subset of vertices is larger than outside (but still bounded). Given a realization of such graph, we aim at identifying the hidden subset of vertices. This can be regarded as a model for the problem of finding a tightly knitted community in a social network, or a cluster in a relational dataset. In this paper we present two sets of contributions: ( i) We use the cavity method from spin glass theory to derive an exact phase diagram for the reconstruction problem. In particular, as the difference in edge probability increases, the problem undergoes two phase transitions, a static phase transition and a dynamic one. ( ii) We establish rigorous bounds on the dynamic phase transition and prove that, above a certain threshold, a local algorithm (belief propagation) correctly identify most of the hidden set. Below the same threshold no local algorithm can achieve this goal. However, in this regime the subset can be identified by exhaustive search. For small hidden sets and large average degree, the phase transition for local algorithms takes an intriguingly simple form. Local algorithms succeed with high probability for deg _in - deg _out > √{deg _out/e} and fail for deg _in - deg _out < √{deg _out/e} (with deg _in, deg _out the average degrees inside and outside the community). We argue that spectral algorithms are also ineffective in the latter regime. It is an open problem whether any polynomial time algorithms might succeed for deg _in - deg _out < √{deg _out/e}.
Detecting novel genes with sparse arrays
Haiminen, Niina; Smit, Bart; Rautio, Jari; Vitikainen, Marika; Wiebe, Marilyn; Martinez, Diego; Chee, Christine; Kunkel, Joe; Sanchez, Charles; Nelson, Mary Anne; Pakula, Tiina; Saloheimo, Markku; Penttilä, Merja; Kivioja, Teemu
2014-01-01
Species-specific genes play an important role in defining the phenotype of an organism. However, current gene prediction methods can only efficiently find genes that share features such as sequence similarity or general sequence characteristics with previously known genes. Novel sequencing methods and tiling arrays can be used to find genes without prior information and they have demonstrated that novel genes can still be found from extensively studied model organisms. Unfortunately, these methods are expensive and thus are not easily applicable, e.g., to finding genes that are expressed only in very specific conditions. We demonstrate a method for finding novel genes with sparse arrays, applying it on the 33.9 Mb genome of the filamentous fungus Trichoderma reesei. Our computational method does not require normalisations between arrays and it takes into account the multiple-testing problem typical for analysis of microarray data. In contrast to tiling arrays, that use overlapping probes, only one 25mer microarray oligonucleotide probe was used for every 100 b. Thus, only relatively little space on a microarray slide was required to cover the intergenic regions of a genome. The analysis was done as a by-product of a conventional microarray experiment with no additional costs. We found at least 23 good candidates for novel transcripts that could code for proteins and all of which were expressed at high levels. Candidate genes were found to neighbour ire1 and cre1 and many other regulatory genes. Our simple, low-cost method can easily be applied to finding novel species-specific genes without prior knowledge of their sequence properties. PMID:20691772
Wiring of Photosystem II to Hydrogenase for Photoelectrochemical Water Splitting.
Mersch, Dirk; Lee, Chong-Yong; Zhang, Jenny Zhenqi; Brinkert, Katharina; Fontecilla-Camps, Juan C; Rutherford, A William; Reisner, Erwin
2015-07-01
In natural photosynthesis, light is used for the production of chemical energy carriers to fuel biological activity. The re-engineering of natural photosynthetic pathways can provide inspiration for sustainable fuel production and insights for understanding the process itself. Here, we employ a semiartificial approach to study photobiological water splitting via a pathway unavailable to nature: the direct coupling of the water oxidation enzyme, photosystem II, to the H2 evolving enzyme, hydrogenase. Essential to this approach is the integration of the isolated enzymes into the artificial circuit of a photoelectrochemical cell. We therefore developed a tailor-made hierarchically structured indium-tin oxide electrode that gives rise to the excellent integration of both photosystem II and hydrogenase for performing the anodic and cathodic half-reactions, respectively. When connected together with the aid of an applied bias, the semiartificial cell demonstrated quantitative electron flow from photosystem II to the hydrogenase with the production of H2 and O2 being in the expected two-to-one ratio and a light-to-hydrogen conversion efficiency of 5.4% under low-intensity red-light irradiation. We thereby demonstrate efficient light-driven water splitting using a pathway inaccessible to biology and report on a widely applicable in vitro platform for the controlled coupling of enzymatic redox processes to meaningfully study photocatalytic reactions. PMID:26046591
The potential versus current state of water splitting with hematite.
Zandi, Omid; Hamann, Thomas W
2015-09-21
This review describes the potential of hematite as a photoanode material for photoelectrochemical (PEC) water splitting. The current understanding of key loss-mechanisms of hematite are introduced and correlated to performance enhancement strategies. The significant voltage loss associated with overcoming the competitive water oxidation and surface state recombination has recently been surmounted through a combination of high temperature annealing and surface modification with water oxidation catalysts. Substantial efforts have been made at nanostructuring electrodes to increase the charge separation efficiency without sacrificing light absorption. Even in optimized nanostructured electrodes, however, charge separation continues to be the primary barrier to achieving efficient water splitting with hematite. Specifically, significant depletion region recombination results in voltage dependant photocurrent which constrains the fill factor. Thus, future directions to enhance the efficiency of hematite electrodes are discussed with an emphasis on circumventing depletion region recombination. PMID:26267040
Imaging in scattering media using correlation image sensors and sparse convolutional coding.
Heide, Felix; Xiao, Lei; Kolb, Andreas; Hullin, Matthias B; Heidrich, Wolfgang
2014-10-20
Correlation image sensors have recently become popular low-cost devices for time-of-flight, or range cameras. They usually operate under the assumption of a single light path contributing to each pixel. We show that a more thorough analysis of the sensor data from correlation sensors can be used can be used to analyze the light transport in much more complex environments, including applications for imaging through scattering and turbid media. The key of our method is a new convolutional sparse coding approach for recovering transient (light-in-flight) images from correlation image sensors. This approach is enabled by an analysis of sparsity in complex transient images, and the derivation of a new physically-motivated model for transient images with drastically improved sparsity. PMID:25401666
Dephasing in coherently split quasicondensates
Stimming, H.-P.; Mauser, N. J.; Mazets, I. E.
2011-02-15
We numerically model the evolution of a pair of coherently split quasicondensates. A truly one-dimensional case is assumed, so that the loss of the (initially high) coherence between the two quasicondensates is due to dephasing only, but not due to the violation of integrability and subsequent thermalization (which are excluded from the present model). We confirm the subexponential time evolution of the coherence between two quasicondensates {proportional_to}exp[-(t/t{sub 0}){sup 2/3}], experimentally observed by Hofferberth et al. [Nature 449, 324 (2007)]. The characteristic time t{sub 0} is found to scale as the square of the ratio of the linear density of a quasicondensate to its temperature, and we analyze the full distribution function of the interference contrast and the decay of the phase correlation.
Minimal Doubling and Point Splitting
Creutz, M.
2010-06-14
Minimally-doubled chiral fermions have the unusual property of a single local field creating two fermionic species. Spreading the field over hypercubes allows construction of combinations that isolate specific modes. Combining these fields into bilinears produces meson fields of specific quantum numbers. Minimally-doubled fermion actions present the possibility of fast simulations while maintaining one exact chiral symmetry. They do, however, introduce some peculiar aspects. An explicit breaking of hyper-cubic symmetry allows additional counter-terms to appear in the renormalization. While a single field creates two different species, spreading this field over nearby sites allows isolation of specific states and the construction of physical meson operators. Finally, lattice artifacts break isospin and give two of the three pseudoscalar mesons an additional contribution to their mass. Depending on the sign of this mass splitting, one can either have a traditional Goldstone pseudoscalar meson or a parity breaking Aoki-like phase.
NASA Astrophysics Data System (ADS)
Kusenko, Alexander; Takahashi, Fuminobu; Yanagida, Tsutomu T.
2010-09-01
The seesaw mechanism in models with extra dimensions is shown to be generically consistent with a broad range of Majorana masses. The resulting democracy of scales implies that the seesaw mechanism can naturally explain the smallness of neutrino masses for an arbitrarily small right-handed neutrino mass. If the scales of the seesaw parameters are split, with two right-handed neutrinos at a high scale and one at a keV scale, one can explain the matter-antimatter asymmetry of the universe, as well as dark matter. The dark matter candidate, a sterile right-handed neutrino with mass of several keV, can account for the observed pulsar velocities and for the recent data from Chandra X-ray Observatory, which suggest the existence of a 5 keV sterile right-handed neutrino.
Salt splitting with ceramic membranes
Kurath, D.
1996-10-01
The purpose of this task is to develop ceramic membrane technologies for salt splitting of radioactively contaminated sodium salt solutions. This technology has the potential to reduce the low-level waste (LLW) disposal volume, the pH and sodium hydroxide content for subsequent processing steps, the sodium content of interstitial liquid in high-level waste (HLW) sludges, and provide sodium hydroxide free of aluminum for recycle within processing plants at the DOE complex. Potential deployment sites include Hanford, Savannah River, and Idaho National Engineering Laboratory (INEL). The technical approach consists of electrochemical separation of sodium ions from the salt solution using sodium (Na) Super Ion Conductors (NaSICON). As the name implies, sodium ions are transported rapidly through these ceramic crystals even at room temperatures.
NASA Astrophysics Data System (ADS)
Cohen, Timothy; Craig, Nathaniel; Knapen, Simon
2016-03-01
We propose a simple model of split supersymmetry from gauge mediation. This model features gauginos that are parametrically a loop factor lighter than scalars, accommodates a Higgs boson mass of 125 GeV, and incorporates a simple solution to the μ- b μ problem. The gaugino mass suppression can be understood as resulting from collective symmetry breaking. Imposing collider bounds on μ and requiring viable electroweak symmetry breaking implies small a-terms and small tan β — the stop mass ranges from 105 to 108 GeV. In contrast with models with anomaly + gravity mediation (which also predict a one-loop loop suppression for gaugino masses), our gauge mediated scenario predicts aligned squark masses and a gravitino LSP. Gluinos, electroweakinos and Higgsinos can be accessible at the LHC and/or future colliders for a wide region of the allowed parameter space.
Anomalous complete opaqueness in a sparse array of gold nanoparticle chains
Bai Benfeng; Li Xiaowei; Vartiainen, Ismo; Lehmuskero, Anni; Turunen, Jari; Kuittinen, Markku; Vahimaa, Pasi; Kang Guoguo
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.
Ensemble polarimetric SAR image classification based on contextual sparse representation
NASA Astrophysics Data System (ADS)
Zhang, Lamei; Wang, Xiao; Zou, Bin; Qiao, Zhijun
2016-05-01
Polarimetric SAR image interpretation has become one of the most interesting topics, in which the construction of the reasonable and effective technique of image classification is of key importance. Sparse representation represents the data using the most succinct sparse atoms of the over-complete dictionary and the advantages of sparse representation also have been confirmed in the field of PolSAR classification. However, it is not perfect, like the ordinary classifier, at different aspects. So ensemble learning is introduced to improve the issue, which makes a plurality of different learners training and obtained the integrated results by combining the individual learner to get more accurate and ideal learning results. Therefore, this paper presents a polarimetric SAR image classification method based on the ensemble learning of sparse representation to achieve the optimal classification.
Image inpainting based on sparse representations with a perceptual metric
NASA Astrophysics Data System (ADS)
Ogawa, Takahiro; Haseyama, Miki
2013-12-01
This paper presents an image inpainting method based on sparse representations optimized with respect to a perceptual metric. In the proposed method, the structural similarity (SSIM) index is utilized as a criterion to optimize the representation performance of image data. Specifically, the proposed method enables the formulation of two important procedures in the sparse representation problem, 'estimation of sparse representation coefficients' and 'update of the dictionary', based on the SSIM index. Then, using the generated dictionary, approximation of target patches including missing areas via the SSIM-based sparse representation becomes feasible. Consequently, image inpainting for which procedures are totally derived from the SSIM index is realized. Experimental results show that the proposed method enables successful inpainting of missing areas.
Multiple kernel learning for sparse representation-based classification.
Shrivastava, Ashish; Patel, Vishal M; Chellappa, Rama
2014-07-01
In this paper, we propose a multiple kernel learning (MKL) algorithm that is based on the sparse representation-based classification (SRC) method. Taking advantage of the nonlinear kernel SRC in efficiently representing the nonlinearities in the high-dimensional feature space, we propose an MKL method based on the kernel alignment criteria. Our method uses a two step training method to learn the kernel weights and sparse codes. At each iteration, the sparse codes are updated first while fixing the kernel mixing coefficients, and then the kernel mixing coefficients are updated while fixing the sparse codes. These two steps are repeated until a stopping criteria is met. The effectiveness of the proposed method is demonstrated using several publicly available image classification databases and it is shown that this method can perform significantly better than many competitive image classification algorithms. PMID:24835226
Generation of high-power laser light with Gigahertz splitting.
Unks, B E; Proite, N A; Yavuz, D D
2007-08-01
We demonstrate the generation of two high-power laser beams whose frequencies are separated by the ground state hyperfine transition frequency in (87)Rb. The system uses a single master diode laser appropriately shifted by high frequency acousto-optic modulators and amplified by semiconductor tapered amplifiers. This produces two 1 W laser beams with a frequency spacing of 6.834 GHz and a relative frequency stability of 1 Hz. We discuss possible applications of this apparatus, including electromagnetically induced transparency-like effects and ultrafast qubit rotations. PMID:17764314
Split Immunological Tolerance to Trophoblast
de Mestre, Amanda; Noronha, Leela; Wagner, Bettina; Antczak, Douglas F.
2010-01-01
Split immunological tolerance refers to states in which an individual is capable of mounting certain types of immune responses to a particular antigenic challenge, but is tolerant of the same antigen in other compartments of the immune system. This concept is applicable to the immunological relationship between mother and fetus, and particularly relevant in equine pregnancy. In pregnant mares, antibody responses to paternal foreign Major Histocompatibility Complex class I antigens are robust, while anti-paternal cytotoxic T cell responses are diminished compared to those mounted by non-pregnant mares. Here we compared the distribution of the major lymphocyte subsets, the percentage of lymphocytes expressing Interferon Gamma (IFNG) and Interleukin 4 (IL4) and the level of expression of the immunoregulatory transcription factor FOXP3 between pregnant and non-pregnant mares, and between peripheral blood and the endometrium during pregnancy. In a cohort of mares in which peripheral blood lymphocytes were tested during early pregnancy and in the non-pregnant state, there were only slight changes observed during pregnancy. In contrast, comparison of peripheral blood lymphocytes with lymphocytes isolated from the endometrial cups of pregnant mares revealed striking differences in lymphocyte sub-populations. The endometrial cups contained higher numbers of IFNG+ lymphocytes, and lower numbers of lymphocytes expressing IL4. The endometrial cup lymphocytes also had higher numbers of FOXP3+ cells compared to peripheral blood lymphocytes. Taken together, these results strengthen the evidence for a state of split tolerance to trophoblast, and furthermore define sharp differences in immune reactivity during equine pregnancy between peripheral blood lymphocytes and lymphocytes at the maternal-fetal interface. PMID:19876828
Salt splitting using ceramic membranes
Kurath, D.E.
