GaAs-oxide interface states - A gigantic photoionization effect and its implications to the origin of these states

NASA Technical Reports Server (NTRS)

Lagowski, J.; Walukiewicz, W.; Kazior, T. E.; Gatos, H. C.; Siejka, J.

1981-01-01

Gigantic photoionization was discovered on GaAs-oxide interfaces leading to the discharge of deep surface states with rates exceeding 1000 times those of photoionization transitions to the conduction band. It exhibits a peak similar to acceptor-donor transitions and is explained as due to energy transfer from photo-excited donor-acceptor pairs to deep surface states. This new process indicates the presence of significant concentrations of shallow donor and acceptor levels not recognized in previous interface models.

Iron and intrinsic deep level states in Ga2O3

NASA Astrophysics Data System (ADS)

Ingebrigtsen, M. E.; Varley, J. B.; Kuznetsov, A. Yu.; Svensson, B. G.; Alfieri, G.; Mihaila, A.; Badstübner, U.; Vines, L.

2018-01-01

Using a combination of deep level transient spectroscopy, secondary ion mass spectrometry, proton irradiation, and hybrid functional calculations, we identify two similar deep levels that are associated with Fe impurities and intrinsic defects in bulk crystals and molecular beam epitaxy and hydride vapor phase epitaxi-grown epilayers of β-Ga2O3. First, our results indicate that FeGa, and not an intrinsic defect, acts as the deep acceptor responsible for the often dominating E2 level at ˜0.78 eV below the conduction band minimum. Second, by provoking additional intrinsic defect generation via proton irradiation, we identified the emergence of a new level, labeled as E2*, having the ionization energy very close to that of E2, but exhibiting an order of magnitude larger capture cross section. Importantly, the properties of E2* are found to be consistent with its intrinsic origin. As such, contradictory opinions of a long standing literature debate on either extrinsic or intrinsic origin of the deep acceptor in question converge accounting for possible contributions from E2 and E2* in different experimental conditions.

DeepSurv: personalized treatment recommender system using a Cox proportional hazards deep neural network.

PubMed

Katzman, Jared L; Shaham, Uri; Cloninger, Alexander; Bates, Jonathan; Jiang, Tingting; Kluger, Yuval

2018-02-26

Medical practitioners use survival models to explore and understand the relationships between patients' covariates (e.g. clinical and genetic features) and the effectiveness of various treatment options. Standard survival models like the linear Cox proportional hazards model require extensive feature engineering or prior medical knowledge to model treatment interaction at an individual level. While nonlinear survival methods, such as neural networks and survival forests, can inherently model these high-level interaction terms, they have yet to be shown as effective treatment recommender systems. We introduce DeepSurv, a Cox proportional hazards deep neural network and state-of-the-art survival method for modeling interactions between a patient's covariates and treatment effectiveness in order to provide personalized treatment recommendations. We perform a number of experiments training DeepSurv on simulated and real survival data. We demonstrate that DeepSurv performs as well as or better than other state-of-the-art survival models and validate that DeepSurv successfully models increasingly complex relationships between a patient's covariates and their risk of failure. We then show how DeepSurv models the relationship between a patient's features and effectiveness of different treatment options to show how DeepSurv can be used to provide individual treatment recommendations. Finally, we train DeepSurv on real clinical studies to demonstrate how it's personalized treatment recommendations would increase the survival time of a set of patients. The predictive and modeling capabilities of DeepSurv will enable medical researchers to use deep neural networks as a tool in their exploration, understanding, and prediction of the effects of a patient's characteristics on their risk of failure.

Deep level transient spectroscopy (DLTS) on colloidal-synthesized nanocrystal solids.

PubMed

Bozyigit, Deniz; Jakob, Michael; Yarema, Olesya; Wood, Vanessa

2013-04-24

We demonstrate current-based, deep level transient spectroscopy (DLTS) on semiconductor nanocrystal solids to obtain quantitative information on deep-lying trap states, which play an important role in the electronic transport properties of these novel solids and impact optoelectronic device performance. Here, we apply this purely electrical measurement to an ethanedithiol-treated, PbS nanocrystal solid and find a deep trap with an activation energy of 0.40 eV and a density of NT = 1.7 × 10(17) cm(-3). We use these findings to draw and interpret band structure models to gain insight into charge transport in PbS nanocrystal solids and the operation of PbS nanocrystal-based solar cells.

Hello World Deep Learning in Medical Imaging.

PubMed

Lakhani, Paras; Gray, Daniel L; Pett, Carl R; Nagy, Paul; Shih, George

2018-05-03

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.

Discriminating a deep defect from shallow acceptors in supercell calculations: gallium antisite in GaAs

NASA Astrophysics Data System (ADS)

Schultz, Peter

To make reliable first principles predictions of defect energies in semiconductors, it is crucial to discriminate between effective-mass-like defects--for which existing supercell methods fail--and deep defects--for which density functional theory calculations can yield reliable predictions of defect energy levels. The gallium antisite GaAs is often associated with the 78/203 meV shallow double acceptor in Ga-rich gallium arsenide. Within a framework of level occupation patterns, analyses of structure and spin stabilization can be used within a supercell approach to distinguish localized deep defect states from shallow acceptors such as BAs. This systematic analysis determines that the gallium antisite is inconsistent with a shallow state, and cannot be the 78/203 shallow double acceptor. The properties of the Ga antisite in GaAs are described, predicting that the Ga antisite is a deep double acceptor and has two donor states, one of which might be accidentally shallow. -- Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Company, for the U.S. Department of Energy's National Nuclear Security Administration under Contract DE-AC04-94AL85000.

DeepMirTar: a deep-learning approach for predicting human miRNA targets.

PubMed

Wen, Ming; Cong, Peisheng; Zhang, Zhimin; Lu, Hongmei; Li, Tonghua

2018-06-01

MicroRNAs (miRNAs) are small noncoding RNAs that function in RNA silencing and post-transcriptional regulation of gene expression by targeting messenger RNAs (mRNAs). Because the underlying mechanisms associated with miRNA binding to mRNA are not fully understood, a major challenge of miRNA studies involves the identification of miRNA-target sites on mRNA. In silico prediction of miRNA-target sites can expedite costly and time-consuming experimental work by providing the most promising miRNA-target-site candidates. In this study, we reported the design and implementation of DeepMirTar, a deep-learning-based approach for accurately predicting human miRNA targets at the site level. The predicted miRNA-target sites are those having canonical or non-canonical seed, and features, including high-level expert-designed, low-level expert-designed, and raw-data-level, were used to represent the miRNA-target site. Comparison with other state-of-the-art machine-learning methods and existing miRNA-target-prediction tools indicated that DeepMirTar improved overall predictive performance. DeepMirTar is freely available at https://github.com/Bjoux2/DeepMirTar_SdA. lith@tongji.edu.cn, hongmeilu@csu.edu.cn. Supplementary data are available at Bioinformatics online.

NCI Scientists Get Deep Look at CRISPR Complex Through Deep Freeze | Poster

Cancer.gov

To get a closer look at one CRISPR complex, researchers from NCI’s Center for Cancer Research and their collaborators recently put it “on ice” with cryo-electron microscopy, creating highly detailed images that show its biological structures in multiple states at a molecular level.

Influence of oxygen vacancy on the electronic structure of CaCu3Ti4O12 and its deep-level vacancy trap states by first-principle calculation

NASA Astrophysics Data System (ADS)

Xiao, H. B.; Yang, C. P.; Huang, C.; Xu, L. F.; Shi, D. W.; Marchenkov, V. V.; Medvedeva, I. V.; Bärner, K.

2012-03-01

The electronic structure, formation energy, and transition energy levels of intrinsic defects have been studied using the density-functional method within the generalized gradient approximation for neutral and charged oxygen vacancy in CaCu3Ti4O12 (CCTO). It is found that oxygen vacancies with different charge states can be formed in CCTO under both oxygen-rich and poor conditions for nonequilibrium and higher-energy sintering processes; especially, a lower formation energy is obtained for poor oxygen environment. The charge transition level (0/1+) of the oxygen vacancy in CCTO is located at 0.53 eV below the conduction-band edge. The (1+/2+) transition occurs at 1.06 eV below the conduction-band edge. Oxygen vacancies of Vo1+ and Vo2+ are positive stable charge states in most gap regions and can act as a moderately deep donor for Vo1+ and a borderline deep for Vo2+, respectively. The polarization and dielectric constant are considerably enhanced by oxygen vacancy dipoles, due to the off-center Ti and Cu ions in CCTO.

Advanced Solid State Lighting for AES Deep Space Hab Project

NASA Technical Reports Server (NTRS)

Holbert, Eirik

2015-01-01

The advanced Solid State Lighting (SSL) assemblies augmented 2nd generation modules under development for the Advanced Exploration Systems Deep Space Habitat in using color therapy to synchronize crew circadian rhythms. Current RGB LED technology does not produce sufficient brightness to adequately address general lighting in addition to color therapy. The intent is to address both through a mix of white and RGB LEDs designing for fully addressable alertness/relaxation levels as well as more dramatic circadian shifts.

Surface acceptor states in MBE-grown CdTe layers

NASA Astrophysics Data System (ADS)

Wichrowska, Karolina; Wosinski, Tadeusz; Tkaczyk, Zbigniew; Kolkovsky, Valery; Karczewski, Grzegorz

2018-04-01

A deep-level hole trap associated with surface defect states has been revealed with deep-level transient spectroscopy investigations of metal-semiconductor junctions fabricated on nitrogen doped p-type CdTe layers grown by the molecular-beam epitaxy technique. The trap displayed the hole-emission activation energy of 0.33 eV and the logarithmic capture kinetics indicating its relation to extended defect states at the metal-semiconductor interface. Strong electric-field-induced enhancement of the thermal emission rate of holes from the trap has been attributed to the phonon-assisted tunneling effect from defect states involving very large lattice relaxation around the defect and metastability of its occupied state. Passivation with ammonium sulfide of the CdTe surface, prior to metallization, results in a significant decrease in the trap density. It also results in a distinct reduction in the width of the surface-acceptor-state-induced hysteresis loops in the capacitance vs. voltage characteristics of the metal-semiconductor junctions.

Processes governing transient responses of the deep ocean buoyancy budget to a doubling of CO2

NASA Astrophysics Data System (ADS)

Palter, J. B.; Griffies, S. M.; Hunter Samuels, B. L.; Galbraith, E. D.; Gnanadesikan, A.

2012-12-01

Recent observational analyses suggest there is a temporal trend and high-frequency variability in deep ocean buoyancy in the last twenty years, a phenomenon reproduced even in low-mixing models. Here we use an earth system model (GFDL's ESM2M) to evaluate physical processes that influence buoyancy (and thus steric sea level) budget of the deep ocean in quasi-steady state and under a doubling of CO2. A new suite of model diagnostics allows us to quantitatively assess every process that influences the buoyancy budget and its temporal evolution, revealing surprising dynamics governing both the equilibrium budget and its transient response to climate change. The results suggest that the temporal evolution of the deep ocean contribution to sea level rise is due to a diversity of processes at high latitudes, whose net effect is then advected in the Eulerian mean flow to mid and low latitudes. In the Southern Ocean, a slowdown in convection and spin up of the residual mean advection are approximately equal players in the deep steric sea level rise. In the North Atlantic, the region of greatest deep steric sea level variability in our simulations, a decrease in mixing of cold, dense waters from the marginal seas and a reduction in open ocean convection causes an accumulation of buoyancy in the deep subpolar gyre, which is then advected equatorward.

Novel Intrinsic Ignition Method Measuring Local-Global Integration Characterizes Wakefulness and Deep Sleep

PubMed Central

Tagliazucchi, Enzo; Sanjuán, Ana

2017-01-01

Abstract A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of brain regions is computed by the elicited level of integration for each intrinsic ignition event in each brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Importantly, whole-brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across brain regions. Thus, combining whole brain models with intrinsic ignition can provide novel insights into underlying mechanisms of brain states. PMID:28966977

Novel Intrinsic Ignition Method Measuring Local-Global Integration Characterizes Wakefulness and Deep Sleep.

PubMed

Deco, Gustavo; Tagliazucchi, Enzo; Laufs, Helmut; Sanjuán, Ana; Kringelbach, Morten L

2017-01-01

A precise definition of a brain state has proven elusive. Here, we introduce the novel local-global concept of intrinsic ignition characterizing the dynamical complexity of different brain states. Naturally occurring intrinsic ignition events reflect the capability of a given brain area to propagate neuronal activity to other regions, giving rise to different levels of integration. The ignitory capability of brain regions is computed by the elicited level of integration for each intrinsic ignition event in each brain region, averaged over all events. This intrinsic ignition method is shown to clearly distinguish human neuroimaging data of two fundamental brain states (wakefulness and deep sleep). Importantly, whole-brain computational modelling of this data shows that at the optimal working point is found where there is maximal variability of the intrinsic ignition across brain regions. Thus, combining whole brain models with intrinsic ignition can provide novel insights into underlying mechanisms of brain states.

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

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

Seyidov, MirHasan Yu., E-mail: smirhasan@gyte.edu.tr; Suleymanov, Rauf A.; Mikailzade, Faik A.

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. Themore » 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, which are originated from the charged B5 deep level defects, are aligned in the direction of the applied electric field and the equilibrium polarization can be reached in a relatively short time. When the polarization field is maintained, while cooling the temperature of sample to a sufficiently low degrees, the relaxation times of the aligned dipoles drastically increases. Practically, frozen internal electric field or electrets states remain inside the TlInS{sub 2}:La when the applied bias field is switched off. The influence of deep level defects on TSDC spectra of TlInS{sub 2}:La has been revealed for the first time.« less

Positron deep level transient spectroscopy — a new application of positron annihilation to semiconductor physics

NASA Astrophysics Data System (ADS)

Beling, C. D.; Fung, S.; Au, H. L.; Ling, C. C.; Reddy, C. V.; Deng, A. H.; Panda, B. K.

1997-05-01

Recent positron mobility and lifetime measurements made on ac-biased metal on semi-insulating GaAs junctions, which have identified the native EL2 defect through a determination of the characteristic ionization energy of the donor level, are reviewed. It is shown that these measurements point towards a new spectroscopy, tentatively named positron-DLTS (deep level transient spectroscopy), that is the direct complement to conventional DLTS in that it monitors transients in the electric field of the depletion region rather than the inversely related depletion width, as deep levels undergo ionization. In this new spectroscopy, which may be applied to doped material by use of a suitable positron beam, electric field transients are monitored through the Doppler shift of the annihilation radiation resulting from the drift velocity of the positron in the depletion region. Two useful extensions of the new spectroscopy beyond conventional capacitance-DLTS are suggested. The first is that in some instances information on the microstructure of the defect causing the deep level may be inferred from the sensitivity of the positron to vacancy defects of negative and neutral charge states. The second is that the positron annihilation technique is intrinsically much faster than conventional DLTS with the capability of observing transients some 10 6 times faster, thus allowing deep levels (and even shallow levels) to be investigated without problems associated with carrier freeze-out.

Structure of ²⁰⁷Pb populated in ²⁰⁸Pb + ²⁰⁸Pb deep-inelastic collisions*

DOE PAGES

Shand, C. M.; Wilson, E.; Podolyák, Zs.; ...

2015-01-01

The yrast structure of 207Pb above the 13/2 + isomeric state has been investigated in deep-inelastic collisions of 208Pb and 208Pb at ATLAS, Argonne National Laboratory. New and previously observed transitions were measured using the Gammasphere detector array. The level scheme of 207Pb is presented up to ~ 6 MeV, built using coincidence and γ-ray intensity analyses. In addition, the spin and parity assignments of states were made, based on angular distributions and comparisons to shell model calculations.

Structure of ²⁰⁷Pb populated in ²⁰⁸Pb + ²⁰⁸Pb deep-inelastic collisions*

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

Shand, C. M.; Wilson, E.; Podolyák, Zs.

The yrast structure of 207Pb above the 13/2 + isomeric state has been investigated in deep-inelastic collisions of 208Pb and 208Pb at ATLAS, Argonne National Laboratory. New and previously observed transitions were measured using the Gammasphere detector array. The level scheme of 207Pb is presented up to ~ 6 MeV, built using coincidence and γ-ray intensity analyses. In addition, the spin and parity assignments of states were made, based on angular distributions and comparisons to shell model calculations.

Possible origin of photoconductivity in La0.7Ca0.3MnO3

NASA Astrophysics Data System (ADS)

Sagdeo, P. R.; Choudhary, R. J.; Phase, D. M.

2010-01-01

The effect of photon energy on the density of states near Fermi level of pulsed laser deposited La0.7Ca0.3MnO3 thin film has been studied to investigate the possible origin of change in the conductivity of these manganites upon photon exposure. For this purpose the photoelectron spectroscopy measurements were carried out using CSR beamline (BL-2) on Indus-1 synchrotron radiation source. The valance band spectra were measured at room temperature with photon energy ranging from 40 to 60 eV. We could see huge change in the density of states near Fermi level and this change is observed to be highest at 56 eV which is due to the resonance between Mn 3p to Mn 3d level. Our results suggest that the probability of electron transfer from deep Mn 3p level to Mn 3d-eg level is higher than that of Mn 3d-t2g level. It appears that this transfer of electron from deep Mn level to Mn 3d-eg level not only modifies the density of state near Fermi level but also changes the mobility of electrons by modifying the electron lattice coupling due to presence of Mn+3 Jahn-Teller ion.

Squeeze-SegNet: a new fast deep convolutional neural network for semantic segmentation

NASA Astrophysics Data System (ADS)

Nanfack, Geraldin; Elhassouny, Azeddine; Oulad Haj Thami, Rachid

2018-04-01

The recent researches in Deep Convolutional Neural Network have focused their attention on improving accuracy that provide significant advances. However, if they were limited to classification tasks, nowadays with contributions from Scientific Communities who are embarking in this field, they have become very useful in higher level tasks such as object detection and pixel-wise semantic segmentation. Thus, brilliant ideas in the field of semantic segmentation with deep learning have completed the state of the art of accuracy, however this architectures become very difficult to apply in embedded systems as is the case for autonomous driving. We present a new Deep fully Convolutional Neural Network for pixel-wise semantic segmentation which we call Squeeze-SegNet. The architecture is based on Encoder-Decoder style. We use a SqueezeNet-like encoder and a decoder formed by our proposed squeeze-decoder module and upsample layer using downsample indices like in SegNet and we add a deconvolution layer to provide final multi-channel feature map. On datasets like Camvid or City-states, our net gets SegNet-level accuracy with less than 10 times fewer parameters than SegNet.

Capacitance Techniques | Photovoltaic Research | NREL

Science.gov Websites

transient spectroscopy generated graph showing six defect levels; DLTS signal (Y-axis) versus Temperature (X -axis). DLTS characterizes defect levels to assist in identification of impurities and potential levels of interface states (or both) that often exist between the surfaces of dissimilar materials. Deep

DeepInfer: open-source deep learning deployment toolkit for image-guided therapy

NASA Astrophysics Data System (ADS)

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-03-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research work ows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy.

PubMed

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A; Kapur, Tina; Wells, William M; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-02-11

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" - an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

DeepInfer: Open-Source Deep Learning Deployment Toolkit for Image-Guided Therapy

PubMed Central

Mehrtash, Alireza; Pesteie, Mehran; Hetherington, Jorden; Behringer, Peter A.; Kapur, Tina; Wells, William M.; Rohling, Robert; Fedorov, Andriy; Abolmaesumi, Purang

2017-01-01

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose “DeepInfer” – an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections. PMID:28615794

Correlated electron-hole mechanism for molecular doping in organic semiconductors

NASA Astrophysics Data System (ADS)

Li, Jing; D'Avino, Gabriele; Pershin, Anton; Jacquemin, Denis; Duchemin, Ivan; Beljonne, David; Blase, Xavier

2017-07-01

The electronic and optical properties of the paradigmatic F4TCNQ-doped pentacene in the low-doping limit are investigated by a combination of state-of-the-art many-body ab initio methods accounting for environmental screening effects, and a carefully parametrized model Hamiltonian. We demonstrate that while the acceptor level lies very deep in the gap, the inclusion of electron-hole interactions strongly stabilizes dopant-semiconductor charge transfer states and, together with spin statistics and structural relaxation effects, rationalize the possibility for room-temperature dopant ionization. Our findings reconcile available experimental data, shedding light on the partial vs. full charge transfer scenario discussed in the literature, and question the relevance of the standard classification in shallow or deep impurity levels prevailing for inorganic semiconductors.

U.S. electric power sector transitions required to achieve 80% reductions in economy-wide greenhouse gas emissions: Results based on a state-level model of the U.S. energy system

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

Iyer, Gokul C.; Clarke, Leon E.; Edmonds, James A.

The United States has articulated a deep decarbonization strategy for achieving a reduction in economy-wide greenhouse gas (GHG) emissions of 80% below 2005 levels by 2050. Achieving such deep emissions reductions will entail a major transformation of the energy system and of the electric power sector in particular. , This study uses a detailed state-level model of the U.S. energy system embedded within a global integrated assessment model (GCAM-USA) to demonstrate pathways for the evolution of the U.S. electric power sector that achieve 80% economy-wide reductions in GHG emissions by 2050. The pathways presented in this report are based onmore » feedback received during a workshop of experts organized by the U.S. Department of Energy’s Office of Energy Policy and Systems Analysis. Our analysis demonstrates that achieving deep decarbonization by 2050 will require substantial decarbonization of the electric power sector resulting in an increase in the deployment of zero-carbon and low-carbon technologies such as renewables and carbon capture utilization and storage. The present results also show that the degree to which the electric power sector will need to decarbonize and low-carbon technologies will need to deploy depends on the nature of technological advances in the energy sector, the ability of end-use sectors to electrify and level of electricity demand.« less

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

King, M. P.; Kaplar, R. J.; Dickerson, J. R.

Electrical performance and characterization of deep levels in vertical GaN P-i-N diodes grown on low threading dislocation density (~10 4 –10 6 cm –2) bulk GaN substrates are investigated. The lightly doped n drift region of these devices is observed to be highly compensated by several prominent deep levels detected using deep level optical spectroscopy at E c-2.13, 2.92, and 3.2 eV. A combination of steady-state photocapacitance and lighted capacitance-voltage profiling indicates the concentrations of these deep levels to be N t = 3 × 10 12, 2 × 10 15, and 5 × 10 14 cm –3, respectively. Themore » E c-2.92 eV level is observed to be the primary compensating defect in as-grown n-type metal-organic chemical vapor deposition GaN, indicating this level acts as a limiting factor for achieving controllably low doping. The device blocking voltage should increase if compensating defects reduce the free carrier concentration of the n drift region. Understanding the incorporation of as-grown and native defects in thick n-GaN is essential for enabling large V BD in the next-generation wide-bandgap power semiconductor devices. Furthermore, controlling the as-grown defects induced by epitaxial growth conditions is critical to achieve blocking voltage capability above 5 kV.« less

Influence of annealing atmosphere on formation of electrically-active defects in rutile TiO2

NASA Astrophysics Data System (ADS)

Zimmermann, C.; Bonkerud, J.; Herklotz, F.; Sky, T. N.; Hupfer, A.; Monakhov, E.; Svensson, B. G.; Vines, L.

2018-04-01

Electronic states in the upper part of the bandgap of reduced and/or hydrogenated n-type rutile TiO2 single crystals have been studied by means of thermal admittance and deep-level transient spectroscopy measurements. The studies were performed at sample temperatures between 28 and 300 K. The results reveal limited charge carrier freeze-out even at 28 K and evidence the existence of dominant shallow donors with ionization energies below 25 meV. Interstitial atomic hydrogen is considered to be a major contributor to these shallow donors, substantiated by infrared absorption measurements. Three defect energy levels with positions of about 70 meV, 95 meV, and 120 meV below the conduction band edge occur in all the studied samples, irrespective of the sample production batch and the post-growth heat treatment used. The origin of these levels is discussed in terms of electron polarons, intrinsic point defects, and/or common residual impurities, where especially interstitial titanium atoms, oxygen vacancies, and complexes involving Al atoms appear as likely candidates. In contrast, no common deep-level defect, exhibiting a charge state transition in the 200-700 meV range below the conduction band edge, is found in different samples. This may possibly indicate a strong influence on deep-level defects by the post-growth heat treatments employed.

