WorldWideScience

Sample records for isopycnal deep layers

  1. Ocean bio-geophysical modeling using mixed layer-isopycnal general circulation model coupled with photosynthesis process

    Digital Repository Service at National Institute of Oceanography (India)

    Nakamoto, S.; Saito, H.; Muneyama, K.; Sato, T.; PrasannaKumar, S.; Kumar, A.; Frouin, R.

    -chemical system that supports steady carbon circulation in geological time scale in the world ocean using Mixed Layer-Isopycnal ocean General Circulation model with remotely sensed Coastal Zone Color Scanner (CZCS) chlorophyll pigment concentration....

  2. Response of the equatorial Pacific to chlorophyll pigment in a mixed layer isopycnal ocean general circulation model

    Digital Repository Service at National Institute of Oceanography (India)

    Nakamoto, S.; PrasannaKumar, S.; Oberhuber, J.M.; Ishizaka, J.; Muneyama, K.; Frouin, R.

    The influence of phytoplankton on the upper ocean dynamics and thermodynamics in the equatorial Pacific is investigated using an isopycnal ocean general circulation model (OPYC) coupled with a mixed layer model and remotely sensed chlorophyll...

  3. Chlorophyll modulation of sea surface temperature in the Arabian Sea in a mixed-layer isopycnal general circulation model

    Digital Repository Service at National Institute of Oceanography (India)

    Nakamoto, S.; PrasannaKumar, S.; Muneyama, K.; Frouin, R.

    , embedded in the ocean isopycnal general circulation model (OPYC). A higher abundance of chlorophyll in October than in April in the Arabian Sea increases absorption of solar irradiance and heating rate in the upper ocean, resulting in decreasing the mixed...

  4. An isopycnic ocean carbon cycle model

    Directory of Open Access Journals (Sweden)

    K. M. Assmann

    2010-02-01

    Full Text Available The carbon cycle is a major forcing component in the global climate system. Modelling studies, aiming to explain recent and past climatic changes and to project future ones, increasingly include the interaction between the physical and biogeochemical systems. Their ocean components are generally z-coordinate models that are conceptually easy to use but that employ a vertical coordinate that is alien to the real ocean structure. Here, we present first results from a newly-developed isopycnic carbon cycle model and demonstrate the viability of using an isopycnic physical component for this purpose. As expected, the model represents well the interior ocean transport of biogeochemical tracers and produces realistic tracer distributions. Difficulties in employing a purely isopycnic coordinate lie mainly in the treatment of the surface boundary layer which is often represented by a bulk mixed layer. The most significant adjustments of the ocean biogeochemistry model HAMOCC, for use with an isopycnic coordinate, were in the representation of upper ocean biological production. We present a series of sensitivity studies exploring the effect of changes in biogeochemical and physical processes on export production and nutrient distribution. Apart from giving us pointers for further model development, they highlight the importance of preformed nutrient distributions in the Southern Ocean for global nutrient distributions. The sensitivity studies show that iron limitation for biological particle production, the treatment of light penetration for biological production, and the role of diapycnal mixing result in significant changes of nutrient distributions and liniting factors of biological production.

  5. Chlorophyll modulation of mixed layer thermodynamics in a mixed-layer isopycnal general circulation model - An example from Arabian Sea and Equatorial Pacific

    Digital Repository Service at National Institute of Oceanography (India)

    Nakamoto, S.; PrasannaKumar, S.; Oberhuber, J.M.; Saito, H.; Muneyama, K.

    and supported by quasi-steady upwelling. Remotely sensed chlorophyll pigment concentrations from the Coastal Zone Color Scanner (CZCS) are used to investigate the chlorophyll modulation of ocean mixed layer thermodynamics in a bulk mixed-layer model, embedded...

  6. Chlorophyll modulation of mixed layer thermodynamics in a mixed-layer isopycnal General Circulation Model - An example from Arabian Sea and equatorial Pacific

    Digital Repository Service at National Institute of Oceanography (India)

    Nakamoto, S.; PrasannaKumar, S.; Oberhuber, J.M.; Saito, H.; Muneyama, K.; Frouin, R.

    is influenced not only by local vertical mixing but also by horizontal con- vergence of mass and heat, a mixed layer model must consider both full dynamics due to the use of primitive equations and a parameterization for the vertical mass transfer and related... is dynamically determined without such a con- straint. Instantaneous atmospheric elds are inter- polated from the monthly means. Monthly mean climatology of chlorophyll pigment concentrations were obtained from the Coastal Zone Color Scan- ner (CZCS) from...

  7. An isopycnal view of the Nordic Seas hydrography with focus on properties of the Lofoten Basin

    Science.gov (United States)

    Rossby, T.; Ozhigin, Vladimir; Ivshin, Victor; Bacon, Sheldon

    2009-11-01

    Basin that eroded away much of the Lofoten eddy and induced the greatest temperature anomaly in the entire 50-year record. Interannual variations in isopycnal layer temperature correlate with the NAO index such that waters in the Iceland Sea become warmer than average with warming air temperatures and conversely in the Lofoten Basin.

  8. Exploring the isopycnal mixing and helium-heat paradoxes in a suite of Earth system models

    Science.gov (United States)

    Gnanadesikan, A.; Pradal, M.-A.; Abernathey, R.

    2015-07-01

    This paper uses a suite of Earth system models which simulate the distribution of He isotopes and radiocarbon to examine two paradoxes in Earth science, each of which results from an inconsistency between theoretically motivated global energy balances and direct observations. The helium-heat paradox refers to the fact that helium emissions to the deep ocean are far lower than would be expected given the rate of geothermal heating, since both are thought to be the result of radioactive decay in Earth's interior. The isopycnal mixing paradox comes from the fact that many theoretical parameterizations of the isopycnal mixing coefficient ARedi that link it to baroclinic instability project it to be small (of order a few hundred m2 s-1) in the ocean interior away from boundary currents. However, direct observations using tracers and floats (largely in the upper ocean) suggest that values of this coefficient are an order of magnitude higher. Helium isotopes equilibrate rapidly with the atmosphere and thus exhibit large gradients along isopycnals while radiocarbon equilibrates slowly and thus exhibits smaller gradients along isopycnals. Thus it might be thought that resolving the isopycnal mixing paradox in favor of the higher observational estimates of ARedi might also solve the helium paradox, by increasing the transport of mantle helium to the surface more than it would radiocarbon. In this paper we show that this is not the case. In a suite of models with different spatially constant and spatially varying values of ARedi the distribution of radiocarbon and helium isotopes is sensitive to the value of ARedi. However, away from strong helium sources in the southeastern Pacific, the relationship between the two is not sensitive, indicating that large-scale advection is the limiting process for removing helium and radiocarbon from the deep ocean. The helium isotopes, in turn, suggest a higher value of ARedi below the thermocline than is seen in theoretical

  9. Fine-scale variability of isopycnal salinity in the California Current System

    Science.gov (United States)

    Itoh, Sachihiko; Rudnick, Daniel L.

    2017-09-01

    This paper examines the fine-scale structure and seasonal fluctuations of the isopycnal salinity of the California Current System from 2007 to 2013 using temperature and salinity profiles obtained from a series of underwater glider surveys. The seasonal mean distributions of the spectral power of the isopycnal salinity gradient averaged over submesoscale (12-30 km) and mesoscale (30-60 km) ranges along three survey lines off Monterey Bay, Point Conception, and Dana Point were obtained from 298 transects. The mesoscale and submesoscale variance increased as coastal upwelling caused the isopycnal salinity gradient to steepen. Areas of elevated variance were clearly observed around the salinity front during the summer then spread offshore through the fall and winter. The high fine-scale variances were observed typically above 25.8 kg m-3 and decreased with depth to a minimum at around 26.3 kg m-3. The mean spectral slope of the isopycnal salinity gradient with respect to wavenumber was 0.19 ± 0.27 over the horizontal scale of 12-60 km, and 31%-35% of the spectra had significantly positive slopes. In contrast, the spectral slope over 12-30 km was mostly flat, with mean values of -0.025 ± 0.32. An increase in submesoscale variability accompanying the steepening of the spectral slope was often observed in inshore areas; e.g., off Monterey Bay in winter, where a sharp front developed between the California Current and the California Under Current, and the lower layers of the Southern California Bight, where vigorous interaction between a synoptic current and bottom topography is to be expected.

  10. Exploring the isopycnal mixing and helium–heat paradoxes in a suite of Earth system models

    Directory of Open Access Journals (Sweden)

    A. Gnanadesikan

    2015-07-01

    this paper we show that this is not the case. In a suite of models with different spatially constant and spatially varying values of ARedi the distribution of radiocarbon and helium isotopes is sensitive to the value of ARedi. However, away from strong helium sources in the southeastern Pacific, the relationship between the two is not sensitive, indicating that large-scale advection is the limiting process for removing helium and radiocarbon from the deep ocean. The helium isotopes, in turn, suggest a higher value of ARedi below the thermocline than is seen in theoretical parameterizations based on baroclinic growth rates. We argue that a key part of resolving the isopycnal mixing paradox is to abandon the idea that ARedi has a direct relationship to local baroclinic instability and to the so-called "thickness" mixing coefficient AGM.

  11. Deep Learning and Developmental Learning: Emergence of Fine-to-Coarse Conceptual Categories at Layers of Deep Belief Network.

    Science.gov (United States)

    Sadeghi, Zahra

    2016-09-01

    In this paper, I investigate conceptual categories derived from developmental processing in a deep neural network. The similarity matrices of deep representation at each layer of neural network are computed and compared with their raw representation. While the clusters generated by raw representation stand at the basic level of abstraction, conceptual categories obtained from deep representation shows a bottom-up transition procedure. Results demonstrate a developmental course of learning from specific to general level of abstraction through learned layers of representations in a deep belief network. © The Author(s) 2016.

  12. Reactive Fe(II) layers in deep-sea sediments

    Science.gov (United States)

    König, Iris; Haeckel, Matthias; Drodt, Matthias; Suess, Erwin; Trautwein, Alfred X.

    1999-05-01

    The percentage of the structural Fe(II) in clay minerals that is readily oxidized to Fe(III) upon contact with atmospheric oxygen was determined across the downcore tan-green color change in Peru Basin sediments. This latent fraction of reactive Fe(II) was only found in the green strata, where it proved to be large enough to constitute a deep reaction layer with respect to the pore water O 2 and NO 3-. Large variations were detected in the proportion of the reactive Fe(II) concentration to the organic matter content along core profiles. Hence, the commonly observed tan-green color change in marine sediments marks the top of a reactive Fe(II) layer, which may represent the major barrier to the movement of oxidation fronts in pelagic subsurface sediments. This is also demonstrated by numerical model simulations. The findings imply that geochemical barriers to pore water oxidation fronts form diagenetically in the sea floor wherever the stage of iron reduction is reached, provided that the sediments contain a significant amount of structural iron in clay minerals.

  13. Parameterization of Mixed Layer and Deep-Ocean Mesoscales Including Nonlinearity

    Science.gov (United States)

    Canuto, V. M.; Cheng, Y.; Dubovikov, M. S.; Howard, A. M.; Leboissetier, A.

    2018-01-01

    In 2011, Chelton et al. carried out a comprehensive census of mesoscales using altimetry data and reached the following conclusions: "essentially all of the observed mesoscale features are nonlinear" and "mesoscales do not move with the mean velocity but with their own drift velocity," which is "the most germane of all the nonlinear metrics."� Accounting for these results in a mesoscale parameterization presents conceptual and practical challenges since linear analysis is no longer usable and one needs a model of nonlinearity. A mesoscale parameterization is presented that has the following features: 1) it is based on the solutions of the nonlinear mesoscale dynamical equations, 2) it describes arbitrary tracers, 3) it includes adiabatic (A) and diabatic (D) regimes, 4) the eddy-induced velocity is the sum of a Gent and McWilliams (GM) term plus a new term representing the difference between drift and mean velocities, 5) the new term lowers the transfer of mean potential energy to mesoscales, 6) the isopycnal slopes are not as flat as in the GM case, 7) deep-ocean stratification is enhanced compared to previous parameterizations where being more weakly stratified allowed a large heat uptake that is not observed, 8) the strength of the Deacon cell is reduced. The numerical results are from a stand-alone ocean code with Coordinated Ocean-Ice Reference Experiment I (CORE-I) normal-year forcing.

  14. Deep and persistent melt layer in the Archaean mantle

    Science.gov (United States)

    Andrault, Denis; Pesce, Giacomo; Manthilake, Geeth; Monteux, Julien; Bolfan-Casanova, Nathalie; Chantel, Julien; Novella, Davide; Guignot, Nicolas; King, Andrew; Itié, Jean-Paul; Hennet, Louis

    2018-02-01

    The transition from the Archaean to the Proterozoic eon ended a period of great instability at the Earth's surface. The origin of this transition could be a change in the dynamic regime of the Earth's interior. Here we use laboratory experiments to investigate the solidus of samples representative of the Archaean upper mantle. Our two complementary in situ measurements of the melting curve reveal a solidus that is 200-250 K lower than previously reported at depths higher than about 100 km. Such a lower solidus temperature makes partial melting today easier than previously thought, particularly in the presence of volatiles (H2O and CO2). A lower solidus could also account for the early high production of melts such as komatiites. For an Archaean mantle that was 200-300 K hotter than today, significant melting is expected at depths from 100-150 km to more than 400 km. Thus, a persistent layer of melt may have existed in the Archaean upper mantle. This shell of molten material may have progressively disappeared because of secular cooling of the mantle. Crystallization would have increased the upper mantle viscosity and could have enhanced mechanical coupling between the lithosphere and the asthenosphere. Such a change might explain the transition from surface dynamics dominated by a stagnant lid on the early Earth to modern-like plate tectonics with deep slab subduction.

  15. Gradual DropIn of Layers to Train Very Deep Neural Networks

    OpenAIRE

    Smith, Leslie N.; Hand, Emily M.; Doster, Timothy

    2015-01-01

    We introduce the concept of dynamically growing a neural network during training. In particular, an untrainable deep network starts as a trainable shallow network and newly added layers are slowly, organically added during training, thereby increasing the network's depth. This is accomplished by a new layer, which we call DropIn. The DropIn layer starts by passing the output from a previous layer (effectively skipping over the newly added layers), then increasingly including units from the ne...

  16. Computations in the deep vs superficial layers of the cerebral cortex.

    Science.gov (United States)

    Rolls, Edmund T; Mills, W Patrick C

    2017-11-01

    A fundamental question is how the cerebral neocortex operates functionally, computationally. The cerebral neocortex with its superficial and deep layers and highly developed recurrent collateral systems that provide a basis for memory-related processing might perform somewhat different computations in the superficial and deep layers. Here we take into account the quantitative connectivity within and between laminae. Using integrate-and-fire neuronal network simulations that incorporate this connectivity, we first show that attractor networks implemented in the deep layers that are activated by the superficial layers could be partly independent in that the deep layers might have a different time course, which might because of adaptation be more transient and useful for outputs from the neocortex. In contrast the superficial layers could implement more prolonged firing, useful for slow learning and for short-term memory. Second, we show that a different type of computation could in principle be performed in the superficial and deep layers, by showing that the superficial layers could operate as a discrete attractor network useful for categorisation and feeding information forward up a cortical hierarchy, whereas the deep layers could operate as a continuous attractor network useful for providing a spatially and temporally smooth output to output systems in the brain. A key advance is that we draw attention to the functions of the recurrent collateral connections between cortical pyramidal cells, often omitted in canonical models of the neocortex, and address principles of operation of the neocortex by which the superficial and deep layers might be specialized for different types of attractor-related memory functions implemented by the recurrent collaterals. Copyright © 2017 Elsevier Inc. All rights reserved.

  17. Penaeoid and sergestoid shrimps from the deep scattering layer (DSL) in the Arabian Sea

    Digital Repository Service at National Institute of Oceanography (India)

    Karuppasamy, P.K.; Menon, N.G.

    Results of a preliminary study on the occurrence and distribution of seventeen species of Penaeoid and Sergestoid shrimps from the deep scattering layer (DSL) of the Indian EEZ of Arabian Sea are presented here based on the IKMT samples collected...

  18. Layer dividing and zone dividing of physical property of crust and deep structure in Jiangxi province

    International Nuclear Information System (INIS)

    Li Chunhua; Yang Yaxin; Gong Yuling; Huang Linping

    2001-01-01

    On the base of summing experiences both at home and abroad, the Bugar gravitative anomalies are studied by major means of data processing. According to the anomalous character, three layer crust models (surface layer, middle layer in region and material layer under crust) are built up, depth of upper and bottom surfaces for every layer is calculated quantitatively, their varied characters of depth are studied and deep geological tectonics are outlined. The 'density' and 'mass' of every layer are calculated, and according to these two parameters, the shallow geological tectonics are researched. The relation-factor R between the surface altitude and Bugar gravitative anomalies are calculated and the stable or unstable crust zones are divided. The favorable mine zones for uranium deposit in Jiangxi Province are outlined

  19. Simulated North Atlantic-Nordic Seas water mass exchanges in an isopycnic coordinate OGCM

    OpenAIRE

    Nilsen, Jan Even Øie; Gao, Yongqi; Drange, Helge; Furevik, Tore; Bentsen, Mats

    2003-01-01

    The variability in the volume exchanges between the North Atlantic and the Nordic Seas during the last 50 years is investigated using a synoptic forced, global version of the Miami Isopycnic Coordinate Ocean Model (MICOM). The simulated volume fluxes agree with the existing observations. The net volume flux across the Faroe-Shetland Channel (FSC) is positively correlated with the net flux through the Denmark Strait (DS; R = 0.74 for 3 years low pass filtering), but negatively correlated with ...

  20. Luminescence and deep-level transient spectroscopy of grown dislocation-rich Si layers

    Directory of Open Access Journals (Sweden)

    I. I. Kurkina

    2012-09-01

    Full Text Available The charge deep-level transient spectroscopy (Q-DLTS is applied to the study of the dislocation-rich Si layers grown on a surface composed of dense arrays of Ge islands prepared on the oxidized Si surface. This provides revealing three deep-level bands located at EV + 0.31 eV, EC – 0.35 eV and EC – 0.43 eV using the stripe-shaped p-i-n diodes fabricated on the basis of these layers. The most interesting observation is the local state recharging process which proceeds with low activation energy (∼50 meV or without activation. The recharging may occur by carrier tunneling within deep-level bands owing to the high dislocation density ∼ 1011 - 1012 cm-2. This result is in favor of the suggestion on the presence of carrier transport between the deep states, which was previously derived from the excitation dependence of photoluminescence (PL intensity. Electroluminescence (EL spectra measured from the stripe edge of the same diodes contain two peaks centered near 1.32 and 1.55 μm. Comparison with PL spectra indicates that the EL peaks are generated from arsenic-contaminated and pure areas of the layers, respectively.

  1. Chlorophyll modulation of mixed layer thermodynamics in a mixed ...

    Indian Academy of Sciences (India)

    M. Senthilkumar (Newgen Imaging) 1461 1996 Oct 15 13:05:22

    in a mixed-layer isopycnal General Circulation Model – An ... three dimensional ocean circulation theory combined with solar radiation transfer process. 1. .... temperature decrease compared with simulation without chlorophyll (bottom panel).

  2. Deep-Layer Microvasculature Dropout by Optical Coherence Tomography Angiography and Microstructure of Parapapillary Atrophy.

    Science.gov (United States)

    Suh, Min Hee; Zangwill, Linda M; Manalastas, Patricia Isabel C; Belghith, Akram; Yarmohammadi, Adeleh; Akagi, Tadamichi; Diniz-Filho, Alberto; Saunders, Luke; Weinreb, Robert N

    2018-04-01

    To investigate the association between the microstructure of β-zone parapapillary atrophy (βPPA) and parapapillary deep-layer microvasculature dropout assessed by optical coherence tomography angiography (OCT-A). Thirty-seven eyes with βPPA devoid of the Bruch's membrane (BM) (γPPA) ranging between completely absent and discontinuous BM were matched by severity of the visual field (VF) damage with 37 eyes with fully intact BM (βPPA+BM) based on the spectral-domain (SD) OCT imaging. Parapapillary deep-layer microvasculature dropout was defined as a dropout of the microvasculature within choroid or scleral flange in the βPPA on the OCT-A. The widths of βPPA, γPPA, and βPPA+BM were measured on six radial SD-OCT images. Prevalence of the dropout was compared between eyes with and without γPPA. Logistic regression was performed for evaluating association of the dropout with the width of βPPA, γPPA, and βPPA+BM, and the γPPA presence. Eyes with γPPA had significantly higher prevalence of the dropout than did those without γPPA (75.7% versus 40.8%; P = 0.004). In logistic regression, presence and longer width of the γPPA, worse VF mean deviation, and presence of focal lamina cribrosa defects were significantly associated with the dropout (P 0.10). Parapapillary deep-layer microvasculature dropout was associated with the presence and larger width of γPPA, but not with the βPPA+BM width. Presence and width of the exposed scleral flange, rather than the retinal pigmented epithelium atrophy, may be associated with deep-layer microvasculature dropout.

  3. Methane oxidation and methane fluxes in the ocean surface layer and deep anoxic waters

    Science.gov (United States)

    Ward, B. B.; Kilpatrick, K. A.; Novelli, P. C.; Scranton, M. I.

    1987-01-01

    Measured biological oxidation rates of methane in near-surface waters of the Cariaco Basin are compared with the diffusional fluxes computed from concentration gradients of methane in the surface layer. Methane fluxes and oxidation rates were investigated in surface waters, at the oxic/anoxic interface, and in deep anoxic waters. It is shown that the surface-waters oxidation of methane is a mechanism which modulates the flux of methane from marine waters to the atmosphere.

  4. Property changes of deep and bottom waters in the Western Tropical Atlantic

    Science.gov (United States)

    Herrford, Josefine; Brandt, Peter; Zenk, Walter

    2017-06-01

    The flow of North Atlantic Deep Water (NADW) and Antarctic Bottom Water (AABW) contributes to the Atlantic meridional overturning circulation. Changes in the associated water mass formation might impact the deep ocean's capacity to take up anthropogenic CO2 while a warming of the deep ocean significantly contributes to global sea level rise. Here we compile historic and recent shipboard measurements of hydrography and velocity to provide a comprehensive view of water mass distribution, pathways, along-path transformation and long-term temperature changes of NADW and AABW in the western South and Equatorial Atlantic. We confirm previous results which show that the northwest corner of the Brazil Basin represents a splitting point for the southward/northward flow of NADW/AABW. The available measurements sample water mass transformation along the two major routes for deep and bottom waters in the tropical to South Atlantic - along the deep western boundary and eastward, parallel to the equator - as well as the hot-spots of extensive mixing. We find lower NADW and lighter AABW to form a highly interactive transition layer in the northern Brazil Basin. The AABW north of 5°S is relatively homogeneous with only lighter AABW being able to pass through the Equatorial Channel (EQCH) into the North Atlantic. Spanning a period of 26 years, our data also allow an estimation of long-term temperature trends in abyssal waters. We find a warming of 2.5±0.7•10-3 °C yr-1 of the waters in the northern Brazil Basin at temperatures colder than 0.6 °C throughout the period 1989-2014 and can relate this warming to a thinning of the dense AABW layer. Whereas isopycnal heave is the dominant effect which defines the vertical distribution of temperature trends on isobars, we also find temperature changes on isopycnals in the lower NADW and AABW layers. There temperatures on isopycnals exhibit decadal variations with warming in the 1990s and cooling in the 2000s - the contributions to the

  5. Reconstruction of Hyaline Cartilage Deep Layer Properties in 3-Dimensional Cultures of Human Articular Chondrocytes.

    Science.gov (United States)

    Nanduri, Vibudha; Tattikota, Surendra Mohan; T, Avinash Raj; Sriramagiri, Vijaya Rama Rao; Kantipudi, Suma; Pande, Gopal

    2014-06-01

    Articular cartilage (AC) injuries and malformations are commonly noticed because of trauma or age-related degeneration. Many methods have been adopted for replacing or repairing the damaged tissue. Currently available AC repair methods, in several cases, fail to yield good-quality long-lasting results, perhaps because the reconstructed tissue lacks the cellular and matrix properties seen in hyaline cartilage (HC). To reconstruct HC tissue from 2-dimensional (2D) and 3-dimensional (3D) cultures of AC-derived human chondrocytes that would specifically exhibit the cellular and biochemical properties of the deep layer of HC. Descriptive laboratory study. Two-dimensional cultures of human AC-derived chondrocytes were established in classical medium (CM) and newly defined medium (NDM) and maintained for a period of 6 weeks. These cells were suspended in 2 mm-thick collagen I gels, placed in 24-well culture inserts, and further cultured up to 30 days. Properties of chondrocytes, grown in 2D cultures and the reconstructed 3D cartilage tissue, were studied by optical and scanning electron microscopic techniques, immunohistochemistry, and cartilage-specific gene expression profiling by reverse transcription polymerase chain reaction and were compared with those of the deep layer of native human AC. Two-dimensional chondrocyte cultures grown in NDM, in comparison with those grown in CM, showed more chondrocyte-specific gene activity and matrix properties. The NDM-grown chondrocytes in 3D cultures also showed better reproduction of deep layer properties of HC, as confirmed by microscopic and gene expression analysis. The method used in this study can yield cartilage tissue up to approximately 1.6 cm in diameter and 2 mm in thickness that satisfies the very low cell density and matrix composition properties present in the deep layer of normal HC. This study presents a novel and reproducible method for long-term culture of AC-derived chondrocytes and reconstruction of cartilage

  6. A Fusion Face Recognition Approach Based on 7-Layer Deep Learning Neural Network

    Directory of Open Access Journals (Sweden)

    Jianzheng Liu

    2016-01-01

    Full Text Available This paper presents a method for recognizing human faces with facial expression. In the proposed approach, a motion history image (MHI is employed to get the features in an expressive face. The face can be seen as a kind of physiological characteristic of a human and the expressions are behavioral characteristics. We fused the 2D images of a face and MHIs which were generated from the same face’s image sequences with expression. Then the fusion features were used to feed a 7-layer deep learning neural network. The previous 6 layers of the whole network can be seen as an autoencoder network which can reduce the dimension of the fusion features. The last layer of the network can be seen as a softmax regression; we used it to get the identification decision. Experimental results demonstrated that our proposed method performs favorably against several state-of-the-art methods.

  7. An environmental model study of the deep layers of the North East Atlantic

    International Nuclear Information System (INIS)

    Bork, I.

    1989-01-01

    The field work of the north Atlantic monitoring Program (NOAMP) was supplemented by numerical simulations of the transport of radionuclides in the North Atlantic Ocean by annual mean flows and mixing processes. During the last year of NOAMP, a different attempt was made to compute the current field and three-dimensional trajectories of particles released in deep layers of the NOAMP area. It is the subject of this paper. The model used is of Bryan/Semtner type, but with smoothed topography and climatological (winter) temperature and salinity data. The results form a compromise between interpretation of climatological temperature and salinity data and the complete prediction of the current field by prognostic calculations, which yields a deep flow pattern that agrees with some ideas of the abyssal circulation

  8. Light penetration structures the deep acoustic scattering layers in the global ocean

    DEFF Research Database (Denmark)

    Aksnes, Dag L.; Rostad, Anders; Kaartvedt, Stein

    2017-01-01

    The deep scattering layer (DSL) is a ubiquitous acoustic signature found across all oceans and arguably the dominant feature structuring the pelagic open ocean ecosystem. It is formed by mesopelagic fishes and pelagic invertebrates. The DSL animals are an important food source for marine megafauna...... distributions with hypoxic waters. In enhancing understanding of this phenomenon, our results should improve the ability to predict and model the dynamics of one of the largest animal biomass components on earth, with key roles in the oceanic biological carbon pump and food web....

  9. Jetting from impact of a spherical drop with a deep layer

    Science.gov (United States)

    Zhang, Li; Toole, Jameson; Fazzaa, Kamel; Deegan, Robert; Deegan Group Team; X-Ray Science Division, Advanced Photon Source Collaboration

    2011-11-01

    We performed an experimental study of jets during the impact of a spherical drop with a deep layer of same liquid. Using high speed optical and X-ray imaging, we observe two types of jets: the so-called ejecta sheet which emerges almost immediately after impact and the lamella which emerges later. For high Reynolds number the two jets are distinct, while for low Reynolds number the two jets combine into a single continuous jet. We also measured the emergence time, speed, and position of the ejecta sheet and found simple scaling relations for these quantities.

  10. Modelling deep convection and its impacts on the tropical tropopause layer

    Directory of Open Access Journals (Sweden)

    J. S. Hosking

    2010-11-01

    Full Text Available The UK Met Office's Unified Model is used at a climate resolution (N216, ~0.83°×~0.56°, ~60 km to assess the impact of deep tropical convection on the structure of the tropical tropopause layer (TTL. We focus on the potential for rapid transport of short-lived ozone depleting species to the stratosphere by rapid convective uplift. The modelled horizontal structure of organised convection is shown to match closely with signatures found in the OLR satellite data. In the model, deep convective elevators rapidly lift air from 4–5 km up to 12–14 km. The influx of tropospheric air entering the TTL (11–12 km is similar for all tropical regions with most convection stopping below ~14 km. The tropical tropopause is coldest and driest between November and February, coinciding with the greatest upwelling over the tropical warm pool. As this deep convection is co-located with bromine-rich biogenic coastal emissions, this period and location could potentially be the preferential gateway for stratospheric bromine.

  11. Magnetic susceptibility in the deep layers of the primary motor cortex in Amyotrophic Lateral Sclerosis

    Directory of Open Access Journals (Sweden)

    M. Costagli

    2016-01-01

    Full Text Available Amyotrophic Lateral Sclerosis (ALS is a progressive neurological disorder that entails degeneration of both upper and lower motor neurons. The primary motor cortex (M1 in patients with upper motor neuron (UMN impairment is pronouncedly hypointense in Magnetic Resonance (MR T2* contrast. In the present study, 3D gradient-recalled multi-echo sequences were used on a 7 Tesla MR system to acquire T2*-weighted images targeting M1 at high spatial resolution. MR raw data were used for Quantitative Susceptibility Mapping (QSM. Measures of magnetic susceptibility correlated with the expected concentration of non-heme iron in different regions of the cerebral cortex in healthy subjects. In ALS patients, significant increases in magnetic susceptibility co-localized with the T2* hypointensity observed in the middle and deep layers of M1. The magnetic susceptibility, hence iron concentration, of the deep cortical layers of patients' M1 subregions corresponding to Penfield's areas of the hand and foot in both hemispheres significantly correlated with the clinical scores of UMN impairment of the corresponding limbs. QSM therefore reflects the presence of iron deposits related to neuroinflammatory reaction and cortical microgliosis, and might prove useful in estimating M1 iron concentration, as a possible radiological sign of severe UMN burden in ALS patients.

  12. Picocyanobacteria Dominance in Deep Biomass Layers: Relation to Diatom Presence and Episodic Events.

    Science.gov (United States)

    Aguilar, C.; Cuhel, R. L.

    2016-02-01

    In Offshore Marine and Large Lake Waters, most of the biomass and the productivity of phytoplankton occur below surface observation capabilities. Sub-mixed layer phytoplankton populations develop, increase, persist, and decay in relation to physical structure such as pycnocline density gradients interacting with progressively changing light fields. Basin-scale meteorological events and persistence of major invasive species have also left marks on biogeochemical cycling and ecosystem function in Lake Michigan. Among the former are precipitation and turbulence alterations brought on by unusual winter ice cover and a century-scale flood during 2008. Dampened seasonal silicate cycling indicated a basin-wide reduction of diatom production following mussel establishment. Communities in Lake Michigan shifted from diatom and big cell-dominated to small cell picocyanobacteria-dominated phytoplankton. Picocyanobacteria were beneficiaries of profound oligotrophication of the ecosystem starting in 2003. Photosynthetic parameters of pre-2003 Deep Biomass populations dominated by diatoms were systematically different from the cyanobacterial epoch following quagga mussel establishment and increase in depth of 1% incident light to 50-60m. Deep cyanobacterial production has now often been on the same scale as overlying waters. Photophysiology changes in a smooth depth gradient in this clear water as opposed to previous abrupt transition to shade adaptation. Among these many physicochemical permutations, community structure has consistently been a tradeoff between diatoms and picocyanobacteria. Opposing fluctuations of biomass favor one or the other on seasonal time frames of sequential years, with a complete system reset between each (winter mixing). For the Great Flood example, diatom surface blooms increased light extinction and drove the deep biomass maximum up - as populations settled into the pycnocline they had already outcompeted the picocyanobacteria. The opposite was true

  13. Mixed layer depth calculation in deep convection regions in ocean numerical models

    Science.gov (United States)

    Courtois, Peggy; Hu, Xianmin; Pennelly, Clark; Spence, Paul; Myers, Paul G.

    2017-12-01

    Mixed Layer Depths (MLDs) diagnosed by conventional numerical models are generally based on a density difference with the surface (e.g., 0.01 kg.m-3). However, the temperature-salinity compensation and the lack of vertical resolution contribute to over-estimated MLD, especially in regions of deep convection. In the present work, we examined the diagnostic MLD, associated with the deep convection of the Labrador Sea Water (LSW), calculated with a simple density difference criterion. The over-estimated MLD led us to develop a new tool, based on an observational approach, to recalculate MLD from model output. We used an eddy-permitting, 1/12° regional configuration of the Nucleus for European Modelling of the Ocean (NEMO) to test and discuss our newly defined MLD. We compared our new MLD with that from observations, and we showed a major improvement with our new algorithm. To show the new MLD is not dependent on a single model and its horizontal resolution, we extended our analysis to include 1/4° eddy-permitting simulations, and simulations using the Modular Ocean Model (MOM) model.

  14. [Soil organic carbon mineralization of Black Locust forest in the deep soil layer of the hilly region of the Loess Plateau, China].

    Science.gov (United States)

    Ma, Xin-Xin; Xu, Ming-Xiang; Yang, Kai

    2012-11-01

    The deep soil layer (below 100 cm) stores considerable soil organic carbon (SOC). We can reveal its stability and provide the basis for certification of the deep soil carbon sinks by studying the SOC mineralization in the deep soil layer. With the shallow soil layer (0-100 cm) as control, the SOC mineralization under the condition (temperature 15 degrees C, the soil water content 8%) of Black Locust forest in the deep soil layer (100-400 cm) of the hilly region of the Loess Plateau was studied. The results showed that: (1) There was a downward trend in the total SOC mineralization with the increase of soil depth. The total SOC mineralization in the sub-deep soil (100-200 cm) and deep soil (200-400 cm) were equivalent to approximately 88.1% and 67.8% of that in the shallow layer (0-100 cm). (2) Throughout the carbon mineralization process, the same as the shallow soil, the sub-deep and deep soil can be divided into 3 stages. In the rapid decomposition phase, the ratio of the mineralization or organic carbon to the total mineralization in the sub-deep and deep layer (0-10 d) was approximately 50% of that in the shallow layer (0-17 d). In the slow decomposition phase, the ratio of organic carbon mineralization to total mineralization in the sub-deep, deep layer (11-45 d) was 150% of that in the shallow layer (18-45 d). There was no significant difference in this ratio among these three layers (46-62 d) in the relatively stable stage. (3) There was no significant difference (P > 0.05) in the mineralization rate of SOC among the shallow, sub-deep, deep layers. The stability of SOC in the deep soil layer (100-400 cm) was similar to that in the shallow soil layer and the SOC in the deep soil layer was also involved in the global carbon cycle. The change of SOC in the deep soil layer should be taken into account when estimating the effects of soil carbon sequestration in the Hilly Region of the Loess Plateau, China.

  15. Light penetration structures the deep acoustic scattering layers in the global ocean.

    KAUST Repository

    Aksnes, Dag L.

    2017-05-01

    The deep scattering layer (DSL) is a ubiquitous acoustic signature found across all oceans and arguably the dominant feature structuring the pelagic open ocean ecosystem. It is formed by mesopelagic fishes and pelagic invertebrates. The DSL animals are an important food source for marine megafauna and contribute to the biological carbon pump through the active flux of organic carbon transported in their daily vertical migrations. They occupy depths from 200 to 1000 m at daytime and migrate to a varying degree into surface waters at nighttime. Their daytime depth, which determines the migration amplitude, varies across the global ocean in concert with water mass properties, in particular the oxygen regime, but the causal underpinning of these correlations has been unclear. We present evidence that the broad variability in the oceanic DSL daytime depth observed during the Malaspina 2010 Circumnavigation Expedition is governed by variation in light penetration. We find that the DSL depth distribution conforms to a common optical depth layer across the global ocean and that a correlation between dissolved oxygen and light penetration provides a parsimonious explanation for the association of shallow DSL distributions with hypoxic waters. In enhancing understanding of this phenomenon, our results should improve the ability to predict and model the dynamics of one of the largest animal biomass components on earth, with key roles in the oceanic biological carbon pump and food web.

  16. Light penetration structures the deep acoustic scattering layers in the global ocean.

    KAUST Repository

    Aksnes, Dag L.; Rø stad, Anders; Kaartvedt, Stein; Martinez, Udane; Duarte, Carlos M.; Irigoien, Xabier

    2017-01-01

    The deep scattering layer (DSL) is a ubiquitous acoustic signature found across all oceans and arguably the dominant feature structuring the pelagic open ocean ecosystem. It is formed by mesopelagic fishes and pelagic invertebrates. The DSL animals are an important food source for marine megafauna and contribute to the biological carbon pump through the active flux of organic carbon transported in their daily vertical migrations. They occupy depths from 200 to 1000 m at daytime and migrate to a varying degree into surface waters at nighttime. Their daytime depth, which determines the migration amplitude, varies across the global ocean in concert with water mass properties, in particular the oxygen regime, but the causal underpinning of these correlations has been unclear. We present evidence that the broad variability in the oceanic DSL daytime depth observed during the Malaspina 2010 Circumnavigation Expedition is governed by variation in light penetration. We find that the DSL depth distribution conforms to a common optical depth layer across the global ocean and that a correlation between dissolved oxygen and light penetration provides a parsimonious explanation for the association of shallow DSL distributions with hypoxic waters. In enhancing understanding of this phenomenon, our results should improve the ability to predict and model the dynamics of one of the largest animal biomass components on earth, with key roles in the oceanic biological carbon pump and food web.

  17. An Algorithm to Generate Deep-Layer Temperatures from Microwave Satellite Observations for the Purpose of Monitoring Climate Change. Revised

    Science.gov (United States)

    Goldberg, Mitchell D.; Fleming, Henry E.

    1994-01-01

    An algorithm for generating deep-layer mean temperatures from satellite-observed microwave observations is presented. Unlike traditional temperature retrieval methods, this algorithm does not require a first guess temperature of the ambient atmosphere. By eliminating the first guess a potentially systematic source of error has been removed. The algorithm is expected to yield long-term records that are suitable for detecting small changes in climate. The atmospheric contribution to the deep-layer mean temperature is given by the averaging kernel. The algorithm computes the coefficients that will best approximate a desired averaging kernel from a linear combination of the satellite radiometer's weighting functions. The coefficients are then applied to the measurements to yield the deep-layer mean temperature. Three constraints were used in deriving the algorithm: (1) the sum of the coefficients must be one, (2) the noise of the product is minimized, and (3) the shape of the approximated averaging kernel is well-behaved. Note that a trade-off between constraints 2 and 3 is unavoidable. The algorithm can also be used to combine measurements from a future sensor (i.e., the 20-channel Advanced Microwave Sounding Unit (AMSU)) to yield the same averaging kernel as that based on an earlier sensor (i.e., the 4-channel Microwave Sounding Unit (MSU)). This will allow a time series of deep-layer mean temperatures based on MSU measurements to be continued with AMSU measurements. The AMSU is expected to replace the MSU in 1996.

  18. The deep chlorophyll layer in Lake Ontario: Extent, mechanisms of formation, and abiotic predictors

    Science.gov (United States)

    Scofield, Anne E.; Watkins, James M.; Weidel, Brian C.; Luckey, Frederick J.; Rudstam, Lars G.

    2017-01-01

    Epilimnetic production has declined in Lake Ontario, but increased production in metalimnetic deep chlorophyll layers (DCLs) may compensate for these losses. We investigated the spatial and temporal extent of DCLs, the mechanisms driving DCL formation, and the use of physical variables for predicting the depth and concentration of the deep chlorophyll maximum (DCM) during April–September 2013. A DCL with DCM concentrations 2 to 3 times greater than those in the epilimnion was present when the euphotic depth extended below the epilimnion, which occurred primarily from late June through mid-August. In situ growth was important for DCL formation in June and July, but settling and photoadaptation likely also contributed to the later-season DCL. Supporting evidence includes: phytoplankton biovolume was 2.4 × greater in the DCL than in the epilimnion during July, the DCL phytoplankton community of July was different from that of May and the July epilimnion (p = 0.004), and there were concurrences of DCM with maxima in fine particle concentration and dissolved oxygen saturation. Higher nutrient levels in the metalimnion may also be a necessary condition for DCL formation because July metalimnetic concentrations were 1.5 × (nitrate) and 3.5 × (silica) greater than in the epilimnion. Thermal structure variables including epilimnion depth, thermocline depth, and thermocline steepness were useful for predicting DCM depth; the inclusion of euphotic depth only marginally improved these predictions. However, euphotic depth was critical for predicting DCM concentrations. The DCL is a productive and predictable feature of the Lake Ontario ecosystem during the stratified period.

  19. Temperature and moisture effects on greenhouse gas emissions from deep active-layer boreal soils

    Science.gov (United States)

    Bond-Lamberty, Ben; Smith, A. Peyton; Bailey, Vanessa

    2016-12-01

    Rapid climatic changes, rising air temperatures, and increased fires are expected to drive permafrost degradation and alter soil carbon (C) cycling in many high-latitude ecosystems. How these soils will respond to changes in their temperature, moisture, and overlying vegetation is uncertain but critical to understand given the large soil C stocks in these regions. We used a laboratory experiment to examine how temperature and moisture control CO2 and CH4 emissions from mineral soils sampled from the bottom of the annual active layer, i.e., directly above permafrost, in an Alaskan boreal forest. Gas emissions from 30 cores, subjected to two temperatures and either field moisture conditions or experimental drought, were tracked over a 100-day incubation; we also measured a variety of physical and chemical characteristics of the cores. Gravimetric water content was 0.31 ± 0.12 (unitless) at the beginning of the incubation; cores at field moisture were unchanged at the end, but drought cores had declined to 0.06 ± 0.04. Daily CO2 fluxes were positively correlated with incubation chamber temperature, core water content, and percent soil nitrogen. They also had a temperature sensitivity (Q10) of 1.3 and 1.9 for the field moisture and drought treatments, respectively. Daily CH4 emissions were most strongly correlated with percent nitrogen, but neither temperature nor water content was a significant first-order predictor of CH4 fluxes. The cumulative production of C from CO2 was over 6 orders of magnitude higher than that from CH4; cumulative CO2 was correlated with incubation temperature and moisture treatment, with drought cores producing 52-73 % lower C. Cumulative CH4 production was unaffected by any treatment. These results suggest that deep active-layer soils may be sensitive to changes in soil moisture under aerobic conditions, a critical factor as discontinuous permafrost thaws in interior Alaska. Deep but unfrozen high-latitude soils have been shown to be

  20. The Deep Atmospheric Boundary Layer and Its Significance to the Stratosphere and Troposphere Exchange over the Tibetan Plateau

    Science.gov (United States)

    Chen, Xuelong; Añel, Juan A.; Su, Zhongbo; de la Torre, Laura; Kelder, Hennie; van Peet, Jacob; Ma, Yaoming

    2013-01-01

    In this study the depth of the atmospheric boundary layer (ABL) over the Tibetan Plateau was measured during a regional radiosonde observation campaign in 2008 and found to be deeper than indicated by previously measurements. Results indicate that during fair weather conditions on winter days, the top of the mixed layers can be up to 5 km above the ground (9.4 km above sea level). Measurements also show that the depth of the ABL is quite distinct for three different periods (winter, monsoon-onset, and monsoon seasons). Turbulence at the top of a deep mixing layer can rise up to the upper troposphere. As a consequence, as confirmed by trajectory analysis, interaction occurs between deep ABLs and the low tropopause during winter over the Tibetan Plateau. PMID:23451108

  1. Towards a more consistent picture of isopycnal mixing in climate models

    Science.gov (United States)

    Gnanadesikan, A.; Pradal, M. A. S.; Koszalka, I.; Abernathey, R. P.

    2014-12-01

    The stirring of tracers by mesoscale eddies along isopycnal surfaces is often represented in coarse-resolution models by the Redi diffusion parameter ARedi. Theoretical treatments of ARedi often assume it should scale as the eddy energy or the growth rate of mesoscale eddies,. producing a picture where it is high in boundary currents and low )of order a few hundred m2/s) in the gyre interiors. However, observational estimates suggest that ARedi should be very large (of order thousands of m2/s) in the gyre interior. We present results of recent simulations comparing a range of spatially constant values ARedi (with values of 400, 800, 1200 and 2400 m2/s) to a spatially resolved estimate based on altimetry and a zonally averaged version of the same estimate. In general, increasing the ARedi coefficient destratifies and warms the high latitudes. Relative to our control simulation, the spatially dependent coefficient is lower in the Southern Ocean, but high in the North Pacific, and so the temperature changes mirror this. We also examine the response of ocean hypoxia to these changes. In general, the zonally averaged version of the altimetry-based estimate of ARedi does not capture the full 2d representation.

  2. Role of ocean isopycnal mixing in setting the uptake of anthropogenic carbon

    Science.gov (United States)

    Gnanadesikan, A.; Pradal, M. A. S.; Abernathey, R. P.

    2014-12-01

    The magnitude of the isopycnal stirring coefficient ARedi is poorly constrained from data and varies greatly across Earth System Models. This paper documents the impact of such uncertainty on the oceanic carbon cycle. We compare six spatial representations of ARedi. Four constant values (400, 800, 1200 and 2400 m2/s) are used to explore the difference between using the low values found in many models and the higher values seen in observational estimates. Models are also run with two spatially dependent values of ARedi based on altimetry, one which captures the fully two-dimensional structure of the mixing coefficient, the other of which looks at the zonally averaged structure alone. Under global warming significant changes are seen in the biological pump in convective regions, but these changes are largely locally compensated by changes in preformed DIC. Instead, differences in anthropogenic uptake of carbon are largely centered in the tropics, and can be well described in terms of a relatively simple diffusive approximation. Using ideal age as a tracer can give insight into the expected behavior of the models. The rate of oceanic mixing represents a quantitatively significant uncertainty in future projections of the global carbon cycle, amounting to about 20% of the oceanic uptake.

  3. Unexpectedly high soil organic carbon stocks under impervious surfaces contributed by urban deep cultural layers

    Science.gov (United States)

    Bae, J.; Ryu, Y.

    2017-12-01

    The expansion of urban artificial structures has altered the spatial distribution of soil organic carbon (SOC) stocks. The majority of the urban soil studies within the land-cover types, however, focused on top soils despite the potential of deep soils to store large amounts of SOC. Here, we investigate vertical distribution of SOC stocks in both impervious surfaces (n = 11) and adjacent green spaces (n = 8) to a depth of 4 m with in an apartment complex area, Seoul, Republic of Korea. We found that more than six times differences in SOC stocks were observed at 0-1 m depth between the impervious surfaces (1.90 kgC m-2) and the green spaces (12.03 kgC m-2), but no significant differences appeared when comparing them at the depth of 0-4 m. We found "cultural layers" with the largest SOC stocks at 1-2 m depth in the impervious surfaces (15.85 kgC m-2) and 2-3 m depths in urban green spaces (12.52 kgC m-2). Thus, the proportions of SOC stocks at the 0-1 m depth to the total of 0-4 m depth were 6.83% in impervious surfaces and 32.15% in urban green spaces, respectively. The 13C and 15N stable isotope data with historical aerial photographs revealed that the cropland which existed before 1978 formed the SOC in the cultural layers. Our results highlight that impervious surface could hold large amount of SOC stock which has been overlooked in urban carbon cycles. We believe this finding will help city planners and policy makers to develop carbon management programs better towards sustainable urban ecosystems.

  4. Beyond Retinal Layers: A Deep Voting Model for Automated Geographic Atrophy Segmentation in SD-OCT Images.

    Science.gov (United States)

    Ji, Zexuan; Chen, Qiang; Niu, Sijie; Leng, Theodore; Rubin, Daniel L

    2018-01-01

    To automatically and accurately segment geographic atrophy (GA) in spectral-domain optical coherence tomography (SD-OCT) images by constructing a voting system with deep neural networks without the use of retinal layer segmentation. An automatic GA segmentation method for SD-OCT images based on the deep network was constructed. The structure of the deep network was composed of five layers, including one input layer, three hidden layers, and one output layer. During the training phase, the labeled A-scans with 1024 features were directly fed into the network as the input layer to obtain the deep representations. Then a soft-max classifier was trained to determine the label of each individual pixel. Finally, a voting decision strategy was used to refine the segmentation results among 10 trained models. Two image data sets with GA were used to evaluate the model. For the first dataset, our algorithm obtained a mean overlap ratio (OR) 86.94% ± 8.75%, absolute area difference (AAD) 11.49% ± 11.50%, and correlation coefficients (CC) 0.9857; for the second dataset, the mean OR, AAD, and CC of the proposed method were 81.66% ± 10.93%, 8.30% ± 9.09%, and 0.9952, respectively. The proposed algorithm was capable of improving over 5% and 10% segmentation accuracy, respectively, when compared with several state-of-the-art algorithms on two data sets. Without retinal layer segmentation, the proposed algorithm could produce higher segmentation accuracy and was more stable when compared with state-of-the-art methods that relied on retinal layer segmentation results. Our model may provide reliable GA segmentations from SD-OCT images and be useful in the clinical diagnosis of advanced nonexudative AMD. Based on the deep neural networks, this study presents an accurate GA segmentation method for SD-OCT images without using any retinal layer segmentation results, and may contribute to improved understanding of advanced nonexudative AMD.

  5. Refinement of learned skilled movement representation in motor cortex deep output layer

    Science.gov (United States)

    Li, Qian; Ko, Ho; Qian, Zhong-Ming; Yan, Leo Y. C.; Chan, Danny C. W.; Arbuthnott, Gordon; Ke, Ya; Yung, Wing-Ho

    2017-01-01

    The mechanisms underlying the emergence of learned motor skill representation in primary motor cortex (M1) are not well understood. Specifically, how motor representation in the deep output layer 5b (L5b) is shaped by motor learning remains virtually unknown. In rats undergoing motor skill training, we detect a subpopulation of task-recruited L5b neurons that not only become more movement-encoding, but their activities are also more structured and temporally aligned to motor execution with a timescale of refinement in tens-of-milliseconds. Field potentials evoked at L5b in vivo exhibit persistent long-term potentiation (LTP) that parallels motor performance. Intracortical dopamine denervation impairs motor learning, and disrupts the LTP profile as well as the emergent neurodynamical properties of task-recruited L5b neurons. Thus, dopamine-dependent recruitment of L5b neuronal ensembles via synaptic reorganization may allow the motor cortex to generate more temporally structured, movement-encoding output signal from M1 to downstream circuitry that drives increased uniformity and precision of movement during motor learning. PMID:28598433

  6. Spatial extent and dissipation of the deep chlorophyll layer in Lake Ontario during the Lake Ontario lower foodweb assessment, 2003 and 2008

    Science.gov (United States)

    Watkins, J. M.; Weidel, Brian M.; Rudstam, L. G.; Holek, K. T.

    2014-01-01

    Increasing water clarity in Lake Ontario has led to a vertical redistribution of phytoplankton and an increased importance of the deep chlorophyll layer in overall primary productivity. We used in situ fluorometer profiles collected in lakewide surveys of Lake Ontario in 2008 to assess the spatial extent and intensity of the deep chlorophyll layer. In situ fluorometer data were corrected with extracted chlorophyll data using paired samples from Lake Ontario collected in August 2008. The deep chlorophyll layer was present offshore during the stratified conditions of late July 2008 with maximum values from 4-13 μg l-1 corrected chlorophyll a at 10 to 17 m depth within the metalimnion. Deep chlorophyll layer was closely associated with the base of the thermocline and a subsurface maximum of dissolved oxygen, indicating the feature's importance as a growth and productivity maximum. Crucial to the deep chlorophyll layer formation, the photic zone extended deeper than the surface mixed layer in mid-summer. The layer extended through most of the offshore in July 2008, but was not present in the easternmost transect that had a deeper surface mixed layer. By early September 2008, the lakewide deep chlorophyll layer had dissipated. A similar formation and dissipation was observed in the lakewide survey of Lake Ontario in 2003.

  7. Handwritten Devanagari Character Recognition Using Layer-Wise Training of Deep Convolutional Neural Networks and Adaptive Gradient Methods

    Directory of Open Access Journals (Sweden)

    Mahesh Jangid

    2018-02-01

    Full Text Available Handwritten character recognition is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Devanagari characters using deep convolutional neural networks (DCNN which are one of the recent techniques adopted from the deep learning community. We experimented the ISIDCHAR database provided by (Information Sharing Index ISI, Kolkata and V2DMDCHAR database with six different architectures of DCNN to evaluate the performance and also investigate the use of six recently developed adaptive gradient methods. A layer-wise technique of DCNN has been employed that helped to achieve the highest recognition accuracy and also get a faster convergence rate. The results of layer-wise-trained DCNN are favorable in comparison with those achieved by a shallow technique of handcrafted features and standard DCNN.

  8. Quantitative evaluation of deep and shallow tissue layers' contribution to fNIRS signal using multi-distance optodes and independent component analysis.

    Science.gov (United States)

    Funane, Tsukasa; Atsumori, Hirokazu; Katura, Takusige; Obata, Akiko N; Sato, Hiroki; Tanikawa, Yukari; Okada, Eiji; Kiguchi, Masashi

    2014-01-15

    To quantify the effect of absorption changes in the deep tissue (cerebral) and shallow tissue (scalp, skin) layers on functional near-infrared spectroscopy (fNIRS) signals, a method using multi-distance (MD) optodes and independent component analysis (ICA), referred to as the MD-ICA method, is proposed. In previous studies, when the signal from the shallow tissue layer (shallow signal) needs to be eliminated, it was often assumed that the shallow signal had no correlation with the signal from the deep tissue layer (deep signal). In this study, no relationship between the waveforms of deep and shallow signals is assumed, and instead, it is assumed that both signals are linear combinations of multiple signal sources, which allows the inclusion of a "shared component" (such as systemic signals) that is contained in both layers. The method also assumes that the partial optical path length of the shallow layer does not change, whereas that of the deep layer linearly increases along with the increase of the source-detector (S-D) distance. Deep- and shallow-layer contribution ratios of each independent component (IC) are calculated using the dependence of the weight of each IC on the S-D distance. Reconstruction of deep- and shallow-layer signals are performed by the sum of ICs weighted by the deep and shallow contribution ratio. Experimental validation of the principle of this technique was conducted using a dynamic phantom with two absorbing layers. Results showed that our method is effective for evaluating deep-layer contributions even if there are high correlations between deep and shallow signals. Next, we applied the method to fNIRS signals obtained on a human head with 5-, 15-, and 30-mm S-D distances during a verbal fluency task, a verbal working memory task (prefrontal area), a finger tapping task (motor area), and a tetrametric visual checker-board task (occipital area) and then estimated the deep-layer contribution ratio. To evaluate the signal separation

  9. Deep reactive ion etching of fused silica using a single-coated soft mask layer for bio-analytical applications

    International Nuclear Information System (INIS)

    Ray, Tathagata; Zhu, Haixin; Meldrum, Deirdre R

    2010-01-01

    In this note, we present our results from process development and characterization of reactive ion etching (RIE) of fused silica using a single-coated soft masking layer (KMPR® 1025, Microchem Corporation, Newton, MA). The effects of a number of fluorine-radical-based gaseous chemistries, the gas flow rate, RF power and chamber pressure on the etch rate and etching selectivity of fused silica were studied using factorial experimental designs. RF power and pressure were found to be the most important factors in determining the etch rate. The highest fused silica etch rate obtained was about 933 Å min −1 by using SF 6 -based gas chemistry, and the highest etching selectivity between the fused silica and KMPR® 1025 was up to 1.2 using a combination of CF 4 , CHF 3 and Ar. Up to 30 µm deep microstructures have been successfully fabricated using the developed processes. The average area roughness (R a ) of the etched surface was measured and results showed it is comparable to the roughness obtained using a wet etching technique. Additionally, near-vertical sidewalls (with a taper angle up to 85°) have been obtained for the etched microstructures. The processes developed here can be applied to any application requiring fabrication of deep microstructures in fused silica with near-vertical sidewalls. To our knowledge, this is the first note on deep RIE of fused silica using a single-coated KMPR® 1025 masking layer and a non-ICP-based reactive ion etcher. (technical note)

  10. The influence of Congo River discharges in the surface and deep layers of the Gulf of Guinea

    OpenAIRE

    Vangriesheim, A.; Pierre, C.; Aminot, A.; Metzl, N.; Baurand, François; Caprais, J. C.

    2009-01-01

    The main feature of the Congo-Angola margin in the Gulf of Guinea is the Congo (ex-Zaire) deep-sea fan composed of a submarine canyon directly connected to the Congo River, a channel and a [sediment] lobe area. During the multi-disciplinary programme called BIOZAIRE conducted by Ifremer from 2000 to 2005, two CTD-O2 sections with discrete water column samples were performed (BIOZAIRE3 cruise: 2003-2004) to study the influence of the Congo River discharges, both in the surface layer and in the...

  11. Object recognition using deep convolutional neural networks with complete transfer and partial frozen layers

    NARCIS (Netherlands)

    Kruithof, M.C.; Bouma, H.; Fischer, N.M.; Schutte, K.

    2016-01-01

    Object recognition is important to understand the content of video and allow flexible querying in a large number of cameras, especially for security applications. Recent benchmarks show that deep convolutional neural networks are excellent approaches for object recognition. This paper describes an

  12. Contrasting effects of strabismic amblyopia on metabolic activity in superficial and deep layers of striate cortex.

    Science.gov (United States)

    Adams, Daniel L; Economides, John R; Horton, Jonathan C

    2015-05-01

    To probe the mechanism of visual suppression, we have raised macaques with strabismus by disinserting the medial rectus muscle in each eye at 1 mo of age. Typically, this operation produces a comitant, alternating exotropia with normal acuity in each eye. Here we describe an unusual occurrence: the development of severe amblyopia in one eye of a monkey after induction of exotropia. Shortly after surgery, the animal demonstrated a strong fixation preference for the left eye, with apparent suppression of the right eye. Later, behavioral testing showed inability to track or to saccade to targets with the right eye. With the left eye occluded, the animal demonstrated no visually guided behavior. Optokinetic nystagmus was absent in the right eye. Metabolic activity in striate cortex was assessed by processing the tissue for cytochrome oxidase (CO). Amblyopia caused loss of CO in one eye's rows of patches, presumably those serving the blind eye. Layers 4A and 4B showed columns of reduced CO, in register with pale rows of patches in layer 2/3. Layers 4C, 5, and 6 also showed columns of CO activity, but remarkably, comparison with more superficial layers showed a reversal in contrast. In other words, pale CO staining in layers 2/3, 4A, and 4B was aligned with dark CO staining in layers 4C, 5, and 6. No experimental intervention or deprivation paradigm has been reported previously to produce opposite effects on metabolic activity in layers 2/3, 4A, and 4B vs. layers 4C, 5, and 6 within a given eye's columns. Copyright © 2015 the American Physiological Society.

  13. Low absorption loss p-AlGaN superlattice cladding layer for current-injection deep ultraviolet laser diodes

    Energy Technology Data Exchange (ETDEWEB)

    Martens, M.; Kuhn, C.; Ziffer, E.; Simoneit, T.; Rass, J.; Wernicke, T. [Institute of Solid State Physics, Technische Universität Berlin, Hardenbergstr. 36, EW 6-1, 10623 Berlin (Germany); Kueller, V.; Knauer, A.; Einfeldt, S.; Weyers, M. [Ferdinand-Braun-Institut, Leibniz-Institut für Höchstfrequenztechnik, Gustav-Kirchhoff-Str. 4, 12489 Berlin (Germany); Kneissl, M. [Institute of Solid State Physics, Technische Universität Berlin, Hardenbergstr. 36, EW 6-1, 10623 Berlin (Germany); Ferdinand-Braun-Institut, Leibniz-Institut für Höchstfrequenztechnik, Gustav-Kirchhoff-Str. 4, 12489 Berlin (Germany)

    2016-04-11

    Current injection into AlGaN-based laser diode structures with high aluminum mole fractions for deep ultraviolet emission is investigated. The electrical characteristics of laser diode structures with different p-AlGaN short period superlattice (SPSL) cladding layers with various aluminum mole fractions are compared. The heterostructures contain all elements that are needed for a current-injection laser diode including cladding and waveguide layers as well as an AlGaN quantum well active region emitting near 270 nm. We found that with increasing aluminum content in the p-AlGaN cladding, the diode turn-on voltage increases, while the series resistance slightly decreases. By introducing an SPSL instead of bulk layers, the operating voltage is significantly reduced. A gain guided broad area laser diode structure with transparent p-Al{sub 0.70}Ga{sub 0.30}N waveguide layers and a transparent p-cladding with an average aluminum content of 81% was designed for strong confinement of the transverse optical mode and low optical losses. Using an optimized SPSL, this diode could sustain current densities of more than 4.5 kA/cm{sup 2}.