1997-10-01
Many radioactive aqueous wastes in the DOE complex have high concentrations of sodium that can negatively affect waste treatment and disposal operations. Sodium can decrease the durability of waste forms such as glass and is the primary contributor to large disposal volumes. Waste treatment processes such as cesium ion exchange, sludge washing, and calcination are made less efficient and more expensive because of the high sodium concentrations. Pacific Northwest National Laboratory (PNNL) and Ceramatec Inc. (Salt Lake City UT) are developing an electrochemical salt splitting process based on inorganic ceramic sodium (Na), super-ionic conductor (NaSICON) membranes that shows promise for mitigating the impact of sodium. In this process, the waste is added to the anode compartment, and an electrical potential is applied to the cell. This drives sodium ions through the membrane, but the membrane rejects most other cations (e.g., Sr{sup +2}, Cs{sup +}). The charge balance in the anode compartment is maintained by generating H{sup +} from the electrolysis of water. The charge balance in the cathode is maintained by generating OH{sup {minus}}, either from the electrolysis of water or from oxygen and water using an oxygen cathode. The normal gaseous products of the electrolysis of water are oxygen at the anode and hydrogen at the cathode. Potentially flammable gas mixtures can be prevented by providing adequate volumes of a sweep gas, using an alternative reductant or destruction of the hydrogen as it is generated. As H{sup +} is generated in the anode compartment, the pH drops. The process may be operated with either an alkaline (pH>12) or an acidic anolyte (pH <1). The benefits of salt splitting using ceramic membranes are (1) waste volume reduction and reduced chemical procurement costs by recycling of NaOH; and (2) direct reduction of sodium in process streams, which enhances subsequent operations such as cesium ion exchange, calcination, and vitrification.
Maverick Comet Splits during Dramatic Outburst
NASA Astrophysics Data System (ADS)
1996-01-01
magnitude 13, quite normal for this comet at the given heliocentric distance. This corresponds to about 700 times fainter than what can be seen with the unaided eye. However, on September 8-12, Jacques Crovisier and his colleagues from the Paris Observatory (Meudon) used the large Nancay radiotelescope to determine the amount of hydroxyle molecules (OH) around SW-3. To their great surprise, they found that it was much larger than predicted. Their careful measurements showed beyond any doubt that SW-3 was emitting enormous amounts of water (H2O), the ``parent'' molecule from which OH is produced by dissociation. Subsequent visual observations by amateur astronomers all over the world confirmed that a dramatic outburst was in progress; on September 17, the comet was recorded at magnitude 8, or 100 times brighter than expected. The overall intensity kept increasing until mid-October, when the magnitude was brighter than 6, or nearly 1000 times brighter than expected, and the comet was in principle visible to the naked eye from dark sites. Only a few comets have ever experienced such an enormous, unexpected increase in brightness. At that time, SW-3 had just passed its perihelion and was ``crossing'' the Earth's orbit on its way out. The distance to the Earth was close to 200 million kilometres. The Observations at ESO It was therefore with particular expectations that the two astronomers went to their respectively ESO telescopes on December 12-14, 1995, to observe SW-3. Although the comet at this time had become about 5 times fainter than when it was brightest, they still hoped that their observations would be able to cast some light on its very peculiar behaviour. And indeed, thanks to the very good image quality, both the EMMI and TIMMI instruments immediately and for the first time showed that the nucleus of SW-3 had split. On the CCD images from EMMI, three distinct light condensations were visible, well aligned with the two outermost at an angular distance of 4
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
Solar photochemical and thermochemical splitting of water.
Rao, C N R; Lingampalli, S R; Dey, Sunita; Roy, Anand
2016-02-28
Artificial photosynthesis to carry out both the oxidation and the reduction of water has emerged to be an exciting area of research. It has been possible to photochemically generate oxygen by using a scheme similar to the Z-scheme, by using suitable catalysts in place of water-oxidation catalyst in the Z-scheme in natural photosynthesis. The best oxidation catalysts are found to be Co and Mn oxides with the e(1) g configuration. The more important aspects investigated pertain to the visible-light-induced generation of hydrogen by using semiconductor heterostructures of the type ZnO/Pt/Cd1-xZnxS and dye-sensitized semiconductors. In the case of heterostructures, good yields of H2 have been obtained. Modifications of the heterostructures, wherein Pt is replaced by NiO, and the oxide is substituted with different anions are discussed. MoS2 and MoSe2 in the 1T form yield high quantities of H2 when sensitized by Eosin Y. Two-step thermochemical splitting of H2O using metal oxide redox pairs provides a strategy to produce H2 and CO. Performance of the Ln0.5A0.5MnO3 (Ln = rare earth ion, A = Ca, Sr) family of perovskites is found to be promising in this context. The best results to date are found with Y0.5Sr0.5MnO3. PMID:26755752
Testing of Error-Correcting Sparse Permutation Channel Codes
NASA Technical Reports Server (NTRS)
Shcheglov, Kirill, V.; Orlov, Sergei S.
2008-01-01
A computer program performs Monte Carlo direct numerical simulations for testing sparse permutation channel codes, which offer strong error-correction capabilities at high code rates and are considered especially suitable for storage of digital data in holographic and volume memories. A word in a code of this type is characterized by, among other things, a sparseness parameter (M) and a fixed number (K) of 1 or "on" bits in a channel block length of N.
Out-of-Core Solutions of Complex Sparse Linear Equations
NASA Technical Reports Server (NTRS)
Yip, E. L.
1982-01-01
ETCLIB is library of subroutines for obtaining out-of-core solutions of complex sparse linear equations. Routines apply to dense and sparse matrices too large to be stored in core. Useful for solving any set of linear equations, but particularly useful in cases where coefficient matrix has no special properties that guarantee convergence with any of interative processes. The only assumption made is that coefficient matrix is not singular.
Visual Tracking Based on Extreme Learning Machine and Sparse Representation
Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen
2015-01-01
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. PMID:26506359
Vector sparse representation of color image using quaternion matrix analysis.
Xu, Yi; Yu, Licheng; Xu, Hongteng; Zhang, Hao; Nguyen, Truong
2015-04-01
Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain. PMID:25643407
A sparse embedding and least variance encoding approach to hashing.
Zhu, Xiaofeng; Zhang, Lei; Huang, Zi
2014-09-01
Hashing is becoming increasingly important in large-scale image retrieval for fast approximate similarity search and efficient data storage. Many popular hashing methods aim to preserve the kNN graph of high dimensional data points in the low dimensional manifold space, which is, however, difficult to achieve when the number of samples is big. In this paper, we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary. To this end, we partition the sample space into clusters via a linear spectral clustering method, and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters. This actually embeds each sample sparsely in the sample space. The sparse embedding vector is employed as the feature of each sample for hashing. We then propose a least variance encoding model, which learns a dictionary to encode the sparse embedding feature, and consequently binarize the coding coefficients as the hash codes. The dictionary and the binarization threshold are jointly optimized in our model. Experimental results on benchmark data sets demonstrated the effectiveness of the proposed approach in comparison with state-of-the-art methods. PMID:24968174
Representation-Independent Iteration of Sparse Data Arrays
NASA Technical Reports Server (NTRS)
James, Mark
2007-01-01
An approach is defined that describes a method of iterating over massively large arrays containing sparse data using an approach that is implementation independent of how the contents of the sparse arrays are laid out in memory. What is unique and important here is the decoupling of the iteration over the sparse set of array elements from how they are internally represented in memory. This enables this approach to be backward compatible with existing schemes for representing sparse arrays as well as new approaches. What is novel here is a new approach for efficiently iterating over sparse arrays that is independent of the underlying memory layout representation of the array. A functional interface is defined for implementing sparse arrays in any modern programming language with a particular focus for the Chapel programming language. Examples are provided that show the translation of a loop that computes a matrix vector product into this representation for both the distributed and not-distributed cases. This work is directly applicable to NASA and its High Productivity Computing Systems (HPCS) program that JPL and our current program are engaged in. The goal of this program is to create powerful, scalable, and economically viable high-powered computer systems suitable for use in national security and industry by 2010. This is important to NASA for its computationally intensive requirements for analyzing and understanding the volumes of science data from our returned missions.
Visual tracking based on extreme learning machine and sparse representation.
Wang, Baoxian; Tang, Linbo; Yang, Jinglin; Zhao, Baojun; Wang, Shuigen
2015-01-01
The existing sparse representation-based visual trackers mostly suffer from both being time consuming and having poor robustness problems. To address these issues, a novel tracking method is presented via combining sparse representation and an emerging learning technique, namely extreme learning machine (ELM). Specifically, visual tracking can be divided into two consecutive processes. Firstly, ELM is utilized to find the optimal separate hyperplane between the target observations and background ones. Thus, the trained ELM classification function is able to remove most of the candidate samples related to background contents efficiently, thereby reducing the total computational cost of the following sparse representation. Secondly, to further combine ELM and sparse representation, the resultant confidence values (i.e., probabilities to be a target) of samples on the ELM classification function are used to construct a new manifold learning constraint term of the sparse representation framework, which tends to achieve robuster results. Moreover, the accelerated proximal gradient method is used for deriving the optimal solution (in matrix form) of the constrained sparse tracking model. Additionally, the matrix form solution allows the candidate samples to be calculated in parallel, thereby leading to a higher efficiency. Experiments demonstrate the effectiveness of the proposed tracker. PMID:26506359
Kernelized Elastic Net Regularization: Generalization Bounds, and Sparse Recovery.
Feng, Yunlong; Lv, Shao-Gao; Hang, Hanyuan; Suykens, Johan A K
2016-03-01
Kernelized elastic net regularization (KENReg) is a kernelization of the well-known elastic net regularization (Zou & Hastie, 2005 ). The kernel in KENReg is not required to be a Mercer kernel since it learns from a kernelized dictionary in the coefficient space. Feng, Yang, Zhao, Lv, and Suykens ( 2014 ) showed that KENReg has some nice properties including stability, sparseness, and generalization. In this letter, we continue our study on KENReg by conducting a refined learning theory analysis. This letter makes the following three main contributions. First, we present refined error analysis on the generalization performance of KENReg. The main difficulty of analyzing the generalization error of KENReg lies in characterizing the population version of its empirical target function. We overcome this by introducing a weighted Banach space associated with the elastic net regularization. We are then able to conduct elaborated learning theory analysis and obtain fast convergence rates under proper complexity and regularity assumptions. Second, we study the sparse recovery problem in KENReg with fixed design and show that the kernelization may improve the sparse recovery ability compared to the classical elastic net regularization. Finally, we discuss the interplay among different properties of KENReg that include sparseness, stability, and generalization. We show that the stability of KENReg leads to generalization, and its sparseness confidence can be derived from generalization. Moreover, KENReg is stable and can be simultaneously sparse, which makes it attractive theoretically and practically. PMID:26735744
Sparse methods for Quantitative Susceptibility Mapping
NASA Astrophysics Data System (ADS)
Bilgic, Berkin; Chatnuntawech, Itthi; Langkammer, Christian; Setsompop, Kawin
2015-09-01
Quantitative Susceptibility Mapping (QSM) aims to estimate the tissue susceptibility distribution that gives rise to subtle changes in the main magnetic field, which are captured by the image phase in a gradient echo (GRE) experiment. The underlying susceptibility distribution is related to the acquired tissue phase through an ill-posed linear system. To facilitate its inversion, spatial regularization that imposes sparsity or smoothness assumptions can be employed. This paper focuses on efficient algorithms for regularized QSM reconstruction. Fast solvers that enforce sparsity under Total Variation (TV) and Total Generalized Variation (TGV) constraints are developed using Alternating Direction Method of Multipliers (ADMM). Through variable splitting that permits closed-form iterations, the computation efficiency of these solvers are dramatically improved. An alternative approach to improve the conditioning of the ill-posed inversion is to acquire multiple GRE volumes at different head orientations relative to the main magnetic field. The phase information from such multi-orientation acquisition can be combined to yield exquisite susceptibility maps and obviate the need for regularized reconstruction, albeit at the cost of increased data acquisition time.
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…
Precision aligned split V-block
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.
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…
Tracking Water Absorption in Split Susceptible Blueberries
Technology Transfer Automated Retrieval System (TEKTRAN)
Rain related fruit splitting in blueberries has been a problem for commercial blueberry growers in the Southeastern US. The presence of split berries can cause an entire batch of berries to be rejected. Rejection of batches can be devastating to the growers and their income. Previous studies ha...
Code of Federal Regulations, 2014 CFR
2014-01-01
....2002 Split shell. Split shell means a shell having any crack which is open and conspicuous for a... Practices), DEPARTMENT OF AGRICULTURE REGULATIONS AND STANDARDS UNDER THE AGRICULTURAL MARKETING ACT OF 1946 AND THE EGG PRODUCTS INSPECTION ACT FRESH FRUITS, VEGETABLES AND OTHER PRODUCTS 1 2...
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.
Efficient solar water-splitting using a nanocrystalline CoO photocatalyst.
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. PMID:24336404
MEASURING X-RAY VARIABILITY IN FAINT/SPARSELY SAMPLED ACTIVE GALACTIC NUCLEI
Allevato, V.; Paolillo, M.; Papadakis, I.; Pinto, C.
2013-07-01
We study the statistical properties of the normalized excess variance of variability process characterized by a ''red-noise'' power spectral density (PSD), as in the case of active galactic nuclei (AGNs). We perform Monte Carlo simulations of light curves, assuming both a continuous and a sparse sampling pattern and various signal-to-noise ratios (S/Ns). We show that the normalized excess variance is a biased estimate of the variance even in the case of continuously sampled light curves. The bias depends on the PSD slope and on the sampling pattern, but not on the S/N. We provide a simple formula to account for the bias, which yields unbiased estimates with an accuracy better than 15%. We show that the normalized excess variance estimates based on single light curves (especially for sparse sampling and S/N < 3) are highly uncertain (even if corrected for bias) and we propose instead the use of an ''ensemble estimate'', based on multiple light curves of the same object, or on the use of light curves of many objects. These estimates have symmetric distributions, known errors, and can also be corrected for biases. We use our results to estimate the ability to measure the intrinsic source variability in current data, and show that they could also be useful in the planning of the observing strategy of future surveys such as those provided by X-ray missions studying distant and/or faint AGN populations and, more in general, in the estimation of the variability amplitude of sources that will result from future surveys such as Pan-STARRS and LSST.