De novo peptide sequencing by deep learning

PubMed Central

Tran, Ngoc Hieu; Zhang, Xianglilan; Xin, Lei; Shan, Baozhen; Li, Ming

2017-01-01

De novo peptide sequencing from tandem MS data is the key technology in proteomics for the characterization of proteins, especially for new sequences, such as mAbs. In this study, we propose a deep neural network model, DeepNovo, for de novo peptide sequencing. DeepNovo architecture combines recent advances in convolutional neural networks and recurrent neural networks to learn features of tandem mass spectra, fragment ions, and sequence patterns of peptides. The networks are further integrated with local dynamic programming to solve the complex optimization task of de novo sequencing. We evaluated the method on a wide variety of species and found that DeepNovo considerably outperformed state of the art methods, achieving 7.7–22.9% higher accuracy at the amino acid level and 38.1–64.0% higher accuracy at the peptide level. We further used DeepNovo to automatically reconstruct the complete sequences of antibody light and heavy chains of mouse, achieving 97.5–100% coverage and 97.2–99.5% accuracy, without assisting databases. Moreover, DeepNovo is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution to the de novo sequencing problem. Not only does our study extend the deep learning revolution to a new field, but it also shows an innovative approach in solving optimization problems by using deep learning and dynamic programming. PMID:28720701

Deep learning

NASA Astrophysics Data System (ADS)

Lecun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-01

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Deep learning.

PubMed

LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey

2015-05-28

Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.

Search for Intruder States in 68Ni and 67Co

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

Chiara, C. J.; Walters, W. B.; Janssens, R. V. F.

The level schemes of 68Ni and 67Co were extended following 70Zn-induced deep-inelastic reactions. No evidence for a previously reported proton intruder 0 + state at 2202 keV in 68Ni was found. In 67Co, two new states at 3216 and 3415 keV have been established; additional states associated with the intruder configuration have yet to be identified.

On the Tip-of-the-Tongue.

ERIC Educational Resources Information Center

Moses, Barbara

1988-01-01

Discusses the organization and retrieval of information. Describes the tip-of-the-tongue state during mathematics problem solving. Provides five rules for a deep level of processing of new concepts. (YP)

TOPICAL REVIEW: The shallow-to-deep instability of hydrogen and muonium in II VI and III V semiconductors

NASA Astrophysics Data System (ADS)

Cox, S. F. J.

2003-11-01

The structure and electrical activity of monatomic hydrogen defect centres are inferred from the spectroscopy and charge-state transitions of muonium, the light pseudo-isotope of hydrogen. Introductions are given to all these topics. Special attention is paid to the shallow-donor behaviour recently established in a number of II VI compounds and one III nitride. This contrasts with trapped-atom states suggestive of an acceptor function in other members of the II VI family as well as with the deep-level amphoteric behaviour which has long been known in the elemental group-IV semiconductors and certain III V compounds. The systematics of this remarkable shallow-to-deep instability are examined in terms of simple chemical considerations, as well as current theoretical and computational models. The muonium data appear to confirm predictions that the switch from shallow to deep behaviour is governed primarily by the depth of the conduction-band minimum below the vacuum continuum. The threshold electron affinity is around 3.5 eV, which compares favourably with computational estimates of a so-called pinning level for hydrogen (+/-) charge-state transitions of between -3 and -4.5 eV. A purely ionic model gives some intuitive understanding of this behaviour as well as the invariance of the threshold. Another current description applies equally to covalent materials and relates the threshold to the origin of the electrochemical scale. At the present level of approximation, zero-point energy corrections to the transition levels are small, so that muonium data should provide a reliable guide to the behaviour of hydrogen. Muonium spectroscopy proves to be more sensitive to the (0/+) donor level than to the (+/-) pinning level but, as a tool which does not rely on favourable hydrogen solubility, it looks set to test further predictions of these models in a large number of other materials, notably oxides. Certain candidate thin-film insulators and high-permittivity gate dielectrics appear to be uncomfortably close to conditions in which hydrogen impurity may cause electronic conduction.

Revisiting the role of trap-assisted-tunneling process on current-voltage characteristics in tunnel field-effect transistors

NASA Astrophysics Data System (ADS)

Omura, Yasuhisa; Mori, Yoshiaki; Sato, Shingo; Mallik, Abhijit

2018-04-01

This paper discusses the role of trap-assisted-tunneling process in controlling the ON- and OFF-state current levels and its impacts on the current-voltage characteristics of a tunnel field-effect transistor. Significant impacts of high-density traps in the source region are observed that are discussed in detail. With regard to recent studies on isoelectronic traps, it has been discovered that deep level density must be minimized to suppress the OFF-state leakage current, as is well known, whereas shallow levels can be utilized to control the ON-state current level. A possible mechanism is discussed based on simulation results.

Bond-center hydrogen in dilute Si1-xGex alloys: Laplace deep-level transient spectroscopy

NASA Astrophysics Data System (ADS)

Bonde Nielsen, K.; Dobaczewski, L.; Peaker, A. R.; Abrosimov, N. V.

2003-07-01

We apply Laplace deep-level transient spectroscopy in situ after low-temperature proton implantation into dilute Si1-xGex alloys and identify the deep donor state of hydrogen occupying a strained Si-Si bond-center site next to Ge. The activation energy of the electron emission from the donor is ˜158 meV when extrapolated to zero electrical field. We construct a configuration diagram of the Ge-strained site from formation and annealing data and deduce that alloying with ˜1% Ge does not significantly influence the low-temperature migration of hydrogen as compared to elemental Si. We observe two bond-center-type carbon-hydrogen centers and conclude that carbon impurities act as much stronger traps for hydrogen than the alloy Ge atoms.

Electronic characterization of defects in narrow gap semiconductors

NASA Technical Reports Server (NTRS)

Patterson, James D.

1994-01-01

We use a Green's function technique to calculate the position of deep defects in narrow gap semiconductors. We consider substitutional (including antisite), vacancy, and interstitial (self and foreign) deep defects. We also use perturbation theory to look at the effect of nonparabolic bands on shallow defect energies and find nonparabolicity can increase the binding by 10 percent or so. We consider mercury cadmium telluride (MCT), mercury zinc telluride (MZT), and mercury zinc selenide (MZS). For substitutional and interstitial defects we look at the situation with and without relaxation. For substitutional impurities in MCT, MZT, and MZS, we consider x (the concentration of Cd or Zn) in the range 0.1 less than x less than 0.3 and also consider appropriate x so E(sub g) = 0.1 eV for each of the three compounds. We consider several cation site s-like deep levels and anion site p-like levels. For E(sub g) = 0.1 eV, we also consider the effects of relaxation. Similar comments apply to the interstitial deep levels whereas no relaxation is considered for the ideal vacancy model. Relaxation effects can be greater for the interstitial than the substitutional cases. Specific results are given in figures and tables and comparison to experiment is made in a limited number of cases. We find, for example, that I, Se, S, Rn, and N are possible cation site, s-like deep levels in MCT and Zn and Mg are for anion site, p-like levels (both levels for substitutional cases). The corresponding cation and anion site levels for interstitial deep defects are (Au, Ag, Hg, Cd, Cu, Zn) and (N, Ar, O, F). For the substitutional cases we have some examples of relaxation moving the levels into the band gap, whereas for the interstitial case we have examples where relaxation moves it out of the band gap. Future work involves calculating the effects of charge state interaction and seeing the effect of relaxation on vacancy levels.

Identification of the primary compensating defect level responsible for determining blocking voltage of vertical GaN power diodes

DOE PAGES

King, M. P.; Kaplar, R. J.; Dickerson, J. R.; ...

2016-10-31

Electrical performance and characterization of deep levels in vertical GaN P-i-N diodes grown on low threading dislocation density (~10 4 –10 6 cm –2) bulk GaN substrates are investigated. The lightly doped n drift region of these devices is observed to be highly compensated by several prominent deep levels detected using deep level optical spectroscopy at E c-2.13, 2.92, and 3.2 eV. A combination of steady-state photocapacitance and lighted capacitance-voltage profiling indicates the concentrations of these deep levels to be N t = 3 × 10 12, 2 × 10 15, and 5 × 10 14 cm –3, respectively. Themore » E c-2.92 eV level is observed to be the primary compensating defect in as-grown n-type metal-organic chemical vapor deposition GaN, indicating this level acts as a limiting factor for achieving controllably low doping. The device blocking voltage should increase if compensating defects reduce the free carrier concentration of the n drift region. Understanding the incorporation of as-grown and native defects in thick n-GaN is essential for enabling large V BD in the next-generation wide-bandgap power semiconductor devices. Furthermore, controlling the as-grown defects induced by epitaxial growth conditions is critical to achieve blocking voltage capability above 5 kV.« less

Inversion of Qubit Energy Levels in Qubit-Oscillator Circuits in the Deep-Strong-Coupling Regime.

PubMed

Yoshihara, F; Fuse, T; Ao, Z; Ashhab, S; Kakuyanagi, K; Saito, S; Aoki, T; Koshino, K; Semba, K

2018-05-04

We report on experimentally measured light shifts of superconducting flux qubits deep-strongly coupled to LC oscillators, where the coupling constants are comparable to the qubit and oscillator resonance frequencies. By using two-tone spectroscopy, the energies of the six lowest levels of each circuit are determined. We find huge Lamb shifts that exceed 90% of the bare qubit frequencies and inversions of the qubits' ground and excited states when there are a finite number of photons in the oscillator. Our experimental results agree with theoretical predictions based on the quantum Rabi model.

Inversion of Qubit Energy Levels in Qubit-Oscillator Circuits in the Deep-Strong-Coupling Regime

NASA Astrophysics Data System (ADS)

Yoshihara, F.; Fuse, T.; Ao, Z.; Ashhab, S.; Kakuyanagi, K.; Saito, S.; Aoki, T.; Koshino, K.; Semba, K.

2018-05-01

We report on experimentally measured light shifts of superconducting flux qubits deep-strongly coupled to L C oscillators, where the coupling constants are comparable to the qubit and oscillator resonance frequencies. By using two-tone spectroscopy, the energies of the six lowest levels of each circuit are determined. We find huge Lamb shifts that exceed 90% of the bare qubit frequencies and inversions of the qubits' ground and excited states when there are a finite number of photons in the oscillator. Our experimental results agree with theoretical predictions based on the quantum Rabi model.

Suppress carrier recombination by introducing defects. The case of Si solar cell

DOE PAGES

Liu, Yuanyue; Stradins, Paul; Deng, Huixiong; ...

2016-01-11

Deep level defects are usually harmful to solar cells. Here we show that incorporation of selected deep level defects in the carrier-collecting region, however, can be utilized to improve the efficiency of optoelectronic devices. The designed defects can help the transport of the majority carriers by creating defect levels that is resonant with the band edge state, and/or reduce the concentration of minority carriers through Coulomb repulsion, thus suppressing the recombination at the carrier-collecting region. The selection process is demonstrated by using Si solar cell as an example. In conclusion, our work enriches the understanding and utilization of the semiconductormore » defects.« less

How large is the subducted water flux? New constraints on mantle regassing rates

NASA Astrophysics Data System (ADS)

Parai, R.; Mukhopadhyay, S.

2012-02-01

Estimates of the subducted water (H2O) flux have been used to discuss the regassing of the mantle over Earth history. However, these estimates vary widely, and some are large enough to have reduced the volume of water in the global ocean by a factor of two over the Phanerozoic. In light of uncertainties in the hydration state of subducting slabs, magma production rates and mantle source water contents, we use a Monte Carlo simulation to set limits on long-term global water cycling and the return flux of water to the deep Earth. Estimates of magma production rates and water contents in primary magmas generated at ocean islands, mid-ocean ridges, arcs and back-arcs are paired with estimates of water entering trenches via subducting oceanic slab in order to construct a model of the deep Earth water cycle. The simulation is constrained by reconstructions of Phanerozoic sea level change, which suggest that ocean volume is near steady-state, though a sea level decrease of up to 360 m may be supported. We provide limits on the return flux of water to the deep Earth over the Phanerozoic corresponding to a near steady-state exosphere (0-100 meter sea level decrease) and a maximum sea level decrease of 360 m. For the near steady-state exosphere, the return flux is 1.4 - 2.0- 0.3+ 0.4 × 1013 mol/yr, corresponding to 2-3% serpentinization in 10 km of lithospheric mantle. The return flux that generates the maximum sea level decrease over the Phanerozoic is 3.5- 0.3+ 0.4 × 1013 mol/yr, corresponding to 5% serpentinization in 10 km of lithospheric mantle. Our estimates of the return flux of water to the mantle are up to 7 times lower than previously suggested. The imbalance between our estimates of the return flux and mantle output flux leads to a low rate of increase in bulk mantle water content of up to 24 ppm/Ga.

Optical modulation in silicon waveguides via charge state control of deep levels.

PubMed

Logan, D F; Jessop, P E; Knights, A P; Wojcik, G; Goebel, A

2009-10-12

The control of defect mediated optical absorption at a wavelength of 1550 nm via charge state manipulation is demonstrated using optical absorption measurements of indium doped Silicon-On-Insulator (SOI) rib waveguides. These measurements introduce the potential for modulation of waveguide transmission by using the local depletion and injection of free-carriers to change deep-level occupancy. The extinction ratio and modulating speed are simulated for a proposed device structure. A 'normally-off' depletion modulator is described with an extinction coefficient limited to 5 dB/cm and switching speeds in excess of 1 GHz. For a carrier injection modulator a fourfold enhancement in extinction ratio is provided relative to free carrier absorption alone. This significant improvement in performance is achieved with negligible increase in driving power but slightly degraded switching speed.

Progress to a Gallium-Arsenide Deep-Center Laser

PubMed Central

Pan, Janet L.

2009-01-01

Although photoluminescence from gallium-arsenide (GaAs) deep-centers was first observed in the 1960s, semiconductor lasers have always utilized conduction-to-valence-band transitions. Here we review recent materials studies leading to the first GaAs deep-center laser. First, we summarize well-known properties: nature of deep-center complexes, Franck-Condon effect, photoluminescence. Second, we describe our recent work: insensitivity of photoluminescence with heating, striking differences between electroluminescence and photoluminescence, correlation between transitions to deep-states and absence of bandgap-emission. Room-temperature stimulated-emission from GaAs deep-centers was observed at low electrical injection, and could be tuned from the bandgap to half-the-bandgap (900–1,600 nm) by changing the electrical injection. The first GaAs deep-center laser was demonstrated with electrical injection, and exhibited a threshold of less than 27 mA/cm2 in continuous-wave mode at room temperature at the important 1.54 μm fiber-optic wavelength. This small injection for laser action was explained by fast depopulation of the lower state of the optical transition (fast capture of free holes onto deep-centers), which maintains the population inversion. The evidence for laser action included: superlinear L-I curve, quasi-Fermi level separations satisfying Bernard-Duraffourg’s criterion, optical gains larger than known significant losses, clamping of the optical-emission from lossy modes unable to reach laser action, pinning of the population distribution during laser action.

Origin of High Electronic Quality in Solar Cell Absorber CH3NH3PbI3

NASA Astrophysics Data System (ADS)

Yin, Wanjian; Shi, Tingting; Wei, Suhua; Yan, Yanfa

Thin-film solar cells based on CH3NH3PbI3 halide perovskites have recently shown remarkable performance. First-principle calculations and molecular dynamic simulations show that the structure of pristine CH3NH3PbI3 is much more disordered than the inorganic archetypal thin-film semiconductor CdTe. However, the structural disorders from thermal fluctuation, point defects and grain boundaries introduce rare deep defect states within the bandgaps; therefore, the material has high electronic quality. We have further shown that this unusually high electronic quality is attributed to the unique electronic structures of halide perovskite: the strong coupling between cation lone-pair Pb s orbitals and anion p orbitals and the large atomic size of constitute cation atoms. We further found that although CH3NH3PbI3 GBs do not introduce a deep gap state, the defect level close to the VBM can still act as a shallow hole trap state. Cl and O can spontaneously segregate into GBs and passivate those defect levels and deactivate the trap state.

Pathways to Deep Decarbonization in the United States

NASA Astrophysics Data System (ADS)

Torn, M. S.; Williams, J.

2015-12-01

Limiting anthropogenic warming to less than 2°C will require a reduction in global net greenhouse gas (GHG) emissions on the order of 80% below 1990 levels by 2050. Thus, there is a growing need to understand what would be required to achieve deep decarbonization (DD) in different economies. We examined the technical and economic feasibility of such a transition in the United States, evaluating the infrastructure and technology changes required to reduce U.S. GHG emissions in 2050 by 80% below 1990 levels. Using the PATHWAYS and GCAM models, we found that this level of decarbonization in the U.S. can be accomplished with existing commercial or near-commercial technologies, while providing the same level of energy services and economic growth as a reference case based on the U.S. DOE Annual Energy Outlook. Reductions are achieved through high levels of energy efficiency, decarbonization of electric generation, electrification of most end uses, and switching the remaining end uses to lower carbon fuels. Incremental energy system cost would be equivalent to roughly 1% of gross domestic product, not including potential non-energy benefits such as avoided human and infrastructure costs of climate change. Starting now on the deep decarbonization path would allow infrastructure stock turnover to follow natural replacement rates, which reduces costs, eases demand on manufacturing, and allows gradual consumer adoption. Energy system changes must be accompanied by reductions in non-energy and non-CO2 GHG emissions.

Do deep convolutional neural networks really need to be deep when applied for remote scene classification?

NASA Astrophysics Data System (ADS)

Luo, Chang; Wang, Jie; Feng, Gang; Xu, Suhui; Wang, Shiqiang

2017-10-01

Deep convolutional neural networks (CNNs) have been widely used to obtain high-level representation in various computer vision tasks. However, for remote scene classification, there are not sufficient images to train a very deep CNN from scratch. From two viewpoints of generalization power, we propose two promising kinds of deep CNNs for remote scenes and try to find whether deep CNNs need to be deep for remote scene classification. First, we transfer successful pretrained deep CNNs to remote scenes based on the theory that depth of CNNs brings the generalization power by learning available hypothesis for finite data samples. Second, according to the opposite viewpoint that generalization power of deep CNNs comes from massive memorization and shallow CNNs with enough neural nodes have perfect finite sample expressivity, we design a lightweight deep CNN (LDCNN) for remote scene classification. With five well-known pretrained deep CNNs, experimental results on two independent remote-sensing datasets demonstrate that transferred deep CNNs can achieve state-of-the-art results in an unsupervised setting. However, because of its shallow architecture, LDCNN cannot obtain satisfactory performance, regardless of whether in an unsupervised, semisupervised, or supervised setting. CNNs really need depth to obtain general features for remote scenes. This paper also provides baseline for applying deep CNNs to other remote sensing tasks.

RNA-protein binding motifs mining with a new hybrid deep learning based cross-domain knowledge integration approach.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2017-02-28

RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in iDeep is also able to automatically capture the interpretable binding motifs for RBPs. Large-scale experiments demonstrate that these mined binding motifs agree well with the experimentally verified results, suggesting iDeep is a promising approach in the real-world applications. The iDeep framework not only can achieve promising performance than the state-of-the-art predictors, but also easily capture interpretable binding motifs. iDeep is available at http://www.csbio.sjtu.edu.cn/bioinf/iDeep.

78 FR 10163 - Notice of Availability of Draft Section 3116 Basis for Determination for Closure of H Tank Farm...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-13

... isolation in a deep geologic repository for spent fuel or high-level radioactive waste; (2) has had highly... in 10 CFR Part 61, Subpart C and pursuant to a State approved closure plan or State-issued permit; or... with the performance objectives of 10 CFR Part 61, Subpart C; pursuant to a State approved closure plan...

Policy Implications of Deep Decarbonization in the United States

NASA Astrophysics Data System (ADS)

Williams, J.

2015-12-01

Independent research teams from sixteen of the largest greenhouse gas (GHG) emitting countries have participated in a collaborative two-year project developing emission reduction scenarios for their own countries consistent with limiting anthropogenic warming to 2 C or less. This talk discusses the policy implications of the work done by the Deep Decarbonization Pathways Project (DDPP) at the US federal and international levels, including new ways of informing decision makers about the requirements of an energy system transformation.

Learning representation hierarchies by sharing visual features: a computational investigation of Persian character recognition with unsupervised deep learning.

PubMed

Sadeghi, Zahra; Testolin, Alberto

2017-08-01

In humans, efficient recognition of written symbols is thought to rely on a hierarchical processing system, where simple features are progressively combined into more abstract, high-level representations. Here, we present a computational model of Persian character recognition based on deep belief networks, where increasingly more complex visual features emerge in a completely unsupervised manner by fitting a hierarchical generative model to the sensory data. Crucially, high-level internal representations emerging from unsupervised deep learning can be easily read out by a linear classifier, achieving state-of-the-art recognition accuracy. Furthermore, we tested the hypothesis that handwritten digits and letters share many common visual features: A generative model that captures the statistical structure of the letters distribution should therefore also support the recognition of written digits. To this aim, deep networks trained on Persian letters were used to build high-level representations of Persian digits, which were indeed read out with high accuracy. Our simulations show that complex visual features, such as those mediating the identification of Persian symbols, can emerge from unsupervised learning in multilayered neural networks and can support knowledge transfer across related domains.

Correlation between cortical beta power and gait speed is suppressed in a parkinsonian model, but restored by therapeutic deep brain stimulation.

PubMed

Polar, Christian A; Gupta, Rahul; Lehmkuhle, Mark J; Dorval, Alan D

2018-05-30

The motor cortex and subthalamic nucleus (STN) of patients with Parkinson's disease (PD) exhibit abnormally high levels of electrophysiological oscillations in the ~12-35 Hz beta-frequency range. Recent studies have shown that beta is partly carried forward to regulate future motor states in the healthy condition, suggesting that steady state beta power is lower when a sequence of movements occurs in a short period of time, such as during fast gait. However, whether this relationship between beta power and motor states persists upon parkinsonian onset or in response to effective therapy is unclear. Using a 6-hydroxy dopamine (6-OHDA) rat model of PD and a custom-built behavioral and neurophysiological recording system, we aimed to elucidate a better understanding of the mechanisms underlying cortical beta power and PD symptoms. In addition to elevated levels of beta oscillations, we show that parkinsonian onset was accompanied by a decoupling of movement intensity - quantified as gait speed - from cortical beta power. Although subthalamic deep brain stimulation (DBS) reduced general levels of beta oscillations in the cortex of all PD animals, the brain's capacity to regulate steady state levels of beta power as a function of movement intensity was only restored in animals with therapeutic DBS. We propose that, in addition to lowering general levels of cortical beta power, restoring the brain's ability to maintain this inverse relationship is critical for effective symptom suppression. Copyright © 2017. Published by Elsevier Inc.

Bistability of Hydrogen in ZnO: Origin of Doping Limit and Persistent Photoconductivity

PubMed Central

Nahm, Ho-Hyun; Park, C. H.; Kim, Yong-Sung

2014-01-01

Substitutional hydrogen at oxygen site (HO) is well-known to be a robust source of n-type conductivity in ZnO, but a puzzling aspect is that the doping limit by hydrogen is only about 1018 cm−3, even if solubility limit is much higher. Another puzzling aspect of ZnO is persistent photoconductivity, which prevents the wide applications of the ZnO-based thin film transistor. Up to now, there is no satisfactory theory about two puzzles. We report the bistability of HO in ZnO through first-principles electronic structure calculations. We find that as Fermi level is close to conduction bands, the HO can undergo a large lattice relaxation, through which a deep level can be induced, capturing electrons and the deep state can be transformed into shallow donor state by a photon absorption. We suggest that the bistability can give explanations to two puzzling aspects. PMID:24535157

Bistability of hydrogen in ZnO: origin of doping limit and persistent photoconductivity.