  14. Low absorption loss p-AlGaN superlattice cladding layer for current-injection deep ultraviolet laser diodes

    International Nuclear Information System (INIS)

    Martens, M.; Kuhn, C.; Ziffer, E.; Simoneit, T.; Rass, J.; Wernicke, T.; Kueller, V.; Knauer, A.; Einfeldt, S.; Weyers, M.; Kneissl, M.

    2016-01-01

    Current injection into AlGaN-based laser diode structures with high aluminum mole fractions for deep ultraviolet emission is investigated. The electrical characteristics of laser diode structures with different p-AlGaN short period superlattice (SPSL) cladding layers with various aluminum mole fractions are compared. The heterostructures contain all elements that are needed for a current-injection laser diode including cladding and waveguide layers as well as an AlGaN quantum well active region emitting near 270 nm. We found that with increasing aluminum content in the p-AlGaN cladding, the diode turn-on voltage increases, while the series resistance slightly decreases. By introducing an SPSL instead of bulk layers, the operating voltage is significantly reduced. A gain guided broad area laser diode structure with transparent p-Al_0_._7_0Ga_0_._3_0N waveguide layers and a transparent p-cladding with an average aluminum content of 81% was designed for strong confinement of the transverse optical mode and low optical losses. Using an optimized SPSL, this diode could sustain current densities of more than 4.5 kA/cm"2.

  15. Deep levels in metamorphic InAs/InGaAs quantum dot structures with different composition of the embedding layers

    Science.gov (United States)

    Golovynskyi, S.; Datsenko, O.; Seravalli, L.; Kozak, O.; Trevisi, G.; Frigeri, P.; Babichuk, I. S.; Golovynska, I.; Qu, Junle

    2017-12-01

    Deep levels in metamorphic InAs/In x Ga1-x As quantum dot (QD) structures are studied with deep level thermally stimulated conductivity (TSC), photoconductivity (PC) and photoluminescence (PL) spectroscopy and compared with data from pseudomorphic InGaAs/GaAs QDs investigated previously by the same techniques. We have found that for a low content of indium (x = 0.15) the trap density in the plane of self-assembled QDs is comparable or less than the one for InGaAs/GaAs QDs. However, the trap density increases with x, resulting in a rise of the defect photoresponse in PC and TSC spectra as well as a reduction of the QD PL intensity. The activation energies of the deep levels and some traps correspond to known defect complexes EL2, EL6, EL7, EL9, and EL10 inherent in GaAs, and three traps are attributed to the extended defects, located in InGaAs embedding layers. The rest of them have been found as concentrated mainly close to QDs, as their density in the deeper InGaAs buffers is much lower. This an important result for the development of light-emitting and light-sensitive devices based on metamorphic InAs QDs, as it is a strong indication that the defect density is not higher than in pseudomorphic InAs QDs.

  16. Tracing the Ventilation Pathways of the Deep North Pacific Ocean Using Lagrangian Particles and Eulerian Tracers

    NARCIS (Netherlands)

    Syed, H.A.M.S.; Primeau, F.W.; Deleersnijder, E.L.C.; Heemink, A.W.

    2017-01-01

    Lagrangian forward and backward models are introduced into a coarse-grid ocean global circulation model to trace the ventilation routes of the deep North Pacific Ocean. The random walk aspect in the Lagrangian model is dictated by a rotated isopycnal diffusivity tensor in the circulation model,

  17. Electronic THz-spectrometer for plasmonic enhanced deep subwavelength layer detection

    NARCIS (Netherlands)

    Berrier, A.; Schaafsma, M.C.; Gómez Rivas, J.; Schäfer-Eberwein, H.; Haring Bolivar, P.; Tripodi, L.; Matters-Kammerer, M.K.

    2015-01-01

    We demonstrate the operation of a miniaturized all-electronic CMOS based THz spectrometer with performances comparable to that of a THz-TDS spectrometer in the frequency range 20 to 220 GHz. The use of this all-electronic THz spectrometer for detection of a thin TiO2 layer and a B. subtilis bacteria

  18. Flow-dependent assimilation of sea surface temperature in isopycnal coordinates with the Norwegian Climate Prediction Model

    Directory of Open Access Journals (Sweden)

    François Counillon

    2016-12-01

    Full Text Available We document a pilot stochastic re-analysis computed by assimilating sea surface temperature (SST anomalies into the ocean component of the coupled Norwegian Climate Prediction Model (NorCPM for the period 1950–2010 (doi: 10.11582/2016.00002. NorCPM is based on the Norwegian Earth System Model and uses the ensemble Kalman filter for data assimilation (DA. Here, we assimilate SST from the stochastic HadISST2 historical reconstruction. The accuracy, reliability and drift are investigated using both assimilated and independent observations. NorCPM is slightly overdispersive against assimilated observations but shows stable performance through the analysis period. It demonstrates skills against independent measurements: sea surface height, heat and salt content, in particular in the Equatorial and North Pacific, the North Atlantic Subpolar Gyre (SPG region and the Nordic Seas. Furthermore, NorCPM provides a reliable monitoring of the SPG index and represents the vertical temperature variability there, in good agreement with observations. The monitoring of the Atlantic meridional overturning circulation is also encouraging. The benefit of using a flow-dependent assimilation method and constructing the covariance in isopycnal coordinates are investigated in the SPG region. Isopycnal coordinates discretisation is found to better capture the vertical structure than standard depth-coordinate discretisation, because it leads to a deeper influence of the assimilated surface observations. The vertical covariance shows a pronounced seasonal and decadal variability that highlights the benefit of flow-dependent DA method. This study demonstrates the potential of NorCPM to compute an ocean re-analysis for the 19th and 20th centuries when SST observations are available.

  19. Exploring the isopycnal mixing and helium-heat paradoxes in a suite of Earth System Models

    OpenAIRE

    A. Gnanadesikan; R. Abernathey; M.-A. Pradal

    2014-01-01

    This paper uses a suite of Earth system models which simulate the distribution of He isotopes and radiocarbon to examine two paradoxes in Earth science, each of which results from an inconsistency between theoretically motivated global energy balances and direct observations. The helium–heat paradox refers to the fact that helium emissions to the deep ocean are far lower than would be expected given the rate of geothermal heating, since both are thought to b...

  20. Impact of deep convection in the tropical tropopause layer in West Africa: in-situ observations and mesoscale modelling

    Directory of Open Access Journals (Sweden)

    F. Fierli

    2011-01-01

    Full Text Available We present the analysis of the impact of convection on the composition of the tropical tropopause layer region (TTL in West-Africa during the AMMA-SCOUT campaign. Geophysica M55 aircraft observations of water vapor, ozone, aerosol and CO2 during August 2006 show perturbed values at altitudes ranging from 14 km to 17 km (above the main convective outflow and satellite data indicates that air detrainment is likely to have originated from convective cloud east of the flights. Simulations of the BOLAM mesoscale model, nudged with infrared radiance temperatures, are used to estimate the convective impact in the upper troposphere and to assess the fraction of air processed by convection. The analysis shows that BOLAM correctly reproduces the location and the vertical structure of convective outflow. Model-aided analysis indicates that convection can influence the composition of the upper troposphere above the level of main outflow for an event of deep convection close to the observation site. Model analysis also shows that deep convection occurring in the entire Sahelian transect (up to 2000 km E of the measurement area has a non negligible role in determining TTL composition.

  1. The Impact of Microphysics and Planetary Boundary Layer Physics on Model Simulation of U.S. Deep South Summer Convection

    Science.gov (United States)

    McCaul, Eugene W., Jr.; Case, Jonathan L.; Zavodsky, Bradley T.; Srikishen, Jayanthi; Medlin, Jeffrey M.; Wood, Lance

    2014-01-01

    Inspection of output from various configurations of high-resolution, explicit convection forecast models such as the Weather Research and Forecasting (WRF) model indicates significant sensitivity to the choices of model physics pararneterizations employed. Some of the largest apparent sensitivities are related to the specifications of the cloud microphysics and planetary boundary layer physics packages. In addition, these sensitivities appear to be especially pronounced for the weakly-sheared, multicell modes of deep convection characteristic of the Deep South of the United States during the boreal summer. Possible ocean-land sensitivities also argue for further examination of the impacts of using unique ocean-land surface initialization datasets provided by the NASA Short-term Prediction Research and Transition (SPoRn Center to select NOAAlNWS weather forecast offices. To obtain better quantitative understanding of these sensitivities and also to determine the utility of the ocean-land initialization data, we have executed matrices of regional WRF forecasts for selected convective events near Mobile, AL (MOB), and Houston, TX (HGX). The matrices consist of identically initialized WRF 24-h forecasts using any of eight microphysics choices and any of three planetary boWldary layer choices. The resulting 24 simulations performed for each event within either the MOB or HGX regions are then compared to identify the sensitivities of various convective storm metrics to the physics choices. Particular emphasis is placed on sensitivities of precipitation timing, intensity, and coverage, as well as amount and coverage oflightuing activity diagnosed from storm kinematics and graupel in the mixed phase layer. The results confirm impressions gleaned from study of the behavior of variously configured WRF runs contained in the ensembles produced each spring at the Center for the Analysis and Prediction of Storms, but with the benefit of more straightforward control of the

  2. An Isopycnic Coordinate Numerical Model of the Agulhas Current with Comparison to Observations

    Science.gov (United States)

    1990-12-01

    anomalous temperature signature at the surface due to the intense heat flux from the sea surface in the eastern South Atlantic (Walker and Mey, 1988...to discern since the sea surface temperature signal is erased very quickly due to the high heat flux in the eastern South Atlantic. Consequently...ITERACE0 .... LAYER I P1.0W PATTERN AND INTERFACE DUP’I AT3100DAYS .KNL AT3120DAYS SOUTHI AFRICAI ..* ... SOUTHIAPRICAI... AGUAS BAN AOUUIAS BAN Figure

  3. Active constrained layer damping treatments for shell structures: a deep-shell theory, some intuitive results, and an energy analysis

    Science.gov (United States)

    Shen, I. Y.

    1997-02-01

    This paper studies vibration control of a shell structure through use of an active constrained layer (ACL) damping treatment. A deep-shell theory that assumes arbitrary Lamé parameters 0964-1726/6/1/011/img1 and 0964-1726/6/1/011/img2 is first developed. Application of Hamilton's principle leads to the governing Love equations, the charge equation of electrostatics, and the associated boundary conditions. The Love equations and boundary conditions imply that the control action of the ACL for shell treatments consists of two components: free-end boundary actuation and membrane actuation. The free-end boundary actuation is identical to that of beam and plate ACL treatments, while the membrane actuation is unique to shell treatments as a result of the curvatures of the shells. In particular, the membrane actuation may reinforce or counteract the boundary actuation, depending on the location of the ACL treatment. Finally, an energy analysis is developed to determine the proper control law that guarantees the stability of ACL shell treatments. Moreover, the energy analysis results in a simple rule predicting whether or not the membrane actuation reinforces the boundary actuation.

  4. Biogeochemical characteristics of suspended particulate matter in deep chlorophyll maximum layers in the southern East China Sea

    Directory of Open Access Journals (Sweden)

    Q. Liu

    2018-04-01

    Full Text Available Continental shelves and marginal seas are key sites of particulate organic matter (POM production, remineralization and sequestration, playing an important role in the global carbon cycle. Elemental and stable isotopic compositions of organic carbon and nitrogen are thus frequently used to characterize and distinguish POM and its sources in suspended particles and surface sediments in the marginal seas. Here we investigated suspended particulate matter (SPM collected around deep chlorophyll maximum (DCM layers in the southern East China Sea for particulate organic carbon and nitrogen (POC and PN contents and their isotopic compositions (δ13CPOC and δ15NPN to understand provenance and dynamics of POM. Hydrographic parameters (temperature, salinity and turbidity indicated that the study area was weakly influenced by freshwater derived from the Yangtze River during summer 2013. Elemental and isotopic results showed a large variation in δ13CPOC (−25.8 to −18.2 ‰ and δ15NPN (3.8 to 8.0 ‰, but a narrow molar C ∕ N ratio (4.1–6.3 and low POC ∕ Chl a ratio ( <  200 g g−1 in POM, and indicated that the POM in DCM layers was newly produced by phytoplankton. In addition to temperature effects, the range and distribution of δ13CPOC were controlled by variations in primary productivity and phytoplankton species composition; the former explained  ∼  70 % of the variability in δ13CPOC. However, the variation in δ15NPN was controlled by the nutrient status and δ15NNO3− in seawater, as indicated by similar spatial distribution between δ15NPN and the current pattern and water masses in the East China Sea; although interpretations of δ15NPN data should be verified with the nutrient data in future studies. Furthermore, the POM investigated was weakly influenced by the terrestrial OM supplied by the Yangtze River during summer 2013 due to the reduced sediment supply by the Yangtze River and north

  5. Biogeochemical characteristics of suspended particulate matter in deep chlorophyll maximum layers in the southern East China Sea

    Science.gov (United States)

    Liu, Qianqian; Kandasamy, Selvaraj; Lin, Baozhi; Wang, Huawei; Chen, Chen-Tung Arthur

    2018-04-01

    Continental shelves and marginal seas are key sites of particulate organic matter (POM) production, remineralization and sequestration, playing an important role in the global carbon cycle. Elemental and stable isotopic compositions of organic carbon and nitrogen are thus frequently used to characterize and distinguish POM and its sources in suspended particles and surface sediments in the marginal seas. Here we investigated suspended particulate matter (SPM) collected around deep chlorophyll maximum (DCM) layers in the southern East China Sea for particulate organic carbon and nitrogen (POC and PN) contents and their isotopic compositions (δ13CPOC and δ15NPN) to understand provenance and dynamics of POM. Hydrographic parameters (temperature, salinity and turbidity) indicated that the study area was weakly influenced by freshwater derived from the Yangtze River during summer 2013. Elemental and isotopic results showed a large variation in δ13CPOC (-25.8 to -18.2 ‰) and δ15NPN (3.8 to 8.0 ‰), but a narrow molar C / N ratio (4.1-6.3) and low POC / Chl a ratio ( < 200 g g-1) in POM, and indicated that the POM in DCM layers was newly produced by phytoplankton. In addition to temperature effects, the range and distribution of δ13CPOC were controlled by variations in primary productivity and phytoplankton species composition; the former explained ˜ 70 % of the variability in δ13CPOC. However, the variation in δ15NPN was controlled by the nutrient status and δ15NNO3- in seawater, as indicated by similar spatial distribution between δ15NPN and the current pattern and water masses in the East China Sea; although interpretations of δ15NPN data should be verified with the nutrient data in future studies. Furthermore, the POM investigated was weakly influenced by the terrestrial OM supplied by the Yangtze River during summer 2013 due to the reduced sediment supply by the Yangtze River and north-eastward transport of riverine particles to the northern East China

  6. Improved PECVD Si x N y film as a mask layer for deep wet etching of the silicon

    Science.gov (United States)

    Han, Jianqiang; Yin, Yi Jun; Han, Dong; Dong, LiZhen

    2017-09-01

    Although plasma enhanced chemical vapor deposition (PECVD) silicon nitride (Si x N y ) films have been extensively investigated by many researchers, requirements of film properties vary from device to device. For some applications utilizing Si x N y film as the mask Layer for deep wet etching of the silicon, it is very desirable to obtain a high quality film. In this study, Si x N y films were deposited on silicon substrates by PECVD technique from the mixtures of NH3 and 5% SiH4 diluted in Ar. The deposition temperature and RF power were fixed at 400 °C and 20 W, respectively. By adjusting the SiH4/NH3 flow ratio, Si x N y films of different compositions were deposited on silicon wafers. The stoichiometry, residual stress, etch rate in 1:50 HF, BHF solution and 40% KOH solution of deposited Si x N y films were measured. The experimental results show that the optimum SiH4/NH3 flow ratio at which deposited Si x N y films can perfectly protect the polysilicon resistors on the front side of wafers during KOH etching is between 1.63 and 2.24 under the given temperature and RF power. Polysilicon resistors protected by the Si x N y films can withstand 6 h 40% KOH double-side etching at 80 °C. At the range of SiH4/NH3 flow ratios, the Si/N atom ratio of films ranges from 0.645 to 0.702, which slightly deviate the ideal stoichiometric ratio of LPCVD Si3N4 film. In addition, the silicon nitride films with the best protection effect are not the films of minimum etch rate in KOH solution.

  7. 3D Voronoi grid dedicated software for modeling gas migration in deep layered sedimentary formations with TOUGH2-TMGAS

    Science.gov (United States)

    Bonduà, Stefano; Battistelli, Alfredo; Berry, Paolo; Bortolotti, Villiam; Consonni, Alberto; Cormio, Carlo; Geloni, Claudio; Vasini, Ester Maria

    2017-11-01

    As is known, a full three-dimensional (3D) unstructured grid permits a great degree of flexibility when performing accurate numerical reservoir simulations. However, when the Integral Finite Difference Method (IFDM) is used for spatial discretization, constraints (arising from the required orthogonality between the segment connecting the blocks nodes and the interface area between blocks) pose difficulties in the creation of grids with irregular shaped blocks. The full 3D Voronoi approach guarantees the respect of IFDM constraints and allows generation of grids conforming to geological formations and structural objects and at the same time higher grid resolution in volumes of interest. In this work, we present dedicated pre- and post-processing gridding software tools for the TOUGH family of numerical reservoir simulators, developed by the Geothermal Research Group of the DICAM Department, University of Bologna. VORO2MESH is a new software coded in C++, based on the voro++ library, allowing computation of the 3D Voronoi tessellation for a given domain and the creation of a ready to use TOUGH2 MESH file. If a set of geological surfaces is available, the software can directly generate the set of Voronoi seed points used for tessellation. In order to reduce the number of connections and so to decrease computation time, VORO2MESH can produce a mixed grid with regular blocks (orthogonal prisms) and irregular blocks (polyhedron Voronoi blocks) at the point of contact between different geological formations. In order to visualize 3D Voronoi grids together with the results of numerical simulations, the functionality of the TOUGH2Viewer post-processor has been extended. We describe an application of VORO2MESH and TOUGH2Viewer to validate the two tools. The case study deals with the simulation of the migration of gases in deep layered sedimentary formations at basin scale using TOUGH2-TMGAS. A comparison between the simulation performances of unstructured and structured

  8. High-resolution AUV mapping and sampling of a deep hydrocarbon plume in the Gulf of Mexico

    Science.gov (United States)

    Ryan, J. P.; Zhang, Y.; Thomas, H.; Rienecker, E.; Nelson, R.; Cummings, S.

    2010-12-01

    During NOAA cruise GU-10-02 on the Ship Gordon Gunter, the Monterey Bay Aquarium Research Institute (MBARI) autonomous underwater vehicle (AUV) Dorado was deployed to map and sample a deep (900-1200 m) volume centered approximately seven nautical miles southwest of the Deepwater Horizon wellhead. Dorado was equipped to detect optical and chemical signals of hydrocarbons and to acquire targeted samples. The primary sensor reading used for hydrocarbon detection was colored dissolved organic matter (CDOM) fluorescence (CF). On June 2 and 3, ship cast and subsequent AUV surveys detected elevated CF in a layer between 1100 and 1200 m depth. While the deep volume was mapped in a series of parallel vertical sections, the AUV ran a peak-capture algorithm to target sample acquisition at layer signal peaks. Samples returned by ship CTD/CF rosette sampling and by AUV were preliminarily examined at sea, and they exhibited odor and fluorometric signal consistent with oil. More definitive and detailed results on these samples are forthcoming from shore-based laboratory analyses. During post-cruise analysis, all of the CF data were analyzed to objectively define and map the deep plume feature. Specifically, the maximum expected background CF over the depth range 1000-1200 m was extrapolated from a linear relationship between depth and maximum CF over the depth range 200 to 1000 m. Values exceeding the maximum expected background in the depth range 1000-1200 m were interpreted as signal from a hydrocarbon-enriched plume. Using this definition we examine relationships between CF and other AUV measurements within the plume, illustrate the three-dimensional structure of the plume boundary region that was mapped, describe small-scale layering on isopycnals, and examine short-term variations in plume depth, intensity and hydrographic relationships. Three-dimensional representation of part of a deep hydrocarbon plume mapped and sampled by AUV on June 2-3, 2010.

  9. Autonomous Gliders Observed Physical and Biogeochemical Interplay at Submesoscale during Deep Convection in the Gulf of Lions (NW Mediterranean)

    Science.gov (United States)

    Bosse, A.; Testor, P.; Damien, P.; D'Ortenzio, F.; Prieur, L. M.; Estournel, C.; Marsaleix, P.; Mortier, L.

    2016-02-01

    Since 2010, sustained observations of the circulation and water properties of the NW Mediterranean Sea have been carried out by gliders in the framework of the MOOSE observatory (Mediterranean Ocean Observatory System for the Environment: http://www.moose-network.fr/). They regularly sampled the wintertime Northern Current (NC), the deep convection zone as well as the North Balearic Front (NBF) collecting a great amount of physical and biogeochemical measurements.During periods of deep convection, the offshore mixed layer can reach great depths (>2300 m) in the Gulf of Lions. Baroclinic fronts then become very intense and reveal a lot of variability at submesoscale in the upper 500 m or so. In terms of process, symmetric instability has been evidenced to occurr during strong wind events by gliders measurements. Complementary analysis performed with the help of a high-resolution regional model (dx,dy=1 km) highlight the prominent role of downfront winds in triggering this instability. Important vertical exchanges of oceanic tracers at the front approximately aligned with isopycnals of magnitude O(100m/day) occur in response to this strong atmospheric forcing. Finally, gliders measurements of Chl-a fluorescence show how this frontal instability seems to stimulate phytoplankton growth in frontal regions during harsh wintertime conditions.

  10. Crude oil treatment leads to shift of bacterial communities in soils from the deep active layer and upper permafrost along the China-Russia Crude Oil Pipeline route.

    Science.gov (United States)

    Yang, Sizhong; Wen, Xi; Zhao, Liang; Shi, Yulan; Jin, Huijun

    2014-01-01

    The buried China-Russia Crude Oil Pipeline (CRCOP) across the permafrost-associated cold ecosystem in northeastern China carries a risk of contamination to the deep active layers and upper permafrost in case of accidental rupture of the embedded pipeline or migration of oil spills. As many soil microbes are capable of degrading petroleum, knowledge about the intrinsic degraders and the microbial dynamics in the deep subsurface could extend our understanding of the application of in-situ bioremediation. In this study, an experiment was conducted to investigate the bacterial communities in response to simulated contamination to deep soil samples by using 454 pyrosequencing amplicons. The result showed that bacterial diversity was reduced after 8-weeks contamination. A shift in bacterial community composition was apparent in crude oil-amended soils with Proteobacteria (esp. α-subdivision) being the dominant phylum, together with Actinobacteria and Firmicutes. The contamination led to enrichment of indigenous bacterial taxa like Novosphingobium, Sphingobium, Caulobacter, Phenylobacterium, Alicylobacillus and Arthrobacter, which are generally capable of degrading polycyclic aromatic hydrocarbons (PAHs). The community shift highlighted the resilience of PAH degraders and their potential for in-situ degradation of crude oil under favorable conditions in the deep soils.

  11. Crude oil treatment leads to shift of bacterial communities in soils from the deep active layer and upper permafrost along the China-Russia Crude Oil Pipeline route.

    Directory of Open Access Journals (Sweden)

    Sizhong Yang

    Full Text Available The buried China-Russia Crude Oil Pipeline (CRCOP across the permafrost-associated cold ecosystem in northeastern China carries a risk of contamination to the deep active layers and upper permafrost in case of accidental rupture of the embedded pipeline or migration of oil spills. As many soil microbes are capable of degrading petroleum, knowledge about the intrinsic degraders and the microbial dynamics in the deep subsurface could extend our understanding of the application of in-situ bioremediation. In this study, an experiment was conducted to investigate the bacterial communities in response to simulated contamination to deep soil samples by using 454 pyrosequencing amplicons. The result showed that bacterial diversity was reduced after 8-weeks contamination. A shift in bacterial community composition was apparent in crude oil-amended soils with Proteobacteria (esp. α-subdivision being the dominant phylum, together with Actinobacteria and Firmicutes. The contamination led to enrichment of indigenous bacterial taxa like Novosphingobium, Sphingobium, Caulobacter, Phenylobacterium, Alicylobacillus and Arthrobacter, which are generally capable of degrading polycyclic aromatic hydrocarbons (PAHs. The community shift highlighted the resilience of PAH degraders and their potential for in-situ degradation of crude oil under favorable conditions in the deep soils.

  12. Crude Oil Treatment Leads to Shift of Bacterial Communities in Soils from the Deep Active Layer and Upper Permafrost along the China-Russia Crude Oil Pipeline Route

    Science.gov (United States)

    Yang, Sizhong; Wen, Xi; Zhao, Liang; Shi, Yulan; Jin, Huijun

    2014-01-01

    The buried China-Russia Crude Oil Pipeline (CRCOP) across the permafrost-associated cold ecosystem in northeastern China carries a risk of contamination to the deep active layers and upper permafrost in case of accidental rupture of the embedded pipeline or migration of oil spills. As many soil microbes are capable of degrading petroleum, knowledge about the intrinsic degraders and the microbial dynamics in the deep subsurface could extend our understanding of the application of in-situ bioremediation. In this study, an experiment was conducted to investigate the bacterial communities in response to simulated contamination to deep soil samples by using 454 pyrosequencing amplicons. The result showed that bacterial diversity was reduced after 8-weeks contamination. A shift in bacterial community composition was apparent in crude oil-amended soils with Proteobacteria (esp. α-subdivision) being the dominant phylum, together with Actinobacteria and Firmicutes. The contamination led to enrichment of indigenous bacterial taxa like Novosphingobium, Sphingobium, Caulobacter, Phenylobacterium, Alicylobacillus and Arthrobacter, which are generally capable of degrading polycyclic aromatic hydrocarbons (PAHs). The community shift highlighted the resilience of PAH degraders and their potential for in-situ degradation of crude oil under favorable conditions in the deep soils. PMID:24794099

  13. Effect of deep native defects on ultrasound propagation in TlInS{sub 2} layered crystal

    Energy Technology Data Exchange (ETDEWEB)

    Seyidov, MirHasan Yu., E-mail: smirhasan@gtu.edu.tr [Department of Physics, Gebze Technical University, 41400 Gebze, Kocaeli (Turkey); Institute of Physics of NAS of Azerbaijan, H. Javid Avenue, 33, AZ-1143 Baku (Azerbaijan); Suleymanov, Rauf A. [Department of Physics, Gebze Technical University, 41400 Gebze, Kocaeli (Turkey); Institute of Physics of NAS of Azerbaijan, H. Javid Avenue, 33, AZ-1143 Baku (Azerbaijan); Odrinsky, Andrei P. [Institute of Technical Acoustics, National Academy of Sciences of Belarus, Lyudnikov Avenue 13, Vitebsk 210717 (Belarus); Kırbaş, Cafer [Department of Physics, Gebze Technical University, 41400 Gebze, Kocaeli (Turkey); The Scientific and Technological Research Council of Turkey, National Metrology Institute (TUBITAK UME), PQ 54 41470 Gebze, Kocaeli (Turkey)

    2016-09-15

    We have investigated p-type semiconductor–ferroelectric TlInS{sub 2} by means of Photo-Induced Current Transient Spectroscopy (PICTS) technique in the temperature range 77–350 K for the detection of native deep defect levels in TlInS{sub 2}. Five native deep defect levels were detected and their energy levels and capture cross sections were evaluated. Focusing on these data, the influence of these defects on the longitudinal and transverse ultrasound waves propagation as well as the effect of electric field on ultrasound waves were studied at different temperatures. The acoustic properties were investigated by the pulse-echo method. The direct contribution of thermally activated charged defects to the acoustic properties of TlInS{sub 2} was demonstrated. The key role of charged native deep level defects in elastic properties of TlInS{sub 2} was shown.

  14. Deep learning

    CERN Document Server

    Goodfellow, Ian; Courville, Aaron

    2016-01-01

    Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language proces...

  15. A 3D Vertically Integrated Deep N-Well CMOS MAPS for the SuperB Layer0

    International Nuclear Information System (INIS)

    Traversi, G; Manghisoni, M; Re, V; Gaioni, L; Ratti, L

    2011-01-01

    Deep N-Well (DNW) Monolithic Active Pixel Sensors (MAPS) have been developed in the last few years with the aim of building monolithic sensors with similar functionalities as hybrid pixels systems. In these devices the triple well option, available in deep submicron processes, is exploited to implement analog and digital signal processing at the pixel level. Many prototypes have been fabricated in a planar (2D) 130nm CMOS technology. A new kind of DNW-MAPS, namely Apsel5 3 D, which exploits the capabilities of vertical integration (3D) processes, is presented and discussed in this paper. The impact of 3D processes on the design and performance of DNW pixel sensors could be large, with significant advantages in terms of detection efficiency, pixel cell size and immunity to cross-talk, therefore complying with the severe constraints set by future HEP experiments.

  16. A 3D Vertically Integrated Deep N-Well CMOS MAPS for the SuperB Layer0

    Energy Technology Data Exchange (ETDEWEB)

    Traversi, G; Manghisoni, M; Re, V [University of Bergamo, Via Marconi 5, 24044 Dalmine (Italy); Gaioni, L; Ratti, L, E-mail: gianluca.traversi@unibg.it [INFN Pavia, Via Bassi 6, 27100 Pavia (Italy)

    2011-01-15

    Deep N-Well (DNW) Monolithic Active Pixel Sensors (MAPS) have been developed in the last few years with the aim of building monolithic sensors with similar functionalities as hybrid pixels systems. In these devices the triple well option, available in deep submicron processes, is exploited to implement analog and digital signal processing at the pixel level. Many prototypes have been fabricated in a planar (2D) 130nm CMOS technology. A new kind of DNW-MAPS, namely Apsel5{sub 3}D, which exploits the capabilities of vertical integration (3D) processes, is presented and discussed in this paper. The impact of 3D processes on the design and performance of DNW pixel sensors could be large, with significant advantages in terms of detection efficiency, pixel cell size and immunity to cross-talk, therefore complying with the severe constraints set by future HEP experiments.

  17. Greedy Deep Dictionary Learning

    OpenAIRE

    Tariyal, Snigdha; Majumdar, Angshul; Singh, Richa; Vatsa, Mayank

    2016-01-01

    In this work we propose a new deep learning tool called deep dictionary learning. Multi-level dictionaries are learnt in a greedy fashion, one layer at a time. This requires solving a simple (shallow) dictionary learning problem, the solution to this is well known. We apply the proposed technique on some benchmark deep learning datasets. We compare our results with other deep learning tools like stacked autoencoder and deep belief network; and state of the art supervised dictionary learning t...

  18. Dynamics of dissolved organic matter in fjord ecosystems: Contributions of terrestrial dissolved organic matter in the deep layer

    Science.gov (United States)

    Yamashita, Youhei; McCallister, S. Leigh; Koch, Boris P.; Gonsior, Michael; Jaffé, Rudolf

    2015-06-01

    Annually, rivers and inland water systems deliver a significant amount of terrestrial organic matter (OM) to the adjacent coastal ocean in both particulate and dissolved forms; however, the metabolic and biogeochemical transformations of OM during its seaward transport remains one of the least understood components of the global carbon cycle. This transfer of terrestrial carbon to marine ecosystems is crucial in maintaining trophic dynamics in coastal areas and critical in global carbon cycling. Although coastal regions have been proposed as important sinks for exported terrestrial materials, most of the global carbon cycling data, have not included fjords in their budgets. Here we present distributional patterns on the quantity and quality of dissolved OM in Fiordland National Park, New Zealand. Specifically, we describe carbon dynamics under diverse environmental settings based on dissolved organic carbon (DOC) depth profiles, oxygen concentrations, optical properties (fluorescence) and stable carbon isotopes. We illustrate a distinct change in the character of DOC in deep waters compared to surface and mid-depth waters. Our results suggest that, both, microbial reworking of terrestrially derived plant detritus and subsequent desorption of DOC from its particulate counterpart (as verified in a desorption experiment) are the main sources of the humic-like enriched DOC in the deep basins of the studied fjords. While it has been suggested that short transit times and protection of OM by mineral sorption may ultimately result in significant terrestrial carbon burial and preservation in fjords, our data suggests the existence of an additional source of terrestrial OM in the form of DOC generated in deep, fjord water.

  19. Deep sequencing of ESTs from nacreous and prismatic layer producing tissues and a screen for novel shell formation-related genes in the pearl oyster.

    Directory of Open Access Journals (Sweden)

    Shigeharu Kinoshita

    Full Text Available BACKGROUND: Despite its economic importance, we have a limited understanding of the molecular mechanisms underlying shell formation in pearl oysters, wherein the calcium carbonate crystals, nacre and prism, are formed in a highly controlled manner. We constructed comprehensive expressed gene profiles in the shell-forming tissues of the pearl oyster Pinctada fucata and identified novel shell formation-related genes candidates. PRINCIPAL FINDINGS: We employed the GS FLX 454 system and constructed transcriptome data sets from pallial mantle and pearl sac, which form the nacreous layer, and from the mantle edge, which forms the prismatic layer in P. fucata. We sequenced 260477 reads and obtained 29682 unique sequences. We also screened novel nacreous and prismatic gene candidates by a combined analysis of sequence and expression data sets, and identified various genes encoding lectin, protease, protease inhibitors, lysine-rich matrix protein, and secreting calcium-binding proteins. We also examined the expression of known nacreous and prismatic genes in our EST library and identified novel isoforms with tissue-specific expressions. CONCLUSIONS: We constructed EST data sets from the nacre- and prism-producing tissues in P. fucata and found 29682 unique sequences containing novel gene candidates for nacreous and prismatic layer formation. This is the first report of deep sequencing of ESTs in the shell-forming tissues of P. fucata and our data provide a powerful tool for a comprehensive understanding of the molecular mechanisms of molluscan biomineralization.

  20. Decadal trends in deep ocean salinity and regional effects on steric sea level

    Science.gov (United States)

    Purkey, S. G.; Llovel, W.

    2017-12-01

    We present deep (below 2000 m) and abyssal (below 4000 m) global ocean salinity trends from the 1990s through the 2010s and assess the role of deep salinity in local and global sea level budgets. Deep salinity trends are assessed using all deep basins with available full-depth, high-quality hydrographic section data that have been occupied two or more times since the 1980s through either the World Ocean Circulation Experiment (WOCE) Hydrographic Program or the Global Ship-Based Hydrographic Investigations Program (GO-SHIP). All salinity data is calibrated to standard seawater and any intercruise offsets applied. While the global mean deep halosteric contribution to sea level rise is close to zero (-0.017 +/- 0.023 mm/yr below 4000 m), there is a large regional variability with the southern deep basins becoming fresher and northern deep basins becoming more saline. This meridional gradient in the deep salinity trend reflects different mechanisms driving the deep salinity variability. The deep Southern Ocean is freshening owing to a recent increased flux of freshwater to the deep ocean. Outside of the Southern Ocean, the deep salinity and temperature changes are tied to isopycnal heave associated with a falling of deep isopycnals in recent decades. Therefore, regions of the ocean with a deep salinity minimum are experiencing both a halosteric contraction with a thermosteric expansion. While the thermosteric expansion is larger in most cases, in some regions the halosteric compensates for as much as 50% of the deep thermal expansion, making a significant contribution to local sea level rise budgets.

  1. Deep level defects in Ge-doped (010) β-Ga2O3 layers grown by plasma-assisted molecular beam epitaxy

    Science.gov (United States)

    Farzana, Esmat; Ahmadi, Elaheh; Speck, James S.; Arehart, Aaron R.; Ringel, Steven A.

    2018-04-01

    Deep level defects were characterized in Ge-doped (010) β-Ga2O3 layers grown by plasma-assisted molecular beam epitaxy (PAMBE) using deep level optical spectroscopy (DLOS) and deep level transient (thermal) spectroscopy (DLTS) applied to Ni/β-Ga2O3:Ge (010) Schottky diodes that displayed Schottky barrier heights of 1.50 eV. DLOS revealed states at EC - 2.00 eV, EC - 3.25 eV, and EC - 4.37 eV with concentrations on the order of 1016 cm-3, and a lower concentration level at EC - 1.27 eV. In contrast to these states within the middle and lower parts of the bandgap probed by DLOS, DLTS measurements revealed much lower concentrations of states within the upper bandgap region at EC - 0.1 - 0.2 eV and EC - 0.98 eV. There was no evidence of the commonly observed trap state at ˜EC - 0.82 eV that has been reported to dominate the DLTS spectrum in substrate materials synthesized by melt-based growth methods such as edge defined film fed growth (EFG) and Czochralski methods [Zhang et al., Appl. Phys. Lett. 108, 052105 (2016) and Irmscher et al., J. Appl. Phys. 110, 063720 (2011)]. This strong sensitivity of defect incorporation on crystal growth method and conditions is unsurprising, which for PAMBE-grown β-Ga2O3:Ge manifests as a relatively "clean" upper part of the bandgap. However, the states at ˜EC - 0.98 eV, EC - 2.00 eV, and EC - 4.37 eV are reminiscent of similar findings from these earlier results on EFG-grown materials, suggesting that possible common sources might also be present irrespective of growth method.

  2. Persistent photoconductivity in AlGaN/GaN heterojunction channels caused by the ionization of deep levels in the AlGaN barrier layer

    International Nuclear Information System (INIS)

    Murayama, H.; Akiyama, Y.; Niwa, R.; Sakashita, H.; Sakaki, H.; Kachi, T.; Sugimoto, M.

    2013-01-01

    Time-dependent responses of drain current (I d ) in an AlGaN/GaN HEMT under UV (3.3 eV) and red (2.0 eV) light illumination have been studied at 300 K and 250 K. UV illumination enhances I d by about 10 %, indicating that the density of two-dimensional electrons is raised by about 10 12 cm −2 . When UV light is turned off at 300 K, a part of increased I d decays quickly but the other part of increment is persistent, showing a slow decay. At 250 K, the majority of increment remains persistent. It is found that such a persistent increase of I d at 250 K can be partially erased by the illumination of red light. These photo-responses are explained by a simple band-bending model in which deep levels in the AlGaN barrier get positively charged by the UV light, resulting in a parabolic band bending in the AlGaN layer, while some potion of those deep levels are neutralized by the red light

  3. Physical oceanographic characteristics influencing the dispersion of dissolved tracers released at the sea floor in selected deep ocean study areas

    International Nuclear Information System (INIS)

    Kupferman, S.L.; Moore, D.E.

    1981-02-01

    Scenarios which follow the development in space and time of the concentration field of a dissolved tracer released at the sea floor are presented for a Pacific and two Atlantic study areas. The scenarios are closely tied to available data by means of simple analytical models and proceed in stages from short time and space scales in the immediate vicinity of a release point to those scales characteristic of ocean basins. The concepts of internal mixing time and residence time in the benthic mixed layer, useful for developing an intuitive feeling for the behavior of a tracer in this feature, are introduced and discussed. We also introduce the concept of domain of occupation, which is useful in drawing distinctions between mixing and stirring in the ocean. From this study it is apparent that reliable estimation of mixing will require careful consideration of the dynamics of the eddy fields in the ocean. Another area in which more information is urgently needed is in the relation of deep isopycnal structure and bottom topography to local near-bottom circulation

  4. A deep-level transient spectroscopy study of gamma-ray irradiation on the passivation properties of silicon nitride layer on silicon

    Science.gov (United States)

    Dong, Peng; Yu, Xuegong; Ma, Yao; Xie, Meng; Li, Yun; Huang, Chunlai; Li, Mo; Dai, Gang; Zhang, Jian

    2017-08-01

    Plasma-enhanced chemical vapor deposited silicon nitride (SiNx) films are extensively used as passivation material in the solar cell industry. Such SiNx passivation layers are the most sensitive part to gamma-ray irradiation in solar cells. In this work, deep-level transient spectroscopy has been applied to analyse the influence of gamma-ray irradiation on the passivation properties of SiNx layer on silicon. It is shown that the effective carrier lifetime decreases with the irradiation dose. At the same time, the interface state density is significantly increased after irradiation, and its energy distribution is broadened and shifts deeper with respect to the conduction band edge, which makes the interface states becoming more efficient recombination centers for carriers. Besides, C-V characteristics show a progressive negative shift with increasing dose, indicating the generation of effective positive charges in SiNx films. Such positive charges are beneficial for shielding holes from the n-type silicon substrates, i. e. the field-effect passivation. However, based on the reduced carrier lifetime after irradiation, it can be inferred that the irradiation induced interface defects play a dominant role over the trapped positive charges, and therefore lead to the degradation of passivation properties of SiNx on silicon.

  5. Irrigation and Nitrogen Regimes Promote the Use of Soil Water and Nitrate Nitrogen from Deep Soil Layers by Regulating Root Growth in Wheat.

    Science.gov (United States)

    Liu, Weixing; Ma, Geng; Wang, Chenyang; Wang, Jiarui; Lu, Hongfang; Li, Shasha; Feng, Wei; Xie, Yingxin; Ma, Dongyun; Kang, Guozhang

    2018-01-01

    Unreasonably high irrigation levels and excessive nitrogen (N) supplementation are common occurrences in the North China Plain that affect winter wheat production. Therefore, a 6-yr-long stationary field experiment was conducted to investigate the effects of irrigation and N regimes on root development and their relationship with soil water and N use in different soil layers. Compared to the non-irrigated treatment (W0), a single irrigation at jointing (W1) significantly increased yield by 3.6-45.6%. With increases in water (W2, a second irrigation at flowering), grain yield was significantly improved by 14.1-45.3% compared to the W1 treatments during the drier growing seasons (2010-2011, 2012-2013, and 2015-2016). However, under sufficient pre-sowing soil moisture conditions, grain yield was not increased, and water use efficiency (WUE) decreased significantly in the W2 treatments during normal precipitation seasons (2011-2012, 2013-2014, and 2014-2015). Irrigating the soil twice inhibited root growth into the deeper soil depth profiles and thus weakened the utilization of soil water and NO 3 -N from the deep soil layers. N applications increased yield by 19.1-64.5%, with a corresponding increase in WUE of 66.9-83.9% compared to the no-N treatment (N0). However, there was no further increase in grain yield and the WUE response when N rates exceeded 240 and 180 kg N ha -1 , respectively. A N application rate of 240 kg ha -1 facilitated root growth in the deep soil layers, which was conducive to utilization of soil water and NO 3 -N and also in reducing the residual NO 3 -N. Correlation analysis indicated that the grain yield was significantly positively correlated with soil water storage (SWS) and nitrate nitrogen accumulation (SNA) prior to sowing. Therefore, N rates of 180-240 kg ha -1 with two irrigations can reduce the risk of yield loss that occurs due to reduced precipitation during the wheat growing seasons, while under better soil moisture conditions, a

  6. Revealing Layers of Pristine Oriented Crystals Embedded Within Deep Ice Clouds Using Differential Reflectivity and the Copolar Correlation Coefficient

    Science.gov (United States)

    Keat, W. J.; Westbrook, C. D.

    2017-11-01

    Pristine ice crystals typically have high aspect ratios (≫ 1), have a high density and tend to fall preferentially with their major axis aligned horizontally. Consequently, they can, in certain circumstances, be readily identified by measurements of differential reflectivity (ZDR), which is related to their average aspect ratio. However, because ZDR is reflectivity weighted, its interpretation becomes ambiguous in the presence of even a few, larger aggregates or irregular polycrystals. An example of this is in mixed-phase regions that are embedded within deeper ice cloud. Currently, our understanding of the microphysical processes within these regions is hindered by a lack of good observations. In this paper, a novel technique is presented that removes this ambiguity using measurements from the 3 GHz Chilbolton Advanced Meteorological Radar in Southern England. By combining measurements of ZDR and the copolar correlation coefficient (ρhv), we show that it is possible to retrieve both the relative contribution to the radar signal and "intrinsic" ZDR (ZDRIP) of the pristine oriented crystals, even in circumstances where their signal is being masked by the presence of aggregates. Results from two case studies indicate that enhancements in ZDR embedded within deep ice clouds are typically produced by pristine oriented crystals with ZDRIP values between 3 and 7 dB (equivalent to 5-9 dB at horizontal incidence) but with varying contributions to the radar reflectivity. Vertically pointing 35 GHz cloud radar Doppler spectra and in situ particle images from the Facility for Airborne Atmospheric Measurements BAe-146 aircraft support the conceptual model used and are consistent with the retrieval interpretation.

  7. BIG1 is required for the survival of deep layer neurons, neuronal polarity, and the formation of axonal tracts between the thalamus and neocortex in developing brain.

    Directory of Open Access Journals (Sweden)

    Jia-Jie Teoh

    Full Text Available BIG1, an activator protein of the small GTPase, Arf, and encoded by the Arfgef1 gene, is one of candidate genes for epileptic encephalopathy. To know the involvement of BIG1 in epileptic encephalopathy, we analyzed BIG1-deficient mice and found that BIG1 regulates neurite outgrowth and brain development in vitro and in vivo. The loss of BIG1 decreased the size of the neocortex and hippocampus. In BIG1-deficient mice, the neuronal progenitor cells (NPCs and the interneurons were unaffected. However, Tbr1+ and Ctip2+ deep layer (DL neurons showed spatial-temporal dependent apoptosis. This apoptosis gradually progressed from the piriform cortex (PIR, peaked in the neocortex, and then progressed into the hippocampus from embryonic day 13.5 (E13.5 to E17.5. The upper layer (UL and DL order in the neocortex was maintained in BIG1-deficient mice, but the excitatory neurons tended to accumulate before their destination layers. Further pulse-chase migration assay showed that the migration defect was non-cell autonomous and secondary to the progression of apoptosis into the BIG1-deficient neocortex after E15.5. In BIG1-deficient mice, we observed an ectopic projection of corticothalamic axons from the primary somatosensory cortex (S1 into the dorsal lateral geniculate nucleus (dLGN. The thalamocortical axons were unable to cross the diencephalon-telencephalon boundary (DTB. In vitro, BIG1-deficient neurons showed a delay in neuronal polarization. BIG1-deficient neurons were also hypersensitive to low dose glutamate (5 μM, and died via apoptosis. This study showed the role of BIG1 in the survival of DL neurons in developing embryonic brain and in the generation of neuronal polarity.

  8. Iron accumulation in deep cortical layers accounts for MRI signal abnormalities in ALS: correlating 7 tesla MRI and pathology.

    Directory of Open Access Journals (Sweden)

    Justin Y Kwan

    Full Text Available Amyotrophic lateral sclerosis (ALS is a progressive neurodegenerative disorder characterized by cortical and spinal motor neuron dysfunction. Routine magnetic resonance imaging (MRI studies have previously shown hypointense signal in the motor cortex on T(2-weighted images in some ALS patients, however, the cause of this finding is unknown. To investigate the utility of this MR signal change as a marker of cortical motor neuron degeneration, signal abnormalities on 3T and 7T MR images of the brain were compared, and pathology was obtained in two ALS patients to determine the origin of the motor cortex hypointensity. Nineteen patients with clinically probable or definite ALS by El Escorial criteria and 19 healthy controls underwent 3T MRI. A 7T MRI scan was carried out on five ALS patients who had motor cortex hypointensity on the 3T FLAIR sequence and on three healthy controls. Postmortem 7T MRI of the brain was performed in one ALS patient and histological studies of the brains and spinal cords were obtained post-mortem in two patients. The motor cortex hypointensity on 3T FLAIR images was present in greater frequency in ALS patients. Increased hypointensity correlated with greater severity of upper motor neuron impairment. Analysis of 7T T(2(*-weighted gradient echo imaging localized the signal alteration to the deeper layers of the motor cortex in both ALS patients. Pathological studies showed increased iron accumulation in microglial cells in areas corresponding to the location of the signal changes on the 3T and 7T MRI of the motor cortex. These findings indicate that the motor cortex hypointensity on 3T MRI FLAIR images in ALS is due to increased iron accumulation by microglia.

  9. Brine/Rock Interaction in Deep Oceanic Layered Gabbros: Petrological Evidence from Cl-Rich Amphibole, High-Temperature Hydrothermal Veins, and Experiments

    Science.gov (United States)

    Currin Sala, A. M.; Koepke, J.; Almeev, R. R.; Teagle, D. A. H.; Zihlmann, B.; Wolff, P. E.

    2017-12-01

    Evidence of high temperature brine/rock interaction is found in hydrothermal veins and dykelets that cross-cut layered olivine gabbros in the deep palaeocrust of the Sumail Ophiolite, Sultanate of Oman. Here we present petrological and geochemical data from these samples, and an experimental attempt to simulate brine/gabbro interaction using externally heated cold seal pressure vessels. The studied natural veins and dykelets contain pargasite, hornblende, actinolite, and Cl-rich pargasite with up to 5 wt% Cl, showing a range of formation conditions from magmatic to metamorphic (hydrothermal) and thus a complex history of brine/rock interaction. In addition, the isotopic study of the radiogenic 87/86Sr and stable 18O in different amphibole types provide an estimate for the extent of seawater influence as alteration agent in the veins of the studied samples. Experiments performed at 750 °C and 200 MPa with different starting materials (chlorine-free amphibole, olivine gabbro powder) and 20 wt% NaCl aqueous brine, illustrate the process by which gabbro-hosted amphibole-rich veins evolve at subsolidus temperatures in the presence of a seawater-derived fluid. Our results demonstrate a decrease in olivine, plagioclase and magnetite content in favour of hastingsite, pargasite and magnesiohornblende, a decrease of IVAl and Ti in the starting amphibole, and an increase in Cl in amphibole, up to 0.2 Cl wt%. Our experiments show the change of magmatic pargasite towards more magnesium and silica-rich end members with results comparable to mildly chlorine-rich pargasites and hornblendes found in the natural samples studied. However, the experimental setup also presents limitations in the attainment of very high-chlorine amphibole (up to 5 wt%). Our analytical and experimental results provide further evidence for the existence of a hydrothermal cooling system in the deep oceanic crust.

  10. Enhanced sensitivity of DNA- and rRNA-based stable isotope probing by fractionation and quantitative analysis of isopycnic centrifugation gradients.

    Science.gov (United States)

    Lueders, Tillmann; Manefield, Mike; Friedrich, Michael W

    2004-01-01

    Stable isotope probing (SIP) of nucleic acids allows the detection and identification of active members of natural microbial populations that are involved in the assimilation of an isotopically labelled compound into nucleic acids. SIP is based on the separation of isotopically labelled DNA or rRNA by isopycnic density gradient centrifugation. We have developed a highly sensitive protocol for the detection of 'light' and 'heavy' nucleic acids in fractions of centrifugation gradients. It involves the fluorometric quantification of total DNA or rRNA, and the quantification of either 16S rRNA genes or 16S rRNA in gradient fractions by real-time PCR with domain-specific primers. Using this approach, we found that fully 13C-labelled DNA or rRNA of Methylobacterium extorquens was quantitatively resolved from unlabelled DNA or rRNA of Methanosarcina barkeri by cesium chloride or cesium trifluoroacetate density gradient centrifugation respectively. However, a constant low background of unspecific nucleic acids was detected in all DNA or rRNA gradient fractions, which is important for the interpretation of environmental SIP results. Consequently, quantitative analysis of gradient fractions provides a higher precision and finer resolution for retrieval of isotopically enriched nucleic acids than possible using ethidium bromide or gradient fractionation combined with fingerprinting analyses. This is a prerequisite for the fine-scale tracing of microbial populations metabolizing 13C-labelled compounds in natural ecosystems.

  11. Massively Parallel Assimilation of TOGA/TAO and Topex/Poseidon Measurements into a Quasi Isopycnal Ocean General Circulation Model Using an Ensemble Kalman Filter

    Science.gov (United States)

    Keppenne, Christian L.; Rienecker, Michele; Borovikov, Anna Y.; Suarez, Max

    1999-01-01

    A massively parallel ensemble Kalman filter (EnKF)is used to assimilate temperature data from the TOGA/TAO array and altimetry from TOPEX/POSEIDON into a Pacific basin version of the NASA Seasonal to Interannual Prediction Project (NSIPP)ls quasi-isopycnal ocean general circulation model. The EnKF is an approximate Kalman filter in which the error-covariance propagation step is modeled by the integration of multiple instances of a numerical model. An estimate of the true error covariances is then inferred from the distribution of the ensemble of model state vectors. This inplementation of the filter takes advantage of the inherent parallelism in the EnKF algorithm by running all the model instances concurrently. The Kalman filter update step also occurs in parallel by having each processor process the observations that occur in the region of physical space for which it is responsible. The massively parallel data assimilation system is validated by withholding some of the data and then quantifying the extent to which the withheld information can be inferred from the assimilation of the remaining data. The distributions of the forecast and analysis error covariances predicted by the ENKF are also examined.

  12. Recharge Rates to Deep Aquifer Layers Estimated with {sup 39}Ar, {sup 85}Kr and {sup 14}C Data: A Case Study in Odense (Denmark)

    Energy Technology Data Exchange (ETDEWEB)

    Purtschert, R. [Climate and Environmental Physics, University of Bern (Switzerland); Bjerre, T. K.; Linderberg, J. [Odense Water, Odense (Denmark); Hinsby, K. [Geological Survey of Denmark and Greenland (GEUS), Copenhagen (Denmark)

    2013-07-15

    The increasing pressure on groundwater resources due to overexploitation, contamination and also due to the effects of climate change leads to an increasing need and interest to investigate the recharge and flow dynamics of pre-modern groundwater (>50 a old). Knowledge about turnover times and groundwater age is crucial e.g. in vulnerability assessment studies as shallow and young groundwater is more susceptible to environmental changes than deeper and generally older groundwater. The presence and age of young water can be determined by a series of transient tracers (e.g. {sup 3}H/{sup 3}He, CFC's, SF{sub 6}, etc). However, with the need to exploit deeper unpolluted resources a tracer on timescales of hundreds of years is required. {sup 39}Ar (T{sub 1/2}: 269 a), {sup 85}Kr (T{sub 1/2}: 10.8 a) and {sup 14}C (T{sub 1/2}: 5730 a) results from 35 wells around the city of Odense (Denmark) are presented. The determined age spectra range from a few years to several hundreds of years. Based on the space-depth distribution of age gradients, renewal/recharge rates to the deep aquifer layers have been estimated. The comparison of {sup 39}Ar and {sup 14}C activities reveals a remarkably good correlation but with a distinct difference in age scale suggesting mixing due to broad age distributions and activity shifts due to rock-water interaction. (author)

  13. Atomic force microscopy stiffness tomography on living Arabidopsis thaliana cells reveals the mechanical properties of surface and deep cell-wall layers during growth.

    Science.gov (United States)

    Radotić, Ksenija; Roduit, Charles; Simonović, Jasna; Hornitschek, Patricia; Fankhauser, Christian; Mutavdžić, Dragosav; Steinbach, Gabor; Dietler, Giovanni; Kasas, Sandor

    2012-08-08

    Cell-wall mechanical properties play a key role in the growth and the protection of plants. However, little is known about genuine wall mechanical properties and their growth-related dynamics at subcellular resolution and in living cells. Here, we used atomic force microscopy (AFM) stiffness tomography to explore stiffness distribution in the cell wall of suspension-cultured Arabidopsis thaliana as a model of primary, growing cell wall. For the first time that we know of, this new imaging technique was performed on living single cells of a higher plant, permitting monitoring of the stiffness distribution in cell-wall layers as a function of the depth and its evolution during the different growth phases. The mechanical measurements were correlated with changes in the composition of the cell wall, which were revealed by Fourier-transform infrared (FTIR) spectroscopy. In the beginning and end of cell growth, the average stiffness of the cell wall was low and the wall was mechanically homogenous, whereas in the exponential growth phase, the average wall stiffness increased, with increasing heterogeneity. In this phase, the difference between the superficial and deep wall stiffness was highest. FTIR spectra revealed a relative increase in the polysaccharide/lignin content. Copyright © 2012 Biophysical Society. Published by Elsevier Inc. All rights reserved.

  14. Improving the performance of AlGaN-based deep-ultraviolet light-emitting diodes using electron blocking layer with a heart-shaped graded Al composition

    Science.gov (United States)

    Kwon, M. R.; Park, T. H.; Lee, T. H.; Lee, B. R.; Kim, T. G.

    2018-04-01

    We propose a design for highly efficient AlGaN-based deep-ultraviolet light-emitting diodes (DUV LEDs) using a heart-shaped graded Al composition electron-blocking layer (EBL). This novel structure reduced downward band bending at the interface between the last quantum barrier and the EBL and flattened the electrostatic field in the interlayer between the barriers of the multi-quantum barrier EBL. Consequently, electron leakage was significantly suppressed and hole injection efficiency was found to have improved. The parameter values of simulation were extracted from the experimental data of the reference DUV LEDs. Using the SimuLED, we compared the electrical and optical properties of three structures with different Al compositions in the active region and the EBL. The internal quantum efficiency of the proposed structure was shown to exceed those of the reference DUV LEDs by a factor of 1.9. Additionally, the output power at 20 mA was found to increase by a factor of 2.1.

  15. Observations of the vertical and temporal evolution of a Natal Pulse along the Eastern Agulhas Bank

    CSIR Research Space (South Africa)

    Pivan, X

    2016-09-01

    Full Text Available describe the evolution of a Natal Pulse along three density surfaces referred to as the surface (satellite-observed), shallow (isopycnal 1026.8 kg m-3), and deep (isopycnal 1027.2 kg m-3) layer. Our observations show that this Natal Pulse extended to a...

  16. Lithology, hydro-dynamism and thermicity in the multi-layer sedimentary system intersected by the Andra deep borehole of Montiers-sur-Saulx (Meuse, France)

    International Nuclear Information System (INIS)

    Landrein, Philippe; Vigneron, Georges; Delay, Jacques; Lebon, Patrick; Pagel, Maurice

    2013-01-01

    Andra (French national radioactive waste management agency) has conducted a research program to determine the feasibility of a disposal facility for long-lived high level and medium level (LL-HLML) nuclear wastes in a deep geological environment. In 2005, following 15 years of research, Andra established the feasibility of this type of disposal in the Callovo-Oxfordian clay formations located in the eastern region of the Paris basin. Between 2006 and 2008, a geological survey campaign was carried out over 250 km 2 at the boundary of the Meuse and Haute-Marne departments. Six drilling platforms were involved on the studied zone and a 2D seismic campaign was carried out using a 2 x 2 km to 3 x 3 km grid as regular as possible. One of the drilling platforms was located in the central area of the studied zone, at a distance of the faults located at the boundary of the studied sector. Three boreholes called EST431, EST432 and EST433 were drilled from this platform. Their main objectives were to contribute to the sitting of the disposal facility, and enhance the knowledge on the formations overlying and underlying the host layer (Dogger, carbonated Oxfordian and Kimmeridgian), as well as on the deep Lias and Trias formations. More specifically, it is meant (i) to acquire a better understanding of the global functioning of the hydrogeological System and the vertical exchanges between the formations and (ii) to assess the potential geothermal resources on the zone in order to verify the absence of exceptional exploitable resources in the vicinity of the site earmarked for the disposal facility. Beyond these objectives, Andra associated the French scientific community to the exploitation of the results yielded by this borehole. Thus, as early as the design stage, Andra entered into a partnership with university laboratories through a program called 'Current and Past transfers in an Aquiferous - Aquitard Sedimentary System'. The partner laboratories were able to carry out

  17. Improving model biases in an ESM with an isopycnic ocean component by accounting for wind work on oceanic near-inertial motions.

    Science.gov (United States)

    de Wet, P. D.; Bentsen, M.; Bethke, I.