Sparse modeling of spatial environmental variables associated with asthma
Chang, Timothy S.; Gangnon, Ronald E.; Page, C. David; Buckingham, William R.; Tandias, Aman; Cowan, Kelly J.; Tomasallo, Carrie D.; Arndt, Brian G.; Hanrahan, Lawrence P.; Guilbert, Theresa W.
2014-01-01
Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin’s Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5–50 years over a three-year period. Each patient’s home address was geocoded to one of 3,456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin’s geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437
Sparse modeling of spatial environmental variables associated with asthma.
Chang, Timothy S; Gangnon, Ronald E; David Page, C; Buckingham, William R; Tandias, Aman; Cowan, Kelly J; Tomasallo, Carrie D; Arndt, Brian G; Hanrahan, Lawrence P; Guilbert, Theresa W
2015-02-01
Geographically distributed environmental factors influence the burden of diseases such as asthma. Our objective was to identify sparse environmental variables associated with asthma diagnosis gathered from a large electronic health record (EHR) dataset while controlling for spatial variation. An EHR dataset from the University of Wisconsin's Family Medicine, Internal Medicine and Pediatrics Departments was obtained for 199,220 patients aged 5-50years over a three-year period. Each patient's home address was geocoded to one of 3456 geographic census block groups. Over one thousand block group variables were obtained from a commercial database. We developed a Sparse Spatial Environmental Analysis (SASEA). Using this method, the environmental variables were first dimensionally reduced with sparse principal component analysis. Logistic thin plate regression spline modeling was then used to identify block group variables associated with asthma from sparse principal components. The addresses of patients from the EHR dataset were distributed throughout the majority of Wisconsin's geography. Logistic thin plate regression spline modeling captured spatial variation of asthma. Four sparse principal components identified via model selection consisted of food at home, dog ownership, household size, and disposable income variables. In rural areas, dog ownership and renter occupied housing units from significant sparse principal components were associated with asthma. Our main contribution is the incorporation of sparsity in spatial modeling. SASEA sequentially added sparse principal components to Logistic thin plate regression spline modeling. This method allowed association of geographically distributed environmental factors with asthma using EHR and environmental datasets. SASEA can be applied to other diseases with environmental risk factors. PMID:25533437
Hyperfine meson splittings: chiral symmetry versus transverse gluon exchange
Felipe J. Llanes-Estrada; Stephen R. Cotanch; Adam P. Szczepaniak; Eric S. Swanson
2004-02-01
Meson spin splittings are examined within an effective Coulomb gauge QCD Hamiltonian incorporating chiral symmetry and a transverse hyperfine interaction necessary for heavy quarks. For light and heavy quarkonium systems the pseudoscalar-vector meson spectrum is generated by approximate BCS-RPA diagonalizations. This relativistic formulation includes both S and D waves for the vector mesons which generates a set of coupled integral equations. A smooth transition from the heavy to the light quark regime is found with chiral symmetry dominating the /pi-/rho mass difference. A good, consistent description of the observed meson spin splittings and chiral quantities, such as the quark condensate and the /pi mass, is obtained. Similar comparisons with TDA diagonalizations, which violate chiral symmetry, are deficient for light pseudoscalar mesons indicating the need to simultaneously include both chiral symmetry and a hyperfine interaction. The /eta{sub b} mass is predicted to be around 9400 MeV consistent with other theoretical expectations and above the unconfirmed 9300 MeV candidate. Finally, for comparison with lattice results, the J reliability parameter is also evaluated.
Lightweight electrical connector split backshell
NASA Technical Reports Server (NTRS)
Goldman, Elliot (Inventor)
2009-01-01
An electrical connector split backshell is provided, comprising two substantially identical backshell halves. Each half includes a first side and a cam projecting therefrom along an axis perpendicular thereto, the cam having an alignment tooth with a constant radius and an engagement section with a radius that increases with angular distance from the alignment tooth. Each half further includes a second side parallel to the first side and a circular sector opening disposed in the second side, the circular sector opening including an inner surface configured as a ramp with a constant radius, the ramp being configured to engage with an engagement section of a cam of the other half, the circular sector opening further including a relieved pocket configured to receive an alignment tooth of the cam of the other half. Each half further includes a back side perpendicular to the first and second sides and a wire bundle notch disposed in the back side, the wire bundle notch configured to align with a wire bundle notch of the other half to form a wire bundle opening. The two substantially identical halves are rotatably coupled by engaging the engagement section of each half to the ramp of the other half.
Innovative solar thermochemical water splitting.
Hogan, Roy E. Jr.; Siegel, Nathan P.; Evans, Lindsey R.; Moss, Timothy A.; Stuecker, John Nicholas; Diver, Richard B., Jr.; Miller, James Edward; Allendorf, Mark D.; James, Darryl L.
2008-02-01
Sandia National Laboratories (SNL) is evaluating the potential of an innovative approach for splitting water into hydrogen and oxygen using two-step thermochemical cycles. Thermochemical cycles are heat engines that utilize high-temperature heat to produce chemical work. Like their mechanical work-producing counterparts, their efficiency depends on operating temperature and on the irreversibility of their internal processes. With this in mind, we have invented innovative design concepts for two-step solar-driven thermochemical heat engines based on iron oxide and iron oxide mixed with other metal oxides (ferrites). The design concepts utilize two sets of moving beds of ferrite reactant material in close proximity and moving in opposite directions to overcome a major impediment to achieving high efficiency--thermal recuperation between solids in efficient counter-current arrangements. They also provide inherent separation of the product hydrogen and oxygen and are an excellent match with high-concentration solar flux. However, they also impose unique requirements on the ferrite reactants and materials of construction as well as an understanding of the chemical and cycle thermodynamics. In this report the Counter-Rotating-Ring Receiver/Reactor/Recuperator (CR5) solar thermochemical heat engine and its basic operating principals are described. Preliminary thermal efficiency estimates are presented and discussed. Our ferrite reactant material development activities, thermodynamic studies, test results, and prototype hardware development are also presented.
Unveiling the photonic spin Hall effect with asymmetric spin-dependent splitting.
Zhou, Xinxing; Ling, Xiaohui
2016-02-01
The photonic spin Hall effect (SHE) manifests itself as the spin-dependent splitting of light beam. Usually, it shows a symmetric spin-dependent splitting, i.e., the left- and right-handed circularly polarized components are equally separated in position and intensity for linear polarization incidence. In this paper, we theoretically propose an asymmetric spin-dependent splitting at an air-glass interface under the illumination of elliptical polarization beam and experimentally demonstrate it with the weak measurement method. The left- and right-handed circularly polarized components show expectedly unequal intensity distributions and unexpectedly different spin-dependent shifts. Remarkably, the asymmetric spin-dependent splitting can be modulated by adjusting the handedness of incident polarization. The inherent physics behind this interesting phenomenon is attributed to the additional spatial Imbert-Fedorov shift. These findings offer us potential methods for developing new spin-based nanophotonic applications. PMID:26906868
Orientation modulated charge transport in hematite for photoelectrochemical water splitting
NASA Astrophysics Data System (ADS)
Cai, Jiajia; Liu, Yinglei; Li, Song; Gao, Meiqi; Wang, Dunwei; Qin, Gaowu
2016-05-01
Hematite is currently considered one of the most promising photoanode materials for light-driven water splitting. The photoelectrochemical performance of hematite is limited by its low conductivity. In this work, we demonstrate that the conductivity of hematite films can be tuned by controlling the orientation of hematite crystals. By applying a high magnetic field (up to 10 T) during the drop-casting preparation, hematite films composed of single crystal particles show featured texture by promoting those particles alignment with (001) normal to the substrate. By enhancing the photocurrent densities with tuned hematite orientation, the current method provides an effective way for increasing the number of carriers that can reach the surface.
Ultrathin planar hematite film for solar photoelectrochemical water splitting.
Liu, Dong; Bierman, David M; Lenert, Andrej; Yu, Hai-Tong; Yang, Zhen; Wang, Evelyn N; Duan, Yuan-Yuan
2015-11-30
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. 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 of hematite photoanodes. PMID:26698797
NASA Astrophysics Data System (ADS)
Deglint, Jason; Kazemzadeh, Farnoud; Wong, Alexander; Clausi, David A.
2015-09-01
One method to acquire multispectral images is to sequentially capture a series of images where each image contains information from a different bandwidth of light. Another method is to use a series of beamsplitters and dichroic filters to guide different bandwidths of light onto different cameras. However, these methods are very time consuming and expensive and perform poorly in dynamic scenes or when observing transient phenomena. An alternative strategy to capturing multispectral data is to infer this data using sparse spectral reflectance measurements captured using an imaging device with overlapping bandpass filters, such as a consumer digital camera using a Bayer filter pattern. Currently the only method of inferring dense reflectance spectra is the Wiener adaptive filter, which makes Gaussian assumptions about the data. However, these assumptions may not always hold true for all data. We propose a new technique to infer dense reflectance spectra from sparse spectral measurements through the use of a non-linear regression model. The non-linear regression model used in this technique is the random forest model, which is an ensemble of decision trees and trained via the spectral characterization of the optical imaging system and spectral data pair generation. This model is then evaluated by spectrally characterizing different patches on the Macbeth color chart, as well as by reconstructing inferred multispectral images. Results show that the proposed technique can produce inferred dense reflectance spectra that correlate well with the true dense reflectance spectra, which illustrates the merits of the technique.
Lensless wide-field fluorescent imaging on a chip using compressive decoding of sparse objects
Coskun, Ahmet F.; Sencan, Ikbal; Su, Ting-Wei; Ozcan, Aydogan
2010-01-01
We demonstrate the use of a compressive sampling algorithm for on-chip fluorescent imaging of sparse objects over an ultra-large field-of-view (>8 cm2) without the need for any lenses or mechanical scanning. In this lensfree imaging technique, fluorescent samples placed on a chip are excited through a prism interface, where the pump light is filtered out by total internal reflection after exciting the entire sample volume. The emitted fluorescent light from the specimen is collected through an on-chip fiber-optic faceplate and is delivered to a wide field-of-view opto-electronic sensor array for lensless recording of fluorescent spots corresponding to the samples. A compressive sampling based optimization algorithm is then used to rapidly reconstruct the sparse distribution of fluorescent sources to achieve ~10 µm spatial resolution over the entire active region of the sensor-array, i.e., over an imaging field-of-view of >8 cm2. Such a wide-field lensless fluorescent imaging platform could especially be significant for high-throughput imaging cytometry, rare cell analysis, as well as for micro-array research. PMID:20588904
Sparsely sampling the sky: Regular vs. random sampling
NASA Astrophysics Data System (ADS)
Paykari, P.; Pires, S.; Starck, J.-L.; Jaffe, A. H.
2015-09-01
Aims: The next generation of galaxy surveys, aiming to observe millions of galaxies, are expensive both in time and money. This raises questions regarding the optimal investment of this time and money for future surveys. In a previous work, we have shown that a sparse sampling strategy could be a powerful substitute for the - usually favoured - contiguous observation of the sky. In our previous paper, regular sparse sampling was investigated, where the sparse observed patches were regularly distributed on the sky. The regularity of the mask introduces a periodic pattern in the window function, which induces periodic correlations at specific scales. Methods: In this paper, we use a Bayesian experimental design to investigate a "random" sparse sampling approach, where the observed patches are randomly distributed over the total sparsely sampled area. Results: We find that in this setting, the induced correlation is evenly distributed amongst all scales as there is no preferred scale in the window function. Conclusions: This is desirable when we are interested in any specific scale in the galaxy power spectrum, such as the matter-radiation equality scale. As the figure of merit shows, however, there is no preference between regular and random sampling to constrain the overall galaxy power spectrum and the cosmological parameters.
X-ray computed tomography using curvelet sparse regularization
Wieczorek, Matthias Vogel, Jakob; Lasser, Tobias; Frikel, Jürgen; Demaret, Laurent; Eggl, Elena; Pfeiffer, Franz; Kopp, Felix; Noël, Peter B.
2015-04-15
Purpose: Reconstruction of x-ray computed tomography (CT) data remains a mathematically challenging problem in medical imaging. Complementing the standard analytical reconstruction methods, sparse regularization is growing in importance, as it allows inclusion of prior knowledge. The paper presents a method for sparse regularization based on the curvelet frame for the application to iterative reconstruction in x-ray computed tomography. Methods: In this work, the authors present an iterative reconstruction approach based on the alternating direction method of multipliers using curvelet sparse regularization. Results: Evaluation of the method is performed on a specifically crafted numerical phantom dataset to highlight the method’s strengths. Additional evaluation is performed on two real datasets from commercial scanners with different noise characteristics, a clinical bone sample acquired in a micro-CT and a human abdomen scanned in a diagnostic CT. The results clearly illustrate that curvelet sparse regularization has characteristic strengths. In particular, it improves the restoration and resolution of highly directional, high contrast features with smooth contrast variations. The authors also compare this approach to the popular technique of total variation and to traditional filtered backprojection. Conclusions: The authors conclude that curvelet sparse regularization is able to improve reconstruction quality by reducing noise while preserving highly directional features.
Sparse approximation problem: how rapid simulated annealing succeeds and fails
NASA Astrophysics Data System (ADS)
Obuchi, Tomoyuki; Kabashima, Yoshiyuki
2016-03-01
Information processing techniques based on sparseness have been actively studied in several disciplines. Among them, a mathematical framework to approximately express a given dataset by a combination of a small number of basis vectors of an overcomplete basis is termed the sparse approximation. In this paper, we apply simulated annealing, a metaheuristic algorithm for general optimization problems, to sparse approximation in the situation where the given data have a planted sparse representation and noise is present. The result in the noiseless case shows that our simulated annealing works well in a reasonable parameter region: the planted solution is found fairly rapidly. This is true even in the case where a common relaxation of the sparse approximation problem, the G-relaxation, is ineffective. On the other hand, when the dimensionality of the data is close to the number of non-zero components, another metastable state emerges, and our algorithm fails to find the planted solution. This phenomenon is associated with a first-order phase transition. In the case of very strong noise, it is no longer meaningful to search for the planted solution. In this situation, our algorithm determines a solution with close-to-minimum distortion fairly quickly.
Online Hierarchical Sparse Representation of Multifeature for Robust Object Tracking
Qu, Shiru
2016-01-01
Object tracking based on sparse representation has given promising tracking results in recent years. However, the trackers under the framework of sparse representation always overemphasize the sparse representation and ignore the correlation of visual information. In addition, the sparse coding methods only encode the local region independently and ignore the spatial neighborhood information of the image. In this paper, we propose a robust tracking algorithm. Firstly, multiple complementary features are used to describe the object appearance; the appearance model of the tracked target is modeled by instantaneous and stable appearance features simultaneously. A two-stage sparse-coded method which takes the spatial neighborhood information of the image patch and the computation burden into consideration is used to compute the reconstructed object appearance. Then, the reliability of each tracker is measured by the tracking likelihood function of transient and reconstructed appearance models. Finally, the most reliable tracker is obtained by a well established particle filter framework; the training set and the template library are incrementally updated based on the current tracking results. Experiment results on different challenging video sequences show that the proposed algorithm performs well with superior tracking accuracy and robustness.