PubMed

Nahm, Ho-Hyun; Park, C H; Kim, Yong-Sung

2014-02-18

Substitutional hydrogen at oxygen site (HO) is well-known to be a robust source of n-type conductivity in ZnO, but a puzzling aspect is that the doping limit by hydrogen is only about 10(18) cm(-3), even if solubility limit is much higher. Another puzzling aspect of ZnO is persistent photoconductivity, which prevents the wide applications of the ZnO-based thin film transistor. Up to now, there is no satisfactory theory about two puzzles. We report the bistability of HO in ZnO through first-principles electronic structure calculations. We find that as Fermi level is close to conduction bands, the HO can undergo a large lattice relaxation, through which a deep level can be induced, capturing electrons and the deep state can be transformed into shallow donor state by a photon absorption. We suggest that the bistability can give explanations to two puzzling aspects.

Ab initio studies of isolated hydrogen vacancies in graphane

NASA Astrophysics Data System (ADS)

Mapasha, R. E.; Molepo, M. P.; Chetty, N.

2016-05-01

We present a density functional study of various hydrogen vacancies located on a single hexagonal ring of graphane (fully hydrogenated graphene) considering the effects of charge states and the position of the Fermi level. We find that uncharged vacancies that lead to a carbon sublattice balance are energetically favorable and are wide band gap systems just like pristine graphane. Vacancies that do create a sublattice imbalance introduce spin polarized states into the band gap, and exhibit a half-metallic behavior with a magnetic moment of 1.00 μB per vacancy. The results show the possibility of using vacancies in graphane for novel spin-based applications. When charging such vacancy configurations, the deep donor (+1/0) and deep acceptor (0/-1) transition levels within the band gap are noted. We also note a half-metallic to metallic transition and a significant reduction of the induced magnetic moment due to both negative and positive charge doping.

Southern Ocean Bottom Water Characteristics in CMIP5 Models

NASA Astrophysics Data System (ADS)

Heuzé, Céline; Heywood, Karen; Stevens, David; Ridley, Jeff

2013-04-01

The depiction of Southern Ocean deep water properties and formation processes in climate models is an indicator of their capability to simulate future climate, heat and carbon uptake, and sea level rise. Southern Ocean potential temperature and density averaged over 1986-2005 from fifteen CMIP5 climate models are compared with an observed climatology, focusing on bottom water properties. The mean bottom properties are reasonably accurate for half of the models, but the other half may not yet have approached an equilibrium state. Eleven models create dense water on the Antarctic shelf, but it does not spill off and propagate northwards, alternatively mixing rapidly with less dense water. Instead most models create deep water by open ocean deep convection. Models with large deep convection areas are those with a strong seasonal cycle in sea ice. The most accurate bottom properties occur in models hosting deep convection in the Weddell and Ross gyres.

Slow and deep respiration suppresses steady-state sympathetic nerve activity in patients with chronic heart failure: from modeling to clinical application.

PubMed

Harada, Daisuke; Asanoi, Hidetsugu; Takagawa, Junya; Ishise, Hisanari; Ueno, Hiroshi; Oda, Yoshitaka; Goso, Yukiko; Joho, Shuji; Inoue, Hiroshi

2014-10-15

Influences of slow and deep respiration on steady-state sympathetic nerve activity remain controversial in humans and could vary depending on disease conditions and basal sympathetic nerve activity. To elucidate the respiratory modulation of steady-state sympathetic nerve activity, we modeled the dynamic nature of the relationship between lung inflation and muscle sympathetic nerve activity (MSNA) in 11 heart failure patients with exaggerated sympathetic outflow at rest. An autoregressive exogenous input model was utilized to simulate entire responses of MSNA to variable respiratory patterns. In another 18 patients, we determined the influence of increasing tidal volume and slowing respiratory frequency on MSNA; 10 patients underwent a 15-min device-guided slow respiration and the remaining 8 had no respiratory modification. The model predicted that a 1-liter, step increase of lung volume decreased MSNA dynamically; its nadir (-33 ± 22%) occurred at 2.4 s; and steady-state decrease (-15 ± 5%), at 6 s. Actually, in patients with the device-guided slow and deep respiration, respiratory frequency effectively fell from 16.4 ± 3.9 to 6.7 ± 2.8/min (P < 0.0001) with a concomitant increase in tidal volume from 499 ± 206 to 1,177 ± 497 ml (P < 0.001). Consequently, steady-state MSNA was decreased by 31% (P < 0.005). In patients without respiratory modulation, there were no significant changes in respiratory frequency, tidal volume, and steady-state MSNA. Thus slow and deep respiration suppresses steady-state sympathetic nerve activity in patients with high levels of resting sympathetic tone as in heart failure. Copyright © 2014 the American Physiological Society.

Deep learning based tissue analysis predicts outcome in colorectal cancer.

PubMed

Bychkov, Dmitrii; Linder, Nina; Turkki, Riku; Nordling, Stig; Kovanen, Panu E; Verrill, Clare; Walliander, Margarita; Lundin, Mikael; Haglund, Caj; Lundin, Johan

2018-02-21

Image-based machine learning and deep learning in particular has recently shown expert-level accuracy in medical image classification. In this study, we combine convolutional and recurrent architectures to train a deep network to predict colorectal cancer outcome based on images of tumour tissue samples. The novelty of our approach is that we directly predict patient outcome, without any intermediate tissue classification. We evaluate a set of digitized haematoxylin-eosin-stained tumour tissue microarray (TMA) samples from 420 colorectal cancer patients with clinicopathological and outcome data available. The results show that deep learning-based outcome prediction with only small tissue areas as input outperforms (hazard ratio 2.3; CI 95% 1.79-3.03; AUC 0.69) visual histological assessment performed by human experts on both TMA spot (HR 1.67; CI 95% 1.28-2.19; AUC 0.58) and whole-slide level (HR 1.65; CI 95% 1.30-2.15; AUC 0.57) in the stratification into low- and high-risk patients. Our results suggest that state-of-the-art deep learning techniques can extract more prognostic information from the tissue morphology of colorectal cancer than an experienced human observer.

Analytical solutions by squeezing to the anisotropic Rabi model in the nonperturbative deep-strong-coupling regime

NASA Astrophysics Data System (ADS)

Zhang, Yu-Yu; Chen, Xiang-You

2017-12-01

An unexplored nonperturbative deep strong coupling (npDSC) achieved in superconducting circuits has been studied in the anisotropic Rabi model by the generalized squeezing rotating-wave approximation. Energy levels are evaluated analytically from the reformulated Hamiltonian and agree well with numerical ones in a wide range of coupling strength. Such improvement ascribes to deformation effects in the displaced-squeezed state presented by the squeezed momentum variance, which are omitted in previous displaced states. The atom population dynamics confirms the validity of our approach for the npDSC strength. Our approach offers the possibility to explore interesting phenomena analytically in the npDSC regime in qubit-oscillator experiments.

Progressive reactivation of the volcanic plumbing system beneath Tolbachik volcano (Kamchatka, Russia) revealed by long-period seismicity

NASA Astrophysics Data System (ADS)

Frank, William B.; Shapiro, Nikolaï M.; Gusev, Alexander A.

2018-07-01

After lying dormant for 36 yr, the Tolbachik volcano of the Klyuchevskoy group started to erupt on 27 November 2012. We investigate the preparatory phase of this eruption via a statistical analysis of the temporal behavior of long-period (LP) earthquakes that occurred beneath this volcanic system. The LP seismicity occurs close to the surface beneath the main volcanic edifices and at 30 km depth in the vicinity of a deep magmatic reservoir. The deep LP earthquakes and those beneath the Klyuchevskoy volcano occur quasi-periodically, while the LP earthquakes beneath Tolbachik are clustered in time. As the seismicity rate increased beneath Tolbachik days before the eruption, the level of the time clustering decreased. We interpret this as a manifestation of the evolution of the volcano plumbing system. We suggest that when a plumbing system awakes after quiescence, multiple cracks and channels are reactivated simultaneously and their interaction results in the strong time clustering of LP earthquakes. With time, this network of channels and cracks evolves into a more stable state with an overall increased permeability, where fluids flow uninhibited throughout the plumbing system except for a few remaining impediments that continue to generate seismic radiation. The inter-seismic source interaction and the level of earthquake time clustering in this latter state is weak. This scenario suggests that the observed evolution of the statistical behavior of the shallow LP seismicity beneath Tolbachik is an indicator of the reactivation and consolidation of the near-surface plumbing system prior to the Tolbachik eruption. The parts of the plumbing system above the deep magmatic reservoir and beneath the Klyuchevskoy volcano remain in nearly permanent activity, as demonstrated by the continuous occurrence of the deep LP earthquakes and very frequent Klyuchevskoy eruptions. This implies that these parts of the plumbing system remain in a stable permeable state and contain a few weakly interacting seismogenic sources. Our results provide new constraints on future mechanical models of the magmatic plumbing systems and demonstrate that the level of time clustering of LP earthquakes can be a useful parameter to infer information about the state of the plumbing system.

Computer analysis of the negative differential resistance switching phenomenon of double-injection devices

NASA Technical Reports Server (NTRS)

Shieh, Tsay-Jiu

1989-01-01

By directly solving the semiconductor differential equations for the double-injection (DI) devices involving two interacting deep levels, the authors studied the negative differential resistance switching characteristic and its relationship with the device dimension, doping level, and dependence on the deep impurity profile. Computer simulation showed that although one can increase the threshold voltage by increasing the device length, the excessive holding voltage that would follow would put this device in a very limited application such as pulse power source. The excessive leakage current in the low conductance state also jeopardizes the attempt to use the device for any practical purpose. Unless there are new materials and deep impurities found that have a great differential hole and electron capture cross sections and a reasonable energy bandgap for low intrinsic carrier concentration, no big improvement in the fate of DI devices is expected in the near future.

Convective transport over the central United States and its role in regional CO and ozone budgets

NASA Technical Reports Server (NTRS)

Thompson, Anne M.; Pickering, Kenneth E.; Dickerson, Russell R.; Ellis, William G., Jr.; Jacob, Daniel J.; Scala, John R.; Tao, Wei-Kuo; Mcnamara, Donna P.; Simpson, Joanne

1994-01-01

We have constructed a regional budget for boundary layer carbon monoxide over the central United States (32.5 deg - 50 deg N, 90 deg - 105 deg W), emphasizing a detailed evaluation of deep convective vertical fluxes appropriate for the month of June. Deep convective venting of the boundary layer (upward) dominates other components of the CO budget, e.g., downward convective transport, loss of CO by oxidation, anthropogenic emissions, and CO produced from oxidation of methane, isoprene, and anthropogenic nonmethane hydrocarbons (NMHCs). Calculations of deep convective venting are based on the method pf Pickering et al.(1992a) which uses a satellite-derived deep convective cloud climatology along with transport statistics from convective cloud model simulations of observed prototype squall line events. This study uses analyses of convective episodes in 1985 and 1989 and CO measurements taken during several midwestern field campaigns. Deep convective venting of the boundary layer over this moderately polluted region provides a net (upward minus downward) flux of 18.1 x 10(exp 8) kg CO/month to the free troposphere during early summer. Shallow cumulus and synoptic-scale weather systems together make a comparable contribution (total net flux 16.2 x 10(exp 8) kg CO/month). Boundary layer venting of CO with other O3 precursors leads to efficient free troposheric O3 formation. We estimate that deep convective transport of CO and other precursors over the central United States in early summer leads to a gross production of 0.66 - 1.1 Gmol O3/d in good agreement with estimates of O3 production from boundary layer venting in a continental-scale model (Jacob et al., 1993a, b). On this respect the central U.S. region acts as s `chimney' for the country, and presumably this O3 contributes to high background levels of O3 in the eastern United States and O3 export to the North Atlantic.

An equation-of-state-meter of quantum chromodynamics transition from deep learning.

PubMed

Pang, Long-Gang; Zhou, Kai; Su, Nan; Petersen, Hannah; Stöcker, Horst; Wang, Xin-Nian

2018-01-15

A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering the nature of the phase transition in quantum chromodynamics. Such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.

Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures.

PubMed

Trullo, Roger; Petitjean, Caroline; Nie, Dong; Shen, Dinggang; Ruan, Su

2017-09-01

Computed Tomography (CT) is the standard imaging technique for radiotherapy planning. The delineation of Organs at Risk (OAR) in thoracic CT images is a necessary step before radiotherapy, for preventing irradiation of healthy organs. However, due to low contrast, multi-organ segmentation is a challenge. In this paper, we focus on developing a novel framework for automatic delineation of OARs. Different from previous works in OAR segmentation where each organ is segmented separately, we propose two collaborative deep architectures to jointly segment all organs, including esophagus, heart, aorta and trachea. Since most of the organ borders are ill-defined, we believe spatial relationships must be taken into account to overcome the lack of contrast. The aim of combining two networks is to learn anatomical constraints with the first network, which will be used in the second network, when each OAR is segmented in turn. Specifically, we use the first deep architecture, a deep SharpMask architecture, for providing an effective combination of low-level representations with deep high-level features, and then take into account the spatial relationships between organs by the use of Conditional Random Fields (CRF). Next, the second deep architecture is employed to refine the segmentation of each organ by using the maps obtained on the first deep architecture to learn anatomical constraints for guiding and refining the segmentations. Experimental results show superior performance on 30 CT scans, comparing with other state-of-the-art methods.

Defect-Reduction Mechanism for Improving Radiative Efficiency in InGaN/GaN Light-Emitting Diodes using InGaN Underlayers

DOE PAGES

Armstrong, Andrew M.; Bryant, Benjamin N.; Crawford, Mary H.; ...

2015-04-01

The influence of a dilute In xGa 1-xN (x~0.03) underlayer (UL) grown below a single In 0.16Ga 0.84N quantum well (SQW), within a light-emitting diode(LED), on the radiative efficiency and deep level defect properties was studied using differential carrier lifetime (DCL) measurements and deep level optical spectroscopy (DLOS). DCL measurements found that inclusion of the UL significantly improved LED radiative efficiency. At low current densities, the non-radiative recombination rate of the LED with an UL was found to be 3.9 times lower than theLED without an UL, while the radiative recombination rates were nearly identical. This, then, suggests that themore » improved radiative efficiency resulted from reduced non-radiative defect concentration within the SQW. DLOS measurement found the same type of defects in the InGaN SQWs with and without ULs. However, lighted capacitance-voltage measurements of the LEDs revealed a 3.4 times reduction in a SQW-related near-mid-gap defect state for the LED with an UL. Furthermore, quantitative agreement in the reduction of both the non-radiative recombination rate (3.9×) and deep level density (3.4×) upon insertion of an UL corroborates deep level defect reduction as the mechanism for improved LED efficiency.« less

Single and double acceptor-levels of a carbon-hydrogen defect in n-type silicon

NASA Astrophysics Data System (ADS)

Stübner, R.; Scheffler, L.; Kolkovsky, Vl.; Weber, J.

2016-05-01

In the present study, we discuss the origin of two dominant deep levels (E42 and E262) observed in n-type Si, which is subjected to hydrogenation by wet chemical etching or a dc H-plasma treatment. Their activation enthalpies determined from Laplace deep level transient spectroscopy measurements are EC-0.06 eV (E42) and EC-0.51 eV (E262). The similar annealing behavior and identical depth profiles of E42 and E262 correlate them with two different charge states of the same defect. E262 is attributed to a single acceptor state due to the absence of the Poole-Frenkel effect and the lack of a capture barrier for electrons. The emission rate of E42 shows a characteristic enhancement with the electric field, which is consistent with the assignment to a double acceptor state. In samples with different carbon and hydrogen content, the depth profiles of E262 can be explained by a defect with one H-atom and one C-atom. From a comparison with earlier calculations [Andersen et al., Phys. Rev. B 66, 235205 (2002)], we attribute E42 to the double acceptor and E262 to the single acceptor state of the CH1AB configuration, where one H atom is directly bound to carbon in the anti-bonding position.

Single and double acceptor-levels of a carbon-hydrogen defect in n-type silicon

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

Stübner, R.; Scheffler, L.; Kolkovsky, Vl., E-mail: kolkov@ifpan.edu.pl

In the present study, we discuss the origin of two dominant deep levels (E42 and E262) observed in n-type Si, which is subjected to hydrogenation by wet chemical etching or a dc H-plasma treatment. Their activation enthalpies determined from Laplace deep level transient spectroscopy measurements are E{sub C}-0.06 eV (E42) and E{sub C}-0.51 eV (E262). The similar annealing behavior and identical depth profiles of E42 and E262 correlate them with two different charge states of the same defect. E262 is attributed to a single acceptor state due to the absence of the Poole-Frenkel effect and the lack of a capture barrier formore » electrons. The emission rate of E42 shows a characteristic enhancement with the electric field, which is consistent with the assignment to a double acceptor state. In samples with different carbon and hydrogen content, the depth profiles of E262 can be explained by a defect with one H-atom and one C-atom. From a comparison with earlier calculations [Andersen et al., Phys. Rev. B 66, 235205 (2002)], we attribute E42 to the double acceptor and E262 to the single acceptor state of the CH{sub 1AB} configuration, where one H atom is directly bound to carbon in the anti-bonding position.« less

Resting state functional MRI in Parkinson’s disease: the impact of deep brain stimulation on ‘effective’ connectivity

PubMed Central

Kahan, Joshua; Urner, Maren; Moran, Rosalyn; Flandin, Guillaume; Marreiros, Andre; Mancini, Laura; White, Mark; Thornton, John; Yousry, Tarek; Zrinzo, Ludvic; Hariz, Marwan; Limousin, Patricia; Friston, Karl

2014-01-01

Depleted of dopamine, the dynamics of the parkinsonian brain impact on both ‘action’ and ‘resting’ motor behaviour. Deep brain stimulation has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Non-invasive characterizations of induced brain responses, and the effective connectivity underlying them, generally appeals to dynamic causal modelling of neuroimaging data. When the brain is at rest, however, this sort of characterization has been limited to correlations (functional connectivity). In this work, we model the ‘effective’ connectivity underlying low frequency blood oxygen level-dependent fluctuations in the resting Parkinsonian motor network—disclosing the distributed effects of deep brain stimulation on cortico-subcortical connections. Specifically, we show that subthalamic nucleus deep brain stimulation modulates all the major components of the motor cortico-striato-thalamo-cortical loop, including the cortico-striatal, thalamo-cortical, direct and indirect basal ganglia pathways, and the hyperdirect subthalamic nucleus projections. The strength of effective subthalamic nucleus afferents and efferents were reduced by stimulation, whereas cortico-striatal, thalamo-cortical and direct pathways were strengthened. Remarkably, regression analysis revealed that the hyperdirect, direct, and basal ganglia afferents to the subthalamic nucleus predicted clinical status and therapeutic response to deep brain stimulation; however, suppression of the sensitivity of the subthalamic nucleus to its hyperdirect afferents by deep brain stimulation may subvert the clinical efficacy of deep brain stimulation. Our findings highlight the distributed effects of stimulation on the resting motor network and provide a framework for analysing effective connectivity in resting state functional MRI with strong a priori hypotheses. PMID:24566670

Resting state functional MRI in Parkinson's disease: the impact of deep brain stimulation on 'effective' connectivity.

PubMed

Kahan, Joshua; Urner, Maren; Moran, Rosalyn; Flandin, Guillaume; Marreiros, Andre; Mancini, Laura; White, Mark; Thornton, John; Yousry, Tarek; Zrinzo, Ludvic; Hariz, Marwan; Limousin, Patricia; Friston, Karl; Foltynie, Tom

2014-04-01

Depleted of dopamine, the dynamics of the parkinsonian brain impact on both 'action' and 'resting' motor behaviour. Deep brain stimulation has become an established means of managing these symptoms, although its mechanisms of action remain unclear. Non-invasive characterizations of induced brain responses, and the effective connectivity underlying them, generally appeals to dynamic causal modelling of neuroimaging data. When the brain is at rest, however, this sort of characterization has been limited to correlations (functional connectivity). In this work, we model the 'effective' connectivity underlying low frequency blood oxygen level-dependent fluctuations in the resting Parkinsonian motor network-disclosing the distributed effects of deep brain stimulation on cortico-subcortical connections. Specifically, we show that subthalamic nucleus deep brain stimulation modulates all the major components of the motor cortico-striato-thalamo-cortical loop, including the cortico-striatal, thalamo-cortical, direct and indirect basal ganglia pathways, and the hyperdirect subthalamic nucleus projections. The strength of effective subthalamic nucleus afferents and efferents were reduced by stimulation, whereas cortico-striatal, thalamo-cortical and direct pathways were strengthened. Remarkably, regression analysis revealed that the hyperdirect, direct, and basal ganglia afferents to the subthalamic nucleus predicted clinical status and therapeutic response to deep brain stimulation; however, suppression of the sensitivity of the subthalamic nucleus to its hyperdirect afferents by deep brain stimulation may subvert the clinical efficacy of deep brain stimulation. Our findings highlight the distributed effects of stimulation on the resting motor network and provide a framework for analysing effective connectivity in resting state functional MRI with strong a priori hypotheses.

State of HIV in the US Deep South.

PubMed

Reif, Susan; Safley, Donna; McAllaster, Carolyn; Wilson, Elena; Whetten, Kathryn

2017-10-01

The Southern United States has been disproportionately affected by HIV diagnoses and mortality. To inform efforts to effectively address HIV in the South, this manuscript synthesizes recent data on HIV epidemiology, care financing, and current research literature on factors that predispose this region to experience a greater impact of HIV. The manuscript focuses on a specific Southern region, the Deep South, which has been particularly affected by HIV. Epidemiologic data from the Centers from Disease Control and Prevention indicate that the Deep South had the highest HIV diagnosis rate and the highest number of individuals diagnosed with HIV (18,087) in 2014. The percentage of new HIV diagnoses that were female has decreased over time (2008-2014) while increasing among minority MSM. The Deep South also had the highest death rates with HIV as an underlying cause of any US region in 2014. Despite higher diagnosis and death rates, the Deep South received less federal government and private foundation funding per person living with HIV than the US overall. Factors that have been identified as contributors to the disproportionate effects of HIV in the Deep South include pervasive HIV-related stigma, poverty, higher levels of sexually transmitted infections, racial inequality and bias, and laws that further HIV-related stigma and fear. Interventions that address and abate the contributors to the spread of HIV disease and the poorer HIV-related outcomes in the Deep South are warranted. Funding inequalities by region must also be examined and addressed to reduce the regional disparities in HIV incidence and mortality.

Energy Levels in Quantum Wells.