    2016-02-01

    It is well-known that, when comparing climatological parameters such as ocean temperature and salinity to the output of an Earth System Model (ESM), the model exhibits biases. In ESMs with an isopycnic ocean component, such as NorESM, insufficient vertical mixing is thought to be one of the causes of such differences between observational and model data. However, enhancing the vertical mixing of the model's ocean component not only requires increasing the energy input, but also sound physical reasoning for doing so. Various authors have shown that the action of atmospheric winds on the ocean's surface is a major source of energy input into the upper ocean. However, due to model and computational constraints, oceanic processes linked to surface winds are incompletely accounted for. Consequently, despite significantly contributing to the energy required to maintain ocean stratification, most ESMs do not directly make provision for this energy. In this study we investigate the implementation of a routine in which the energy from work done on oceanic near-inertial motions is calculated in an offline slab model. The slab model, which has been well-documented in the literature, runs parallel to but independently from the ESM's ocean component. It receives wind fields with a frequency higher than that of the coupling frequency, allowing it to capture the fluctuations in the winds on shorter time scales. The additional energy calculated thus is then passed to the ocean component, avoiding the need for increased coupling between the components of the ESM. Results show localised reduction in, amongst others, the salinity and temperature biases of NorESM, confirming model sensitivity to wind-forcing and points to the need for better representation of surface processes in ESMs.

  18. Nonlinear Dynamics Mechanism of Rock Burst Induced by the Instability of the Layer-Crack Plate Structure in the Coal Wall in Deep Coal Mining

    Directory of Open Access Journals (Sweden)

    Yanlong Chen

    2017-01-01

    Full Text Available The instability of layer-crack plate structure in coal wall is one of the causes of rock burst. In the present paper, we investigate the formation and instability processes of layer-crack plate structure in coal wall by experiments and theoretical analysis. The results reveal that layer-crack plate structure formed near the free surface of the coal wall during the loading. During the formation of the layer-crack plate structure, the lateral displacement curve of the coal wall experiences a jagged variation, which suggests the nonlinear instability failure of the coal wall with a sudden release of the elastic energy. Then, a dynamic model for the stability analysis of the layer-crack plate structure was proposed, which takes consideration of the dynamic disturbance factor. Based on the dynamic model, the criterion for dynamic instability of the layer-crack plate structure was determined and demonstrated by an example. According to the analytical results, some control methods of dynamic stability of the layer-crack plate structure was put forward.

  19. Joint Training of Deep Boltzmann Machines

    OpenAIRE

    Goodfellow, Ian; Courville, Aaron; Bengio, Yoshua

    2012-01-01

    We introduce a new method for training deep Boltzmann machines jointly. Prior methods require an initial learning pass that trains the deep Boltzmann machine greedily, one layer at a time, or do not perform well on classifi- cation tasks.

  20. Deep Visual Attention Prediction

    Science.gov (United States)

    Wang, Wenguan; Shen, Jianbing

    2018-05-01

    In this work, we aim to predict human eye fixation with view-free scenes based on an end-to-end deep learning architecture. Although Convolutional Neural Networks (CNNs) have made substantial improvement on human attention prediction, it is still needed to improve CNN based attention models by efficiently leveraging multi-scale features. Our visual attention network is proposed to capture hierarchical saliency information from deep, coarse layers with global saliency information to shallow, fine layers with local saliency response. Our model is based on a skip-layer network structure, which predicts human attention from multiple convolutional layers with various reception fields. Final saliency prediction is achieved via the cooperation of those global and local predictions. Our model is learned in a deep supervision manner, where supervision is directly fed into multi-level layers, instead of previous approaches of providing supervision only at the output layer and propagating this supervision back to earlier layers. Our model thus incorporates multi-level saliency predictions within a single network, which significantly decreases the redundancy of previous approaches of learning multiple network streams with different input scales. Extensive experimental analysis on various challenging benchmark datasets demonstrate our method yields state-of-the-art performance with competitive inference time.

  1. Influence of a deep-level-defect band formed in a heavily Mg-doped GaN contact layer on the Ni/Au contact to p-GaN

    International Nuclear Information System (INIS)

    Li Xiao-Jing; Zhao De-Gang; Jiang De-Sheng; Chen Ping; Zhu Jian-Jun; Liu Zong-Shun; Yang Jing; He Xiao-Guang; Yang Hui; Zhang Li-Qun; Zhang Shu-Ming; Le Ling-Cong; Liu Jian-Ping

    2015-01-01

    The influence of a deep-level-defect (DLD) band formed in a heavily Mg-doped GaN contact layer on the performance of Ni/Au contact to p-GaN is investigated. The thin heavily Mg-doped GaN (p ++ -GaN) contact layer with DLD band can effectively improve the performance of Ni/Au ohmic contact to p-GaN. The temperature-dependent I–V measurement shows that the variable-range hopping (VRH) transportation through the DLD band plays a dominant role in the ohmic contact. The thickness and Mg/Ga flow ratio of p ++ -GaN contact layer have a significant effect on ohmic contact by controlling the Mg impurity doping and the formation of a proper DLD band. When the thickness of the p ++ -GaN contact layer is 25 nm thick and the Mg/Ga flow rate ratio is 10.29%, an ohmic contact with low specific contact resistivity of 6.97× 10 −4 Ω·cm 2 is achieved. (paper)

  2. Deep levels in as-grown and Si-implanted In(0.2)Ga(0.8)As-GaAs strained-layer superlattice optical guiding structures

    Science.gov (United States)

    Dhar, S.; Das, U.; Bhattacharya, P. K.

    1986-01-01

    Trap levels in about 2-micron In(0.2)Ga(0.8)As(94 A)/GaAs(25 A) strained-layer superlattices, suitable for optical waveguides, have been identified and characterized by deep-level transient spectroscopy and optical deep-level transient spectroscopy measurements. Several dominant electron and hole traps with concentrations of approximately 10 to the 14th/cu cm, and thermal ionization energies Delta-E(T) varying from 0.20 to 0.75 eV have been detected. Except for a 0.20-eV electron trap, which might be present in the In(0.2)Ga(0.8)As well regions, all the other traps have characteristics similar to those identified in molecular-beam epitaxial GaAs. Of these, a 0.42-eV hole trap is believed to originate from Cu impurities, and the others are probably related to native defects. Upon Si implantation and halogen lamp annealing, new deep centers are created. These are electron traps with Delta-E(T) = 0.81 eV and hole traps with Delta-E(T) = 0.46 eV. Traps occurring at room temperature may present limitations for optical devices.

  3. Deep levels in a-plane, high Mg-content MgxZn1−xO epitaxial layers grown by molecular beam epitaxy

    International Nuclear Information System (INIS)

    Gür, Emre; Tabares, G.; Hierro, A.; Arehart, A.; Ringel, S. A.; Chauveau, J. M.

    2012-01-01

    Deep level defects in n-type unintentionally doped a-plane Mg x Zn 1−x O, grown by molecular beam epitaxy on r-plane sapphire were fully characterized using deep level optical spectroscopy (DLOS) and related methods. Four compositions of Mg x Zn 1−x O were examined with x = 0.31, 0.44, 0.52, and 0.56 together with a control ZnO sample. DLOS measurements revealed the presence of five deep levels in each Mg-containing sample, having energy levels of E c − 1.4 eV, 2.1 eV, 2.6 V, and E v + 0.3 eV and 0.6 eV. For all Mg compositions, the activation energies of the first three states were constant with respect to the conduction band edge, whereas the latter two revealed constant activation energies with respect to the valence band edge. In contrast to the ternary materials, only three levels, at E c − 2.1 eV, E v + 0.3 eV, and 0.6 eV, were observed for the ZnO control sample in this systematically grown series of samples. Substantially higher concentrations of the deep levels at E v + 0.3 eV and E c − 2.1 eV were observed in ZnO compared to the Mg alloyed samples. Moreover, there is a general invariance of trap concentration of the E v + 0.3 eV and 0.6 eV levels on Mg content, while at least and order of magnitude dependency of the E c − 1.4 eV and E c − 2.6 eV levels in Mg alloyed samples.

  4. Enhancement of the surface methane hydrate-bearing layer based on the specific microorganisms form deep seabed sediment in Japan Sea.

    Science.gov (United States)

    Hata, T.; Yoneda, J.; Yamamoto, K.

    2017-12-01

    A methane hydrate-bearing layer located near the Japan Sea has been investigated as a new potential energy resource. In this study examined the feasibility of the seabed surface sediment strength located in the Japan Sea improvement technologies for enhancing microbial induced carbonate precipitation (MICP) process. First, the authors cultivated the specific urease production bacterium culture medium from this surface methane hydrate-bearing layer in the seabed (-600m depth) of Japan Sea. After that, two types of the laboratory test (consolidated-drained triaxial tests) were conducted using this specific culture medium from the seabed in the Japan Sea near the Toyama Prefecture and high urease activities bacterium named Bacillus pasteurii. The main outcomes of this research are as follows. 1) Specific culture medium focused on the urease production bacterium can enhancement of the urease activities from the methane hydrate-bearing layer near the Japan Sea side, 2) This specific culture medium can be enhancement of the surface layer strength, 3) The microbial induced carbonate precipitation process can increase the particle size compared to that of the original particles coating the calcite layer surface, 4) The mechanism for increasing the soil strength is based on the addition of cohesion like a cement stabilized soil.

  5. Characterization of deep level defects and thermally stimulated depolarization phenomena in La-doped TlInS{sub 2} layered semiconductor

    Energy Technology Data Exchange (ETDEWEB)

    Seyidov, MirHasan Yu., E-mail: smirhasan@gyte.edu.tr; Suleymanov, Rauf A.; Mikailzade, Faik A. [Department of Physics, Gebze Technical University, Gebze, Kocaeli 41400 (Turkey); Institute of Physics of NAS of Azerbaijan, H. Javid ave. 33, Baku AZ-1143 (Azerbaijan); Kargın, Elif Orhan [Department of Physics, Gebze Technical University, Gebze, Kocaeli 41400 (Turkey); Odrinsky, Andrei P. [Institute of Technical Acoustics, National Academy of Sciences of Belarus, Lyudnikov ave. 13, Vitebsk 210717 (Belarus)

    2015-06-14

    Lanthanum-doped high quality TlInS{sub 2} (TlInS{sub 2}:La) ferroelectric-semiconductor was characterized by photo-induced current transient spectroscopy (PICTS). Different impurity centers are resolved and identified. Analyses of the experimental data were performed in order to determine the characteristic parameters of the extrinsic and intrinsic defects. The energies and capturing cross section of deep traps were obtained by using the heating rate method. The observed changes in the Thermally Stimulated Depolarization Currents (TSDC) near the phase transition points in TlInS{sub 2}:La ferroelectric-semiconductor are interpreted as a result of self-polarization of the crystal due to the internal electric field caused by charged defects. The TSDC spectra show the depolarization peaks, which are attributed to defects of dipolar origin. These peaks provide important information on the defect structure and localized energy states in TlInS{sub 2}:La. Thermal treatments of TlInS{sub 2}:La under an external electric field, which was applied at different temperatures, allowed us to identify a peak in TSDC which was originated from La-dopant. It was established that deep energy level trap BTE43, which are active at low temperature (T ≤ 156 K) and have activation energy 0.29 eV and the capture cross section 2.2 × 10{sup −14} cm{sup 2}, corresponds to the La dopant. According to the PICTS results, the deep level trap center B5 is activated in the temperature region of incommensurate (IC) phases of TlInS{sub 2}:La, having the giant static dielectric constant due to the structural disorders. From the PICTS simulation results for B5, native deep level trap having an activation energy of 0.3 eV and the capture cross section of 1.8 × 10{sup −16} cm{sup 2} were established. A substantial amount of residual space charges is trapped by the deep level localized energy states of B5 in IC-phase. While the external electric field is applied, permanent dipoles

  6. Characterization of deep level defects and thermally stimulated depolarization phenomena in La-doped TlInS2 layered semiconductor

    International Nuclear Information System (INIS)

    Seyidov, MirHasan Yu.; Suleymanov, Rauf A.; Mikailzade, Faik A.; Kargın, Elif Orhan; Odrinsky, Andrei P.

    2015-01-01

    Lanthanum-doped high quality TlInS 2 (TlInS 2 :La) ferroelectric-semiconductor was characterized by photo-induced current transient spectroscopy (PICTS). Different impurity centers are resolved and identified. Analyses of the experimental data were performed in order to determine the characteristic parameters of the extrinsic and intrinsic defects. The energies and capturing cross section of deep traps were obtained by using the heating rate method. The observed changes in the Thermally Stimulated Depolarization Currents (TSDC) near the phase transition points in TlInS 2 :La ferroelectric-semiconductor are interpreted as a result of self-polarization of the crystal due to the internal electric field caused by charged defects. The TSDC spectra show the depolarization peaks, which are attributed to defects of dipolar origin. These peaks provide important information on the defect structure and localized energy states in TlInS 2 :La. Thermal treatments of TlInS 2 :La under an external electric field, which was applied at different temperatures, allowed us to identify a peak in TSDC which was originated from La-dopant. It was established that deep energy level trap BTE43, which are active at low temperature (T ≤ 156 K) and have activation energy 0.29 eV and the capture cross section 2.2 × 10 −14 cm 2 , corresponds to the La dopant. According to the PICTS results, the deep level trap center B5 is activated in the temperature region of incommensurate (IC) phases of TlInS 2 :La, having the giant static dielectric constant due to the structural disorders. From the PICTS simulation results for B5, native deep level trap having an activation energy of 0.3 eV and the capture cross section of 1.8 × 10 −16 cm 2 were established. A substantial amount of residual space charges is trapped by the deep level localized energy states of B5 in IC-phase. While the external electric field is applied, permanent dipoles, which are originated from the charged B5

  7. Alterations in Location, Magnitude, and Community Composition of Discrete Layers of Phytoplankton in Cold, Deep Waters Near the 1% Isolume of the Laurentian Great Lake Michigan Among Years With Dramatically Different Meteorological Conditions

    Science.gov (United States)

    Cuhel, R. L.; Aguilar, C.

    2016-02-01

    Phytoplankton deep populations have dominated both biomass and productivity in deep basins of Lake Michigan for much of the anthropocene. In recent decades, chronically phosphorus-deficient waters have progressed from lower thermocline diatom assemblages in the 2000s to much deeper picocyanobacterial dominance in the late 2000s. Overwhelming establishment of benthic filter-feeding quagga mussels was instrumental in selection for picoplankton in the 2003-2007 time frame, but in 2008 a return to diatom dominance occurred in conjunction with monumental runoff from the Storm of the Century. Picoplankton gradually returned to significance in ensuing years, but suffered after lakewide ice cover and extremely slow spring warming of winters 2013-2015. Extremely calm summer conditions favored the picoplankton, and a decade of 1% light penetration of 50-60m has consistently enabled very deep productivity by several different divisions of algae. An unusual persistent south wind with basin-scale upwelling stimulated a return of fall diatom bloom for the first time in 2015. Repeated expeditions to offshore deep stations (100-150m) with detailed water sampling based on hydrographic observations often include thin peaks of biogenic silica (diatoms, chrysophytes) offset from one or more distinct layers of picocyanobacteria and mixed eucaryotic phytoplankton. In 2014 large, stable populations of the diatom Tabellaria sp. flourished at 50-60m with highly shade-adapted photosynthetic characteristics but assimilation numbers >1. In 2014-2015, picocyanobacterial maxima moved up in the water column and were dissociated from signals in either in vivo fluorescence or transmission. Physical structure, within-year basin physics sequence timing, and now seemingly ammonium availability may each contribute to phytoplankton ecology in this ocean-scale freshwater ecosystem.

  8. Deep Super Learner: A Deep Ensemble for Classification Problems

    OpenAIRE

    Young, Steven; Abdou, Tamer; Bener, Ayse

    2018-01-01

    Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datase...

  9. Accelerating Deep Learning with Shrinkage and Recall

    OpenAIRE

    Zheng, Shuai; Vishnu, Abhinav; Ding, Chris

    2016-01-01

    Deep Learning is a very powerful machine learning model. Deep Learning trains a large number of parameters for multiple layers and is very slow when data is in large scale and the architecture size is large. Inspired from the shrinking technique used in accelerating computation of Support Vector Machines (SVM) algorithm and screening technique used in LASSO, we propose a shrinking Deep Learning with recall (sDLr) approach to speed up deep learning computation. We experiment shrinking Deep Lea...

  10. Background synaptic activity in rat entorhinal cortex shows a progressively greater dominance of inhibition over excitation from deep to superficial layers.

    Directory of Open Access Journals (Sweden)

    Stuart David Greenhill

    Full Text Available The entorhinal cortex (EC controls hippocampal input and output, playing major roles in memory and spatial navigation. Different layers of the EC subserve different functions and a number of studies have compared properties of neurones across layers. We have studied synaptic inhibition and excitation in EC neurones, and we have previously compared spontaneous synaptic release of glutamate and GABA using patch clamp recordings of synaptic currents in principal neurones of layers II (L2 and V (L5. Here, we add comparative studies in layer III (L3. Such studies essentially look at neuronal activity from a presynaptic viewpoint. To correlate this with the postsynaptic consequences of spontaneous transmitter release, we have determined global postsynaptic conductances mediated by the two transmitters, using a method to estimate conductances from membrane potential fluctuations. We have previously presented some of this data for L3 and now extend to L2 and L5. Inhibition dominates excitation in all layers but the ratio follows a clear rank order (highest to lowest of L2>L3>L5. The variance of the background conductances was markedly higher for excitation and inhibition in L2 compared to L3 or L5. We also show that induction of synchronized network epileptiform activity by blockade of GABA inhibition reveals a relative reluctance of L2 to participate in such activity. This was associated with maintenance of a dominant background inhibition in L2, whereas in L3 and L5 the absolute level of inhibition fell below that of excitation, coincident with the appearance of synchronized discharges. Further experiments identified potential roles for competition for bicuculline by ambient GABA at the GABAA receptor, and strychnine-sensitive glycine receptors in residual inhibition in L2. We discuss our results in terms of control of excitability in neuronal subpopulations of EC neurones and what these may suggest for their functional roles.

  11. NH4 Be2 BO3 F2 and γ-Be2 BO3 F: Overcoming the Layering Habit in KBe2 BO3 F2 for the Next-Generation Deep-Ultraviolet Nonlinear Optical Materials.

    Science.gov (United States)

    Peng, Guang; Ye, Ning; Lin, Zheshuai; Kang, Lei; Pan, Shilie; Zhang, Min; Lin, Chensheng; Long, Xifa; Luo, Min; Chen, Yu; Tang, Yu-Huan; Xu, Feng; Yan, Tao

    2018-05-12

    KBe 2 BO 3 F 2 (KBBF) is still the only practically usable crystal that can generate deep-ultraviolet (DUV) coherent light by direct second harmonic generation (SHG). However, applications are hindered by layering, leading to difficulty in the growth of thick crystals and compromised mechanical integrity. Despite efforts, it is still a great challenge to discover new nonlinear optical (NLO) materials that overcome the layering while keeping the DUV SHG available. Now, two new DUV NLO beryllium borates have been successfully designed and synthesized, NH 4 Be 2 BO 3 F 2 (ABBF) and γ-Be 2 BO 3 F (γ-BBF), which not only overcome the layering but also can be used as next-generation DUV NLO materials with the shortest type I phase-matching second-harmonic wavelength down to 173.9 nm and 146 nm, respectively. Significantly, γ-BBF is superior to KBBF in all metrics and would be the most outstanding DUV NLO crystal. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  12. Organic light-emitting diodes based on 9-(2-naphthyl)anthracene derivatives with a triphenylsilane unit as the deep-blue emitting layer

    Energy Technology Data Exchange (ETDEWEB)

    Song, Ji Young; Lee, Seul Bee [Department of Chemistry, Sungkyunkwan University, Suwon 440-746 (Korea, Republic of); Lee, Seok Jae [Department of Information Display, Hongik University, Seoul 121-791 (Korea, Republic of); Kim, Young Kwan, E-mail: kimyk@wow.hongik.ac.kr [Department of Information Display, Hongik University, Seoul 121-791 (Korea, Republic of); Yoon, Seung Soo, E-mail: ssyoon@skku.edu [Department of Chemistry, Sungkyunkwan University, Suwon 440-746 (Korea, Republic of)

    2015-02-27

    A series of 9-(2-naphthyl)anthracene derivatives with a triphenylsilane unit, which prevented molecular aggregation and self-quenching effect, was designed and synthesized. By using various bridges between the 9-(2-naphthyl)anthracene group and the triphenylsilane unit, five deep-blue emitters were obtained and applied as non-doped emitting materials in organic light-emitting diodes (OLEDs) with a device structure of indium–tin-oxide (ITO) (180 nm)/4,4-bis(N-(1-naphthyl)-N-phenylamino)biphenyl (NPB) (50 nm)/emitting materials (30 nm)/4,7-diphenyl-1,10-phenanthroline (Bphen) (30 nm)/lithium quinolate (Liq) (2 nm)/Aluminium (100 nm). All devices showed blue emissions and their electroluminescence efficiencies are sensitive to the structural changes of the emitting materials. In particular, a device using 9-(2-naphthalenyl)-10-[6-(triphenylsilyl)-2-naphthalenyl]-anthracene (4) exhibited high luminous, power and quantum efficiencies of 2.28 cd/A, 1.42 lm/W and 2.40% at 20 mA/cm{sup 2}, respectively, and this device showed the deep blue emission with the CIE coordinates of (0.16, 0.10) at 6.0 V. - Highlights: • We synthesized 9-(2-naphthyl)anthracene derivatives with a triphenylsilane unit. • We study the conjugation-length effect on the electroluminescence properties. • The bulky triphenylsilane-anthracene derivatives show resistance to self-aggregation.

  13. Hydrocarbon test in lower-layer atmosphere to predict deep-sea petroleum or hydrate in the Okinawa Trough: an example

    Institute of Scientific and Technical Information of China (English)

    Gong Jianming; Chen Jianwen; Li Gang; Zhang Xunhua; Li Jipeng; Huang Fulin

    2003-01-01

    Light hydrocarbon (methane, ethane, propane, butane and CO2) test and C isotopic analysis of CO2 are conducted for over 100 lower-layer atmospheric samples from the East China Sea slope and the Okinawa Trough. The results show that the lower-layer atmosphere mainly consists of CO2 and then of CH4, and the CO2 concentrations are calculated to have a high average value of 0.87 ω/10-2 ,about three times that of the regional background (0.3 ω/10-2). The result also shows that the average value of C isotope - 20.8 × 10 -3 is given to the CO2, inferring that it is inorganic gas.Thus, for the future ' s work in the Okinawa Trough, special attention should be paid to CO2 hydrate,which is very possibly an important hydrate type.

  14. Physical properties of a new Deep Eutectic Solvent based on lithium bis[(trifluoromethyl)sulfonyl]imide and N-methylacetamide as superionic suitable electrolyte for lithium ion batteries and electric double layer capacitors

    International Nuclear Information System (INIS)

    Boisset, Aurélien; Jacquemin, Johan; Anouti, Mérièm

    2013-01-01

    Highlights: • Preparation of new Deep Eutectic Solvent (DES) based on N-methylacetamide and TFSI. • Characterization of conductivity, viscosity and thermal properties of DES. • DES presents a superionic character in Walden classification. • DES is suitable electrolyte for lithium ion batteries and electric double layer capacitors. -- Abstract: Herein we present a study on the physical/chemical properties of a new Deep Eutectic Solvent (DES) based on N-methylacetamide (MAc) and lithium bis[(trifluoromethyl)sulfonyl]imide (LiTFSI). Due to its interesting properties, such as wide liquid-phase range from −60 °C to 280 °C, low vapor pressure, and high ionic conductivity up to 28.4 mS cm −1 at 150 °C and at x LiTFSI = 1/4, this solution can be practically used as electrolyte for electrochemical storage systems such as electric double-layer capacitors (EDLCs) and/or lithium ion batteries (LiBs). Firstly, relationships between its transport properties (conductivity and viscosity) as a function of composition and temperature were discussed through Arrhenius’ Law and Vogel–Tamman–Fulcher (VTF) equations, as well as by using the Walden classification. From this investigation, it appears that this complex electrolyte possesses a number of excellent transport properties, like a superionic character for example. Based on which, we then evaluated its electrochemical performances as electrolyte for EDLCs and LiBs applications by using activated carbon (AC) and lithium iron phosphate (LiFePO 4 ) electrodes, respectively. These results demonstrate that this electrolyte has a good compatibility with both electrodes (AC and LiFePO 4 ) in each testing cell driven also by excellent electrochemical properties in specific capacitance, rate and cycling performances, indicating that the LiTFSI/MAc DES can be a promising electrolyte for EDLCs and LiBs applications especially for those requiring high safety and stability

  15. Effect of deep structure and surface layer (a) simulation study on the heavy damage belt of the 1995 Hyogoken Nanbu earthquake; Hado simulation wo mochiita `shinsai no obi` ni tsuite no kento

    Energy Technology Data Exchange (ETDEWEB)

    Feng, S; Ishikawa, K; Kaji, Y [Chuokaihatsu Corp., Tokyo (Japan)

    1996-05-01

    A simulation study was done to identify the causes for the so-called heavy damage belt observed as a result of the Hyogoken Nanbu Earthquake. This study determines distributions of the maximum amplitudes on the ground surfaces, and discusses the effects of deep structures and low-velocity surface layers, based on the simulation by the wave equation with the underground model of Higashinada-ku and its vicinity in the north-south direction, observed seismic records and artificial waves. The two-dimensional scalar wave equation is used for the analysis. The velocity structure model used for the simulation is established, based on the elastic wave seismic survey results. The focus function is drawn by expanding or contracting the time scale, using the seismic records at Kobe Port Island and artificial waves. The analysis results show that the damage belt coincides with the areas at which the focusing zone of the deep structure overlap the amplification zone in the low-velocity ground surfaces, where relative density is amplified 1.5 to 2 times. It is also observed that large peaks repeat 2 to 3 times on the time scale. 5 refs., 8 figs., 1 tab.

  16. The γ Dor stars as revealed by Kepler: A key to reveal deep-layer rotation in A and F stars

    Directory of Open Access Journals (Sweden)

    Salmon S. J. A. J.

    2017-01-01

    Full Text Available The γ Dor pulsating stars present high-order gravity modes, which make them important targets in the intermediate-and low-mass main-sequence region of the Hertzsprung-Russell diagram. Whilst we have only access to rotation in the envelope of the Sun, the g modes of γ Dor stars can in principle deliver us constraints on the inner layers. With the puzzling discovery of unexpectedly low rotation rates in the core of red giants, the γ Dor stars appear now as unique targets to explore internal angular momentum transport in the progenitors of red giants. Yet, the γ Dor pulsations remain hard to detect from the ground for their periods are close to 1 day. While the CoRoT space mission first revealed intriguing frequency spectra, the almost uninterrupted 4-year photometry from the Kepler mission eventually shed a new light on them. It revealed regularities in the spectra, expected to bear signature of physical processes, including rotation, in the shear layers close to the convective core. We present here the first results of our effort to derive exploitable seismic diagnosis for mid- to fast rotators among γ Dor stars. We confirm their potential to explore the rotation history of this early phase of stellar evolution.

  17. Deep Belief Networks for dimensionality reduction

    NARCIS (Netherlands)

    Noulas, A.K.; Kröse, B.J.A.

    2008-01-01

    Deep Belief Networks are probabilistic generative models which are composed by multiple layers of latent stochastic variables. The top two layers have symmetric undirected connections, while the lower layers receive directed top-down connections from the layer above. The current state-of-the-art

  18. A 2-d modeling approach for studying the formation, maintenance, and decay of Tropical Tropopause Layer Cirrus associated with Deep Convection

    Science.gov (United States)

    Henz, D. R.; Hashino, T.; Tripoli, G. J.; Smith, E. A.

    2009-12-01

    This study is being conducted to examine the distribution, variability, and formation-decay processes of TTL cirrus associated with tropical deep convection using the University of Wisconsin Non-Hydrostatic modeling system (NMS). The experimental design is based on Tripoli, Hack and Kiehl (1992) which explicitly simulates the radiative-convective equilibrium of the tropical atmosphere over extended periods of weeks or months using a 2D periodic cloud resolving model. The experiment design includes a radiation parameterization to explicitly simulate radiative transfer through simulated crystals. Advanced Microphysics Prediction System (AMP) will be used to simulate microphysics by employing SHIPS (Spectral Habit Ice Prediction System) for ice, SLiPS (Spectral Liquid Prediction System) for droplets, and SAPS (Spectral Aerosol Prediction System) for aerosols. The ice scheme called SHIPS is unique in that ice particle properties (such as size, particle density, and crystal habitats) are explicitly predicted in a CRM (Hashino and Tripoli, 2007, 2008). The Advanced Microphysics Prediction System (AMPS) technology provides a particularly strong tool that effectively enables the explicit modeling of the TTL cloud microphysics and dynamical processes which has yet to be accomplished by more traditional bulk microphysics approaches.

  19. Effect of Hydrogen Post-Annealing on Transparent Conductive ITO/Ga2O3 Bi-Layer Films for Deep Ultraviolet Light-Emitting Diodes.

    Science.gov (United States)

    Kim, Kyeong Heon; Kim, Su Jin; Park, Sang Young; Kim, Tae Geun

    2015-10-01

    The effect of hydrogen post-annealing on the electrical and optical properties of ITO/Ga2O bi-layer films, deposited by RF magnetron sputtering, is investigated for potential applications to transparent conductive electrodes of ultraviolet (UV) light-emitting diodes. Three samples--an as-deposited sample and two samples post-annealed in N2 gas and N2-H2 gas mixture--were prepared and annealed at different temperatures ranging from 100 °C to 500 °C for comparison. Among these samples, the sample annealed at 300 °C in a mixture of N2 and H2 gases shows the lowest sheet resistance of 301.3 Ω/square and a high UV transmittance of 87.1% at 300 nm.

  20. A study on vertical distribution of radionuclides in the soil layers 0-30 cm deep in relation to their particle size

    International Nuclear Information System (INIS)

    Megumi, K.; Sato, Y.; Matsunami, T.; Fukuda, K.; Ishiyama, T.; Kimura, S.; Tsujimoto, T.

    1991-01-01

    It is fundamentally important to have a detailed knowledge of distribution of radionuclides in soils in natural environments for an understanding of behavior of nuclides during a lengthy period beyond the limit of the possible in experimental systems. Core soil samples (30 cm) were taken from surface of the ground in the central parts (Osaka and Wakasa) of Japan. Each core sample was sliced into 5 cm sections and the parts of the soil larger than 10 mesh were excluded by standard sieves. Some of the samples were further sieved into four classes of soil particles, such as, 10-60, 60-100, 100-200 and more than 200 mesh. The concentrations of U-series nuclides ( 238 U, 226 Ra, 210 Pb), Th-series nuclides ( 232 Th, 228 Ra), 40 K and 137 Cs in the samples were determined by gamma ray spectrometry and photon activation analysis. The photon activation analysis with a planer type of Ge detector was made by irradiating the samples with bremsstrahlung of tungsten target from electron accelerator, 16 MeV, at Res. Inst. for Advanced Sci. and Tech., Univ. of Osaka Pref. Fallout 137 Cs and 210 Pb deposition in the soil layers showed vertical variation mainly depending on the organic substances contents, the ratios of water contents and particle sizes at each location. A good correlation was found between the concentrations of these two nuclides. This correlation is available to evaluate of the 137 Cs contamination levels in soils. The concentrations of 238 U, 226 Ra, 232 Th, 228 Ra and 40 K contained originally in soils, changed slightly with the depth and the vertical distributions of these nuclides were found to relate mainly to the soil particle size in the layer. This tendency was evidently observed in the soil originating from the weathering of granite rock. (author)

  1. Oxidative stress in deep scattering layers: Heat shock response and antioxidant enzymes activities of myctophid fishes thriving in oxygen minimum zones

    Science.gov (United States)

    Lopes, Ana Rita; Trübenbach, Katja; Teixeira, Tatiana; Lopes, Vanessa M.; Pires, Vanessa; Baptista, Miguel; Repolho, Tiago; Calado, Ricardo; Diniz, Mário; Rosa, Rui

    2013-12-01

    Diel vertical migrators, such as myctophid fishes, are known to encounter oxygen minimum zones (OMZ) during daytime in the Eastern Pacific Ocean and, therefore, have to cope with temperature and oxidative stress that arise while ascending to warmer, normoxic surface waters at night-time. The aim of this study was to investigate the antioxidant defense strategies and heat shock response (HSR) in two myctophid species, namely Triphoturus mexicanus and Benthosema panamense, at shallow and warm surface waters (21 kPa, 20-25 °C) and at hypoxic, cold (≤1 kPa, 10 °C) mesopelagic depths. More specifically, we quantified (i) heat shock protein concentrations (HSP70/HSC70) (ii) antioxidant enzyme activities [including superoxide dismutase (SOD), catalase (CAT) and glutathione-S-transferase (GST)], and (iii) lipid peroxidation [malondialdehyde (MDA) levels]. HSP70/HSC70 levels increased in both myctophid species at warmer, well-oxygenated surface waters probably to prevent cellular damage (oxidative stress) due to increased oxygen demand under elevated temperatures and reactive oxygen species (ROS) formation. On the other hand, CAT and GST activities were augmented under hypoxic conditions, probably as preparatory response to a burst of oxyradicals during the reoxygenation phase (while ascending). SOD activity decreased under hypoxia in B. panamense, but was kept unchanged in T. mexicanus. MDA levels in B. panamense did not change between the surface and deep-sea conditions, whereas T. mexicanus showed elevated MDA and HSP70/HSC70 concentrations at warmer surface waters. This indicated that T. mexicanus seems to be not so well tuned to temperature and oxidative stress associated to diel vertical migrations. The understanding of such physiological strategies that are linked to oxygen deprivation and reoxygenation phases may provide valuable information about how different species might respond to the impacts of environmental stressors (e.g. expanding mesopelagic hypoxia

  2. Learning Transferable Features with Deep Adaptation Networks

    OpenAIRE

    Long, Mingsheng; Cao, Yue; Wang, Jianmin; Jordan, Michael I.

    2015-01-01

    Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation. However, as deep features eventually transition from general to specific along the network, the feature transferability drops significantly in higher layers with increasing domain discrepancy. Hence, it is important to formally reduce the dataset bias and enhance the transferability in task-specific layers. In this paper, we propose a new Deep Adaptation...

  3. Auxiliary Deep Generative Models

    DEFF Research Database (Denmark)

    Maaløe, Lars; Sønderby, Casper Kaae; Sønderby, Søren Kaae

    2016-01-01

    Deep generative models parameterized by neural networks have recently achieved state-of-the-art performance in unsupervised and semi-supervised learning. We extend deep generative models with auxiliary variables which improves the variational approximation. The auxiliary variables leave...... the generative model unchanged but make the variational distribution more expressive. Inspired by the structure of the auxiliary variable we also propose a model with two stochastic layers and skip connections. Our findings suggest that more expressive and properly specified deep generative models converge...... faster with better results. We show state-of-the-art performance within semi-supervised learning on MNIST (0.96%), SVHN (16.61%) and NORB (9.40%) datasets....

  4. Deep Learning from Crowds

    DEFF Research Database (Denmark)

    Rodrigues, Filipe; Pereira, Francisco Camara

    Over the last few years, deep learning has revolutionized the field of machine learning by dramatically improving the stateof-the-art in various domains. However, as the size of supervised artificial neural networks grows, typically so does the need for larger labeled datasets. Recently...... networks from crowds. We begin by describing an EM algorithm for jointly learning the parameters of the network and the reliabilities of the annotators. Then, a novel general-purpose crowd layer is proposed, which allows us to train deep neural networks end-to-end, directly from the noisy labels......, crowdsourcing has established itself as an efficient and cost-effective solution for labeling large sets of data in a scalable manner, but it often requires aggregating labels from multiple noisy contributors with different levels of expertise. In this paper, we address the problem of learning deep neural...

  5. Deep frying

    NARCIS (Netherlands)

    Koerten, van K.N.

    2016-01-01

    Deep frying is one of the most used methods in the food processing industry. Though practically any food can be fried, French fries are probably the most well-known deep fried products. The popularity of French fries stems from their unique taste and texture, a crispy outside with a mealy soft

  6. Deep Support Vector Machines for Regression Problems

    NARCIS (Netherlands)

    Wiering, Marco; Schutten, Marten; Millea, Adrian; Meijster, Arnold; Schomaker, Lambertus

    2013-01-01

    In this paper we describe a novel extension of the support vector machine, called the deep support vector machine (DSVM). The original SVM has a single layer with kernel functions and is therefore a shallow model. The DSVM can use an arbitrary number of layers, in which lower-level layers contain

  7. A Global Survey of Deep Underground Facilities; Examples of Geotechnical and Engineering Capabilities, Achievements, Challenges (Mines, Shafts, Tunnels, Boreholes, Sites and Underground Facilities for Nuclear Waste and Physics R&D): A Guide to Interactive Global Map Layers, Table Database, References and Notes

    International Nuclear Information System (INIS)

    Tynan, Mark C.; Russell, Glenn P.; Perry, Frank V.; Kelley, Richard E.; Champenois, Sean T.

    2017-01-01

    These associated tables, references, notes, and report present a synthesis of some notable geotechnical and engineering information used to create four interactive layer maps for selected: 1) deep mines and shafts; 2) existing, considered or planned radioactive waste management deep underground studies or disposal facilities 3) deep large diameter boreholes, and 4) physics underground laboratories and facilities from around the world. These data are intended to facilitate user access to basic information and references regarding “deep underground” facilities, history, activities, and plans. In general, the interactive maps and database provide each facility’s approximate site location, geology, and engineered features (e.g.: access, geometry, depth, diameter, year of operations, groundwater, lithology, host unit name and age, basin; operator, management organization, geographic data, nearby cultural features, other). Although the survey is not comprehensive, it is representative of many of the significant existing and historical underground facilities discussed in the literature addressing radioactive waste management and deep mined geologic disposal safety systems. The global survey is intended to support and to inform: 1) interested parties and decision makers; 2) radioactive waste disposal and siting option evaluations, and 3) safety case development applicable to any mined geologic disposal facility as a demonstration of historical and current engineering and geotechnical capabilities available for use in deep underground facility siting, planning, construction, operations and monitoring.

  8. A Global Survey of Deep Underground Facilities; Examples of Geotechnical and Engineering Capabilities, Achievements, Challenges (Mines, Shafts, Tunnels, Boreholes, Sites and Underground Facilities for Nuclear Waste and Physics R&D): A Guide to Interactive Global Map Layers, Table Database, References and Notes

    Energy Technology Data Exchange (ETDEWEB)

    Tynan, Mark C. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Russell, Glenn P. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Perry, Frank V. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Kelley, Richard E. [Idaho National Lab. (INL), Idaho Falls, ID (United States); Champenois, Sean T. [Idaho National Lab. (INL), Idaho Falls, ID (United States)

    2017-06-13

    These associated tables, references, notes, and report present a synthesis of some notable geotechnical and engineering information used to create four interactive layer maps for selected: 1) deep mines and shafts; 2) existing, considered or planned radioactive waste management deep underground studies or disposal facilities 3) deep large diameter boreholes, and 4) physics underground laboratories and facilities from around the world. These data are intended to facilitate user access to basic information and references regarding “deep underground” facilities, history, activities, and plans. In general, the interactive maps and database provide each facility’s approximate site location, geology, and engineered features (e.g.: access, geometry, depth, diameter, year of operations, groundwater, lithology, host unit name and age, basin; operator, management organization, geographic data, nearby cultural features, other). Although the survey is not comprehensive, it is representative of many of the significant existing and historical underground facilities discussed in the literature addressing radioactive waste management and deep mined geologic disposal safety systems. The global survey is intended to support and to inform: 1) interested parties and decision makers; 2) radioactive waste disposal and siting option evaluations, and 3) safety case development applicable to any mined geologic disposal facility as a demonstration of historical and current engineering and geotechnical capabilities available for use in deep underground facility siting, planning, construction, operations and monitoring.

  9. Deep Learning for ECG Classification

    Science.gov (United States)

    Pyakillya, B.; Kazachenko, N.; Mikhailovsky, N.

    2017-10-01

    The importance of ECG classification is very high now due to many current medical applications where this problem can be stated. Currently, there are many machine learning (ML) solutions which can be used for analyzing and classifying ECG data. However, the main disadvantages of these ML results is use of heuristic hand-crafted or engineered features with shallow feature learning architectures. The problem relies in the possibility not to find most appropriate features which will give high classification accuracy in this ECG problem. One of the proposing solution is to use deep learning architectures where first layers of convolutional neurons behave as feature extractors and in the end some fully-connected (FCN) layers are used for making final decision about ECG classes. In this work the deep learning architecture with 1D convolutional layers and FCN layers for ECG classification is presented and some classification results are showed.

  10. Deep Galaxy: Classification of Galaxies based on Deep Convolutional Neural Networks

    OpenAIRE

    Khalifa, Nour Eldeen M.; Taha, Mohamed Hamed N.; Hassanien, Aboul Ella; Selim, I. M.

    2017-01-01

    In this paper, a deep convolutional neural network architecture for galaxies classification is presented. The galaxy can be classified based on its features into main three categories Elliptical, Spiral, and Irregular. The proposed deep galaxies architecture consists of 8 layers, one main convolutional layer for features extraction with 96 filters, followed by two principles fully connected layers for classification. It is trained over 1356 images and achieved 97.272% in testing accuracy. A c...

  11. Cocaine- and amphetamine-regulated transcript peptide and calcium binding proteins immunoreactivity in the deep layers of the superior colliculus of the guinea pig: Implications for multisensory and visuomotor processing.

    Science.gov (United States)

    Najdzion, Janusz

    2018-03-01

    The superior colliculus (SC) of mammals is a midbrain center, that can be subdivided into the superficial (SCs) and deep layers (SCd). In contrast to the visual SCs, the SCd are involved in multisensory and motor processing. This study investigated the pattern of distribution and colocalization of cocaine- and amphetamine-regulated transcript peptide (CART) and three calcium-binding proteins (CaBPs) i.e. calbindin (CB), calretinin (CR) and parvalbumin (PV) in the SCd of the guinea pig. CART labeling was seen almost exclusively in the neuropil and fibers, which differed in regard to morphology and location. CART-positive neurons were very rare and restricted to a narrow area of the SCd. The most intense CART immunoreactivity was observed in the most dorsally located sublayer of the SCd, which is anatomically and functionally connected with the SCs. CART immunoreactivity in the remaining SCd was less intensive, but still relatively high. This characteristic pattern of immunoreactivity indicates that CART as a putative neurotransmitter or neuromodulator may play an important role in processing of visual information, while its involvement in the auditory and visuomotor processing is less significant, but still possible. CaBPs-positive neurons were morphologically diverse and widely distributed throughout all SCd. From studied CaBPs, CR showed a markedly different distribution compared to CB and PV. Overall, the patterns of distribution of CB and PV were similar in the entire SCd. Consequently, the complementarity of these patterns in the guinea pig was very weak. Double immunostaining revealed that CART did not colocalize with either CaBPs, which suggested that these neurochemical substances might not coexist in the multisensory and visuomotor parts of the SC. Copyright © 2017 Elsevier B.V. All rights reserved.

  12. Deep Transfer Metric Learning.

    Science.gov (United States)

    Junlin Hu; Jiwen Lu; Yap-Peng Tan; Jie Zhou

    2016-12-01

    Conventional metric learning methods usually assume that the training and test samples are captured in similar scenarios so that their distributions are assumed to be the same. This assumption does not hold in many real visual recognition applications, especially when samples are captured across different data sets. In this paper, we propose a new deep transfer metric learning (DTML) method to learn a set of hierarchical nonlinear transformations for cross-domain visual recognition by transferring discriminative knowledge from the labeled source domain to the unlabeled target domain. Specifically, our DTML learns a deep metric network by maximizing the inter-class variations and minimizing the intra-class variations, and minimizing the distribution divergence between the source domain and the target domain at the top layer of the network. To better exploit the discriminative information from the source domain, we further develop a deeply supervised transfer metric learning (DSTML) method by including an additional objective on DTML, where the output of both the hidden layers and the top layer are optimized jointly. To preserve the local manifold of input data points in the metric space, we present two new methods, DTML with autoencoder regularization and DSTML with autoencoder regularization. Experimental results on face verification, person re-identification, and handwritten digit recognition validate the effectiveness of the proposed methods.

  13. Law proposal detailing the modalities of creation of an installation for the reversible storage in deep geological layers of high and intermediate level and long life radioactive wastes (Sent back to the Commission for Sustainable Development and Land Planning, because of a failure of constituting a special commission within delays as foreseen in articles 30 and 31 of the rules) - Nr 3210

    International Nuclear Information System (INIS)

    Le Deaut, Jean-Yves; Dumont, Jean-Louis; Bataille, Christian; Le Dain, Anne-Yvonne

    2015-01-01

    This law proposal contains adjustments to the legal arrangement implemented in 2006 for a normal continuation of the project of a reversible storage in deep geological layers. It defines the notion of reversibility, specifies that the exploitation of the installation must start with a pilot industrial phase, and defines the authorisation procedure for such an installation and the project schedule. It also contains technical arrangements which are required for the request of authorisation of creation of the installation

  14. Deep Unfolding for Topic Models.

    Science.gov (United States)

    Chien, Jen-Tzung; Lee, Chao-Hsi

    2018-02-01

    Deep unfolding provides an approach to integrate the probabilistic generative models and the deterministic neural networks. Such an approach is benefited by deep representation, easy interpretation, flexible learning and stochastic modeling. This study develops the unsupervised and supervised learning of deep unfolded topic models for document representation and classification. Conventionally, the unsupervised and supervised topic models are inferred via the variational inference algorithm where the model parameters are estimated by maximizing the lower bound of logarithm of marginal likelihood using input documents without and with class labels, respectively. The representation capability or classification accuracy is constrained by the variational lower bound and the tied model parameters across inference procedure. This paper aims to relax these constraints by directly maximizing the end performance criterion and continuously untying the parameters in learning process via deep unfolding inference (DUI). The inference procedure is treated as the layer-wise learning in a deep neural network. The end performance is iteratively improved by using the estimated topic parameters according to the exponentiated updates. Deep learning of topic models is therefore implemented through a back-propagation procedure. Experimental results show the merits of DUI with increasing number of layers compared with variational inference in unsupervised as well as supervised topic models.

  15. Short-term Memory of Deep RNN

    OpenAIRE

    Gallicchio, Claudio

    2018-01-01

    The extension of deep learning towards temporal data processing is gaining an increasing research interest. In this paper we investigate the properties of state dynamics developed in successive levels of deep recurrent neural networks (RNNs) in terms of short-term memory abilities. Our results reveal interesting insights that shed light on the nature of layering as a factor of RNN design. Noticeably, higher layers in a hierarchically organized RNN architecture results to be inherently biased ...

  16. Modification of the deep salinity-maximum in the Southern Ocean by circulation in the Antarctic Circumpolar Current and the Weddell Gyre

    Science.gov (United States)

    Donnelly, Matthew; Leach, Harry; Strass, Volker

    2017-07-01

    The evolution of the deep salinity-maximum associated with the Lower Circumpolar Deep Water (LCDW) is assessed using a set of 37 hydrographic sections collected over a 20-year period in the Southern Ocean as part of the WOCE/CLIVAR programme. A circumpolar decrease in the value of the salinity-maximum is observed eastwards from the North Atlantic Deep Water (NADW) in the Atlantic sector of the Southern Ocean through the Indian and Pacific sectors to Drake Passage. Isopycnal mixing processes are limited by circumpolar fronts, and in the Atlantic sector, this acts to limit the direct poleward propagation of the salinity signal. Limited entrainment occurs into the Weddell Gyre, with LCDW entering primarily through the eddy-dominated eastern limb. A vertical mixing coefficient, κV of (2.86 ± 1.06) × 10-4 m2 s-1 and an isopycnal mixing coefficient, κI of (8.97 ± 1.67) × 102 m2 s-1 are calculated for the eastern Indian and Pacific sectors of the Antarctic Circumpolar Current (ACC). A κV of (2.39 ± 2.83) × 10-5 m2 s-1, an order of magnitude smaller, and a κI of (2.47 ± 0.63) × 102 m2 s-1, three times smaller, are calculated for the southern and eastern Weddell Gyre reflecting a more turbulent regime in the ACC and a less turbulent regime in the Weddell Gyre. In agreement with other studies, we conclude that the ACC acts as a barrier to direct meridional transport and mixing in the Atlantic sector evidenced by the eastward propagation of the deep salinity-maximum signal, insulating the Weddell Gyre from short-term changes in NADW characteristics.

  17. Deep Learning

    DEFF Research Database (Denmark)

    Jensen, Morten Bornø; Bahnsen, Chris Holmberg; Nasrollahi, Kamal

    2018-01-01

    I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning.......I løbet af de sidste 10 år er kunstige neurale netværk gået fra at være en støvet, udstødt tekno-logi til at spille en hovedrolle i udviklingen af kunstig intelligens. Dette fænomen kaldes deep learning og er inspireret af hjernens opbygning....

  18. Invited talk: Deep Learning Meets Physics

    CERN Multimedia

    CERN. Geneva

    2018-01-01

    Deep Learning has emerged as one of the most successful fields of machine learning and artificial intelligence with overwhelming success in industrial speech, text and vision benchmarks. Consequently it evolved into the central field of research for IT giants like Google, facebook, Microsoft, Baidu, and Amazon. Deep Learning is founded on novel neural network techniques, the recent availability of very fast computers, and massive data sets. In its core, Deep Learning discovers multiple levels of abstract representations of the input. The main obstacle to learning deep neural networks is the vanishing gradient problem. The vanishing gradient impedes credit assignment to the first layers of a deep network or to early elements of a sequence, therefore limits model selection. Major advances in Deep Learning can be related to avoiding the vanishing gradient like stacking, ReLUs, residual networks, highway networks, and LSTM. For Deep Learning, we suggested self-normalizing neural networks (SNNs) which automatica...

  19. Deep geothermics

    International Nuclear Information System (INIS)

    Anon.

    1995-01-01

    The hot-dry-rocks located at 3-4 km of depth correspond to low permeable rocks carrying a large amount of heat. The extraction of this heat usually requires artificial hydraulic fracturing of the rock to increase its permeability before water injection. Hot-dry-rocks geothermics or deep geothermics is not today a commercial channel but only a scientific and technological research field. The Soultz-sous-Forets site (Northern Alsace, France) is characterized by a 6 degrees per meter geothermal gradient and is used as a natural laboratory for deep geothermal and geological studies in the framework of a European research program. Two boreholes have been drilled up to 3600 m of depth in the highly-fractured granite massif beneath the site. The aim is to create a deep heat exchanger using only the natural fracturing for water transfer. A consortium of german, french and italian industrial companies (Pfalzwerke, Badenwerk, EdF and Enel) has been created for a more active participation to the pilot phase. (J.S.). 1 fig., 2 photos

  20. Formation of conductive spontaneous via holes in AlN buffer layer on n+Si substrate by filling the vias with n-AlGaN by metal organic chemical vapor deposition and application to vertical deep ultraviolet photo-sensor

    Directory of Open Access Journals (Sweden)

    N. Kurose

    2014-12-01

    Full Text Available We have grown conductive aluminum nitride (AlN layers using the spontaneous via holes formation technique on an n+-Si substrate for vertical-type device fabrication. The size and density of the via holes are controlled through the crystal growth conditions used for the layer, and this enables the conductance of the layer to be controlled. Using this technique, we demonstrate the fabrication of a vertical-type deep ultraviolet (DUV photo-sensor. This technique opens up the possibility of fabrication of monolithically integrated on-chip DUV sensors and DUV light-emitting devices (LEDs, including amplifiers, controllers and other necessary functional circuits, on a Si substrate.

  1. Towards deep learning with segregated dendrites.

    Science.gov (United States)

    Guerguiev, Jordan; Lillicrap, Timothy P; Richards, Blake A

    2017-12-05

    Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear whether deep learning could occur in the real brain. Here, we show that a deep learning algorithm that utilizes multi-compartment neurons might help us to understand how the neocortex optimizes cost functions. Like neocortical pyramidal neurons, neurons in our model receive sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, neurons in different layers of the network can coordinate synaptic weight updates. As a result, the network learns to categorize images better than a single layer network. Furthermore, we show that our algorithm takes advantage of multilayer architectures to identify useful higher-order representations-the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments, which may help to explain the morphology of neocortical pyramidal neurons.

  2. Deep smarts.

    Science.gov (United States)

    Leonard, Dorothy; Swap, Walter

    2004-09-01

    When a person sizes up a complex situation and rapidly comes to a decision that proves to be not just good but brilliant, you think, "That was smart." After you watch him do this a few times, you realize you're in the presence of something special. It's not raw brainpower, though that helps. It's not emotional intelligence, either, though that, too, is often involved. It's deep smarts. Deep smarts are not philosophical--they're not"wisdom" in that sense, but they're as close to wisdom as business gets. You see them in the manager who understands when and how to move into a new international market, in the executive who knows just what kind of talk to give when her organization is in crisis, in the technician who can track a product failure back to an interaction between independently produced elements. These are people whose knowledge would be hard to purchase on the open market. Their insight is based on know-how more than on know-what; it comprises a system view as well as expertise in individual areas. Because deep smarts are experienced based and often context specific, they can't be produced overnight or readily imported into an organization. It takes years for an individual to develop them--and no time at all for an organization to lose them when a valued veteran walks out the door. They can be taught, however, with the right techniques. Drawing on their forthcoming book Deep Smarts, Dorothy Leonard and Walter Swap say the best way to transfer such expertise to novices--and, on a larger scale, to make individual knowledge institutional--isn't through PowerPoint slides, a Web site of best practices, online training, project reports, or lectures. Rather, the sage needs to teach the neophyte individually how to draw wisdom from experience. Companies have to be willing to dedicate time and effort to such extensive training, but the investment more than pays for itself.

  3. Gas Classification Using Deep Convolutional Neural Networks

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-01

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

  4. Gas Classification Using Deep Convolutional Neural Networks.

    Science.gov (United States)

    Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

    2018-01-08

    In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

  5. Modeling the dispersal of Levantine Intermediate Water and its role in Mediterranean deep water formation

    Science.gov (United States)

    Wu, Peili; Haines, Keith

    1996-03-01

    This paper demonstrates the importance of Levantine Intermediate Water (LIW) in the deep water formation process in the Mediterranean using the modular ocean general circulation model at 0.25° resolution, 19 vertical levels, over the entire Mediterranean with an open Gibraltar strait. LIW formation is strongly prescribed in the Rhodes Gyre region by Haney [1971] relaxation, while in other regions, surface salinity relaxation is much reduced by applying the `mixed' thermohaline surface boundary conditions. Isopycnal diagnostics are used to trace water mass movements, and volume fluxes are monitored at straits. Low viscosity and diffusion are used to permit baroclinic eddies to play a role in water mass dispersal. The overall water budget is measured by an average flux at Gibraltar of 0.8 Sv, of which 0.7 Sv is exchanged with the eastern basin at Sicily. LIW (density around 28.95) spreads rapidly after formation throughout the entire Levantine due to baroclinic eddies. Toward the west, LIW accumulates in the northern and central Ionian, with some entering the Adriatic through Otranto and some mixing southward in eddies and exiting to the western Mediterranean through Sicily. LIW is converted to deep water in the south Adriatic at an average rate of 0.4 Sv. Water exchange through the Otranto strait appears to be buoyancy driven, with a strong bias to the end of winter (March-April), while at Sicily the exchange has a strong symmetric seasonal cycle, with maximum transport of 1.1 Sv in December indicating the effects of wind driving. LIW pathways in the west are complex and variable. In the Tyrrhenian, intermediate water becomes uniform on isopycnal surfaces due to eddy stirring. West of Sardinia, two LIW boundary currents are formed in the Balearic basin; one flows northward up the west coast of Sardinia and Corsica, and one westward along the northern African coast. The northward current is consistent with observations, while the westward current is intermittent for

  6. Soft-Deep Boltzmann Machines

    OpenAIRE

    Kiwaki, Taichi

    2015-01-01

    We present a layered Boltzmann machine (BM) that can better exploit the advantages of a distributed representation. It is widely believed that deep BMs (DBMs) have far greater representational power than its shallow counterpart, restricted Boltzmann machines (RBMs). However, this expectation on the supremacy of DBMs over RBMs has not ever been validated in a theoretical fashion. In this paper, we provide both theoretical and empirical evidences that the representational power of DBMs can be a...

  7. On the AlxGa1-xN/AlyGa1-yN/AlxGa1-xN (x>y) p-electron blocking layer to improve the hole injection for AlGaN based deep ultraviolet light-emitting diodes

    Science.gov (United States)

    Chu, Chunshuang; Tian, Kangkai; Fang, Mengqian; Zhang, Yonghui; Li, Luping; Bi, Wengang; Zhang, Zi-Hui

    2018-01-01

    This work proposes the [0001] oriented AlGaN-based deep ultraviolet (DUV) light-emitting diode (LED) possessing a specifically designed p-electron blocking layer (p-EBL) to achieve the high internal quantum efficiency. Both electrons and holes can be efficiently injected into the active region by adopting the Al0.60Ga0.40N/Al0.50Ga0.50N/Al0.60Ga0.40N structured p-EBL, in which a p-Al0.50Ga0.50N layer is embedded into the p-EBL. Moreover, the impact of different thicknesses for the p-Al0.50Ga0.50N insertion layer on the hole and electron injections has also been investigated. Compared with the DUV LED with the bulk p-Al0.60Ga0.40N as the EBL, the proposed LED architectures improve the light output power if the thickness of the p-Al0.50Ga0.50N insertion layer is properly designed.

  8. DeepPy: Pythonic deep learning

    DEFF Research Database (Denmark)

    Larsen, Anders Boesen Lindbo

    This technical report introduces DeepPy – a deep learning framework built on top of NumPy with GPU acceleration. DeepPy bridges the gap between highperformance neural networks and the ease of development from Python/NumPy. Users with a background in scientific computing in Python will quickly...... be able to understand and change the DeepPy codebase as it is mainly implemented using high-level NumPy primitives. Moreover, DeepPy supports complex network architectures by letting the user compose mathematical expressions as directed graphs. The latest version is available at http...

  9. Deep primary production in coastal pelagic systems

    DEFF Research Database (Denmark)

    Lyngsgaard, Maren Moltke; Richardson, Katherine; Markager, Stiig

    2014-01-01

    produced. The primary production (PP) occurring below the surface layer, i.e. in the pycnocline-bottom layer (PBL), is shown to contribute significantly to total PP. Oxygen concentrations in the PBL are shown to correlate significantly with the deep primary production (DPP) as well as with salinity...... that eutrophication effects may include changes in the structure of planktonic food webs and element cycling in the water column, both brought about through an altered vertical distribution of PP....

  10. DeepNAT: Deep convolutional neural network for segmenting neuroanatomy.

    Science.gov (United States)

    Wachinger, Christian; Reuter, Martin; Klein, Tassilo

    2018-04-15

    We introduce DeepNAT, a 3D Deep convolutional neural network for the automatic segmentation of NeuroAnaTomy in T1-weighted magnetic resonance images. DeepNAT is an end-to-end learning-based approach to brain segmentation that jointly learns an abstract feature representation and a multi-class classification. We propose a 3D patch-based approach, where we do not only predict the center voxel of the patch but also neighbors, which is formulated as multi-task learning. To address a class imbalance problem, we arrange two networks hierarchically, where the first one separates foreground from background, and the second one identifies 25 brain structures on the foreground. Since patches lack spatial context, we augment them with coordinates. To this end, we introduce a novel intrinsic parameterization of the brain volume, formed by eigenfunctions of the Laplace-Beltrami operator. As network architecture, we use three convolutional layers with pooling, batch normalization, and non-linearities, followed by fully connected layers with dropout. The final segmentation is inferred from the probabilistic output of the network with a 3D fully connected conditional random field, which ensures label agreement between close voxels. The roughly 2.7million parameters in the network are learned with stochastic gradient descent. Our results show that DeepNAT compares favorably to state-of-the-art methods. Finally, the purely learning-based method may have a high potential for the adaptation to young, old, or diseased brains by fine-tuning the pre-trained network with a small training sample on the target application, where the availability of larger datasets with manual annotations may boost the overall segmentation accuracy in the future. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Deep Learning in the Automotive Industry: Applications and Tools

    OpenAIRE

    Luckow, Andre; Cook, Matthew; Ashcraft, Nathan; Weill, Edwin; Djerekarov, Emil; Vorster, Bennie

    2017-01-01

    Deep Learning refers to a set of machine learning techniques that utilize neural networks with many hidden layers for tasks, such as image classification, speech recognition, language understanding. Deep learning has been proven to be very effective in these domains and is pervasively used by many Internet services. In this paper, we describe different automotive uses cases for deep learning in particular in the domain of computer vision. We surveys the current state-of-the-art in libraries, ...