Image fusion via nonlocal sparse K-SVD dictionary learning.
Li, Ying; Li, Fangyi; Bai, Bendu; Shen, Qiang
2016-03-01
Image fusion aims to merge two or more images captured via various sensors of the same scene to construct a more informative image by integrating their details. Generally, such integration is achieved through the manipulation of the representations of the images concerned. Sparse representation plays an important role in the effective description of images, offering a great potential in a variety of image processing tasks, including image fusion. Supported by sparse representation, in this paper, an approach for image fusion by the use of a novel dictionary learning scheme is proposed. The nonlocal self-similarity property of the images is exploited, not only at the stage of learning the underlying description dictionary but during the process of image fusion. In particular, the property of nonlocal self-similarity is combined with the traditional sparse dictionary. This results in an improved learned dictionary, hereafter referred to as the nonlocal sparse K-SVD dictionary (where K-SVD stands for the K times singular value decomposition that is commonly used in the literature), and abbreviated to NL_SK_SVD. The performance of the NL_SK_SVD dictionary is applied for image fusion using simultaneous orthogonal matching pursuit. The proposed approach is evaluated with different types of images, and compared with a number of alternative image fusion techniques. The resultant superior fused images using the present approach demonstrates the efficacy of the NL_SK_SVD dictionary in sparse image representation. PMID:26974648
Analysis of Seismic Anisotropy Across Central Anatolia by Shear Wave Splitting
NASA Astrophysics Data System (ADS)
Pamir, Dilekcan; Abgarmi, Bizhan; Arda Özacar, A.
2014-05-01
Analysis of Seismic Anisotropy Across Central Anatolia by Shear Wave Splitting Dilekcan Pamir, Bizhan Abgarmi, A. Arda Özacar Department of Geological Engineering, Middle East Technical University (METU), Dumlupinar Bulvari 1, 06800 Ankara, Turkey Central Anatolia holds the key to connect the theories about the ongoing tectonic escape, the African Plate subduction along Cyprus Arc and the indenter-style collision of Arabian Plate along Bitlis Suture. However, the shear wave splitting measurements which are needed to characterize seismic anisotropy are very sparse in the region. Recently, seismic data recorded by national seismic networks (KOERI, ERI-DAD) with dense coverage, provided a unique opportunity to analyze the effect of present slab geometry (slab tears, slab break-off) on mantle deformation and test different models of anisotropy forming mechanisms. In this study, the anisotropic structure beneath the Central Anatolia is investigated via splitting of SKS and SKKS phases recorded at 46 broadband seismic stations. Our measurements yielded 1171 well-constrained splitting and 433 null results. Overall, the region displays NE-SW trending fast splitting directions and delay times on the order of 1 sec. On the other hand, a large number of stations which are spatially correlated with Cyprus Slab, Neogene volcanism and major tectonic structures present significant back azimuthal variations on splitting parameters that cannot be explained by one-layered anisotropy with horizontal symmetry. Thus, we have modeled anisotropy for two-layered structures using a forward approach and identified NE-SW trending fast splitting directions with delay times close to 1 sec at the lower layer and N-S, NW-SE trending fast splitting with limited time delays (0.1 - 0.3 sec) at the upper layer. Fast directions and delay times of the lower layer are similar to one-layered anisotropy and parallel or sub-parallel to the absolute plate motions which favors asthenospheric flow model
On Valence-Band Splitting in Layered MoS2.
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. PMID:26222731
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
NASA Technical Reports Server (NTRS)
2003-01-01
[figure removed for brevity, see original site]
Released 23 September 2003
A 22 km-diameter crater has been sliced by the tectonic forces that produced the rift known as Sirenum Fossae. The orientation of this rift is roughly radial to the great Tharsis volcano Arsia Mons, probably indicating a link between the formation of the rift and the volcano. Note how the rift cuts through a jumble of mounds on the floor of the crater. This indicates a sequence of events beginning with the formation of the crater followed by an infilling of material that was then eroded into the mounds and ultimately split open by the shifting martian crust.
Image information: VIS instrument. Latitude -29.7, Longitude 211.7 East (148.3 West). 19 meter/pixel resolution.
Note: this THEMIS visual image has not been radiometrically nor geometrically calibrated for this preliminary release. An empirical correction has been performed to remove instrumental effects. A linear shift has been applied in the cross-track and down-track direction to approximate spacecraft and planetary motion. Fully calibrated and geometrically projected images will be released through the Planetary Data System in accordance with Project policies at a later time.
NASA's Jet Propulsion Laboratory manages the 2001 Mars Odyssey mission for NASA's Office of Space Science, Washington, D.C. The Thermal Emission Imaging System (THEMIS) was developed by Arizona State University, Tempe, in collaboration with Raytheon Santa Barbara Remote Sensing. The THEMIS investigation is led by Dr. Philip Christensen at Arizona State University. Lockheed Martin Astronautics, Denver, is the prime contractor for the Odyssey project, and developed and built the orbiter. Mission operations are conducted jointly from Lockheed Martin and from JPL, a division of the California Institute of Technology in Pasadena.
Phenomena and Performance of High-Efficiency Split Spectrum Photovoltaics
NASA Astrophysics Data System (ADS)
Downs, Chandler
High-efficiency photovoltaics are one of the most promising technologies for supplying sustainable energy in the near future. These technologies allow for high energy conversion efficiencies and long system lifetimes, which is becoming an increasingly profitable power generation option. One high-efficiency photovoltaic technology gaining increasing attention recent years is that of split-spectrum photovoltaics. This technology divides the incident solar spectrum on the basis of wavelength, directing each portion of the spectrum to a different cell where the light can be utilized most efficiently. In this dissertation, a number of aspects of high-efficiency photovoltaics, most notably split-spectrum photovoltaics, are examined. First, the ideal bandgap placements of the subcells of a split-spectrum photovoltaic system are calculated, specifically determined with an eye towards practical fabrication of the cells. Two viable designs are determined which improve theoretical absolute conversion efficiency by 4-5%. Next, those systems are simulated using the TCAD Sentaurus software package to project conversion efficiencies and determine additional device specifications (doping levels, layer thicknesses, etc.). These cells show comparable conversion efficiencies to high performing, full-spectrum multijunction photovoltaics in fabrication today. In the last section, a theoretical examination of semiconductor performance under high optical concentration is performed, including the prediction and characterization of various phenomena in those devices. This work aims to improve the understanding of the performance of high concentration photovoltaics, most notably split-spectrum photovoltaics. This understanding will aid in the advancement of this technology as a widespread, sustainable energy source for use worldwide, reducing greenhouse emissions and providing cheap, clean energy.
Communication: Tunnelling splitting in the phosphine molecule.
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. PMID:27608982
Human interaction recognition through two-phase sparse coding
NASA Astrophysics Data System (ADS)
Zhang, B.; Conci, N.; De Natale, Francesco G. B.
2014-03-01
In this paper, we propose a novel method to recognize two-person interactions through a two-phase sparse coding approach. In the first phase, we adopt the non-negative sparse coding on the spatio-temporal interest points (STIPs) extracted from videos, and then construct the feature vector for each video by sum-pooling and l2-normalization. At the second stage, we apply the label-consistent KSVD (LC-KSVD) algorithm on the video feature vectors to train a new dictionary. The algorithm has been validated on the TV human interaction dataset, and the experimental results show that the classification performance is considerably improved compared with the standard bag-of-words approach and the single layer non-negative sparse coding.
Analyzing Sparse Dictionaries for Online Learning With Kernels
NASA Astrophysics Data System (ADS)
Honeine, Paul
2015-12-01
Many signal processing and machine learning methods share essentially the same linear-in-the-parameter model, with as many parameters as available samples as in kernel-based machines. Sparse approximation is essential in many disciplines, with new challenges emerging in online learning with kernels. To this end, several sparsity measures have been proposed in the literature to quantify sparse dictionaries and constructing relevant ones, the most prolific ones being the distance, the approximation, the coherence and the Babel measures. In this paper, we analyze sparse dictionaries based on these measures. By conducting an eigenvalue analysis, we show that these sparsity measures share many properties, including the linear independence condition and inducing a well-posed optimization problem. Furthermore, we prove that there exists a quasi-isometry between the parameter (i.e., dual) space and the dictionary's induced feature space.
Sparse Partial Equilibrium Tables in Chemically Resolved Reactive Flow
Vitello, P; Fried, L E; Pudliner, B; McAbee, T
2003-07-14
The detonation of an energetic material is the result of a complex interaction between kinetic chemical reactions and hydrodynamics. Unfortunately, little is known concerning the detailed chemical kinetics of detonations in energetic materials. CHEETAH uses rate laws to treat species with the slowest chemical reactions, while assuming other chemical species are in equilibrium. CHEETAH supports a wide range of elements and condensed detonation products and can also be applied to gas detonations. A sparse hash table of equation of state values, called the ''cache'' is used in CHEETAH to enhance the efficiency of kinetic reaction calculations. For large-scale parallel hydrodynamic calculations, CHEETAH uses MPI communication to updates to the cache. We present here details of the sparse caching model used in the CHEETAH. To demonstrate the efficiency of modeling using a sparse cache model we consider detonations in energetic materials.
Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model
NASA Astrophysics Data System (ADS)
Meng, Jia; Zhang, Jianqiu(Michelle); Qi, Yuan(Alan); Chen, Yidong; Huang, Yufei
2010-12-01
The problem of uncovering transcriptional regulation by transcription factors (TFs) based on microarray data is considered. A novel Bayesian sparse correlated rectified factor model (BSCRFM) is proposed that models the unknown TF protein level activity, the correlated regulations between TFs, and the sparse nature of TF-regulated genes. The model admits prior knowledge from existing database regarding TF-regulated target genes based on a sparse prior and through a developed Gibbs sampling algorithm, a context-specific transcriptional regulatory network specific to the experimental condition of the microarray data can be obtained. The proposed model and the Gibbs sampling algorithm were evaluated on the simulated systems, and results demonstrated the validity and effectiveness of the proposed approach. The proposed model was then applied to the breast cancer microarray data of patients with Estrogen Receptor positive ([InlineEquation not available: see fulltext.]) status and Estrogen Receptor negative ([InlineEquation not available: see fulltext.]) status, respectively.
PSPIKE: A Parallel Hybrid Sparse Linear System Solver
NASA Astrophysics Data System (ADS)
Manguoglu, Murat; Sameh, Ahmed H.; Schenk, Olaf
The availability of large-scale computing platforms comprised of tens of thousands of multicore processors motivates the need for the next generation of highly scalable sparse linear system solvers. These solvers must optimize parallel performance, processor (serial) performance, as well as memory requirements, while being robust across broad classes of applications and systems. In this paper, we present a new parallel solver that combines the desirable characteristics of direct methods (robustness) and effective iterative solvers (low computational cost), while alleviating their drawbacks (memory requirements, lack of robustness). Our proposed hybrid solver is based on the general sparse solver PARDISO, and the “Spike” family of hybrid solvers. The resulting algorithm, called PSPIKE, is as robust as direct solvers, more reliable than classical preconditioned Krylov subspace methods, and much more scalable than direct sparse solvers. We support our performance and parallel scalability claims using detailed experimental studies and comparison with direct solvers, as well as classical preconditioned Krylov methods.
Automatic landslide and mudflow detection method via multichannel sparse representation
NASA Astrophysics Data System (ADS)
Chao, Chen; Zhou, Jianjun; Hao, Zhuo; Sun, Bo; He, Jun; Ge, Fengxiang
2015-10-01
Landslide and mudflow detection is an important application of aerial images and high resolution remote sensing images, which is crucial for national security and disaster relief. Since the high resolution images are often large in size, it's necessary to develop an efficient algorithm for landslide and mudflow detection. Based on the theory of sparse representation and, we propose a novel automatic landslide and mudflow detection method in this paper, which combines multi-channel sparse representation and eight neighbor judgment methods. The whole process of the detection is totally automatic. We make the experiment on a high resolution image of ZhouQu district of Gansu province in China on August, 2010 and get a promising result which proved the effective of using sparse representation on landslide and mudflow detection.
A Multiobjective Sparse Feature Learning Model for Deep Neural Networks.
Gong, Maoguo; Liu, Jia; Li, Hao; Cai, Qing; Su, Linzhi
2015-12-01
Hierarchical deep neural networks are currently popular learning models for imitating the hierarchical architecture of human brain. Single-layer feature extractors are the bricks to build deep networks. Sparse feature learning models are popular models that can learn useful representations. But most of those models need a user-defined constant to control the sparsity of representations. In this paper, we propose a multiobjective sparse feature learning model based on the autoencoder. The parameters of the model are learnt by optimizing two objectives, reconstruction error and the sparsity of hidden units simultaneously to find a reasonable compromise between them automatically. We design a multiobjective induced learning procedure for this model based on a multiobjective evolutionary algorithm. In the experiments, we demonstrate that the learning procedure is effective, and the proposed multiobjective model can learn useful sparse features. PMID:26340790
Sparse representation based face recognition using weighted regions
NASA Astrophysics Data System (ADS)
Bilgazyev, Emil; Yeniaras, E.; Uyanik, I.; Unan, Mahmut; Leiss, E. L.
2013-12-01
Face recognition is a challenging research topic, especially when the training (gallery) and recognition (probe) images are acquired using different cameras under varying conditions. Even a small noise or occlusion in the images can compromise the accuracy of recognition. Lately, sparse encoding based classification algorithms gave promising results for such uncontrollable scenarios. In this paper, we introduce a novel methodology by modeling the sparse encoding with weighted patches to increase the robustness of face recognition even further. In the training phase, we define a mask (i.e., weight matrix) using a sparse representation selecting the facial regions, and in the recognition phase, we perform comparison on selected facial regions. The algorithm was evaluated both quantitatively and qualitatively using two comprehensive surveillance facial image databases, i.e., SCfaceandMFPV, with the results clearly superior to common state-of-the-art methodologies in different scenarios.
SAR target classification based on multiscale sparse representation
NASA Astrophysics Data System (ADS)
Ruan, Huaiyu; Zhang, Rong; Li, Jingge; Zhan, Yibing
2016-03-01
We propose a novel multiscale sparse representation approach for SAR target classification. It firstly extracts the dense SIFT descriptors on multiple scales, then trains a global multiscale dictionary by sparse coding algorithm. After obtaining the sparse representation, the method applies spatial pyramid matching (SPM) and max pooling to summarize the features for each image. The proposed method can provide more information and descriptive ability than single-scale ones. Moreover, it costs less extra computation than existing multiscale methods which compute a dictionary for each scale. The MSTAR database and ship database collected from TerraSAR-X images are used in classification setup. Results show that the best overall classification rate of the proposed approach can achieve 98.83% on the MSTAR database and 92.67% on the TerraSAR-X ship database.