NASA Astrophysics Data System (ADS)

Zang, Jan Xin

Normalized analytical equations for eigenstates of an arbitrary one-dimensional configuration of square potentials in a well have been derived. The general formulation is used to evaluate the energy levels of a particle in a very deep potential well containing seven internal barriers. The configuration can be considered as a finite superlattice sample or as a simplified model for a sample with only several atom layers. The results are shown in graphical forms as functions of the height and width of the potential barriers and as functions of the ratio of the effective mass in barrier to the mass in well. The formation of energy bands and surface eigenstates from eigenstates of a deep single well, the coming close of two energy bands and a surface state which are separate ordinarily, and mixing of the wave function of a surface state with the bulk energy bands are seen. Then the normalized derivation is extended to study the effect of a uniform electric field applied across a one-dimensional well containing an internal configuration of square potentials The general formulation is used to calculate the electric field dependence of the energy levels of a deep well with five internal barriers. Typical results are shown in graphical forms as functions of the barrier height, barrier width, barrier effective mass and the field strength. The formation of Stark ladders and surface states from the eigenstates of a single deep well in an electric field, the localization process of wave functions with changing barrier height, width, and field strength and their anticrossing behaviors are seen. The energy levels of a hydrogenic impurity in a uniform medium and in a uniform magnetic field are calculated with variational methods. The energy eigenvalues for the eigenstates with major quantum number less than or equal to 3 are obtained. The results are consistent with previous results. Furthermore, the energy levels of a hydrogenic impurity at the bottom of a one-dimensional parabolic quantum well with a magnetic field normal to the plane of the well are calculated with the finite-basis-set variational method. The limit of small radial distance and the limit of great radial distance are considered to choose a set of proper basis functions. It is found that the energy levels increase with increasing parabolic parameter alpha and increase with increasing normalized magnetic field strength gamma except those levels with magnetic quantum number m < 0 at small gamma.

Auditory processing during deep propofol sedation and recovery from unconsciousness.

PubMed

Koelsch, Stefan; Heinke, Wolfgang; Sammler, Daniela; Olthoff, Derk

2006-08-01

Using evoked potentials, this study investigated effects of deep propofol sedation, and effects of recovery from unconsciousness, on the processing of auditory information with stimuli suited to elicit a physical MMN, and a (music-syntactic) ERAN. Levels of sedation were assessed using the Bispectral Index (BIS) and the Modified Observer's Assessment of Alertness and Sedation Scale (MOAAS). EEG-measurements were performed during wakefulness, deep propofol sedation (MOAAS 2-3, mean BIS=68), and a recovery period. Between deep sedation and recovery period, the infusion rate of propofol was increased to achieve unconsciousness (MOAAS 0-1, mean BIS=35); EEG measurements of recovery period were performed after subjects regained consciousness. During deep sedation, the physical MMN was markedly reduced, but still significant. No ERAN was observed in this level. A clear P3a was elicited during deep sedation by those deviants, which were task-relevant during the awake state. As soon as subjects regained consciousness during the recovery period, a normal MMN was elicited. By contrast, the P3a was absent in the recovery period, and the P3b was markedly reduced. Results indicate that the auditory sensory memory (as indexed by the physical MMN) is still active, although strongly reduced, during deep sedation (MOAAS 2-3). The presence of the P3a indicates that attention-related processes are still operating during this level. Processes of syntactic analysis appear to be abolished during deep sedation. After propofol-induced anesthesia, the auditory sensory memory appears to operate normal as soon as subjects regain consciousness, whereas the attention-related processes indexed by P3a and P3b are markedly impaired. Results inform about effects of sedative drugs on auditory and attention-related mechanisms. The findings are important because these mechanisms are prerequisites for auditory awareness, auditory learning and memory, as well as language perception during anesthesia.

Demand Driven Acquisition of E-Books in a Small Online Academic Library: Growing Pains and Assessing Gains

ERIC Educational Resources Information Center

Longley, Dana H.

2016-01-01

How does a smaller, fully online academic library offer a wide and deep collection of academic level e-books to its distance learners in a sustainable and affordable way? The State University of New York (SUNY) Empire State College Online Library, with a staff of four, has used demand-driven e-book acquisitions since September 2013. Despite…

Nuclear structure studies of 141Ce and 147Sm using deep-inelastic collisions

NASA Astrophysics Data System (ADS)

Gass, E. J.; McCutchan, E. A.; Sonzogni, A. A.; Loveland, W.; Barrett, J. S.; Yanez, R.; Chiara, C. J.; Harker, J. L.; Walters, W. B.; Zhu, S.; Ayangeakaai, A. D.; Carpenter, M. P.; Greene, J. P.; Janssens, R. V. F.; Lauritsen, T.; Naïdja, H.

2017-09-01

Nuclei with a few valence nucleons outside of the magic numbers are essential for testing the nuclear shell model and gathering information on the residual interactions and energies of single-particle levels. The present work focused on the high-spin structures of 141Ce (N = 83) and 147Sm (N = 85). These nuclei are not produced by heavy-ion fusion-evaporation or fission reactions, therefore little was known about their high-spin structure. A deep-inelastic reaction using a beam of 136Xe incident on a thick target of 208Pb was used to populate excited states in the nuclei. The Gammasphere array at Argonne National Laboratory was used to detect the resulting de-excitation -ray transitions. The level schemes of both nuclei were significantly extended to high angular momentum and high excitation energy. In 141Ce, this included a number of states built on the i13/2, 1369-keV level. Results of the present analysis will be compared to state-of-the-art shell model calculations. Supported by US DOE under the SULI Program and Grant Nos. DE-FG06-97ER41026 and DE-FG02-94ER40834 and Contract Nos. DE-AC02-06CH11357 and DE-AC02-06CH10886.

ACTIVIS: Visual Exploration of Industry-Scale Deep Neural Network Models.

PubMed

Kahng, Minsuk; Andrews, Pierre Y; Kalro, Aditya; Polo Chau, Duen Horng

2017-08-30

While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ACTIVIS, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ACTIVIS has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ACTIVIS may work with different models.

Native hole trap in bulk GaAs and its association with the double-charge state of the arsenic antisite defect

NASA Technical Reports Server (NTRS)

Lagowski, J.; Lin, D. G.; Chen, T.-P.; Skowronski, M.; Gatos, H. C.

1985-01-01

A dominant hole trap has been identified in p-type bulk GaAs employing deep level transient and photocapacitance spectroscopies. The trap is present at a concentration up to about 4 x 10 to the 16th per cu cm, and it has two charge states with energies 0.54 + or - 0.02 and 0.77 + or - 0.02 eV above the top of the valence band (at 77 K). From the upper level the trap can be photoexcited to a persistent metastable state just as the dominant midgap level, EL2. Impurity analysis and the photoionization characteristics rule out association of the trap with impurities Fe, Cu, or Mn. Taking into consideration theoretical results, it appears most likely that the two charge states of the trap are the single and double donor levels of the arsenic antisite As(Ga) defect.

An equation-of-state-meter of quantum chromodynamics transition from deep learning

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

Pang, Long-Gang; Zhou, Kai; Su, Nan

A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering themore » nature of the phase transition in quantum chromodynamics. Finally, such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.« less

An equation-of-state-meter of quantum chromodynamics transition from deep learning

DOE PAGES

Pang, Long-Gang; Zhou, Kai; Su, Nan; ...

2018-01-15

A primordial state of matter consisting of free quarks and gluons that existed in the early universe a few microseconds after the Big Bang is also expected to form in high-energy heavy-ion collisions. Determining the equation of state (EoS) of such a primordial matter is the ultimate goal of high-energy heavy-ion experiments. Here we use supervised learning with a deep convolutional neural network to identify the EoS employed in the relativistic hydrodynamic simulations of heavy ion collisions. High-level correlations of particle spectra in transverse momentum and azimuthal angle learned by the network act as an effective EoS-meter in deciphering themore » nature of the phase transition in quantum chromodynamics. Finally, such EoS-meter is model-independent and insensitive to other simulation inputs including the initial conditions for hydrodynamic simulations.« less

Ecological impacts of large-scale disposal of mining waste in the deep sea

PubMed Central

Hughes, David J.; Shimmield, Tracy M.; Black, Kenneth D.; Howe, John A.

2015-01-01

Deep-Sea Tailings Placement (DSTP) from terrestrial mines is one of several large-scale industrial activities now taking place in the deep sea. The scale and persistence of its impacts on seabed biota are unknown. We sampled around the Lihir and Misima island mines in Papua New Guinea to measure the impacts of ongoing DSTP and assess the state of benthic infaunal communities after its conclusion. At Lihir, where DSTP has operated continuously since 1996, abundance of sediment infauna was substantially reduced across the sampled depth range (800–2020 m), accompanied by changes in higher-taxon community structure, in comparison with unimpacted reference stations. At Misima, where DSTP took place for 15 years, ending in 2004, effects on community composition persisted 3.5 years after its conclusion. Active tailings deposition has severe impacts on deep-sea infaunal communities and these impacts are detectable at a coarse level of taxonomic resolution. PMID:25939397

Ecological impacts of large-scale disposal of mining waste in the deep sea.

PubMed

Hughes, David J; Shimmield, Tracy M; Black, Kenneth D; Howe, John A

2015-05-05

Deep-Sea Tailings Placement (DSTP) from terrestrial mines is one of several large-scale industrial activities now taking place in the deep sea. The scale and persistence of its impacts on seabed biota are unknown. We sampled around the Lihir and Misima island mines in Papua New Guinea to measure the impacts of ongoing DSTP and assess the state of benthic infaunal communities after its conclusion. At Lihir, where DSTP has operated continuously since 1996, abundance of sediment infauna was substantially reduced across the sampled depth range (800-2020 m), accompanied by changes in higher-taxon community structure, in comparison with unimpacted reference stations. At Misima, where DSTP took place for 15 years, ending in 2004, effects on community composition persisted 3.5 years after its conclusion. Active tailings deposition has severe impacts on deep-sea infaunal communities and these impacts are detectable at a coarse level of taxonomic resolution.

Blue whales respond to simulated mid-frequency military sonar.

PubMed

Goldbogen, Jeremy A; Southall, Brandon L; DeRuiter, Stacy L; Calambokidis, John; Friedlaender, Ari S; Hazen, Elliott L; Falcone, Erin A; Schorr, Gregory S; Douglas, Annie; Moretti, David J; Kyburg, Chris; McKenna, Megan F; Tyack, Peter L

2013-08-22

Mid-frequency military (1-10 kHz) sonars have been associated with lethal mass strandings of deep-diving toothed whales, but the effects on endangered baleen whale species are virtually unknown. Here, we used controlled exposure experiments with simulated military sonar and other mid-frequency sounds to measure behavioural responses of tagged blue whales (Balaenoptera musculus) in feeding areas within the Southern California Bight. Despite using source levels orders of magnitude below some operational military systems, our results demonstrate that mid-frequency sound can significantly affect blue whale behaviour, especially during deep feeding modes. When a response occurred, behavioural changes varied widely from cessation of deep feeding to increased swimming speed and directed travel away from the sound source. The variability of these behavioural responses was largely influenced by a complex interaction of behavioural state, the type of mid-frequency sound and received sound level. Sonar-induced disruption of feeding and displacement from high-quality prey patches could have significant and previously undocumented impacts on baleen whale foraging ecology, individual fitness and population health.

Electronic Characterization of Defects in Narrow Gap Semiconductors-Comparison of Electronic Energy Levels and Formation Energies in Mercury Cadmium Telluride, Mercury Zinc Telluride, and Mercury Zinc Selenide

NASA Technical Reports Server (NTRS)

Patterson, James D.

1996-01-01

We have used a Green's function technique to calculate the energy levels and formation energy of deep defects in the narrow gap semiconductors mercury cadmium telluride (MCT), mercury zinc telluride (MZT) and mercury zinc selenide (MZS). The formation energy is calculated from the difference between the total energy with an impurity cluster and the total energy for the perfect crystal. Substitutional (including antisite), interstitial (self and foreign), and vacancy deep defects are considered. Relaxation effects are calculated (with molecular dynamics). By use of a pseudopotential, we generalize the ideal vacancy model so as to be able to consider relaxation for vacancies. Different charge states are considered and the charged state energy shift (as computed by a modified Haldane-Anderson model) can be twice that due to relaxation. Different charged states for vacancies were not calculated to have much effect on the formation energy. For all cases we find deep defects in the energy gap only for cation site s-like orbitals or anion site p-like orbitals, and for the substitutional case only the latter are appreciably effected by relaxation. For most cases for MCT, MZT, MZS, we consider x (the concentration of Cd or Zn) in the range appropriate for a band gap of 0.1 eV. For defect energy levels, the absolute accuracy of our results is limited, but the precision is good, and hence chemical trends are accurately predicted. For the same reason, defect formation energies are more accurately predicted than energy level position. We attempt, in Appendix B, to calculate vacancy formation energies using relatively simple chemical bonding ideas due to Harrison. However, these results are only marginally accurate for estimating vacancy binding energies. Appendix C lists all written reports and publications produced for the grant. We include abstracts and a complete paper that summarizes our work which is not yet available.

Mobile Strike force 2010

DTIC Science & Technology

1994-09-23

States Army Inrtntry Center, Fort Beanning Georgia, served as the MSF 0-3. MAY Kevin Lee, Course Manager, United States Army ntelligence Center, Fort...possess mobility and protei levels sufficent for it to operate in conjunction with the FMBT. Firepower will include a lnran-mge minkge (TOW follow...Experience has shown that composite artillery battalions don’t work very well due to logistics resupply and ammo problem. £ To achieve effectivenus vith deep

Deep Visual Attention Prediction

NASA Astrophysics Data System (ADS)

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.

Tutorial: Junction spectroscopy techniques and deep-level defects in semiconductors

NASA Astrophysics Data System (ADS)

Peaker, A. R.; Markevich, V. P.; Coutinho, J.

2018-04-01

The term junction spectroscopy embraces a wide range of techniques used to explore the properties of semiconductor materials and semiconductor devices. In this tutorial review, we describe the most widely used junction spectroscopy approaches for characterizing deep-level defects in semiconductors and present some of the early work on which the principles of today's methodology are based. We outline ab-initio calculations of defect properties and give examples of how density functional theory in conjunction with formation energy and marker methods can be used to guide the interpretation of experimental results. We review recombination, generation, and trapping of charge carriers associated with defects. We consider thermally driven emission and capture and describe the techniques of Deep Level Transient Spectroscopy (DLTS), high resolution Laplace DLTS, admittance spectroscopy, and scanning DLTS. For the study of minority carrier related processes and wide gap materials, we consider Minority Carrier Transient Spectroscopy (MCTS), Optical DLTS, and deep level optical transient spectroscopy together with some of their many variants. Capacitance, current, and conductance measurements enable carrier exchange processes associated with the defects to be detected. We explain how these methods are used in order to understand the behaviour of point defects and the determination of charge states and negative-U (Hubbard correlation energy) behaviour. We provide, or reference, examples from a wide range of materials including Si, SiGe, GaAs, GaP, GaN, InGaN, InAlN, and ZnO.

deepNF: Deep network fusion for protein function prediction.

PubMed

Gligorijevic, Vladimir; Barot, Meet; Bonneau, Richard

2018-06-01

The prevalence of high-throughput experimental methods has resulted in an abundance of large-scale molecular and functional interaction networks. The connectivity of these networks provides a rich source of information for inferring functional annotations for genes and proteins. An important challenge has been to develop methods for combining these heterogeneous networks to extract useful protein feature representations for function prediction. Most of the existing approaches for network integration use shallow models that encounter difficulty in capturing complex and highly-nonlinear network structures. Thus, we propose deepNF, a network fusion method based on Multimodal Deep Autoencoders to extract high-level features of proteins from multiple heterogeneous interaction networks. We apply this method to combine STRING networks to construct a common low-dimensional representation containing high-level protein features. We use separate layers for different network types in the early stages of the multimodal autoencoder, later connecting all the layers into a single bottleneck layer from which we extract features to predict protein function. We compare the cross-validation and temporal holdout predictive performance of our method with state-of-the-art methods, including the recently proposed method Mashup. Our results show that our method outperforms previous methods for both human and yeast STRING networks. We also show substantial improvement in the performance of our method in predicting GO terms of varying type and specificity. deepNF is freely available at: https://github.com/VGligorijevic/deepNF. vgligorijevic@flatironinstitute.org, rb133@nyu.edu. Supplementary data are available at Bioinformatics online.

Deep Packet/Flow Analysis using GPUs

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

Gong, Qian; Wu, Wenji; DeMar, Phil

Deep packet inspection (DPI) faces severe performance challenges in high-speed networks (40/100 GE) as it requires a large amount of raw computing power and high I/O throughputs. Recently, researchers have tentatively used GPUs to address the above issues and boost the performance of DPI. Typically, DPI applications involve highly complex operations in both per-packet and per-flow data level, often in real-time. The parallel architecture of GPUs fits exceptionally well for per-packet network traffic processing. However, for stateful network protocols such as TCP, their data stream need to be reconstructed in a per-flow level to deliver a consistent content analysis. Sincemore » the flow-centric operations are naturally antiparallel and often require large memory space for buffering out-of-sequence packets, they can be problematic for GPUs, whose memory is normally limited to several gigabytes. In this work, we present a highly efficient GPU-based deep packet/flow analysis framework. The proposed design includes a purely GPU-implemented flow tracking and TCP stream reassembly. Instead of buffering and waiting for TCP packets to become in sequence, our framework process the packets in batch and uses a deterministic finite automaton (DFA) with prefix-/suffix- tree method to detect patterns across out-of-sequence packets that happen to be located in different batches. In conclusion, evaluation shows that our code can reassemble and forward tens of millions of packets per second and conduct a stateful signature-based deep packet inspection at 55 Gbit/s using an NVIDIA K40 GPU.« less

Deep level study of Mg-doped GaN using deep level transient spectroscopy and minority carrier transient spectroscopy

NASA Astrophysics Data System (ADS)

Duc, Tran Thien; Pozina, Galia; Amano, Hiroshi; Monemar, Bo; Janzén, Erik; Hemmingsson, Carl

2016-07-01

Deep levels in Mg-doped GaN grown by metal organic chemical vapor deposition (MOCVD), undoped GaN grown by MOCVD, and halide vapor phase epitaxy (HVPE)-grown GaN have been studied using deep level transient spectroscopy and minority charge carrier transient spectroscopy on Schottky diodes. One hole trap, labeled HT1, was detected in the Mg-doped sample. It is observed that the hole emission rate of the trap is enhanced by increasing electric field. By fitting four different theoretical models for field-assisted carrier emission processes, the three-dimensional Coulombic Poole-Frenkel (PF) effect, three-dimensional square well PF effect, phonon-assisted tunneling, and one-dimensional Coulombic PF effect including phonon-assisted tunneling, it is found that the one-dimensional Coulombic PF model, including phonon-assisted tunneling, is consistent with the experimental data. Since the trap exhibits the PF effect, we suggest it is acceptorlike. From the theoretical model, the zero field ionization energy of the trap and an estimate of the hole capture cross section have been determined. Depending on whether the charge state is -1 or -2 after hole emission, the zero field activation energy Ei 0 is 0.57 eV or 0.60 eV, respectively, and the hole capture cross section σp is 1.3 ×10-15c m2 or 1.6 ×10-16c m2 , respectively. Since the level was not observed in undoped GaN, it is suggested that the trap is associated with an Mg related defect.

Fusion of Deep Learning and Compressed Domain features for Content Based Image Retrieval.

PubMed

Liu, Peizhong; Guo, Jing-Ming; Wu, Chi-Yi; Cai, Danlin

2017-08-29

This paper presents an effective image retrieval method by combining high-level features from Convolutional Neural Network (CNN) model and low-level features from Dot-Diffused Block Truncation Coding (DDBTC). The low-level features, e.g., texture and color, are constructed by VQ-indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features (DL-TLCF) is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate (APR) and average recall rate (ARR), are employed to examine various datasets. As documented in the experimental results, the proposed schemes can achieve superior performance compared to the state-of-the-art methods with either low- or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.

75 FR 49420 - Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab Fisheries; 2010 Atlantic...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-13

.... 100513223-0289-02] RIN 0648-AY88 Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab Fisheries; 2010 Atlantic Deep-Sea Red Crab Specifications In- season Adjustment AGENCY: National Marine...-sea (DAS) allocation for the Atlantic deep- sea red crab fishery that were implemented in May 2010...

75 FR 7435 - Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab Fisheries; 2010 Atlantic...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-02-19

.... 100105009-0053-01] RIN 0648-AY51 Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab Fisheries; 2010 Atlantic Deep-Sea Red Crab Specifications AGENCY: National Marine Fisheries Service (NMFS... comments. SUMMARY: NMFS proposes 2010 specifications for the Atlantic deep-sea red crab fishery, including...

75 FR 27219 - Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab Fisheries; 2010 Atlantic...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-05-14

.... 100105009-0167-02] RIN 0648-AY51 Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab Fisheries; 2010 Atlantic Deep-Sea Red Crab Specifications AGENCY: National Marine Fisheries Service (NMFS... final specifications for the 2010 Atlantic deep- sea red crab fishery, including a target total...

Extended Deterrence, Nuclear Proliferation, and START III

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

Speed, R.D.

2000-06-20

Early in the Cold War, the United States adopted a policy of ''extended nuclear deterrence'' to protect its allies by threatening a nuclear strike against any state that attacks these allies. This threat can (in principle) be used to try to deter an enemy attack using conventional weapons or one using nuclear, chemical, or biological weapons. The credibility of a nuclear threat has long been subject to debate and is dependent on many complex geopolitical factors, not the least of which is the military capabilities of the opposing sides. The ending of the Cold War has led to a significantmore » decrease in the number of strategic nuclear weapons deployed by the United States and Russia. START II, which was recently ratified by the Russian Duma, will (if implemented) reduce the number deployed strategic nuclear weapons on each side to 3500, compared to a level of over 11,000 at the end of the Cold War in 1991. The tentative limit established by Presidents Clinton and Yeltsin for START III would reduce the strategic force level to 2000-2500. However, the Russians (along with a number of arms control advocates) now argue that the level should be reduced even further--to 1500 warheads or less. The conventional view is that ''deep cuts'' in nuclear weapons are necessary to discourage nuclear proliferation. Thus, as part of the bargain to get the non-nuclear states to agree to the renewal of the Nuclear Non-Proliferation Treaty, the United States pledged to work towards greater reductions in strategic forces. Without movement in the direction of deep cuts, it is thought by many analysts that some countries may decide to build their own nuclear weapons. Indeed, this was part of the rationale India used to justify its own nuclear weapons program. However, there is also some concern that deep cuts (to 1500 or lower) in the U.S. strategic nuclear arsenal could have the opposite effect. The fear is that such cuts might undermine extended deterrence and cause a crisis in confidence among U.S. allies to such an extent that they could seek nuclear weapons of their own to protect themselves.« less

Data fusion for CD metrology: heterogeneous hybridization of scatterometry, CDSEM, and AFM data

NASA Astrophysics Data System (ADS)

Hazart, J.; Chesneau, N.; Evin, G.; Largent, A.; Derville, A.; Thérèse, R.; Bos, S.; Bouyssou, R.; Dezauzier, C.; Foucher, J.

2014-04-01

The manufacturing of next generation semiconductor devices forces metrology tool providers for an exceptional effort in order to meet the requirements for precision, accuracy and throughput stated in the ITRS. In the past years hybrid metrology (based on data fusion theories) has been investigated as a new methodology for advanced metrology [1][2][3]. This paper provides a new point of view of data fusion for metrology through some experiments and simulations. The techniques are presented concretely in terms of equations to be solved. The first point of view is High Level Fusion which is the use of simple numbers with their associated uncertainty postprocessed by tools. In this paper, it is divided into two stages: one for calibration to reach accuracy, the second to reach precision thanks to Bayesian Fusion. From our perspective, the first stage is mandatory before applying the second stage which is commonly presented [1]. However a reference metrology system is necessary for this fusion. So, precision can be improved if and only if the tools to be fused are perfectly matched at least for some parameters. We provide a methodology similar to a multidimensional TMU able to perform this matching exercise. It is demonstrated on a 28 nm node backend lithography case. The second point of view is Deep Level Fusion which works on the contrary with raw data and their combination. In the approach presented here, the analysis of each raw data is based on a parametric model and connections between the parameters of each tool. In order to allow OCD/SEM Deep Level Fusion, a SEM Compact Model derived from [4] has been developed and compared to AFM. As far as we know, this is the first time such techniques have been coupled at Deep Level. A numerical study on the case of a simple stack for lithography is performed. We show strict equivalence of Deep Level Fusion and High Level Fusion when tools are sensitive and models are perfect. When one of the tools can be considered as a reference and the second is biased, High Level Fusion is far superior to standard Deep Level Fusion. Otherwise, only the second stage of High Level Fusion is possible (Bayesian Fusion) and do not provide substantial advantage. Finally, when OCD is equipped with methods for bias detection [5], Deep Level Fusion outclasses the two-stage High Level Fusion and will benefit to the industry for most advanced nodes production.