  12. Sacrificial wafer bonding for planarization after very deep etching

    NARCIS (Netherlands)

    Spiering, V.L.; Spiering, Vincent L.; Berenschot, Johan W.; Elwenspoek, Michael Curt; Fluitman, J.H.J.

    A new technique is presented that provides planarization after a very deep etching step in silicon. This offers the possibility for as well resist spinning and layer patterning as realization of bridges or cantilevers across deep holes or grooves. The sacrificial wafer bonding technique contains a

  13. The storage of nuclear wastes; General problematic of radioactive waste management; The currently operated ANDRA's storage centres in France; The Aube storage centre (CSA) and the industrial centre for gathering, warehousing and storage (Cires); The Cigeo project - Industrial centre of radioactive waste storage in deep geological layers; From R and D to innovation within the ANDRA

    International Nuclear Information System (INIS)

    Abadie, Pierre-Marie; Tallec, Michele; Legee, Frederic; Krieguer, Jean-Marie; Plas, Frederic

    2016-01-01

    This publication proposes a set of four articles which address various aspects related to the storage of nuclear wastes. The authors respectively propose an overview of the general problematic of nuclear waste management, a detailed description of existing storage sites which are currently operated by the ANDRA with a focus on the Aube storage centre or CSA, and on the industrial centre for gathering, warehousing and storage or Cires (The currently operated ANDRA's storage centres in France - The Aube Storage Centre or CSA, and the Industrial Centre for Regrouping, Warehousing and Storage or CIRES), a comprehensive overview of the current status of the Cigeo project which could become one of the most important technological works in France (The Cigeo project - Industrial centre of radioactive waste storage in deep geological layers), and a presentation showing how the ANDRA is involved in R and D activities and innovation (From R and D to innovation within the ANDRA)

  14. Taoism and Deep Ecology.

    Science.gov (United States)

    Sylvan, Richard; Bennett, David

    1988-01-01

    Contrasted are the philosophies of Deep Ecology and ancient Chinese. Discusses the cosmology, morality, lifestyle, views of power, politics, and environmental philosophies of each. Concludes that Deep Ecology could gain much from Taoism. (CW)

  15. Applications of Deep Learning in Biomedicine.

    Science.gov (United States)

    Mamoshina, Polina; Vieira, Armando; Putin, Evgeny; Zhavoronkov, Alex

    2016-05-02

    Increases in throughput and installed base of biomedical research equipment led to a massive accumulation of -omics data known to be highly variable, high-dimensional, and sourced from multiple often incompatible data platforms. While this data may be useful for biomarker identification and drug discovery, the bulk of it remains underutilized. Deep neural networks (DNNs) are efficient algorithms based on the use of compositional layers of neurons, with advantages well matched to the challenges -omics data presents. While achieving state-of-the-art results and even surpassing human accuracy in many challenging tasks, the adoption of deep learning in biomedicine has been comparatively slow. Here, we discuss key features of deep learning that may give this approach an edge over other machine learning methods. We then consider limitations and review a number of applications of deep learning in biomedical studies demonstrating proof of concept and practical utility.

  16. Deep Incremental Boosting

    OpenAIRE

    Mosca, Alan; Magoulas, George D

    2017-01-01

    This paper introduces Deep Incremental Boosting, a new technique derived from AdaBoost, specifically adapted to work with Deep Learning methods, that reduces the required training time and improves generalisation. We draw inspiration from Transfer of Learning approaches to reduce the start-up time to training each incremental Ensemble member. We show a set of experiments that outlines some preliminary results on some common Deep Learning datasets and discuss the potential improvements Deep In...

  17. Deep Space Telecommunications

    Science.gov (United States)

    Kuiper, T. B. H.; Resch, G. M.

    2000-01-01

    The increasing load on NASA's deep Space Network, the new capabilities for deep space missions inherent in a next-generation radio telescope, and the potential of new telescope technology for reducing construction and operation costs suggest a natural marriage between radio astronomy and deep space telecommunications in developing advanced radio telescope concepts.

  18. The deep lymphatic anatomy of the hand.

    Science.gov (United States)

    Ma, Chuan-Xiang; Pan, Wei-Ren; Liu, Zhi-An; Zeng, Fan-Qiang; Qiu, Zhi-Qiang

    2018-04-03

    The deep lymphatic anatomy of the hand still remains the least described in medical literature. Eight hands were harvested from four nonembalmed human cadavers amputated above the wrist. A small amount of 6% hydrogen peroxide was employed to detect the lymphatic vessels around the superficial and deep palmar vascular arches, in webs from the index to little fingers, the thenar and hypothenar areas. A 30-gauge needle was inserted into the vessels and injected with a barium sulphate compound. Each specimen was dissected, photographed and radiographed to demonstrate deep lymphatic distribution of the hand. Five groups of deep collecting lymph vessels were found in the hand: superficial palmar arch lymph vessel (SPALV); deep palmar arch lymph vessel (DPALV); thenar lymph vessel (TLV); hypothenar lymph vessel (HTLV); deep finger web lymph vessel (DFWLV). Each group of vessels drained in different directions first, then all turned and ran towards the wrist in different layers. The deep lymphatic drainage of the hand has been presented. The results will provide an anatomical basis for clinical management, educational reference and scientific research. Copyright © 2018 Elsevier GmbH. All rights reserved.

  19. Deep kernel learning method for SAR image target recognition

    Science.gov (United States)

    Chen, Xiuyuan; Peng, Xiyuan; Duan, Ran; Li, Junbao

    2017-10-01

    With the development of deep learning, research on image target recognition has made great progress in recent years. Remote sensing detection urgently requires target recognition for military, geographic, and other scientific research. This paper aims to solve the synthetic aperture radar image target recognition problem by combining deep and kernel learning. The model, which has a multilayer multiple kernel structure, is optimized layer by layer with the parameters of Support Vector Machine and a gradient descent algorithm. This new deep kernel learning method improves accuracy and achieves competitive recognition results compared with other learning methods.

  20. Deep learning with Python

    CERN Document Server

    Chollet, Francois

    2018-01-01

    DESCRIPTION Deep learning is applicable to a widening range of artificial intelligence problems, such as image classification, speech recognition, text classification, question answering, text-to-speech, and optical character recognition. Deep Learning with Python is structured around a series of practical code examples that illustrate each new concept introduced and demonstrate best practices. By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects. KEY FEATURES • Practical code examples • In-depth introduction to Keras • Teaches the difference between Deep Learning and AI ABOUT THE TECHNOLOGY Deep learning is the technology behind photo tagging systems at Facebook and Google, self-driving cars, speech recognition systems on your smartphone, and much more. AUTHOR BIO Francois Chollet is the author of Keras, one of the most widely used libraries for deep learning in Python. He has been working with deep neural ...

  1. Deep learning evaluation using deep linguistic processing

    OpenAIRE

    Kuhnle, Alexander; Copestake, Ann

    2017-01-01

    We discuss problems with the standard approaches to evaluation for tasks like visual question answering, and argue that artificial data can be used to address these as a complement to current practice. We demonstrate that with the help of existing 'deep' linguistic processing technology we are able to create challenging abstract datasets, which enable us to investigate the language understanding abilities of multimodal deep learning models in detail, as compared to a single performance value ...

  2. Mixed layer depth trends in the Bay of Biscay over the period 1975-2010.

    Directory of Open Access Journals (Sweden)

    Xurxo Costoya

    Full Text Available Wintertime trends in mixed layer depth (MLD were calculated in the Bay of Biscay over the period 1975-2010 using the Simple Ocean Data Assimilation (SODA package. The reliability of the SODA database was confirmed correlating its results with those obtained from the experimental Argo database over the period 2003-2010. An iso-thermal layer depth (TLD and an iso-pycnal layer depth (PLD were defined using the threshold difference method with ΔT = 0.5°C and Δσθ = 0.125 kg/m3. Wintertime trends of the MLD were calculated using winter extended (December-March anomalies and annual maxima. Trends calculated for the whole Bay of Biscay using both parameters (TLD and PLD showed to be dependent on the area. Thus, MLD became deeper in the southeastern corner and shallower in the rest of the area. Air temperature was shown to play a key role in regulating the different spatial behavior of the MLD. Negative air temperature trends localized in the southeastern corner coincide with MLD deepening in this area, while, positive air temperature trends are associated to MLD shoaling in the rest of the bay. Additionally, the temperature trend calculated along the first 700 m of the water column is in good agreement with the different spatial behavior revealed for the MLD trend.

  3. Deep water characteristics and circulation in the South China Sea

    Science.gov (United States)

    Wang, Aimei; Du, Yan; Peng, Shiqiu; Liu, Kexiu; Huang, Rui Xin

    2018-04-01

    This study investigates the deep circulation in the South China Sea (SCS) using oceanographic observations combined with results from a bottom layer reduced gravity model. The SCS water, 2000 m below the surface, is quite different from that in the adjacent Pacific Ocean, and it is characterized by its low dissolved oxygen (DO), high temperature and low salinity. The horizontal distribution of deep water properties indicates a basin-scale cyclonic circulation driven by the Luzon overflow. The results of the bottom layer reduced gravity model are consistent with the existence of the cyclonic circulation in the deep SCS. The circulation is stronger at the northern/western boundary. After overflowing the sill of the Luzon Strait, the deep water moves broadly southwestward, constrained by the 3500 m isobath. The broadening of the southward flow is induced by the downwelling velocity in the interior of the deep basin. The main deep circulation bifurcates into two branches after the Zhongsha Islands. The southward branch continues flowing along the 3500 m isobath, and the eastward branch forms the sub-basin scale cyclonic circulation around the seamounts in the central deep SCS. The returning flow along the east boundary is fairly weak. The numerical experiments of the bottom layer reduced gravity model reveal the important roles of topography, bottom friction, and the upwelling/downwelling pattern in controlling the spatial structure, particularly the strong, deep western boundary current.

  4. The application of deep confidence network in the problem of image recognition

    Directory of Open Access Journals (Sweden)

    Chumachenko О.І.

    2016-12-01

    Full Text Available In order to study the concept of deep learning, in particular the substitution of multilayer perceptron on the corresponding network of deep confidence, computer simulations of the learning process to test voters was carried out. Multi-layer perceptron has been replaced by a network of deep confidence, consisting of successive limited Boltzmann machines. After training of a network of deep confidence algorithm of layer-wise training it was found that the use of networks of deep confidence greatly improves the accuracy of multilayer perceptron training by method of reverse distribution errors.

  5. Deep learning relevance

    DEFF Research Database (Denmark)

    Lioma, Christina; Larsen, Birger; Petersen, Casper

    2016-01-01

    train a Recurrent Neural Network (RNN) on existing relevant information to that query. We then use the RNN to "deep learn" a single, synthetic, and we assume, relevant document for that query. We design a crowdsourcing experiment to assess how relevant the "deep learned" document is, compared...... to existing relevant documents. Users are shown a query and four wordclouds (of three existing relevant documents and our deep learned synthetic document). The synthetic document is ranked on average most relevant of all....

  6. A reason of fast and deep fading of centimeter wave

    International Nuclear Information System (INIS)

    Danzan, D.; Damdinsuren, E.; Hiamjav, J.; Chuluunbaatar, Ch.; Battulga, S.

    1992-01-01

    First discovered experimentally exactly correlation between of appearance and of disappearance of optical mirage and fast and deep fading of horizontal polarization of centimeter wave. Proved the interference of the straight and reflected rays from the thin layer of air in mirage a reason of this fading. The physical parameters data of the layer of mirage: change of dielectric permeability and n/ h gradient of refraction index of air in this layer are been showed

  7. Two-layer anti-reflection strategies for implant applications

    Science.gov (United States)

    Guerrero, Douglas J.; Smith, Tamara; Kato, Masakazu; Kimura, Shigeo; Enomoto, Tomoyuki

    2006-03-01

    A two-layer bottom anti-reflective coating (BARC) concept in which a layer that develops slowly is coated on top of a bottom layer that develops more rapidly was demonstrated. Development rate control was achieved by selection of crosslinker amount and BARC curing conditions. A single-layer BARC was compared with the two-layer BARC concept. The single-layer BARC does not clear out of 200-nm deep vias. When the slower developing single-layer BARC was coated on top of the faster developing layer, the vias were cleared. Lithographic evaluation of the two-layer BARC concept shows the same resolution advantages as the single-layer system. Planarization properties of a two-layer BARC system are better than for a single-layer system, when comparing the same total nominal thicknesses.

  8. Layered materials

    Science.gov (United States)

    Johnson, David; Clarke, Simon; Wiley, John; Koumoto, Kunihito

    2014-06-01

    Layered compounds, materials with a large anisotropy to their bonding, electrical and/or magnetic properties, have been important in the development of solid state chemistry, physics and engineering applications. Layered materials were the initial test bed where chemists developed intercalation chemistry that evolved into the field of topochemical reactions where researchers are able to perform sequential steps to arrive at kinetically stable products that cannot be directly prepared by other approaches. Physicists have used layered compounds to discover and understand novel phenomena made more apparent through reduced dimensionality. The discovery of charge and spin density waves and more recently the remarkable discovery in condensed matter physics of the two-dimensional topological insulating state were discovered in two-dimensional materials. The understanding developed in two-dimensional materials enabled subsequent extension of these and other phenomena into three-dimensional materials. Layered compounds have also been used in many technologies as engineers and scientists used their unique properties to solve challenging technical problems (low temperature ion conduction for batteries, easy shear planes for lubrication in vacuum, edge decorated catalyst sites for catalytic removal of sulfur from oil, etc). The articles that are published in this issue provide an excellent overview of the spectrum of activities that are being pursued, as well as an introduction to some of the most established achievements in the field. Clusters of papers discussing thermoelectric properties, electronic structure and transport properties, growth of single two-dimensional layers, intercalation and more extensive topochemical reactions and the interleaving of two structures to form new materials highlight the breadth of current research in this area. These papers will hopefully serve as a useful guideline for the interested reader to different important aspects in this field and

  9. Tracing Internal Radar Layers in the Greenland Ice Sheet

    DEFF Research Database (Denmark)

    Panton, Christian

    Internal layers in radio-echograms from the sounding of ice sheets have long been a valuable resource in glaciology, but their usefulness have been limited by availability of traced (digitized) layers. To speed up this process, we have developed an algorithm for semi-automatic tracing the internal...... layers and a fully automated algorithm for mapping the layer slope. The layer slope is inferred by the intensity response to a slanted Gaussian filter, whereafter layers can be traced using an active contour model. With these techniques we show that it possible to trace internal layers over distances...... of hundreds kilometers with minimal operator intervention, and the methods have been successfully validated between two Greenland deep ice cores with internal match points. In order to remove any operator assistance, we show how the layer slope can be used to detect disturbances in the deep radiostratigraphy...

  10. Deep Vein Thrombosis

    African Journals Online (AJOL)

    OWNER

    Deep Vein Thrombosis: Risk Factors and Prevention in Surgical Patients. Deep Vein ... preventable morbidity and mortality in hospitalized surgical patients. ... the elderly.3,4 It is very rare before the age ... depends on the risk level; therefore an .... but also in the post-operative period. ... is continuing uncertainty regarding.

  11. Construction of Neural Networks for Realization of Localized Deep Learning

    Directory of Open Access Journals (Sweden)

    Charles K. Chui

    2018-05-01

    Full Text Available The subject of deep learning has recently attracted users of machine learning from various disciplines, including: medical diagnosis and bioinformatics, financial market analysis and online advertisement, speech and handwriting recognition, computer vision and natural language processing, time series forecasting, and search engines. However, theoretical development of deep learning is still at its infancy. The objective of this paper is to introduce a deep neural network (also called deep-net approach to localized manifold learning, with each hidden layer endowed with a specific learning task. For the purpose of illustrations, we only focus on deep-nets with three hidden layers, with the first layer for dimensionality reduction, the second layer for bias reduction, and the third layer for variance reduction. A feedback component is also designed to deal with outliers. The main theoretical result in this paper is the order O(m-2s/(2s+d of approximation of the regression function with regularity s, in terms of the number m of sample points, where the (unknown manifold dimension d replaces the dimension D of the sampling (Euclidean space for shallow nets.

  12. Applied model for the growth of the daytime mixed layer

    DEFF Research Database (Denmark)

    Batchvarova, E.; Gryning, Sven-Erik

    1991-01-01

    numerically. When the mixed layer is shallow or the atmosphere nearly neutrally stratified, the growth is controlled mainly by mechanical turbulence. When the layer is deep, its growth is controlled mainly by convective turbulence. The model is applied on a data set of the evolution of the height of the mixed...... layer in the morning hours, when both mechanical and convective turbulence contribute to the growth process. Realistic mixed-layer developments are obtained....

  13. Deep Echo State Network (DeepESN): A Brief Survey

    OpenAIRE

    Gallicchio, Claudio; Micheli, Alessio

    2017-01-01

    The study of deep recurrent neural networks (RNNs) and, in particular, of deep Reservoir Computing (RC) is gaining an increasing research attention in the neural networks community. The recently introduced deep Echo State Network (deepESN) model opened the way to an extremely efficient approach for designing deep neural networks for temporal data. At the same time, the study of deepESNs allowed to shed light on the intrinsic properties of state dynamics developed by hierarchical compositions ...

  14. Deep levels in silicon–oxygen superlattices

    International Nuclear Information System (INIS)

    Simoen, E; Jayachandran, S; Delabie, A; Caymax, M; Heyns, M

    2016-01-01

    This work reports on the deep levels observed in Pt/Al 2 O 3 /p-type Si metal-oxide-semiconductor capacitors containing a silicon–oxygen superlattice (SL) by deep-level transient spectroscopy. It is shown that the presence of the SL gives rise to a broad band of hole traps occurring around the silicon mid gap, which is absent in reference samples with a silicon epitaxial layer. In addition, the density of states of the deep layers roughly scales with the number of SL periods for the as-deposited samples. Annealing in a forming gas atmosphere reduces the maximum concentration significantly, while the peak energy position shifts from close-to mid-gap towards the valence band edge. Based on the flat-band voltage shift of the Capacitance–Voltage characteristics it is inferred that positive charge is introduced by the oxygen atomic layers in the SL, indicating the donor nature of the underlying hole traps. In some cases, a minor peak associated with P b dangling bond centers at the Si/SiO 2 interface has been observed as well. (paper)

  15. Deep learning in bioinformatics.

    Science.gov (United States)

    Min, Seonwoo; Lee, Byunghan; Yoon, Sungroh

    2017-09-01

    In the era of big data, transformation of biomedical big data into valuable knowledge has been one of the most important challenges in bioinformatics. Deep learning has advanced rapidly since the early 2000s and now demonstrates state-of-the-art performance in various fields. Accordingly, application of deep learning in bioinformatics to gain insight from data has been emphasized in both academia and industry. Here, we review deep learning in bioinformatics, presenting examples of current research. To provide a useful and comprehensive perspective, we categorize research both by the bioinformatics domain (i.e. omics, biomedical imaging, biomedical signal processing) and deep learning architecture (i.e. deep neural networks, convolutional neural networks, recurrent neural networks, emergent architectures) and present brief descriptions of each study. Additionally, we discuss theoretical and practical issues of deep learning in bioinformatics and suggest future research directions. We believe that this review will provide valuable insights and serve as a starting point for researchers to apply deep learning approaches in their bioinformatics studies. © The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  16. Deep subsurface microbial processes

    Science.gov (United States)

    Lovley, D.R.; Chapelle, F.H.

    1995-01-01

    Information on the microbiology of the deep subsurface is necessary in order to understand the factors controlling the rate and extent of the microbially catalyzed redox reactions that influence the geophysical properties of these environments. Furthermore, there is an increasing threat that deep aquifers, an important drinking water resource, may be contaminated by man's activities, and there is a need to predict the extent to which microbial activity may remediate such contamination. Metabolically active microorganisms can be recovered from a diversity of deep subsurface environments. The available evidence suggests that these microorganisms are responsible for catalyzing the oxidation of organic matter coupled to a variety of electron acceptors just as microorganisms do in surface sediments, but at much slower rates. The technical difficulties in aseptically sampling deep subsurface sediments and the fact that microbial processes in laboratory incubations of deep subsurface material often do not mimic in situ processes frequently necessitate that microbial activity in the deep subsurface be inferred through nonmicrobiological analyses of ground water. These approaches include measurements of dissolved H2, which can predict the predominant microbially catalyzed redox reactions in aquifers, as well as geochemical and groundwater flow modeling, which can be used to estimate the rates of microbial processes. Microorganisms recovered from the deep subsurface have the potential to affect the fate of toxic organics and inorganic contaminants in groundwater. Microbial activity also greatly influences 1 the chemistry of many pristine groundwaters and contributes to such phenomena as porosity development in carbonate aquifers, accumulation of undesirably high concentrations of dissolved iron, and production of methane and hydrogen sulfide. Although the last decade has seen a dramatic increase in interest in deep subsurface microbiology, in comparison with the study of

  17. Deep Ocean Contribution to Sea Level Rise

    Science.gov (United States)

    Chang, L.; Sun, W.; Tang, H.; Wang, Q.

    2017-12-01

    The ocean temperature and salinity change in the upper 2000m can be detected by Argo floats, so we can know the steric height change of the ocean. But the ocean layers above 2000m represent only 50% of the total ocean volume. Although the temperature and salinity change are small compared to the upper ocean, the deep ocean contribution to sea level might be significant because of its large volume. There has been some research on the deep ocean rely on the very sparse situ observation and are limited to decadal and longer-term rates of change. The available observational data in the deep ocean are too spares to determine the temporal variability, and the long-term changes may have a bias. We will use the Argo date and combine the situ data and topographic data to estimate the temperature and salinity of the sea water below 2000m, so we can obtain a monthly data. We will analyze the seasonal and annual change of the steric height change due to the deep ocean between 2005 and 2016. And we will evaluate the result combination the present-day satellite and in situ observing systems. The deep ocean contribution can be inferred indirectly as the difference between the altimetry minus GRACE and Argo-based steric sea level.

  18. Deep neural mapping support vector machines.

    Science.gov (United States)

    Li, Yujian; Zhang, Ting

    2017-09-01

    The choice of kernel has an important effect on the performance of a support vector machine (SVM). The effect could be reduced by NEUROSVM, an architecture using multilayer perceptron for feature extraction and SVM for classification. In binary classification, a general linear kernel NEUROSVM can be theoretically simplified as an input layer, many hidden layers, and an SVM output layer. As a feature extractor, the sub-network composed of the input and hidden layers is first trained together with a virtual ordinary output layer by backpropagation, then with the output of its last hidden layer taken as input of the SVM classifier for further training separately. By taking the sub-network as a kernel mapping from the original input space into a feature space, we present a novel model, called deep neural mapping support vector machine (DNMSVM), from the viewpoint of deep learning. This model is also a new and general kernel learning method, where the kernel mapping is indeed an explicit function expressed as a sub-network, different from an implicit function induced by a kernel function traditionally. Moreover, we exploit a two-stage procedure of contrastive divergence learning and gradient descent for DNMSVM to jointly training an adaptive kernel mapping instead of a kernel function, without requirement of kernel tricks. As a whole of the sub-network and the SVM classifier, the joint training of DNMSVM is done by using gradient descent to optimize the objective function with the sub-network layer-wise pre-trained via contrastive divergence learning of restricted Boltzmann machines. Compared to the separate training of NEUROSVM, the joint training is a new algorithm for DNMSVM to have advantages over NEUROSVM. Experimental results show that DNMSVM can outperform NEUROSVM and RBFSVM (i.e., SVM with the kernel of radial basis function), demonstrating its effectiveness. Copyright © 2017 Elsevier Ltd. All rights reserved.

  19. Deep Water Survey Data

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The deep water biodiversity surveys explore and describe the biodiversity of the bathy- and bentho-pelagic nekton using Midwater and bottom trawls centered in the...

  20. Deep Space Habitat Project

    Data.gov (United States)

    National Aeronautics and Space Administration — The Deep Space Habitat was closed out at the end of Fiscal Year 2013 (September 30, 2013). Results and select content have been incorporated into the new Exploration...

  1. Deep Learning in Neuroradiology.

    Science.gov (United States)

    Zaharchuk, G; Gong, E; Wintermark, M; Rubin, D; Langlotz, C P

    2018-02-01

    Deep learning is a form of machine learning using a convolutional neural network architecture that shows tremendous promise for imaging applications. It is increasingly being adapted from its original demonstration in computer vision applications to medical imaging. Because of the high volume and wealth of multimodal imaging information acquired in typical studies, neuroradiology is poised to be an early adopter of deep learning. Compelling deep learning research applications have been demonstrated, and their use is likely to grow rapidly. This review article describes the reasons, outlines the basic methods used to train and test deep learning models, and presents a brief overview of current and potential clinical applications with an emphasis on how they are likely to change future neuroradiology practice. Facility with these methods among neuroimaging researchers and clinicians will be important to channel and harness the vast potential of this new method. © 2018 by American Journal of Neuroradiology.

  2. Deep inelastic lepton scattering

    International Nuclear Information System (INIS)

    Nachtmann, O.

    1977-01-01

    Deep inelastic electron (muon) nucleon and neutrino nucleon scattering as well as electron positron annihilation into hadrons are reviewed from a theoretical point of view. The emphasis is placed on comparisons of quantum chromodynamics with the data. (orig.) [de

  3. Neuromorphic Deep Learning Machines

    OpenAIRE

    Neftci, E; Augustine, C; Paul, S; Detorakis, G

    2017-01-01

    An ongoing challenge in neuromorphic computing is to devise general and computationally efficient models of inference and learning which are compatible with the spatial and temporal constraints of the brain. One increasingly popular and successful approach is to take inspiration from inference and learning algorithms used in deep neural networks. However, the workhorse of deep learning, the gradient descent Back Propagation (BP) rule, often relies on the immediate availability of network-wide...

  4. Pathogenesis of deep endometriosis.

    Science.gov (United States)

    Gordts, Stephan; Koninckx, Philippe; Brosens, Ivo

    2017-12-01

    The pathophysiology of (deep) endometriosis is still unclear. As originally suggested by Cullen, change the definition "deeper than 5 mm" to "adenomyosis externa." With the discovery of the old European literature on uterine bleeding in 5%-10% of the neonates and histologic evidence that the bleeding represents decidual shedding, it is postulated/hypothesized that endometrial stem/progenitor cells, implanted in the pelvic cavity after birth, may be at the origin of adolescent and even the occasionally premenarcheal pelvic endometriosis. Endometriosis in the adolescent is characterized by angiogenic and hemorrhagic peritoneal and ovarian lesions. The development of deep endometriosis at a later age suggests that deep infiltrating endometriosis is a delayed stage of endometriosis. Another hypothesis is that the endometriotic cell has undergone genetic or epigenetic changes and those specific changes determine the development into deep endometriosis. This is compatible with the hereditary aspects, and with the clonality of deep and cystic ovarian endometriosis. It explains the predisposition and an eventual causal effect by dioxin or radiation. Specific genetic/epigenetic changes could explain the various expressions and thus typical, cystic, and deep endometriosis become three different diseases. Subtle lesions are not a disease until epi(genetic) changes occur. A classification should reflect that deep endometriosis is a specific disease. In conclusion the pathophysiology of deep endometriosis remains debated and the mechanisms of disease progression, as well as the role of genetics and epigenetics in the process, still needs to be unraveled. Copyright © 2017 American Society for Reproductive Medicine. Published by Elsevier Inc. All rights reserved.

  5. Multiscale deep features learning for land-use scene recognition

    Science.gov (United States)

    Yuan, Baohua; Li, Shijin; Li, Ning

    2018-01-01

    The features extracted from deep convolutional neural networks (CNNs) have shown their promise as generic descriptors for land-use scene recognition. However, most of the work directly adopts the deep features for the classification of remote sensing images, and does not encode the deep features for improving their discriminative power, which can affect the performance of deep feature representations. To address this issue, we propose an effective framework, LASC-CNN, obtained by locality-constrained affine subspace coding (LASC) pooling of a CNN filter bank. LASC-CNN obtains more discriminative deep features than directly extracted from CNNs. Furthermore, LASC-CNN builds on the top convolutional layers of CNNs, which can incorporate multiscale information and regions of arbitrary resolution and sizes. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

  6. Why & When Deep Learning Works: Looking Inside Deep Learnings

    OpenAIRE

    Ronen, Ronny

    2017-01-01

    The Intel Collaborative Research Institute for Computational Intelligence (ICRI-CI) has been heavily supporting Machine Learning and Deep Learning research from its foundation in 2012. We have asked six leading ICRI-CI Deep Learning researchers to address the challenge of "Why & When Deep Learning works", with the goal of looking inside Deep Learning, providing insights on how deep networks function, and uncovering key observations on their expressiveness, limitations, and potential. The outp...

  7. Benthic boundary layer modelling studies

    International Nuclear Information System (INIS)

    Richards, K.J.

    1984-01-01

    A numerical model has been developed to study the factors which control the height of the benthic boundary layer in the deep ocean and the dispersion of a tracer within and directly above the layer. This report covers tracer clouds of horizontal scales of 10 to 100 km. The dispersion of a tracer has been studied in two ways. Firstly, a number of particles have been introduced into the flow. The trajectories of these particles provide information on dispersion rates. For flow conditions similar to those observed in the abyssal N.E. Atlantic the diffusivity of a tracer was found to be 5 x 10 6 cm 2 s -1 for a tracer within the boundary layer and 8 x 10 6 cm 2 s -1 for a tracer above the boundary layer. The results are in accord with estimates made from current meter measurements. The second method of studying dispersion was to calculate the evolution of individual tracer clouds. Clouds within and above the benthic boundary layer often show quite different behaviour from each other although the general structure of the clouds in the two regions were found to have no significant differences. (author)

  8. Stellar Atmospheric Parameterization Based on Deep Learning

    Science.gov (United States)

    Pan, Ru-yang; Li, Xiang-ru

    2017-07-01

    Deep learning is a typical learning method widely studied in the fields of machine learning, pattern recognition, and artificial intelligence. This work investigates the problem of stellar atmospheric parameterization by constructing a deep neural network with five layers, and the node number in each layer of the network is respectively 3821-500-100-50-1. The proposed scheme is verified on both the real spectra measured by the Sloan Digital Sky Survey (SDSS) and the theoretic spectra computed with the Kurucz's New Opacity Distribution Function (NEWODF) model, to make an automatic estimation for three physical parameters: the effective temperature (Teff), surface gravitational acceleration (lg g), and metallic abundance (Fe/H). The results show that the stacked autoencoder deep neural network has a better accuracy for the estimation. On the SDSS spectra, the mean absolute errors (MAEs) are 79.95 for Teff/K, 0.0058 for (lg Teff/K), 0.1706 for lg (g/(cm·s-2)), and 0.1294 dex for the [Fe/H], respectively; On the theoretic spectra, the MAEs are 15.34 for Teff/K, 0.0011 for lg (Teff/K), 0.0214 for lg(g/(cm · s-2)), and 0.0121 dex for [Fe/H], respectively.

  9. Deep boreholes; Tiefe Bohrloecher

    Energy Technology Data Exchange (ETDEWEB)

    Bracke, Guido [Gesellschaft fuer Anlagen- und Reaktorsicherheit gGmbH Koeln (Germany); Charlier, Frank [NSE international nuclear safety engineering gmbh, Aachen (Germany); Geckeis, Horst [Karlsruher Institut fuer Technologie (Germany). Inst. fuer Nukleare Entsorgung; and others

    2016-02-15

    The report on deep boreholes covers the following subject areas: methods for safe enclosure of radioactive wastes, requirements concerning the geological conditions of possible boreholes, reversibility of decisions and retrievability, status of drilling technology. The introduction covers national and international activities. Further chapters deal with the following issues: basic concept of the storage in deep bore holes, status of the drilling technology, safe enclosure, geomechanics and stability, reversibility of decisions, risk scenarios, compliancy with safe4ty requirements and site selection criteria, research and development demand.

  10. Deep Water Acoustics

    Science.gov (United States)

    2016-06-28

    the Deep Water project and participate in the NPAL Workshops, including Art Baggeroer (MIT), J. Beron- Vera (UMiami), M. Brown (UMiami), T...Kathleen E . Wage. The North Pacific Acoustic Laboratory deep-water acoustic propagation experiments in the Philippine Sea. J. Acoust. Soc. Am., 134(4...estimate of the angle α during PhilSea09, made from ADCP measurements at the site of the DVLA. Sim. A B1 B2 B3 C D E F Prof. # 0 4 4 4 5 10 16 20 α

  11. Atomic Layer Deposition of Chemical Passivation Layers and High Performance Anti-Reflection Coatings on Back-Illuminated Detectors

    Science.gov (United States)

    Hoenk, Michael E. (Inventor); Greer, Frank (Inventor); Nikzad, Shouleh (Inventor)

    2014-01-01

    A back-illuminated silicon photodetector has a layer of Al2O3 deposited on a silicon oxide surface that receives electromagnetic radiation to be detected. The Al2O3 layer has an antireflection coating deposited thereon. The Al2O3 layer provides a chemically resistant separation layer between the silicon oxide surface and the antireflection coating. The Al2O3 layer is thin enough that it is optically innocuous. Under deep ultraviolet radiation, the silicon oxide layer and the antireflection coating do not interact chemically. In one embodiment, the silicon photodetector has a delta-doped layer near (within a few nanometers of) the silicon oxide surface. The Al2O3 layer is expected to provide similar protection for doped layers fabricated using other methods, such as MBE, ion implantation and CVD deposition.

  12. Distribution of phytoplankton groups within the deep chlorophyll maximum

    KAUST Repository

    Latasa, Mikel

    2016-11-01

    The fine vertical distribution of phytoplankton groups within the deep chlorophyll maximum (DCM) was studied in the NE Atlantic during summer stratification. A simple but unconventional sampling strategy allowed examining the vertical structure with ca. 2 m resolution. The distribution of Prochlorococcus, Synechococcus, chlorophytes, pelagophytes, small prymnesiophytes, coccolithophores, diatoms, and dinoflagellates was investigated with a combination of pigment-markers, flow cytometry and optical and FISH microscopy. All groups presented minimum abundances at the surface and a maximum in the DCM layer. The cell distribution was not vertically symmetrical around the DCM peak and cells tended to accumulate in the upper part of the DCM layer. The more symmetrical distribution of chlorophyll than cells around the DCM peak was due to the increase of pigment per cell with depth. We found a vertical alignment of phytoplankton groups within the DCM layer indicating preferences for different ecological niches in a layer with strong gradients of light and nutrients. Prochlorococcus occupied the shallowest and diatoms the deepest layers. Dinoflagellates, Synechococcus and small prymnesiophytes preferred shallow DCM layers, and coccolithophores, chlorophytes and pelagophytes showed a preference for deep layers. Cell size within groups changed with depth in a pattern related to their mean size: the cell volume of the smallest group increased the most with depth while the cell volume of the largest group decreased the most. The vertical alignment of phytoplankton groups confirms that the DCM is not a homogeneous entity and indicates groups’ preferences for different ecological niches within this layer.

  13. Deep diode atomic battery

    International Nuclear Information System (INIS)

    Anthony, T.R.; Cline, H.E.

    1977-01-01

    A deep diode atomic battery is made from a bulk semiconductor crystal containing three-dimensional arrays of columnar and lamellar P-N junctions. The battery is powered by gamma rays and x-ray emission from a radioactive source embedded in the interior of the semiconductor crystal

  14. Deep Learning Policy Quantization

    NARCIS (Netherlands)

    van de Wolfshaar, Jos; Wiering, Marco; Schomaker, Lambertus

    2018-01-01

    We introduce a novel type of actor-critic approach for deep reinforcement learning which is based on learning vector quantization. We replace the softmax operator of the policy with a more general and more flexible operator that is similar to the robust soft learning vector quantization algorithm.

  15. Deep-sea fungi

    Digital Repository Service at National Institute of Oceanography (India)

    Raghukumar, C; Damare, S.R.

    significant in terms of carbon sequestration (5, 8). In light of this, the diversity, abundance, and role of fungi in deep-sea sediments may form an important link in the global C biogeochemistry. This review focuses on issues related to collection...

  16. Deep inelastic scattering

    International Nuclear Information System (INIS)

    Aubert, J.J.

    1982-01-01

    Deep inelastic lepton-nucleon interaction experiments are renewed. Singlet and non-singlet structure functions are measured and the consistency of the different results is checked. A detailed analysis of the scaling violation is performed in terms of the quantum chromodynamics predictions [fr

  17. Deep Vein Thrombosis

    Centers for Disease Control (CDC) Podcasts

    2012-04-05

    This podcast discusses the risk for deep vein thrombosis in long-distance travelers and ways to minimize that risk.  Created: 4/5/2012 by National Center for Emerging and Zoonotic Infectious Diseases (NCEZID).   Date Released: 4/5/2012.

  18. Deep Learning Microscopy

    KAUST Repository

    Rivenson, Yair; Gorocs, Zoltan; Gunaydin, Harun; Zhang, Yibo; Wang, Hongda; Ozcan, Aydogan

    2017-01-01

    regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with remarkably

  19. The deep universe

    CERN Document Server

    Sandage, AR; Longair, MS

    1995-01-01

    Discusses the concept of the deep universe from two conflicting theoretical viewpoints: firstly as a theory embracing the evolution of the universe from the Big Bang to the present; and secondly through observations gleaned over the years on stars, galaxies and clusters.

  20. Teaching for Deep Learning

    Science.gov (United States)

    Smith, Tracy Wilson; Colby, Susan A.

    2007-01-01

    The authors have been engaged in research focused on students' depth of learning as well as teachers' efforts to foster deep learning. Findings from a study examining the teaching practices and student learning outcomes of sixty-four teachers in seventeen different states (Smith et al. 2005) indicated that most of the learning in these classrooms…

  1. Deep Trawl Dataset

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — Otter trawl (36' Yankee and 4-seam net deepwater gear) catches from mid-Atlantic slope and canyons at 200 - 800 m depth. Deep-sea (200-800 m depth) flat otter trawls...

  2. [Deep vein thrombosis prophylaxis.

    Science.gov (United States)

    Sandoval-Chagoya, Gloria Alejandra; Laniado-Laborín, Rafael

    2013-01-01

    Background: despite the proven effectiveness of preventive therapy for deep vein thrombosis, a significant proportion of patients at risk for thromboembolism do not receive prophylaxis during hospitalization. Our objective was to determine the adherence to thrombosis prophylaxis guidelines in a general hospital as a quality control strategy. Methods: a random audit of clinical charts was conducted at the Tijuana General Hospital, Baja California, Mexico, to determine the degree of adherence to deep vein thrombosis prophylaxis guidelines. The instrument used was the Caprini's checklist for thrombosis risk assessment in adult patients. Results: the sample included 300 patient charts; 182 (60.7 %) were surgical patients and 118 were medical patients. Forty six patients (15.3 %) received deep vein thrombosis pharmacologic prophylaxis; 27.1 % of medical patients received deep vein thrombosis prophylaxis versus 8.3 % of surgical patients (p < 0.0001). Conclusions: our results show that adherence to DVT prophylaxis at our hospital is extremely low. Only 15.3 % of our patients at risk received treatment, and even patients with very high risk received treatment in less than 25 % of the cases. We have implemented strategies to increase compliance with clinical guidelines.

  3. Tephrostratigraphy the DEEP site record, Lake Ohrid

    Science.gov (United States)

    Leicher, N.; Zanchetta, G.; Sulpizio, R.; Giaccio, B.; Wagner, B.; Francke, A.

    2016-12-01

    In the central Mediterranean region, tephrostratigraphy has been proofed to be a suitable and powerful tool for dating and correlating marine and terrestrial records. However, for the period older 200 ka, tephrostratigraphy is incomplete and restricted to some Italian continental basins (e.g. Sulmona, Acerno, Mercure), and continuous records downwind of the Italian volcanoes are rare. Lake Ohrid (Macedonia/Albania) in the eastern Mediterranean region fits this requisite and is assumed to be the oldest continuously existing lake of Europe. A continous record (DEEP) was recovered within the scope of the ICDP deep-drilling campaign SCOPSCO (Scientific Collaboration on Past Speciation Conditions in Lake Ohrid). In the uppermost 450 meters of the record, covering more than 1.2 Myrs of Italian volcanism, 54 tephra layers were identified during core-opening and description. A first tephrostratigraphic record was established for the uppermost 248 m ( 637 ka). Major element analyses (EDS/WDS) were carried out on juvenile glass fragments and 15 out of 35 tephra layers have been identified and correlated with known and dated eruptions of Italian volcanoes. Existing 40Ar/39Ar ages were re-calculated by using the same flux standard and used as first order tie points to develop a robust chronology for the DEEP site succession. Between 248 and 450 m of the DEEP site record, another 19 tephra horizons were identified and are subject of ongoing work. These deposits, once correlated with known and dated tephra, will hopefully enable dating this part of the succession, likely supported by major paleomagnetic events, such as the Brunhes-Matuyama boundary, or the Cobb-Mountain or the Jaramillo excursions. This makes the Lake Ohrid record a unique continuous, distal record of Italian volcanic activity, which is candidate to become the template for the central Mediterranean tephrostratigraphy, especially for the hitherto poorly known and explored lower Middle Pleistocene period.

  4. Layer-specific morphological and molecular differences in neocortical astrocytes and their dependence on neuronal layers.

    Science.gov (United States)

    Lanjakornsiripan, Darin; Pior, Baek-Jun; Kawaguchi, Daichi; Furutachi, Shohei; Tahara, Tomoaki; Katsuyama, Yu; Suzuki, Yutaka; Fukazawa, Yugo; Gotoh, Yukiko

    2018-04-24

    Non-pial neocortical astrocytes have historically been thought to comprise largely a nondiverse population of protoplasmic astrocytes. Here we show that astrocytes of the mouse somatosensory cortex manifest layer-specific morphological and molecular differences. Two- and three-dimensional observations revealed that astrocytes in the different layers possess distinct morphologies as reflected by differences in cell orientation, territorial volume, and arborization. The extent of ensheathment of synaptic clefts by astrocytes in layer II/III was greater than that by those in layer VI. Moreover, differences in gene expression were observed between upper-layer and deep-layer astrocytes. Importantly, layer-specific differences in astrocyte properties were abrogated in reeler and Dab1 conditional knockout mice, in which neuronal layers are disturbed, suggesting that neuronal layers are a prerequisite for the observed morphological and molecular differences of neocortical astrocytes. This study thus demonstrates the existence of layer-specific interactions between neurons and astrocytes, which may underlie their layer-specific functions.

  5. A system of automated processing of deep water hydrological information

    Science.gov (United States)

    Romantsov, V. A.; Dyubkin, I. A.; Klyukbin, L. N.

    1974-01-01

    An automated system for primary and scientific analysis of deep water hydrological information is presented. Primary processing of the data in this system is carried out on a drifting station, which also calculates the parameters of vertical stability of the sea layers, as well as their depths and altitudes. Methods of processing the raw data are described.

  6. Dilution limits dissolved organic carbon utilization in the deep ocean

    NARCIS (Netherlands)

    Arrieta, J.M.; Mayol, E.; Hansman, R.L.; Herndl, G.J.; Dittmar, T.; Duarte, C.M.

    2015-01-01

    Oceanic dissolved organic carbon (DOC) is the second largest reservoir of organic carbon in the biosphere. About 72% of the global DOC inventory is stored in deep oceanic layers for years to centuries, supporting the current view that it consists of materials resistant to microbial degradation. An

  7. Layering and Ordering in Electrochemical Double Layers

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Yihua [Materials Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States; Kawaguchi, Tomoya [Materials Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States; Pierce, Michael S. [Rochester Institute of Technology, School of Physics and Astronomy, Rochester, New York 14623, United States; Komanicky, Vladimir [Faculty of Science, Safarik University, 041 54 Kosice, Slovakia; You, Hoydoo [Materials Science Division, Argonne National Laboratory, Argonne, Illinois 60439, United States

    2018-02-26

    Electrochemical double layers (EDL) form at electrified interfaces. While Gouy-Chapman model describes moderately charged EDL, formation of Stern layers was predicted for highly charged EDL. Our results provide structural evidence for a Stern layer of cations, at potentials close to hydrogen evolution in alkali fluoride and chloride electrolytes. Layering was observed by x-ray crystal truncation rods and atomic-scale recoil responses of Pt(111) surface layers. Ordering in the layer is confirmed by glancing-incidence in-plane diffraction measurements.

  8. Unsupervised deep learning reveals prognostically relevant subtypes of glioblastoma.

    Science.gov (United States)

    Young, Jonathan D; Cai, Chunhui; Lu, Xinghua

    2017-10-03

    One approach to improving the personalized treatment of cancer is to understand the cellular signaling transduction pathways that cause cancer at the level of the individual patient. In this study, we used unsupervised deep learning to learn the hierarchical structure within cancer gene expression data. Deep learning is a group of machine learning algorithms that use multiple layers of hidden units to capture hierarchically related, alternative representations of the input data. We hypothesize that this hierarchical structure learned by deep learning will be related to the cellular signaling system. Robust deep learning model selection identified a network architecture that is biologically plausible. Our model selection results indicated that the 1st hidden layer of our deep learning model should contain about 1300 hidden units to most effectively capture the covariance structure of the input data. This agrees with the estimated number of human transcription factors, which is approximately 1400. This result lends support to our hypothesis that the 1st hidden layer of a deep learning model trained on gene expression data may represent signals related to transcription factor activation. Using the 3rd hidden layer representation of each tumor as learned by our unsupervised deep learning model, we performed consensus clustering on all tumor samples-leading to the discovery of clusters of glioblastoma multiforme with differential survival. One of these clusters contained all of the glioblastoma samples with G-CIMP, a known methylation phenotype driven by the IDH1 mutation and associated with favorable prognosis, suggesting that the hidden units in the 3rd hidden layer representations captured a methylation signal without explicitly using methylation data as input. We also found differentially expressed genes and well-known mutations (NF1, IDH1, EGFR) that were uniquely correlated with each of these clusters. Exploring these unique genes and mutations will allow us to

  9. Exploring orange peel treatment with deep eutectic solvents and diluted organic acids

    NARCIS (Netherlands)

    van den Bruinhorst, A.; Kouris, P.; Timmer, J.M.K.; de Croon, M.H.J.M.; Kroon, M.C.

    2016-01-01

    The disintegration of orange peel waste in deep eutectic solvents and diluted organic acids is presented in this work. The albedo and flavedo layers of the peel were studied separately, showing faster disintegration of the latter. Addition of water to the deep eutectic solvents lowered the amount of

  10. Low temperature sacrificial wafer bonding for planarization after very deep etching

    NARCIS (Netherlands)

    Spiering, V.L.; Spiering, V.L.; Berenschot, Johan W.; Elwenspoek, Michael Curt; Fluitman, J.H.J.

    1994-01-01

    A new technique, at temperatures of 150°C or 450°C, that provides planarization after a very deep etching step in silicon is presented. Resist spinning and layer patterning as well as realization of bridges or cantilevers across deep holes becomes possible. The sacrificial wafer bonding technique

  11. Silicon germanium mask for deep silicon etching

    KAUST Repository

    Serry, Mohamed

    2014-07-29

    Polycrystalline silicon germanium (SiGe) can offer excellent etch selectivity to silicon during cryogenic deep reactive ion etching in an SF.sub.6/O.sub.2 plasma. Etch selectivity of over 800:1 (Si:SiGe) may be achieved at etch temperatures from -80 degrees Celsius to -140 degrees Celsius. High aspect ratio structures with high resolution may be patterned into Si substrates using SiGe as a hard mask layer for construction of microelectromechanical systems (MEMS) devices and semiconductor devices.

  12. Deep Learning for Distribution Channels' Management

    Directory of Open Access Journals (Sweden)

    Sabina-Cristiana NECULA

    2017-01-01

    Full Text Available This paper presents an experiment of using deep learning models for distribution channel management. We present an approach that combines self-organizing maps with artificial neural network with multiple hidden layers in order to identify the potential sales that might be addressed for channel distribution change/ management. Our study aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. This paper will allow researchers to choose best suited techniques and features to prepare their churn prediction models.

  13. Silicon germanium mask for deep silicon etching

    KAUST Repository

    Serry, Mohamed; Rubin, Andrew; Refaat, Mohamed; Sedky, Sherif; Abdo, Mohammad

    2014-01-01

    Polycrystalline silicon germanium (SiGe) can offer excellent etch selectivity to silicon during cryogenic deep reactive ion etching in an SF.sub.6/O.sub.2 plasma. Etch selectivity of over 800:1 (Si:SiGe) may be achieved at etch temperatures from -80 degrees Celsius to -140 degrees Celsius. High aspect ratio structures with high resolution may be patterned into Si substrates using SiGe as a hard mask layer for construction of microelectromechanical systems (MEMS) devices and semiconductor devices.

  14. Bidirectional Nonnegative Deep Model and Its Optimization in Learning

    Directory of Open Access Journals (Sweden)

    Xianhua Zeng

    2016-01-01

    Full Text Available Nonnegative matrix factorization (NMF has been successfully applied in signal processing as a simple two-layer nonnegative neural network. Projective NMF (PNMF with fewer parameters was proposed, which projects a high-dimensional nonnegative data onto a lower-dimensional nonnegative subspace. Although PNMF overcomes the problem of out-of-sample of NMF, it does not consider the nonlinear characteristic of data and is only a kind of narrow signal decomposition method. In this paper, we combine the PNMF with deep learning and nonlinear fitting to propose a bidirectional nonnegative deep learning (BNDL model and its optimization learning algorithm, which can obtain nonlinear multilayer deep nonnegative feature representation. Experiments show that the proposed model can not only solve the problem of out-of-sample of NMF but also learn hierarchical nonnegative feature representations with better clustering performance than classical NMF, PNMF, and Deep Semi-NMF algorithms.

  15. Deep learning—Accelerating Next Generation Performance Analysis Systems?

    Directory of Open Access Journals (Sweden)

    Heike Brock

    2018-02-01

    Full Text Available Deep neural network architectures show superior performance in recognition and prediction tasks of the image, speech and natural language domains. The success of such multi-layered networks encourages their implementation in further application scenarios as the retrieval of relevant motion information for performance enhancement in sports. However, to date deep learning is only seldom applied to activity recognition problems of the human motion domain. Therefore, its use for sports data analysis might remain abstract to many practitioners. This paper provides a survey on recent works in the field of high-performance motion data and examines relevant technologies for subsequent deployment in real training systems. In particular, it discusses aspects of data acquisition, processing and network modeling. Analysis suggests the advantage of deep neural networks under difficult and noisy data conditions. However, further research is necessary to confirm the benefit of deep learning for next generation performance analysis systems.

  16. Dilution limits dissolved organic carbon utilization in the deep ocean

    KAUST Repository

    Arrieta, Jesus

    2015-03-19

    Oceanic dissolved organic carbon (DOC) is the second largest reservoir of organic carbon in the biosphere. About 72% of the global DOC inventory is stored in deep oceanic layers for years to centuries, supporting the current view that it consists of materials resistant to microbial degradation. An alternative hypothesis is that deep-water DOC consists of many different, intrinsically labile compounds at concentrations too low to compensate for the metabolic costs associated to their utilization. Here, we present experimental evidence showing that low concentrations rather than recalcitrance preclude consumption of a substantial fraction of DOC, leading to slow microbial growth in the deep ocean. These findings demonstrate an alternative mechanism for the long-term storage of labile DOC in the deep ocean, which has been hitherto largely ignored. © 2015, American Association for the Advancement of Science. All rights reserved.

  17. Dilution limits dissolved organic carbon utilization in the deep ocean

    KAUST Repository

    Arrieta, J M; Mayol, Eva; Hansman, Roberta L.; Herndl, Gerhard J.; Dittmar, Thorsten; Duarte, Carlos M.

    2015-01-01

    Oceanic dissolved organic carbon (DOC) is the second largest reservoir of organic carbon in the biosphere. About 72% of the global DOC inventory is stored in deep oceanic layers for years to centuries, supporting the current view that it consists of materials resistant to microbial degradation. An alternative hypothesis is that deep-water DOC consists of many different, intrinsically labile compounds at concentrations too low to compensate for the metabolic costs associated to their utilization. Here, we present experimental evidence showing that low concentrations rather than recalcitrance preclude consumption of a substantial fraction of DOC, leading to slow microbial growth in the deep ocean. These findings demonstrate an alternative mechanism for the long-term storage of labile DOC in the deep ocean, which has been hitherto largely ignored. © 2015, American Association for the Advancement of Science. All rights reserved.

  18. Plant Species Identification by Bi-channel Deep Convolutional Networks

    Science.gov (United States)

    He, Guiqing; Xia, Zhaoqiang; Zhang, Qiqi; Zhang, Haixi; Fan, Jianping

    2018-04-01

    Plant species identification achieves much attention recently as it has potential application in the environmental protection and human life. Although deep learning techniques can be directly applied for plant species identification, it still needs to be designed for this specific task to obtain the state-of-art performance. In this paper, a bi-channel deep learning framework is developed for identifying plant species. In the framework, two different sub-networks are fine-tuned over their pretrained models respectively. And then a stacking layer is used to fuse the output of two different sub-networks. We construct a plant dataset of Orchidaceae family for algorithm evaluation. Our experimental results have demonstrated that our bi-channel deep network can achieve very competitive performance on accuracy rates compared to the existing deep learning algorithm.

  19. Evaluation of vertical coordinate and vertical mixing algorithms in the HYbrid-Coordinate Ocean Model (HYCOM)

    Science.gov (United States)

    Halliwell, George R.

    Vertical coordinate and vertical mixing algorithms included in the HYbrid Coordinate Ocean Model (HYCOM) are evaluated in low-resolution climatological simulations of the Atlantic Ocean. The hybrid vertical coordinates are isopycnic in the deep ocean interior, but smoothly transition to level (pressure) coordinates near the ocean surface, to sigma coordinates in shallow water regions, and back again to level coordinates in very shallow water. By comparing simulations to climatology, the best model performance is realized using hybrid coordinates in conjunction with one of the three available differential vertical mixing models: the nonlocal K-Profile Parameterization, the NASA GISS level 2 turbulence closure, and the Mellor-Yamada level 2.5 turbulence closure. Good performance is also achieved using the quasi-slab Price-Weller-Pinkel dynamical instability model. Differences among these simulations are too small relative to other errors and biases to identify the "best" vertical mixing model for low-resolution climate simulations. Model performance deteriorates slightly when the Kraus-Turner slab mixed layer model is used with hybrid coordinates. This deterioration is smallest when solar radiation penetrates beneath the mixed layer and when shear instability mixing is included. A simulation performed using isopycnic coordinates to emulate the Miami Isopycnic Coordinate Ocean Model (MICOM), which uses Kraus-Turner mixing without penetrating shortwave radiation and shear instability mixing, demonstrates that the advantages of switching from isopycnic to hybrid coordinates and including more sophisticated turbulence closures outweigh the negative numerical effects of maintaining hybrid vertical coordinates.

  20. Deep inelastic scattering

    International Nuclear Information System (INIS)

    Zakharov, V.I.

    1977-01-01

    The present status of the quark-parton-gluon picture of deep inelastic scattering is reviewed. The general framework is mostly theoretical and covers investigations since 1970. Predictions of the parton model and of the asymptotically free field theories are compared with experimental data available. The valence quark approximation is concluded to be valid in most cases, but fails to account for the data on the total momentum transfer. On the basis of gluon corrections introduced to the parton model certain predictions concerning both the deep inelastic structure functions and form factors are made. The contributions of gluon exchanges and gluon bremsstrahlung are highlighted. Asymptotic freedom is concluded to be very attractive and provide qualitative explanation to some experimental observations (scaling violations, breaking of the Drell-Yan-West type relations). Lepton-nuclear scattering is pointed out to be helpful in probing the nature of nuclear forces and studying the space-time picture of the parton model

  1. Deep Energy Retrofit

    DEFF Research Database (Denmark)

    Zhivov, Alexander; Lohse, Rüdiger; Rose, Jørgen

    Deep Energy Retrofit – A Guide to Achieving Significant Energy User Reduction with Major Renovation Projects contains recommendations for characteristics of some of core technologies and measures that are based on studies conducted by national teams associated with the International Energy Agency...... Energy Conservation in Buildings and Communities Program (IEA-EBC) Annex 61 (Lohse et al. 2016, Case, et al. 2016, Rose et al. 2016, Yao, et al. 2016, Dake 2014, Stankevica et al. 2016, Kiatreungwattana 2014). Results of these studies provided a base for setting minimum requirements to the building...... envelope-related technologies to make Deep Energy Retrofit feasible and, in many situations, cost effective. Use of energy efficiency measures (EEMs) in addition to core technologies bundle and high-efficiency appliances will foster further energy use reduction. This Guide also provides best practice...

  2. Deep groundwater chemistry

    International Nuclear Information System (INIS)

    Wikberg, P.; Axelsen, K.; Fredlund, F.

    1987-06-01

    Starting in 1977 and up till now a number of places in Sweden have been investigated in order to collect the necessary geological, hydrogeological and chemical data needed for safety analyses of repositories in deep bedrock systems. Only crystalline rock is considered and in many cases this has been gneisses of sedimentary origin but granites and gabbros are also represented. Core drilled holes have been made at nine sites. Up to 15 holes may be core drilled at one site, the deepest down to 1000 m. In addition to this a number of boreholes are percussion drilled at each site to depths of about 100 m. When possible drilling water is taken from percussion drilled holes. The first objective is to survey the hydraulic conditions. Core drilled boreholes and sections selected for sampling of deep groundwater are summarized. (orig./HP)

  3. Deep Reinforcement Fuzzing

    OpenAIRE

    Böttinger, Konstantin; Godefroid, Patrice; Singh, Rishabh

    2018-01-01

    Fuzzing is the process of finding security vulnerabilities in input-processing code by repeatedly testing the code with modified inputs. In this paper, we formalize fuzzing as a reinforcement learning problem using the concept of Markov decision processes. This in turn allows us to apply state-of-the-art deep Q-learning algorithms that optimize rewards, which we define from runtime properties of the program under test. By observing the rewards caused by mutating with a specific set of actions...

  4. Laminar recordings in frontal cortex suggest distinct layers for maintenance and control of working memory.

    Science.gov (United States)

    Bastos, André M; Loonis, Roman; Kornblith, Simon; Lundqvist, Mikael; Miller, Earl K

    2018-01-30

    All of the cerebral cortex has some degree of laminar organization. These different layers are composed of neurons with distinct connectivity patterns, embryonic origins, and molecular profiles. There are little data on the laminar specificity of cognitive functions in the frontal cortex, however. We recorded neuronal spiking/local field potentials (LFPs) using laminar probes in the frontal cortex (PMd, 8A, 8B, SMA/ACC, DLPFC, and VLPFC) of monkeys performing working memory (WM) tasks. LFP power in the gamma band (50-250 Hz) was strongest in superficial layers, and LFP power in the alpha/beta band (4-22 Hz) was strongest in deep layers. Memory delay activity, including spiking and stimulus-specific gamma bursting, was predominately in superficial layers. LFPs from superficial and deep layers were synchronized in the alpha/beta bands. This was primarily unidirectional, with alpha/beta bands in deep layers driving superficial layer activity. The phase of deep layer alpha/beta modulated superficial gamma bursting associated with WM encoding. Thus, alpha/beta rhythms in deep layers may regulate the superficial layer gamma bands and hence maintenance of the contents of WM. Copyright © 2018 the Author(s). Published by PNAS.

  5. An improved advertising CTR prediction approach based on the fuzzy deep neural network.

    Science.gov (United States)

    Jiang, Zilong; Gao, Shu; Li, Mingjiang

    2018-01-01

    Combining a deep neural network with fuzzy theory, this paper proposes an advertising click-through rate (CTR) prediction approach based on a fuzzy deep neural network (FDNN). In this approach, fuzzy Gaussian-Bernoulli restricted Boltzmann machine (FGBRBM) is first applied to input raw data from advertising datasets. Next, fuzzy restricted Boltzmann machine (FRBM) is used to construct the fuzzy deep belief network (FDBN) with the unsupervised method layer by layer. Finally, fuzzy logistic regression (FLR) is utilized for modeling the CTR. The experimental results show that the proposed FDNN model outperforms several baseline models in terms of both data representation capability and robustness in advertising click log datasets with noise.

  6. Image quality assessment using deep convolutional networks

    Science.gov (United States)

    Li, Yezhou; Ye, Xiang; Li, Yong

    2017-12-01

    This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes.