A note on rank reduction in sparse multivariate regression
Chen, Kun; Chan, Kung-Sik
2016-01-01
A reduced-rank regression with sparse singular value decomposition (RSSVD) approach was proposed by Chen et al. for conducting variable selection in a reduced-rank model. To jointly model the multivariate response, the method efficiently constructs a prespecified number of latent variables as some sparse linear combinations of the predictors. Here, we generalize the method to also perform rank reduction, and enable its usage in reduced-rank vector autoregressive (VAR) modeling to perform automatic rank determination and order selection. We show that in the context of stationary time-series data, the generalized approach correctly identifies both the model rank and the sparse dependence structure between the multivariate response and the predictors, with probability one asymptotically. We demonstrate the efficacy of the proposed method by simulations and analyzing a macro-economical multivariate time series using a reduced-rank VAR model. PMID:26997938
Sparse-based multispectral image encryption via ptychography
NASA Astrophysics Data System (ADS)
Rawat, Nitin; Shi, Yishi; Kim, Byoungho; Lee, Byung-Geun
2015-12-01
Recently, we proposed a model of securing a ptychography-based monochromatic image encryption system via the classical Photon-counting imaging (PCI) technique. In this study, we examine a single-channel multispectral sparse-based photon-counting ptychography imaging (SMPI)-based cryptosystem. A ptychography-based cryptosystem creates a complex object wave field, which can be reconstructed by a series of diffraction intensity patterns through an aperture movement. The PCI sensor records only a few complex Bayer patterned samples that have been utilized in the decryption process. Sparse sensing and nonlinear properties of the classical PCI system, together with the scanning probes, enlarge the key space, and such a combination therefore enhances the system's security. We demonstrate that the sparse samples have adequate information for image decryption, as well as information authentication by means of optical correlation.
P-SPARSLIB: A parallel sparse iterative solution package
Saad, Y.
1994-12-31
Iterative methods are gaining popularity in engineering and sciences at a time where the computational environment is changing rapidly. P-SPARSLIB is a project to build a software library for sparse matrix computations on parallel computers. The emphasis is on iterative methods and the use of distributed sparse matrices, an extension of the domain decomposition approach to general sparse matrices. One of the goals of this project is to develop a software package geared towards specific applications. For example, the author will test the performance and usefulness of P-SPARSLIB modules on linear systems arising from CFD applications. Equally important is the goal of portability. In the long run, the author wishes to ensure that this package is portable on a variety of platforms, including SIMD environments and shared memory environments.
Sparse Partial Equilibrium Tables in Chemically Resolved Reactive Flow
NASA Astrophysics Data System (ADS)
Vitello, Peter; Fried, Laurence E.; Pudliner, Brian; McAbee, Tom
2004-07-01
The detonation of an energetic material is the result of a complex interaction between kinetic chemical reactions and hydrodynamics. Unfortunately, little is known concerning the detailed chemical kinetics of detonations in energetic materials. CHEETAH uses rate laws to treat species with the slowest chemical reactions, while assuming other chemical species are in equilibrium. CHEETAH supports a wide range of elements and condensed detonation products and can also be applied to gas detonations. A sparse hash table of equation of state values is used in CHEETAH to enhance the efficiency of kinetic reaction calculations. For large-scale parallel hydrodynamic calculations, CHEETAH uses parallel communication to updates to the cache. We present here details of the sparse caching model used in the CHEETAH coupled to an ALE hydrocode. To demonstrate the efficiency of modeling using a sparse cache model we consider detonations in energetic materials.
Sparse Sensing of Aerodynamic Loads on Insect Wings
NASA Astrophysics Data System (ADS)
Manohar, Krithika; Brunton, Steven; Kutz, J. Nathan
2015-11-01
We investigate how insects use sparse sensors on their wings to detect aerodynamic loading and wing deformation using a coupled fluid-structure model given periodically flapping input motion. Recent observations suggest that insects collect sensor information about their wing deformation to inform control actions for maneuvering and rejecting gust disturbances. Given a small number of point measurements of the chordwise aerodynamic loads from the sparse sensors, we reconstruct the entire chordwise loading using sparsesensing - a signal processing technique that reconstructs a signal from a small number of measurements using l1 norm minimization of sparse modal coefficients in some basis. We compare reconstructions from sensors randomly sampled from probability distributions biased toward different regions along the wing chord. In this manner, we determine the preferred regions along the chord for sensor placement and for estimating chordwise loads to inform control decisions in flight.
Joint sparse representation for robust multimodal biometrics recognition.
Shekhar, Sumit; Patel, Vishal M; Nasrabadi, Nasser M; Chellappa, Rama
2014-01-01
Traditional biometric recognition systems rely on a single biometric signature for authentication. While the advantage of using multiple sources of information for establishing the identity has been widely recognized, computational models for multimodal biometrics recognition have only recently received attention. We propose a multimodal sparse representation method, which represents the test data by a sparse linear combination of training data, while constraining the observations from different modalities of the test subject to share their sparse representations. Thus, we simultaneously take into account correlations as well as coupling information among biometric modalities. A multimodal quality measure is also proposed to weigh each modality as it gets fused. Furthermore, we also kernelize the algorithm to handle nonlinearity in data. The optimization problem is solved using an efficient alternative direction method. Various experiments show that the proposed method compares favorably with competing fusion-based methods. PMID:24231870
The sparseness of neuronal responses in ferret primary visual cortex.
Tolhurst, David J; Smyth, Darragh; Thompson, Ian D
2009-02-25
Various arguments suggest that neuronal coding of natural sensory stimuli should be sparse (i.e., individual neurons should respond rarely but should respond reliably). We examined sparseness of visual cortical neurons in anesthetized ferret to flashed natural scenes. Response behavior differed widely between neurons. The median firing rate of 4.1 impulses per second was slightly higher than predicted from consideration of metabolic load. Thirteen percent of neurons (12 of 89) responded to <5% of the images, but one-half responded to >25% of images. Multivariate analysis of the range of sparseness values showed that 67% of the variance was accounted for by differing response patterns to moving gratings. Repeat presentation of images showed that response variance for natural images exaggerated sparseness measures; variance was scaled with mean response, but with a lower Fano factor than for the responses to moving gratings. This response variability and the "soft" sparse responses (Rehn and Sommer, 2007) raise the question of what constitutes a reliable neuronal response and imply parallel signaling by multiple neurons. We investigated whether the temporal structure of responses might be reliable enough to give additional information about natural scenes. Poststimulus time histogram shape was similar for "strong" and "weak" stimuli, with no systematic change in first-spike latency with stimulus strength. The variance of first-spike latency for repeat presentations of the same image was greater than the latency variance between images. In general, responses to flashed natural scenes do not seem compatible with a sparse encoding in which neurons fire rarely but reliably. PMID:19244512
Content addressable systolic array for sparse matrix computation
Wing, O.
1983-01-01
A systolic array is proposed which is specifically designed to solve a system of sparse linear equations. The array consists of a number of processing elements connected in a ring. Each processing element has its own content addressable memory where the nonzero elements of the sparse matrix are stored. Matrix elements to which elementary operations are applied are extracted from the memory by content addressing. The system of equations is solved in a systolic fashion and the solution is obtained in nz+5n-2 steps where nz is the number of nonzero elements along and below the diagonal and n is the number of equations. 13 references.
Reconstruction Techniques for Sparse Multistatic Linear Array Microwave Imaging
Sheen, David M.; Hall, Thomas E.
2014-06-09
Sequentially-switched linear arrays are an enabling technology for a number of near-field microwave imaging applications. Electronically sequencing along the array axis followed by mechanical scanning along an orthogonal axis allows dense sampling of a two-dimensional aperture in near real-time. In this paper, a sparse multi-static array technique will be described along with associated Fourier-Transform-based and back-projection-based image reconstruction algorithms. Simulated and measured imaging results are presented that show the effectiveness of the sparse array technique along with the merits and weaknesses of each image reconstruction approach.
Sparse Matrix for ECG Identification with Two-Lead Features
Tseng, Kuo-Kun; Luo, Jiao; Wang, Wenmin; Haiting, Dong
2015-01-01
Electrocardiograph (ECG) human identification has the potential to improve biometric security. However, improvements in ECG identification and feature extraction are required. Previous work has focused on single lead ECG signals. Our work proposes a new algorithm for human identification by mapping two-lead ECG signals onto a two-dimensional matrix then employing a sparse matrix method to process the matrix. And that is the first application of sparse matrix techniques for ECG identification. Moreover, the results of our experiments demonstrate the benefits of our approach over existing methods. PMID:25961074
D Super-Resolution Approach for Sparse Laser Scanner Data
NASA Astrophysics Data System (ADS)
Hosseinyalamdary, S.; Yilmaz, A.
2015-08-01
Laser scanner point cloud has been emerging in Photogrammetry and computer vision to achieve high level tasks such as object tracking, object recognition and scene understanding. However, low cost laser scanners are noisy, sparse and prone to systematic errors. This paper proposes a novel 3D super resolution approach to reconstruct surface of the objects in the scene. This method works on sparse, unorganized point clouds and has superior performance over other surface recovery approaches. Since the proposed approach uses anisotropic diffusion equation, it does not deteriorate the object boundaries and it preserves topology of the object.
Three-dimensional sparse-aperture moving-target imaging
NASA Astrophysics Data System (ADS)
Ferrara, Matthew; Jackson, Julie; Stuff, Mark
2008-04-01
If a target's motion can be determined, the problem of reconstructing a 3D target image becomes a sparse-aperture imaging problem. That is, the data lies on a random trajectory in k-space, which constitutes a sparse data collection that yields very low-resolution images if backprojection or other standard imaging techniques are used. This paper investigates two moving-target imaging algorithms: the first is a greedy algorithm based on the CLEAN technique, and the second is a version of Basis Pursuit Denoising. The two imaging algorithms are compared for a realistic moving-target motion history applied to a Xpatch-generated backhoe data set.
BIRD: A general interface for sparse distributed memory simulators
NASA Technical Reports Server (NTRS)
Rogers, David
1990-01-01
Kanerva's sparse distributed memory (SDM) has now been implemented for at least six different computers, including SUN3 workstations, the Apple Macintosh, and the Connection Machine. A common interface for input of commands would both aid testing of programs on a broad range of computer architectures and assist users in transferring results from research environments to applications. A common interface also allows secondary programs to generate command sequences for a sparse distributed memory, which may then be executed on the appropriate hardware. The BIRD program is an attempt to create such an interface. Simplifying access to different simulators should assist developers in finding appropriate uses for SDM.
Learning control for robotic manipulators with sparse data
NASA Technical Reports Server (NTRS)
Morita, Atsushi; Dubowsky, Steven; Hootsmans, Norbert A. M.
1987-01-01
Learning control algorithms have been proposed for error compensation in repetitive robotic manipulator tasks. It is shown that the performance of such control algorithms can be seriously degraded when the feedback data they use is relatively sparse in time, such as might be provided by vision systems. It is also shown that learning control algorithms can be modified to compensate for the effects of sparse data and thereby yield performance which approaches that of systems without limitations on the sensory information available for control.
A LONE code for the sparse control of quantum systems
NASA Astrophysics Data System (ADS)
Ciaramella, G.; Borzì, A.
2016-03-01
In many applications with quantum spin systems, control functions with a sparse and pulse-shaped structure are often required. These controls can be obtained by solving quantum optimal control problems with L1-penalized cost functionals. In this paper, the MATLAB package LONE is presented aimed to solving L1-penalized optimal control problems governed by unitary-operator quantum spin models. This package implements a new strategy that includes a globalized semi-smooth Krylov-Newton scheme and a continuation procedure. Results of numerical experiments demonstrate the ability of the LONE code in computing accurate sparse optimal control solutions.
Supramolecular Control over Split-Luciferase Complementation.
Bosmans, Ralph P G; Briels, Jeroen M; Milroy, Lech-Gustav; de Greef, Tom F A; Merkx, Maarten; Brunsveld, Luc
2016-07-25
Supramolecular split-enzyme complementation restores enzymatic activity and allows for on-off switching. Split-luciferase fragment pairs were provided with an N-terminal FGG sequence and screened for complementation through host-guest binding to cucurbit[8]uril (Q8). Split-luciferase heterocomplex formation was induced in a Q8 concentration dependent manner, resulting in a 20-fold upregulation of luciferase activity. Supramolecular split-luciferase complementation was fully reversible, as revealed by using two types of Q8 inhibitors. Competition studies with the weak-binding FGG peptide revealed a 300-fold enhanced stability for the formation of the ternary heterocomplex compared to binding of two of the same fragments to Q8. Stochiometric binding by the potent inhibitor memantine could be used for repeated cycling of luciferase activation and deactivation in conjunction with Q8, providing a versatile module for in vitro supramolecular signaling networks. PMID:27356091
Small-molecule-dependent split aptamer ligation.
Sharma, Ashwani K; Heemstra, Jennifer M
2011-08-17
Here we describe the first use of small-molecule binding to direct a chemical reaction between two nucleic acid strands. The reported reaction is a ligation between two fragments of a DNA split aptamer using strain-promoted azide-alkyne cycloaddition. Utilizing the split aptamer for cocaine, we demonstrate small-molecule-dependent ligation that is dose-dependent over a wide range of cocaine concentrations and is compatible with complex biological fluids such as human blood serum. Moreover, studies of split aptamer ligation at varying salt concentrations and using structurally similar analogues of cocaine have revealed new insight into the assembly and small-molecule binding properties of the cocaine split aptamer. The ability to translate the presence of a small-molecule target into the output of DNA ligation is anticipated to enable the development of new, broadly applicable small-molecule detection assays. PMID:21761903
Divided Opinions on the Split Fovea
ERIC Educational Resources Information Center
Ellis, Andrew W.; Brysbaert, Marc
2010-01-01
We explain once again the distinction between the "split fovea theory" and the "bilateral projection theory", and consider the implications of the two theories for understanding the processing of centrally fixated words and faces.
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.
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. PMID:25473850
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. PMID:25473850
Radial spin Hall effect of light
NASA Astrophysics Data System (ADS)
Shu, Weixing; Ke, Yougang; Liu, Yachao; Ling, Xiaohui; Luo, Hailu; Yin, Xiaobo
2016-01-01
We propose and realize a radial spin Hall effect (SHE) of light by using a dielectric metasurface. The metasurface with radially varying optical axes introduces a Pancharatnam-Berry (PB) geometrical phase to the incident light. The spatial gradient of PB phase accounts for a shift in the momentum space and thus leads the light to split radially into two concentric rays with opposite spin in the position space, which is called a radial SHE. Further experiments verify that the magnitude of the splitting increases with the rotation rate of the optical-axis orientation and the propagation distance, thereby allowing for macroscopic observation of the SHE. We also find that the phase of the incident light influences the profiles of the two split rays, while the polarization determines their intensities. The results provide methods to tune the SHE of light by engineering metasurfaces and modulating the incident light, and this radial SHE may be extrapolated to other physical systems.