76 FR 36511 - Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab; Amendment 3

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-22

...-BA22 Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab; Amendment 3 AGENCY... the Atlantic Deep-Sea Red Crab Fishery Management Plan (FMP) (Amendment 3), incorporating a draft... current trap limit regulations state that red crab may not be harvested from gear other than a marked red...

Roadmap for Growth

ERIC Educational Resources Information Center

Business Roundtable, 2010

2010-01-01

The United States is at a critical juncture. The deep recession and weak economic recovery have left one in 10 American workers without a job, and the federal budget is driving the country's debt to unprecedented levels. Business Roundtable believes that the nation's business community, the White House and Congress must work together to encourage…

Constructing fine-granularity functional brain network atlases via deep convolutional autoencoder.

PubMed

Zhao, Yu; Dong, Qinglin; Chen, Hanbo; Iraji, Armin; Li, Yujie; Makkie, Milad; Kou, Zhifeng; Liu, Tianming

2017-12-01

State-of-the-art functional brain network reconstruction methods such as independent component analysis (ICA) or sparse coding of whole-brain fMRI data can effectively infer many thousands of volumetric brain network maps from a large number of human brains. However, due to the variability of individual brain networks and the large scale of such networks needed for statistically meaningful group-level analysis, it is still a challenging and open problem to derive group-wise common networks as network atlases. Inspired by the superior spatial pattern description ability of the deep convolutional neural networks (CNNs), a novel deep 3D convolutional autoencoder (CAE) network is designed here to extract spatial brain network features effectively, based on which an Apache Spark enabled computational framework is developed for fast clustering of larger number of network maps into fine-granularity atlases. To evaluate this framework, 10 resting state networks (RSNs) were manually labeled from the sparsely decomposed networks of Human Connectome Project (HCP) fMRI data and 5275 network training samples were obtained, in total. Then the deep CAE models are trained by these functional networks' spatial maps, and the learned features are used to refine the original 10 RSNs into 17 network atlases that possess fine-granularity functional network patterns. Interestingly, it turned out that some manually mislabeled outliers in training networks can be corrected by the deep CAE derived features. More importantly, fine granularities of networks can be identified and they reveal unique network patterns specific to different brain task states. By further applying this method to a dataset of mild traumatic brain injury study, it shows that the technique can effectively identify abnormal small networks in brain injury patients in comparison with controls. In general, our work presents a promising deep learning and big data analysis solution for modeling functional connectomes, with fine granularities, based on fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

Thermally Stimulated Currents in Nanocrystalline Titania

PubMed Central

Bruzzi, Mara; Mori, Riccardo; Baldi, Andrea; Cavallaro, Alessandro; Scaringella, Monica

2018-01-01

A thorough study on the distribution of defect-related active energy levels has been performed on nanocrystalline TiO2. Films have been deposited on thick-alumina printed circuit boards equipped with electrical contacts, heater and temperature sensors, to carry out a detailed thermally stimulated currents analysis on a wide temperature range (5–630 K), in view to evidence contributions from shallow to deep energy levels within the gap. Data have been processed by numerically modelling electrical transport. The model considers both free and hopping contribution to conduction, a density of states characterized by an exponential tail of localized states below the conduction band and the convolution of standard Thermally Stimulated Currents (TSC) emissions with gaussian distributions to take into account the variability in energy due to local perturbations in the highly disordered network. Results show that in the low temperature range, up to 200 K, hopping within the exponential band tail represents the main contribution to electrical conduction. Above room temperature, electrical conduction is dominated by free carriers contribution and by emissions from deep energy levels, with a defect density ranging within 1014–1018 cm−3, associated with physio- and chemi-sorbed water vapour, OH groups and to oxygen vacancies. PMID:29303976

Thermally Stimulated Currents in Nanocrystalline Titania.

PubMed

Bruzzi, Mara; Mori, Riccardo; Baldi, Andrea; Carnevale, Ennio Antonio; Cavallaro, Alessandro; Scaringella, Monica

2018-01-05

A thorough study on the distribution of defect-related active energy levels has been performed on nanocrystalline TiO₂. Films have been deposited on thick-alumina printed circuit boards equipped with electrical contacts, heater and temperature sensors, to carry out a detailed thermally stimulated currents analysis on a wide temperature range (5-630 K), in view to evidence contributions from shallow to deep energy levels within the gap. Data have been processed by numerically modelling electrical transport. The model considers both free and hopping contribution to conduction, a density of states characterized by an exponential tail of localized states below the conduction band and the convolution of standard Thermally Stimulated Currents (TSC) emissions with gaussian distributions to take into account the variability in energy due to local perturbations in the highly disordered network. Results show that in the low temperature range, up to 200 K, hopping within the exponential band tail represents the main contribution to electrical conduction. Above room temperature, electrical conduction is dominated by free carriers contribution and by emissions from deep energy levels, with a defect density ranging within 10 14 -10 18 cm -3 , associated with physio- and chemi-sorbed water vapour, OH groups and to oxygen vacancies.

The role of nurse functional types in seedling recruitment dynamics of alternative states in rangelands

NASA Astrophysics Data System (ADS)

López, Dardo R.; Cavallero, Laura

2017-02-01

In arid ecosystems, recruitment dynamics are limited by harsh environmental conditions and greatly depend on the net outcome of the balance between facilitation and competition. This outcome can change as a consequence of degradation caused by livestock overgrazing. Also, distinct plant species may show a differential response to a common neighbour under the same environmental conditions. Therefore, ecosystem degradation could affect the net balance of plant-plant interactions, which can also depend on the functional traits of potential nurse species. The aim of this study is to assess the influence of alternative degradation states on (i) the density of seedlings of perennial species emerging in four microsite types, and on (ii) the relative interaction intensity (RII) between seedlings and potential nurses belonging to three functional types (deep- and shallow-rooted shrubs, and tussock grasses). During three years, we recorded seedling density of perennial species in four alternative degradation states in grass-shrubby steppes from northwestern Patagonia. The density of emerged seedlings of perennial species decreased sharply as degradation increased, showing non-linear responses in most microsites. Seedling density underneath deep-rooted shrubs was higher than underneath shallow-rooted shrubs and tussock grasses. Also, deep-rooted shrubs were the only functional type that recorded seedling emergence in highly degraded states. Deep-rooted shrubs had facilitative effects on the seedlings emerging and surviving underneath them, independently of ecosystem degradation. In contrast, RII between shallow-rooted shrubs and recently emerged seedlings, switched from positive effects in the less degraded states, to negative effects in the most degraded state. Tussock grasses recorded the weakest intensity of facilitative interactions with recently emerged seedlings, switching to competitive interactions as degradation increased. Our results suggest that species with key functional traits should be considered in management and restoration plans for rangelands with different degradation levels, since they have a strong influence in the net outcome of plant-plant interactions and in the recruitment dynamics of arid ecosystems.

75 FR 35435 - Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab Fisheries; 2010 Atlantic...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-22

.... 100513223-0254-01] RIN 0648-AY88 Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab Fisheries; 2010 Atlantic Deep-Sea Red Crab Specifications In- season Adjustment AGENCY: National Marine... deep-sea red crab fishery, including a target total allowable catch (TAC) and a fleet-wide days-at-sea...

Spectroscopic analysis of electron trapping levels in pentacene field-effect transistors

NASA Astrophysics Data System (ADS)

Park, Chang Bum

2014-08-01

Electron trapping phenomena have been investigated with respect to the energy levels of localized trap states and bias-induced device instability effects in pentacene field-effect transistors. The mechanism of the photoinduced threshold voltage shift (ΔVT) is presented by providing a ΔVT model governed by the electron trapping. The trap-and-release behaviour functionalized by photo-irradiation also shows that the trap state for electrons is associated with the energy levels in different positions in the forbidden gap of pentacene. Spectroscopic analysis identifies two kinds of electron trap states distributed above and below the energy of 2.5 eV in the band gap of the pentacene crystal. The study of photocurrent spectra shows the specific trap levels of electrons in energy space that play a substantial role in causing device instability. The shallow and deep trapping states are distributed at two centroidal energy levels of ˜1.8 and ˜2.67 eV in the pentacene band gap. Moreover, we present a systematic energy profile of electron trap states in the pentacene crystal for the first time.

Yrast excitations of neutron-rich nuclei around doubly magic Tin-132

NASA Astrophysics Data System (ADS)

Bhattacharyya, Pallab Kumar

Investigation of the yrast structures of neutron-rich nuclei around the double closed shell nucleus 132Sn is important in the understanding of simple two-body nucleon-nucleon interactions in that region. However conventional fusion-evaporation methods do not populate these nuclei and β-decay studies are useful only in studying low spin states. The spectroscopy of these nuclei from thick target γ-γ coincidence measurements of deep inelastic heavy ion collisions as well as from fission fragment γ-ray studies using large multidetector arrays are presented in this thesis. Analyses of data from the 124Sn + 665 MeV 136Xe and 130Te + 272 MeV 64Ni deep inelastic experiments identified new yrast isomers in the N = 80 nuclei 134Xe and 136Ba which de- excite by γ-ray cascades concluding with their known 4+/to2+ and 2+/to0+ transitions. The isomeric decay characteristics are presented and discussed in light of the systematic features in N = 80 isotones. By analyzing fission product γ-ray data measured at Eurogam II using a 248Cm source, yrast level structures of the two-, three- and four-proton N = 82 isotones 134Te, 135I and 136Xe were developed, and the proton-proton interactions from the two-body nucleus 134Te were used in interpreting 135I and 136Xe levels using shell model calculations. From the same data the yrast states in the N = 83 isotones 134Sb, 135Te, 136I and 137Xe were explored, and key proton-neutron interactions were extracted from the 134Sb level spectrum which were used in interpreting the levels of the other N = 83 isotones. Similarly yrast states in previously unexplored N = 81 isotones 132Sb and 133Te were also identified and interpreted with shell model calculations; the 132Sb level spectrum yielded important proton-neutron hole interactions. Neutron core-excited states at higher energies were also identified in most of these nuclei. For establishing isotopic assignments of unknown cascades, the γgamma cross coincidences between heavy and light fission partners were vital. Overall, both deep inelastic and fission product studies have contributed to the exploration of an otherwise inaccessible region of the nuclidic chart. This opens up a new horizon in studying the structure of these important neutron-rich nuclei.

Study on Seismogenesis of 2013 Ms5.1 Badong Earthquake in the Three Gorges Reservoir Region

NASA Astrophysics Data System (ADS)

Li, X.; Zeng, Z.; Xu, S.; He, C.

2015-12-01

On 16 December, 2013, an earthquake of Ms5.1 occurred in Badong County, the Three Gorges Reservoir area, China. We collected all the 150 published focal mechanism solutions (FMS) and inversed the tectonic stress field in Badong, the Three Gorges Dam and Huangling anticline area using the software SATSI (Hardebeck and Michael, 2006). Inversion results show that the orientations of maximum principle stress axis (σ1) in Badong plunge to NNE or SSW. Detailed characteristics of the stress field indicate that the σ1 axis is almost vertical in the center of Huangling anticline and turns horizontal to the west. As to deep structures, we studied the satellite gravity anomalies of 8-638 order in this area using the EIGEN-6C2 model provided by ICGRM. Combining the seismic sounding profile through the epicenter of Badong earthquake and the petrology data, we reinterpreted the deep structure in the study area. The results show that the deep crust in Badong is unstable and the deep material's upwelling leads to Huangling anticline continued uplifting, which is consistent with the result indicated from the stress filed. Both of them provide energy for the preparation of earthquake. The FMS shows that Gaoqiao Fault is the causative fault of this Ms5.1 earthquake. Field investigations indicated that the lithology and fracture characteristic in Badong is beneficial to reservoir water infiltration. Before the earthquake, reservoir water level raised to 175m, the highest storage level, which increased the loading. Based on above researches, we believe that the Ms5.1 Badong earthquake is controlled by deep tectonic environment and stress field in shallow crust. The reservoir water infiltration and uploading increase generated by water storage of the Three Gorges area reduced the strength of Gaoqiao Fault and changed its stress state. These factors jointly promoted an abrupt movement of the fault in the critical stress state, and triggered the Ms5.1 Badong earthquake.

Role of defects in ferromagnetism in Zn1-xCoxO : A hybrid density-functional study

NASA Astrophysics Data System (ADS)

Patterson, C. H.

2006-10-01

Experimental studies of Zn1-xCoxO as thin films or nanocrystals have found ferromagnetism and Curie temperatures above room temperature and that p - or n -type doping of Zn1-xCoxO can change its magnetic state. Bulk Zn1-xCoxO with a low defect density and x in the range used in experimental thin-film studies exhibits ferromagnetism only at very low temperatures. Therefore defects in thin-film samples or nanocrystals may play an important role in promoting magnetic interactions between Co ions in Zn1-xCoxO . The mechanism of exchange coupling induced by defect states is considered and compared to a model for ferromagnetism in dilute magnetic semiconductors [T. Dietl , Science 287, 1019 (2000)]. The electronic structures of Co substituted for Zn in ZnO, Zn, and O vacancies, substituted N, and interstitial Zn in ZnO were calculated using the B3LYP hybrid density functional in a supercell. The B3LYP functional predicts a band gap of 3.34eV for bulk ZnO, close to the experimental value of 3.47eV . Occupied minority-spin Co 3d levels are at the top of the valence band and unoccupied levels lie above the conduction-band minimum. Majority-spin Co 3d levels hybridize strongly with bulk ZnO states. The neutral O vacancy defect level is predicted to lie deep in the band gap, and interstitial Zn is predicted to be a deep donor. The Zn vacancy is a deep acceptor, and the acceptor level for substituted N is at midgap. The possibility that p - or n -type dopants promote exchange coupling of Co ions was investigated by computing the total energies of magnetic states of ZnO supercells containing two Co ions and an oxygen vacancy, substituted N, or interstitial Zn in various charge states. The neutral N defect and the singly positively charged O vacancy are the only defects which strongly promote ferromagnetic exchange coupling of Co ions at intermediate range. Total energy calculations on supercells containing two O vacancies and one Zn vacancy clearly show that pairs of singly positively charged O vacancies are unstable with respect to dissociation into neutral and doubly positively charged vacancies; the oxygen vacancy is a “negative U ” defect. This apparently precludes simple charged O vacancies as a mediator of ferromagnetism in Zn1-xCoxO .

Functional reasoning in diagnostic problem solving

NASA Technical Reports Server (NTRS)

Sticklen, Jon; Bond, W. E.; Stclair, D. C.

1988-01-01

This work is one facet of an integrated approach to diagnostic problem solving for aircraft and space systems currently under development. The authors are applying a method of modeling and reasoning about deep knowledge based on a functional viewpoint. The approach recognizes a level of device understanding which is intermediate between a compiled level of typical Expert Systems, and a deep level at which large-scale device behavior is derived from known properties of device structure and component behavior. At this intermediate functional level, a device is modeled in three steps. First, a component decomposition of the device is defined. Second, the functionality of each device/subdevice is abstractly identified. Third, the state sequences which implement each function are specified. Given a functional representation and a set of initial conditions, the functional reasoner acts as a consequence finder. The output of the consequence finder can be utilized in diagnostic problem solving. The paper also discussed ways in which this functional approach may find application in the aerospace field.

Can frequent precipitation moderate drought impact on peatmoss carbon uptake in northern peatlands?

NASA Astrophysics Data System (ADS)

Nijp, Jelmer; Limpens, Juul; Metselaar, Klaas; van der Zee, Sjoerd; Berendse, Frank; Robroek, Bjorn

2014-05-01

Northern peatlands represent one of the largest global carbon stores that can potentially be released by water table drawdown during extreme summer droughts. Small precipitation events may moderate negative impacts of deep water levels on carbon uptake by sustaining photosynthesis of peatmoss (Sphagnum spp.), the key species in these ecosystems. We experimentally assessed the importance of the temporal distribution of precipitation for Sphagnum water supply and carbon uptake during a stepwise decrease in water levels in a growth chamber. CO2 exchange and the water balance were measured for intact cores of three peatmoss species representative of three contrasting habitats in northern peatlands (Sphagnum fuscum, S. balticum and S. majus). For shallow water levels, capillary rise was the most important source of water for peatmoss photosynthesis and precipitation did not promote carbon uptake irrespective of peatmoss species. For deep water levels, however, precipitation dominated over capillary rise and moderated adverse effects of drought on carbon uptake by peat mosses. The ability to use the transient water supply by precipitation was species-specific: carbon uptake of S. fuscum increased linearly with precipitation frequency for deep water levels, whereas S. balticum and S. majus showed depressed carbon uptake at intermediate precipitation frequencies. Our results highlight the importance of precipitation for carbon uptake by peatmosses. The potential of precipitation to moderate drought impact, however, is species specific and depends on the temporal distribution of precipitation and water level. These results also suggest that modelling approaches in which water level depth is used as the only state variable determining water availability in the living moss layer and (in)directly linked to Sphagnum carbon uptake may have serious drawbacks. The predictive power of peatland ecosystem models may be reduced when deep water levels prevail, as precipitation frequency and quantity are likely the main variables controlling carbon uptake.

Pressure-Photoluminescence Study of the Zn Vacancy and Donor Zn-Vacancy Complexes in ZnSe

NASA Astrophysics Data System (ADS)

Iota, V.; Weinstein, B. A.

1997-03-01

We report photoluminescence (PL) results to 65kbar (at 8K) on n-type electron irradiated ZnSe containing high densities of isolated Zn vacancies (V_Zn) and donor-V_Zn complexes (A-centers).^1 Isotropic pressure is applied using a diamond-anvil cell with He medium, and laser excitations above and below the ZnSe bandgap (2.82eV) are employed. The 1 atm. spectra exhibit excitonic lines, shallow donor-acceptor pair (DAP) peaks, and two broad bands due to DAP transitions between shallow donors and deep acceptor states at A-centers (2.07eV) or V_Zn (1.72eV). At all pressures, these broad bands are prominent only for sub-gap excitation, which results in: i) A-center PL at energies above the laser line, and ii) strong enhancement of the first LO-replica in the shallow DAP series compared to 3.41eV UV excitation. This suggests that sub-gap excitation produces long-lived metastable acceptor states. The broad PL bands shift to higher energy with pressure faster than the ZnSe direct gap, indicating that compression causes the A-center and V_Zn deep acceptor levels to approach the hole continuum. This behavior is similar to that found by our group for P and As deep acceptor levels in ZnSe, supporting the view that deep substitutional defects often resemble the limiting case of a vacancy. ^1D. Y. Jeon, H. P. Gislason, G. D. Watkins Phys. Rev. B 48, 7872 (1993); we thank G. D. Watkins for providing the samples. (figures)

Optoelectronically probing the density of nanowire surface trap states to the single state limit

NASA Astrophysics Data System (ADS)

Dan, Yaping

2015-02-01

Surface trap states play a dominant role in the optoelectronic properties of nanoscale devices. Understanding the surface trap states allows us to properly engineer the device surfaces for better performance. But characterization of surface trap states at nanoscale has been a formidable challenge using the traditional capacitive techniques. Here, we demonstrate a simple but powerful optoelectronic method to probe the density of nanowire surface trap states to the single state limit. In this method, we choose to tune the quasi-Fermi level across the bandgap of a silicon nanowire photoconductor, allowing for capture and emission of photogenerated charge carriers by surface trap states. The experimental data show that the energy density of nanowire surface trap states is in a range from 109 cm-2/eV at deep levels to 1012 cm-2/eV near the conduction band edge. This optoelectronic method allows us to conveniently probe trap states of ultra-scaled nano/quantum devices at extremely high precision.

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions.

PubMed

Akkus, Zeynettin; Galimzianova, Alfiia; Hoogi, Assaf; Rubin, Daniel L; Erickson, Bradley J

2017-08-01

Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest. Deep learning-based segmentation approaches for brain MRI are gaining interest due to their self-learning and generalization ability over large amounts of data. As the deep learning architectures are becoming more mature, they gradually outperform previous state-of-the-art classical machine learning algorithms. This review aims to provide an overview of current deep learning-based segmentation approaches for quantitative brain MRI. First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions. Next, the performance, speed, and properties of deep learning approaches are summarized and discussed. Finally, we provide a critical assessment of the current state and identify likely future developments and trends.

Jumping into the Deep End: Developing Culturally Responsive Urban Teachers through Community-Immersive Partnerships

ERIC Educational Resources Information Center

Mustian, April L.; Lee, Robert E.; Nelson, Carlos; Gamboa-Turner, Valentina; Roule, Lisa

2017-01-01

Preparing special educators for the highest-need schools remains an ongoing challenge in urban districts across the United States. One university's collaborative community-based immersive partnership model, with emphasis on service learning, has demonstrated promising levels of impact on candidates' preparation as preservice teachers learning how…

The New Politics of Race and Gender. Education Policy Perspective Series.

ERIC Educational Resources Information Center

Marshall, Catherine, Ed.

This book demonstrates that liberal policy cannot adequately address deep-seated assumptions and traditional practices that undermine minorities and women in schools. Chapters focus on educational policy and its implementation at all levels of school politics in the United States, Australia, and Israel. Part 1, "Cutting Across Race and…

Recombination activity of light-activated copper defects in p-type silicon studied by injection- and temperature-dependent lifetime spectroscopy

NASA Astrophysics Data System (ADS)

Inglese, Alessandro; Lindroos, Jeanette; Vahlman, Henri; Savin, Hele

2016-09-01

The presence of copper contamination is known to cause strong light-induced degradation (Cu-LID) in silicon. In this paper, we parametrize the recombination activity of light-activated copper defects in terms of Shockley—Read—Hall recombination statistics through injection- and temperature dependent lifetime spectroscopy (TDLS) performed on deliberately contaminated float zone silicon wafers. We obtain an accurate fit of the experimental data via two non-interacting energy levels, i.e., a deep recombination center featuring an energy level at Ec-Et=0.48 -0.62 eV with a moderate donor-like capture asymmetry ( k =1.7 -2.6 ) and an additional shallow energy state located at Ec-Et=0.1 -0.2 eV , which mostly affects the carrier lifetime only at high-injection conditions. Besides confirming these defect parameters, TDLS measurements also indicate a power-law temperature dependence of the capture cross sections associated with the deep energy state. Eventually, we compare these results with the available literature data, and we find that the formation of copper precipitates is the probable root cause behind Cu-LID.

High Probability of Cyclone Development in the Bay of Bengal

NASA Image and Video Library

2014-05-22

The Joint Typhoon Warning Center states that formation of a significant tropical cyclone is possible in the Bay of Bengal within the next 12 - 24 hours as of 0730Z on May 21, 2014. Along with deep convective banding associated with a consolidating low-level circulation center, warm sea surface temperatures are conducive for further development. This image was taken by the Suomi NPP satellite's VIIRS instrument in two passes, the east pass around 0615Z and the west pass around 0755Z on May 21, 2014. Credit: NASA/NOAA/NPP/VIIRS The Joint Typhoon Warning Center states that formation of a significant tropical cyclone is possible in the Bay of Bengal within the next 12 - 24 hours as of 0730Z on May 21, 2014. Along with deep convective banding associated with a consolidating low-level circulation center, warm sea surface temperatures are conducive for further development. This image was taken by the Suomi NPP satellite's VIIRS instrument in two passes, the east pass around 0615Z and the west pass around 0755Z on May 21, 2014.

Metastable self-trapping of positrons in MgO

NASA Astrophysics Data System (ADS)

Monge, M. A.; Pareja, R.; González, R.; Chen, Y.