  7. pDeep: Predicting MS/MS Spectra of Peptides with Deep Learning.

    Science.gov (United States)

    Zhou, Xie-Xuan; Zeng, Wen-Feng; Chi, Hao; Luo, Chunjie; Liu, Chao; Zhan, Jianfeng; He, Si-Min; Zhang, Zhifei

    2017-12-05

    In tandem mass spectrometry (MS/MS)-based proteomics, search engines rely on comparison between an experimental MS/MS spectrum and the theoretical spectra of the candidate peptides. Hence, accurate prediction of the theoretical spectra of peptides appears to be particularly important. Here, we present pDeep, a deep neural network-based model for the spectrum prediction of peptides. Using the bidirectional long short-term memory (BiLSTM), pDeep can predict higher-energy collisional dissociation, electron-transfer dissociation, and electron-transfer and higher-energy collision dissociation MS/MS spectra of peptides with >0.9 median Pearson correlation coefficients. Further, we showed that intermediate layer of the neural network could reveal physicochemical properties of amino acids, for example the similarities of fragmentation behaviors between amino acids. We also showed the potential of pDeep to distinguish extremely similar peptides (peptides that contain isobaric amino acids, for example, GG = N, AG = Q, or even I = L), which were very difficult to distinguish using traditional search engines.

  8. Shakeout: A New Approach to Regularized Deep Neural Network Training.

    Science.gov (United States)

    Kang, Guoliang; Li, Jun; Tao, Dacheng

    2018-05-01

    Recent years have witnessed the success of deep neural networks in dealing with a plenty of practical problems. Dropout has played an essential role in many successful deep neural networks, by inducing regularization in the model training. In this paper, we present a new regularized training approach: Shakeout. Instead of randomly discarding units as Dropout does at the training stage, Shakeout randomly chooses to enhance or reverse each unit's contribution to the next layer. This minor modification of Dropout has the statistical trait: the regularizer induced by Shakeout adaptively combines , and regularization terms. Our classification experiments with representative deep architectures on image datasets MNIST, CIFAR-10 and ImageNet show that Shakeout deals with over-fitting effectively and outperforms Dropout. We empirically demonstrate that Shakeout leads to sparser weights under both unsupervised and supervised settings. Shakeout also leads to the grouping effect of the input units in a layer. Considering the weights in reflecting the importance of connections, Shakeout is superior to Dropout, which is valuable for the deep model compression. Moreover, we demonstrate that Shakeout can effectively reduce the instability of the training process of the deep architecture.

  9. Deep-level transient spectroscopy of low-energy ion-irradiated silicon

    DEFF Research Database (Denmark)

    Kolkovsky, Vladimir; Privitera, V.; Nylandsted Larsen, Arne

    2009-01-01

     During electron-gun deposition of metal layers on semiconductors, the semiconductor is bombarded with low-energy metal ions creating defects in the outermost surface layer. For many years, it has been a puzzle why deep-level transient spectroscopy spectra of the as-deposited, electron-gun evapor...

  10. Deep Red (Profondo Rosso)

    CERN Multimedia

    Cine Club

    2015-01-01

    Wednesday 29 April 2015 at 20:00 CERN Council Chamber    Deep Red (Profondo Rosso) Directed by Dario Argento (Italy, 1975) 126 minutes A psychic who can read minds picks up the thoughts of a murderer in the audience and soon becomes a victim. An English pianist gets involved in solving the murders, but finds many of his avenues of inquiry cut off by new murders, and he begins to wonder how the murderer can track his movements so closely. Original version Italian; English subtitles

  11. Reversible deep disposal

    International Nuclear Information System (INIS)

    2009-10-01

    This presentation, given by the national agency of radioactive waste management (ANDRA) at the meeting of October 8, 2009 of the high committee for the nuclear safety transparency and information (HCTISN), describes the concept of deep reversible disposal for high level/long living radioactive wastes, as considered by the ANDRA in the framework of the program law of June 28, 2006 about the sustainable management of radioactive materials and wastes. The document presents the social and political reasons of reversibility, the technical means considered (containers, disposal cavities, monitoring system, test facilities and industrial prototypes), the decisional process (progressive development and blocked off of the facility, public information and debate). (J.S.)

  12. Deep inelastic neutron scattering

    International Nuclear Information System (INIS)

    Mayers, J.

    1989-03-01

    The report is based on an invited talk given at a conference on ''Neutron Scattering at ISIS: Recent Highlights in Condensed Matter Research'', which was held in Rome, 1988, and is intended as an introduction to the techniques of Deep Inelastic Neutron Scattering. The subject is discussed under the following topic headings:- the impulse approximation I.A., scaling behaviour, kinematical consequences of energy and momentum conservation, examples of measurements, derivation of the I.A., the I.A. in a harmonic system, and validity of the I.A. in neutron scattering. (U.K.)

  13. [Deep mycoses rarely described].

    Science.gov (United States)

    Charles, D

    1986-01-01

    Beside deep mycoses very well known: histoplasmosis, candidosis, cryptococcosis, there are other mycoses less frequently described. Some of them are endemic in some countries: South American blastomycosis in Brazil, coccidioidomycosis in California; some others are cosmopolitan and may affect everyone: sporotrichosis, or may affect only immunodeficient persons: mucormycosis. They do not spare Africa, we may encounter basidiobolomycosis, rhinophycomycosis, dermatophytosis, sporotrichosis and, more recently reported, rhinosporidiosis. Important therapeutic progresses have been accomplished with amphotericin B and with antifungus imidazole compounds (miconazole and ketoconazole). Surgical intervention is sometime recommended in chromomycosis and rhinosporidiosis.

  14. Deep penetration calculations

    International Nuclear Information System (INIS)

    Thompson, W.L.; Deutsch, O.L.; Booth, T.E.

    1980-04-01

    Several Monte Carlo techniques are compared in the transport of neutrons of different source energies through two different deep-penetration problems each with two parts. The first problem involves transmission through a 200-cm concrete slab. The second problem is a 90 0 bent pipe jacketed by concrete. In one case the pipe is void, and in the other it is filled with liquid sodium. Calculations are made with two different Los Alamos Monte Carlo codes: the continuous-energy code MCNP and the multigroup code MCMG

  15. Classification of Exacerbation Frequency in the COPDGene Cohort Using Deep Learning with Deep Belief Networks.

    Science.gov (United States)

    Ying, Jun; Dutta, Joyita; Guo, Ning; Hu, Chenhui; Zhou, Dan; Sitek, Arkadiusz; Li, Quanzheng

    2016-12-21

    This study aims to develop an automatic classifier based on deep learning for exacerbation frequency in patients with chronic obstructive pulmonary disease (COPD). A threelayer deep belief network (DBN) with two hidden layers and one visible layer was employed to develop classification models and the models' robustness to exacerbation was analyzed. Subjects from the COPDGene cohort were labeled with exacerbation frequency, defined as the number of exacerbation events per year. 10,300 subjects with 361 features each were included in the analysis. After feature selection and parameter optimization, the proposed classification method achieved an accuracy of 91.99%, using a 10-fold cross validation experiment. The analysis of DBN weights showed that there was a good visual spatial relationship between the underlying critical features of different layers. Our findings show that the most sensitive features obtained from the DBN weights are consistent with the consensus showed by clinical rules and standards for COPD diagnostics. We thus demonstrate that DBN is a competitive tool for exacerbation risk assessment for patients suffering from COPD.

  16. Deep Structures of The Angola Margin

    Science.gov (United States)

    Moulin, M.; Contrucci, I.; Olivet, J.-L.; Aslanian, D.; Géli, L.; Sibuet, J.-C.

    1 Ifremer Centre de Brest, DRO/Géosciences Marines, B.P. 70, 29280 Plouzané cedex (France) mmoulin@ifremer.fr/Fax : 33 2 98 22 45 49 2 Université de Bretagne Occidentale, Institut Universitaire Europeen de la Mer, Place Nicolas Copernic, 29280 Plouzane (France) 3 Total Fina Elf, DGEP/GSR/PN -GEOLOGIE, 2,place de la Coupole-La Defense 6, 92078 Paris la Defense Cedex Deep reflection and refraction seismic data were collected in April 2000 on the West African margin, offshore Angola, within the framework of the Zaiango Joint Project, conducted by Ifremer and Total Fina Elf Production. Vertical multichannel reflection seismic data generated by a « single-bubble » air gun array array (Avedik et al., 1993) were recorded on a 4.5 km long, digital streamer, while refraction and wide angle reflection seismic data were acquired on OBSs (Ocean Bottom Seismometers). Despite the complexity of the margin (5 s TWT of sediment, salt tectonics), the combination of seismic reflection and refraction methods results in an image and a velocity model of the ground structures below the Aptian salt layer. Three large seismic units appear in the reflection seismic section from the deep part on the margin under the base of salt. The upper seismic unit is layered with reflectors parallel to the base of the salt ; it represents unstructured sediments, filling a basin. The middle unit is seismically transparent. The lower unit is characterized by highly energetic reflectors. According to the OBS refraction data, these two units correspond to the continental crust and the base of the high energetic unit corresponds to the Moho. The margin appears to be divided in 3 domains, from east to west : i) a domain with an unthinned, 30 km thick, continental crust ; ii) a domain located between the hinge line and the foot of the continental slope, where the crust thins sharply, from 30 km to less than 7 km, this domain is underlain by an anormal layer with velocities comprising between 7,2 and 7

  17. Deep Convolutional Neural Networks: Structure, Feature Extraction and Training

    Directory of Open Access Journals (Sweden)

    Namatēvs Ivars

    2017-12-01

    Full Text Available Deep convolutional neural networks (CNNs are aimed at processing data that have a known network like topology. They are widely used to recognise objects in images and diagnose patterns in time series data as well as in sensor data classification. The aim of the paper is to present theoretical and practical aspects of deep CNNs in terms of convolution operation, typical layers and basic methods to be used for training and learning. Some practical applications are included for signal and image classification. Finally, the present paper describes the proposed block structure of CNN for classifying crucial features from 3D sensor data.

  18. Deep Learning for Plant Identification in Natural Environment.

    Science.gov (United States)

    Sun, Yu; Liu, Yuan; Wang, Guan; Zhang, Haiyan

    2017-01-01

    Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry.

  19. Deep Learning for Plant Identification in Natural Environment

    Directory of Open Access Journals (Sweden)

    Yu Sun

    2017-01-01

    Full Text Available Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry.

  20. Shear Strengthening of RC Deep Beam Using Externally Bonded GFRP Fabrics

    Science.gov (United States)

    Kumari, A.; Patel, S. S.; Nayak, A. N.

    2018-06-01

    This work presents the experimental investigation of RC deep beams wrapped with externally bonded Glass Fibre Reinforced Polymer (GFRP) fabrics in order to study the Load versus deflection behavior, cracking pattern, failure modes and ultimate shear strength. A total number of five deep beams have been casted, which is designed with conventional steel reinforcement as per IS: 456 (Indian standard plain and reinforced concrete—code for practice, Bureau of Indian Standards, New Delhi, 2000). The spans to depth ratio for all RC deep beams have been kept less than 2 as per the above specification. Out of five RC deep beams, one without retrofitting serves as a reference beam and the rest four have been wrapped with GFRP fabrics in multiple layers and tested with two point loading condition. The first cracking load, ultimate load and the shear contribution of GFRP to the deep beams have been observed. A critical discussion is made with respect to the enhancement of the strength, behaviour and performance of retrofitted deep beams in comparison to the deep beam without GFRP in order to explore the potential use of GFRP for strengthening the RC deep beams. Test results have demonstrated that the deep beams retrofitted with GFRP shows a slower development of the diagonal cracks and improves shear carrying capacity of the RC deep beam. A comparative study of the experimental results with the theoretical ones predicted by various researchers available in the literatures has also been presented. It is observed that the ultimate load of the beams retrofitted with GFRP fabrics increases with increase of number of GFRP layers up to a specific number of layers, i.e. 3 layers, beyond which it decreases.

  1. Deep SOMs for automated feature extraction and classification from big data streaming

    Science.gov (United States)

    Sakkari, Mohamed; Ejbali, Ridha; Zaied, Mourad

    2017-03-01

    In this paper, we proposed a deep self-organizing map model (Deep-SOMs) for automated features extracting and learning from big data streaming which we benefit from the framework Spark for real time streams and highly parallel data processing. The SOMs deep architecture is based on the notion of abstraction (patterns automatically extract from the raw data, from the less to more abstract). The proposed model consists of three hidden self-organizing layers, an input and an output layer. Each layer is made up of a multitude of SOMs, each map only focusing at local headmistress sub-region from the input image. Then, each layer trains the local information to generate more overall information in the higher layer. The proposed Deep-SOMs model is unique in terms of the layers architecture, the SOMs sampling method and learning. During the learning stage we use a set of unsupervised SOMs for feature extraction. We validate the effectiveness of our approach on large data sets such as Leukemia dataset and SRBCT. Results of comparison have shown that the Deep-SOMs model performs better than many existing algorithms for images classification.

  2. Processing of chromatic information in a deep convolutional neural network.

    Science.gov (United States)

    Flachot, Alban; Gegenfurtner, Karl R

    2018-04-01

    Deep convolutional neural networks are a class of machine-learning algorithms capable of solving non-trivial tasks, such as object recognition, with human-like performance. Little is known about the exact computations that deep neural networks learn, and to what extent these computations are similar to the ones performed by the primate brain. Here, we investigate how color information is processed in the different layers of the AlexNet deep neural network, originally trained on object classification of over 1.2M images of objects in their natural contexts. We found that the color-responsive units in the first layer of AlexNet learned linear features and were broadly tuned to two directions in color space, analogously to what is known of color responsive cells in the primate thalamus. Moreover, these directions are decorrelated and lead to statistically efficient representations, similar to the cardinal directions of the second-stage color mechanisms in primates. We also found, in analogy to the early stages of the primate visual system, that chromatic and achromatic information were segregated in the early layers of the network. Units in the higher layers of AlexNet exhibit on average a lower responsivity for color than units at earlier stages.

  3. Deep sea biophysics

    International Nuclear Information System (INIS)

    Yayanos, A.A.

    1982-01-01

    A collection of deep-sea bacterial cultures was completed. Procedures were instituted to shelter the culture collection from accidential warming. A substantial data base on the rates of reproduction of more than 100 strains of bacteria from that collection was obtained from experiments and the analysis of that data was begun. The data on the rates of reproduction were obtained under conditions of temperature and pressure found in the deep sea. The experiments were facilitated by inexpensively fabricated pressure vessels, by the streamlining of the methods for the study of kinetics at high pressures, and by computer-assisted methods. A polybarothermostat was used to study the growth of bacteria along temperature gradients at eight distinct pressures. This device should allow for the study of microbial processes in the temperature field simulating the environment around buried HLW. It is small enough to allow placement in a radiation field in future studies. A flow fluorocytometer was fabricated. This device will be used to determine the DNA content per cell in bacteria grown in laboratory culture and in microorganisms in samples from the ocean. The technique will be tested for its rapidity in determining the concentration of cells (standing stock of microorganisms) in samples from the ocean

  4. Deep Learning in Radiology.

    Science.gov (United States)

    McBee, Morgan P; Awan, Omer A; Colucci, Andrew T; Ghobadi, Comeron W; Kadom, Nadja; Kansagra, Akash P; Tridandapani, Srini; Auffermann, William F

    2018-03-29

    As radiology is inherently a data-driven specialty, it is especially conducive to utilizing data processing techniques. One such technique, deep learning (DL), has become a remarkably powerful tool for image processing in recent years. In this work, the Association of University Radiologists Radiology Research Alliance Task Force on Deep Learning provides an overview of DL for the radiologist. This article aims to present an overview of DL in a manner that is understandable to radiologists; to examine past, present, and future applications; as well as to evaluate how radiologists may benefit from this remarkable new tool. We describe several areas within radiology in which DL techniques are having the most significant impact: lesion or disease detection, classification, quantification, and segmentation. The legal and ethical hurdles to implementation are also discussed. By taking advantage of this powerful tool, radiologists can become increasingly more accurate in their interpretations with fewer errors and spend more time to focus on patient care. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

  5. Deep Learning Microscopy

    KAUST Repository

    Rivenson, Yair

    2017-05-12

    We demonstrate that a deep neural network can significantly improve optical microscopy, enhancing its spatial resolution over a large field-of-view and depth-of-field. After its training, the only input to this network is an image acquired using a regular optical microscope, without any changes to its design. We blindly tested this deep learning approach using various tissue samples that are imaged with low-resolution and wide-field systems, where the network rapidly outputs an image with remarkably better resolution, matching the performance of higher numerical aperture lenses, also significantly surpassing their limited field-of-view and depth-of-field. These results are transformative for various fields that use microscopy tools, including e.g., life sciences, where optical microscopy is considered as one of the most widely used and deployed techniques. Beyond such applications, our presented approach is broadly applicable to other imaging modalities, also spanning different parts of the electromagnetic spectrum, and can be used to design computational imagers that get better and better as they continue to image specimen and establish new transformations among different modes of imaging.

  6. Comet Dust After Deep Impact

    Science.gov (United States)

    Wooden, Diane H.; Harker, David E.; Woodward, Charles E.

    2006-01-01

    a more diverse mineralogy. The lower spatial resolution, high sensitivity Spitzer IRS data reveal resonances of refractory minerals (those seen by GEMINI+Michelle plus ortho-pyroxene)) as well resonances that can be attributed to phillosilicates (layer lattice silicates such as Montmorillonite) (Lisse et al. 2006). Pre- and post-impact, micron to submicron grains were deciphered to be present in the coma by the modeling the high spatial resolution images to account for nucleus plus inner coma fluxes (Wooden et al. 2005, 2006; Harker et al. 2005, 2006a). Note also that crystalline silicates were released from the interior of 73P-B/SW-3 as it disintegrated (Harker et al. 2006b). From the Deep Impact and the disintegration of 73P-B, we are led to ask the questians: Why is the mineralogy of the dust released from a volatile-rich pocket beneath the surface different from the dust that is released from the nominally active areas? Could the most volatile pockets be exhausted quickly? Why would crystalline silicates be associated with more volatile materials? Perhaps the structure of the comet is so inhomogeneous, e.g., the layered pile mode2 of the nucleus (Belton et al. 2006), that a reservoir of crystalline silicate and submicron grains just happens to not be released by the nominally active areas of comet 9P? Perhaps comets lose matter through their mantles from below their surfaces, thus preserving ancient topographic structures and radiation damaged silicates and carbon? We will discuss and ponder different scenarios. We will discuss future directions for coordinated observations of JF comets.

  7. Deep Learning- and Transfer Learning-Based Super Resolution Reconstruction from Single Medical Image

    Directory of Open Access Journals (Sweden)

    YiNan Zhang

    2017-01-01

    Full Text Available Medical images play an important role in medical diagnosis and research. In this paper, a transfer learning- and deep learning-based super resolution reconstruction method is introduced. The proposed method contains one bicubic interpolation template layer and two convolutional layers. The bicubic interpolation template layer is prefixed by mathematics deduction, and two convolutional layers learn from training samples. For saving training medical images, a SIFT feature-based transfer learning method is proposed. Not only can medical images be used to train the proposed method, but also other types of images can be added into training dataset selectively. In empirical experiments, results of eight distinctive medical images show improvement of image quality and time reduction. Further, the proposed method also produces slightly sharper edges than other deep learning approaches in less time and it is projected that the hybrid architecture of prefixed template layer and unfixed hidden layers has potentials in other applications.

  8. The biomass of the deep-sea benthopelagic plankton

    Science.gov (United States)

    Wishner, K. F.

    1980-04-01

    Deep-sea benthopelagic plankton samples were collected with a specially designed opening-closing net system 10 to 100 m above the bottom in five different oceanic regions at depths from 1000 to 4700 m. Benthopelagic plankton biomasses decrease exponentially with depth. At 1000 m the biomass is about 1% that of the surface zooplankton, at 5000 m about 0.1%. Effects of differences in surface primary productivity on deep-sea plankton biomass are much less than the effect of depth and are detectable only in a few comparisons of extreme oceanic regions. The biomass at 10 m above the bottom is greater than that at 100 m above the bottom (in a three-sample comparison), which could be a consequence of an enriched near-bottom environment. The deep-sea plankton biomass in the Red Sea is anomalously low. This may be due to increased decomposition rates in the warm (22°C) deep Red Sea water, which prevent much detritus from reaching the deep sea. A model of organic carbon utilization in the benthic boundary layer (bottom 100 m), incorporating results from deep-sea sediment trap and respiration studies, indicates that the benthopelagic plankton use only a small amount of the organic carbon flux. A large fraction of the flux is unaccounted for by present estimates of benthic and benthopelagic respiration.

  9. Deep Reinforcement Learning: An Overview

    OpenAIRE

    Li, Yuxi

    2017-01-01

    We give an overview of recent exciting achievements of deep reinforcement learning (RL). We discuss six core elements, six important mechanisms, and twelve applications. We start with background of machine learning, deep learning and reinforcement learning. Next we discuss core RL elements, including value function, in particular, Deep Q-Network (DQN), policy, reward, model, planning, and exploration. After that, we discuss important mechanisms for RL, including attention and memory, unsuperv...

  10. Deep Feature Consistent Variational Autoencoder

    OpenAIRE

    Hou, Xianxu; Shen, Linlin; Sun, Ke; Qiu, Guoping

    2016-01-01

    We present a novel method for constructing Variational Autoencoder (VAE). Instead of using pixel-by-pixel loss, we enforce deep feature consistency between the input and the output of a VAE, which ensures the VAE's output to preserve the spatial correlation characteristics of the input, thus leading the output to have a more natural visual appearance and better perceptual quality. Based on recent deep learning works such as style transfer, we employ a pre-trained deep convolutional neural net...

  11. Deep learning for image classification

    Science.gov (United States)

    McCoppin, Ryan; Rizki, Mateen

    2014-06-01

    This paper provides an overview of deep learning and introduces the several subfields of deep learning including a specific tutorial of convolutional neural networks. Traditional methods for learning image features are compared to deep learning techniques. In addition, we present our preliminary classification results, our basic implementation of a convolutional restricted Boltzmann machine on the Mixed National Institute of Standards and Technology database (MNIST), and we explain how to use deep learning networks to assist in our development of a robust gender classification system.

  12. Deep Neural Yodelling

    OpenAIRE

    Pfäffli, Daniel (Autor/in)

    2018-01-01

    Yodel music differs from most other genres by exercising the transition from chest voice to falsetto with an audible glottal stop which is recognised even by laymen. Yodel often consists of a yodeller with a choir accompaniment. In Switzerland, it is differentiated between the natural yodel and yodel songs. Today's approaches to music generation with machine learning algorithms are based on neural networks, which are best described by stacked layers of neurons which are connected with neurons...

  13. Deep learning? What deep learning? | Fourie | South African ...

    African Journals Online (AJOL)

    In teaching generally over the past twenty years, there has been a move towards teaching methods that encourage deep, rather than surface approaches to learning. The reason for this being that students, who adopt a deep approach to learning are considered to have learning outcomes of a better quality and desirability ...

  14. On the renewal of the denitrifying layer in the Arabian Sea

    Digital Repository Service at National Institute of Oceanography (India)

    Somasundar, K.; Naqvi, S.W.A.

    A one-dimensional (vertical) advection-diffusion model has been applied to the deep layer characterized by a linear potential temperature(theta)-salinity relationship in the Arabian Sea to estimate the velocity of ascending motion. The results...

  15. Deep sea radionuclides

    International Nuclear Information System (INIS)

    Kanisch, G.; Vobach, M.

    1993-01-01

    Every year since 1979, either in sping or in summer, the fishing research vessel 'Walther Herwig' goes to the North Atlantic disposal areas of solid radioactive wastes, and, for comparative purposes, to other areas, in order to collect water samples, plankton and nekton, and, from the deep sea bed, sediment samples and benthos organisms. In addition to data on the radionuclide contents of various media, information about the plankton, nekton and benthos organisms living in those areas and about their biomasses could be gathered. The investigations are aimed at acquiring scientifically founded knowledge of the uptake of radioactive substances by microorganisms, and their migration from the sea bottom to the areas used by man. (orig.) [de

  16. Deep inelastic phenomena

    International Nuclear Information System (INIS)

    Aubert, J.J.

    1982-01-01

    The experimental situation of the deep inelastic scattering for electrons (muons) is reviewed. A brief history of experimentation highlights Mohr and Nicoll's 1932 experiment on electron-atom scattering and Hofstadter's 1950 experiment on electron-nucleus scattering. The phenomenology of electron-nucleon scattering carried out between 1960 and 1970 is described, with emphasis on the parton model, and scaling. Experiments at SLAC and FNAL since 1974 exhibit scaling violations. Three muon-nucleon scattering experiments at BFP, BCDMA, and EMA, currently producing new results in the high Q 2 domain suggest a rather flat behaviour of the structure function at fixed x as a function of Q 2 . It is seen that the structure measured in DIS can then be projected into a pure hadronic process to predict a cross section. Protonneutron difference, moment analysis, and Drell-Yan pairs are also considered

  17. Deep-well injection of radioactive waste in Russia

    International Nuclear Information System (INIS)

    Hoek, J.

    1998-01-01

    In the Russian federation, deep borehole injection of liquid radioactive waste has been established practice since at least 1963. The liquid is injected into sandy or other formations with high porosity, which are isolated by water-tight layers. This technique has also been used elsewhere for toxic liquid waste and residues from mining operations. Deep-well injection of radioactive waste is not currently used in any of the European Commission (EC) countries. In this paper the results of a EC-funded study were presented. The study is entitled 'Measurements, modelling of migration and possible radiological consequences at deep well injection sites for liquid radioactive waste in Russia', COSU-CT94-0099-UK. The study was carried out jointly by AEA Technology, CAG and the Research Institute for Nuclear Reactors (NIIAR) at Dimitrovgrad. Many scientists have contributed to the results reported here. The aims of the study are: Provision of extensive information on the deep-well injection repositories and their use in the former Soviet Union; Provision of a methodology to assess safety aspects of deep-well injection of liquid radioactive waste in deep geological formations; This will allow evaluation of proposals to use deep-well injection techniques in other regions; Support for Russian regulatory bodies through evaluation of the suitability of the sites, including estimates of the maximum amount of waste that can be safely stored in them; and Provision of a methodology to assess the use of deep-well injection repositories as an alternative disposal technique for EC countries. 7 refs

  18. Relative Humidity in the Tropopause Saturation Layer

    Science.gov (United States)

    Selkirk, H. B.; Schoeberl, M. R.; Pfister, L.; Thornberry, T. D.; Bui, T. V.

    2017-12-01

    The tropical tropopause separates two very different atmospheric regimes: the stable lower stratosphere where the air is both extremely dry and nearly always so, and a transition layer in the uppermost tropical troposphere, where humidity on average increases rapidly downward but can undergo substantial temporal fluctuations. The processes that control the humidity in this layer below the tropopause include convective detrainment (which can result in either a net hydration or dehydration), slow ascent, wave motions and advection. Together these determine the humidity of the air that eventually passes through the tropopause and into the stratosphere, and we refer to this layer as the tropopause saturation layer or TSL. We know from in situ water vapor observations such as Ticosonde's 12-year balloonsonde record at Costa Rica that layers of supersaturation are frequently observed in the TSL. While their frequency is greatest during the local rainy season from June through October, supersaturation is also observed in the boreal winter dry season when deep convection is well south of Costa Rica. In other words, local convection is not a necessary condition for the presence of supersaturation. Furthermore, there are indications from airborne measurements during the recent POSIDON campaign at Guam that if anything deep convection tends to `reset' the TSL locally to a state of just-saturation. Conversely, it may be that layers of supersaturation are the result of slow ascent. To explore these ideas we take Ticosonde water vapor observations from the TSL, stratify them on the basis of relative humidity and report on the differences in the the history of upstream convective influence between supersaturated parcels and those that are not.

  19. Detection of Pitting in Gears Using a Deep Sparse Autoencoder

    Directory of Open Access Journals (Sweden)

    Yongzhi Qu

    2017-05-01

    Full Text Available In this paper; a new method for gear pitting fault detection is presented. The presented method is developed based on a deep sparse autoencoder. The method integrates dictionary learning in sparse coding into a stacked autoencoder network. Sparse coding with dictionary learning is viewed as an adaptive feature extraction method for machinery fault diagnosis. An autoencoder is an unsupervised machine learning technique. A stacked autoencoder network with multiple hidden layers is considered to be a deep learning network. The presented method uses a stacked autoencoder network to perform the dictionary learning in sparse coding and extract features from raw vibration data automatically. These features are then used to perform gear pitting fault detection. The presented method is validated with vibration data collected from gear tests with pitting faults in a gearbox test rig and compared with an existing deep learning-based approach.

  20. Context and Deep Learning Design

    Science.gov (United States)

    Boyle, Tom; Ravenscroft, Andrew

    2012-01-01

    Conceptual clarification is essential if we are to establish a stable and deep discipline of technology enhanced learning. The technology is alluring; this can distract from deep design in a surface rush to exploit the affordances of the new technology. We need a basis for design, and a conceptual unit of organization, that are applicable across…

  1. Exploring Ocean Animal Trajectory Pattern via Deep Learning

    KAUST Repository

    Wang, Su

    2016-01-01

    We trained a combined deep convolutional neural network to predict seals’ age (3 categories) and gender (2 categories). The entire dataset contains 110 seals with around 489 thousand location records. Most records are continuous and measured in a certain step. We created five convolutional layers for feature representation and established two fully connected structure as age’s and gender’s classifier, respectively. Each classifier consists of three fully connected layers. Treating seals’ latitude and longitude as input, entire deep learning network, which includes 780,000 neurons and 2,097,000 parameters, can reach to 70.72% accuracy rate for predicting seals’ age and simultaneously achieve 79.95% for gender estimation.

  2. Realization of Chinese word segmentation based on deep learning method

    Science.gov (United States)

    Wang, Xuefei; Wang, Mingjiang; Zhang, Qiquan

    2017-08-01

    In recent years, with the rapid development of deep learning, it has been widely used in the field of natural language processing. In this paper, I use the method of deep learning to achieve Chinese word segmentation, with large-scale corpus, eliminating the need to construct additional manual characteristics. In the process of Chinese word segmentation, the first step is to deal with the corpus, use word2vec to get word embedding of the corpus, each character is 50. After the word is embedded, the word embedding feature is fed to the bidirectional LSTM, add a linear layer to the hidden layer of the output, and then add a CRF to get the model implemented in this paper. Experimental results show that the method used in the 2014 People's Daily corpus to achieve a satisfactory accuracy.

  3. Deep-Learning-Based Approach for Prediction of Algal Blooms

    Directory of Open Access Journals (Sweden)

    Feng Zhang

    2016-10-01

    Full Text Available Algal blooms have recently become a critical global environmental concern which might put economic development and sustainability at risk. However, the accurate prediction of algal blooms remains a challenging scientific problem. In this study, a novel prediction approach for algal blooms based on deep learning is presented—a powerful tool to represent and predict highly dynamic and complex phenomena. The proposed approach constructs a five-layered model to extract detailed relationships between the density of phytoplankton cells and various environmental parameters. The algal blooms can be predicted by the phytoplankton density obtained from the output layer. A case study is conducted in coastal waters of East China using both our model and a traditional back-propagation neural network for comparison. The results show that the deep-learning-based model yields better generalization and greater accuracy in predicting algal blooms than a traditional shallow neural network does.

  4. Exploring Ocean Animal Trajectory Pattern via Deep Learning

    KAUST Repository

    Wang, Su

    2016-05-23

    We trained a combined deep convolutional neural network to predict seals’ age (3 categories) and gender (2 categories). The entire dataset contains 110 seals with around 489 thousand location records. Most records are continuous and measured in a certain step. We created five convolutional layers for feature representation and established two fully connected structure as age’s and gender’s classifier, respectively. Each classifier consists of three fully connected layers. Treating seals’ latitude and longitude as input, entire deep learning network, which includes 780,000 neurons and 2,097,000 parameters, can reach to 70.72% accuracy rate for predicting seals’ age and simultaneously achieve 79.95% for gender estimation.

  5. Deep Joint Rain Detection and Removal from a Single Image

    OpenAIRE

    Yang, Wenhan; Tan, Robby T.; Feng, Jiashi; Liu, Jiaying; Guo, Zongming; Yan, Shuicheng

    2016-01-01

    In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in the new rain image models and a novel deep learning architecture. We first modify an existing model comprising a rain streak layer and a background layer, by adding a binary map that locates rain streak regions. Second, we create a new model consisting of a component representing rain streak accumulation (where individual streaks cannot b...

  6. VSWI Wetlands Advisory Layer

    Data.gov (United States)

    Vermont Center for Geographic Information — This dataset represents the DEC Wetlands Program's Advisory layer. This layer makes the most up-to-date, non-jurisdictional, wetlands mapping avaiable to the public...

  7. Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

    OpenAIRE

    Sugiarto Indar; Pasila Felix

    2018-01-01

    Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning. Conceptually, it relies on a many-layer network that exhibits a hierarchically non-linear processing capability. Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence. However, many researchers are still scept...

  8. Assumed Probability Density Functions for Shallow and Deep Convection

    OpenAIRE

    Steven K Krueger; Peter A Bogenschutz; Marat Khairoutdinov

    2010-01-01

    The assumed joint probability density function (PDF) between vertical velocity and conserved temperature and total water scalars has been suggested to be a relatively computationally inexpensive and unified subgrid-scale (SGS) parameterization for boundary layer clouds and turbulent moments. This paper analyzes the performance of five families of PDFs using large-eddy simulations of deep convection, shallow convection, and a transition from stratocumulus to trade wind cumulus. Three of the PD...

  9. Application of deep convolutional neural networks for ocean front recognition

    Science.gov (United States)

    Lima, Estanislau; Sun, Xin; Yang, Yuting; Dong, Junyu

    2017-10-01

    Ocean fronts have been a subject of study for many years, a variety of methods and algorithms have been proposed to address the problem of ocean fronts. However, all these existing ocean front recognition methods are built upon human expertise in defining the front based on subjective thresholds of relevant physical variables. This paper proposes a deep learning approach for ocean front recognition that is able to automatically recognize the front. We first investigated four existing deep architectures, i.e., AlexNet, CaffeNet, GoogLeNet, and VGGNet, for the ocean front recognition task using remote sensing (RS) data. We then propose a deep network with fewer layers compared to existing architecture for the front recognition task. This network has a total of five learnable layers. In addition, we extended the proposed network to recognize and classify the front into strong and weak ones. We evaluated and analyzed the proposed network with two strategies of exploiting the deep model: full-training and fine-tuning. Experiments are conducted on three different RS image datasets, which have different properties. Experimental results show that our model can produce accurate recognition results.

  10. Electronic structure properties of deep defects in hBN

    Science.gov (United States)

    Dev, Pratibha; Prdm Collaboration

    In recent years, the search for room-temperature solid-state qubit (quantum bit) candidates has revived interest in the study of deep-defect centers in semiconductors. The charged NV-center in diamond is the best known amongst these defects. However, as a host material, diamond poses several challenges and so, increasingly, there is an interest in exploring deep defects in alternative semiconductors such as hBN. The layered structure of hBN makes it a scalable platform for quantum applications, as there is a greater potential for controlling the location of the deep defect in the 2D-matrix through careful experiments. Using density functional theory-based methods, we have studied the electronic and structural properties of several deep defects in hBN. Native defects within hBN layers are shown to have high spin ground states that should survive even at room temperature, making them interesting solid-state qubit candidates in a 2D matrix. Partnership for Reduced Dimensional Material (PRDM) is part of the NSF sponsored Partnerships for Research and Education in Materials (PREM).

  11. Layer-by-layer cell membrane assembly

    Science.gov (United States)

    Matosevic, Sandro; Paegel, Brian M.

    2013-11-01

    Eukaryotic subcellular membrane systems, such as the nuclear envelope or endoplasmic reticulum, present a rich array of architecturally and compositionally complex supramolecular targets that are as yet inaccessible. Here we describe layer-by-layer phospholipid membrane assembly on microfluidic droplets, a route to structures with defined compositional asymmetry and lamellarity. Starting with phospholipid-stabilized water-in-oil droplets trapped in a static droplet array, lipid monolayer deposition proceeds as oil/water-phase boundaries pass over the droplets. Unilamellar vesicles assembled layer-by-layer support functional insertion both of purified and of in situ expressed membrane proteins. Synthesis and chemical probing of asymmetric unilamellar and double-bilayer vesicles demonstrate the programmability of both membrane lamellarity and lipid-leaflet composition during assembly. The immobilized vesicle arrays are a pragmatic experimental platform for biophysical studies of membranes and their associated proteins, particularly complexes that assemble and function in multilamellar contexts in vivo.

  12. Factors governing the deep ventilation of the Red Sea

    KAUST Repository

    Papadopoulos, Vassilis P.

    2015-11-19

    A variety of data based on hydrographic measurements, satellite observations, reanalysis databases, and meteorological observations are used to explore the interannual variability and factors governing the deep water formation in the northern Red Sea. Historical and recent hydrographic data consistently indicate that the ventilation of the near-bottom layer in the Red Sea is a robust feature of the thermohaline circulation. Dense water capable to reach the bottom layers of the Red Sea can be regularly produced mostly inside the Gulfs of Aqaba and Suez. Occasionally, during colder than usual winters, deep water formation may also take place over coastal areas in the northernmost end of the open Red Sea just outside the Gulfs of Aqaba and Suez. However, the origin as well as the amount of deep waters exhibit considerable interannual variability depending not only on atmospheric forcing but also on the water circulation over the northern Red Sea. Analysis of several recent winters shows that the strength of the cyclonic gyre prevailing in the northernmost part of the basin can effectively influence the sea surface temperature (SST) and intensify or moderate the winter surface cooling. Upwelling associated with periods of persistent gyre circulation lowers the SST over the northernmost part of the Red Sea and can produce colder than normal winter SST even without extreme heat loss by the sea surface. In addition, the occasional persistence of the cyclonic gyre feeds the surface layers of the northern Red Sea with nutrients, considerably increasing the phytoplankton biomass.

  13. Comparison of frailty of primary neurons, embryonic, and aging mouse cortical layers.

    Science.gov (United States)

    Fugistier, Patrick; Vallet, Philippe G; Leuba, Geneviève; Piotton, Françoise; Marin, Pascale; Bouras, Constantin; Savioz, Armand

    2014-02-01

    Superficial layers I to III of the human cerebral cortex are more vulnerable toward Aβ peptides than deep layers V to VI in aging. Three models of layers were used to investigate this pattern of frailty. First, primary neurons from E14 and E17 embryonic murine cortices, corresponding respectively to future deep and superficial layers, were treated either with Aβ(1-42), okadaic acid, or kainic acid. Second, whole E14 and E17 embryonic cortices, and third, in vitro separated deep and superficial layers of young and old C57BL/6J mice, were treated identically. We observed that E14 and E17 neurons in culture were prone to death after the Aβ and particularly the kainic acid treatment. This was also the case for the superficial layers of the aged cortex, but not for the embryonic, the young cortex, and the deep layers of the aged cortex. Thus, the aged superficial layers appeared to be preferentially vulnerable against Aβ and kainic acid. This pattern of vulnerability corresponds to enhanced accumulation of senile plaques in the superficial cortical layers with aging and Alzheimer's disease. Copyright © 2014 Elsevier Inc. All rights reserved.

  14. A Flat World with Deep Fractures

    Directory of Open Access Journals (Sweden)

    Emil Constantinescu

    2016-10-01

    Full Text Available The Internet manages to connect different parts of the world, defies geographical distances and gives the impression that our planet is flat, but the Internet is there only for the ones who have the possibility and the ability to use it. Our contemporary flat world has deep transversal fractures which, like in many geological structures, make a direct connection between layers with different characteristics. The elites are moving across information avenues with targets set in the future; at the same time, in many parts of our planet, there are people organizing their lives in pre-modern agrarian cycles. Diversity in ways of living and in social organization is a sign of human freedom, not a sign of error, so, having different alternatives to achieving prosperity and happiness should be good news. Holding dear to a society’s lifestyle should not push for the destruction of societies with different sets of values.

  15. Deep hierarchical attention network for video description

    Science.gov (United States)

    Li, Shuohao; Tang, Min; Zhang, Jun

    2018-03-01

    Pairing video to natural language description remains a challenge in computer vision and machine translation. Inspired by image description, which uses an encoder-decoder model for reducing visual scene into a single sentence, we propose a deep hierarchical attention network for video description. The proposed model uses convolutional neural network (CNN) and bidirectional LSTM network as encoders while a hierarchical attention network is used as the decoder. Compared to encoder-decoder models used in video description, the bidirectional LSTM network can capture the temporal structure among video frames. Moreover, the hierarchical attention network has an advantage over single-layer attention network on global context modeling. To make a fair comparison with other methods, we evaluate the proposed architecture with different types of CNN structures and decoders. Experimental results on the standard datasets show that our model has a more superior performance than the state-of-the-art techniques.

  16. Deep Learning Improves Antimicrobial Peptide Recognition.

    Science.gov (United States)

    Veltri, Daniel; Kamath, Uday; Shehu, Amarda

    2018-03-24

    Bacterial resistance to antibiotics is a growing concern. Antimicrobial peptides (AMPs), natural components of innate immunity, are popular targets for developing new drugs. Machine learning methods are now commonly adopted by wet-laboratory researchers to screen for promising candidates. In this work we utilize deep learning to recognize antimicrobial activity. We propose a neural network model with convolutional and recurrent layers that leverage primary sequence composition. Results show that the proposed model outperforms state-of-the-art classification models on a comprehensive data set. By utilizing the embedding weights, we also present a reduced-alphabet representation and show that reasonable AMP recognition can be maintained using nine amino-acid types. Models and data sets are made freely available through the Antimicrobial Peptide Scanner vr.2 web server at: www.ampscanner.com. amarda@gmu.edu for general inquiries and dan.veltri@gmail.com for web server information. Supplementary data are available at Bioinformatics online.

  17. Deep Learning Fluid Mechanics

    Science.gov (United States)

    Barati Farimani, Amir; Gomes, Joseph; Pande, Vijay

    2017-11-01

    We have developed a new data-driven model paradigm for the rapid inference and solution of the constitutive equations of fluid mechanic by deep learning models. Using generative adversarial networks (GAN), we train models for the direct generation of solutions to steady state heat conduction and incompressible fluid flow without knowledge of the underlying governing equations. Rather than using artificial neural networks to approximate the solution of the constitutive equations, GANs can directly generate the solutions to these equations conditional upon an arbitrary set of boundary conditions. Both models predict temperature, velocity and pressure fields with great test accuracy (>99.5%). The application of our framework for inferring and generating the solutions of partial differential equations can be applied to any physical phenomena and can be used to learn directly from experiments where the underlying physical model is complex or unknown. We also have shown that our framework can be used to couple multiple physics simultaneously, making it amenable to tackle multi-physics problems.

  18. Deep video deblurring

    KAUST Repository

    Su, Shuochen

    2016-11-25

    Motion blur from camera shake is a major problem in videos captured by hand-held devices. Unlike single-image deblurring, video-based approaches can take advantage of the abundant information that exists across neighboring frames. As a result the best performing methods rely on aligning nearby frames. However, aligning images is a computationally expensive and fragile procedure, and methods that aggregate information must therefore be able to identify which regions have been accurately aligned and which have not, a task which requires high level scene understanding. In this work, we introduce a deep learning solution to video deblurring, where a CNN is trained end-to-end to learn how to accumulate information across frames. To train this network, we collected a dataset of real videos recorded with a high framerate camera, which we use to generate synthetic motion blur for supervision. We show that the features learned from this dataset extend to deblurring motion blur that arises due to camera shake in a wide range of videos, and compare the quality of results to a number of other baselines.

  19. Deep space telescopes

    CERN Multimedia

    CERN. Geneva

    2006-01-01

    The short series of seminars will address results and aims of current and future space astrophysics as the cultural framework for the development of deep space telescopes. It will then present such new tools, as they are currently available to, or imagined by, the scientific community, in the context of the science plans of ESA and of all major world space agencies. Ground-based astronomy, in the 400 years since Galileo’s telescope, has given us a profound phenomenological comprehension of our Universe, but has traditionally been limited to the narrow band(s) to which our terrestrial atmosphere is transparent. Celestial objects, however, do not care about our limitations, and distribute most of the information about their physics throughout the complete electromagnetic spectrum. Such information is there for the taking, from millimiter wavelengths to gamma rays. Forty years astronomy from space, covering now most of the e.m. spectrum, have thus given us a better understanding of our physical Universe then t...

  20. Deep inelastic final states

    International Nuclear Information System (INIS)

    Girardi, G.

    1980-11-01

    In these lectures we attempt to describe the final states of deep inelastic scattering as given by QCD. In the first section we shall briefly comment on the parton model and give the main properties of decay functions which are of interest for the study of semi-inclusive leptoproduction. The second section is devoted to the QCD approach to single hadron leptoproduction. First we recall basic facts on QCD log's and derive after that the evolution equations for the fragmentation functions. For this purpose we make a short detour in e + e - annihilation. The rest of the section is a study of the factorization of long distance effects associated with the initial and final states. We then show how when one includes next to leading QCD corrections one induces factorization breaking and describe the double moments useful for testing such effects. The next section contains a review on the QCD jets in the hadronic final state. We begin by introducing the notion of infrared safe variable and defining a few useful examples. Distributions in these variables are studied to first order in QCD, with some comments on the resummation of logs encountered in higher orders. Finally the last section is a 'gaullimaufry' of jet studies

  1. Double layers in space

    International Nuclear Information System (INIS)

    Carlqvist, P.

    1982-07-01

    For more than a decade it has been realised that electrostatic double layers are likely to occur in space. We briefly discuss the theoretical background of such double layers. Most of the paper is devoted to an account of the observational evidence for double layers in the ionosphere and magnetosphere of the Earth. Several different experiments are reviewed including rocket and satellite measurements and ground based observations. It is concluded that the observational evidence for double layers in space is very strong. The experimental results indicate that double layers with widely different properties may exist in space. (Author)

  2. Double layers in space

    International Nuclear Information System (INIS)

    Carlqvist, P.

    1982-01-01

    For more than a decade it has been realised that electrostatic double layers are likely to occur in space. The author briefly discusses the theoretical background of such double layers. Most of the paper is devoted to an account of the observational evidence for double layers in the ionosphere and magnetosphere of the Earth. Several different experiments are reviewed including rocket and satellite measurements and ground based observations. It is concluded that the observational evidence for double layers in space is very strong. The experimental results indicate that double layers with widely different properties may exist in space. (Auth.)

  3. Deep lake water cooling a renewable technology

    Energy Technology Data Exchange (ETDEWEB)

    Eliadis, C.

    2003-06-01

    In the face of increasing electrical demand for air conditioning, the damage to the ozone layer by CFCs used in conventional chillers, and efforts to reduce the greenhouse gases emitted into the atmosphere by coal-fired power generating stations more and more attention is focused on developing alternative strategies for sustainable energy. This article describes one such strategy, namely deep lake water cooling, of which the Enwave project recently completed on the north shore of Lake Ontario is a prime example. The Enwave Deep Lake Water Cooling (DLWC) project is a joint undertaking by Enwave and the City of Toronto. The $180 million project is unique in design and concept, using the coldness of the lake water from the depths of Lake Ontario (not the water itself) to provide environmentally friendly air conditioning to office towers. Concurrently, the system also provides improved quality raw cold water to the city's potable water supply. The plant has a rated capacity of 52,200 tons of refrigeration. The DLWC project is estimated to save 75-90 per cent of the electricity that would have been generated by a coal-fired power station. Enwave, established over 20 years ago, is North America's largest district energy system, delivering steam, hot water and chilled water to buildings from a central plant via an underground piping distribution network. 2 figs.

  4. Matrix completion by deep matrix factorization.

    Science.gov (United States)

    Fan, Jicong; Cheng, Jieyu

    2018-02-01

    Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

  5. Deep Mapping and Spatial Anthropology

    Directory of Open Access Journals (Sweden)

    Les Roberts

    2016-01-01

    Full Text Available This paper provides an introduction to the Humanities Special Issue on “Deep Mapping”. It sets out the rationale for the collection and explores the broad-ranging nature of perspectives and practices that fall within the “undisciplined” interdisciplinary domain of spatial humanities. Sketching a cross-current of ideas that have begun to coalesce around the concept of “deep mapping”, the paper argues that rather than attempting to outline a set of defining characteristics and “deep” cartographic features, a more instructive approach is to pay closer attention to the multivalent ways deep mapping is performatively put to work. Casting a critical and reflexive gaze over the developing discourse of deep mapping, it is argued that what deep mapping “is” cannot be reduced to the otherwise a-spatial and a-temporal fixity of the “deep map”. In this respect, as an undisciplined survey of this increasing expansive field of study and practice, the paper explores the ways in which deep mapping can engage broader discussion around questions of spatial anthropology.

  6. DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

    Science.gov (United States)

    Chen, Liang-Chieh; Papandreou, George; Kokkinos, Iasonas; Murphy, Kevin; Yuille, Alan L

    2018-04-01

    In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks. It also allows us to effectively enlarge the field of view of filters to incorporate larger context without increasing the number of parameters or the amount of computation. Second, we propose atrous spatial pyramid pooling (ASPP) to robustly segment objects at multiple scales. ASPP probes an incoming convolutional feature layer with filters at multiple sampling rates and effective fields-of-views, thus capturing objects as well as image context at multiple scales. Third, we improve the localization of object boundaries by combining methods from DCNNs and probabilistic graphical models. The commonly deployed combination of max-pooling and downsampling in DCNNs achieves invariance but has a toll on localization accuracy. We overcome this by combining the responses at the final DCNN layer with a fully connected Conditional Random Field (CRF), which is shown both qualitatively and quantitatively to improve localization performance. Our proposed "DeepLab" system sets the new state-of-art at the PASCAL VOC-2012 semantic image segmentation task, reaching 79.7 percent mIOU in the test set, and advances the results on three other datasets: PASCAL-Context, PASCAL-Person-Part, and Cityscapes. All of our code is made publicly available online.

  7. Deep learning for computational chemistry.

    Science.gov (United States)

    Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

    2017-06-15

    The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  8. Deep learning for computational chemistry

    Energy Technology Data Exchange (ETDEWEB)

    Goh, Garrett B. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Hodas, Nathan O. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Vishnu, Abhinav [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354

    2017-03-08

    The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.

  9. DeepSimulator: a deep simulator for Nanopore sequencing

    KAUST Repository

    Li, Yu; Han, Renmin; Bi, Chongwei; Li, Mo; Wang, Sheng; Gao, Xin

    2017-01-01

    or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments

  10. Flood frequency matters: Why climate change degrades deep-water quality of peri-alpine lakes

    Science.gov (United States)

    Fink, Gabriel; Wessels, Martin; Wüest, Alfred

    2016-09-01

    Sediment-laden riverine floods transport large quantities of dissolved oxygen into the receiving deep layers of lakes. Hence, the water quality of deep lakes is strongly influenced by the frequency of riverine floods. Although flood frequency reflects climate conditions, the effects of climate variability on the water quality of deep lakes is largely unknown. We quantified the effects of climate variability on the potential shifts in the flood regime of the Alpine Rhine, the main catchment of Lake Constance, and determined the intrusion depths of riverine density-driven underflows and the subsequent effects on water exchange rates in the lake. A simplified hydrodynamic underflow model was developed and validated with observed river inflow and underflow events. The model was implemented to estimate underflow statistics for different river inflow scenarios. Using this approach, we integrated present and possible future flood frequencies to underflow occurrences and intrusion depths in Lake Constance. The results indicate that more floods will increase the number of underflows and the intensity of deep-water renewal - and consequently will cause higher deep-water dissolved oxygen concentrations. Vice versa, fewer floods weaken deep-water renewal and lead to lower deep-water dissolved oxygen concentrations. Meanwhile, a change from glacial nival regime (present) to a nival pluvial regime (future) is expected to decrease deep-water renewal. While flood frequencies are not expected to change noticeably for the next decades, it is most likely that increased winter discharge and decreased summer discharge will reduce the number of deep density-driven underflows by 10% and favour shallower riverine interflows in the upper hypolimnion. The renewal in the deepest layers is expected to be reduced by nearly 27%. This study underlines potential consequences of climate change on the occurrence of deep river underflows and water residence times in deep lakes.

  11. Microbially induced corrosion of carbon steel in deep groundwater environment

    Directory of Open Access Journals (Sweden)

    Pauliina eRajala

    2015-07-01

    Full Text Available The metallic low and intermediate level radioactive waste generally consists of carbon steel and stainless steels. The corrosion rate of carbon steel in deep groundwater is typically low, unless the water is very acidic or microbial activity in the environment is high. Therefore, the assessment of microbially induced corrosion of carbon steel in deep bedrock environment has become important for evaluating the safety of disposal of radioactive waste. Here we studied the corrosion inducing ability of indigenous microbial community from a deep bedrock aquifer. Carbon steel coupons were exposed to anoxic groundwater from repository site 100 m depth (Olkiluoto, Finland for periods of three and eight months. The experiments were conducted at both in situ temperature and room temperature to investigate the response of microbial population to elevated temperature. Our results demonstrate that microorganisms from the deep bedrock aquifer benefit from carbon steel introduced to the nutrient poor anoxic deep groundwater environment. In the groundwater incubated with carbon steel the planktonic microbial community was more diverse and 100-fold more abundant compared to the environment without carbon steel. The betaproteobacteria were the most dominant bacterial class in all samples where carbon steel was present, whereas in groundwater incubated without carbon steel the microbial community had clearly less diversity. Microorganisms induced pitting corrosion and were found to cluster inside the corrosion pits. Temperature had an effect on the species composition of microbial community and also affected the corrosion deposits layer formed on the surface of carbon steel.

  12. Deep and intermediate mediterranean water in the western Alboran Sea

    Science.gov (United States)

    Parrilla, Gregorio; Kinder, Thomas H.; Preller, Ruth H.

    1986-01-01

    Hydrographic and current meter data, obtained during June to October 1982, and numerical model experiments are used to study the distribution and flow of Mediterranean waters in the western Alboran Sea. The Intermediate Water is more pronounced in the northern three-fourths of the sea, but its distribution is patchy as manifested by variability of the temperature and salinity maxima at scales ≤10 km. Current meters in the lower Intermediate Water showed mean flow toward the Strait at 2 cm s -1. A reversal of this flow lasted about 2 weeks. A rough estimate of the mean westward Intermediate Water transport was 0.4 × 10 6 m 3 s -1, about one-third of the total outflow, so that the best estimates of the contributions of traditionally defined Intermediate Water and Deep Water account for only about one-half of the total outflow. The Deep Water was uplifted against the southern continental slope from Alboran Island (3°W) to the Strait. There was also a similar but much weaker banking against the Spanish slope, but a deep current record showed that the eastward recirculation implied by this banking is probably intermittent. Two-layer numerical model experiments simulated the Intermediate Water flow with a flat bottom and the Deep Water with realistic bottom topography. Both experiments replicated the major circulation features, and the Intermediate Water flow was concentrated in the north because of rotation and the Deep Water flow in the south because of topographic control.

  13. Active semi-supervised learning method with hybrid deep belief networks.

    Science.gov (United States)

    Zhou, Shusen; Chen, Qingcai; Wang, Xiaolong

    2014-01-01

    In this paper, we develop a novel semi-supervised learning algorithm called active hybrid deep belief networks (AHD), to address the semi-supervised sentiment classification problem with deep learning. First, we construct the previous several hidden layers using restricted Boltzmann machines (RBM), which can reduce the dimension and abstract the information of the reviews quickly. Second, we construct the following hidden layers using convolutional restricted Boltzmann machines (CRBM), which can abstract the information of reviews effectively. Third, the constructed deep architecture is fine-tuned by gradient-descent based supervised learning with an exponential loss function. Finally, active learning method is combined based on the proposed deep architecture. We did several experiments on five sentiment classification datasets, and show that AHD is competitive with previous semi-supervised learning algorithm. Experiments are also conducted to verify the effectiveness of our proposed method with different number of labeled reviews and unlabeled reviews respectively.

  14. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  15. Breast cancer molecular subtype classification using deep features: preliminary results

    Science.gov (United States)

    Zhu, Zhe; Albadawy, Ehab; Saha, Ashirbani; Zhang, Jun; Harowicz, Michael R.; Mazurowski, Maciej A.

    2018-02-01

    Radiogenomics is a field of investigation that attempts to examine the relationship between imaging characteris- tics of cancerous lesions and their genomic composition. This could offer a noninvasive alternative to establishing genomic characteristics of tumors and aid cancer treatment planning. While deep learning has shown its supe- riority in many detection and classification tasks, breast cancer radiogenomic data suffers from a very limited number of training examples, which renders the training of the neural network for this problem directly and with no pretraining a very difficult task. In this study, we investigated an alternative deep learning approach referred to as deep features or off-the-shelf network approach to classify breast cancer molecular subtypes using breast dynamic contrast enhanced MRIs. We used the feature maps of different convolution layers and fully connected layers as features and trained support vector machines using these features for prediction. For the feature maps that have multiple layers, max-pooling was performed along each channel. We focused on distinguishing the Luminal A subtype from other subtypes. To evaluate the models, 10 fold cross-validation was performed and the final AUC was obtained by averaging the performance of all the folds. The highest average AUC obtained was 0.64 (0.95 CI: 0.57-0.71), using the feature maps of the last fully connected layer. This indicates the promise of using this approach to predict the breast cancer molecular subtypes. Since the best performance appears in the last fully connected layer, it also implies that breast cancer molecular subtypes may relate to high level image features

  16. Opportunities and obstacles for deep learning in biology and medicine

    Science.gov (United States)

    2018-01-01

    Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems—patient classification, fundamental biological processes and treatment of patients—and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine. PMID:29618526

  17. Opportunities and obstacles for deep learning in biology and medicine.

    Science.gov (United States)

    Ching, Travers; Himmelstein, Daniel S; Beaulieu-Jones, Brett K; Kalinin, Alexandr A; Do, Brian T; Way, Gregory P; Ferrero, Enrico; Agapow, Paul-Michael; Zietz, Michael; Hoffman, Michael M; Xie, Wei; Rosen, Gail L; Lengerich, Benjamin J; Israeli, Johnny; Lanchantin, Jack; Woloszynek, Stephen; Carpenter, Anne E; Shrikumar, Avanti; Xu, Jinbo; Cofer, Evan M; Lavender, Christopher A; Turaga, Srinivas C; Alexandari, Amr M; Lu, Zhiyong; Harris, David J; DeCaprio, Dave; Qi, Yanjun; Kundaje, Anshul; Peng, Yifan; Wiley, Laura K; Segler, Marwin H S; Boca, Simina M; Swamidass, S Joshua; Huang, Austin; Gitter, Anthony; Greene, Casey S

    2018-04-01

    Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine. © 2018 The Authors.