Antenna Splitting Functions for Massive Particles
Larkoski, Andrew J.; Peskin, Michael E.; /SLAC
2011-06-22
An antenna shower is a parton shower in which the basic move is a color-coherent 2 {yields} 3 parton splitting process. In this paper, we give compact forms for the spin-dependent antenna splitting functions involving massive partons of spin 0 and spin 1/2. We hope that this formalism we have presented will be useful in describing the QCD dynamics of the top quark and other heavy particles at LHC.
Spin-dependent antenna splitting functions
Larkoski, Andrew J.; Peskin, Michael E.
2010-03-01
We consider parton showers based on radiation from QCD dipoles or 'antennas'. These showers are built from 2{yields}3 parton splitting processes. The question then arises of what functions replace the Altarelli-Parisi splitting functions in this approach. We give a detailed answer to this question, applicable to antenna showers in which partons carry definite helicity, and to both initial- and final-state emissions.
Spontaneous splitting of a quadruply charged vortex.
Isoshima, T; Okano, M; Yasuda, H; Kasa, K; Huhtamäki, J A M; Kumakura, M; Takahashi, Y
2007-11-16
We studied the splitting instability of a quadruply charged vortex both experimentally and theoretically. The density defect, which is a signature of the vortex core, is experimentally observed to deform into a linear shape. The deformed defect is theoretically confirmed to be an array of four linearly aligned singly charged vortices. The array of vortices rotates and precesses simultaneously with different angular velocities. The initial state of the system is not rotationally symmetric, which enables spontaneous splitting without external perturbations. PMID:18233124
Spontaneous Splitting of a Quadruply Charged Vortex
Isoshima, T.; Okano, M.; Yasuda, H.; Kasa, K.; Huhtamaeki, J. A. M.; Kumakura, M.; Takahashi, Y.
2007-11-16
We studied the splitting instability of a quadruply charged vortex both experimentally and theoretically. The density defect, which is a signature of the vortex core, is experimentally observed to deform into a linear shape. The deformed defect is theoretically confirmed to be an array of four linearly aligned singly charged vortices. The array of vortices rotates and precesses simultaneously with different angular velocities. The initial state of the system is not rotationally symmetric, which enables spontaneous splitting without external perturbations.
Smallness of baryon asymmetry from split supersymmetry
Kasuya, Shinta; Takahashi, Fuminobu
2005-06-15
The smallness of the baryon asymmetry in our universe is one of the greatest mysteries and may originate from some profound physics beyond the standard model. We investigate the Affleck-Dine baryogenesis in split supersymmetry, and find that the smallness of the baryon asymmetry is directly related to the hierarchy between the supersymmetry breaking squark/slepton masses and the weak scale. Put simply, the baryon asymmetry is small because of the split mass spectrum.
Laser beam splitting by polarization encoding.
Wan, Chenhao
2015-03-20
A scheme is proposed to design a polarization grating that splits an incident linearly polarized beam to an array of linearly polarized beams of identical intensity distribution and various azimuth angles of linear polarization. The grating is equivalent to a wave plate with space-variant azimuth angle and space-variant phase retardation. The linear polarization states of all split beams make the grating suitable for coherent beam combining architectures based on Dammann gratings. PMID:25968540
Replacement of split-pin assemblies in nuclear reactors
Nee, J.D.; Green, R.A.
1989-12-12
This patent describes a pin-insertion/torque tool for the replacement of old split-pin assemblies. Each of the new split-pin assemblies including a new split-pin having times and a new nut for securing the new split pin in the guide tube, a new nut being inserted in the guide tube in position to receive a split pin. The the pin-insertion/torque tool including a blade means for engaging a new split pin with the blade with the tines of the new split pins straddling the blade, means, connected to the blade, for advancing the split-pin into the guide tube into threading engagement with the new nut positioned to receive a new split pin and means, to be connected to the nut for securing the new nut onto the new split pin while the split pin is engaged by the blade.
Quintessence and phantom emerging from the split-complex field and the split-quaternion field
NASA Astrophysics Data System (ADS)
Gao, Changjun; Chen, Xuelei; Shen, You-Gen
2016-01-01
Motivated by the mathematic theory of split-complex numbers (or hyperbolic numbers, also perplex numbers) and the split-quaternion numbers (or coquaternion numbers), we define the notion of split-complex scalar field and the split-quaternion scalar field. Then we explore the cosmic evolution of these scalar fields in the background of spatially flat Friedmann-Robertson-Walker Universe. We find that both the quintessence field and the phantom field could naturally emerge in these scalar fields. Introducing the metric of field space, these theories fall into a subclass of the multi-field theories which have been extensively studied in inflationary cosmology.
Spin splitting generated in a Y-shaped semiconductor nanostructure with a quantum point contact
Wójcik, P. Adamowski, J. Wołoszyn, M.; Spisak, B. J.
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 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
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.
Two demonstrators and a simulator for a sparse, distributed memory
NASA Technical Reports Server (NTRS)
Brown, Robert L.
1987-01-01
Described are two programs demonstrating different aspects of Kanerva's Sparse, Distributed Memory (SDM). These programs run on Sun 3 workstations, one using color, and have straightforward graphically oriented user interfaces and graphical output. Presented are descriptions of the programs, how to use them, and what they show. Additionally, this paper describes the software simulator behind each program.
Sparse-flowering orchardgrass is stable across temperate North America
Technology Transfer Automated Retrieval System (TEKTRAN)
Three sparse-flowering orchardgrass populations were developed by selective breeding as a mechanism to reduce stem production during the early spring season in management-intensive grazing systems. These populations and three check cultivars were evaluated under frequent- and infrequent-harvest syst...
Voxel selection in FMRI data analysis based on sparse representation.
Li, Yuanqing; Namburi, Praneeth; Yu, Zhuliang; Guan, Cuntai; Feng, Jianfeng; Gu, Zhenghui
2009-10-01
Multivariate pattern analysis approaches toward detection of brain regions from fMRI data have been gaining attention recently. In this study, we introduce an iterative sparse-representation-based algorithm for detection of voxels in functional MRI (fMRI) data with task relevant information. In each iteration of the algorithm, a linear programming problem is solved and a sparse weight vector is subsequently obtained. The final weight vector is the mean of those obtained in all iterations. The characteristics of our algorithm are as follows: 1) the weight vector (output) is sparse; 2) the magnitude of each entry of the weight vector represents the significance of its corresponding variable or feature in a classification or regression problem; and 3) due to the convergence of this algorithm, a stable weight vector is obtained. To demonstrate the validity of our algorithm and illustrate its application, we apply the algorithm to the Pittsburgh Brain Activity Interpretation Competition 2007 functional fMRI dataset for selecting the voxels, which are the most relevant to the tasks of the subjects. Based on this dataset, the aforementioned characteristics of our algorithm are analyzed, and a comparison between our method with the univariate general-linear-model-based statistical parametric mapping is performed. Using our method, a combination of voxels are selected based on the principle of effective/sparse representation of a task. Data analysis results in this paper show that this combination of voxels is suitable for decoding tasks and demonstrate the effectiveness of our method. PMID:19567340
Global and Local Sparse Subspace Optimization for Motion Segmentation
NASA Astrophysics Data System (ADS)
Yang, M. Ying; Feng, S.; Ackermann, H.; Rosenhahn, B.
2015-08-01
In this paper, we propose a new framework for segmenting feature-based moving objects under affine subspace model. Since the feature trajectories in practice are high-dimensional and contain a lot of noise, we firstly apply the sparse PCA to represent the original trajectories with a low-dimensional global subspace, which consists of the orthogonal sparse principal vectors. Subsequently, the local subspace separation will be achieved via automatically searching the sparse representation of the nearest neighbors for each projected data. In order to refine the local subspace estimation result, we propose an error estimation to encourage the projected data that span a same local subspace to be clustered together. In the end, the segmentation of different motions is achieved through the spectral clustering on an affinity matrix, which is constructed with both the error estimation and sparse neighbors optimization. We test our method extensively and compare it with state-of-the-art methods on the Hopkins 155 dataset. The results show that our method is comparable with the other motion segmentation methods, and in many cases exceed them in terms of precision and computation time.
Multiple kernel sparse representations for supervised and unsupervised learning.
Thiagarajan, Jayaraman J; Ramamurthy, Karthikeyan Natesan; Spanias, Andreas
2014-07-01
In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods. PMID:24833593
Automatic stellar spectral classification via sparse representations and dictionary learning
NASA Astrophysics Data System (ADS)
Díaz-Hernández, R.; Peregrina-Barreto, H.; Altamirano-Robles, L.; González-Bernal, J. A.; Ortiz-Esquivel, A. E.
2014-11-01
Stellar classification is an important topic in astronomical tasks such as the study of stellar populations. However, stellar classification of a region of the sky is a time-consuming process due to the large amount of objects present in an image. Therefore, automatic techniques to speed up the process are required. In this work, we study the application of a sparse representation and a dictionary learning for automatic spectral stellar classification. Our dataset consist of 529 calibrated stellar spectra of classes B to K, belonging to the Pulkovo Spectrophotometric catalog, in the 3400-5500Å range. These stellar spectra are used for both training and testing of the proposed methodology. The sparse technique is applied by using the greedy algorithm OMP (Orthogonal Matching Pursuit) for finding an approximated solution, and the K-SVD (K-Singular Value Decomposition) for the dictionary learning step. Thus, sparse classification is based on the recognition of the common characteristics of a particular stellar type through the construction of a trained basis. In this work, we propose a classification criterion that evaluates the results of the sparse representation techniques and determines the final classification of the spectra. This methodology demonstrates its ability to achieve levels of classification comparable with automatic methodologies previously reported such as the Maximum Correlation Coefficient (MCC) and Artificial Neural Networks (ANN).
SAR imaging via iterative adaptive approach and sparse Bayesian learning
NASA Astrophysics Data System (ADS)
Xue, Ming; Santiago, Enrique; Sedehi, Matteo; Tan, Xing; Li, Jian
2009-05-01
We consider sidelobe reduction and resolution enhancement in synthetic aperture radar (SAR) imaging via an iterative adaptive approach (IAA) and a sparse Bayesian learning (SBL) method. The nonparametric weighted least squares based IAA algorithm is a robust and user parameter-free adaptive approach originally proposed for array processing. We show that it can be used to form enhanced SAR images as well. SBL has been used as a sparse signal recovery algorithm for compressed sensing. It has been shown in the literature that SBL is easy to use and can recover sparse signals more accurately than the l 1 based optimization approaches, which require delicate choice of the user parameter. We consider using a modified expectation maximization (EM) based SBL algorithm, referred to as SBL-1, which is based on a three-stage hierarchical Bayesian model. SBL-1 is not only more accurate than benchmark SBL algorithms, but also converges faster. SBL-1 is used to further enhance the resolution of the SAR images formed by IAA. Both IAA and SBL-1 are shown to be effective, requiring only a limited number of iterations, and have no need for polar-to-Cartesian interpolation of the SAR collected data. This paper characterizes the achievable performance of these two approaches by processing the complex backscatter data from both a sparse case study and a backhoe vehicle in free space with different aperture sizes.
HYPOTHESIS TESTING FOR HIGH-DIMENSIONAL SPARSE BINARY REGRESSION
Mukherjee, Rajarshi; Pillai, Natesh S.; Lin, Xihong
2015-01-01
In this paper, we study the detection boundary for minimax hypothesis testing in the context of high-dimensional, sparse binary regression models. Motivated by genetic sequencing association studies for rare variant effects, we investigate the complexity of the hypothesis testing problem when the design matrix is sparse. We observe a new phenomenon in the behavior of detection boundary which does not occur in the case of Gaussian linear regression. We derive the detection boundary as a function of two components: a design matrix sparsity index and signal strength, each of which is a function of the sparsity of the alternative. For any alternative, if the design matrix sparsity index is too high, any test is asymptotically powerless irrespective of the magnitude of signal strength. For binary design matrices with the sparsity index that is not too high, our results are parallel to those in the Gaussian case. In this context, we derive detection boundaries for both dense and sparse regimes. For the dense regime, we show that the generalized likelihood ratio is rate optimal; for the sparse regime, we propose an extended Higher Criticism Test and show it is rate optimal and sharp. We illustrate the finite sample properties of the theoretical results using simulation studies. PMID:26246645
Tensor methods for large sparse systems of nonlinear equations
Bouaricha, A.; Schnabel, R.B.
1996-12-31
This paper introduces censor methods for solving, large sparse systems of nonlinear equations. Tensor methods for nonlinear equations were developed in the context of solving small to medium- sized dense problems. They base each iteration on a quadratic model of the nonlinear equations. where the second-order term is selected so that the model requires no more derivative or function information per iteration than standard linear model-based methods, and hardly more storage or arithmetic operations per iteration. Computational experiments on small to medium-sized problems have shown censor methods to be considerably more efficient than standard Newton-based methods, with a particularly large advantage on singular problems. This paper considers the extension of this approach to solve large sparse problems. The key issue that must be considered is how to make efficient use of sparsity in forming and solving the censor model problem at each iteration. Accomplishing this turns out to require an entirely new way of solving the tensor model that successfully exploits the sparsity of the Jacobian, whether the Jacobian is nonsingular or singular. We develop such an approach and, based upon it, an efficient tensor method for solving large sparse systems of nonlinear equations. Test results indicate that this tensor method is significantly more efficient and robust than an efficient sparse Newton-based method. in terms of iterations, function evaluations. and execution time.
Spatiotemporal System Identification With Continuous Spatial Maps and Sparse Estimation.
Aram, Parham; Kadirkamanathan, Visakan; Anderson, Sean R
2015-11-01
We present a framework for the identification of spatiotemporal linear dynamical systems. We use a state-space model representation that has the following attributes: 1) the number of spatial observation locations are decoupled from the model order; 2) the model allows for spatial heterogeneity; 3) the model representation is continuous over space; and 4) the model parameters can be identified in a simple and sparse estimation procedure. The model identification procedure we propose has four steps: 1) decomposition of the continuous spatial field using a finite set of basis functions where spatial frequency analysis is used to determine basis function width and spacing, such that the main spatial frequency contents of the underlying field can be captured; 2) initialization of states in closed form; 3) initialization of state-transition and input matrix model parameters using sparse regression-the least absolute shrinkage and selection operator method; and 4) joint state and parameter estimation using an iterative Kalman-filter/sparse-regression algorithm. To investigate the performance of the proposed algorithm we use data generated by the Kuramoto model of spatiotemporal cortical dynamics. The identification algorithm performs successfully, predicting the spatiotemporal field with high accuracy, whilst the sparse regression leads to a compact model. PMID:25647667
Sparsely sampling the sky: a Bayesian experimental design approach
NASA Astrophysics Data System (ADS)
Paykari, P.; Jaffe, A. H.