1997-01-01

Low-temperature positron annihilation measurements have been performed on MgO single crystals containing either cation or anion vacancies. The temperature dependence of the S parameter is explained in terms of metastable self-trapped positrons which thermally hop through the crystal lattice. The experimental results are analyzed using a three-state trapping model assuming transitions from both delocalized and self-trapped states to deep trapped states at vacancies. The energy level of the self-trapped state was determined to be (62+/-5) meV above the delocalized state. The activation enthalpy for the hopping process of self-trapped positrons appears to depend on the kind of defect present in the crystals.

An Examination of Guided Imagery and Its Relationship to Self-Efficacy and the Implications for Employability in College Graduates Seeking Employment

ERIC Educational Resources Information Center

Goins, Robin R.

2012-01-01

The study examined the relationship between Guided-Imagery (GI) and Self-Efficacy (SE) as means to better understand how GI techniques affect SE levels, particularly in relation to career related performance. The use of GI has been found to elicit a state of relaxation where a deep level of focus is achieved which some would call an altered state…

Convolutional networks for fast, energy-efficient neuromorphic computing

PubMed Central

Esser, Steven K.; Merolla, Paul A.; Arthur, John V.; Cassidy, Andrew S.; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J.; McKinstry, Jeffrey L.; Melano, Timothy; Barch, Davis R.; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D.; Modha, Dharmendra S.

2016-01-01

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware’s underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer. PMID:27651489

Convolutional networks for fast, energy-efficient neuromorphic computing.

PubMed

Esser, Steven K; Merolla, Paul A; Arthur, John V; Cassidy, Andrew S; Appuswamy, Rathinakumar; Andreopoulos, Alexander; Berg, David J; McKinstry, Jeffrey L; Melano, Timothy; Barch, Davis R; di Nolfo, Carmelo; Datta, Pallab; Amir, Arnon; Taba, Brian; Flickner, Myron D; Modha, Dharmendra S

2016-10-11

Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented energy-efficiency through a new chip architecture based on spiking neurons, low precision synapses, and a scalable communication network. Here, we demonstrate that neuromorphic computing, despite its novel architectural primitives, can implement deep convolution networks that (i) approach state-of-the-art classification accuracy across eight standard datasets encompassing vision and speech, (ii) perform inference while preserving the hardware's underlying energy-efficiency and high throughput, running on the aforementioned datasets at between 1,200 and 2,600 frames/s and using between 25 and 275 mW (effectively >6,000 frames/s per Watt), and (iii) can be specified and trained using backpropagation with the same ease-of-use as contemporary deep learning. This approach allows the algorithmic power of deep learning to be merged with the efficiency of neuromorphic processors, bringing the promise of embedded, intelligent, brain-inspired computing one step closer.

Understanding Deep Representations Learned in Modeling Users Likes.

PubMed

Guntuku, Sharath Chandra; Zhou, Joey Tianyi; Roy, Sujoy; Lin, Weisi; Tsang, Ivor W

2016-08-01

Automatically understanding and discriminating different users' liking for an image is a challenging problem. This is because the relationship between image features (even semantic ones extracted by existing tools, viz., faces, objects, and so on) and users' likes is non-linear, influenced by several subtle factors. This paper presents a deep bi-modal knowledge representation of images based on their visual content and associated tags (text). A mapping step between the different levels of visual and textual representations allows for the transfer of semantic knowledge between the two modalities. Feature selection is applied before learning deep representation to identify the important features for a user to like an image. The proposed representation is shown to be effective in discriminating users based on images they like and also in recommending images that a given user likes, outperforming the state-of-the-art feature representations by  ∼ 15 %-20%. Beyond this test-set performance, an attempt is made to qualitatively understand the representations learned by the deep architecture used to model user likes.

Shallow trapping vs. deep polarons in a hybrid lead halide perovskite, CH3NH3PbI3.

PubMed

Kang, Byungkyun; Biswas, Koushik

2017-10-18

There has been considerable speculation over the nature of charge carriers in organic-inorganic hybrid perovskites, i.e., whether they are free and band-like, or they are prone to self-trapping via short range deformation potentials. Unusually long minority-carrier diffusion lengths and moderate-to-low mobilities, together with relatively few deep defects add to their intrigue. Here we implement density functional methods to investigate the room-temperature, tetragonal phase of CH 3 NH 3 PbI 3 . We compare charge localization behavior at shallow levels and associated lattice relaxation versus those at deep polaronic states. The shallow level originates from screened Coulomb interaction between the perturbed host and an excited electron or hole. The host lattice has a tendency towards forming these shallow traps where the electron or hole is localized not too far from the band edge. In contrast, there is a considerable potential barrier that must be overcome in order to initiate polaronic hole trapping. The formation of a hole polaron (I 2 - center) involves strong lattice relaxation, including large off-center displacement of the organic cation, CH 3 NH 3 + . This type of deep polaron is energetically unfavorable, and active shallow traps are expected to shape the carrier dynamics in this material.

Visual Saliency Detection Based on Multiscale Deep CNN Features.

PubMed

Guanbin Li; Yizhou Yu

2016-11-01

Visual saliency is a fundamental problem in both cognitive and computational sciences, including computer vision. In this paper, we discover that a high-quality visual saliency model can be learned from multiscale features extracted using deep convolutional neural networks (CNNs), which have had many successes in visual recognition tasks. For learning such saliency models, we introduce a neural network architecture, which has fully connected layers on top of CNNs responsible for feature extraction at three different scales. The penultimate layer of our neural network has been confirmed to be a discriminative high-level feature vector for saliency detection, which we call deep contrast feature. To generate a more robust feature, we integrate handcrafted low-level features with our deep contrast feature. To promote further research and evaluation of visual saliency models, we also construct a new large database of 4447 challenging images and their pixelwise saliency annotations. Experimental results demonstrate that our proposed method is capable of achieving the state-of-the-art performance on all public benchmarks, improving the F-measure by 6.12% and 10%, respectively, on the DUT-OMRON data set and our new data set (HKU-IS), and lowering the mean absolute error by 9% and 35.3%, respectively, on these two data sets.

Effect of swift heavy ion irradiation on deep levels in Au /n-Si (100) Schottky diode studied by deep level transient spectroscopy

NASA Astrophysics Data System (ADS)

Kumar, Sandeep; Katharria, Y. S.; Kumar, Sugam; Kanjilal, D.

2007-12-01

In situ deep level transient spectroscopy has been applied to investigate the influence of 100MeV Si7+ ion irradiation on the deep levels present in Au/n-Si (100) Schottky structure in a wide fluence range from 5×109to1×1012ions cm-2. The swift heavy ion irradiation introduces a deep level at Ec-0.32eV. It is found that initially, trap level concentration of the energy level at Ec-0.40eV increases with irradiation up to a fluence value of 1×1010cm-2 while the deep level concentration decreases as irradiation fluence increases beyond the fluence value of 5×1010cm-2. These results are discussed, taking into account the role of energy transfer mechanism of high energy ions in material.

Deep levels in osmium doped p-type GaAs grown by metal organic chemical vapor deposition

NASA Astrophysics Data System (ADS)

Iqbal, M. Zafar; Majid, A.; Dadgar, A.; Bimberg, D.

2005-06-01

Results of a preliminary study on deep level transient spectroscopy (DLTS) investigations of osmium (Os) impurity in p-type GaAs, introduced in situ during MOCVD crystal growth, are reported for the first time. Os is clearly shown to introduce two prominent deep levels in the lower half-bandgap of GaAs at energy positions Ev + 0.42 eV (OsA) and Ev + 0.72 eV (OsB). A minority-carrier emitting defect feature observed in the upper half-bandgap is shown to consist of a band of Os-related deep levels with a concentration significantly higher than that of the majority carrier emitting deep levels. Detailed data on the emission rate signatures and related parameters of the Os-related deep levels are reported.

Blue whales respond to simulated mid-frequency military sonar

PubMed Central

Goldbogen, Jeremy A.; Southall, Brandon L.; DeRuiter, Stacy L.; Calambokidis, John; Friedlaender, Ari S.; Hazen, Elliott L.; Falcone, Erin A.; Schorr, Gregory S.; Douglas, Annie; Moretti, David J.; Kyburg, Chris; McKenna, Megan F.; Tyack, Peter L.

2013-01-01

Mid-frequency military (1–10 kHz) sonars have been associated with lethal mass strandings of deep-diving toothed whales, but the effects on endangered baleen whale species are virtually unknown. Here, we used controlled exposure experiments with simulated military sonar and other mid-frequency sounds to measure behavioural responses of tagged blue whales (Balaenoptera musculus) in feeding areas within the Southern California Bight. Despite using source levels orders of magnitude below some operational military systems, our results demonstrate that mid-frequency sound can significantly affect blue whale behaviour, especially during deep feeding modes. When a response occurred, behavioural changes varied widely from cessation of deep feeding to increased swimming speed and directed travel away from the sound source. The variability of these behavioural responses was largely influenced by a complex interaction of behavioural state, the type of mid-frequency sound and received sound level. Sonar-induced disruption of feeding and displacement from high-quality prey patches could have significant and previously undocumented impacts on baleen whale foraging ecology, individual fitness and population health. PMID:23825206

NOAA tsunami water level archive - scientific perspectives and discoveries

NASA Astrophysics Data System (ADS)

Mungov, G.; Eble, M. C.; McLean, S. J.

2013-12-01

The National Oceanic and Atmospheric Administration (NOAA) National Geophysical Data Center (NGDC) and co-located World Data Service for Geophysics (WDS) provides long-term archive, data management, and access to national and global tsunami data. Currently, NGDC archives and processes high-resolution data recorded by the Deep-ocean Assessment and Reporting of Tsunami (DART) network, the coastal-tide-gauge network from the National Ocean Service (NOS) as well as tide-gauge data recorded by all gauges in the two National Weather Service (NWS) Tsunami Warning Centers' (TWCs) regional networks. The challenge in processing these data is that the observations from the deep-ocean, Pacific Islands, Alaska region, and United States West and East Coasts display commonalities, but, at the same time, differ significantly, especially when extreme events are considered. The focus of this work is on how time integration of raw observations (10-seconds to 1-minute) could mask extreme water levels. Analysis of the statistical and spectral characteristics obtained from records with different time step of integration will be presented. Results show the need to precisely calibrate the despiking procedure against raw data due to the significant differences in the variability of deep-ocean and coastal tide-gauge observations. It is shown that special attention should be drawn to the very strong water level declines associated with the passage of the North Atlantic cyclones. Strong changes for the deep ocean and for the West Coast have implications for data quality but these same features are typical for the East Coast regime.

Blubber morphology in wild bottlenose dolphins (Tursiops truncatus) from the Southeastern United States: influence of geographic location, age class, and reproductive state.

PubMed

Montie, Eric W; Garvin, Scott R; Fair, Patricia A; Bossart, Gregory D; Mitchum, Greg B; McFee, Wayne E; Speakman, Todd; Starczak, Victoria R; Hahn, Mark E

2008-04-01

This study investigated blubber morphology and correlations of histological measurements with ontogeny, geography, and reproductive state in live, wild bottlenose dolphins (Tursiops truncatus) from the southeastern United States. Surgical skin-blubber biopsies (N=74) were collected from dolphins during capture-release studies conducted in two geographic locations: Charleston, SC (N=38) and Indian River Lagoon, FL (N=36). Histological analysis of blubber revealed stratification into superficial, middle, and deep layers. Adipocytes of the middle blubber were 1.6x larger in Charleston subadults than in Indian River Lagoon subadults (4,590+/-340 compared to 2,833+/-335 microm2 per cell). Charleston subadult dolphins contained higher levels of total blubber lipids than Charleston adult animals (49.3%+/-1.9% compared to 34.2%+/-1.7%), and this difference was manifested in more adipocytes in the middle blubber layer (19.2+/-0.9 compared to 14.9+/-0.5 cells per field). However, dolphins from Indian River Lagoon did not exhibit this pattern, and the adipocyte cell counts of subadults were approximately equal to those of the adults (16.0+/-1.4 compared to 13.4+/-0.8 cells per field). The colder year-round water temperatures in Charleston compared to Indian River Lagoon may explain these differences. Adipocytes in the deep blubber layer were significantly smaller in lactating and simultaneously pregnant and lactating animals compared to pregnant dolphins (840+/-179, 627+/-333, and 2,776+/-586 microm2 per cell, respectively). Total blubber lipid content and adipocyte size in the deep blubber of mothers with calves decreased linearly with calf length. Lactating females may utilize lipids from the deep blubber during periods of increased energetic demands associated with offspring care. This study demonstrates that ontogeny, geography, and reproductive state may influence morphological parameters such as structural fiber densities and adipocyte numbers and sizes, measured in bottlenose dolphin blubber. Copyright (c) 2007 Wiley-Liss, Inc.

Study of GaAs-oxide interface by transient capacitance spectroscopy - Discrete energy interface states

NASA Technical Reports Server (NTRS)

Kamieniecki, E.; Kazior, T. E.; Lagowski, J.; Gatos, H. C.

1980-01-01

Interface states and bulk GaAs energy levels were simultaneously investigated in GaAs MOS structures prepared by anodic oxidation. These two types of energy levels were successfully distinguished by carrying out a comparative analysis of deep level transient capacitance spectra of the MOS structures and MS structures prepared on the same samples of epitaxially grown GaAs. The identification and study of the interface states and bulk levels was also performed by investigating the transient capacitance spectra as a function of the filling pulse magnitude. It was found that in the GaAs-anodic oxide interface there are states present with a discrete energy rather than with a continuous energy distribution. The value of the capture cross section of the interface states was found to be 10 to the 14th to 10 to the 15th/sq cm, which is more accurate than the extremely large values of 10 to the -8th to 10 to the -9th/sq cm reported on the basis of conductance measurements.

Multi-level deep supervised networks for retinal vessel segmentation.

PubMed

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.

Physical activity levels and perceived benefits and barriers to physical activity in HIV-infected women living in the deep south of the United States.

PubMed

Rehm, Kristina E; Konkle-Parker, Deborah

2016-09-01

Engaging in regular physical activity (PA) is important in maintaining health and increasing the overall quality of life of people living with HIV (PLWH). The deep south of the USA is known for its high rate of sedentary behavior although data on the activity levels and perceptions of the benefits and barriers to exercise in women living with HIV in the deep south are lacking. Understanding the perceived benefits and barriers to exercise can guide the development of PA interventions. We conducted a cross-sectional study to determine the PA levels and perceived benefits and barriers to exercise associated with both age and depression level in a group of HIV+ women living in the deep south. We recruited a total of 50 participants from a cohort site for the Women's Interagency HIV Study. Depression was assessed using the Center for Epidemiological Studies Depression Scale (CES-D) and benefits/barriers to exercise were measured using the Exercise Benefits and Barriers Scale (EBBS). We measured PA both subjectively and objectively using the International Physical Activity Questionnaire (IPAQ) and a Fitbit PA monitor, respectively. Our sample was predominantly African-American (96%) and the mean ±SD age, body mass index, and CES-D score were 42 ± 8.8 years, 36.6 ± 11.5 kg/m(2), and 15.6 ± 11.4, respectively. Both subjective and objective measures of PA indicated that our participants were sedentary. The greatest perceived benefit to exercise was physical performance and the greatest barrier to exercise was physical exertion. Higher overall perceived benefits were reported by women ≥43 years and women reporting higher levels of depression. There was no difference in overall barriers associated with age and depression level, but women with depression felt more fatigued by exercise. The results of this study can be helpful when designing and implementing PA interventions in women living with HIV in the deep south.

Thermal quenching effect of an infrared deep level in Mg-doped p-type GaN films

NASA Astrophysics Data System (ADS)

Kim, Keunjoo; Chung, Sang Jo

2002-03-01

The thermal quenching of an infrared deep level of 1.2-1.5 eV has been investigated on Mg-doped p-type GaN films, using one- and two-step annealing processes and photocurrent measurements. The deep level appeared in the one-step annealing process at a relatively high temperature of 900 °C, but disappeared in the two-step annealing process with a low-temperature step and a subsequent high-temperature step. The persistent photocurrent was residual in the sample including the deep level, while it was terminated in the sample without the deep level. This indicates that the deep level is a neutral hole center located above a quasi-Fermi level, estimated with an energy of EpF=0.1-0.15 eV above the valence band at a hole carrier concentration of 2.0-2.5×1017/cm3.

Transient luminescence induced by electrical refilling of charge carrier traps of dislocation network at hydrophilically bonded Si wafers interface

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

Bondarenko, Anton; Vyvenko, Oleg

2014-02-21

Dislocation network (DN) at hydrophilically bonded Si wafers interface is placed in space charge region (SCR) of a Schottky diode at a depth of about 150 nm from Schottky electrode for simultaneous investigation of its electrical and luminescent properties. Our recently proposed pulsed traps refilling enhanced luminescence (Pulsed-TREL) technique based on the effect of transient luminescence induced by refilling of charge carrier traps with electrical pulses is further developed and used as a tool to establish DN energy levels responsible for D1 band of dislocation-related luminescence in Si (DRL). In present work we do theoretical analysis and simulation of trapsmore » refilling kinetics dependence on refilling pulse magnitude (Vp) in two levels model: shallow and deep. The influence of initial charge state of deep level on shallow level occupation-Vp dependence is discussed. Characteristic features predicted by simulations are used for Pulsed-TREL experimental results interpretation. We conclude that only shallow (∼0.1 eV from conduction and valence band) energetic levels in the band gap participate in D1 DRL.« less

Traps in AlGaN /GaN/SiC heterostructures studied by deep level transient spectroscopy

NASA Astrophysics Data System (ADS)

Fang, Z.-Q.; Look, D. C.; Kim, D. H.; Adesida, I.

2005-10-01

AlGaN /GaN/SiC Schottky barrier diodes (SBDs), with and without Si3N4 passivation, have been characterized by temperature-dependent current-voltage and capacitance-voltage measurements, and deep level transient spectroscopy (DLTS). A dominant trap A1, with activation energy of 1.0 eV and apparent capture cross section of 2×10-12cm2, has been observed in both unpassivated and passivated SBDs. Based on the well-known logarithmic dependence of DLTS peak height with filling pulse width for a line-defect related trap, A1, which is commonly observed in thin GaN layers grown by various techniques, is believed to be associated with threading dislocations. At high temperatures, the DLTS signal sometimes becomes negative, likely due to an artificial surface-state effect.

Detection of resting state functional connectivity using partial correlation analysis: A study using multi-distance and whole-head probe near-infrared spectroscopy.

PubMed

Sakakibara, Eisuke; Homae, Fumitaka; Kawasaki, Shingo; Nishimura, Yukika; Takizawa, Ryu; Koike, Shinsuke; Kinoshita, Akihide; Sakurada, Hanako; Yamagishi, Mika; Nishimura, Fumichika; Yoshikawa, Akane; Inai, Aya; Nishioka, Masaki; Eriguchi, Yosuke; Matsuoka, Jun; Satomura, Yoshihiro; Okada, Naohiro; Kakiuchi, Chihiro; Araki, Tsuyoshi; Kan, Chiemi; Umeda, Maki; Shimazu, Akihito; Uga, Minako; Dan, Ippeita; Hashimoto, Hideki; Kawakami, Norito; Kasai, Kiyoto

2016-11-15

Multichannel near-infrared spectroscopy (NIRS) is a functional neuroimaging modality that enables easy-to-use and noninvasive measurement of changes in blood oxygenation levels. We developed a clinically-applicable method for estimating resting state functional connectivity (RSFC) with NIRS using a partial correlation analysis to reduce the influence of extraneural components. Using a multi-distance probe arrangement NIRS, we measured resting state brain activity for 8min in 17 healthy participants. Independent component analysis was used to extract shallow and deep signals from the original NIRS data. Pearson's correlation calculated from original signals was significantly higher than that calculated from deep signals, while partial correlation calculated from original signals was comparable to that calculated from deep (cerebral-tissue) signals alone. To further test the validity of our method, we also measured 8min of resting state brain activity using a whole-head NIRS arrangement consisting of 17 cortical regions in 80 healthy participants. Significant RSFC between neighboring, interhemispheric homologous, and some distant ipsilateral brain region pairs was revealed. Additionally, females exhibited higher RSFC between interhemispheric occipital region-pairs, in addition to higher connectivity between some ipsilateral pairs in the left hemisphere, when compared to males. The combined results of the two component experiments indicate that partial correlation analysis is effective in reducing the influence of extracerebral signals, and that NIRS is able to detect well-described resting state networks and sex-related differences in RSFC. Copyright © 2016 Elsevier Inc. All rights reserved.

Dynamical interferences to probe short-pulse photoassociation of Rb atoms and stabilization of Rb{sub 2} dimers

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

Mur-Petit, Jordi; Luc-Koenig, Eliane; Masnou-Seeuws, Francoise

2007-06-15

We analyze the formation of Rb{sub 2} molecules with short photoassociation pulses applied to a cold {sup 85}Rb sample. A pump laser pulse couples a continuum level of the ground electronic state X {sup 1}{sigma}{sub g}{sup +} with bound levels in the 0{sub u}{sup +}(5S+5P{sub 1/2}) and 0{sub u}{sup +}(5S+5P{sub 3/2}) vibrational series. The nonadiabatic coupling between the two excited channels induces time-dependent beatings in the populations. We propose to take advantage of these oscillations to design further laser pulses that probe the photoassociation process via photoionization or that optimize the stabilization in deep levels of the ground state.

Mo isotope record of shales points to deep ocean oxygenation in the early Paleoproterozoic

NASA Astrophysics Data System (ADS)

Asael, Dan; Scott, Clint; Rouxel, Olivier; Poulton, Simon; Lyons, Timothy; Javaux, Emmanuelle; Bekker, Andrey

2014-05-01

Two steps in Earth's surface oxidation lie at either end of the Proterozoic Eon. The first step, known as the Great Oxidation Event (GOE), occurred at ca. 2.32 Ga (1), when atmospheric oxygen first exceeded 0.001% of present atmospheric levels (2). The second step, occurred at ca. 0.58 Ga, resulting in the pervasive oxygenation of the deep oceans, a feature that persisted through most of the Phanerozoic (3). The conventional model envisions two progressive and unidirectional increases in free oxygen. However, recent studies have challenged this simplistic view of the GOE (4, 5). A dramatic increase and decline in Earth oxidation state between 2.3 and 2.0 Ga is now well supported (6-9) and raises the question of how well-oxygenated the Earth surface was in the immediate aftermath of the GOE. In order to constrain the response of the deep oceans to the GOE, we present a study of Mo isotope composition and Mo concentration from three key early Paleoproterozoic black shale units with ages ranging from 2.32 to 2.06 Ga. Our results suggest high and unstable surface oxygen levels at 2.32 Ga, leading to an abrupt increase in Mo supply to the still globally anoxic ocean, and producing extreme seawater Mo isotopic enrichments in these black shales. We thus infer a period of significant Mo isotopic Rayleigh effects and non-steady state behaviour of the Mo oceanic system at the beginning of the GOE. Between 2.2-2.1 Ga, we observe smaller Mo isotopic variations and estimate the δ98Mo of seawater to be 1.42 ± 0.27 ‰W conclude that oxygen levels must have stabilized at a relatively high level and that the deep oceans were oxygenated for the first time in Earth's history. By ca. 2.06 Ga, immediately after the Lomagundi Event, the Mo isotopic composition decreased dramatically to δ98MoSW = 0.80 ± 0.21 o reflecting the end of deep ocean oxygenation and the return of largely anoxic deep oceans. References: [1] A. Bekker et al., 2004, Nature 427, 117-20. [2] A. Pavlov and J. Kasting, 2002, Astrobiology 2, 27-41. [3] C. Scott et al., 2008, Nature 452, 456-9. [4] C. Goldblatt et al., 2006, Nature 443, 683-6. [5] L. Kump et al., 2011, Science 334, 1694-6. [6] A. Bekker and D. Holland, 2012, Earth Planet. Sci. Lett. 317-318, 295-304. [7] N. Planavsky et al., 2012, Proc. Natl. Acad. Sci. U. S. A. 109, 18300-5. [8] C. Partin et al., 2013, Chem. Geol. 362, 82-90. [9] C. Scott et al., 2014, Earth Planet. Sci. Lett. 389, 95-104.