  18. Deep UV LEDs

    Science.gov (United States)

    Han, Jung; Amano, Hiroshi; Schowalter, Leo

    2014-06-01

    Deep ultraviolet (DUV) photons interact strongly with a broad range of chemical and biological molecules; compact DUV light sources could enable a wide range of applications in chemi/bio-sensing, sterilization, agriculture, and industrial curing. The much shorter wavelength also results in useful characteristics related to optical diffraction (for lithography) and scattering (non-line-of-sight communication). The family of III-N (AlGaInN) compound semiconductors offers a tunable energy gap from infrared to DUV. While InGaN-based blue light emitters have been the primary focus for the obvious application of solid state lighting, there is a growing interest in the development of efficient UV and DUV light-emitting devices. In the past few years we have witnessed an increasing investment from both government and industry sectors to further the state of DUV light-emitting devices. The contributions in Semiconductor Science and Technology 's special issue on DUV devices provide an up-to-date snapshot covering many relevant topics in this field. Given the expected importance of bulk AlN substrate in DUV technology, we are pleased to include a review article by Hartmann et al on the growth of AlN bulk crystal by physical vapour transport. The issue of polarization field within the deep ultraviolet LEDs is examined in the article by Braut et al. Several commercial companies provide useful updates in their development of DUV emitters, including Nichia (Fujioka et al ), Nitride Semiconductors (Muramoto et al ) and Sensor Electronic Technology (Shatalov et al ). We believe these articles will provide an excellent overview of the state of technology. The growth of AlGaN heterostructures by molecular beam epitaxy, in contrast to the common organo-metallic vapour phase epitaxy, is discussed by Ivanov et al. Since hexagonal boron nitride (BN) has received much attention as both a UV and a two-dimensional electronic material, we believe it serves readers well to include the

  19. DEEP INFILTRATING ENDOMETRIOSIS

    Directory of Open Access Journals (Sweden)

    Martina Ribič-Pucelj

    2018-02-01

    Full Text Available Background: Endometriosis is not considered a unified disease, but a disease encompassing three differ- ent forms differentiated by aetiology and pathogenesis: peritoneal endometriosis, ovarian endometriosis and deep infiltrating endometriosis (DIE. The disease is classified as DIE when the lesions penetrate 5 mm or more into the retroperitoneal space. The estimated incidence of endometriosis in women of reproductive age ranges from 10–15 % and that of DIE from 3–10 %, the highest being in infertile women and in those with chronic pelvic pain. The leading symptoms of DIE are chronic pelvic pain which increases with age and correlates with the depth of infiltration and infertility. The most important diagnostic procedures are patient’s history and proper gynecological examination. The diagnosis is confirmed with laparoscopy. DIE can affect, beside reproductive organs, also bowel, bladder and ureters, therefore adi- tional diagnostic procedures must be performed preopertively to confirm or to exclude the involvement of the mentioned organs. Endometriosis is hormon dependent disease, there- fore several hormonal treatment regims are used to supress estrogen production but the symptoms recurr soon after caesation of the treatment. At the moment, surgical treatment with excision of all lesions, including those of bowel, bladder and ureters, is the method of choice but requires frequently interdisciplinary approach. Surgical treatment significantly reduces pain and improves fertility in inferile patients. Conclusions: DIE is not a rare form of endometriosis characterized by chronic pelvic pain and infertility. Medical treatment is not efficient. The method of choice is surgical treatment with excision of all lesions. It significantly reduces pelvic pain and enables high spontaneus and IVF preg- nacy rates.Therefore such patients should be treated at centres with experience in treatment of DIE and with possibility of interdisciplinary approach.

  20. Adapted ECC ozonesonde for long-duration flights aboard boundary-layer pressurised balloons

    Science.gov (United States)

    Gheusi, François; Durand, Pierre; Verdier, Nicolas; Dulac, François; Attié, Jean-Luc; Commun, Philippe; Barret, Brice; Basdevant, Claude; Clenet, Antoine; Derrien, Solène; Doerenbecher, Alexis; El Amraoui, Laaziz; Fontaine, Alain; Hache, Emeric; Jambert, Corinne; Jaumouillé, Elodie; Meyerfeld, Yves; Roblou, Laurent; Tocquer, Flore

    2016-12-01

    Since the 1970s, the French space agency CNES has developed boundary-layer pressurised balloons (BLPBs) with the capability to transport lightweight scientific payloads at isopycnic level and offer a quasi-Lagrangian sampling of the lower atmosphere over very long distances and durations (up to several weeks).Electrochemical concentration cell (ECC) ozonesondes are widely used under small sounding balloons. However, their autonomy is limited to a few hours owing to power consumption and electrolyte evaporation. An adaptation of the ECC sonde has been developed specifically for long-duration BLPB flights. Compared to conventional ECC sondes, the main feature is the possibility of programming periodic measurement sequences (with possible remote control during the flight). To increase the ozonesonde autonomy, the strategy has been adopted of short measurement sequences (2-3 min) regularly spaced in time (e.g. every 15 min). The rest of the time, the sonde pump is turned off. Results of preliminary ground-based tests are first presented. In particular, the sonde was able to provide correct ozone concentrations against a reference UV-absorption ozone analyser every 15 min for 4 days. Then we illustrate results from 16 BLBP flights launched over the western Mediterranean during three summer field campaigns of the ChArMEx project (http://charmex.lsce.ipsl.fr): TRAQA in 2012, and ADRIMED and SAFMED in 2013. BLPB drifting altitudes were in the range 0.25-3.2 km. The longest flight lasted more than 32 h and covered more than 1000 km. Satisfactory data were obtained when compared to independent ozone measurements close in space and time. The quasi-Lagrangian measurements allowed a first look at ozone diurnal evolution in the marine boundary layer as well as in the lower free troposphere. During some flight segments, there was indication of photochemical ozone production in the marine boundary layer or even in the free troposphere, at rates ranging from 1 to 2 ppbv h -1, which

  1. Modeling Language and Cognition with Deep Unsupervised Learning:A Tutorial Overview

    OpenAIRE

    Marco eZorzi; Marco eZorzi; Alberto eTestolin; Ivilin Peev Stoianov; Ivilin Peev Stoianov

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cog...

  2. Modeling language and cognition with deep unsupervised learning: a tutorial overview

    OpenAIRE

    Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P.

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cog...

  3. Model-free prediction of noisy chaotic time series by deep learning

    OpenAIRE

    Yeo, Kyongmin

    2017-01-01

    We present a deep neural network for a model-free prediction of a chaotic dynamical system from noisy observations. The proposed deep learning model aims to predict the conditional probability distribution of a state variable. The Long Short-Term Memory network (LSTM) is employed to model the nonlinear dynamics and a softmax layer is used to approximate a probability distribution. The LSTM model is trained by minimizing a regularized cross-entropy function. The LSTM model is validated against...

  4. Integrating deep and shallow natural language processing components : representations and hybrid architectures

    OpenAIRE

    Schäfer, Ulrich

    2006-01-01

    We describe basic concepts and software architectures for the integration of shallow and deep (linguistics-based, semantics-oriented) natural language processing (NLP) components. The main goal of this novel, hybrid integration paradigm is improving robustness of deep processing. After an introduction to constraint-based natural language parsing, we give an overview of typical shallow processing tasks. We introduce XML standoff markup as an additional abstraction layer that eases integration ...

  5. Adaptive Learning Rule for Hardware-based Deep Neural Networks Using Electronic Synapse Devices

    OpenAIRE

    Lim, Suhwan; Bae, Jong-Ho; Eum, Jai-Ho; Lee, Sungtae; Kim, Chul-Heung; Kwon, Dongseok; Park, Byung-Gook; Lee, Jong-Ho

    2017-01-01

    In this paper, we propose a learning rule based on a back-propagation (BP) algorithm that can be applied to a hardware-based deep neural network (HW-DNN) using electronic devices that exhibit discrete and limited conductance characteristics. This adaptive learning rule, which enables forward, backward propagation, as well as weight updates in hardware, is helpful during the implementation of power-efficient and high-speed deep neural networks. In simulations using a three-layer perceptron net...

  6. The circulation of deep water in the Tasman and Coral seas

    International Nuclear Information System (INIS)

    Harries, J.R.

    1976-07-01

    The physical oceanography of the Tasman and Coral Seas is reviewed with an emphasis on the deep currents. There are many uncertainties in the deep circulation pattern. The available data are used to develop an idealised circulation to estimate the likely path taken by water flowing from a depth of 5000 m in the Tasman Sea. The model suggests that the water would finally reach the surface layers south of the Antarctic Convergence with a median delay of 600 years. (author)

  7. Telepresence for Deep Space Missions

    Data.gov (United States)

    National Aeronautics and Space Administration — Incorporating telepresence technologies into deep space mission operations can give the crew and ground personnel the impression that they are in a location at time...

  8. Hybrid mask for deep etching

    KAUST Repository

    Ghoneim, Mohamed T.

    2017-01-01

    Deep reactive ion etching is essential for creating high aspect ratio micro-structures for microelectromechanical systems, sensors and actuators, and emerging flexible electronics. A novel hybrid dual soft/hard mask bilayer may be deposited during

  9. Diffusive boundary layers over varying topography

    KAUST Repository

    Dell, R. W.

    2015-03-25

    Diffusive bottom boundary layers can produce upslope flows in a stratified fluid. Accumulating observations suggest that these boundary layers may drive upwelling and mixing in mid-ocean ridge flank canyons. However, most studies of diffusive bottom boundary layers to date have concentrated on constant bottom slopes. We present a study of how diffusive boundary layers interact with various idealized topography, such as changes in bottom slope, slopes with corrugations and isolated sills. We use linear theory and numerical simulations in the regional ocean modeling system (ROMS) model to show changes in bottom slope can cause convergences and divergences within the boundary layer, in turn causing fluid exchanges that reach far into the overlying fluid and alter stratification far from the bottom. We also identify several different regimes of boundary-layer behaviour for topography with oceanographically relevant size and shape, including reversing flows and overflows, and we develop a simple theory that predicts the regime boundaries, including what topographies will generate overflows. As observations also suggest there may be overflows in deep canyons where the flow passes over isolated bumps and sills, this parameter range may be particularly significant for understanding the role of boundary layers in the deep ocean.

  10. Deep Learning and Bayesian Methods

    OpenAIRE

    Prosper Harrison B.

    2017-01-01

    A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such meth...

  11. Density functionals from deep learning

    OpenAIRE

    McMahon, Jeffrey M.

    2016-01-01

    Density-functional theory is a formally exact description of a many-body quantum system in terms of its density; in practice, however, approximations to the universal density functional are required. In this work, a model based on deep learning is developed to approximate this functional. Deep learning allows computational models that are capable of naturally discovering intricate structure in large and/or high-dimensional data sets, with multiple levels of abstraction. As no assumptions are ...

  12. A deep belief network with PLSR for nonlinear system modeling.

    Science.gov (United States)

    Qiao, Junfei; Wang, Gongming; Li, Wenjing; Li, Xiaoli

    2017-10-31

    Nonlinear system modeling plays an important role in practical engineering, and deep learning-based deep belief network (DBN) is now popular in nonlinear system modeling and identification because of the strong learning ability. However, the existing weights optimization for DBN is based on gradient, which always leads to a local optimum and a poor training result. In this paper, a DBN with partial least square regression (PLSR-DBN) is proposed for nonlinear system modeling, which focuses on the problem of weights optimization for DBN using PLSR. Firstly, unsupervised contrastive divergence (CD) algorithm is used in weights initialization. Secondly, initial weights derived from CD algorithm are optimized through layer-by-layer PLSR modeling from top layer to bottom layer. Instead of gradient method, PLSR-DBN can determine the optimal weights using several PLSR models, so that a better performance of PLSR-DBN is achieved. Then, the analysis of convergence is theoretically given to guarantee the effectiveness of the proposed PLSR-DBN model. Finally, the proposed PLSR-DBN is tested on two benchmark nonlinear systems and an actual wastewater treatment system as well as a handwritten digit recognition (nonlinear mapping and modeling) with high-dimension input data. The experiment results show that the proposed PLSR-DBN has better performances of time and accuracy on nonlinear system modeling than that of other methods. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Multi-layers castings

    Directory of Open Access Journals (Sweden)

    J. Szajnar

    2010-01-01

    Full Text Available In paper is presented the possibility of making of multi-layers cast steel castings in result of connection of casting and welding coating technologies. First layer was composite surface layer on the basis of Fe-Cr-C alloy, which was put directly in founding process of cast carbon steel 200–450 with use of preparation of mould cavity method. Second layer were padding welds, which were put with use of TIG – Tungsten Inert Gas surfacing by welding technology with filler on Ni matrix, Ni and Co matrix with wolfram carbides WC and on the basis on Fe-Cr-C alloy, which has the same chemical composition with alloy, which was used for making of composite surface layer. Usability for industrial applications of surface layers of castings were estimated by criterion of hardness and abrasive wear resistance of type metal-mineral.

  14. The posterior layer of the thoracolumbar fascia. Its function in load transfer from spine to legs.

    NARCIS (Netherlands)

    Pool-Goudzwaard, A.L.; Vleeming, A; Stoeckart, R.; Wingerden, Jan Paul; Snijders, Chris

    1996-01-01

    STUDY DESIGN: The superficial and deep lamina of the posterior layer of the thoracolumbar fascia have been studied anatomically and biomechanically. In embalmed human specimens, the posterior layer has been loaded by simulating the action of various muscles. The effect has been studied using raster

  15. A vertically integrated dynamic RAM-cell: Buried bit line memory cell with floating transfer layer

    NARCIS (Netherlands)

    Mouthaan, A.J.; Vertregt, Maarten

    1986-01-01

    A charge injection device has been realized in which charge can be injected on to an MOS-capacitor from a buried layer via an isolated transfer layer. The cell is positioned vertically between word and bit line. LOCOS (local oxidation) is used to isolate the cells and (deep) ion implantation to

  16. Hot, deep origin of petroleum: deep basin evidence and application

    Science.gov (United States)

    Price, Leigh C.

    1978-01-01

    Use of the model of a hot deep origin of oil places rigid constraints on the migration and entrapment of crude oil. Specifically, oil originating from depth migrates vertically up faults and is emplaced in traps at shallower depths. Review of petroleum-producing basins worldwide shows oil occurrence in these basins conforms to the restraints of and therefore supports the hypothesis. Most of the world's oil is found in the very deepest sedimentary basins, and production over or adjacent to the deep basin is cut by or directly updip from faults dipping into the basin deep. Generally the greater the fault throw the greater the reserves. Fault-block highs next to deep sedimentary troughs are the best target areas by the present concept. Traps along major basin-forming faults are quite prospective. The structural style of a basin governs the distribution, types, and amounts of hydrocarbons expected and hence the exploration strategy. Production in delta depocenters (Niger) is in structures cut by or updip from major growth faults, and structures not associated with such faults are barren. Production in block fault basins is on horsts next to deep sedimentary troughs (Sirte, North Sea). In basins whose sediment thickness, structure and geologic history are known to a moderate degree, the main oil occurrences can be specifically predicted by analysis of fault systems and possible hydrocarbon migration routes. Use of the concept permits the identification of significant targets which have either been downgraded or ignored in the past, such as production in or just updip from thrust belts, stratigraphic traps over the deep basin associated with major faulting, production over the basin deep, and regional stratigraphic trapping updip from established production along major fault zones.

  17. A double layer review

    International Nuclear Information System (INIS)

    Block, L.P.

    1977-06-01

    A review of the main results on electrostatic double layers (sometimes called space charge layers or sheaths) obtained from theory, and laboratory and space experiments up to the spring of 1977 is given. By means of barium jets and satellite probes, double layers have now been found at the altitudes, earlier predicted theoretically. The general potential distribution above the auroral zone, suggested by inverted V-events and electric field reversals, is corroborated. (author)

  18. Two layer powder pressing

    International Nuclear Information System (INIS)

    Schreiner, H.

    1979-01-01

    First, significance and advantages of sintered materials consisting of two layers are pointed out. By means of the two layer powder pressing technique metal powders are formed resulting in compacts with high accuracy of shape and mass. Attributes of basic powders, different filling methods and pressing techniques are discussed. The described technique is supposed to find further applications in the field of two layer compacts in the near future

  19. Economical Atomic Layer Deposition

    Science.gov (United States)

    Wyman, Richard; Davis, Robert; Linford, Matthew

    2010-10-01

    Atomic Layer Deposition is a self limiting deposition process that can produce films at a user specified height. At BYU we have designed a low cost and automated atomic layer deposition system. We have used the system to deposit silicon dioxide at room temperature using silicon tetrachloride and tetramethyl orthosilicate. Basics of atomic layer deposition, the system set up, automation techniques and our system's characterization are discussed.

  20. Stable Boundary Layer Issues

    OpenAIRE

    Steeneveld, G.J.

    2012-01-01

    Understanding and prediction of the stable atmospheric boundary layer is a challenging task. Many physical processes are relevant in the stable boundary layer, i.e. turbulence, radiation, land surface coupling, orographic turbulent and gravity wave drag, and land surface heterogeneity. The development of robust stable boundary layer parameterizations for use in NWP and climate models is hampered by the multiplicity of processes and their unknown interactions. As a result, these models suffer ...

  1. IMPROVEMENT OF RECOGNITION QUALITY IN DEEP LEARNING NETWORKS BY SIMULATED ANNEALING METHOD

    Directory of Open Access Journals (Sweden)

    A. S. Potapov

    2014-09-01

    Full Text Available The subject of this research is deep learning methods, in which automatic construction of feature transforms is taken place in tasks of pattern recognition. Multilayer autoencoders have been taken as the considered type of deep learning networks. Autoencoders perform nonlinear feature transform with logistic regression as an upper classification layer. In order to verify the hypothesis of possibility to improve recognition rate by global optimization of parameters for deep learning networks, which are traditionally trained layer-by-layer by gradient descent, a new method has been designed and implemented. The method applies simulated annealing for tuning connection weights of autoencoders while regression layer is simultaneously trained by stochastic gradient descent. Experiments held by means of standard MNIST handwritten digit database have shown the decrease of recognition error rate from 1.1 to 1.5 times in case of the modified method comparing to the traditional method, which is based on local optimization. Thus, overfitting effect doesn’t appear and the possibility to improve learning rate is confirmed in deep learning networks by global optimization methods (in terms of increasing recognition probability. Research results can be applied for improving the probability of pattern recognition in the fields, which require automatic construction of nonlinear feature transforms, in particular, in the image recognition. Keywords: pattern recognition, deep learning, autoencoder, logistic regression, simulated annealing.

  2. Layered plasma polymer composite membranes

    Science.gov (United States)

    Babcock, Walter C.

    1994-01-01

    Layered plasma polymer composite fluid separation membranes are disclosed, which comprise alternating selective and permeable layers for a total of at least 2n layers, where n is .gtoreq.2 and is the number of selective layers.

  3. Formation of double layers

    International Nuclear Information System (INIS)

    Leung, P.; Wong, A.Y.; Quon, B.H.

    1981-01-01

    Experiments on both stationary and propagating double layers and a related analytical model are described. Stationary double layers were produced in a multiple plasma device, in which an electron drift current was present. An investigation of the plasma parameters for the stable double layer condition is described. The particle distribution in the stable double layer establishes a potential profile, which creates electron and ion beams that excite plasma instabilities. The measured characteristics of the instabilities are consistent with the existence of the double layer. Propagating double layers are formed when the initial electron drift current is large. Ths slopes of the transition region increase as they propagate. A physical model for the formation of a double layer in the experimental device is described. This model explains the formation of the low potential region on the basis of the space charge. This space charge is created by the electron drift current. The model also accounts for the role of ions in double layer formation and explains the formation of moving double layers. (Auth.)

  4. Electroless atomic layer deposition

    Science.gov (United States)

    Robinson, David Bruce; Cappillino, Patrick J.; Sheridan, Leah B.; Stickney, John L.; Benson, David M.

    2017-10-31

    A method of electroless atomic layer deposition is described. The method electrolessly generates a layer of sacrificial material on a surface of a first material. The method adds doses of a solution of a second material to the substrate. The method performs a galvanic exchange reaction to oxidize away the layer of the sacrificial material and deposit a layer of the second material on the surface of the first material. The method can be repeated for a plurality of iterations in order to deposit a desired thickness of the second material on the surface of the first material.

  5. Double seismic zone for deep earthquakes in the izu-bonin subduction zone.

    Science.gov (United States)

    Iidaka, T; Furukawa, Y

    1994-02-25

    A double seismic zone for deep earthquakes was found in the Izu-Bonin region. An analysis of SP-converted phases confirms that the deep seismic zone consists of two layers separated by approximately 20 kilometers. Numerical modeling of the thermal structure implies that the hypocenters are located along isotherms of 500 degrees to 550 degrees C, which is consistent with the hypothesis that deep earthquakes result from the phase transition of metastable olivine to a high-pressure phase in the subducting slab.

  6. Moving object detection in video satellite image based on deep learning

    Science.gov (United States)

    Zhang, Xueyang; Xiang, Junhua

    2017-11-01

    Moving object detection in video satellite image is studied. A detection algorithm based on deep learning is proposed. The small scale characteristics of remote sensing video objects are analyzed. Firstly, background subtraction algorithm of adaptive Gauss mixture model is used to generate region proposals. Then the objects in region proposals are classified via the deep convolutional neural network. Thus moving objects of interest are detected combined with prior information of sub-satellite point. The deep convolution neural network employs a 21-layer residual convolutional neural network, and trains the network parameters by transfer learning. Experimental results about video from Tiantuo-2 satellite demonstrate the effectiveness of the algorithm.

  7. Development and application of deep convolutional neural network in target detection

    Science.gov (United States)

    Jiang, Xiaowei; Wang, Chunping; Fu, Qiang

    2018-04-01

    With the development of big data and algorithms, deep convolution neural networks with more hidden layers have more powerful feature learning and feature expression ability than traditional machine learning methods, making artificial intelligence surpass human level in many fields. This paper first reviews the development and application of deep convolutional neural networks in the field of object detection in recent years, then briefly summarizes and ponders some existing problems in the current research, and the future development of deep convolutional neural network is prospected.

  8. Spectroscopy of Deep Traps in Cu2S-CdS Junction Structures

    Directory of Open Access Journals (Sweden)

    Eugenijus Gaubas

    2012-12-01

    Full Text Available Cu2S-CdS junctions of the polycrystalline material layers have been examined by combining the capacitance deep level transient spectroscopy technique together with white LED light additional illumination (C-DLTS-WL and the photo-ionization spectroscopy (PIS implemented by the photocurrent probing. Three types of junction structures, separated by using the barrier capacitance characteristics of the junctions and correlated with XRD distinguished precipitates of the polycrystalline layers, exhibit different deep trap spectra within CdS substrates.

  9. Controls on deep drainage beneath the root soil zone in snowmelt-dominated environments

    Science.gov (United States)

    Hammond, J. C.; Harpold, A. A.; Kampf, S. K.

    2017-12-01

    Snowmelt is the dominant source of streamflow generation and groundwater recharge in many high elevation and high latitude locations, yet we still lack a detailed understanding of how snowmelt is partitioned between the soil, deep drainage, and streamflow under a variety of soil, climate, and snow conditions. Here we use Hydrus 1-D simulations with historical inputs from five SNOTEL snow monitoring sites in each of three regions, Cascades, Sierra, and Southern Rockies, to investigate how inter-annual variability on water input rate and duration affects soil saturation and deep drainage. Each input scenario was run with three different soil profiles of varying hydraulic conductivity, soil texture, and bulk density. We also created artificial snowmelt scenarios to test how snowmelt intermittence affects deep drainage. Results indicate that precipitation is the strongest predictor (R2 = 0.83) of deep drainage below the root zone, with weaker relationships observed between deep drainage and snow persistence, peak snow water equivalent, and melt rate. The ratio of deep drainage to precipitation shows a stronger positive relationship to melt rate suggesting that a greater fraction of input becomes deep drainage at higher melt rates. For a given amount of precipitation, rapid, concentrated snowmelt may create greater deep drainage below the root zone than slower, intermittent melt. Deep drainage requires saturation below the root zone, so saturated hydraulic conductivity serves as a primary control on deep drainage magnitude. Deep drainage response to climate is mostly independent of soil texture because of its reliance on saturated conditions. Mean water year saturations of deep soil layers can predict deep drainage and may be a useful way to compare sites in soils with soil hydraulic porosities. The unit depth of surface runoff often is often greater than deep drainage at daily and annual timescales, as snowmelt exceeds infiltration capacity in near-surface soil layers

  10. Probabilistic analysis of soil : Diaphragm wall friction used for value engineering of deep excavation, north/south metro Amsterdam

    NARCIS (Netherlands)

    Buykx, S.M.; Delfgaauw, S.; Bosch, J.W.

    2009-01-01

    The excavation of deep building pits often requires a check against failure by uplift of low permeability ground layers below excavation level. Whenever the weight of these soil layers is less than the pore-water pressure underneath, measures to resist buoyancy are to be considered. The measures

  11. How Stressful Is "Deep Bubbling"?

    Science.gov (United States)

    Tyrmi, Jaana; Laukkanen, Anne-Maria

    2017-03-01

    Water resistance therapy by phonating through a tube into the water is used to treat dysphonia. Deep submersion (≥10 cm in water, "deep bubbling") is used for hypofunctional voice disorders. Using it with caution is recommended to avoid vocal overloading. This experimental study aimed to investigate how strenuous "deep bubbling" is. Fourteen subjects, half of them with voice training, repeated the syllable [pa:] in comfortable speaking pitch and loudness, loudly, and in strained voice. Thereafter, they phonated a vowel-like sound both in comfortable loudness and loudly into a glass resonance tube immersed 10 cm into the water. Oral pressure, contact quotient (CQ, calculated from electroglottographic signal), and sound pressure level were studied. The peak oral pressure P(oral) during [p] and shuttering of the outer end of the tube was measured to estimate the subglottic pressure P(sub) and the mean P(oral) during vowel portions to enable calculation of transglottic pressure P(trans). Sensations during phonation were reported with an open-ended interview. P(sub) and P(oral) were higher in "deep bubbling" and P(trans) lower than in loud syllable phonation, but the CQ did not differ significantly. Similar results were obtained for the comparison between loud "deep bubbling" and strained phonation, although P(sub) did not differ significantly. Most of the subjects reported "deep bubbling" to be stressful only for respiratory and lip muscles. No big differences were found between trained and untrained subjects. The CQ values suggest that "deep bubbling" may increase vocal fold loading. Further studies should address impact stress during water resistance exercises. Copyright © 2017 The Voice Foundation. Published by Elsevier Inc. All rights reserved.

  12. Microbial diversity in methane hydrate-bearing deep marine sediments core preserved in the original pressure.

    Science.gov (United States)

    Takahashi, Y.; Hata, T.; Nishida, H.

    2017-12-01

    In normal coring of deep marine sediments, the sampled cores are exposed to the pressure of the atmosphere, which results in dissociation of gas-hydrates and might change microbial diversity. In this study, we analyzed microbial composition in methane hydrate-bearing sediment core sampled and preserved by Hybrid-PCS (Pressure Coring System). We sliced core into three layers; (i) outside layer, which were most affected by drilling fluids, (ii) middle layer, and (iii) inner layer, which were expected to be most preserved as the original state. From each layer, we directly extracted DNA, and amplified V3-V4 region of 16S rRNA gene. We determined at least 5000 of nucleotide sequences of the partial 16S rDNA from each layer by Miseq (Illumina). In the all layers, facultative anaerobes, which can grow with or without oxygen because they can metabolize energy aerobically or anaerobically, were detected as majority. However, the genera which are often detected anaerobic environment is abundant in the inner layer compared to the outside layer, indicating that condition of drilling and preservation affect the microbial composition in the deep marine sediment core. This study was conducted as a part of the activity of the Research Consortium for Methane Hydrate Resources in Japan [MH21 consortium], and supported by JOGMEC (Japan Oil, Gas and Metals National Corporation). The sample was provided by AIST (National Institute of Advanced Industrial Science and Technology).

  13. Multi-layer monochromator

    International Nuclear Information System (INIS)

    Schoenborn, B.P.; Caspar, D.L.D.

    1975-01-01

    This invention provides an artificial monochromator crystal for efficiently selecting a narrow band of neutron wavelengths from a neutron beam having a Maxwellian wavelength distribution, by providing on a substrate a plurality of germanium layers, and alternate periodic layers of a different metal having tailored thicknesses, shapes, and volumetric and neutron scattering densities. (U.S.)

  14. Ozone Layer Protection

    Science.gov (United States)

    ... and Research Centers Contact Us Share Ozone Layer Protection The stratospheric ozone layer is Earth’s “sunscreen” – protecting ... GreenChill Partnership Responsible Appliance Disposal (RAD) Program Ozone Protection vs. Ozone Pollution This website addresses stratospheric ozone ...

  15. Skin layer mechanics

    NARCIS (Netherlands)

    Geerligs, M.

    2010-01-01

    The human skin is composed of several layers, each with an unique structure and function. Knowledge about the mechanical behavior of these skin layers is important for clinical and cosmetic research, such as the development of personal care products and the understanding of skin diseases. Until

  16. Stable Boundary Layer Issues

    NARCIS (Netherlands)

    Steeneveld, G.J.

    2012-01-01

    Understanding and prediction of the stable atmospheric boundary layer is a challenging task. Many physical processes are relevant in the stable boundary layer, i.e. turbulence, radiation, land surface coupling, orographic turbulent and gravity wave drag, and land surface heterogeneity. The

  17. Development of boundary layers

    International Nuclear Information System (INIS)

    Herbst, R.

    1980-01-01

    Boundary layers develop along the blade surfaces on both the pressure and the suction side in a non-stationary flow field. This is due to the fact that there is a strongly fluctuating flow on the downstream blade row, especially as a result of the wakes of the upstream blade row. The author investigates the formation of boundary layers under non-stationary flow conditions and tries to establish a model describing the non-stationary boundary layer. For this purpose, plate boundary layers are measured, at constant flow rates but different interferent frequency and variable pressure gradients. By introducing the sample technique, measurements of the non-stationary boundary layer become possible, and the flow rate fluctuation can be divided in its components, i.e. stochastic turbulence and periodical fluctuation. (GL) [de

  18. Improved electron transport layer

    DEFF Research Database (Denmark)

    2012-01-01

    The present invention provides: a method of preparing a coating ink for forming a zinc oxide electron transport layer, comprising mixing zinc acetate and a wetting agent in water or methanol; a coating ink comprising zinc acetate and a wetting agent in aqueous solution or methanolic solution......; a method of preparing a zinc oxide electron transporting layer, which method comprises: i) coating a substrate with the coating ink of the present invention to form a film; ii) drying the film; and iii) heating the dry film to convert the zinc acetate substantially to ZnO; a method of preparing an organic...... photovoltaic device or an organic LED having a zinc oxide electron transport layer, the method comprising, in this order: a) providing a substrate bearing a first electrode layer; b) forming an electron transport layer according to the following method: i) coating a coating ink comprising an ink according...

  19. What Really is Deep Learning Doing?

    OpenAIRE

    Xiong, Chuyu

    2017-01-01

    Deep learning has achieved a great success in many areas, from computer vision to natural language processing, to game playing, and much more. Yet, what deep learning is really doing is still an open question. There are a lot of works in this direction. For example, [5] tried to explain deep learning by group renormalization, and [6] tried to explain deep learning from the view of functional approximation. In order to address this very crucial question, here we see deep learning from perspect...

  20. Late Eocene impact events recorded in deep-sea sediments

    Science.gov (United States)

    Glass, B. P.

    1988-01-01

    Raup and Sepkoski proposed that mass extinctions have occurred every 26 Myr during the last 250 Myr. In order to explain this 26 Myr periodicity, it was proposed that the mass extinctions were caused by periodic increases in cometary impacts. One method to test this hypothesis is to determine if there were periodic increases in impact events (based on crater ages) that correlate with mass extinctions. A way to test the hypothesis that mass extinctions were caused by periodic increases in impact cratering is to look for evidence of impact events in deep-sea deposits. This method allows direct observation of the temporal relationship between impact events and extinctions as recorded in the sedimentary record. There is evidence in the deep-sea record for two (possibly three) impact events in the late Eocene. The younger event, represented by the North American microtektite layer, is not associated with an Ir anomaly. The older event, defined by the cpx spherule layer, is associated with an Ir anomaly. However, neither of the two impact events recorded in late Eocene deposits appears to be associated with an unusual number of extinctions. Thus there is little evidence in the deep-sea record for an impact-related mass extinction in the late Eocene.

  1. Importance of the variability of hydrographic preconditioning for deep convection in the Gulf of Lion, NW Mediterranean

    Directory of Open Access Journals (Sweden)

    L. Grignon

    2010-06-01

    Full Text Available We study the variability of hydrographic preconditioning defined as the heat and salt contents in the Ligurian Sea before convection. The stratification is found to reach a maximum in the intermediate layer in December, whose causes and consequences for the interannual variability of convection are investigated. Further study of the interannual variability and correlation tests between the properties of the deep water formed and the winter surface fluxes support the description of convection as a process that transfers the heat and salt contents from the top and intermediate layers to the deep layer. A proxy for the rate of transfer is given by the final convective mixed layer depth, that is shown to depend equally on the surface fluxes and on the preconditioning. In particular, it is found that deep convection in winter 2004–2005 would have happened even with normal winter conditions, due to low pre-winter stratification.

  2. Boosting water oxidation layer-by-layer.

    Science.gov (United States)

    Hidalgo-Acosta, Jonnathan C; Scanlon, Micheál D; Méndez, Manuel A; Amstutz, Véronique; Vrubel, Heron; Opallo, Marcin; Girault, Hubert H

    2016-04-07

    Electrocatalysis of water oxidation was achieved using fluorinated tin oxide (FTO) electrodes modified with layer-by-layer deposited films consisting of bilayers of negatively charged citrate-stabilized IrO2 NPs and positively charged poly(diallyldimethylammonium chloride) (PDDA) polymer. The IrO2 NP surface coverage can be fine-tuned by controlling the number of bilayers. The IrO2 NP films were amorphous, with the NPs therein being well-dispersed and retaining their as-synthesized shape and sizes. UV/vis spectroscopic and spectro-electrochemical studies confirmed that the total surface coverage and electrochemically addressable surface coverage of IrO2 NPs increased linearly with the number of bilayers up to 10 bilayers. The voltammetry of the modified electrode was that of hydrous iridium oxide films (HIROFs) with an observed super-Nernstian pH response of the Ir(III)/Ir(IV) and Ir(IV)-Ir(IV)/Ir(IV)-Ir(V) redox transitions and Nernstian shift of the oxygen evolution onset potential. The overpotential of the oxygen evolution reaction (OER) was essentially pH independent, varying only from 0.22 V to 0.28 V (at a current density of 0.1 mA cm(-2)), moving from acidic to alkaline conditions. Bulk electrolysis experiments revealed that the IrO2/PDDA films were stable and adherent under acidic and neutral conditions but degraded in alkaline solutions. Oxygen was evolved with Faradaic efficiencies approaching 100% under acidic (pH 1) and neutral (pH 7) conditions, and 88% in alkaline solutions (pH 13). This layer-by-layer approach forms the basis of future large-scale OER electrode development using ink-jet printing technology.

  3. Layered virus protection for the operations and administrative messaging system

    Science.gov (United States)

    Cortez, R. H.

    2002-01-01

    NASA's Deep Space Network (DSN) is critical in supporting the wide variety of operating and plannedunmanned flight projects. For day-to-day operations it relies on email communication between the three Deep Space Communication Complexes (Canberra, Goldstone, Madrid) and NASA's Jet Propulsion Laboratory. The Operations & Administrative Messaging system, based on the Microsoft Windows NTand Exchange platform, provides the infrastructure that is required for reliable, mission-critical messaging. The reliability of this system, however, is threatened by the proliferation of email viruses that continue to spread at alarming rates. A layered approach to email security has been implemented across the DSN to protect against this threat.

  4. DeepNet: An Ultrafast Neural Learning Code for Seismic Imaging

    International Nuclear Information System (INIS)

    Barhen, J.; Protopopescu, V.; Reister, D.

    1999-01-01

    A feed-forward multilayer neural net is trained to learn the correspondence between seismic data and well logs. The introduction of a virtual input layer, connected to the nominal input layer through a special nonlinear transfer function, enables ultrafast (single iteration), near-optimal training of the net using numerical algebraic techniques. A unique computer code, named DeepNet, has been developed, that has achieved, in actual field demonstrations, results unattainable to date with industry standard tools

  5. Deep Learning and Bayesian Methods

    Directory of Open Access Journals (Sweden)

    Prosper Harrison B.

    2017-01-01

    Full Text Available A revolution is underway in which deep neural networks are routinely used to solve diffcult problems such as face recognition and natural language understanding. Particle physicists have taken notice and have started to deploy these methods, achieving results that suggest a potentially significant shift in how data might be analyzed in the not too distant future. We discuss a few recent developments in the application of deep neural networks and then indulge in speculation about how such methods might be used to automate certain aspects of data analysis in particle physics. Next, the connection to Bayesian methods is discussed and the paper ends with thoughts on a significant practical issue, namely, how, from a Bayesian perspective, one might optimize the construction of deep neural networks.

  6. Deep Learning in Drug Discovery.

    Science.gov (United States)

    Gawehn, Erik; Hiss, Jan A; Schneider, Gisbert

    2016-01-01

    Artificial neural networks had their first heyday in molecular informatics and drug discovery approximately two decades ago. Currently, we are witnessing renewed interest in adapting advanced neural network architectures for pharmaceutical research by borrowing from the field of "deep learning". Compared with some of the other life sciences, their application in drug discovery is still limited. Here, we provide an overview of this emerging field of molecular informatics, present the basic concepts of prominent deep learning methods and offer motivation to explore these techniques for their usefulness in computer-assisted drug discovery and design. We specifically emphasize deep neural networks, restricted Boltzmann machine networks and convolutional networks. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  7. Eric Davidson and deep time.

    Science.gov (United States)

    Erwin, Douglas H

    2017-10-13

    Eric Davidson had a deep and abiding interest in the role developmental mechanisms played in generating evolutionary patterns documented in deep time, from the origin of the euechinoids to the processes responsible for the morphological architectures of major animal clades. Although not an evolutionary biologist, Davidson's interests long preceded the current excitement over comparative evolutionary developmental biology. Here I discuss three aspects at the intersection between his research and evolutionary patterns in deep time: First, understanding the mechanisms of body plan formation, particularly those associated with the early diversification of major metazoan clades. Second, a critique of early claims about ancestral metazoans based on the discoveries of highly conserved genes across bilaterian animals. Third, Davidson's own involvement in paleontology through a collaborative study of the fossil embryos from the Ediacaran Doushantuo Formation in south China.

  8. Deep Learning in Gastrointestinal Endoscopy.

    Science.gov (United States)

    Patel, Vivek; Armstrong, David; Ganguli, Malika; Roopra, Sandeep; Kantipudi, Neha; Albashir, Siwar; Kamath, Markad V

    2016-01-01

    Gastrointestinal (GI) endoscopy is used to inspect the lumen or interior of the GI tract for several purposes, including, (1) making a clinical diagnosis, in real time, based on the visual appearances; (2) taking targeted tissue samples for subsequent histopathological examination; and (3) in some cases, performing therapeutic interventions targeted at specific lesions. GI endoscopy is therefore predicated on the assumption that the operator-the endoscopist-is able to identify and characterize abnormalities or lesions accurately and reproducibly. However, as in other areas of clinical medicine, such as histopathology and radiology, many studies have documented marked interobserver and intraobserver variability in lesion recognition. Thus, there is a clear need and opportunity for techniques or methodologies that will enhance the quality of lesion recognition and diagnosis and improve the outcomes of GI endoscopy. Deep learning models provide a basis to make better clinical decisions in medical image analysis. Biomedical image segmentation, classification, and registration can be improved with deep learning. Recent evidence suggests that the application of deep learning methods to medical image analysis can contribute significantly to computer-aided diagnosis. Deep learning models are usually considered to be more flexible and provide reliable solutions for image analysis problems compared to conventional computer vision models. The use of fast computers offers the possibility of real-time support that is important for endoscopic diagnosis, which has to be made in real time. Advanced graphics processing units and cloud computing have also favored the use of machine learning, and more particularly, deep learning for patient care. This paper reviews the rapidly evolving literature on the feasibility of applying deep learning algorithms to endoscopic imaging.

  9. Deep learning-based Diabetic Retinopathy assessment on embedded system.

    Science.gov (United States)

    Ardiyanto, Igi; Nugroho, Hanung Adi; Buana, Ratna Lestari Budiani

    2017-07-01

    Diabetic Retinopathy (DR) is a disease which affect the vision ability. The observation by an ophthalmologist usually conducted by analyzing the retinal images of the patient which are marked by some DR features. However some misdiagnosis are usually found due to human error. Here, a deep learning-based low-cost embedded system is established to assist the doctor for grading the severity of the DR from the retinal images. A compact deep learning algorithm named Deep-DR-Net which fits on a small embedded board is afterwards proposed for such purposes. In the heart of Deep-DR-Net, a cascaded encoder-classifier network is arranged using residual style for ensuring the small model size. The usage of different types of convolutional layers subsequently guarantees the features richness of the network for differentiating the grade of the DR. Experimental results show the capability of the proposed system for detecting the existence as well as grading the severity of the DR symptomps.

  10. Radio-active waste disposal and deep-sea biology

    International Nuclear Information System (INIS)

    Rice, A.L.

    1978-01-01

    The deep-sea has been widely thought of as a remote, sparsely populated, and biologically inactive environment, well suited to receive the noxious products of nuclear fission processes. Much of what is known of abyssal biology tends to support this view, but there are a few disquieting contra-indications. The realisation, in recent years, that many animal groups show a previously unsuspected high species diversity in the deep-sea emphasized the paucity of our knowledge of this environment. More dramatically, the discovery of a large, active, and highly mobile abysso-bentho-pelagic fauna changed the whole concept of abyssal life. Finally, while there is little evidence for the existence of vertical migration patterns linking the deep-sea bottom communities with those of the overlying water layers, there are similarly too few negative results for the possibility of such transport mechanisms to be dismissed. In summary, biological knowledge of the abyss is insufficient to answer the questions raised in connection with deep-sea dumping, but in the absence of adequate answers it might be dangerous to ignore the questions

  11. Deep Learning with Dynamic Spiking Neurons and Fixed Feedback Weights.

    Science.gov (United States)

    Samadi, Arash; Lillicrap, Timothy P; Tweed, Douglas B

    2017-03-01

    Recent work in computer science has shown the power of deep learning driven by the backpropagation algorithm in networks of artificial neurons. But real neurons in the brain are different from most of these artificial ones in at least three crucial ways: they emit spikes rather than graded outputs, their inputs and outputs are related dynamically rather than by piecewise-smooth functions, and they have no known way to coordinate arrays of synapses in separate forward and feedback pathways so that they change simultaneously and identically, as they do in backpropagation. Given these differences, it is unlikely that current deep learning algorithms can operate in the brain, but we that show these problems can be solved by two simple devices: learning rules can approximate dynamic input-output relations with piecewise-smooth functions, and a variation on the feedback alignment algorithm can train deep networks without having to coordinate forward and feedback synapses. Our results also show that deep spiking networks learn much better if each neuron computes an intracellular teaching signal that reflects that cell's nonlinearity. With this mechanism, networks of spiking neurons show useful learning in synapses at least nine layers upstream from the output cells and perform well compared to other spiking networks in the literature on the MNIST digit recognition task.

  12. A global boundary-layer height climatology

    Energy Technology Data Exchange (ETDEWEB)

    Dop, H. van; Krol, M.; Holtslag, B. [Inst. for Marine and Atmospheric Research Utrecht, IMAU, Utrecht (Netherlands)

    1997-10-01

    In principle the ABL (atmospheric boundary layer) height can be retrieved from atmospheric global circulation models since they contain algorithms which determine the intensity of the turbulence as a function of height. However, these data are not routinely available, or on a (vertical) resolution which is too crude in view of the application. This justifies the development of a separate algorithm in order to define the ABL. The algorithm should include the generation of turbulence by both shear and buoyancy and should be based on readily available atmospheric parameters. There is obviously a wide application for boundary heights in off-line global and regional chemistry and transport modelling. It is also a much used parameter in air pollution meteorology. In this article we shall present a theory which is based on current insights in ABL dynamics. The theory is applicable over land and sea surfaces in all seasons. The theory is (for various reasons) not valid in mountainous areas. In areas where boundary-layer clouds or deep cumulus convection are present the theory does not apply. However, the same global atmospheric circulation models contain parameterizations for shallow and deep convection from which separate estimates can be obtained for the extent of vertical mixing. (au)

  13. The Application of Layer Theory to Design: The Control Layer

    Science.gov (United States)

    Gibbons, Andrew S.; Langton, Matthew B.

    2016-01-01

    A theory of design layers proposed by Gibbons ("An Architectural Approach to Instructional Design." Routledge, New York, 2014) asserts that each layer of an instructional design is related to a body of theory closely associated with the concerns of that particular layer. This study focuses on one layer, the control layer, examining…

  14. Deep mycoses in Amazon region.

    Science.gov (United States)

    Talhari, S; Cunha, M G; Schettini, A P; Talhari, A C

    1988-09-01

    Patients with deep mycoses diagnosed in dermatologic clinics of Manaus (state of Amazonas, Brazil) were studied from November 1973 to December 1983. They came from the Brazilian states of Amazonas, Pará, Acre, and Rondônia and the Federal Territory of Roraima. All of these regions, with the exception of Pará, are situated in the western part of the Amazon Basin. The climatic conditions in this region are almost the same: tropical forest, high rainfall, and mean annual temperature of 26C. The deep mycoses diagnosed, in order of frequency, were Jorge Lobo's disease, paracoccidioidomycosis, chromomycosis, sporotrichosis, mycetoma, cryptococcosis, zygomycosis, and histoplasmosis.

  15. Producing deep-water hydrocarbons

    International Nuclear Information System (INIS)

    Pilenko, Thierry

    2011-01-01

    Several studies relate the history and progress made in offshore production from oil and gas fields in relation to reserves and the techniques for producing oil offshore. The intention herein is not to review these studies but rather to argue that the activities of prospecting and producing deep-water oil and gas call for a combination of technology and project management and, above all, of devotion and innovation. Without this sense of commitment motivating men and women in this industry, the human adventure of deep-water production would never have taken place

  16. Topologically nontrivial quantum layers

    International Nuclear Information System (INIS)

    Carron, G.; Exner, P.; Krejcirik, D.

    2004-01-01

    Given a complete noncompact surface Σ embedded in R 3 , we consider the Dirichlet Laplacian in the layer Ω that is defined as a tubular neighborhood of constant width about Σ. Using an intrinsic approach to the geometry of Ω, we generalize the spectral results of the original paper by Duclos et al. [Commun. Math. Phys. 223, 13 (2001)] to the situation when Σ does not possess poles. This enables us to consider topologically more complicated layers and state new spectral results. In particular, we are interested in layers built over surfaces with handles or several cylindrically symmetric ends. We also discuss more general regions obtained by compact deformations of certain Ω

  17. Corium quench in deep pool mixing experiments

    International Nuclear Information System (INIS)

    Spencer, B.W.; McUmber, L.; Gregorash, D.; Aeschlimann, R.; Sienicki, J.J.

    1985-01-01

    The results of two recent corium-water thermal interaction (CWTI) tests are described in which a stream of molten corium was poured into a deep pool of water in order to determine the mixing behavior, the corium-to-water heat transfer rates, and the characteristic sizes of the quenched debris. The corium composition was 60% UO 2 , 16% ZrO 2 , and 24% stainless steel by weight; its initial temperature was 3080 K, approx.160 K above the oxide phase liquidus temperature. The corium pour stream was a single-phase 2.2 cm dia liquid column which entered the water pool in film boiling at approx.4 m/s. The water subcooling was 6 and 75C in the two tests. Test results showed that with low subcooling, rapid steam generation caused the pool to boil up into a high void fraction regime. In contrast, with large subcooling no net steam generation occurred, and the pool remained relatively quiescent. Breakup of the jet appeared to occur by surface stripping. In neither test was the breakup complete during transit through the 32 cm deep water pool, and molten corium channeled to the base where it formed a melt layer. The characteristic heat transfer rates measured 3.5 MJ/s and 2.7 MJ/s during the fall stage for small and large subcooling, respectively; during the initial stage of bed quench, the surface heat fluxes measured 2.4 MW/m 2 and 3.7 MW/m 2 , respectively. A small mass of particles was formed in each test, measuring typically 0.1 to 1 mm and 1 to 5 mm dia for the large and small subcooling conditions, respectively. 9 refs., 13 figs., 1 tab

  18. DeepSimulator: a deep simulator for Nanopore sequencing

    KAUST Repository

    Li, Yu

    2017-12-23

    Motivation: Oxford Nanopore sequencing is a rapidly developed sequencing technology in recent years. To keep pace with the explosion of the downstream data analytical tools, a versatile Nanopore sequencing simulator is needed to complement the experimental data as well as to benchmark those newly developed tools. However, all the currently available simulators are based on simple statistics of the produced reads, which have difficulty in capturing the complex nature of the Nanopore sequencing procedure, the main task of which is the generation of raw electrical current signals. Results: Here we propose a deep learning based simulator, DeepSimulator, to mimic the entire pipeline of Nanopore sequencing. Starting from a given reference genome or assembled contigs, we simulate the electrical current signals by a context-dependent deep learning model, followed by a base-calling procedure to yield simulated reads. This workflow mimics the sequencing procedure more naturally. The thorough experiments performed across four species show that the signals generated by our context-dependent model are more similar to the experimentally obtained signals than the ones generated by the official context-independent pore model. In terms of the simulated reads, we provide a parameter interface to users so that they can obtain the reads with different accuracies ranging from 83% to 97%. The reads generated by the default parameter have almost the same properties as the real data. Two case studies demonstrate the application of DeepSimulator to benefit the development of tools in de novo assembly and in low coverage SNP detection. Availability: The software can be accessed freely at: https://github.com/lykaust15/DeepSimulator.

  19. Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons.

    Directory of Open Access Journals (Sweden)

    Magdalena Guardiola

    Full Text Available Marine sediments are home to one of the richest species pools on Earth, but logistics and a dearth of taxonomic work-force hinders the knowledge of their biodiversity. We characterized α- and β-diversity of deep-sea assemblages from submarine canyons in the western Mediterranean using an environmental DNA metabarcoding. We used a new primer set targeting a short eukaryotic 18S sequence (ca. 110 bp. We applied a protocol designed to obtain extractions enriched in extracellular DNA from replicated sediment corers. With this strategy we captured information from DNA (local or deposited from the water column that persists adsorbed to inorganic particles and buffered short-term spatial and temporal heterogeneity. We analysed replicated samples from 20 localities including 2 deep-sea canyons, 1 shallower canal, and two open slopes (depth range 100-2,250 m. We identified 1,629 MOTUs, among which the dominant groups were Metazoa (with representatives of 19 phyla, Alveolata, Stramenopiles, and Rhizaria. There was a marked small-scale heterogeneity as shown by differences in replicates within corers and within localities. The spatial variability between canyons was significant, as was the depth component in one of the canyons where it was tested. Likewise, the composition of the first layer (1 cm of sediment was significantly different from deeper layers. We found that qualitative (presence-absence and quantitative (relative number of reads data showed consistent trends of differentiation between samples and geographic areas. The subset of exclusively benthic MOTUs showed similar patterns of β-diversity and community structure as the whole dataset. Separate analyses of the main metazoan phyla (in number of MOTUs showed some differences in distribution attributable to different lifestyles. Our results highlight the differentiation that can be found even between geographically close assemblages, and sets the ground for future monitoring and conservation

  20. Stimulation Technologies for Deep Well Completions

    Energy Technology Data Exchange (ETDEWEB)

    None

    2003-09-30

    The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a study to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. An assessment of historical deep gas well drilling activity and forecast of future trends was completed during the first six months of the project; this segment of the project was covered in Technical Project Report No. 1. The second progress report covers the next six months of the project during which efforts were primarily split between summarizing rock mechanics and fracture growth in deep reservoirs and contacting operators about case studies of deep gas well stimulation.

  1. Residual Deep Convolutional Neural Network Predicts MGMT Methylation Status.

    Science.gov (United States)

    Korfiatis, Panagiotis; Kline, Timothy L; Lachance, Daniel H; Parney, Ian F; Buckner, Jan C; Erickson, Bradley J

    2017-10-01

    Predicting methylation of the O6-methylguanine methyltransferase (MGMT) gene status utilizing MRI imaging is of high importance since it is a predictor of response and prognosis in brain tumors. In this study, we compare three different residual deep neural network (ResNet) architectures to evaluate their ability in predicting MGMT methylation status without the need for a distinct tumor segmentation step. We found that the ResNet50 (50 layers) architecture was the best performing model, achieving an accuracy of 94.90% (+/- 3.92%) for the test set (classification of a slice as no tumor, methylated MGMT, or non-methylated). ResNet34 (34 layers) achieved 80.72% (+/- 13.61%) while ResNet18 (18 layers) accuracy was 76.75% (+/- 20.67%). ResNet50 performance was statistically significantly better than both ResNet18 and ResNet34 architectures (p deep neural architectures can be used to predict molecular biomarkers from routine medical images.

  2. STIMULATION TECHNOLOGIES FOR DEEP WELL COMPLETIONS

    Energy Technology Data Exchange (ETDEWEB)

    Stephen Wolhart

    2003-06-01

    The Department of Energy (DOE) is sponsoring a Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies is conducting a project to evaluate the stimulation of deep wells. The objective of the project is to assess U.S. deep well drilling & stimulation activity, review rock mechanics & fracture growth in deep, high pressure/temperature wells and evaluate stimulation technology in several key deep plays. Phase 1 was recently completed and consisted of assessing deep gas well drilling activity (1995-2007) and an industry survey on deep gas well stimulation practices by region. Of the 29,000 oil, gas and dry holes drilled in 2002, about 300 were drilled in the deep well; 25% were dry, 50% were high temperature/high pressure completions and 25% were simply deep completions. South Texas has about 30% of these wells, Oklahoma 20%, Gulf of Mexico Shelf 15% and the Gulf Coast about 15%. The Rockies represent only 2% of deep drilling. Of the 60 operators who drill deep and HTHP wells, the top 20 drill almost 80% of the wells. Six operators drill half the U.S. deep wells. Deep drilling peaked at 425 wells in 1998 and fell to 250 in 1999. Drilling is expected to rise through 2004 after which drilling should cycle down as overall drilling declines.

  3. Sunspot drawings handwritten character recognition method based on deep learning

    Science.gov (United States)

    Zheng, Sheng; Zeng, Xiangyun; Lin, Ganghua; Zhao, Cui; Feng, Yongli; Tao, Jinping; Zhu, Daoyuan; Xiong, Li

    2016-05-01

    High accuracy scanned sunspot drawings handwritten characters recognition is an issue of critical importance to analyze sunspots movement and store them in the database. This paper presents a robust deep learning method for scanned sunspot drawings handwritten characters recognition. The convolution neural network (CNN) is one algorithm of deep learning which is truly successful in training of multi-layer network structure. CNN is used to train recognition model of handwritten character images which are extracted from the original sunspot drawings. We demonstrate the advantages of the proposed method on sunspot drawings provided by Chinese Academy Yunnan Observatory and obtain the daily full-disc sunspot numbers and sunspot areas from the sunspot drawings. The experimental results show that the proposed method achieves a high recognition accurate rate.

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

    Science.gov (United States)

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

    2015-12-01

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

  5. Deep generative learning of location-invariant visual word recognition.

    Science.gov (United States)

    Di Bono, Maria Grazia; Zorzi, Marco

    2013-01-01

    It is widely believed that orthographic processing implies an approximate, flexible coding of letter position, as shown by relative-position and transposition priming effects in visual word recognition. These findings have inspired alternative proposals about the representation of letter position, ranging from noisy coding across the ordinal positions to relative position coding based on open bigrams. This debate can be cast within the broader problem of learning location-invariant representations of written words, that is, a coding scheme abstracting the identity and position of letters (and combinations of letters) from their eye-centered (i.e., retinal) locations. We asked whether location-invariance would emerge from deep unsupervised learning on letter strings and what type of intermediate coding would emerge in the resulting hierarchical generative model. We trained a deep network with three hidden layers on an artificial dataset of letter strings presented at five possible retinal locations. Though word-level information (i.e., word identity) was never provided to the network during training, linear decoding from the activity of the deepest hidden layer yielded near-perfect accuracy in location-invariant word recognition. Conversely, decoding from lower layers yielded a large number of transposition errors. Analyses of emergent internal representations showed that word selectivity and location invariance increased as a function of layer depth. Word-tuning and location-invariance were found at the level of single neurons, but there was no evidence for bigram coding. Finally, the distributed internal representation of words at the deepest layer showed higher similarity to the representation elicited by the two exterior letters than by other combinations of two contiguous letters, in agreement with the hypothesis that word edges have special status. These results reveal that the efficient coding of written words-which was the model's learning objective

  6. Deep generative learning of location-invariant visual word recognition

    Directory of Open Access Journals (Sweden)

    Maria Grazia eDi Bono

    2013-09-01

    Full Text Available It is widely believed that orthographic processing implies an approximate, flexible coding of letter position, as shown by relative-position and transposition priming effects in visual word recognition. These findings have inspired alternative proposals about the representation of letter position, ranging from noisy coding across the ordinal positions to relative position coding based on open bigrams. This debate can be cast within the broader problem of learning location-invariant representations of written words, that is, a coding scheme abstracting the identity and position of letters (and combinations of letters from their eye-centred (i.e., retinal locations. We asked whether location-invariance would emerge from deep unsupervised learning on letter strings and what type of intermediate coding would emerge in the resulting hierarchical generative model. We trained a deep network with three hidden layers on an artificial dataset of letter strings presented at five possible retinal locations. Though word-level information (i.e., word identity was never provided to the network during training, linear decoding from the activity of the deepest hidden layer yielded near-perfect accuracy in location-invariant word recognition. Conversely, decoding from lower layers yielded a large number of transposition errors. Analyses of emergent internal representations showed that word selectivity and location invariance increased as a function of layer depth. Conversely, there was no evidence for bigram coding. Finally, the distributed internal representation of words at the deepest layer showed higher similarity to the representation elicited by the two exterior letters than by other combinations of two contiguous letters, in agreement with the hypothesis that word edges have special status. These results reveal that the efficient coding of written words – which was the model’s learning objective – is largely based on letter-level information.

  7. Deep generative learning of location-invariant visual word recognition

    Science.gov (United States)

    Di Bono, Maria Grazia; Zorzi, Marco

    2013-01-01

    It is widely believed that orthographic processing implies an approximate, flexible coding of letter position, as shown by relative-position and transposition priming effects in visual word recognition. These findings have inspired alternative proposals about the representation of letter position, ranging from noisy coding across the ordinal positions to relative position coding based on open bigrams. This debate can be cast within the broader problem of learning location-invariant representations of written words, that is, a coding scheme abstracting the identity and position of letters (and combinations of letters) from their eye-centered (i.e., retinal) locations. We asked whether location-invariance would emerge from deep unsupervised learning on letter strings and what type of intermediate coding would emerge in the resulting hierarchical generative model. We trained a deep network with three hidden layers on an artificial dataset of letter strings presented at five possible retinal locations. Though word-level information (i.e., word identity) was never provided to the network during training, linear decoding from the activity of the deepest hidden layer yielded near-perfect accuracy in location-invariant word recognition. Conversely, decoding from lower layers yielded a large number of transposition errors. Analyses of emergent internal representations showed that word selectivity and location invariance increased as a function of layer depth. Word-tuning and location-invariance were found at the level of single neurons, but there was no evidence for bigram coding. Finally, the distributed internal representation of words at the deepest layer showed higher similarity to the representation elicited by the two exterior letters than by other combinations of two contiguous letters, in agreement with the hypothesis that word edges have special status. These results reveal that the efficient coding of written words—which was the model's learning objective

  8. Arctic Mixed Layer Dynamics

    National Research Council Canada - National Science Library

    Morison, James

    2003-01-01

    .... Over the years we have sought to understand the heat and mass balance of the mixed layer, marginal ice zone processes, the Arctic internal wave and mixing environment, summer and winter leads, and convection...

  9. Layered inorganic solids

    Czech Academy of Sciences Publication Activity Database

    Čejka, Jiří; Morris, R. E.; Nachtigall, P.; Roth, Wieslaw Jerzy

    2014-01-01

    Roč. 43, č. 27 (2014), s. 10274-10275 ISSN 1477-9226 Institutional support: RVO:61388955 Keywords : layered inorganic solids * physical chemistry * catalysis Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 4.197, year: 2014

  10. Addressing Ozone Layer Depletion

    Science.gov (United States)

    Access information on EPA's efforts to address ozone layer depletion through regulations, collaborations with stakeholders, international treaties, partnerships with the private sector, and enforcement actions under Title VI of the Clean Air Act.

  11. Layered Fault Management Architecture

    National Research Council Canada - National Science Library

    Sztipanovits, Janos

    2004-01-01

    ... UAVs or Organic Air Vehicles. The approach of this effort was to analyze fault management requirements of formation flight for fleets of UAVs, and develop a layered fault management architecture which demonstrates significant...

  12. The Bottom Boundary Layer.

    Science.gov (United States)

    Trowbridge, John H; Lentz, Steven J

    2018-01-03

    The oceanic bottom boundary layer extracts energy and momentum from the overlying flow, mediates the fate of near-bottom substances, and generates bedforms that retard the flow and affect benthic processes. The bottom boundary layer is forced by winds, waves, tides, and buoyancy and is influenced by surface waves, internal waves, and stratification by heat, salt, and suspended sediments. This review focuses on the coastal ocean. The main points are that (a) classical turbulence concepts and modern turbulence parameterizations provide accurate representations of the structure and turbulent fluxes under conditions in which the underlying assumptions hold, (b) modern sensors and analyses enable high-quality direct or near-direct measurements of the turbulent fluxes and dissipation rates, and (c) the remaining challenges include the interaction of waves and currents with the erodible seabed, the impact of layer-scale two- and three-dimensional instabilities, and the role of the bottom boundary layer in shelf-slope exchange.

  13. The Bottom Boundary Layer

    Science.gov (United States)

    Trowbridge, John H.; Lentz, Steven J.