2013-08-01
The next generation of galaxy surveys will observe millions of galaxies over large volumes of the Universe. These surveys are expensive both in time and cost, raising questions regarding the optimal investment of this time and money. In this work, we investigate criteria for selecting amongst observing strategies for constraining the galaxy power spectrum and a set of cosmological parameters. Depending on the parameters of interest, it may be more efficient to observe a larger, but sparsely sampled, area of sky instead of a smaller contiguous area. In this work, by making use of the principles of Bayesian experimental design, we will investigate the advantages and disadvantages of the sparse sampling of the sky and discuss the circumstances in which a sparse survey is indeed the most efficient strategy. For the Dark Energy Survey (DES), we find that by sparsely observing the same area in a smaller amount of time, we only increase the errors on the parameters by a maximum of 0.45 per cent. Conversely, investing the same amount of time as the original DES to observe a sparser but larger area of sky, we can in fact constrain the parameters with errors reduced by 28 per cent.
Sparse PDF Volumes for Consistent Multi-Resolution Volume Rendering
Sicat, Ronell; Krüger, Jens; Möller, Torsten; Hadwiger, Markus
2015-01-01
This paper presents a new multi-resolution volume representation called sparse pdf volumes, which enables consistent multi-resolution volume rendering based on probability density functions (pdfs) of voxel neighborhoods. These pdfs are defined in the 4D domain jointly comprising the 3D volume and its 1D intensity range. Crucially, the computation of sparse pdf volumes exploits data coherence in 4D, resulting in a sparse representation with surprisingly low storage requirements. At run time, we dynamically apply transfer functions to the pdfs using simple and fast convolutions. Whereas standard low-pass filtering and down-sampling incur visible differences between resolution levels, the use of pdfs facilitates consistent results independent of the resolution level used. We describe the efficient out-of-core computation of large-scale sparse pdf volumes, using a novel iterative simplification procedure of a mixture of 4D Gaussians. Finally, our data structure is optimized to facilitate interactive multi-resolution volume rendering on GPUs. PMID:26146475
Energy Efficient Sparse Connectivity from Imbalanced Synaptic Plasticity Rules
Sacramento, João; Wichert, Andreas; van Rossum, Mark C. W.
2015-01-01
It is believed that energy efficiency is an important constraint in brain evolution. As synaptic transmission dominates energy consumption, energy can be saved by ensuring that only a few synapses are active. It is therefore likely that the formation of sparse codes and sparse connectivity are fundamental objectives of synaptic plasticity. In this work we study how sparse connectivity can result from a synaptic learning rule of excitatory synapses. Information is maximised when potentiation and depression are balanced according to the mean presynaptic activity level and the resulting fraction of zero-weight synapses is around 50%. However, an imbalance towards depression increases the fraction of zero-weight synapses without significantly affecting performance. We show that imbalanced plasticity corresponds to imposing a regularising constraint on the L 1-norm of the synaptic weight vector, a procedure that is well-known to induce sparseness. Imbalanced plasticity is biophysically plausible and leads to more efficient synaptic configurations than a previously suggested approach that prunes synapses after learning. Our framework gives a novel interpretation to the high fraction of silent synapses found in brain regions like the cerebellum. PMID:26046817
Robust visual tracking of infrared object via sparse representation model
NASA Astrophysics Data System (ADS)
Ma, Junkai; Liu, Haibo; Chang, Zheng; Hui, Bin
2014-11-01
In this paper, we propose a robust tracking method for infrared object. We introduce the appearance model and the sparse representation in the framework of particle filter to achieve this goal. Representing every candidate image patch as a linear combination of bases in the subspace which is spanned by the target templates is the mechanism behind this method. The natural property, that if the candidate image patch is the target so the coefficient vector must be sparse, can ensure our algorithm successfully. Firstly, the target must be indicated manually in the first frame of the video, then construct the dictionary using the appearance model of the target templates. Secondly, the candidate image patches are selected in following frames and the sparse coefficient vectors of them are calculated via l1-norm minimization algorithm. According to the sparse coefficient vectors the right candidates is determined as the target. Finally, the target templates update dynamically to cope with appearance change in the tracking process. This paper also addresses the problem of scale changing and the rotation of the target occurring in tracking. Theoretic analysis and experimental results show that the proposed algorithm is effective and robust.
Beam hardening correction for sparse-view CT reconstruction
NASA Astrophysics Data System (ADS)
Liu, Wenlei; Rong, Junyan; Gao, Peng; Liao, Qimei; Lu, HongBing
2015-03-01
Beam hardening, which is caused by spectrum polychromatism of the X-ray beam, may result in various artifacts in the reconstructed image and degrade image quality. The artifacts would be further aggravated for the sparse-view reconstruction due to insufficient sampling data. Considering the advantages of the total-variation (TV) minimization in CT reconstruction with sparse-view data, in this paper, we propose a beam hardening correction method for sparse-view CT reconstruction based on Brabant's modeling. In this correction model for beam hardening, the attenuation coefficient of each voxel at the effective energy is modeled and estimated linearly, and can be applied in an iterative framework, such as simultaneous algebraic reconstruction technique (SART). By integrating the correction model into the forward projector of the algebraic reconstruction technique (ART), the TV minimization can recover images when only a limited number of projections are available. The proposed method does not need prior information about the beam spectrum. Preliminary validation using Monte Carlo simulations indicates that the proposed method can provide better reconstructed images from sparse-view projection data, with effective suppression of artifacts caused by beam hardening. With appropriate modeling of other degrading effects such as photon scattering, the proposed framework may provide a new way for low-dose CT imaging.
Varying Coefficient Models for Sparse Noise-contaminated Longitudinal Data
2014-01-01
Summary In this paper we propose a varying coefficient model for highly sparse longitudinal data that allows for error-prone time-dependent variables and time-invariant covariates. We develop a new estimation procedure, based on covariance representation techniques, that enables effective borrowing of information across all subjects in sparse and irregular longitudinal data observed with measurement error, a challenge in which there is no adequate solution currently. More specifically, sparsity is addressed via a functional analysis approach that considers the observed longitudinal data as noise contaminated realizations of a random process that produces smooth trajectories. This approach allows for estimation based on pooled data, borrowing strength from all subjects, in targeting the mean functions and auto- and cross-covariances to overcome sparse noisy designs. The resulting estimators are shown to be uniformly consistent. Consistent prediction for the response trajectories are also obtained via conditional expectation under Gaussian assumptions. Asymptotic distribution of the predicted response trajectories are derived, allowing for construction of asymptotic pointwise confidence bands. Efficacy of the proposed method is investigated in simulation studies and compared to the commonly used local polynomial smoothing method. The proposed method is illustrated with a sparse longitudinal data set, examining the age-varying relationship between calcium absorption and dietary calcium. Prediction of individual calcium absorption curves as a function of age are also examined. PMID:25589822
Inference algorithms and learning theory for Bayesian sparse factor analysis
NASA Astrophysics Data System (ADS)
Rattray, Magnus; Stegle, Oliver; Sharp, Kevin; Winn, John
2009-12-01
Bayesian sparse factor analysis has many applications; for example, it has been applied to the problem of inferring a sparse regulatory network from gene expression data. We describe a number of inference algorithms for Bayesian sparse factor analysis using a slab and spike mixture prior. These include well-established Markov chain Monte Carlo (MCMC) and variational Bayes (VB) algorithms as well as a novel hybrid of VB and Expectation Propagation (EP). For the case of a single latent factor we derive a theory for learning performance using the replica method. We compare the MCMC and VB/EP algorithm results with simulated data to the theoretical prediction. The results for MCMC agree closely with the theory as expected. Results for VB/EP are slightly sub-optimal but show that the new algorithm is effective for sparse inference. In large-scale problems MCMC is infeasible due to computational limitations and the VB/EP algorithm then provides a very useful computationally efficient alternative.
Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering.
Wang, Changqing; Kipping, Judy; Bao, Chenglong; Ji, Hui; Qiu, Anqi
2016-01-01
The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children. PMID:27199650
Scaled norm-based Euclidean projection for sparse speaker adaptation
NASA Astrophysics Data System (ADS)
Kim, Younggwan; Kim, Myung Jong; Kim, Hoirin
2015-12-01
To reduce data storage for speaker adaptive (SA) models, in our previous work, we proposed a sparse speaker adaptation method which can efficiently reduce the number of adapted parameters by using Euclidean projection onto the L 1-ball (EPL1) while maintaining recognition performance comparable to maximum a posteriori (MAP) adaptation. In the EPL1-based sparse speaker adaptation framework, however, the adapted Gaussian mean vectors are mostly concentrated on dimensions having large variances because of assuming unit variance for all dimensions. To make EPL1 more flexible, in this paper, we propose scaled norm-based Euclidean projection (SNEP) which can consider dimension-specific variances. By using SNEP, we also propose a new sparse speaker adaptation method which can consider the variances of a speaker-independent model. Our experiments show that the adapted components of mean vectors are evenly distributed in all dimensions, and we can obtain sparsely adapted models with no loss of phone recognition performance from the proposed method compared with MAP adaptation.
Sparse methods in spectroscopy: an introduction, overview, and perspective.
Andries, Erik; Martin, Shawn
2013-06-01
Multivariate calibration methods such as partial least-squares build calibration models that are not parsimonious: all variables (either wavelengths or samples) are used to define a calibration model. In high-dimensional or large sample size settings, interpretable analysis aims to reduce model complexity by finding a small subset of variables that significantly influences the model. The term "sparsity", as used here, refers to calibration models having many zero-valued regression coefficients. Only the variables associated with non-zero coefficients influence the model. In this paper, we briefly review the regression problems associated with sparse models and discuss their spectroscopic applications. We also discuss how one can re-appropriate sparse modeling algorithms that perform wavelength selection for purposes of sample selection. In particular, we highlight specific sparse modeling algorithms that are easy to use and understand for the spectroscopist, as opposed to the overly complex "black-box" algorithms that dominate much of the statistical learning literature. We apply these sparse modeling approaches to three spectroscopic data sets. PMID:23735242
Block sparse Cholesky algorithms on advanced uniprocessor computers
Ng, E.G.; Peyton, B.W.
1991-12-01
As with many other linear algebra algorithms, devising a portable implementation of sparse Cholesky factorization that performs well on the broad range of computer architectures currently available is a formidable challenge. Even after limiting our attention to machines with only one processor, as we have done in this report, there are still several interesting issues to consider. For dense matrices, it is well known that block factorization algorithms are the best means of achieving this goal. We take this approach for sparse factorization as well. This paper has two primary goals. First, we examine two sparse Cholesky factorization algorithms, the multifrontal method and a blocked left-looking sparse Cholesky method, in a systematic and consistent fashion, both to illustrate the strengths of the blocking techniques in general and to obtain a fair evaluation of the two approaches. Second, we assess the impact of various implementation techniques on time and storage efficiency, paying particularly close attention to the work-storage requirement of the two methods and their variants.
Cerebellar Functional Parcellation Using Sparse Dictionary Learning Clustering
Wang, Changqing; Kipping, Judy; Bao, Chenglong; Ji, Hui; Qiu, Anqi
2016-01-01
The human cerebellum has recently been discovered to contribute to cognition and emotion beyond the planning and execution of movement, suggesting its functional heterogeneity. We aimed to identify the functional parcellation of the cerebellum using information from resting-state functional magnetic resonance imaging (rs-fMRI). For this, we introduced a new data-driven decomposition-based functional parcellation algorithm, called Sparse Dictionary Learning Clustering (SDLC). SDLC integrates dictionary learning, sparse representation of rs-fMRI, and k-means clustering into one optimization problem. The dictionary is comprised of an over-complete set of time course signals, with which a sparse representation of rs-fMRI signals can be constructed. Cerebellar functional regions were then identified using k-means clustering based on the sparse representation of rs-fMRI signals. We solved SDLC using a multi-block hybrid proximal alternating method that guarantees strong convergence. We evaluated the reliability of SDLC and benchmarked its classification accuracy against other clustering techniques using simulated data. We then demonstrated that SDLC can identify biologically reasonable functional regions of the cerebellum as estimated by their cerebello-cortical functional connectivity. We further provided new insights into the cerebello-cortical functional organization in children. PMID:27199650
Hypothesis Generation, Sparse Categories, and the Positive Test Strategy
ERIC Educational Resources Information Center
Navarro, Daniel J.; Perfors, Amy F.
2011-01-01
We consider the situation in which a learner must induce the rule that explains an observed set of data but the hypothesis space of possible rules is not explicitly enumerated or identified. The first part of the article demonstrates that as long as hypotheses are sparse (i.e., index less than half of the possible entities in the domain) then a…
A multithread based new sparse matrix method in bioluminescence tomography
NASA Astrophysics Data System (ADS)
Zhang, Bo; Tian, Jie; Liu, Dan; Sun, Li; Yang, Xin; Han, Dong
2010-03-01
Among many molecular imaging modalities, bioluminescence tomography (BLT) stands out as an effective approach for in vivo imaging because of its noninvasive molecular and cellular level detection ability, high sensitivity and low cost in comparison with other imaging technologies. However, there exists the case that large scale problem with large number of points and elements in the structure of mesh standing for the small animal or phantom. And the large scale problem's system matrix generated by the diffuse approximation (DA) model using finite element method (FEM) is large. So there wouldn't be enough random access memory (RAM) for the program and the related inverse problem couldn't be solved. Considering the sparse property of the BLT system matrix, we've developed a new sparse matrix (ZSM) to overcome the problem. And the related algorithms have all been speeded up by multi-thread technologies. Then the inverse problem is solved by Tikhonov regularization method in adaptive finite element (AFE) framework. Finally, the performance of this method is tested on a heterogeneous phantom and the boundary data is obtained through Monte Carlo simulation. During the process of solving the forward model, the ZSM can save more processing time and memory space than the usual way, such as those not using sparse matrix and those using Triples or Cross Linked sparse matrix. Numerical experiments have shown when more CPU cores are used, the processing speed is increased. By incorporating ZSM, BLT can be applied to large scale problems with large system matrix.
Sparse matrix methods based on orthogonality and conjugacy
NASA Technical Reports Server (NTRS)
Lawson, C. L.
1973-01-01
A matrix having a high percentage of zero elements is called spares. In the solution of systems of linear equations or linear least squares problems involving large sparse matrices, significant saving of computer cost can be achieved by taking advantage of the sparsity. The conjugate gradient algorithm and a set of related algorithms are described.