REPERTOIRE OF MESOSCOPIC CORTICAL ACTIVITY IS NOT REDUCED DURING ANESTHESIA

PubMed Central

HUDETZ, ANTHONY G.; VIZUETE, JEANNETTE A.; PILLAY, SIVESHIGAN; MASHOUR, GEORGE A.

2016-01-01

Consciousness has been linked to the repertoire of brain states at various spatiotemporal scales. Anesthesia is thought to modify consciousness by altering information integration in cortical and thalamocortical circuits. At a mesoscopic scale, neuronal populations in the cortex form synchronized ensembles whose characteristics are presumably state-dependent but this has not been rigorously tested. In this study, spontaneous neuronal activity was recorded with 64-contact microelectrode arrays in primary visual cortex of chronically instrumented, unrestrained rats under stepwise decreasing levels of desflurane anesthesia (8%, 6%, 4%, and 2% inhaled concentrations) and wakefulness (0% concentration). Negative phases of the local field potentials formed compact, spatially contiguous activity patterns (CAPs) that were not due to chance. The number of CAPs was 120% higher in wakefulness and deep anesthesia associated with burst-suppression than at intermediate levels of consciousness. The frequency distribution of CAP sizes followed a power–law with slope −1.5 in relatively deep anesthesia (8–6%) but deviated from that at the lighter levels. Temporal variance and entropy of CAP sizes were lowest in wakefulness (76% and 24% lower at 0% than at 8% desflurane, respectively) but changed little during recovery of consciousness. CAPs categorized by K-means clustering were conserved at all anesthesia levels and wakefulness, although their proportion changed in a state-dependent manner. These observations yield new knowledge about the dynamic landscape of ongoing population activity in sensory cortex at graded levels of anesthesia. The repertoire of population activity and self-organized criticality at the mesoscopic scale do not appear to contribute to anesthetic suppression of consciousness, which may instead depend on large-scale effects, more subtle dynamic properties, or changes outside of primary sensory cortex. PMID:27751957

Repertoire of mesoscopic cortical activity is not reduced during anesthesia.

PubMed

Hudetz, Anthony G; Vizuete, Jeannette A; Pillay, Siveshigan; Mashour, George A

2016-12-17

Consciousness has been linked to the repertoire of brain states at various spatiotemporal scales. Anesthesia is thought to modify consciousness by altering information integration in cortical and thalamocortical circuits. At a mesoscopic scale, neuronal populations in the cortex form synchronized ensembles whose characteristics are presumably state-dependent but this has not been rigorously tested. In this study, spontaneous neuronal activity was recorded with 64-contact microelectrode arrays in primary visual cortex of chronically instrumented, unrestrained rats under stepwise decreasing levels of desflurane anesthesia (8%, 6%, 4%, and 2% inhaled concentrations) and wakefulness (0% concentration). Negative phases of the local field potentials formed compact, spatially contiguous activity patterns (CAPs) that were not due to chance. The number of CAPs was 120% higher in wakefulness and deep anesthesia associated with burst-suppression than at intermediate levels of consciousness. The frequency distribution of CAP sizes followed a power-law with slope -1.5 in relatively deep anesthesia (8-6%) but deviated from that at the lighter levels. Temporal variance and entropy of CAP sizes were lowest in wakefulness (76% and 24% lower at 0% than at 8% desflurane, respectively) but changed little during recovery of consciousness. CAPs categorized by K-means clustering were conserved at all anesthesia levels and wakefulness, although their proportion changed in a state-dependent manner. These observations yield new knowledge about the dynamic landscape of ongoing population activity in sensory cortex at graded levels of anesthesia. The repertoire of population activity and self-organized criticality at the mesoscopic scale do not appear to contribute to anesthetic suppression of consciousness, which may instead depend on large-scale effects, more subtle dynamic properties, or changes outside of primary sensory cortex. Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.

Application of positron annihilation lifetime technique to the study of deep level transients in semiconductors

NASA Astrophysics Data System (ADS)

Deng, A. H.; Shan, Y. Y.; Fung, S.; Beling, C. D.

2002-03-01

Unlike its conventional applications in lattice defect characterization, positron annihilation lifetime technique was applied to study temperature-dependent deep level transients in semiconductors. Defect levels in the band gap can be determined as they are determined by conventional deep level transient spectroscopy (DLTS) studies. The promising advantage of this application of positron annihilation over the conventional DLTS is that it could further extract extra microstructure information of deep-level defects, such as whether a deep level defect is vacancy related or not. A demonstration of EL2 defect level transient study in GaAs was shown and the EL2 level of 0.82±0.02 eV was obtained by a standard Arrhenius analysis, similar to that in conventional DLTS studies.

Sensory processing of deep tissue nociception in the rat spinal cord and thalamic ventrobasal complex.

PubMed

Sikandar, Shafaq; West, Steven J; McMahon, Stephen B; Bennett, David L; Dickenson, Anthony H

2017-07-01

Sensory processing of deep somatic tissue constitutes an important component of the nociceptive system, yet associated central processing pathways remain poorly understood. Here, we provide a novel electrophysiological characterization and immunohistochemical analysis of neural activation in the lateral spinal nucleus (LSN). These neurons show evoked activity to deep, but not cutaneous, stimulation. The evoked responses of neurons in the LSN can be sensitized to somatosensory stimulation following intramuscular hypertonic saline, an acute model of muscle pain, suggesting this is an important spinal relay site for the processing of deep tissue nociceptive inputs. Neurons of the thalamic ventrobasal complex (VBC) mediate both cutaneous and deep tissue sensory processing, but in contrast to the lateral spinal nucleus our electrophysiological studies do not suggest the existence of a subgroup of cells that selectively process deep tissue inputs. The sensitization of polymodal and thermospecific VBC neurons to mechanical somatosensory stimulation following acute muscle stimulation with hypertonic saline suggests differential roles of thalamic subpopulations in mediating cutaneous and deep tissue nociception in pathological states. Overall, our studies at both the spinal (lateral spinal nucleus) and supraspinal (thalamic ventrobasal complex) levels suggest a convergence of cutaneous and deep somatosensory inputs onto spinothalamic pathways, which are unmasked by activation of muscle nociceptive afferents to produce consequent phenotypic alterations in spinal and thalamic neural coding of somatosensory stimulation. A better understanding of the sensory pathways involved in deep tissue nociception, as well as the degree of labeled line and convergent pathways for cutaneous and deep somatosensory inputs, is fundamental to developing targeted analgesic therapies for deep pain syndromes. © 2017 University College London. Physiological Reports published by Wiley Periodicals, Inc. on behalf of The Physiological Society and the American Physiological Society.

Deep learning for hybrid EEG-fNIRS brain-computer interface: application to motor imagery classification.

PubMed

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Brain-computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

DeepFruits: A Fruit Detection System Using Deep Neural Networks

PubMed Central

Sa, Inkyu; Ge, Zongyuan; Dayoub, Feras; Upcroft, Ben; Perez, Tristan; McCool, Chris

2016-01-01

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit. PMID:27527168

DeepFruits: A Fruit Detection System Using Deep Neural Networks.

PubMed

Sa, Inkyu; Ge, Zongyuan; Dayoub, Feras; Upcroft, Ben; Perez, Tristan; McCool, Chris

2016-08-03

This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform; it is a key element for fruit yield estimation and automated harvesting. Recent work in deep neural networks has led to the development of a state-of-the-art object detector termed Faster Region-based CNN (Faster R-CNN). We adapt this model, through transfer learning, for the task of fruit detection using imagery obtained from two modalities: colour (RGB) and Near-Infrared (NIR). Early and late fusion methods are explored for combining the multi-modal (RGB and NIR) information. This leads to a novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0 . 807 to 0 . 838 for the detection of sweet pepper. In addition to improved accuracy, this approach is also much quicker to deploy for new fruits, as it requires bounding box annotation rather than pixel-level annotation (annotating bounding boxes is approximately an order of magnitude quicker to perform). The model is retrained to perform the detection of seven fruits, with the entire process taking four hours to annotate and train the new model per fruit.

Deep learning for hybrid EEG-fNIRS brain–computer interface: application to motor imagery classification

NASA Astrophysics Data System (ADS)

Chiarelli, Antonio Maria; Croce, Pierpaolo; Merla, Arcangelo; Zappasodi, Filippo

2018-06-01

Objective. Brain–computer interface (BCI) refers to procedures that link the central nervous system to a device. BCI was historically performed using electroencephalography (EEG). In the last years, encouraging results were obtained by combining EEG with other neuroimaging technologies, such as functional near infrared spectroscopy (fNIRS). A crucial step of BCI is brain state classification from recorded signal features. Deep artificial neural networks (DNNs) recently reached unprecedented complex classification outcomes. These performances were achieved through increased computational power, efficient learning algorithms, valuable activation functions, and restricted or back-fed neurons connections. By expecting significant overall BCI performances, we investigated the capabilities of combining EEG and fNIRS recordings with state-of-the-art deep learning procedures. Approach. We performed a guided left and right hand motor imagery task on 15 subjects with a fixed classification response time of 1 s and overall experiment length of 10 min. Left versus right classification accuracy of a DNN in the multi-modal recording modality was estimated and it was compared to standalone EEG and fNIRS and other classifiers. Main results. At a group level we obtained significant increase in performance when considering multi-modal recordings and DNN classifier with synergistic effect. Significance. BCI performances can be significantly improved by employing multi-modal recordings that provide electrical and hemodynamic brain activity information, in combination with advanced non-linear deep learning classification procedures.

Classifying the molecular functions of Rab GTPases in membrane trafficking using deep convolutional neural networks.

PubMed

Le, Nguyen-Quoc-Khanh; Ho, Quang-Thai; Ou, Yu-Yen

2018-06-13

Deep learning has been increasingly used to solve a number of problems with state-of-the-art performance in a wide variety of fields. In biology, deep learning can be applied to reduce feature extraction time and achieve high levels of performance. In our present work, we apply deep learning via two-dimensional convolutional neural networks and position-specific scoring matrices to classify Rab protein molecules, which are main regulators in membrane trafficking for transferring proteins and other macromolecules throughout the cell. The functional loss of specific Rab molecular functions has been implicated in a variety of human diseases, e.g., choroideremia, intellectual disabilities, cancer. Therefore, creating a precise model for classifying Rabs is crucial in helping biologists understand the molecular functions of Rabs and design drug targets according to such specific human disease information. We constructed a robust deep neural network for classifying Rabs that achieved an accuracy of 99%, 99.5%, 96.3%, and 97.6% for each of four specific molecular functions. Our approach demonstrates superior performance to traditional artificial neural networks. Therefore, from our proposed study, we provide both an effective tool for classifying Rab proteins and a basis for further research that can improve the performance of biological modeling using deep neural networks. Copyright © 2018 Elsevier Inc. All rights reserved.

Vertical Gravimeter Array Observations and Their Performance in Groundwater-Level Monitoring

NASA Astrophysics Data System (ADS)

Tanaka, T.; Honda, R.

2018-03-01

The gravitational effects of the atmosphere and subsurface water are significant obstacles to observing gravity variations on the sub-μGal (1 μGal = 10 nm/s2) scale. The goal of this study is to detect changes in gravity that are caused by mass redistributions deep underground related to seismological phenomena by reducing environmental gravity effects using multiple gravimeters belowground and aboveground, which we term a "vertical gravimeter array." Based on an evaluation of the responses to atmospheric effects and rainfall events identified in observations made with individual relative gravimeters, the vertical gravimeter array succeeds in stacking the target signals from deep underground and in reducing errors due to rainfall or free groundwater and atmospheric effects. To enable accurate interpretation, we introduce a physical approach that is based on attraction and loading deformation effects for atmospheric reduction using state-of-the-art gridded weather data products. Changes in the water levels of confined groundwater can be regarded as a signal from deep underground, and a response coefficient of approximately -15 μGal/m was obtained. In addition, the response coefficient of the free groundwater level was determined to be approximately 5 μGal/m. Such array observations are expected to contribute to monitoring crustal activity and hydrological studies.

Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.

PubMed

Abbas, Qaisar; Fondon, Irene; Sarmiento, Auxiliadora; Jiménez, Soledad; Alemany, Pedro

2017-11-01

Diabetic retinopathy (DR) is leading cause of blindness among diabetic patients. Recognition of severity level is required by ophthalmologists to early detect and diagnose the DR. However, it is a challenging task for both medical experts and computer-aided diagnosis systems due to requiring extensive domain expert knowledge. In this article, a novel automatic recognition system for the five severity level of diabetic retinopathy (SLDR) is developed without performing any pre- and post-processing steps on retinal fundus images through learning of deep visual features (DVFs). These DVF features are extracted from each image by using color dense in scale-invariant and gradient location-orientation histogram techniques. To learn these DVF features, a semi-supervised multilayer deep-learning algorithm is utilized along with a new compressed layer and fine-tuning steps. This SLDR system was evaluated and compared with state-of-the-art techniques using the measures of sensitivity (SE), specificity (SP) and area under the receiving operating curves (AUC). On 750 fundus images (150 per category), the SE of 92.18%, SP of 94.50% and AUC of 0.924 values were obtained on average. These results demonstrate that the SLDR system is appropriate for early detection of DR and provide an effective treatment for prediction type of diabetes.

Electron Correlation in Oxygen Vacancy in SrTiO3

NASA Astrophysics Data System (ADS)

Lin, Chungwei; Demkov, Alexander A.

2014-03-01

Oxygen vacancies are an important type of defect in transition metal oxides. In SrTiO3 they are believed to be the main donors in an otherwise intrinsic crystal. At the same time, a relatively deep gap state associated with the vacancy is widely reported. To explain this inconsistency we investigate the effect of electron correlation in an oxygen vacancy (OV) in SrTiO3. When taking correlation into account, we find that the OV-induced localized level can at most trap one electron, while the second electron occupies the conduction band. Our results offer a natural explanation of how the OV in SrTiO3 can produce a deep in-gap level (about 1 eV below the conduction band bottom) in photoemission, and at the same time be an electron donor. Our analysis implies an OV in SrTiO3 should be fundamentally regarded as a magnetic impurity, whose deep level is always partially occupied due to the strong Coulomb repulsion. An OV-based Anderson impurity model is derived, and its implications are discussed. This work was supported by Scientific Discovery through Advanced Computing (SciDAC) program funded by U.S. Department of Energy, Office of Science, Advanced Scientific Computing Research and Basic Energy Sciences under award number DESC0008877.

Interpretation of deep levels in Si-GaAs crystals observed by photo-induced current transient spectroscopy

NASA Astrophysics Data System (ADS)

Hlinomaz, P.; Šmíd, V.; Krištofik, J.

1993-05-01

Deep levels measured by Photo-Induced Current Transient Spectroscopy (PICTS) are interpreted taking into account different bulk and surface properties of semi-insulating crystals, results of directly measured isothermal transients and types of observed deep levels determined from the measurements with different voltage polarity. The principal interest is focused on the temperature interval 250-450 K where peaks related to the deep levels causing semiinsulating properties are observed in the PICTS spectra. Majority of deep levels observed in various samples may be ascribed to the EL2, EL3, EL4, HL1 and HL9 levels. Maxima exhibiting inverse polarity in PICTS spectra are not related to EL2 or HL1.

Enriching mission planning approach with state transition graph heuristics for deep space exploration

NASA Astrophysics Data System (ADS)

Jin, Hao; Xu, Rui; Xu, Wenming; Cui, Pingyuan; Zhu, Shengying

2017-10-01

As to support the mission of Mars exploration in China, automated mission planning is required to enhance security and robustness of deep space probe. Deep space mission planning requires modeling of complex operations constraints and focus on the temporal state transitions of involved subsystems. Also, state transitions are ubiquitous in physical systems, but have been elusive for knowledge description. We introduce a modeling approach to cope with these difficulties that takes state transitions into consideration. The key technique we build on is the notion of extended states and state transition graphs. Furthermore, a heuristics that based on state transition graphs is proposed to avoid redundant work. Finally, we run comprehensive experiments on selected domains and our techniques present an excellent performance.

Universal behavior of surface-dangling bonds in hydrogen-terminated Si, Ge, and Si/Ge nanowires.

NASA Astrophysics Data System (ADS)

Nunes, Ricardo; Kagimura, Ricardo; Chacham, Hélio

2007-03-01

We report an ab initio study of the electronic properties of surface dangling bond (SDB) states in hydrogen-terminated Si, Ge, and Si/Ge nanowires with diameters between 1 and 2 nm. We find that the charge transition levels ɛ(+/-) of SDB states are deep in the bandgap for Si wires, and shallow (near the valence band edge) for Ge wires. In both Si and Ge wires, the SDB states are localized. We also find that the SDB ɛ(+/-) levels behave as a ``universal" energy reference level among Si, Ge, and Si/Ge wires within a precision of 0.1 eV. By computing the average bewteen the electron affinity and ionization energy in the atomi limit of several atoms from the III, IV and V columns, we conjecture that the universality is a periodic-table atomic property.

Wavelength-modulated photocapacitance spectroscopy

NASA Technical Reports Server (NTRS)

Kamieniecki, E.; Lagowski, J.; Gatos, H. C.

1980-01-01

Derivative deep-level spectroscopy was achieved with wavelength-modulated photocapacitance employing MOS structures and Schottky barriers. The energy position and photoionization characteristics of deep levels of melt-grown GaAs and the Cr level in high-resistivity GaAs were determined. The advantages of this method over existing methods for deep-level spectroscopy are discussed.

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

NASA Astrophysics Data System (ADS)

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.

Absence of Intramolecular Singlet Fission in Pentacene-Perylenediimide Heterodimers: The Role of Charge Transfer State.

PubMed

Wang, Long; Wu, Yishi; Chen, Jianwei; Wang, Lanfen; Liu, Yanping; Yu, Zhenyi; Yao, Jiannian; Fu, Hongbing

2017-11-16

A new class of donor-acceptor heterodimers based on two singlet fission (SF)-active chromophores, i.e., pentacene (Pc) and perylenediimide (PDI), was developed to investigate the role of charge transfer (CT) state on the excitonic dynamics. The CT state is efficiently generated upon photoexcitation. However, the resulting CT state decays to different energy states depending on the energy levels of the CT state. It undergoes extremely rapid deactivation to the ground state in polar CH 2 Cl 2 , whereas it undergoes transformation to a Pc triplet in nonpolar toluene. The efficient triplet generation in toluene is not due to SF but CT-mediated intersystem crossing. In light of the energy landscape, it is suggested that the deep energy level of the CT state relative to that of the triplet pair state makes the CT state actually serve as a trap state that cannot undergoes an intramolecular singlet fission process. These results provide guidance for the design of SF materials and highlight the requisite for more widely applicable design principles.

76 FR 60379 - Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab; Amendment 3

Federal Register 2010, 2011, 2012, 2013, 2014

2011-09-29

.... 100903433-1531-02] RIN 0648-BA22 Fisheries of the Northeastern United States; Atlantic Deep-Sea Red Crab... approved in Amendment 3 to the Atlantic Deep-Sea Red Crab Fishery Management Plan (FMP). The New England... range and/or witness the at-sea retrieval of the traps to determine compliance. 2. Prohibition on...

Carbon-hydrogen defects with a neighboring oxygen atom in n-type Si

NASA Astrophysics Data System (ADS)

Gwozdz, K.; Stübner, R.; Kolkovsky, Vl.; Weber, J.

2017-07-01

We report on the electrical activation of neutral carbon-oxygen complexes in Si by wet-chemical etching at room temperature. Two deep levels, E65 and E75, are observed by deep level transient spectroscopy in n-type Czochralski Si. The activation enthalpies of E65 and E75 are obtained as EC-0.11 eV (E65) and EC-0.13 eV (E75). The electric field dependence of their emission rates relates both levels to single acceptor states. From the analysis of the depth profiles, we conclude that the levels belong to two different defects, which contain only one hydrogen atom. A configuration is proposed, where the CH1BC defect, with hydrogen in the bond-centered position between neighboring C and Si atoms, is disturbed by interstitial oxygen in the second nearest neighbor position to substitutional carbon. The significant reduction of the CH1BC concentration in samples with high oxygen concentrations limits the use of this defect for the determination of low concentrations of substitutional carbon in Si samples.

Phonon-assisted changes in charge states of deep level defects in germanium

NASA Astrophysics Data System (ADS)

Markevich, A. V.; Litvinov, V. V.; Emtsev, V. V.; Markevich, V. P.; Peaker, A. R.

2006-04-01

Electronic processes associated with changes in the charge states of the vacancy-oxygen center (VO or A center) and vacancy-group-V-impurity atom (P, As, Sb or Bi) pairs (E centers) in irradiated germanium crystals have been studied using deep level transient spectroscopy (DLTS), high-resolution Laplace DLTS and Hall effect measurements. It is found that the electron emission and capture processes related to transitions between the doubly and the singly negatively charged states of the A center and the E centers in Ge are phonon-assisted, i.e., they are accompanied by significant vibrations and re-arrangements of atoms in the vicinity of the defects. Manifestations of the phonon involvements are: (i) temperature-dependent electron capture cross-sections which are well described in the frame of the multi-phonon-assisted capture model; (ii) large changes in entropy related to the ionization of the defects and, associated with these, temperature-dependent positions of energy levels; and (iii) electron emission via phonon-assisted tunneling upon the application of electric field. These effects have been considered in detail for the vacancy-oxygen and the vacancy-donor complexes. On the basis of a combined analysis of the electronic processes a configuration-coordinate diagram of the acceptor states of the A and E centers is plotted. It is found that changes in the entropy of ionization and the energy for electron emission for these traps follow the empirical Meyer-Neldel rule. A model based on multi-phonon-assisted carrier emission from defects is adapted for the explanation of the origin of this rule for the case of electronic processes in Ge.

Two different carbon-hydrogen complexes in silicon with closely spaced energy levels

NASA Astrophysics Data System (ADS)

Stübner, R.; Kolkovsky, Vl.; Weber, J.

2015-08-01

An acceptor and a single donor state of carbon-hydrogen defects (CHA and CHB) are observed by Laplace deep level transient spectroscopy at 90 K. CHA appears directly after hydrogenation by wet chemical etching or hydrogen plasma treatment, whereas CHB can be observed only after a successive annealing under reverse bias at about 320 K. The activation enthalpies of these states are 0.16 eV for CHA and 0.14 eV for CHB. Our results reconcile previous controversial experimental results. We attribute CHA to the configuration where substitutional carbon binds a hydrogen atom on a bond centered position between carbon and the neighboring silicon and CHB to another carbon-hydrogen defect.

An Embodied Multi-Sensor Fusion Approach to Visual Motion Estimation Using Unsupervised Deep Networks.

PubMed

Shamwell, E Jared; Nothwang, William D; Perlis, Donald

2018-05-04

Aimed at improving size, weight, and power (SWaP)-constrained robotic vision-aided state estimation, we describe our unsupervised, deep convolutional-deconvolutional sensor fusion network, Multi-Hypothesis DeepEfference (MHDE). MHDE learns to intelligently combine noisy heterogeneous sensor data to predict several probable hypotheses for the dense, pixel-level correspondence between a source image and an unseen target image. We show how our multi-hypothesis formulation provides increased robustness against dynamic, heteroscedastic sensor and motion noise by computing hypothesis image mappings and predictions at 76⁻357 Hz depending on the number of hypotheses being generated. MHDE fuses noisy, heterogeneous sensory inputs using two parallel, inter-connected architectural pathways and n (1⁻20 in this work) multi-hypothesis generating sub-pathways to produce n global correspondence estimates between a source and a target image. We evaluated MHDE on the KITTI Odometry dataset and benchmarked it against the vision-only DeepMatching and Deformable Spatial Pyramids algorithms and were able to demonstrate a significant runtime decrease and a performance increase compared to the next-best performing method.