    2018-01-01

    The oceanic bottom boundary layer extracts energy and momentum from the overlying flow, mediates the fate of near-bottom substances, and generates bedforms that retard the flow and affect benthic processes. The bottom boundary layer is forced by winds, waves, tides, and buoyancy and is influenced by surface waves, internal waves, and stratification by heat, salt, and suspended sediments. This review focuses on the coastal ocean. The main points are that (a) classical turbulence concepts and modern turbulence parameterizations provide accurate representations of the structure and turbulent fluxes under conditions in which the underlying assumptions hold, (b) modern sensors and analyses enable high-quality direct or near-direct measurements of the turbulent fluxes and dissipation rates, and (c) the remaining challenges include the interaction of waves and currents with the erodible seabed, the impact of layer-scale two- and three-dimensional instabilities, and the role of the bottom boundary layer in shelf-slope exchange.

  14. Distinctive Microbial Community Structure in Highly Stratified Deep-Sea Brine Water Columns

    KAUST Repository

    Bougouffa, Salim; Yang, J. K.; Lee, O. O.; Wang, Y.; Batang, Zenon B.; Al-Suwailem, Abdulaziz M.; Qian, P. Y.

    2013-01-01

    Atlantis II and Discovery are two hydrothermal and hypersaline deep-sea pools in the Red Sea rift that are characterized by strong thermohalo-stratification and temperatures steadily peaking near the bottom. We conducted comprehensive vertical profiling of the microbial populations in both pools and highlighted the influential environmental factors. Pyrosequencing of the 16S rRNA genes revealed shifts in community structures vis-à-vis depth. High diversity and low abundance were features of the deepest convective layers despite the low cell density. Surprisingly, the brine interfaces had significantly higher cell counts than the overlying deep-sea water, yet they were lowest in diversity. Vertical stratification of the bacterial populations was apparent as we moved from the Alphaproteobacteria-dominated deep sea to the Planctomycetaceae- or Deferribacteres-dominated interfaces to the Gammaproteobacteria-dominated brine layers. Archaeal marine group I was dominant in the deep-sea water and interfaces, while several euryarchaeotic groups increased in the brine. Across sites, microbial phylotypes and abundances varied substantially in the brine interface of Discovery compared with Atlantis II, despite the near-identical populations in the overlying deep-sea waters. The lowest convective layers harbored interestingly similar microbial communities, even though temperature and heavy metal concentrations were very different. Multivariate analysis indicated that temperature and salinity were the major influences shaping the communities. The harsh conditions and the low-abundance phylotypes could explain the observed correlation in the brine pools.

  15. Recognition of emotions using multimodal physiological signals and an ensemble deep learning model.

    Science.gov (United States)

    Yin, Zhong; Zhao, Mengyuan; Wang, Yongxiong; Yang, Jingdong; Zhang, Jianhua

    2017-03-01

    Using deep-learning methodologies to analyze multimodal physiological signals becomes increasingly attractive for recognizing human emotions. However, the conventional deep emotion classifiers may suffer from the drawback of the lack of the expertise for determining model structure and the oversimplification of combining multimodal feature abstractions. In this study, a multiple-fusion-layer based ensemble classifier of stacked autoencoder (MESAE) is proposed for recognizing emotions, in which the deep structure is identified based on a physiological-data-driven approach. Each SAE consists of three hidden layers to filter the unwanted noise in the physiological features and derives the stable feature representations. An additional deep model is used to achieve the SAE ensembles. The physiological features are split into several subsets according to different feature extraction approaches with each subset separately encoded by a SAE. The derived SAE abstractions are combined according to the physiological modality to create six sets of encodings, which are then fed to a three-layer, adjacent-graph-based network for feature fusion. The fused features are used to recognize binary arousal or valence states. DEAP multimodal database was employed to validate the performance of the MESAE. By comparing with the best existing emotion classifier, the mean of classification rate and F-score improves by 5.26%. The superiority of the MESAE against the state-of-the-art shallow and deep emotion classifiers has been demonstrated under different sizes of the available physiological instances. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  16. Distinctive Microbial Community Structure in Highly Stratified Deep-Sea Brine Water Columns

    KAUST Repository

    Bougouffa, Salim

    2013-03-29

    Atlantis II and Discovery are two hydrothermal and hypersaline deep-sea pools in the Red Sea rift that are characterized by strong thermohalo-stratification and temperatures steadily peaking near the bottom. We conducted comprehensive vertical profiling of the microbial populations in both pools and highlighted the influential environmental factors. Pyrosequencing of the 16S rRNA genes revealed shifts in community structures vis-à-vis depth. High diversity and low abundance were features of the deepest convective layers despite the low cell density. Surprisingly, the brine interfaces had significantly higher cell counts than the overlying deep-sea water, yet they were lowest in diversity. Vertical stratification of the bacterial populations was apparent as we moved from the Alphaproteobacteria-dominated deep sea to the Planctomycetaceae- or Deferribacteres-dominated interfaces to the Gammaproteobacteria-dominated brine layers. Archaeal marine group I was dominant in the deep-sea water and interfaces, while several euryarchaeotic groups increased in the brine. Across sites, microbial phylotypes and abundances varied substantially in the brine interface of Discovery compared with Atlantis II, despite the near-identical populations in the overlying deep-sea waters. The lowest convective layers harbored interestingly similar microbial communities, even though temperature and heavy metal concentrations were very different. Multivariate analysis indicated that temperature and salinity were the major influences shaping the communities. The harsh conditions and the low-abundance phylotypes could explain the observed correlation in the brine pools.

  17. Deep Space Climate Observatory (DSCOVR)

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The Deep Space Climate ObserVatoRy (DSCOVR) satellite is a NOAA operated asset at the first Lagrange (L1) point. The primary space weather instrument is the PlasMag...

  18. Ploughing the deep sea floor.

    Science.gov (United States)

    Puig, Pere; Canals, Miquel; Company, Joan B; Martín, Jacobo; Amblas, David; Lastras, Galderic; Palanques, Albert

    2012-09-13

    Bottom trawling is a non-selective commercial fishing technique whereby heavy nets and gear are pulled along the sea floor. The direct impact of this technique on fish populations and benthic communities has received much attention, but trawling can also modify the physical properties of seafloor sediments, water–sediment chemical exchanges and sediment fluxes. Most of the studies addressing the physical disturbances of trawl gear on the seabed have been undertaken in coastal and shelf environments, however, where the capacity of trawling to modify the seafloor morphology coexists with high-energy natural processes driving sediment erosion, transport and deposition. Here we show that on upper continental slopes, the reworking of the deep sea floor by trawling gradually modifies the shape of the submarine landscape over large spatial scales. We found that trawling-induced sediment displacement and removal from fishing grounds causes the morphology of the deep sea floor to become smoother over time, reducing its original complexity as shown by high-resolution seafloor relief maps. Our results suggest that in recent decades, following the industrialization of fishing fleets, bottom trawling has become an important driver of deep seascape evolution. Given the global dimension of this type of fishery, we anticipate that the morphology of the upper continental slope in many parts of the world’s oceans could be altered by intensive bottom trawling, producing comparable effects on the deep sea floor to those generated by agricultural ploughing on land.

  19. FOSTERING DEEP LEARNING AMONGST ENTREPRENEURSHIP ...

    African Journals Online (AJOL)

    An important prerequisite for this important objective to be achieved is that lecturers ensure that students adopt a deep learning approach towards entrepreneurship courses been taught, as this will enable them to truly understand key entrepreneurial concepts and strategies and how they can be implemented in the real ...

  20. Deep Space Gateway "Recycler" Mission

    Science.gov (United States)

    Graham, L.; Fries, M.; Hamilton, J.; Landis, R.; John, K.; O'Hara, W.

    2018-02-01

    Use of the Deep Space Gateway provides a hub for a reusable planetary sample return vehicle for missions to gather star dust as well as samples from various parts of the solar system including main belt asteroids, near-Earth asteroids, and Mars moon.

  1. Deep freezers with heat recovery

    Energy Technology Data Exchange (ETDEWEB)

    Kistler, J.

    1981-09-02

    Together with space and water heating systems, deep freezers are the biggest energy consumers in households. The article investigates the possibility of using the waste heat for water heating. The design principle of such a system is presented in a wiring diagram.

  2. A Deep-Sea Simulation.

    Science.gov (United States)

    Montes, Georgia E.

    1997-01-01

    Describes an activity that simulates exploration techniques used in deep-sea explorations and teaches students how this technology can be used to take a closer look inside volcanoes, inspect hazardous waste sites such as nuclear reactors, and explore other environments dangerous to humans. (DDR)

  3. Barbabos Deep-Water Sponges

    NARCIS (Netherlands)

    Soest, van R.W.M.; Stentoft, N.

    1988-01-01

    Deep-water sponges dredged up in two locations off the west coast of Barbados are systematically described. A total of 69 species is recorded, among which 16 are new to science, viz. Pachymatisma geodiformis, Asteropus syringiferus, Cinachyra arenosa, Theonella atlantica. Corallistes paratypus,

  4. Deep learning for visual understanding

    NARCIS (Netherlands)

    Guo, Y.

    2017-01-01

    With the dramatic growth of the image data on the web, there is an increasing demand of the algorithms capable of understanding the visual information automatically. Deep learning, served as one of the most significant breakthroughs, has brought revolutionary success in diverse visual applications,

  5. Deep-Sky Video Astronomy

    CERN Document Server

    Massey, Steve

    2009-01-01

    A guide to using modern integrating video cameras for deep-sky viewing and imaging with the kinds of modest telescopes available commercially to amateur astronomers. It includes an introduction and a brief history of the technology and camera types. It examines the pros and cons of this unrefrigerated yet highly efficient technology

  6. DM Considerations for Deep Drilling

    OpenAIRE

    Dubois-Felsmann, Gregory

    2016-01-01

    An outline of the current situation regarding the DM plans for the Deep Drilling surveys and an invitation to the community to provide feedback on what they would like to see included in the data processing and visualization of these surveys.

  7. Lessons from Earth's Deep Time

    Science.gov (United States)

    Soreghan, G. S.

    2005-01-01

    Earth is a repository of data on climatic changes from its deep-time history. Article discusses the collection and study of these data to predict future climatic changes, the need to create national study centers for the purpose, and the necessary cooperation between different branches of science in climatic research.

  8. Digging Deeper: The Deep Web.

    Science.gov (United States)

    Turner, Laura

    2001-01-01

    Focuses on the Deep Web, defined as Web content in searchable databases of the type that can be found only by direct query. Discusses the problems of indexing; inability to find information not indexed in the search engine's database; and metasearch engines. Describes 10 sites created to access online databases or directly search them. Lists ways…

  9. Deep Learning and Music Adversaries

    DEFF Research Database (Denmark)

    Kereliuk, Corey Mose; Sturm, Bob L.; Larsen, Jan

    2015-01-01

    the minimal perturbation of the input image such that the system misclassifies it with high confidence. We adapt this approach to construct and deploy an adversary of deep learning systems applied to music content analysis. In our case, however, the system inputs are magnitude spectral frames, which require...

  10. Stimulation Technologies for Deep Well Completions

    Energy Technology Data Exchange (ETDEWEB)

    Stephen Wolhart

    2005-06-30

    The Department of Energy (DOE) is sponsoring the Deep Trek Program targeted at improving the economics of drilling and completing deep gas wells. Under the DOE program, Pinnacle Technologies conducted a study to evaluate the stimulation of deep wells. The objective of the project was to review U.S. deep well drilling and stimulation activity, review rock mechanics and fracture growth in deep, high-pressure/temperature wells and evaluate stimulation technology in several key deep plays. This report documents results from this project.

  11. Release pathways for deep seabed disposal of radioactive wastes

    International Nuclear Information System (INIS)

    Anderson, D.R.; Bishop, W.P.; Bowen, V.T.; Brannen, J.P.; Caudle, W.N.; Detry, R.J.; Ewart, T.E.; Hayes, D.E.; Heath, G.R.; Hessler, R.R.; Hollister, C.D.; Keil, K.; McGowan, J.A.; Rohde, R.W.; Schimmel, W.P.; Schuster, C.L.; Silva, A.J.; Smyrl, W.H.; Taft, B.A.; Talbert, D.M.

    1975-01-01

    The concept of disposal of high-level solidified and encapsulated radioactive wastes into the deep sea floor has recently been discussed. Such a scheme has conceptual advantages in that the areas of the mid-plate/mid-gyre regions of the oceans are relatively unproductive biologically and relatively devoid of cataclysmic events, and natural processes there are generally quite slow. Given a lack of singular events, a set of barriers against the dispersion of the radioisotopes may be defined. The inverse of these barriers is the set of mechanisms by which the isotopes are transported from the burial site through the barriers to parts of the ocean of immediate significance to mankind. These include: corrosion of the cask; leaching of the waste material; upward transport of the isotopes with the upward moving pore water (mediated by ion-exchange processes); biological transport through bioturbition in the upper sediment layers and lowest water layer; the slow throughput currents of the deep basins; advection and diffusion through the water column; thermally driven transport through the sediments or the water column; biological transport of incorporated isotopes across the seabed or upward through the water column. In principle, the rates of all these processes are measurable or capable of being estimated. Such estimates are given on the basis of present knowledge of the processes in the deep basins. A methodology is discussed for the analytical treatment of the set of processes to give the amount of the isotopes reaching some part of the environment (e.g., and oceanic regime of immediate significance to man) as a function of time. The authors conclude that disposal in the deep seabed is conceptually attractive because of the stability and predictablity of the environment, but that it is not possible to give a firm estimate of the safety of such a scheme from the current knowledge of the mid-plate/mid-gyre regions. (author)

  12. Multi-level deep supervised networks for retinal vessel segmentation.

    Science.gov (United States)

    Mo, Juan; Zhang, Lei

    2017-12-01

    Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation. A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors. We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set. The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.

  13. High-level radioactive waste disposal in the deep ocean

    International Nuclear Information System (INIS)

    Hill, H.W.

    1977-01-01

    A joint programme has begun between the Fisheries Laboratory, Lowestoft and the Institute of Oceanographic Sciences, Wormley to study the dispersion of radioactivity in the deep ocean arising from the possible dumping of high level waste on the sea bed in vitrified-glass form which would permit slow leakage over a long term scale. The programme consists firstly of the development of a simple diffusion/advection model for the dispersion of radioactivity in a closed and finite ocean, which overcomes many of the criticisms of the earlier model proposed by Webb and Morley. Preliminary results from this new model are comparable to those of the Webb-Morley model for radio isotopes with half-lives of 10-300 years but are considerably more restrictive outside this range, particularly for those which are much longer-lived. The second part of the programme, towards which the emphasis is directed, concerns the field programme planned to measure the advection and diffusion parameters in the deeper layers of the ocean to provide realistic input parameters to the model and increase our fundamental understanding of the environment in which the radioactive materials may be released. The first cruises of the programme will take place in late 1976 and involve deep current meter deployments and float dispersion experiments around the present NEA dump site with some sediment sampling, so that adsorption experiments can be started on typical deep sea sediments. The programme will expand the number of long-term deep moored stations over the next five years and include further float experiments, CTD profiling, and other physical oceanography. In the second half of the 5-year programme, attempts will be made to measure diffusion parameters in the deeper layers of the ocean using radioactive tracers

  14. Comparison of single and dual layer detector blocks for pre-clinical MRI–PET

    International Nuclear Information System (INIS)

    Thompson, Christopher; Stortz, Greg; Goertzen, Andrew; Berg, Eric; Retière, Fabrice; Kozlowski, Piotr; Ryner, Lawrence; Sossi, Vesna; Zhang, Xuezhu

    2013-01-01

    Dual or multi-layer crystal blocks have been proposed to minimise the radial blurring effect in PET scanners with small ring diameters. We measured two potential PET detector blocks' performance in a configuration which would allow 16 blocks in a ring which could be inserted in a small animal 7T MRI scanner. Two crystal sizes, 1.60×1.60 mm 2 and 1.20×1.20 mm 2 , were investigated. Single layer blocks had 10 or 12 mm deep crystals, the dual layer blocks had 4 mm deep crystals on the top layer and 6 mm deep crystals on the bottom layer. The crystals in the dual layer blocks are offset by ½ of the crystal pitch to allow for purely geometric crystal identification. Both were read out with SensL 4×4 SiPM arrays. The software identifies 64 crystals in the single layer and either 85 or 113 crystals in the dual layer array, (either 49 or 64 in the lower layers and 36 or 49 in the upper layers). All the crystals were clearly visible in the crystal identification images and their resolvability indexes (average FWHM/crystal separation) were shown to range from 0.29 for the best single layer block to 0.33 for the densest dual layer block. The best coincidence response FWHM was 0.95 mm for the densest block at the centre of the field. This degraded to 1.83 mm at a simulated radial offset of 16 mm from the centre, while the single layer crystals blurred this result to 3.4 mm. The energy resolution was 16.4±2.2% averaged over the 113 crystals of the densest block

  15. A New Technique for Deep in situ Measurements of the Soil Water Retention Behaviour

    DEFF Research Database (Denmark)

    Rocchi, Irene; Gragnano, Carmine Gerardo; Govoni, Laura

    2018-01-01

    In situ measurements of soil suction and water content in deep soil layers still represent an experimental challenge. Mostly developed within agriculture related disciplines, field techniques for the identification of soil retention behaviour have been so far employed in the geotechnical context ...

  16. A Study on DLC Tool Coating for Deep Drawing and Ironing of Stainless Steel

    DEFF Research Database (Denmark)

    Üstünyagiz, Esmeray; Hafis Sulaiman, Mohd; Christiansen, Peter

    2018-01-01

    ) to replicate industrial ironing of deep drawn, stainless steel parts. Non-hazardous tribo-systems in form of a double layer Diamond-like coated tool applied under dry condition or with an environmentally friendly lubricant were investigated via emulating industrial process conditions in laboratory tests...

  17. Convectively-driven cold layer and its influences on moisture in the UTLS

    Science.gov (United States)

    Kim, J.; Randel, W. J.; Birner, T.

    2016-12-01

    Characteristics of the cold anomaly in the tropical tropopause layer (TTL) that is commonly observed with deep convection are examined using CloudSat and Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) GPS radio occultation measurements. Deep convection is sampled based on the cloud top height (>17 km) from CloudSat 2B-CLDCLASS, and then temperature profiles from COSMIC are composited around the deep convection. The composite temperature shows anomalously warm troposphere (up to 14 km) and a significantly cold layer near the tropopause (at 16-18 km) in the regions of deep convection. Generally in the tropics, the cold layer has very large horizontal scale (2,000 - 6,000 km) compared to that of mesoscale convective cluster, and it lasts one or two weeks with minimum temperature anomaly of - 2K. The cold layer shows slight but clear eastward-tilted vertical structure in the deep tropics indicating a large-scale Kelvin wave response. Further analyses on circulation patterns suggest that the anomaly can be explained as a part of Gill-type response in the TTL to deep convective heating in the troposphere. Response of moisture to the cold layer is also examined in the upper troposphere and lower stratosphere using microwave limb sounder (MLS) measurements. The water vapor anomalies show coherent structures with the temperature and circulation anomalies. A clear dry anomaly is found in the cold layer and its outflow region, implying a large-scale dehydration process due to the convectively driven cold layer in the upper TTL.

  18. Deep UV Native Fluorescence Imaging of Antarctic Cryptoendolithic Communities

    Science.gov (United States)

    Storrie-Lombardi, M. C.; Douglas, S.; Sun, H.; McDonald, G. D.; Bhartia, R.; Nealson, K. H.; Hug, W. F.

    2001-01-01

    An interdisciplinary team at the Jet Propulsion Laboratory Center for Life Detection has embarked on a project to provide in situ chemical and morphological characterization of Antarctic cryptoendolithic microbial communities. We present here in situ deep ultraviolet (UV) native fluorescence and environmental scanning electron microscopy images transiting 8.5 mm into a sandstone sample from the Antarctic Dry Valleys. The deep ultraviolet imaging system employs 224.3, 248.6, and 325 nm lasers to elicit differential fluorescence and resonance Raman responses from biomolecules and minerals. The 224.3 and 248.6 nm lasers elicit a fluorescence response from the aromatic amino and nucleic acids. Excitation at 325 nm may elicit activity from a variety of biomolecules, but is more likely to elicit mineral fluorescence. The resultant fluorescence images provide in situ chemical and morphological maps of microorganisms and the associated organic matrix. Visible broadband reflectance images provide orientation against the mineral background. Environmental scanning electron micrographs provided detailed morphological information. The technique has made possible the construction of detailed fluorescent maps extending from the surface of an Antarctic sandstone sample to a depth of 8.5 mm. The images detect no evidence of microbial life in the superficial 0.2 mm crustal layer. The black lichen component between 0.3 and 0.5 mm deep absorbs all wavelengths of both laser and broadband illumination. Filamentous deep ultraviolet native fluorescent activity dominates in the white layer between 0.6 mm and 5.0 mm from the surface. These filamentous forms are fungi that continue into the red (iron-rich) region of the sample extending from 5.0 to 8.5 mm. Using differential image subtraction techniques it is possible to identify fungal nuclei. The ultraviolet response is markedly attenuated in this region, apparently from the absorption of ultraviolet light by iron-rich particles coating

  19. CFD Simulation of Flow Tones from Grazing Flow past a Deep Cavity

    International Nuclear Information System (INIS)

    T Bagwell

    2006-01-01

    Locked-in flow tones due to shear flow over a deep cavity are investigated using Large Eddy Simulation (LES). An isentropic form of the compressible Navier-Stokes equations (pseudo-compressibility) is used to couple the vertical flow over the cavity mouth with the deep cavity resonances (1). Comparisons to published experimental data (2) show that the pseudo-compressible LES formulation is capable of predicting the feedforward excitation of the deep cavity resonator, as well as the feedback process from the resonator to the flow source. By systematically increasing the resonator damping level, it is shown that strong lock-in results in a more organized shear layer than is observed for the locked-out flow state. By comparison, weak interactions (non-locked-in) produce no change in the shear layer characteristics. This supports the 40 dB definition of lock-in defined in the experiment

  20. Uptake of hazardous radionuclides within layered chalcogenide for environmental protection

    Energy Technology Data Exchange (ETDEWEB)

    Sengupta, Pranesh, E-mail: praneshsengupta@gmail.com [Materials Science Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Dudwadkar, N.L. [Fuel Reprocessing Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Vishwanadh, B. [Materials Science Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Pulhani, V. [Health Physics Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Rao, Rekha [Solid State Physics Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Tripathi, S.C. [Fuel Reprocessing Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India); Dey, G.K. [Materials Science Division, Bhabha Atomic Research Centre, Mumbai 400 085 (India)

    2014-02-15

    Highlights: • Layered chalcogenide with CdI{sub 2} crystal structure prepared by hydrothermal route. • Exploration of the possibilities for radionuclides’ uptake using layered chalcogenide. • Proposing ‘topotactic ionic substitution’ as major uptake mechanism. -- Abstract: Ensuring environmental protection in and around nuclear facilities is a matter of deep concern. Toward this, layered chalcogenide with CdI{sub 2} crystal structure has been prepared. Structural characterizations of layered chalcogenide suggest ‘topotactic ionic substitution’ as the dominant mechanism behind uptake of different cations within its lattice structure. An equilibration time of 45 min and volume to mass ratio of 30:1 are found to absorb {sup 233}U, {sup 239}Pu, {sup 106}Ru, {sup 85+89}Sr, {sup 137}Cs and {sup 241}Am radionuclides to the maximum extents.

  1. Engineering Seismic Base Layer for Defining Design Earthquake Motion

    International Nuclear Information System (INIS)

    Yoshida, Nozomu

    2008-01-01

    Engineer's common sense that incident wave is common in a widespread area at the engineering seismic base layer is shown not to be correct. An exhibiting example is first shown, which indicates that earthquake motion at the ground surface evaluated by the analysis considering the ground from a seismic bedrock to a ground surface simultaneously (continuous analysis) is different from the one by the analysis in which the ground is separated at the engineering seismic base layer and analyzed separately (separate analysis). The reason is investigated by several approaches. Investigation based on eigen value problem indicates that the first predominant period in the continuous analysis cannot be found in the separate analysis, and predominant period at higher order does not match in the upper and lower ground in the separate analysis. The earthquake response analysis indicates that reflected wave at the engineering seismic base layer is not zero, which indicates that conventional engineering seismic base layer does not work as expected by the term ''base''. All these results indicate that wave that goes down to the deep depths after reflecting in the surface layer and again reflects at the seismic bedrock cannot be neglected in evaluating the response at the ground surface. In other words, interaction between the surface layer and/or layers between seismic bedrock and engineering seismic base layer cannot be neglected in evaluating the earthquake motion at the ground surface

  2. Brain tumor segmentation with Deep Neural Networks.

    Science.gov (United States)

    Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

    2017-01-01

    In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. Copyright © 2016 Elsevier B.V. All rights reserved.

  3. [Terahertz Spectroscopic Identification with Deep Belief Network].

    Science.gov (United States)

    Ma, Shuai; Shen, Tao; Wang, Rui-qi; Lai, Hua; Yu, Zheng-tao

    2015-12-01

    Feature extraction and classification are the key issues of terahertz spectroscopy identification. Because many materials have no apparent absorption peaks in the terahertz band, it is difficult to extract theirs terahertz spectroscopy feature and identify. To this end, a novel of identify terahertz spectroscopy approach with Deep Belief Network (DBN) was studied in this paper, which combines the advantages of DBN and K-Nearest Neighbors (KNN) classifier. Firstly, cubic spline interpolation and S-G filter were used to normalize the eight kinds of substances (ATP, Acetylcholine Bromide, Bifenthrin, Buprofezin, Carbazole, Bleomycin, Buckminster and Cylotriphosphazene) terahertz transmission spectra in the range of 0.9-6 THz. Secondly, the DBN model was built by two restricted Boltzmann machine (RBM) and then trained layer by layer using unsupervised approach. Instead of using handmade features, the DBN was employed to learn suitable features automatically with raw input data. Finally, a KNN classifier was applied to identify the terahertz spectrum. Experimental results show that using the feature learned by DBN can identify the terahertz spectrum of different substances with the recognition rate of over 90%, which demonstrates that the proposed method can automatically extract the effective features of terahertz spectrum. Furthermore, this KNN classifier was compared with others (BP neural network, SOM neural network and RBF neural network). Comparisons showed that the recognition rate of KNN classifier is better than the other three classifiers. Using the approach that automatic extract terahertz spectrum features by DBN can greatly reduce the workload of feature extraction. This proposed method shows a promising future in the application of identifying the mass terahertz spectroscopy.

  4. Very deep recurrent convolutional neural network for object recognition

    Science.gov (United States)

    Brahimi, Sourour; Ben Aoun, Najib; Ben Amar, Chokri

    2017-03-01

    In recent years, Computer vision has become a very active field. This field includes methods for processing, analyzing, and understanding images. The most challenging problems in computer vision are image classification and object recognition. This paper presents a new approach for object recognition task. This approach exploits the success of the Very Deep Convolutional Neural Network for object recognition. In fact, it improves the convolutional layers by adding recurrent connections. This proposed approach was evaluated on two object recognition benchmarks: Pascal VOC 2007 and CIFAR-10. The experimental results prove the efficiency of our method in comparison with the state of the art methods.

  5. Deep Web and Dark Web: Deep World of the Internet

    OpenAIRE

    Çelik, Emine

    2018-01-01

    The Internet is undoubtedly still a revolutionary breakthrough in the history of humanity. Many people use the internet for communication, social media, shopping, political and social agenda, and more. Deep Web and Dark Web concepts not only handled by computer, software engineers but also handled by social siciensists because of the role of internet for the States in international arenas, public institutions and human life. By the moving point that very importantrole of internet for social s...

  6. Deep Phenotyping: Deep Learning For Temporal Phenotype/Genotype Classification

    OpenAIRE

    Najafi, Mohammad; Namin, Sarah; Esmaeilzadeh, Mohammad; Brown, Tim; Borevitz, Justin

    2017-01-01

    High resolution and high throughput, genotype to phenotype studies in plants are underway to accelerate breeding of climate ready crops. Complex developmental phenotypes are observed by imaging a variety of accessions in different environment conditions, however extracting the genetically heritable traits is challenging. In the recent years, deep learning techniques and in particular Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long-Short Term Memories (LSTMs), h...

  7. Deep Correlated Holistic Metric Learning for Sketch-Based 3D Shape Retrieval.

    Science.gov (United States)

    Dai, Guoxian; Xie, Jin; Fang, Yi

    2018-07-01

    How to effectively retrieve desired 3D models with simple queries is a long-standing problem in computer vision community. The model-based approach is quite straightforward but nontrivial, since people could not always have the desired 3D query model available by side. Recently, large amounts of wide-screen electronic devices are prevail in our daily lives, which makes the sketch-based 3D shape retrieval a promising candidate due to its simpleness and efficiency. The main challenge of sketch-based approach is the huge modality gap between sketch and 3D shape. In this paper, we proposed a novel deep correlated holistic metric learning (DCHML) method to mitigate the discrepancy between sketch and 3D shape domains. The proposed DCHML trains two distinct deep neural networks (one for each domain) jointly, which learns two deep nonlinear transformations to map features from both domains into a new feature space. The proposed loss, including discriminative loss and correlation loss, aims to increase the discrimination of features within each domain as well as the correlation between different domains. In the new feature space, the discriminative loss minimizes the intra-class distance of the deep transformed features and maximizes the inter-class distance of the deep transformed features to a large margin within each domain, while the correlation loss focused on mitigating the distribution discrepancy across different domains. Different from existing deep metric learning methods only with loss at the output layer, our proposed DCHML is trained with loss at both hidden layer and output layer to further improve the performance by encouraging features in the hidden layer also with desired properties. Our proposed method is evaluated on three benchmarks, including 3D Shape Retrieval Contest 2013, 2014, and 2016 benchmarks, and the experimental results demonstrate the superiority of our proposed method over the state-of-the-art methods.

  8. A deep auto-encoder model for gene expression prediction.

    Science.gov (United States)

    Xie, Rui; Wen, Jia; Quitadamo, Andrew; Cheng, Jianlin; Shi, Xinghua

    2017-11-17

    Gene expression is a key intermediate level that genotypes lead to a particular trait. Gene expression is affected by various factors including genotypes of genetic variants. With an aim of delineating the genetic impact on gene expression, we build a deep auto-encoder model to assess how good genetic variants will contribute to gene expression changes. This new deep learning model is a regression-based predictive model based on the MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE). The model is trained using a stacked denoising auto-encoder for feature selection and a multilayer perceptron framework for backpropagation. We further improve the model by introducing dropout to prevent overfitting and improve performance. To demonstrate the usage of this model, we apply MLP-SAE to a real genomic datasets with genotypes and gene expression profiles measured in yeast. Our results show that the MLP-SAE model with dropout outperforms other models including Lasso, Random Forests and the MLP-SAE model without dropout. Using the MLP-SAE model with dropout, we show that gene expression quantifications predicted by the model solely based on genotypes, align well with true gene expression patterns. We provide a deep auto-encoder model for predicting gene expression from SNP genotypes. This study demonstrates that deep learning is appropriate for tackling another genomic problem, i.e., building predictive models to understand genotypes' contribution to gene expression. With the emerging availability of richer genomic data, we anticipate that deep learning models play a bigger role in modeling and interpreting genomics.

  9. Deep transfer learning for automatic target classification: MWIR to LWIR

    Science.gov (United States)

    Ding, Zhengming; Nasrabadi, Nasser; Fu, Yun

    2016-05-01

    Publisher's Note: This paper, originally published on 5/12/2016, was replaced with a corrected/revised version on 5/18/2016. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance. When dealing with sparse or no labeled data in the target domain, transfer learning shows its appealing performance by borrowing the supervised knowledge from external domains. Recently deep structure learning has been exploited in transfer learning due to its attractive power in extracting effective knowledge through multi-layer strategy, so that deep transfer learning is promising to address the cross-domain mismatch. In general, cross-domain disparity can be resulted from the difference between source and target distributions or different modalities, e.g., Midwave IR (MWIR) and Longwave IR (LWIR). In this paper, we propose a Weighted Deep Transfer Learning framework for automatic target classification through a task-driven fashion. Specifically, deep features and classifier parameters are obtained simultaneously for optimal classification performance. In this way, the proposed deep structures can extract more effective features with the guidance of the classifier performance; on the other hand, the classifier performance is further improved since it is optimized on more discriminative features. Furthermore, we build a weighted scheme to couple source and target output by assigning pseudo labels to target data, therefore we can transfer knowledge from source (i.e., MWIR) to target (i.e., LWIR). Experimental results on real databases demonstrate the superiority of the proposed algorithm by comparing with others.

  10. Seismic Wave Propagation in Layered Viscoelastic Media

    Science.gov (United States)

    Borcherdt, R. D.

    2008-12-01

    Advances in the general theory of wave propagation in layered viscoelastic media reveal new insights regarding seismic waves in the Earth. For example, the theory predicts: 1) P and S waves are predominantly inhomogeneous in a layered anelastic Earth with seismic travel times, particle-motion orbits, energy speeds, Q, and amplitude characteristics that vary with angle of incidence and hence, travel path through the layers, 2) two types of shear waves exist, one with linear and the other with elliptical particle motions each with different absorption coefficients, and 3) surface waves with amplitude and particle motion characteristics not predicted by elasticity, such as Rayleigh-Type waves with tilted elliptical particle motion orbits and Love-Type waves with superimposed sinusoidal amplitude dependencies that decay exponentially with depth. The general theory provides closed-form analytic solutions for body waves, reflection-refraction problems, response of multiple layers, and surface wave problems valid for any material with a viscoelastic response, including the infinite number of models, derivable from various configurations of springs and dashpots, such as elastic, Voight, Maxwell, and Standard Linear. The theory provides solutions independent of the amount of intrinsic absorption and explicit analytic expressions for physical characteristics of body waves in low-loss media such as the deep Earth. The results explain laboratory and seismic observations, such as travel-time and wide-angle reflection amplitude anomalies, not explained by elasticity or one dimensional Q models. They have important implications for some forward modeling and inverse problems. Theoretical advances and corresponding numerical results as recently compiled (Borcherdt, 2008, Viscoelastic Waves in Layered Media, Cambridge University Press) will be reviewed.

  11. Deep Neuromuscular Blockade Improves Laparoscopic Surgical Conditions

    DEFF Research Database (Denmark)

    Rosenberg, Jacob; Herring, W Joseph; Blobner, Manfred

    2017-01-01

    INTRODUCTION: Sustained deep neuromuscular blockade (NMB) during laparoscopic surgery may facilitate optimal surgical conditions. This exploratory study assessed whether deep NMB improves surgical conditions and, in doing so, allows use of lower insufflation pressures during laparoscopic cholecys...

  12. Stability of mixing layers

    Science.gov (United States)

    Tam, Christopher; Krothapalli, A

    1993-01-01

    The research program for the first year of this project (see the original research proposal) consists of developing an explicit marching scheme for solving the parabolized stability equations (PSE). Performing mathematical analysis of the computational algorithm including numerical stability analysis and the determination of the proper boundary conditions needed at the boundary of the computation domain are implicit in the task. Before one can solve the parabolized stability equations for high-speed mixing layers, the mean flow must first be found. In the past, instability analysis of high-speed mixing layer has mostly been performed on mean flow profiles calculated by the boundary layer equations. In carrying out this project, it is believed that the boundary layer equations might not give an accurate enough nonparallel, nonlinear mean flow needed for parabolized stability analysis. A more accurate mean flow can, however, be found by solving the parabolized Navier-Stokes equations. The advantage of the parabolized Navier-Stokes equations is that its accuracy is consistent with the PSE method. Furthermore, the method of solution is similar. Hence, the major part of the effort of the work of this year has been devoted to the development of an explicit numerical marching scheme for the solution of the Parabolized Navier-Stokes equation as applied to the high-seed mixing layer problem.

  13. Modeling language and cognition with deep unsupervised learning: a tutorial overview.

    Science.gov (United States)

    Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

  14. Modeling Language and Cognition with Deep Unsupervised Learning:A Tutorial Overview

    Directory of Open Access Journals (Sweden)

    Marco eZorzi

    2013-08-01

    Full Text Available Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981 is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition.

  15. Modeling language and cognition with deep unsupervised learning: a tutorial overview

    Science.gov (United States)

    Zorzi, Marco; Testolin, Alberto; Stoianov, Ivilin P.

    2013-01-01

    Deep unsupervised learning in stochastic recurrent neural networks with many layers of hidden units is a recent breakthrough in neural computation research. These networks build a hierarchy of progressively more complex distributed representations of the sensory data by fitting a hierarchical generative model. In this article we discuss the theoretical foundations of this approach and we review key issues related to training, testing and analysis of deep networks for modeling language and cognitive processing. The classic letter and word perception problem of McClelland and Rumelhart (1981) is used as a tutorial example to illustrate how structured and abstract representations may emerge from deep generative learning. We argue that the focus on deep architectures and generative (rather than discriminative) learning represents a crucial step forward for the connectionist modeling enterprise, because it offers a more plausible model of cortical learning as well as a way to bridge the gap between emergentist connectionist models and structured Bayesian models of cognition. PMID:23970869

  16. Surface Improvement of Shafts by Turn-Assisted Deep Cold Rolling Process

    Directory of Open Access Journals (Sweden)

    Prabhu Raghavendra

    2016-01-01

    Full Text Available It is well recognized that mechanical surface enhancement methods can significantly improve the characteristics of highly-stressed metallic components. Deep cold rolling is one of such technique which is particularly attractive since it is possible to generate, near the surface, deep compressive residual stresses and work hardened layers while retaining a relatively smooth surface finish. In this paper, the effect of turn-assisted deep cold rolling on AISI 4140 steel is examined, with emphasis on the residual stress state. Based on the X-ray diffraction measurements, it is found that turn-assisted deep cold rolling can be quite effective in retarding the initiation and initial propagation of fatigue cracks in AISI 4140 steel.

  17. Building Program Vector Representations for Deep Learning

    OpenAIRE

    Mou, Lili; Li, Ge; Liu, Yuxuan; Peng, Hao; Jin, Zhi; Xu, Yan; Zhang, Lu

    2014-01-01

    Deep learning has made significant breakthroughs in various fields of artificial intelligence. Advantages of deep learning include the ability to capture highly complicated features, weak involvement of human engineering, etc. However, it is still virtually impossible to use deep learning to analyze programs since deep architectures cannot be trained effectively with pure back propagation. In this pioneering paper, we propose the "coding criterion" to build program vector representations, whi...

  18. Deep-Focusing Time-Distance Helioseismology

    Science.gov (United States)

    Duvall, T. L., Jr.; Jensen, J. M.; Kosovichev, A. G.; Birch, A. C.; Fisher, Richard R. (Technical Monitor)

    2001-01-01

    Much progress has been made by measuring the travel times of solar acoustic waves from a central surface location to points at equal arc distance away. Depth information is obtained from the range of arc distances examined, with the larger distances revealing the deeper layers. This method we will call surface-focusing, as the common point, or focus, is at the surface. To obtain a clearer picture of the subsurface region, it would, no doubt, be better to focus on points below the surface. Our first attempt to do this used the ray theory to pick surface location pairs that would focus on a particular subsurface point. This is not the ideal procedure, as Born approximation kernels suggest that this focus should have zero sensitivity to sound speed inhomogeneities. However, the sensitivity is concentrated below the surface in a much better way than the old surface-focusing method, and so we expect the deep-focusing method to be more sensitive. A large sunspot group was studied by both methods. Inversions based on both methods will be compared.

  19. Deep-sea bioluminescence blooms after dense water formation at the ocean surface.

    Directory of Open Access Journals (Sweden)

    Christian Tamburini

    Full Text Available The deep ocean is the largest and least known ecosystem on Earth. It hosts numerous pelagic organisms, most of which are able to emit light. Here we present a unique data set consisting of a 2.5-year long record of light emission by deep-sea pelagic organisms, measured from December 2007 to June 2010 at the ANTARES underwater neutrino telescope in the deep NW Mediterranean Sea, jointly with synchronous hydrological records. This is the longest continuous time-series of deep-sea bioluminescence ever recorded. Our record reveals several weeks long, seasonal bioluminescence blooms with light intensity up to two orders of magnitude higher than background values, which correlate to changes in the properties of deep waters. Such changes are triggered by the winter cooling and evaporation experienced by the upper ocean layer in the Gulf of Lion that leads to the formation and subsequent sinking of dense water through a process known as "open-sea convection". It episodically renews the deep water of the study area and conveys fresh organic matter that fuels the deep ecosystems. Luminous bacteria most likely are the main contributors to the observed deep-sea bioluminescence blooms. Our observations demonstrate a consistent and rapid connection between deep open-sea convection and bathypelagic biological activity, as expressed by bioluminescence. In a setting where dense water formation events are likely to decline under global warming scenarios enhancing ocean stratification, in situ observatories become essential as environmental sentinels for the monitoring and understanding of deep-sea ecosystem shifts.

  20. HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

    Science.gov (United States)

    Fan, Jianping; Zhao, Tianyi; Kuang, Zhenzhong; Zheng, Yu; Zhang, Ji; Yu, Jun; Peng, Jinye

    2017-02-09

    In this paper, a hierarchical deep multi-task learning (HD-MTL) algorithm is developed to support large-scale visual recognition (e.g., recognizing thousands or even tens of thousands of atomic object classes automatically). First, multiple sets of multi-level deep features are extracted from different layers of deep convolutional neural networks (deep CNNs), and they are used to achieve more effective accomplishment of the coarseto- fine tasks for hierarchical visual recognition. A visual tree is then learned by assigning the visually-similar atomic object classes with similar learning complexities into the same group, which can provide a good environment for determining the interrelated learning tasks automatically. By leveraging the inter-task relatedness (inter-class similarities) to learn more discriminative group-specific deep representations, our deep multi-task learning algorithm can train more discriminative node classifiers for distinguishing the visually-similar atomic object classes effectively. Our hierarchical deep multi-task learning (HD-MTL) algorithm can integrate two discriminative regularization terms to control the inter-level error propagation effectively, and it can provide an end-to-end approach for jointly learning more representative deep CNNs (for image representation) and more discriminative tree classifier (for large-scale visual recognition) and updating them simultaneously. Our incremental deep learning algorithms can effectively adapt both the deep CNNs and the tree classifier to the new training images and the new object classes. Our experimental results have demonstrated that our HD-MTL algorithm can achieve very competitive results on improving the accuracy rates for large-scale visual recognition.

  1. Deep-sea bioluminescence blooms after dense water formation at the ocean surface.

    Science.gov (United States)

    Tamburini, Christian; Canals, Miquel; Durrieu de Madron, Xavier; Houpert, Loïc; Lefèvre, Dominique; Martini, Séverine; D'Ortenzio, Fabrizio; Robert, Anne; Testor, Pierre; Aguilar, Juan Antonio; Samarai, Imen Al; Albert, Arnaud; André, Michel; Anghinolfi, Marco; Anton, Gisela; Anvar, Shebli; Ardid, Miguel; Jesus, Ana Carolina Assis; Astraatmadja, Tri L; Aubert, Jean-Jacques; Baret, Bruny; Basa, Stéphane; Bertin, Vincent; Biagi, Simone; Bigi, Armando; Bigongiari, Ciro; Bogazzi, Claudio; Bou-Cabo, Manuel; Bouhou, Boutayeb; Bouwhuis, Mieke C; Brunner, Jurgen; Busto, José; Camarena, Francisco; Capone, Antonio; Cârloganu, Christina; Carminati, Giada; Carr, John; Cecchini, Stefano; Charif, Ziad; Charvis, Philippe; Chiarusi, Tommaso; Circella, Marco; Coniglione, Rosa; Costantini, Heide; Coyle, Paschal; Curtil, Christian; Decowski, Patrick; Dekeyser, Ivan; Deschamps, Anne; Donzaud, Corinne; Dornic, Damien; Dorosti, Hasankiadeh Q; Drouhin, Doriane; Eberl, Thomas; Emanuele, Umberto; Ernenwein, Jean-Pierre; Escoffier, Stéphanie; Fermani, Paolo; Ferri, Marcelino; Flaminio, Vincenzo; Folger, Florian; Fritsch, Ulf; Fuda, Jean-Luc; Galatà, Salvatore; Gay, Pascal; Giacomelli, Giorgio; Giordano, Valentina; Gómez-González, Juan-Pablo; Graf, Kay; Guillard, Goulven; Halladjian, Garadeb; Hallewell, Gregory; van Haren, Hans; Hartman, Joris; Heijboer, Aart J; Hello, Yann; Hernández-Rey, Juan Jose; Herold, Bjoern; Hößl, Jurgen; Hsu, Ching-Cheng; de Jong, Marteen; Kadler, Matthias; Kalekin, Oleg; Kappes, Alexander; Katz, Uli; Kavatsyuk, Oksana; Kooijman, Paul; Kopper, Claudio; Kouchner, Antoine; Kreykenbohm, Ingo; Kulikovskiy, Vladimir; Lahmann, Robert; Lamare, Patrick; Larosa, Giuseppina; Lattuada, Dario; Lim, Gordon; Presti, Domenico Lo; Loehner, Herbert; Loucatos, Sotiris; Mangano, Salvatore; Marcelin, Michel; Margiotta, Annarita; Martinez-Mora, Juan Antonio; Meli, Athina; Montaruli, Teresa; Moscoso, Luciano; Motz, Holger; Neff, Max; Nezri, Emma Nuel; Palioselitis, Dimitris; Păvălaş, Gabriela E; Payet, Kevin; Payre, Patrice; Petrovic, Jelena; Piattelli, Paolo; Picot-Clemente, Nicolas; Popa, Vlad; Pradier, Thierry; Presani, Eleonora; Racca, Chantal; Reed, Corey; Riccobene, Giorgio; Richardt, Carsten; Richter, Roland; Rivière, Colas; Roensch, Kathrin; Rostovtsev, Andrei; Ruiz-Rivas, Joaquin; Rujoiu, Marius; Russo, Valerio G; Salesa, Francisco; Sánchez-Losa, Augustin; Sapienza, Piera; Schöck, Friederike; Schuller, Jean-Pierre; Schussler, Fabian; Shanidze, Rezo; Simeone, Francesco; Spies, Andreas; Spurio, Maurizio; Steijger, Jos J M; Stolarczyk, Thierry; Taiuti, Mauro G F; Toscano, Simona; Vallage, Bertrand; Van Elewyck, Véronique; Vannoni, Giulia; Vecchi, Manuela; Vernin, Pascal; Wijnker, Guus; Wilms, Jorn; de Wolf, Els; Yepes, Harold; Zaborov, Dmitry; De Dios Zornoza, Juan; Zúñiga, Juan

    2013-01-01

    The deep ocean is the largest and least known ecosystem on Earth. It hosts numerous pelagic organisms, most of which are able to emit light. Here we present a unique data set consisting of a 2.5-year long record of light emission by deep-sea pelagic organisms, measured from December 2007 to June 2010 at the ANTARES underwater neutrino telescope in the deep NW Mediterranean Sea, jointly with synchronous hydrological records. This is the longest continuous time-series of deep-sea bioluminescence ever recorded. Our record reveals several weeks long, seasonal bioluminescence blooms with light intensity up to two orders of magnitude higher than background values, which correlate to changes in the properties of deep waters. Such changes are triggered by the winter cooling and evaporation experienced by the upper ocean layer in the Gulf of Lion that leads to the formation and subsequent sinking of dense water through a process known as "open-sea convection". It episodically renews the deep water of the study area and conveys fresh organic matter that fuels the deep ecosystems. Luminous bacteria most likely are the main contributors to the observed deep-sea bioluminescence blooms. Our observations demonstrate a consistent and rapid connection between deep open-sea convection and bathypelagic biological activity, as expressed by bioluminescence. In a setting where dense water formation events are likely to decline under global warming scenarios enhancing ocean stratification, in situ observatories become essential as environmental sentinels for the monitoring and understanding of deep-sea ecosystem shifts.

  2. GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform

    Directory of Open Access Journals (Sweden)

    Syed Tahir Hussain Rizvi

    2016-12-01

    Full Text Available Deep convolutional neural networks achieve state-of-the-art performance in image classification. The computational and memory requirements of such networks are however huge, and that is an issue on embedded devices due to their constraints. Most of this complexity derives from the convolutional layers and in particular from the matrix multiplications they entail. This paper proposes a complete approach to image classification providing common layers used in neural networks. Namely, the proposed approach relies on a heterogeneous CPU-GPU scheme for performing convolutions in the transform domain. The Compute Unified Device Architecture(CUDA-based implementation of the proposed approach is evaluated over three different image classification networks on a Tegra K1 CPU-GPU mobile processor. Experiments show that the presented heterogeneous scheme boasts a 50× speedup over the CPU-only reference and outperforms a GPU-based reference by 2×, while slashing the power consumption by nearly 30%.

  3. Random synaptic feedback weights support error backpropagation for deep learning

    Science.gov (United States)

    Lillicrap, Timothy P.; Cownden, Daniel; Tweed, Douglas B.; Akerman, Colin J.

    2016-01-01

    The brain processes information through multiple layers of neurons. This deep architecture is representationally powerful, but complicates learning because it is difficult to identify the responsible neurons when a mistake is made. In machine learning, the backpropagation algorithm assigns blame by multiplying error signals with all the synaptic weights on each neuron's axon and further downstream. However, this involves a precise, symmetric backward connectivity pattern, which is thought to be impossible in the brain. Here we demonstrate that this strong architectural constraint is not required for effective error propagation. We present a surprisingly simple mechanism that assigns blame by multiplying errors by even random synaptic weights. This mechanism can transmit teaching signals across multiple layers of neurons and performs as effectively as backpropagation on a variety of tasks. Our results help reopen questions about how the brain could use error signals and dispel long-held assumptions about algorithmic constraints on learning. PMID:27824044

  4. Is Multitask Deep Learning Practical for Pharma?

    Science.gov (United States)

    Ramsundar, Bharath; Liu, Bowen; Wu, Zhenqin; Verras, Andreas; Tudor, Matthew; Sheridan, Robert P; Pande, Vijay

    2017-08-28

    Multitask deep learning has emerged as a powerful tool for computational drug discovery. However, despite a number of preliminary studies, multitask deep networks have yet to be widely deployed in the pharmaceutical and biotech industries. This lack of acceptance stems from both software difficulties and lack of understanding of the robustness of multitask deep networks. Our work aims to resolve both of these barriers to adoption. We introduce a high-quality open-source implementation of multitask deep networks as part of the DeepChem open-source platform. Our implementation enables simple python scripts to construct, fit, and evaluate sophisticated deep models. We use our implementation to analyze the performance of multitask deep networks and related deep models on four collections of pharmaceutical data (three of which have not previously been analyzed in the literature). We split these data sets into train/valid/test using time and neighbor splits to test multitask deep learning performance under challenging conditions. Our results demonstrate that multitask deep networks are surprisingly robust and can offer strong improvement over random forests. Our analysis and open-source implementation in DeepChem provide an argument that multitask deep networks are ready for widespread use in commercial drug discovery.

  5. Evaluation of the DeepWind concept

    DEFF Research Database (Denmark)

    Schmidt Paulsen, Uwe; Borg, Michael; Gonzales Seabra, Luis Alberto

    The report describes the DeepWind 5 MW conceptual design as a baseline for results obtained in the scientific and technical work packages of the DeepWind project. A comparison of DeepWi nd with existing VAWTs and paper projects are carried out and the evaluation of the concept in terms of cost...

  6. Consolidated Deep Actor Critic Networks (DRAFT)

    NARCIS (Netherlands)

    Van der Laan, T.A.

    2015-01-01

    The works [Volodymyr et al. Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602, 2013.] and [Volodymyr et al. Human-level control through deep reinforcement learning. Nature, 518(7540):529–533, 2015.] have demonstrated the power of combining deep neural networks with

  7. Simulator Studies of the Deep Stall

    Science.gov (United States)

    White, Maurice D.; Cooper, George E.

    1965-01-01

    Simulator studies of the deep-stall problem encountered with modern airplanes are discussed. The results indicate that the basic deep-stall tendencies produced by aerodynamic characteristics are augmented by operational considerations. Because of control difficulties to be anticipated in the deep stall, it is desirable that adequate safeguards be provided against inadvertent penetrations.

  8. TOPIC MODELING: CLUSTERING OF DEEP WEBPAGES

    OpenAIRE

    Muhunthaadithya C; Rohit J.V; Sadhana Kesavan; E. Sivasankar

    2015-01-01

    The internet is comprised of massive amount of information in the form of zillions of web pages.This information can be categorized into the surface web and the deep web. The existing search engines can effectively make use of surface web information.But the deep web remains unexploited yet. Machine learning techniques have been commonly employed to access deep web content.

  9. Three-layer magnetoconvection

    International Nuclear Information System (INIS)

    Lin, M.-K.; Silvers, L.J.; Proctor, M.R.E.

    2008-01-01

    It is believed that some stars have two or more convection zones in close proximity near to the stellar photosphere. These zones are separated by convectively stable regions that are relatively narrow. Due to the close proximity of these regions it is important to construct mathematical models to understand the transport and mixing of passive and dynamic quantities. One key quantity of interest is a magnetic field, a dynamic vector quantity, that can drastically alter the convectively driven flows, and have an important role in coupling the different layers. In this Letter we present the first investigation into the effect of an imposed magnetic field in such a geometry. We focus our attention on the effect of field strength and show that, while there are some similarities with results for magnetic field evolution in a single layer, new and interesting phenomena are also present in a three layer system

  10. Layered tin dioxide microrods

    International Nuclear Information System (INIS)

    Duan Junhong; Huang Hongbo; Gong Jiangfeng; Zhao Xiaoning; Cheng Guangxu; Yang Shaoguang

    2007-01-01

    Single-crystalline layered SnO 2 microrods were synthesized by a simple tin-water reaction at 900 deg. C. The structural and optical properties of the sample were characterized by x-ray powder diffraction, energy-dispersive x-ray spectroscopy, scanning electron microscopy, high resolution transmission electron microscopy, Raman scattering and photoluminescence (PL) spectroscopy. High resolution transmission electron microscopy studies and selected area electron diffraction patterns revealed that the layered SnO 2 microrods are single crystalline and their growth direction is along [1 1 0]. The growth mechanism of the microrods was proposed based on SEM, TEM characterization and thermodynamic analysis. It is deduced that the layered microrods grow by the stacking of SnO 2 sheets with a (1 1 0) surface in a vapour-liquid-solid process. Three emission peaks at 523, 569 and 626 nm were detected in room-temperature PL measurements

  11. Superfluid Boundary Layer.

    Science.gov (United States)

    Stagg, G W; Parker, N G; Barenghi, C F

    2017-03-31

    We model the superfluid flow of liquid helium over the rough surface of a wire (used to experimentally generate turbulence) profiled by atomic force microscopy. Numerical simulations of the Gross-Pitaevskii equation reveal that the sharpest features in the surface induce vortex nucleation both intrinsically (due to the raised local fluid velocity) and extrinsically (providing pinning sites to vortex lines aligned with the flow). Vortex interactions and reconnections contribute to form a dense turbulent layer of vortices with a nonclassical average velocity profile which continually sheds small vortex rings into the bulk. We characterize this layer for various imposed flows. As boundary layers conventionally arise from viscous forces, this result opens up new insight into the nature of superflows.

  12. Quantum dot laser optimization: selectively doped layers

    Science.gov (United States)

    Korenev, Vladimir V.; Konoplev, Sergey S.; Savelyev, Artem V.; Shernyakov, Yurii M.; Maximov, Mikhail V.; Zhukov, Alexey E.

    2016-08-01

    Edge emitting quantum dot (QD) lasers are discussed. It has been recently proposed to use modulation p-doping of the layers that are adjacent to QD layers in order to control QD's charge state. Experimentally it has been proven useful to enhance ground state lasing and suppress the onset of excited state lasing at high injection. These results have been also confirmed with numerical calculations involving solution of drift-diffusion equations. However, deep understanding of physical reasons for such behavior and laser optimization requires analytical approaches to the problem. In this paper, under a set of assumptions we provide an analytical model that explains major effects of selective p-doping. Capture rates of elections and holes can be calculated by solving Poisson equations for electrons and holes around the charged QD layer. The charge itself is ruled by capture rates and selective doping concentration. We analyzed this self-consistent set of equations and showed that it can be used to optimize QD laser performance and to explain underlying physics.

  13. Quantum dot laser optimization: selectively doped layers

    International Nuclear Information System (INIS)

    Korenev, Vladimir V; Konoplev, Sergey S; Savelyev, Artem V; Shernyakov, Yurii M; Maximov, Mikhail V; Zhukov, Alexey E

    2016-01-01

    Edge emitting quantum dot (QD) lasers are discussed. It has been recently proposed to use modulation p-doping of the layers that are adjacent to QD layers in order to control QD's charge state. Experimentally it has been proven useful to enhance ground state lasing and suppress the onset of excited state lasing at high injection. These results have been also confirmed with numerical calculations involving solution of drift-diffusion equations. However, deep understanding of physical reasons for such behavior and laser optimization requires analytical approaches to the problem. In this paper, under a set of assumptions we provide an analytical model that explains major effects of selective p-doping. Capture rates of elections and holes can be calculated by solving Poisson equations for electrons and holes around the charged QD layer. The charge itself is ruled by capture rates and selective doping concentration. We analyzed this self-consistent set of equations and showed that it can be used to optimize QD laser performance and to explain underlying physics. (paper)

  14. Stoner magnetism in an inversion layer

    Energy Technology Data Exchange (ETDEWEB)

    Golosov, D.I., E-mail: Denis.Golosov@biu.ac.il

    2016-02-15

    Motivated by recent experimental work on magnetic properties of Si-MOSFETs, we report a calculation of magnetisation and susceptibility of electrons in an inversion layer, taking into account the co-ordinate dependence of electron wave function in the direction perpendicular to the plane. It is assumed that the inversion-layer carriers interact via a contact repulsive potential, which is treated at a mean-field level, resulting in a self-consistent change of profile of the wave functions. We find that the results differ significantly from those obtained in the pure 2DEG case (where no provision is made for a quantum motion in the transverse direction). Specifically, the critical value of interaction needed to attain the ferromagnetic (Stoner) instability is decreased and the Stoner criterion is therefore relaxed. This leads to an increased susceptibility and ultimately to a ferromagnetic transition deep in the high-density metallic regime. In the opposite limit of low carrier densities, a phenomenological treatment of the in-plane correlation effects suggests a ferromagnetic instability above the metal–insulator transition. Results are discussed in the context of the available experimental data. - Highlights: • Stoner-type mean field theory for electrons in an inversion layer is constructed. • Wave function change under an in-plane magnetic field is taken into account. • Tendency toward ferromagnetism is strengthened in comparison with a usual Stoner theory. • In-plane correlations at low densities are taken into account phenomenologically.

  15. DeepFlavour in CMS

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Flavour-tagging of jets is an important task in collider based high energy physics and a field where machine learning tools are applied by all major experiments. A new tagger (DeepFlavour) was developed and commissioned in CMS that is based on an advanced machine learning procedure. A deep neural network is used to do multi-classification of jets that origin from a b-quark, two b-quarks, a c-quark, two c-quarks or light colored particles (u, d, s-quark or gluon). The performance was measured in both, data and simulation. The talk will also include the measured performance of all taggers in CMS. The different taggers and results will be discussed and compared with some focus on details of the newest tagger.

  16. Deep Space Habitat Concept Demonstrator

    Science.gov (United States)

    Bookout, Paul S.; Smitherman, David

    2015-01-01

    This project will develop, integrate, test, and evaluate Habitation Systems that will be utilized as technology testbeds and will advance NASA's understanding of alternative deep space mission architectures, requirements, and operations concepts. Rapid prototyping and existing hardware will be utilized to develop full-scale habitat demonstrators. FY 2014 focused on the development of a large volume Space Launch System (SLS) class habitat (Skylab Gen 2) based on the SLS hydrogen tank components. Similar to the original Skylab, a tank section of the SLS rocket can be outfitted with a deep space habitat configuration and launched as a payload on an SLS rocket. This concept can be used to support extended stay at the Lunar Distant Retrograde Orbit to support the Asteroid Retrieval Mission and provide a habitat suitable for human missions to Mars.

  17. Hybrid mask for deep etching

    KAUST Repository

    Ghoneim, Mohamed T.