Latent subspace sparse representation-based unsupervised domain adaptation
NASA Astrophysics Data System (ADS)
Shuai, Liu; Sun, Hao; Zhao, Fumin; Zhou, Shilin
2015-12-01
In this paper, we introduce and study a novel unsupervised domain adaptation (DA) algorithm, called latent subspace sparse representation based domain adaptation, based on the fact that source and target data that lie in different but related low-dimension subspaces. The key idea is that each point in a union of subspaces can be constructed by a combination of other points in the dataset. In this method, we propose to project the source and target data onto a common latent generalized subspace which is a union of subspaces of source and target domains and learn the sparse representation in the latent generalized subspace. By employing the minimum reconstruction error and maximum mean discrepancy (MMD) constraints, the structure of source and target domain are preserved and the discrepancy is reduced between the source and target domains and thus reflected in the sparse representation. We then utilize the sparse representation to build a weighted graph which reflect the relationship of points from the different domains (source-source, source- target, and target-target) to predict the labels of the target domain. We also proposed an efficient optimization method for the algorithm. Our method does not need to combine with any classifiers and therefore does not need train the test procedures. Various experiments show that the proposed method perform better than the competitive state of art subspace-based domain adaptation.
Robust Methods for Sensing and Reconstructing Sparse Signals
ERIC Educational Resources Information Center
Carrillo, Rafael E.
2012-01-01
Compressed sensing (CS) is an emerging signal acquisition framework that goes against the traditional Nyquist sampling paradigm. CS demonstrates that a sparse, or compressible, signal can be acquired using a low rate acquisition process. Since noise is always present in practical data acquisition systems, sensing and reconstruction methods are…
NASA Astrophysics Data System (ADS)
Hashemi, Nezhad Z.; Karami, A.
2015-10-01
Hyperspectral images (HSI) have high spectral and low spatial resolutions. However, multispectral images (MSI) usually have low spectral and high spatial resolutions. In various applications HSI with high spectral and spatial resolutions are required. In this paper, a new method for spatial resolution enhancement of HSI using high resolution MSI based on sparse coding and linear spectral unmixing (SCLSU) is introduced. In the proposed method (SCLSU), high spectral resolution features of HSI and high spatial resolution features of MSI are fused. In this case, the sparse representation of some high resolution MSI and linear spectral unmixing (LSU) model of HSI and MSI is simultaneously used in order to construct high resolution HSI (HRHSI). The fusion process of HSI and MSI is formulated as an ill-posed inverse problem. It is solved by the Split Augmented Lagrangian Shrinkage Algorithm (SALSA) and an orthogonal matching pursuit (OMP) algorithm. Finally, the proposed algorithm is applied to the Hyperion and ALI datasets. Compared with the other state-of-the-art algorithms such as Coupled Nonnegative Matrix Factorization (CNMF) and local spectral unmixing, the SCLSU has significantly increased the spatial resolution and in addition the spectral content of HSI is well maintained.
Towards robust and effective shape modeling: sparse shape composition.
Zhang, Shaoting; Zhan, Yiqiang; Dewan, Maneesh; Huang, Junzhou; Metaxas, Dimitris N; Zhou, Xiang Sean
2012-01-01
Organ shape plays an important role in various clinical practices, e.g., diagnosis, surgical planning and treatment evaluation. It is usually derived from low level appearance cues in medical images. However, due to diseases and imaging artifacts, low level appearance cues might be weak or misleading. In this situation, shape priors become critical to infer and refine the shape derived by image appearances. Effective modeling of shape priors is challenging because: (1) shape variation is complex and cannot always be modeled by a parametric probability distribution; (2) a shape instance derived from image appearance cues (input shape) may have gross errors; and (3) local details of the input shape are difficult to preserve if they are not statistically significant in the training data. In this paper we propose a novel Sparse Shape Composition model (SSC) to deal with these three challenges in a unified framework. In our method, a sparse set of shapes in the shape repository is selected and composed together to infer/refine an input shape. The a priori information is thus implicitly incorporated on-the-fly. Our model leverages two sparsity observations of the input shape instance: (1) the input shape can be approximately represented by a sparse linear combination of shapes in the shape repository; (2) parts of the input shape may contain gross errors but such errors are sparse. Our model is formulated as a sparse learning problem. Using L1 norm relaxation, it can be solved by an efficient expectation-maximization (EM) type of framework. Our method is extensively validated on two medical applications, 2D lung localization in X-ray images and 3D liver segmentation in low-dose CT scans. Compared to state-of-the-art methods, our model exhibits better performance in both studies. PMID:21963296
Deformable segmentation via sparse representation and dictionary learning.
Zhang, Shaoting; Zhan, Yiqiang; Metaxas, Dimitris N
2012-10-01
"Shape" and "appearance", the two pillars of a deformable model, complement each other in object segmentation. In many medical imaging applications, while the low-level appearance information is weak or mis-leading, shape priors play a more important role to guide a correct segmentation, thanks to the strong shape characteristics of biological structures. Recently a novel shape prior modeling method has been proposed based on sparse learning theory. Instead of learning a generative shape model, shape priors are incorporated on-the-fly through the sparse shape composition (SSC). SSC is robust to non-Gaussian errors and still preserves individual shape characteristics even when such characteristics is not statistically significant. Although it seems straightforward to incorporate SSC into a deformable segmentation framework as shape priors, the large-scale sparse optimization of SSC has low runtime efficiency, which cannot satisfy clinical requirements. In this paper, we design two strategies to decrease the computational complexity of SSC, making a robust, accurate and efficient deformable segmentation system. (1) When the shape repository contains a large number of instances, which is often the case in 2D problems, K-SVD is used to learn a more compact but still informative shape dictionary. (2) If the derived shape instance has a large number of vertices, which often appears in 3D problems, an affinity propagation method is used to partition the surface into small sub-regions, on which the sparse shape composition is performed locally. Both strategies dramatically decrease the scale of the sparse optimization problem and hence speed up the algorithm. Our method is applied on a diverse set of biomedical image analysis problems. Compared to the original SSC, these two newly-proposed modules not only significant reduce the computational complexity, but also improve the overall accuracy. PMID:22959839
lp-lq penalty for sparse linear and sparse multiple kernel multitask learning.
Rakotomamonjy, Alain; Flamary, Rémi; Gasso, Gilles; Canu, Stéphane
2011-08-01
Recently, there has been much interest around multitask learning (MTL) problem with the constraints that tasks should share a common sparsity profile. Such a problem can be addressed through a regularization framework where the regularizer induces a joint-sparsity pattern between task decision functions. We follow this principled framework and focus on l(p)-l(q) (with 0 ≤ p ≤ 1 and 1 ≤ q ≤ 2) mixed norms as sparsity-inducing penalties. Our motivation for addressing such a larger class of penalty is to adapt the penalty to a problem at hand leading thus to better performances and better sparsity pattern. For solving the problem in the general multiple kernel case, we first derive a variational formulation of the l(1)-l(q) penalty which helps us in proposing an alternate optimization algorithm. Although very simple, the latter algorithm provably converges to the global minimum of the l(1)-l(q) penalized problem. For the linear case, we extend existing works considering accelerated proximal gradient to this penalty. Our contribution in this context is to provide an efficient scheme for computing the l(1)-l(q) proximal operator. Then, for the more general case, when , we solve the resulting nonconvex problem through a majorization-minimization approach. The resulting algorithm is an iterative scheme which, at each iteration, solves a weighted l(1)-l(q) sparse MTL problem. Empirical evidences from toy dataset and real-word datasets dealing with brain-computer interface single-trial electroencephalogram classification and protein subcellular localization show the benefit of the proposed approaches and algorithms. PMID:21813358
Sloped terrain segmentation for autonomous drive using sparse 3D point cloud.
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. PMID:25093204
Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud
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
Adaptive sparse signal processing of on-orbit lightning data using learned dictionaries
NASA Astrophysics Data System (ADS)
Moody, Daniela I.; Smith, David A.; Hamlin, Timothy D.; Light, Tess E.; Suszcynsky, David M.
2013-05-01
For the past two decades, there has been an ongoing research effort at Los Alamos National Laboratory to learn more about the Earth's radiofrequency (RF) background utilizing satellite-based RF observations of terrestrial lightning. The Fast On-orbit Recording of Transient Events (FORTE) satellite provided a rich RF lighting database, comprising of five years of data recorded from its two RF payloads. While some classification work has been done previously on the FORTE RF database, application of modern pattern recognition techniques may advance lightning research in the scientific community and potentially improve on-orbit processing and event discrimination capabilities for future satellite payloads. We now develop and implement new event classification capability on the FORTE database using state-of-the-art adaptive signal processing combined with compressive sensing and machine learning techniques. The focus of our work is improved feature extraction using sparse representations in learned dictionaries. Conventional localized data representations for RF transients using analytical dictionaries, such as a short-time Fourier basis or wavelets, can be suitable for analyzing some types of signals, but not others. Instead, we learn RF dictionaries directly from data, without relying on analytical constraints or additional knowledge about the signal characteristics, using several established machine learning algorithms. Sparse classification features are extracted via matching pursuit search over the learned dictionaries, and used in conjunction with a statistical classifier to distinguish between lightning types. We present preliminary results of our work and discuss classification scenarios and future development.
12 CFR 7.2023 - Reverse stock splits.
Code of Federal Regulations, 2011 CFR
2011-01-01
... 12 Banks and Banking 1 2011-01-01 2011-01-01 false Reverse stock splits. 7.2023 Section 7.2023... 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...
Masuda, Y; Aguilar, I; Tsuruta, S; Misztal, I
2015-10-01
The objective of this study was to remove bottlenecks generally found in a computer program for average-information REML. The refinements included improvements to setting-up mixed-model equations on a hash table with a faster hash function as sparse matrix storage, changing sparse structures in calculation of traces, and replacing a sparse matrix package using traditional methods (FSPAK) with a new package using supernodal methods (YAMS); the latter package quickly processed sparse matrices containing large, dense blocks. Comparisons included 23 models with data sets from broiler, swine, beef, and dairy cattle. Models included single-trait, multiple-trait, maternal, and random regression models with phenotypic data; selected models used genomic information in a single-step approach. Setting-up mixed model equations was completed without abnormal termination in all analyses. Calculations in traces were accelerated with a hash format, especially for models with a genomic relationship matrix, and the maximum speed was 67 times faster. Computations with YAMS were, on average, more than 10 times faster than with FSPAK and had greater advantages for large data and more complicated models including multiple traits, random regressions, and genomic effects. These refinements can be applied to general average-information REML programs. PMID:26523559
Spin splitting in 2D monochalcogenide semiconductors
Do, Dat T.; Mahanti, Subhendra D.; Lai, Chih Wei
2015-01-01
We report ab initio calculations of the spin splitting of the uppermost valence band (UVB) and the lowermost conduction band (LCB) in bulk and atomically thin GaS, GaSe, GaTe, and InSe. These layered monochalcogenides appear in four major polytypes depending on the stacking order, except for the monoclinic GaTe. Bulk and few-layer ε-and γ -type, and odd-number β-type GaS, GaSe, and InSe crystals are noncentrosymmetric. The spin splittings of the UVB and the LCB near the Γ-point in the Brillouin zone are finite, but still smaller than those in a zinc-blende semiconductor such as GaAs. On the other hand, the spin splitting is zero in centrosymmetric bulk and even-number few-layer β-type GaS, GaSe, and InSe, owing to the constraint of spatial inversion symmetry. By contrast, GaTe exhibits zero spin splitting because it is centrosymmetric down to a single layer. In these monochalcogenide semiconductors, the separation of the non-degenerate conduction and valence bands from adjacent bands results in the suppression of Elliot-Yafet spin relaxation mechanism. Therefore, the electron- and hole-spin relaxation times in these systems with zero or minimal spin splittings are expected to exceed those in GaAs when the D’yakonov-Perel’ spin relaxation mechanism is also suppressed. PMID:26596907
Conditions for a split diffusion flame
Hertzberg, J.R.
1997-05-01
An unusual phenomenon has been observed in a methane jet diffusion flame subjected to axial acoustic forcing. At specific excitation frequencies and amplitudes, the driven flame splits into a central jet and one or two side jets. The splitting is accompanied by a partial detachment of the flame from the nozzle exit, a shortening of the flame by a factor of 2, and a change from the common yellow color of soot radiation to a clear blue flame. Such a phenomenon may be useful for the control of soot production or product species. The splitting is intermittent in time, bifurcating between the split flame and an ordinary single jet diffusion flame. The experiment consists of an unconfined axisymmetric methane jet formed by a short length of 0.4 cm diameter pipe. The pipe is connected to a large plenum surrounding a bass reflex loudspeaker enclosure that provides the excitation. Conditions producing split and bifurcated flames are presented. The drive frequencies required to cause bifurcation correspond to the first two peaks in the system`s frequency response curve. Bifurcating behavior was observed at a wide range of flow rates, ranging from very small flames of Reynolds number 240 up to turbulent lift-off, at Re = 1,000, based on the inner pipe diameter. It was not sensitive to nozzle length, but the details of the nozzle tip, such as orifice or pipe geometry, can affect the frequency range.
Spin splitting in 2D monochalcogenide semiconductors
NASA Astrophysics Data System (ADS)
Do, Dat T.; Mahanti, Subhendra D.; Lai, Chih Wei
2015-11-01
We report ab initio calculations of the spin splitting of the uppermost valence band (UVB) and the lowermost conduction band (LCB) in bulk and atomically thin GaS, GaSe, GaTe, and InSe. These layered monochalcogenides appear in four major polytypes depending on the stacking order, except for the monoclinic GaTe. Bulk and few-layer ε-and γ -type, and odd-number β-type GaS, GaSe, and InSe crystals are noncentrosymmetric. The spin splittings of the UVB and the LCB near the Γ-point in the Brillouin zone are finite, but still smaller than those in a zinc-blende semiconductor such as GaAs. On the other hand, the spin splitting is zero in centrosymmetric bulk and even-number few-layer β-type GaS, GaSe, and InSe, owing to the constraint of spatial inversion symmetry. By contrast, GaTe exhibits zero spin splitting because it is centrosymmetric down to a single layer. In these monochalcogenide semiconductors, the separation of the non-degenerate conduction and valence bands from adjacent bands results in the suppression of Elliot-Yafet spin relaxation mechanism. Therefore, the electron- and hole-spin relaxation times in these systems with zero or minimal spin splittings are expected to exceed those in GaAs when the D’yakonov-Perel’ spin relaxation mechanism is also suppressed.
Single-Crystal Semiconductors with Narrow Band Gaps for Solar Water Splitting.
Wang, Tuo; Gong, Jinlong
2015-09-01
Solar water splitting provides a clean and renewable approach to produce hydrogen energy. In recent years, single-crystal semiconductors such as Si and InP with narrow band gaps have demonstrated excellent performance to drive the half reactions of water splitting through visible light due to their suitable band gaps and low bulk recombination. This Minireview describes recent research advances that successfully overcome the primary obstacles in using these semiconductors as photoelectrodes, including photocorrosion, sluggish reaction kinetics, low photovoltage, and unfavorable planar substrate surface. Surface modification strategies, such as surface protection, cocatalyst loading, surface energetics tuning, and surface texturization are highlighted as the solutions. PMID:26227831
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.