    2017-08-10

    Deep reactive ion etching is essential for creating high aspect ratio micro-structures for microelectromechanical systems, sensors and actuators, and emerging flexible electronics. A novel hybrid dual soft/hard mask bilayer may be deposited during semiconductor manufacturing for deep reactive etches. Such a manufacturing process may include depositing a first mask material on a substrate; depositing a second mask material on the first mask material; depositing a third mask material on the second mask material; patterning the third mask material with a pattern corresponding to one or more trenches for transfer to the substrate; transferring the pattern from the third mask material to the second mask material; transferring the pattern from the second mask material to the first mask material; and/or transferring the pattern from the first mask material to the substrate.

  18. Resolution of Singularities Introduced by Hierarchical Structure in Deep Neural Networks.

    Science.gov (United States)

    Nitta, Tohru

    2017-10-01

    We present a theoretical analysis of singular points of artificial deep neural networks, resulting in providing deep neural network models having no critical points introduced by a hierarchical structure. It is considered that such deep neural network models have good nature for gradient-based optimization. First, we show that there exist a large number of critical points introduced by a hierarchical structure in deep neural networks as straight lines, depending on the number of hidden layers and the number of hidden neurons. Second, we derive a sufficient condition for deep neural networks having no critical points introduced by a hierarchical structure, which can be applied to general deep neural networks. It is also shown that the existence of critical points introduced by a hierarchical structure is determined by the rank and the regularity of weight matrices for a specific class of deep neural networks. Finally, two kinds of implementation methods of the sufficient conditions to have no critical points are provided. One is a learning algorithm that can avoid critical points introduced by the hierarchical structure during learning (called avoidant learning algorithm). The other is a neural network that does not have some critical points introduced by the hierarchical structure as an inherent property (called avoidant neural network).

  19. Layered semiconductor neutron detectors

    Science.gov (United States)

    Mao, Samuel S; Perry, Dale L

    2013-12-10

    Room temperature operating solid state hand held neutron detectors integrate one or more relatively thin layers of a high neutron interaction cross-section element or materials with semiconductor detectors. The high neutron interaction cross-section element (e.g., Gd, B or Li) or materials comprising at least one high neutron interaction cross-section element can be in the form of unstructured layers or micro- or nano-structured arrays. Such architecture provides high efficiency neutron detector devices by capturing substantially more carriers produced from high energy .alpha.-particles or .gamma.-photons generated by neutron interaction.

  20. Complex approach mechanical properties and formability assessment of selected deep-drawing steels

    Directory of Open Access Journals (Sweden)

    J. Štaba

    2009-07-01

    Full Text Available The paper analyses the properties of deep-drawing sheets of three grades (Re = 320 to 475 MPa, surface-treated with hot-dip galvanizing, made of microalloyed steels. Deformation properties are assessed using tensile tests, technological Erichsen or cupping tests. These characteristics, as well as the behaviour of the surface layer, are also investigated under dynamic conditions (modified Erichsen test using a drop tester, or using flat bending fatigue tests. Using microscopic analysis the deformation properties of the surface layer are evaluated. The results show the compactness of the surface layer, high deformation characteristics, as well as fatigue properties of the investigated deep-drawing materials, suitable for application in the automotive industry.

  1. Evolving Deep Networks Using HPC

    Energy Technology Data Exchange (ETDEWEB)

    Young, Steven R. [ORNL, Oak Ridge; Rose, Derek C. [ORNL, Oak Ridge; Johnston, Travis [ORNL, Oak Ridge; Heller, William T. [ORNL, Oak Ridge; Karnowski, thomas P. [ORNL, Oak Ridge; Potok, Thomas E. [ORNL, Oak Ridge; Patton, Robert M. [ORNL, Oak Ridge; Perdue, Gabriel [Fermilab; Miller, Jonathan [Santa Maria U., Valparaiso

    2017-01-01

    While a large number of deep learning networks have been studied and published that produce outstanding results on natural image datasets, these datasets only make up a fraction of those to which deep learning can be applied. These datasets include text data, audio data, and arrays of sensors that have very different characteristics than natural images. As these “best” networks for natural images have been largely discovered through experimentation and cannot be proven optimal on some theoretical basis, there is no reason to believe that they are the optimal network for these drastically different datasets. Hyperparameter search is thus often a very important process when applying deep learning to a new problem. In this work we present an evolutionary approach to searching the possible space of network hyperparameters and construction that can scale to 18, 000 nodes. This approach is applied to datasets of varying types and characteristics where we demonstrate the ability to rapidly find best hyperparameters in order to enable practitioners to quickly iterate between idea and result.

  2. Deep Space Gateway Science Opportunities

    Science.gov (United States)

    Quincy, C. D.; Charles, J. B.; Hamill, Doris; Sidney, S. C.

    2018-01-01

    The NASA Life Sciences Research Capabilities Team (LSRCT) has been discussing deep space research needs for the last two years. NASA's programs conducting life sciences studies - the Human Research Program, Space Biology, Astrobiology, and Planetary Protection - see the Deep Space Gateway (DSG) as affording enormous opportunities to investigate biological organisms in a unique environment that cannot be replicated in Earth-based laboratories or on Low Earth Orbit science platforms. These investigations may provide in many cases the definitive answers to risks associated with exploration and living outside Earth's protective magnetic field. Unlike Low Earth Orbit or terrestrial locations, the Gateway location will be subjected to the true deep space spectrum and influence of both galactic cosmic and solar particle radiation and thus presents an opportunity to investigate their long-term exposure effects. The question of how a community of biological organisms change over time within the harsh environment of space flight outside of the magnetic field protection can be investigated. The biological response to the absence of Earth's geomagnetic field can be studied for the first time. Will organisms change in new and unique ways under these new conditions? This may be specifically true on investigations of microbial communities. The Gateway provides a platform for microbiology experiments both inside, to improve understanding of interactions between microbes and human habitats, and outside, to improve understanding of microbe-hardware interactions exposed to the space environment.

  3. Deep water recycling through time.

    Science.gov (United States)

    Magni, Valentina; Bouilhol, Pierre; van Hunen, Jeroen

    2014-11-01

    We investigate the dehydration processes in subduction zones and their implications for the water cycle throughout Earth's history. We use a numerical tool that combines thermo-mechanical models with a thermodynamic database to examine slab dehydration for present-day and early Earth settings and its consequences for the deep water recycling. We investigate the reactions responsible for releasing water from the crust and the hydrated lithospheric mantle and how they change with subduction velocity ( v s ), slab age ( a ) and mantle temperature (T m ). Our results show that faster slabs dehydrate over a wide area: they start dehydrating shallower and they carry water deeper into the mantle. We parameterize the amount of water that can be carried deep into the mantle, W (×10 5 kg/m 2 ), as a function of v s (cm/yr), a (Myrs), and T m (°C):[Formula: see text]. We generally observe that a 1) 100°C increase in the mantle temperature, or 2) ∼15 Myr decrease of plate age, or 3) decrease in subduction velocity of ∼2 cm/yr all have the same effect on the amount of water retained in the slab at depth, corresponding to a decrease of ∼2.2×10 5 kg/m 2 of H 2 O. We estimate that for present-day conditions ∼26% of the global influx water, or 7×10 8 Tg/Myr of H 2 O, is recycled into the mantle. Using a realistic distribution of subduction parameters, we illustrate that deep water recycling might still be possible in early Earth conditions, although its efficiency would generally decrease. Indeed, 0.5-3.7 × 10 8 Tg/Myr of H 2 O could still be recycled in the mantle at 2.8 Ga. Deep water recycling might be possible even in early Earth conditions We provide a scaling law to estimate the amount of H 2 O flux deep into the mantle Subduction velocity has a a major control on the crustal dehydration pattern.

  4. Vision in the deep sea.

    Science.gov (United States)

    Warrant, Eric J; Locket, N Adam

    2004-08-01

    The deep sea is the largest habitat on earth. Its three great faunal environments--the twilight mesopelagic zone, the dark bathypelagic zone and the vast flat expanses of the benthic habitat--are home to a rich fauna of vertebrates and invertebrates. In the mesopelagic zone (150-1000 m), the down-welling daylight creates an extended scene that becomes increasingly dimmer and bluer with depth. The available daylight also originates increasingly from vertically above, and bioluminescent point-source flashes, well contrasted against the dim background daylight, become increasingly visible. In the bathypelagic zone below 1000 m no daylight remains, and the scene becomes entirely dominated by point-like bioluminescence. This changing nature of visual scenes with depth--from extended source to point source--has had a profound effect on the designs of deep-sea eyes, both optically and neurally, a fact that until recently was not fully appreciated. Recent measurements of the sensitivity and spatial resolution of deep-sea eyes--particularly from the camera eyes of fishes and cephalopods and the compound eyes of crustaceans--reveal that ocular designs are well matched to the nature of the visual scene at any given depth. This match between eye design and visual scene is the subject of this review. The greatest variation in eye design is found in the mesopelagic zone, where dim down-welling daylight and bio-luminescent point sources may be visible simultaneously. Some mesopelagic eyes rely on spatial and temporal summation to increase sensitivity to a dim extended scene, while others sacrifice this sensitivity to localise pinpoints of bright bioluminescence. Yet other eyes have retinal regions separately specialised for each type of light. In the bathypelagic zone, eyes generally get smaller and therefore less sensitive to point sources with increasing depth. In fishes, this insensitivity, combined with surprisingly high spatial resolution, is very well adapted to the

  5. The deep Canary poleward undercurrent

    Science.gov (United States)

    Velez-Belchi, P. J.; Hernandez-Guerra, A.; González-Pola, C.; Fraile, E.; Collins, C. A.; Machín, F.

    2012-12-01

    Poleward undercurrents are well known features in Eastern Boundary systems. In the California upwelling system (CalCEBS), the deep poleward flow has been observed along the entire outer continental shelf and upper-slope, using indirect methods based on geostrophic estimates and also using direct current measurements. The importance of the poleward undercurrents in the CalCEBS, among others, is to maintain its high productivity by means of the transport of equatorial Pacific waters all the way northward to Vancouver Island and the subpolar gyre but there is also concern about the low oxygen concentration of these waters. However, in the case of the Canary Current Eastern Boundary upwelling system (CanCEBS), there are very few observations of the poleward undercurrent. Most of these observations are short-term mooring records, or drifter trajectories of the upper-slope flow. Hence, the importance of the subsurface poleward flow in the CanCEBS has been only hypothesized. Moreover, due to the large differences between the shape of the coastline and topography between the California and the Canary Current system, the results obtained for the CalCEBS are not completely applicable to the CanCEBS. In this study we report the first direct observations of the continuity of the deep poleward flow of the Canary Deep Poleward undercurrent (CdPU) in the North-Africa sector of the CanCEBS, and one of the few direct observations in the North-Africa sector of the Canary Current eastern boundary. The results indicate that the Canary Island archipelago disrupts the deep poleward undercurrent even at depths where the flow is not blocked by the bathymetry. The deep poleward undercurrent flows west around the eastern-most islands and north east of the Conception Bank to rejoin the intermittent branch that follows the African slope in the Lanzarote Passage. This hypothesis is consistent with the AAIW found west of Lanzarote, as far as 17 W. But also, this hypothesis would be coherent

  6. Physical layer network coding

    DEFF Research Database (Denmark)

    Fukui, Hironori; Popovski, Petar; Yomo, Hiroyuki

    2014-01-01

    Physical layer network coding (PLNC) has been proposed to improve throughput of the two-way relay channel, where two nodes communicate with each other, being assisted by a relay node. Most of the works related to PLNC are focused on a simple three-node model and they do not take into account...

  7. Thin layer activation

    International Nuclear Information System (INIS)

    Schweickert, H.; Fehsenfeld, P.

    1995-01-01

    The reliability of industrial equip ment is substantially influenced by wear and corrosion; monitoring can prevent accidents and avoid down-time. One powerful tool is thin layer activation analysis (TLA) using accelerator systems. The information is used to improve mechanical design and material usage; the technology is used by many large companies, particularly in the automotive industry, e.g. Daimler Benz. A critical area of a machine component receives a thin layer of radioactivity by irradiation with charged particles from an accelerator - usually a cyclotron. The radioactivity can be made homogeneous by suitable selection of particle, beam energy and angle of incidence. Layer thickness can be varied from 20 microns to around 1 mm with different depth distributions; the position and size of the wear zone can be set to within 0.1 mm. The machine is then reassembled and operated so that wear can be measured. An example is a combustion engine comprising piston ring, cylinder wall, cooling water jacket and housing wall, where wear measurements on the cylinder wall are required in a critical zone around the dead-point of the piston ring. Proton beam bombardment creates a radioactive layer whose thickness is known accurately, and characteristic gamma radiation from this radioactive zone penetrates through the engine and is detected externally. Measurements can be made either of the activity removed from the surface, or of the (reduced) residual activity; wear measurement of the order of 10 -9 metres is possible

  8. Our Shrinking Ozone Layer

    Indian Academy of Sciences (India)

    Depletion of the ozone layer is therefore having significant effects on life on .... but there is always a net balance between the rate of formation and destruction ..... award of Commonwealth Fellowship during the present work and also being an ...

  9. Layer-Cake Earth

    Science.gov (United States)

    Tedford, Rebecca; Warny, Sophie

    2006-01-01

    In this article, the authors offer a safe, fun, effective way to introduce geology concepts to elementary school children of all ages: "coring" layer cakes. This activity introduces the concepts and challenges that geologists face and at the same time strengthens students' inferential, observational, and problem-solving skills. It also addresses…

  10. Layered double hydroxides

    DEFF Research Database (Denmark)

    López Rayo, Sandra; Imran, Ahmad; Hansen, Hans Chr. Bruun

    2017-01-01

    A novel zinc (Zn) fertilizer concept based on Zn doped layered double hydroxides (Zn-doped Mg-Fe-LDHs) has been investigated. Zn-doped Mg-Fe-LDHs were synthetized, their chemical composition was analyzed and their nutrient release was studied in buffered solutions with different pH values. Uptake...

  11. Deep iCrawl: An Intelligent Vision-Based Deep Web Crawler

    OpenAIRE

    R.Anita; V.Ganga Bharani; N.Nityanandam; Pradeep Kumar Sahoo

    2011-01-01

    The explosive growth of World Wide Web has posed a challenging problem in extracting relevant data. Traditional web crawlers focus only on the surface web while the deep web keeps expanding behind the scene. Deep web pages are created dynamically as a result of queries posed to specific web databases. The structure of the deep web pages makes it impossible for traditional web crawlers to access deep web contents. This paper, Deep iCrawl, gives a novel and vision-based app...

  12. Ocean barrier layers' effect on tropical cyclone intensification.

    Science.gov (United States)

    Balaguru, Karthik; Chang, Ping; Saravanan, R; Leung, L Ruby; Xu, Zhao; Li, Mingkui; Hsieh, Jen-Shan

    2012-09-04

    Improving a tropical cyclone's forecast and mitigating its destructive potential requires knowledge of various environmental factors that influence the cyclone's path and intensity. Herein, using a combination of observations and model simulations, we systematically demonstrate that tropical cyclone intensification is significantly affected by salinity-induced barrier layers, which are "quasi-permanent" features in the upper tropical oceans. When tropical cyclones pass over regions with barrier layers, the increased stratification and stability within the layer reduce storm-induced vertical mixing and sea surface temperature cooling. This causes an increase in enthalpy flux from the ocean to the atmosphere and, consequently, an intensification of tropical cyclones. On average, the tropical cyclone intensification rate is nearly 50% higher over regions with barrier layers, compared to regions without. Our finding, which underscores the importance of observing not only the upper-ocean thermal structure but also the salinity structure in deep tropical barrier layer regions, may be a key to more skillful predictions of tropical cyclone intensities through improved ocean state estimates and simulations of barrier layer processes. As the hydrological cycle responds to global warming, any associated changes in the barrier layer distribution must be considered in projecting future tropical cyclone activity.

  13. MITRE sensor layer prototype

    Science.gov (United States)

    Duff, Francis; McGarry, Donald; Zasada, David; Foote, Scott

    2009-05-01

    The MITRE Sensor Layer Prototype is an initial design effort to enable every sensor to help create new capabilities through collaborative data sharing. By making both upstream (raw) and downstream (processed) sensor data visible, users can access the specific level, type, and quantities of data needed to create new data products that were never anticipated by the original designers of the individual sensors. The major characteristic that sets sensor data services apart from typical enterprise services is the volume (on the order of multiple terabytes) of raw data that can be generated by most sensors. Traditional tightly coupled processing approaches extract pre-determined information from the incoming raw sensor data, format it, and send it to predetermined users. The community is rapidly reaching the conclusion that tightly coupled sensor processing loses too much potentially critical information.1 Hence upstream (raw and partially processed) data must be extracted, rapidly archived, and advertised to the enterprise for unanticipated uses. The authors believe layered sensing net-centric integration can be achieved through a standardize-encapsulate-syndicateaggregate- manipulate-process paradigm. The Sensor Layer Prototype's technical approach focuses on implementing this proof of concept framework to make sensor data visible, accessible and useful to the enterprise. To achieve this, a "raw" data tap between physical transducers associated with sensor arrays and the embedded sensor signal processing hardware and software has been exploited. Second, we encapsulate and expose both raw and partially processed data to the enterprise within the context of a service-oriented architecture. Third, we advertise the presence of multiple types, and multiple layers of data through geographic-enabled Really Simple Syndication (GeoRSS) services. These GeoRSS feeds are aggregated, manipulated, and filtered by a feed aggregator. After filtering these feeds to bring just the type

  14. Deep Corals, Deep Learning: Moving the Deep Net Towards Real-Time Image Annotation

    OpenAIRE

    Lea-Anne Henry; Sankha S. Mukherjee; Neil M. Roberston; Laurence De Clippele; J. Murray Roberts

    2016-01-01

    The mismatch between human capacity and the acquisition of Big Data such as Earth imagery undermines commitments to Convention on Biological Diversity (CBD) and Aichi targets. Artificial intelligence (AI) solutions to Big Data issues are urgently needed as these could prove to be faster, more accurate, and cheaper. Reducing costs of managing protected areas in remote deep waters and in the High Seas is of great importance, and this is a realm where autonomous technology will be transformative.

  15. The Next Era: Deep Learning in Pharmaceutical Research.

    Science.gov (United States)

    Ekins, Sean

    2016-11-01

    Over the past decade we have witnessed the increasing sophistication of machine learning algorithms applied in daily use from internet searches, voice recognition, social network software to machine vision software in cameras, phones, robots and self-driving cars. Pharmaceutical research has also seen its fair share of machine learning developments. For example, applying such methods to mine the growing datasets that are created in drug discovery not only enables us to learn from the past but to predict a molecule's properties and behavior in future. The latest machine learning algorithm garnering significant attention is deep learning, which is an artificial neural network with multiple hidden layers. Publications over the last 3 years suggest that this algorithm may have advantages over previous machine learning methods and offer a slight but discernable edge in predictive performance. The time has come for a balanced review of this technique but also to apply machine learning methods such as deep learning across a wider array of endpoints relevant to pharmaceutical research for which the datasets are growing such as physicochemical property prediction, formulation prediction, absorption, distribution, metabolism, excretion and toxicity (ADME/Tox), target prediction and skin permeation, etc. We also show that there are many potential applications of deep learning beyond cheminformatics. It will be important to perform prospective testing (which has been carried out rarely to date) in order to convince skeptics that there will be benefits from investing in this technique.

  16. Enhancing Spatial Resolution of Remotely Sensed Imagery Using Deep Learning

    Science.gov (United States)

    Beck, J. M.; Bridges, S.; Collins, C.; Rushing, J.; Graves, S. J.

    2017-12-01

    Researchers at the Information Technology and Systems Center at the University of Alabama in Huntsville are using Deep Learning with Convolutional Neural Networks (CNNs) to develop a method for enhancing the spatial resolutions of moderate resolution (10-60m) multispectral satellite imagery. This enhancement will effectively match the resolutions of imagery from multiple sensors to provide increased global temporal-spatial coverage for a variety of Earth science products. Our research is centered on using Deep Learning for automatically generating transformations for increasing the spatial resolution of remotely sensed images with different spatial, spectral, and temporal resolutions. One of the most important steps in using images from multiple sensors is to transform the different image layers into the same spatial resolution, preferably the highest spatial resolution, without compromising the spectral information. Recent advances in Deep Learning have shown that CNNs can be used to effectively and efficiently upscale or enhance the spatial resolution of multispectral images with the use of an auxiliary data source such as a high spatial resolution panchromatic image. In contrast, we are using both the spatial and spectral details inherent in low spatial resolution multispectral images for image enhancement without the use of a panchromatic image. This presentation will discuss how this technology will benefit many Earth Science applications that use remotely sensed images with moderate spatial resolutions.

  17. A new approach to develop computer-aided diagnosis scheme of breast mass classification using deep learning technology.

    Science.gov (United States)

    Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

    2017-01-01

    To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process.

  18. A New Approach to Develop Computer-aided Diagnosis Scheme of Breast Mass Classification Using Deep Learning Technology

    Science.gov (United States)

    Qiu, Yuchen; Yan, Shiju; Gundreddy, Rohith Reddy; Wang, Yunzhi; Cheng, Samuel; Liu, Hong; Zheng, Bin

    2017-01-01

    PURPOSE To develop and test a deep learning based computer-aided diagnosis (CAD) scheme of mammograms for classifying between malignant and benign masses. METHODS An image dataset involving 560 regions of interest (ROIs) extracted from digital mammograms was used. After down-sampling each ROI from 512×512 to 64×64 pixel size, we applied an 8 layer deep learning network that involves 3 pairs of convolution-max-pooling layers for automatic feature extraction and a multiple layer perceptron (MLP) classifier for feature categorization to process ROIs. The 3 pairs of convolution layers contain 20, 10, and 5 feature maps, respectively. Each convolution layer is connected with a max-pooling layer to improve the feature robustness. The output of the sixth layer is fully connected with a MLP classifier, which is composed of one hidden layer and one logistic regression layer. The network then generates a classification score to predict the likelihood of ROI depicting a malignant mass. A four-fold cross validation method was applied to train and test this deep learning network. RESULTS The results revealed that this CAD scheme yields an area under the receiver operation characteristic curve (AUC) of 0.696±0.044, 0.802±0.037, 0.836±0.036, and 0.822±0.035 for fold 1 to 4 testing datasets, respectively. The overall AUC of the entire dataset is 0.790±0.019. CONCLUSIONS This study demonstrates the feasibility of applying a deep learning based CAD scheme to classify between malignant and benign breast masses without a lesion segmentation, image feature computation and selection process. PMID:28436410

  19. Statistical-Mechanical Analysis of Pre-training and Fine Tuning in Deep Learning

    Science.gov (United States)

    Ohzeki, Masayuki

    2015-03-01

    In this paper, we present a statistical-mechanical analysis of deep learning. We elucidate some of the essential components of deep learning — pre-training by unsupervised learning and fine tuning by supervised learning. We formulate the extraction of features from the training data as a margin criterion in a high-dimensional feature-vector space. The self-organized classifier is then supplied with small amounts of labelled data, as in deep learning. Although we employ a simple single-layer perceptron model, rather than directly analyzing a multi-layer neural network, we find a nontrivial phase transition that is dependent on the number of unlabelled data in the generalization error of the resultant classifier. In this sense, we evaluate the efficacy of the unsupervised learning component of deep learning. The analysis is performed by the replica method, which is a sophisticated tool in statistical mechanics. We validate our result in the manner of deep learning, using a simple iterative algorithm to learn the weight vector on the basis of belief propagation.

  20. Peeling Back the Layers

    Science.gov (United States)

    2004-01-01

    NASA's Mars Exploration Rover Spirit took this panoramic camera image of the rock target named 'Mazatzal' on sol 77 (March 22, 2004). It is a close-up look at the rock face and the targets that will be brushed and ground by the rock abrasion tool in upcoming sols. Mazatzal, like most rocks on Earth and Mars, has layers of material near its surface that provide clues about the history of the rock. Scientists believe that the top layer of Mazatzal is actually a coating of dust and possibly even salts. Under this light coating may be a more solid portion of the rock that has been chemically altered by weathering. Past this layer is the unaltered rock, which may give scientists the best information about how Mazatzal was formed. Because each layer reveals information about the formation and subsequent history of Mazatzal, it is important that scientists get a look at each of them. For this reason, they have developed a multi-part strategy to use the rock abrasion tool to systematically peel back Mazatzal's layers and analyze what's underneath with the rover's microscopic imager, and its Moessbauer and alpha particle X-ray spectrometers. The strategy began on sol 77 when scientists used the microscopic imager to get a closer look at targets on Mazatzal named 'New York,' 'Illinois' and 'Arizona.' These rock areas were targeted because they posed the best opportunity for successfully using the rock abrasion tool; Arizona also allowed for a close-up look at a range of tones. On sol 78, Spirit's rock abrasion tool will do a light brushing on the Illinois target to preserve some of the surface layers. Then, a brushing of the New York target should remove the top coating of any dust and salts and perhaps reveal the chemically altered rock underneath. Finally, on sol 79, the rock abrasion tool will be commanded to grind into the New York target, which will give scientists the best chance of observing Mazatzal's interior. The Mazatzal targets were named after the home states of

  1. Event Recognition Based on Deep Learning in Chinese Texts.

    Directory of Open Access Journals (Sweden)

    Yajun Zhang

    Full Text Available Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM. Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN, then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

  2. Event Recognition Based on Deep Learning in Chinese Texts.

    Science.gov (United States)

    Zhang, Yajun; Liu, Zongtian; Zhou, Wen

    2016-01-01

    Event recognition is the most fundamental and critical task in event-based natural language processing systems. Existing event recognition methods based on rules and shallow neural networks have certain limitations. For example, extracting features using methods based on rules is difficult; methods based on shallow neural networks converge too quickly to a local minimum, resulting in low recognition precision. To address these problems, we propose the Chinese emergency event recognition model based on deep learning (CEERM). Firstly, we use a word segmentation system to segment sentences. According to event elements labeled in the CEC 2.0 corpus, we classify words into five categories: trigger words, participants, objects, time and location. Each word is vectorized according to the following six feature layers: part of speech, dependency grammar, length, location, distance between trigger word and core word and trigger word frequency. We obtain deep semantic features of words by training a feature vector set using a deep belief network (DBN), then analyze those features in order to identify trigger words by means of a back propagation neural network. Extensive testing shows that the CEERM achieves excellent recognition performance, with a maximum F-measure value of 85.17%. Moreover, we propose the dynamic-supervised DBN, which adds supervised fine-tuning to a restricted Boltzmann machine layer by monitoring its training performance. Test analysis reveals that the new DBN improves recognition performance and effectively controls the training time. Although the F-measure increases to 88.11%, the training time increases by only 25.35%.

  3. Dro1, a major QTL involved in deep rooting of rice under upland field conditions.

    Science.gov (United States)

    Uga, Yusaku; Okuno, Kazutoshi; Yano, Masahiro

    2011-05-01

    Developing a deep root system is an important strategy for avoiding drought stress in rice. Using the 'basket' method, the ratio of deep rooting (RDR; the proportion of total roots that elongated through the basket bottom) was calculated to evaluate deep rooting. A new major quantitative trait locus (QTL) controlling RDR was detected on chromosome 9 by using 117 recombinant inbred lines (RILs) derived from a cross between the lowland cultivar IR64, with shallow rooting, and the upland cultivar Kinandang Patong (KP), with deep rooting. This QTL explained 66.6% of the total phenotypic variance in RDR in the RILs. A BC(2)F(3) line homozygous for the KP allele of the QTL had an RDR of 40.4%, compared with 2.6% for the homozygous IR64 allele. Fine mapping of this QTL was undertaken using eight BC(2)F(3) recombinant lines. The RDR QTL Dro1 (Deeper rooting 1) was mapped between the markers RM24393 and RM7424, which delimit a 608.4 kb interval in the reference cultivar Nipponbare. To clarify the influence of Dro1 in an upland field, the root distribution in different soil layers was quantified by means of core sampling. A line homozygous for the KP allele of Dro1 (Dro1-KP) and IR64 did not differ in root dry weight in the shallow soil layers (0-25 cm), but root dry weight of Dro1-KP in deep soil layers (25-50 cm) was significantly greater than that of IR64, suggesting that Dro1 plays a crucial role in increased deep rooting under upland field conditions.

  4. Deep remission: a new concept?

    Science.gov (United States)

    Colombel, Jean-Frédéric; Louis, Edouard; Peyrin-Biroulet, Laurent; Sandborn, William J; Panaccione, Remo

    2012-01-01

    Crohn's disease (CD) is a chronic inflammatory disorder characterized by periods of clinical remission alternating with periods of relapse defined by recurrent clinical symptoms. Persistent inflammation is believed to lead to progressive bowel damage over time, which manifests with the development of strictures, fistulae and abscesses. These disease complications frequently lead to a need for surgical resection, which in turn leads to disability. So CD can be characterized as a chronic, progressive, destructive and disabling disease. In rheumatoid arthritis, treatment paradigms have evolved beyond partial symptom control alone toward the induction and maintenance of sustained biological remission, also known as a 'treat to target' strategy, with the goal of improving long-term disease outcomes. In CD, there is currently no accepted, well-defined, comprehensive treatment goal that entails the treatment of both clinical symptoms and biologic inflammation. It is important that such a treatment concept begins to evolve for CD. A treatment strategy that delays or halts the progression of CD to increasing damage and disability is a priority. As a starting point, a working definition of sustained deep remission (that includes long-term biological remission and symptom control) with defined patient outcomes (including no disease progression) has been proposed. The concept of sustained deep remission represents a goal for CD management that may still evolve. It is not clear if the concept also applies to ulcerative colitis. Clinical trials are needed to evaluate whether treatment algorithms that tailor therapy to achieve deep remission in patients with CD can prevent disease progression and disability. Copyright © 2012 S. Karger AG, Basel.

  5. The Role of Peat Layers on Iron Dynamics in Peatlands

    Directory of Open Access Journals (Sweden)

    Arifin Fahmi

    2010-09-01

    Full Text Available The research aimed to study the effect of peat thickness and humification stage of the peat material on Fe solubility at the peatlands with sulfidic material as substratum. The research was conducted at three conditionals of ombrogen peatlands ie ; deep, moderate and shallow peat. Soil samples were collected by using peat borer according to interlayer (the border layer of peat and mineral layer and conditional of soil horizons. The sample point depth were (cm G.s2 : 25, G.s1 : 50, Int.s : 70, M.s1 : 90 and M.s2 : 100 for shallow peat, G.m2 : 47, G.m1 : 100, Int.m : 120 and M.m1 : 135 for moderate peat and G.d3 : 50, G.d2 : 150, G.d1 : 200, Int.d : 220 and M.d1 : 235 for deep peat respectively. The results showed that most of Fe on the tested soils was found in organic forms. The peat layers above the sulfidic material decreased the Fe2+ solubility at peatlands. Fe2+ concentration in peat layer decreased with its increasing distance from sulfidic material. There was any other processes beside complexation and chelation of Fe2+ by humic material and its processes was reduction of Fe3+ and this conditions was reflected in redox potential values (Eh.

  6. Topics in deep inelastic scattering

    International Nuclear Information System (INIS)

    Wandzura, S.M.

    1977-01-01

    Several topics in deep inelastic lepton--nucleon scattering are discussed, with emphasis on the structure functions appearing in polarized experiments. The major results are: infinite set of new sum rules reducing the number of independent spin dependent structure functions (for electroproduction) from two to one; the application of the techniques of Nachtmann to extract the coefficients appearing in the Wilson operator product expansion; and radiative corrections to the Wilson coefficients of free field theory. Also discussed are the use of dimensional regularization to simplify the calculation of these radiative corrections

  7. Physical Layer Network Coding

    DEFF Research Database (Denmark)

    Fukui, Hironori; Yomo, Hironori; Popovski, Petar

    2013-01-01

    of interfering nodes and usage of spatial reservation mechanisms. Specifically, we introduce a reserved area in order to protect the nodes involved in two-way relaying from the interference caused by neighboring nodes. We analytically derive the end-to-end rate achieved by PLNC considering the impact......Physical layer network coding (PLNC) has the potential to improve throughput of multi-hop networks. However, most of the works are focused on the simple, three-node model with two-way relaying, not taking into account the fact that there can be other neighboring nodes that can cause....../receive interference. The way to deal with this problem in distributed wireless networks is usage of MAC-layer mechanisms that make a spatial reservation of the shared wireless medium, similar to the well-known RTS/CTS in IEEE 802.11 wireless networks. In this paper, we investigate two-way relaying in presence...

  8. Crack layer theory

    Science.gov (United States)

    Chudnovsky, A.

    1987-01-01

    A damage parameter is introduced in addition to conventional parameters of continuum mechanics and consider a crack surrounded by an array of microdefects within the continuum mechanics framework. A system consisting of the main crack and surrounding damage is called crack layer (CL). Crack layer propagation is an irreversible process. The general framework of the thermodynamics of irreversible processes are employed to identify the driving forces (causes) and to derive the constitutive equation of CL propagation, that is, the relationship between the rates of the crack growth and damage dissemination from one side and the conjugated thermodynamic forces from another. The proposed law of CL propagation is in good agreement with the experimental data on fatigue CL propagation in various materials. The theory also elaborates material toughness characterization.

  9. Gravitational double layers

    International Nuclear Information System (INIS)

    Senovilla, José M M

    2014-01-01

    I analyze the properties of thin shells through which the scalar curvature R is discontinuous in gravity theories with Lagrangian F(R) = R − 2Λ + αR 2 on the bulk. These shells/domain walls are of a new kind because they possess, in addition to the standard energy–momentum tensor, an external energy flux vector, an external scalar pressure/tension and, most exotic of all, another energy–momentum contribution resembling classical dipole distributions on a shell: a double layer. I prove that all these contributions are necessary to make the entire energy–momentum tensor divergence-free. This is the first known occurrence of such a type of double layer in a gravity theory. I present explicit examples in constant-curvature five-dimensional bulks, with a brief study of their properties: new physical behaviors arise. (fast track communications)

  10. Boundary-layer theory

    CERN Document Server

    Schlichting (Deceased), Hermann

    2017-01-01

    This new edition of the near-legendary textbook by Schlichting and revised by Gersten presents a comprehensive overview of boundary-layer theory and its application to all areas of fluid mechanics, with particular emphasis on the flow past bodies (e.g. aircraft aerodynamics). The new edition features an updated reference list and over 100 additional changes throughout the book, reflecting the latest advances on the subject.

  11. Barrier layer arrangement for conductive layers on silicon substrates

    International Nuclear Information System (INIS)

    Hung, L.S.; Agostinelli, J.A.

    1990-01-01

    This patent describes a circuit element comprised of a silicon substrate and a conductive layer located on the substrate. It is characterized in that the conductive layer consists essentially of a rare earth alkaline earth copper oxide and a barrier layer triad is interposed between the silicon substrate and the conductive layer comprised of a first triad layer located adjacent the silicon substrate consisting essentially of silica, a third triad layer remote from the silicon substrate consisting essentially of a least one Group 4 heavy metal oxide, and a second triad layer interposed between the first and third triad layers consisting essentially of a mixture of silica and at lease one Group 4 heavy metal oxide

  12. Inferences of the deep solar meridional flow

    Science.gov (United States)

    Böning, Vincent G. A.

    2017-10-01

    Understanding the solar meridional flow is important for uncovering the origin of the solar activity cycle. Yet, recent helioseismic estimates of this flow have come to conflicting conclusions in deeper layers of the solar interior, i.e., at depths below about 0.9 solar radii. The aim of this thesis is to contribute to a better understanding of the deep solar meridional flow. Time-distance helioseismology is the major method for investigating this flow. In this method, travel times of waves propagating between pairs of locations on the solar surface are measured. Until now, the travel-time measurements have been modeled using the ray approximation, which assumes that waves travel along infinitely thin ray paths between these locations. In contrast, the scattering of the full wave field in the solar interior due to the flow is modeled in first order by the Born approximation. It is in general a more accurate model of the physics in the solar interior. In a first step, an existing model for calculating the sensitivity of travel-time measurements to solar interior flows using the Born approximation is extended from Cartesian to spherical geometry. The results are succesfully compared to the Cartesian ones and are tested for self-consistency. In a second step, the newly developed model is validated using an existing numerical simulation of linear wave propagation in the Sun. An inversion of artificial travel times for meridional flow shows excellent agreement for noiseless data and reproduces many features in the input flow profile in the case of noisy data. Finally, the new method is used to infer the deep meridional flow. I used Global Oscillation Network Group (GONG) data that were earlier analyzed using the ray approximation and I employed the same Substractive Optimized Local Averaging (SOLA) inversion technique as in the earlier study. Using an existing formula for the covariance of travel-time measurements, it is shown that the assumption of uncorrelated errors

  13. Deep groundwater flow at Palmottu

    International Nuclear Information System (INIS)

    Niini, H.; Vesterinen, M.; Tuokko, T.

    1993-01-01

    Further observations, measurements, and calculations aimed at determining the groundwater flow regimes and periodical variations in flow at deeper levels were carried out in the Lake Palmottu (a natural analogue study site for radioactive waste disposal in southwestern Finland) drainage basin. These water movements affect the migration of radionuclides from the Palmottu U-Th deposit. The deep water flow is essentially restricted to the bedrock fractures which developed under, and are still affected by, the stress state of the bedrock. Determination of the detailed variations was based on fracture-tectonic modelling of the 12 most significant underground water-flow channels that cross the surficial water of the Palmottu area. According to the direction of the hydraulic gradient the deep water flow is mostly outwards from the Palmottu catchment but in the westernmost section it is partly towards the centre. Estimation of the water flow through the U-Th deposit by the water-balance method is still only approximate and needs continued observation series and improved field measurements

  14. Deep ocean model penetrator experiments

    International Nuclear Information System (INIS)

    Freeman, T.J.; Burdett, J.R.F.

    1986-01-01

    Preliminary trials of experimental model penetrators in the deep ocean have been conducted as an international collaborative exercise by participating members (national bodies and the CEC) of the Engineering Studies Task Group of the Nuclear Energy Agency's Seabed Working Group. This report describes and gives the results of these experiments, which were conducted at two deep ocean study areas in the Atlantic: Great Meteor East and the Nares Abyssal Plain. Velocity profiles of penetrators of differing dimensions and weights have been determined as they free-fell through the water column and impacted the sediment. These velocity profiles are used to determine the final embedment depth of the penetrators and the resistance to penetration offered by the sediment. The results are compared with predictions of embedment depth derived from elementary models of a penetrator impacting with a sediment. It is tentatively concluded that once the resistance to penetration offered by a sediment at a particular site has been determined, this quantity can be used to sucessfully predict the embedment that penetrators of differing sizes and weights would achieve at the same site

  15. Academic Training: Deep Space Telescopes

    CERN Multimedia

    Françoise Benz

    2006-01-01

    2005-2006 ACADEMIC TRAINING PROGRAMME LECTURE SERIES 20, 21, 22, 23, 24 February from 11:00 to 12:00 - Council Chamber on 20, 21, 23, 24 February, TH Auditorium, bldg 4 - 3-006, on 22 February Deep Space Telescopes G. BIGNAMI / CNRS, Toulouse, F & Univ. di Pavia, I The short series of seminars will address results and aims of current and future space astrophysics as the cultural framework for the development of deep space telescopes. It will then present such new tools, as they are currently available to, or imagined by, the scientific community, in the context of the science plans of ESA and of all major world space agencies. Ground-based astronomy, in the 400 years since Galileo's telescope, has given us a profound phenomenological comprehension of our Universe, but has traditionally been limited to the narrow band(s) to which our terrestrial atmosphere is transparent. Celestial objects, however, do not care about our limitations, and distribute most of the information about their physics thro...

  16. Removal of contaminated asphalt layers by using heat generating powder metallic systems

    International Nuclear Information System (INIS)

    Barinov, A.S.; Karlina, O.K.; Ojovan, M.I.

    1996-01-01

    Heat generating systems on the base of powder metallic fuel were used for the removal of contaminated asphalt layers. Decontamination of spots which had complex geometric form was performed. Asphalt layers with deep contamination were removed essentially all radionuclides being retained in asphalt residue. Only a small part (1 - 2 %) of radionuclides could pass to combustion slag. No radionuclides were detected in aerosol-gas phase during decontamination process

  17. E-beam irradiation effect on CdSe/ZnSe QD formation by MBE: deep level transient spectroscopy and cathodoluminescence studies

    International Nuclear Information System (INIS)

    Kozlovsky, V I; Litvinov, V G; Sadofyev, Yu G

    2004-01-01

    CdSe/ZnSe structures containing 1 or 15 thin (3-5 monolayers) CdSe layers were studied by cathodoluminescence (CL) and deep level transient spectroscopy (DLTS). The DLTS spectra consisted of peaks from deep levels (DLs) and an additional intense peak due to electron emission from the ground quantized level in the CdSe layers. Activation energy of this additional peak correlated with an energy of the CdSe-layer emission line in the CL spectra. Electron-beam irradiation of the structure during the growth process was found to influence the DLTS and CL spectra of the CdSe layers, shifting the CdSe-layer emission line to the long-wave side. The obtained results are explained using the assumption that e-beam irradiation stimulates the formation of quantum dots of various sizes in the CdSe layers

  18. Subset of Cortical Layer 6b Neurons Selectively Innervates Higher Order Thalamic Nuclei in Mice.

    Science.gov (United States)

    Hoerder-Suabedissen, Anna; Hayashi, Shuichi; Upton, Louise; Nolan, Zachary; Casas-Torremocha, Diana; Grant, Eleanor; Viswanathan, Sarada; Kanold, Patrick O; Clasca, Francisco; Kim, Yongsoo; Molnár, Zoltán

    2018-05-01

    The thalamus receives input from 3 distinct cortical layers, but input from only 2 of these has been well characterized. We therefore investigated whether the third input, derived from layer 6b, is more similar to the projections from layer 6a or layer 5. We studied the projections of a restricted population of deep layer 6 cells ("layer 6b cells") taking advantage of the transgenic mouse Tg(Drd1a-cre)FK164Gsat/Mmucd (Drd1a-Cre), that selectively expresses Cre-recombinase in a subpopulation of layer 6b neurons across the entire cortical mantle. At P8, 18% of layer 6b neurons are labeled with Drd1a-Cre::tdTomato in somatosensory cortex (SS), and some co-express known layer 6b markers. Using Cre-dependent viral tracing, we identified topographical projections to higher order thalamic nuclei. VGluT1+ synapses formed by labeled layer 6b projections were found in posterior thalamic nucleus (Po) but not in the (pre)thalamic reticular nucleus (TRN). The lack of TRN collaterals was confirmed with single-cell tracing from SS. Transmission electron microscopy comparison of terminal varicosities from layer 5 and layer 6b axons in Po showed that L6b varicosities are markedly smaller and simpler than the majority from L5. Our results suggest that L6b projections to the thalamus are distinct from both L5 and L6a projections.

  19. Macular Choroidal Small-Vessel Layer, Sattler's Layer and Haller's Layer Thicknesses: The Beijing Eye Study.

    Science.gov (United States)

    Zhao, Jing; Wang, Ya Xing; Zhang, Qi; Wei, Wen Bin; Xu, Liang; Jonas, Jost B

    2018-03-13

    To study macular choroidal layer thickness, 3187 study participants from the population-based Beijing Eye Study underwent spectral-domain optical coherence tomography with enhanced depth imaging for thickness measurements of the macular small-vessel layer, including the choriocapillaris, medium-sized choroidal vessel layer (Sattler's layer) and large choroidal vessel layer (Haller's layer). In multivariate analysis, greater thickness of all three choroidal layers was associated (all P  0.05) associated with the prevalence of open-angle glaucoma or diabetic retinopathy. There was a tendency (0.07 > P > 0.02) toward thinner choroidal layers in chronic angle-closure glaucoma. The ratio of small-vessel layer thickness to total choroidal thickness increased (P layer and Haller's layer thickness to total choroidal thickness decreased. A higher ratio of small-vessel layer thickness to total choroidal thickness was significantly associated with a lower prevalence of AMD (early type, intermediate type, late geographic type). Axial elongation-associated and aging-associated choroidal thinning affected Haller's and Sattler's layers more markedly than the small-vessel layer. Non-exudative and exudative AMD, except for geographic atrophy, was associated with slightly increased choroidal thickness.

  20. Image Captioning with Deep Bidirectional LSTMs

    OpenAIRE

    Wang, Cheng; Yang, Haojin; Bartz, Christian; Meinel, Christoph

    2016-01-01

    This work presents an end-to-end trainable deep bidirectional LSTM (Long-Short Term Memory) model for image captioning. Our model builds on a deep convolutional neural network (CNN) and two separate LSTM networks. It is capable of learning long term visual-language interactions by making use of history and future context information at high level semantic space. Two novel deep bidirectional variant models, in which we increase the depth of nonlinearity transition in different way, are propose...

  1. Deep inelastic processes and the parton model

    International Nuclear Information System (INIS)

    Altarelli, G.

    The lecture was intended as an elementary introduction to the physics of deep inelastic phenomena from the point of view of theory. General formulae and facts concerning inclusive deep inelastic processes in the form: l+N→l'+hadrons (electroproduction, neutrino scattering) are first recalled. The deep inelastic annihilation e + e - →hadrons is then envisaged. The light cone approach, the parton model and their relation are mainly emphasized

  2. Deep inelastic electron and muon scattering

    International Nuclear Information System (INIS)

    Taylor, R.E.

    1975-07-01

    From the review of deep inelastic electron and muon scattering it is concluded that the puzzle of deep inelastic scattering versus annihilation was replaced with the challenge of the new particles, that the evidence for the simplest quark-algebra models of deep inelastic processes is weaker than a year ago. Definite evidence of scale breaking was found but the specific form of that scale breaking is difficult to extract from the data. 59 references

  3. Fast, Distributed Algorithms in Deep Networks

    Science.gov (United States)

    2016-05-11

    shallow networks, additional work will need to be done in order to allow for the application of ADMM to deep nets. The ADMM method allows for quick...Quock V Le, et al. Large scale distributed deep networks. In Advances in Neural Information Processing Systems, pages 1223–1231, 2012. [11] Ken-Ichi...A TRIDENT SCHOLAR PROJECT REPORT NO. 446 Fast, Distributed Algorithms in Deep Networks by Midshipman 1/C Ryan J. Burmeister, USN

  4. An overview of latest deep water technologies

    International Nuclear Information System (INIS)

    Anon.

    1995-01-01

    The 8th Deep Offshore Technology Conference (DOT VIII, Rio de Janeiro, October 30 - November 3, 1995) has brought together renowned specialists in deep water development projects, as well as managers from oil companies and engineering/service companies to discuss state-of-the-art technologies and ongoing projects in the deep offshore. This paper is a compilation of the session summaries about sub sea technologies, mooring and dynamic positioning, floaters (Tension Leg Platforms (TLP) and Floating Production Storage and Off loading (FPSO)), pipelines and risers, exploration and drilling, and other deep water techniques. (J.S.)

  5. Deep learning in neural networks: an overview.

    Science.gov (United States)

    Schmidhuber, Jürgen

    2015-01-01

    In recent years, deep artificial neural networks (including recurrent ones) have won numerous contests in pattern recognition and machine learning. This historical survey compactly summarizes relevant work, much of it from the previous millennium. Shallow and Deep Learners are distinguished by the depth of their credit assignment paths, which are chains of possibly learnable, causal links between actions and effects. I review deep supervised learning (also recapitulating the history of backpropagation), unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.

  6. The deep ocean under climate change

    Science.gov (United States)

    Levin, Lisa A.; Le Bris, Nadine

    2015-11-01

    The deep ocean absorbs vast amounts of heat and carbon dioxide, providing a critical buffer to climate change but exposing vulnerable ecosystems to combined stresses of warming, ocean acidification, deoxygenation, and altered food inputs. Resulting changes may threaten biodiversity and compromise key ocean services that maintain a healthy planet and human livelihoods. There exist large gaps in understanding of the physical and ecological feedbacks that will occur. Explicit recognition of deep-ocean climate mitigation and inclusion in adaptation planning by the United Nations Framework Convention on Climate Change (UNFCCC) could help to expand deep-ocean research and observation and to protect the integrity and functions of deep-ocean ecosystems.

  7. Docker Containers for Deep Learning Experiments

    OpenAIRE

    Gerke, Paul K.

    2017-01-01

    Deep learning is a powerful tool to solve problems in the area of image analysis. The dominant compute platform for deep learning is Nvidia’s proprietary CUDA, which can only be used together with Nvidia graphics cards. The nivida-docker project allows exposing Nvidia graphics cards to docker containers and thus makes it possible to run deep learning experiments in docker containers.In our department, we use deep learning to solve problems in the area of medical image analysis and use docker ...

  8. Deep Brain Stimulation for Parkinson's Disease

    Science.gov (United States)

    ... about the BRAIN initiative, see www.nih.gov/science/brain . Show More Show Less Search Disorders SEARCH SEARCH Definition Treatment Prognosis Clinical Trials Organizations Publications Definition Deep ...

  9. Cultivating the Deep Subsurface Microbiome

    Science.gov (United States)

    Casar, C. P.; Osburn, M. R.; Flynn, T. M.; Masterson, A.; Kruger, B.

    2017-12-01

    Subterranean ecosystems are poorly understood because many microbes detected in metagenomic surveys are only distantly related to characterized isolates. Cultivating microorganisms from the deep subsurface is challenging due to its inaccessibility and potential for contamination. The Deep Mine Microbial Observatory (DeMMO) in Lead, SD however, offers access to deep microbial life via pristine fracture fluids in bedrock to a depth of 1478 m. The metabolic landscape of DeMMO was previously characterized via thermodynamic modeling coupled with genomic data, illustrating the potential for microbial inhabitants of DeMMO to utilize mineral substrates as energy sources. Here, we employ field and lab based cultivation approaches with pure minerals to link phylogeny to metabolism at DeMMO. Fracture fluids were directed through reactors filled with Fe3O4, Fe2O3, FeS2, MnO2, and FeCO3 at two sites (610 m and 1478 m) for 2 months prior to harvesting for subsequent analyses. We examined mineralogical, geochemical, and microbiological composition of the reactors via DNA sequencing, microscopy, lipid biomarker characterization, and bulk C and N isotope ratios to determine the influence of mineralogy on biofilm community development. Pre-characterized mineral chips were imaged via SEM to assay microbial growth; preliminary results suggest MnO2, Fe3O4, and Fe2O3 were most conducive to colonization. Solid materials from reactors were used as inoculum for batch cultivation experiments. Media designed to mimic fracture fluid chemistry was supplemented with mineral substrates targeting metal reducers. DNA sequences and microscopy of iron oxide-rich biofilms and fracture fluids suggest iron oxidation is a major energy source at redox transition zones where anaerobic fluids meet more oxidizing conditions. We utilized these biofilms and fluids as inoculum in gradient cultivation experiments targeting microaerophilic iron oxidizers. Cultivation of microbes endemic to DeMMO, a system

  10. Protecting the ozone layer.

    Science.gov (United States)

    Munasinghe, M; King, K

    1992-06-01

    Stratospheric ozone layer depletion has been recognized as a problem by the Vienna Convention for the Protection of the Ozone Layer and the 1987 Montreal Protocol (MP). The ozone layer shields the earth from harmful ultraviolet radiation (UV-B), which is more pronounced at the poles and around the equator. Industrialized countries have contributed significantly to the problem by releasing chlorofluorocarbons (CFCs) and halons into the atmosphere. The effect of these chemicals, which were known for their inertness, nonflammability, and nontoxicity, was discovered in 1874. Action to deal with the effects of CFCs and halons was initiated in 1985 in a 49-nation UN meeting. 21 nations signed a protocol limiting ozone depleting substances (ODS): CFCs and halons. Schedules were set based on each country's use in 1986; the target phaseout was set for the year 2000. The MP restricts trade in ODSs and weights the impact of substances to reflect the extent of damage; i.e., halons are 10 times more damaging than CFCs. ODS requirements for developing countries were eased to accommodate scarce resources and the small fraction of ODS emissions. An Interim Multilateral Fund under the Montreal Protocol (IMFMP) was established to provide loans to finance the costs to developing countries in meeting global environmental requirements. The IMFMP is administered by the World Bank, the UN Environmental Program, and the UN Development Program. Financing is available to eligible countries who use .3 kg of ODS/person/year. Rapid phaseout in developed countries has occurred due to strong support from industry and a lower than expected cost. Although there are clear advantages to rapid phaseout, there were no incentives included in the MP for rapid phaseout. Some of the difficulties occur because the schedules set minimum targets at the lowest possible cost. Also, costs cannot be minimized by a country-specific and ODS-specific process. The ways to improve implementation in scheduling and

  11. Friedel Transition in Layered Superconductors

    International Nuclear Information System (INIS)

    Dzierzawa, M.; Zamora, M.; Baeriswyl, D.; Bagnoud, X.

    1996-01-01

    Weakly coupled superconducting layers are described by the anisotropic 3D XY model. A low-temperature layer decoupling due to a proliferation of fluxons between planes, as proposed by Friedel, does not occur. The same is true for a periodic superlattice of high and low T c layers, although the interplane coherence can become extremely weak. On the other hand a true layer decoupling is found for a random stack. copyright 1996 The American Physical Society

  12. Crystallinity Improvement of Zn O Thin Film on Different Buffer Layers Grown by MBE

    International Nuclear Information System (INIS)

    Shao-Ying, T.; Che-Hao, L.; Wen-Ming, Ch.; Yang, C.C.; Po-Ju, Ch.; Hsiang-Chen, W.; Ya-Ping, H.

    2012-01-01

    The material and optical properties of Zn O thin film samples grown on different buffer layers on sapphire substrates through a two-step temperature variation growth by molecular beam epitaxy were investigated. The thin buffer layer between the Zn O layer and the sapphire substrate decreased the lattice mismatch to achieve higher quality Zn O thin film growth. A Ga N buffer layer slightly increased the quality of the Zn O thin film, but the threading dislocations still stretched along the c-axis of the Ga N layer. The use of Mg O as the buffer layer decreased the surface roughness of the Zn O thin film by 58.8% due to the suppression of surface cracks through strain transfer of the sample. From deep level emission and rocking curve measurements it was found that the threading dislocations play a more important role than oxygen vacancies for high-quality Zn O thin film growth.

  13. Cooperating systems: Layered MAS

    Science.gov (United States)

    Rochowiak, Daniel

    1990-01-01

    Distributed intelligent systems can be distinguished by the models that they use. The model developed focuses on layered multiagent system conceived of as a bureaucracy in which a distributed data base serves as a central means of communication. The various generic bureaus of such a system is described and a basic vocabulary for such systems is presented. In presenting the bureaus and vocabularies, special attention is given to the sorts of reasonings that are appropriate. A bureaucratic model has a hierarchy of master system and work group that organizes E agents and B agents. The master system provides the administrative services and support facilities for the work groups.

  14. Layered double hydroxides

    DEFF Research Database (Denmark)

    López Rayo, Sandra; Imran, Ahmad; Hansen, Hans Chr. Bruun

    2017-01-01

    A novel zinc (Zn) fertilizer concept based on Zn doped layered double hydroxides (Zn-doped Mg-Fe-LDHs) has been investigated. Zn-doped Mg-Fe-LDHs were synthetized, their chemical composition was analyzed and their nutrient release was studied in buffered solutions with different pH values. Uptake...... equation showing maximum release at pH 5.2, reaching approximately 45% of the total Zn content. The Zn concentrations in the plants receiving the LDHs were between 2- and 9.5-fold higher than those in plants without Zn addition. A positive effect of the LDHs was also found in soil. This work documents...

  15. The Keck keyword layer

    Science.gov (United States)

    Conrad, A. R.; Lupton, W. F.

    1992-01-01

    Each Keck instrument presents a consistent software view to the user interface programmer. The view consists of a small library of functions, which are identical for all instruments, and a large set of keywords, that vary from instrument to instrument. All knowledge of the underlying task structure is hidden from the application programmer by the keyword layer. Image capture software uses the same function library to collect data for the image header. Because the image capture software and the instrument control software are built on top of the same keyword layer, a given observation can be 'replayed' by extracting keyword-value pairs from the image header and passing them back to the control system. The keyword layer features non-blocking as well as blocking I/O. A non-blocking keyword write operation (such as setting a filter position) specifies a callback to be invoked when the operation is complete. A non-blocking keyword read operation specifies a callback to be invoked whenever the keyword changes state. The keyword-callback style meshes well with the widget-callback style commonly used in X window programs. The first keyword library was built for the two Keck optical instruments. More recently, keyword libraries have been developed for the infrared instruments and for telescope control. Although the underlying mechanisms used for inter-process communication by each of these systems vary widely (Lick MUSIC, Sun RPC, and direct socket I/O, respectively), a basic user interface has been written that can be used with any of these systems. Since the keyword libraries are bound to user interface programs dynamically at run time, only a single set of user interface executables is needed. For example, the same program, 'xshow', can be used to display continuously the telescope's position, the time left in an instrument's exposure, or both values simultaneously. Less generic tools that operate on specific keywords, for example an X display that controls optical

  16. Earth's ozone layer

    International Nuclear Information System (INIS)

    Lasa, J.

    1991-01-01

    The paper contain the actual results of investigations of the influence of the human activity on the Earth's ozone layer. History of the ozone measurements and of the changes in its concentrations within the last few years are given. The influence of the trace gases on both local and global ozone concentrations are discussed. The probable changes of the ozone concentrations are presented on the basis of the modelling investigations. The effect of a decrease in global ozone concentration on human health and on biosphere are also presented. (author). 33 refs, 36 figs, 5 tabs

  17. How stratospheric are deep stratospheric intrusions? LUAMI 2008

    Directory of Open Access Journals (Sweden)

    T. Trickl

    2016-07-01

    Full Text Available A large-scale comparison of water-vapour vertical-sounding instruments took place over central Europe on 17 October 2008, during a rather homogeneous deep stratospheric intrusion event (LUAMI, Lindenberg Upper-Air Methods Intercomparison. The measurements were carried out at four observational sites: Payerne (Switzerland, Bilthoven (the Netherlands, Lindenberg (north-eastern Germany, and the Zugspitze mountain (Garmisch-Partenkichen, German Alps, and by an airborne water-vapour lidar system creating a transect of humidity profiles between all four stations. A high data quality was verified that strongly underlines the scientific findings. The intrusion layer was very dry with a minimum mixing ratios of 0 to 35 ppm on its lower west side, but did not drop below 120 ppm on the higher-lying east side (Lindenberg. The dryness hardens the findings of a preceding study (“Part 1”, Trickl et al., 2014 that, e.g., 73 % of deep intrusions reaching the German Alps and travelling 6 days or less exhibit minimum mixing ratios of 50 ppm and less. These low values reflect values found in the lowermost stratosphere and indicate very slow mixing with tropospheric air during the downward transport to the lower troposphere. The peak ozone values were around 70 ppb, confirming the idea that intrusion layers depart from the lowermost edge of the stratosphere. The data suggest an increase of ozone from the lower to the higher edge of the intrusion layer. This behaviour is also confirmed by stratospheric aerosol caught in the layer. Both observations are in agreement with the idea that sections of the vertical distributions of these constituents in the source region were transferred to central Europe without major change. LAGRANTO trajectory calculations demonstrated a rather shallow outflow from the stratosphere just above the dynamical tropopause, for the first time confirming the conclusions in “Part 1” from the Zugspitze CO observations. The

  18. Basis reduction for layered lattices

    NARCIS (Netherlands)

    Torreão Dassen, Erwin

    2011-01-01

    We develop the theory of layered Euclidean spaces and layered lattices. We present algorithms to compute both Gram-Schmidt and reduced bases in this generalized setting. A layered lattice can be seen as lattices where certain directions have infinite weight. It can also be

  19. Basis reduction for layered lattices

    NARCIS (Netherlands)

    E.L. Torreão Dassen (Erwin)

    2011-01-01

    htmlabstractWe develop the theory of layered Euclidean spaces and layered lattices. With this new theory certain problems that usually are solved by using classical lattices with a "weighting" gain a new, more natural form. Using the layered lattice basis reduction algorithms introduced here these

  20. Deep inelastic scattering and disquarks

    International Nuclear Information System (INIS)

    Anselmino, M.

    1993-01-01

    The most comprehensive and detailed analyses of the existing data on the structure function F 2 (x, Q 2 ) of free nucleons, from the deep inelastic scattering (DIS) of charged leptons on hydrogen and deuterium targets, have proved beyond any doubt that higher twist, 1/Q 2 corrections are needed in order to obtain a perfect agreement between perturbative QCD predictions and the data. These higher twist corrections take into account two quark correlations inside the nucleon; it is then natural to try to model them in the quark-diquark model of the proton. In so doing all interactions between the two quarks inside the diquark, both perturbative and non perturbative, are supposed to be taken into account. (orig./HSI)