HgCdTe Surface and Defect Study Program.

DTIC Science & Technology

1984-07-01

double layer heterojunction (DLHJ) devices. There are however many complications on this once we consider implanted junctions, LWIR devices or even the...It is not possible from this measurement to discriminate between real interface states and charge nonuniformities . Admittance spectroscopy (discussed...earlier) and deep level transient spectroscopy (DLTS) are not usually affected by these nonuniformities due to their observation of a speci- fic

Joint Services Electronics Program.

DTIC Science & Technology

1987-12-31

and annealing, using deep level transient spectroscopy (DLTS), and the effects of co-implantation on 4l the activation of amphoteric dopants and...theriithe study of optical quantum effects with emphasis on nonlinear optical phenomena. For example, a significant accomplishment write-up describes...Millimeter-Wave Array Components Tatsuo Itoh A number of novel solid state devices such as metal semiconductor field effect transistors (MESFET

"Anti-American Studies" in the Deep South: Dissenting Rhetorics, the Practice of Democracy, and Academic Freedom in Wartime Universities

ERIC Educational Resources Information Center

Powers, M. Karen; Chaput, Catherine

2007-01-01

Using Frederic Jameson, we outline concentric circles of the political unconscious structuring debates about academic freedom at the national and state levels. By drawing parallels between the World War I university and the contemporary university, we suggest that such circles function historically, always bearing traces of an earlier time. To…

Neurostimulation to improve level of consciousness in patients with epilepsy.

PubMed

Gummadavelli, Abhijeet; Kundishora, Adam J; Willie, Jon T; Andrews, John P; Gerrard, Jason L; Spencer, Dennis D; Blumenfeld, Hal

2015-06-01

When drug-resistant epilepsy is poorly localized or surgical resection is contraindicated, current neurostimulation strategies such as deep brain stimulation and vagal nerve stimulation can palliate the frequency or severity of seizures. However, despite medical and neuromodulatory therapy, a significant proportion of patients continue to experience disabling seizures that impair awareness, causing disability and risking injury or sudden unexplained death. We propose a novel strategy in which neuromodulation is used not only to reduce seizures but also to ameliorate impaired consciousness when the patient is in the ictal and postictal states. Improving or preventing alterations in level of consciousness may have an effect on morbidity (e.g., accidents, drownings, falls), risk for death, and quality of life. Recent studies may have elucidated underlying networks and mechanisms of impaired consciousness and yield potential novel targets for neuromodulation. The feasibility, benefits, and pitfalls of potential deep brain stimulation targets are illustrated in human and animal studies involving minimally conscious/vegetative states, movement disorders, depth of anesthesia, sleep-wake regulation, and epilepsy. We review evidence that viable therapeutic targets for impaired consciousness associated with seizures may be provided by key nodes of the consciousness system in the brainstem reticular activating system, hypothalamus, basal ganglia, thalamus, and basal forebrain.

Charge Trapping Properties of Ge Nanocrystals Grown via Solid-State Dewetting

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

Johnston, Steven; Jadli, I.; Aouassa, M.

2018-05-04

In the present work, we report on the charge trapping properties of Germanium Nanocrystals (Ge NCs) self assembled on SiO2 thin layer for promising applications in next-generation non volatile memory by the means of Deep Level Transient Spectroscopy (DLTS) and high frequency C-V method. The Ge NCs were grown via dewetting phenomenon at solid state by Ultra-High Vacuum (UHV) annealing and passivated with silicon before SiO2 capping. The role of the surface passivation is to reduce the electrical defect density at the Ge NCs-SiO2 interface. The presence of the Ge NCs in the oxide of the MOS capacitors strongly affectsmore » the C-V characteristics and increases the accumulation capacitance, causes a negative flat band voltage (VFB) shift. The DLTS has been used to study the individual Ge NCs as a single point deep level defect in the oxide. DLTS reveals two main features: the first electron traps around 255 K could correspond to dangling bonds at the Si/SiO2 interface and the second, at high-temperature (>300 K) response, could be originated from minority carrier generation in Ge NCs.« less

Defect States in InP/InGaAs/InP Heterostructures by Current-Voltage Characteristics and Deep Level Transient Spectroscopy.

PubMed

Vu, Thi Kim Oanh; Lee, Kyoung Su; Lee, Sang Jun; Kim, Eun Kyu

2018-09-01

We studied defect states in In0.53Ga0.47As/InP heterojunctions with interface control by group V atoms during metalorganic chemical vapor (MOCVD) deposition. From deep level transient spectroscopy (DLTS) measurements, two defects with activation energies of 0.28 eV (E1) and 0.15 eV (E2) below the conduction band edge, were observed. The defect density of E1 for In0.53Ga0.47As/InP heterojunctions with an addition of As and P atoms was about 1.5 times higher than that of the heterojunction added P atom only. From the temperature dependence of current- voltage characteristics, the thermal activation energies of In0.53Ga0.47As/InP of heterojunctions were estimated to be 0.27 and 0.25 eV, respectively. It appeared that the reverse light current for In0.53Ga0.47As/InP heterojunction added P atom increased only by illumination of a 940 nm-LED light source. These results imply that only the P addition at the interface can enhance the quality of InGaAs/InP heterojunction.

Crystal Engineering for Low Defect Density and High Efficiency Hybrid Chemical Vapor Deposition Grown Perovskite Solar Cells.

PubMed

Ng, Annie; Ren, Zhiwei; Shen, Qian; Cheung, Sin Hang; Gokkaya, Huseyin Cem; So, Shu Kong; Djurišić, Aleksandra B; Wan, Yangyang; Wu, Xiaojun; Surya, Charles

2016-12-07

Synthesis of high quality perovskite absorber is a key factor in determining the performance of the solar cells. We demonstrate that hybrid chemical vapor deposition (HCVD) growth technique can provide high level of versatility and repeatability to ensure the optimal conditions for the growth of the perovskite films as well as potential for batch processing. It is found that the growth ambient and degree of crystallization of CH 3 NH 3 PbI 3 (MAPI) have strong impact on the defect density of MAPI. We demonstrate that HCVD process with slow postdeposition cooling rate can significantly reduce the density of shallow and deep traps in the MAPI due to enhanced material crystallization, while a mixed O 2 /N 2 carrier gas is effective in passivating both shallow and deep traps. By careful control of the perovskite growth process, a champion device with power conversion efficiency of 17.6% is achieved. Our work complements the existing theoretical studies on different types of trap states in MAPI and fills the gap on the theoretical analysis of the interaction between deep levels and oxygen. The experimental results are consistent with the theoretical predictions.

The deep levels in InGaAlP epilayers grown by metalorganic chemical vapor deposition using tertiarybutylphosphine

NASA Astrophysics Data System (ADS)

Izumiya, T.; Ishikawa, H.; Mashita, M.

1994-12-01

InGaAlP epilayers and double-hetero structure light emitting diodes (LEDs) were grown by metalorganic chemical vapor deposition (MOCVD) using tertiarybutylphosphine (TBP). The photoluminescence (PL) intensities were low compared with the epilayer grown using PH 3, and depended markedly on the TBP synthesis lots. Deep levels, were studied and two oxygen related levels were observed in the epilayers with small PL intensities. An intimate relation between the deep levels and the photoluminescence (PL) intensity has been found. A larger TBP flow rate reduced the deep level concentrations and improved the PL intensity.

Setup for in situ deep level transient spectroscopy of semiconductors during swift heavy ion irradiation.

PubMed

Kumar, Sandeep; Kumar, Sugam; Katharria, Y S; Safvan, C P; Kanjilal, D

2008-05-01

A computerized system for in situ deep level characterization during irradiation in semiconductors has been set up and tested in the beam line for materials science studies of the 15 MV Pelletron accelerator at the Inter-University Accelerator Centre, New Delhi. This is a new facility for in situ irradiation-induced deep level studies, available in the beam line of an accelerator laboratory. It is based on the well-known deep level transient spectroscopy (DLTS) technique. High versatility for data manipulation is achieved through multifunction data acquisition card and LABVIEW. In situ DLTS studies of deep levels produced by impact of 100 MeV Si ions on Aun-Si(100) Schottky barrier diode are presented to illustrate performance of the automated DLTS facility in the beam line.

A first-principles study of carbon-related energy levels in GaN. I. Complexes formed by substitutional/interstitial carbons and gallium/nitrogen vacancies

NASA Astrophysics Data System (ADS)

Matsubara, Masahiko; Bellotti, Enrico

2017-05-01

Various forms of carbon based complexes in GaN are studied with first-principles calculations employing Heyd-Scuseria-Ernzerhof hybrid functionals within the framework of the density functional theory. We consider carbon complexes made of the combinations of single impurities, i.e., CN-CGa, CI-CN , and CI-CGa , where CN, CGa , and CI denote C substituting nitrogen, C substituting gallium, and interstitial C, respectively, and of neighboring gallium/nitrogen vacancies ( VGa / VN ), i.e., CN-VGa and CGa-VN . Formation energies are computed for all these configurations with different charge states after full geometry optimizations. From our calculated formation energies, thermodynamic transition levels are evaluated, which are related to the thermal activation energies observed in experimental techniques such as deep level transient spectroscopy. Furthermore, the lattice relaxation energies (Franck-Condon shift) are computed to obtain optical activation energies, which are observed in experimental techniques such as deep level optical spectroscopy. We compare our calculated values of activation energies with the energies of experimentally observed C-related trap levels and identify the physical origins of these traps, which were unknown before.

A Poor Relationship Between Sea Level and Deep-Water Sand Delivery

NASA Astrophysics Data System (ADS)

Harris, Ashley D.; Baumgardner, Sarah E.; Sun, Tao; Granjeon, Didier

2018-08-01

The most commonly cited control on delivery of sand to deep water is the rate of relative sea-level fall. The rapid rate of accommodation loss on the shelf causes sedimentation to shift basinward. Field and experimental numerical modeling studies have shown that deep-water sand delivery can occur during any stage of relative sea level position and across a large range of values of rate of relative sea-level change. However, these studies did not investigate the impact of sediment transport efficiency on the relationship between rate of relative sea-level change and deep-water sand delivery rate. We explore this relationship using a deterministic nonlinear diffusion-based numerical stratigraphic forward model. We vary across three orders of magnitude the diffusion coefficient value for marine settings, which controls sediment transport efficiency. We find that the rate of relative sea-level change can explain no more than 1% of the variability in deep-water sand delivery rates, regardless of sediment transport efficiency. Model results show a better correlation with relative sea level, with up to 55% of the variability in deep water sand delivery rates explained. The results presented here are consistent with studies of natural settings which suggest stochastic processes such as avulsion and slope failure, and interactions among such processes, may explain the remaining variance. Relative sea level is a better predictor of deep-water sand delivery than rate of relative sea-level change because it is the sea-level fall itself which promotes sand delivery, not the rate of the fall. We conclude that the poor relationship between sea level and sand delivery is not an artifact of the modeling parameters but is instead due to the inadequacy of relative sea level and the rate of relative sea-level change to fully describe the dimensional space in which depositional systems reside. Subsequently, sea level itself is unable to account for the interaction of multiple processes that contribute to sand delivery to deep water.

A patient with acute liver failure and extreme hypoglycaemia with lactic acidosis who was not in a coma: causes and consequences of lactate-protected hypoglycaemia.

PubMed

Oldenbeuving, G; McDonald, J R; Goodwin, M L; Sayilir, R; Reijngoud, D J; Gladden, L B; Nijsten, M W N

2014-07-01

Lactate can substitute for glucose as a metabolic substrate. We report a patient with acute liver failure who was awake despite a glucose level of 0.7 mmol/l with very high lactate level of 25 mmol/l. The hypoglycaemia+hyperlactataemia combination may be considered paradoxical since glucose is the main precursor of lactate and lactate is reconverted into glucose by the Cori cycle. Literature relevant to the underlying mechanism of combined deep hypoglycaemia and severe hyperlactataemia was assessed. We also assessed the literature for evidence of protection against deep hypoglycaemia by hyperlactataemia. Four syndromes demonstrating hypoglycaemia+hyperlactataemia were found: 1) paracetamol-induced acute liver failure, 2) severe malaria, 3) lymphoma and 4) glucose-6-phosphatase deficiency. An impaired Cori cycle is a key component in all of these metabolic states. Apparently the liver, after exhausting its glycogen stores, loses the gluconeogenic pathway to generate glucose and thereby its ability to remove lactate as well. Several patients with lactic acidosis and glucose levels below 1.7 mmol/l who were not in a coma have been reported. These observations and other data coherently indicate that lactate-protected hypoglycaemia is, at least transiently, a viable state under experimental and clinical conditions. Severe hypoglycaemia+hyperlactataemia reflects failure of the gluconeogenic pathway of lactate metabolism. The existence of lactate-protected hypoglycaemia implies that patients who present with this metabolic state should not automatically be considered to have sustained irreversible brain damage. Moreover, therapies that aim to achieve hypoglycaemia might be feasible with concomitant hyperlactataemia.

A better state-of-mind: deep breathing reduces state anxiety and enhances test performance through regulating test cognitions in children.

PubMed

Khng, Kiat Hui

2017-11-01

A pre-test/post-test, intervention-versus-control experimental design was used to examine the effects, mechanisms and moderators of deep breathing on state anxiety and test performance in 122 Primary 5 students. Taking deep breaths before a timed math test significantly reduced self-reported feelings of anxiety and improved test performance. There was a statistical trend towards greater effectiveness in reducing state anxiety for boys compared to girls, and in enhancing test performance for students with higher autonomic reactivity in test-like situations. The latter moderation was significant when comparing high-versus-low autonomic reactivity groups. Mediation analyses suggest that deep breathing reduces state anxiety in test-like situations, creating a better state-of-mind by enhancing the regulation of adaptive-maladaptive thoughts during the test, allowing for better performance. The quick and simple technique can be easily learnt and effectively applied by most children to immediately alleviate some of the adverse effects of test anxiety on psychological well-being and academic performance.

Deep learning and face recognition: the state of the art

NASA Astrophysics Data System (ADS)

Balaban, Stephen

2015-05-01

Deep Neural Networks (DNNs) have established themselves as a dominant technique in machine learning. DNNs have been top performers on a wide variety of tasks including image classification, speech recognition, and face recognition.1-3 Convolutional neural networks (CNNs) have been used in nearly all of the top performing methods on the Labeled Faces in the Wild (LFW) dataset.3-6 In this talk and accompanying paper, I attempt to provide a review and summary of the deep learning techniques used in the state-of-the-art. In addition, I highlight the need for both larger and more challenging public datasets to benchmark these systems. Despite the ability of DNNs and autoencoders to perform unsupervised feature learning, modern facial recognition pipelines still require domain specific engineering in the form of re-alignment. For example, in Facebook's recent DeepFace paper, a 3D "frontalization" step lies at the beginning of the pipeline. This step creates a 3D face model for the incoming image and then uses a series of affine transformations of the fiducial points to "frontalize" the image. This step enables the DeepFace system to use a neural network architecture with locally connected layers without weight sharing as opposed to standard convolutional layers.6 Deep learning techniques combined with large datasets have allowed research groups to surpass human level performance on the LFW dataset.3, 5 The high accuracy (99.63% for FaceNet at the time of publishing) and utilization of outside data (hundreds of millions of images in the case of Google's FaceNet) suggest that current face verification benchmarks such as LFW may not be challenging enough, nor provide enough data, for current techniques.3, 5 There exist a variety of organizations with mobile photo sharing applications that would be capable of releasing a very large scale and highly diverse dataset of facial images captured on mobile devices. Such an "ImageNet for Face Recognition" would likely receive a warm welcome from researchers and practitioners alike.

A sparse autoencoder-based deep neural network for protein solvent accessibility and contact number prediction.

PubMed

Deng, Lei; Fan, Chao; Zeng, Zhiwen

2017-12-28

Direct prediction of the three-dimensional (3D) structures of proteins from one-dimensional (1D) sequences is a challenging problem. Significant structural characteristics such as solvent accessibility and contact number are essential for deriving restrains in modeling protein folding and protein 3D structure. Thus, accurately predicting these features is a critical step for 3D protein structure building. In this study, we present DeepSacon, a computational method that can effectively predict protein solvent accessibility and contact number by using a deep neural network, which is built based on stacked autoencoder and a dropout method. The results demonstrate that our proposed DeepSacon achieves a significant improvement in the prediction quality compared with the state-of-the-art methods. We obtain 0.70 three-state accuracy for solvent accessibility, 0.33 15-state accuracy and 0.74 Pearson Correlation Coefficient (PCC) for the contact number on the 5729 monomeric soluble globular protein dataset. We also evaluate the performance on the CASP11 benchmark dataset, DeepSacon achieves 0.68 three-state accuracy and 0.69 PCC for solvent accessibility and contact number, respectively. We have shown that DeepSacon can reliably predict solvent accessibility and contact number with stacked sparse autoencoder and a dropout approach.

The Institutional Effects of Incarceration: Spillovers From Criminal Justice to Health Care

PubMed Central

Schnittker, Jason; Uggen, Christopher; Shannon, Sarah Ks; Mcelrath, Suzy Maves

2015-01-01

Context This study examines the spillover effects of growth in state-level incarceration rates on the functioning and quality of the US health care system. Methods Our multilevel approach first explored cross-sectional individual-level data on health care behavior merged to aggregate state-level data regarding incarceration. We then conducted an entirely aggregate-level analysis to address between-state heterogeneity and trends over time in health care access and utilization. Findings We found that individuals residing in states with a larger number of former prison inmates have diminished access to care, less access to specialists, less trust in physicians, and less satisfaction with the care they receive. These spillover effects are deep in that they affect even those least likely to be personally affected by incarceration, including the insured, those over 50, women, non-Hispanic whites, and those with incomes far exceeding the federal poverty threshold. These patterns likely reflect the burden of uncompensated care among former inmates, who have both a greater than average need for care and higher than average levels of uninsurance. State-level analyses solidify these claims. Increases in the number of former inmates are associated simultaneously with increases in the percentage of uninsured within a state and increases in emergency room use per capita, both net of controls for between-state heterogeneity. Conclusions Our analyses establish an intersection between systems of care and corrections, linked by inadequate financial and administrative mechanisms for delivering services to former inmates. PMID:26350929

Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance.

PubMed

Ngo, Tuan Anh; Lu, Zhi; Carneiro, Gustavo

2017-01-01

We introduce a new methodology that combines deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance (MR) data. This combination is relevant for segmentation problems, where the visual object of interest presents large shape and appearance variations, but the annotated training set is small, which is the case for various medical image analysis applications, including the one considered in this paper. In particular, level set methods are based on shape and appearance terms that use small training sets, but present limitations for modelling the visual object variations. Deep learning methods can model such variations using relatively small amounts of annotated training, but they often need to be regularised to produce good generalisation. Therefore, the combination of these methods brings together the advantages of both approaches, producing a methodology that needs small training sets and produces accurate segmentation results. We test our methodology on the MICCAI 2009 left ventricle segmentation challenge database (containing 15 sequences for training, 15 for validation and 15 for testing), where our approach achieves the most accurate results in the semi-automated problem and state-of-the-art results for the fully automated challenge. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Minimal Magnetic States of the Sun and the Solar Wind: Implications for the Origin of the Slow Solar Wind

NASA Astrophysics Data System (ADS)

Cliver, E. W.; von Steiger, R.

2017-09-01

During the last decade it has been proposed that both the Sun and the solar wind have minimum magnetic states, lowest order levels of magnetism that underlie the 11-yr cycle as well as longer-term variability. Here we review the literature on basal magnetic states at the Sun and in the heliosphere and draw a connection between the two based on the recent deep 2008-2009 minimum between cycles 23 and 24. In particular, we consider the implications of the low solar activity during the recent minimum for the origin of the slow solar wind.

2-(2-Hydroxyphenyl)benzimidazole-based four-coordinate boron-containing materials with highly efficient deep-blue photoluminescence and electroluminescence.

PubMed

Zhang, Zhenyu; Zhang, Houyu; Jiao, Chuanjun; Ye, Kaiqi; Zhang, Hongyu; Zhang, Jingying; Wang, Yue

2015-03-16

Two novel four-coordinate boron-containing emitters 1 and 2 with deep-blue emissions were synthesized by refluxing a 2-(2-hydroxyphenyl)benzimidazole ligand with triphenylborane or bromodibenzoborole. The boron chelation produced a new π-conjugated skeleton, which rendered the synthesized boron materials with intense fluorescence, good thermal stability, and high carrier mobility. Both compounds displayed deep-blue emissions in solutions with very high fluorescence quantum yields (over 0.70). More importantly, the samples showed identical fluorescence in the solution and solid states, and the efficiency was maintained at a high level (approximately 0.50) because of the bulky substituents between the boron atom and the benzimidazole unit, which can effectively separate the flat luminescent units. In addition, neat thin films composed of 1 or 2 exhibited high electron and hole mobility in the same order of magnitude 10(-4), as determined by time-of-flight. The fabricated electroluminescent devices that employed 1 or 2 as emitting materials showed high-performance deep-blue emissions with Commission Internationale de L'Eclairage (CIE) coordinates of (X = 0.15, Y = 0.09) and (X = 0.16, Y = 0.08), respectively. Thus, the synthesized boron-containing materials are ideal candidates for fabricating high-performance deep-blue organic light-emitting diodes.

Low Thrust, Deep Throttling, US/CIS Integrated NTRE

NASA Astrophysics Data System (ADS)

Culver, Donald W.; Kolganov, Vyacheslav; Rochow, Richard F.

1994-07-01

In 1993 our international team performed a follow-on ``Nuclear Thermal Rocket Engine (NTRE) Extended Life Feasibility Assessment'' study for the Nuclear Propulsion Office (NPO) at NASAs Lewis Research Center. The main purpose of this study was to complete the 1992 study matrix to assess NTRE designs at thrust levels of 22.5, 11.3, and 6.8 tonnes, using Commonwealth of Independent States (CIS) reactor technology. An additional Aerojet goal was to continue improving the NTRE concept we had generated. Deep throttling, mission performance optimized engine design parametrics, and reliability/cost enhancing engine system simplifications were studied, because they seem to be the last three basic design improvements sorely needed by post-NERVA NTRE. Deep throttling improves engine life by eliminating damaging thermal and mechanical shocks caused by after-cooling with pulsed coolant flow. Alternately, it improves mission performance with steady flow after-cooling by minimizing reactor over-cooling. Deep throttling also provides a practical transition from high pressures and powers of the high thrust power cycle to the low pressures and powers of our electric power generating mode. Two deep throttling designs are discussed; a workable system that was studied and a simplified system that is recommended for future study. Mission-optimized engine thrust/weight (T/W) and Isp predictions are included along with system flow schemes and concept sketches.

Electrical characterisation of defects in wide bandgap semiconductors

NASA Astrophysics Data System (ADS)

Elsherif, Osama S.

Defects usually have a very large influence on the semiconductor material properties and hence on fabricated electronic devices. The nature and properties of defects in semiconducting materials can be investigated by applying electrical characterization techniques such as thermal admittance spectroscopy (TAS), deep level transient spectroscopy (DLTS) and high resolution Laplace-DLTS measurements. This dissertation presents the electrical characterisation of two different wide bandgap semiconducting materials (polycrystalline diamond and GaN) which have both recently attracted a great deal of attention because of their potential applications in the fields of power electronics and optoelectronics. Raman spectroscopy, I-V and C-V measurements were carried out as supporting experiments for the above investigations. The first part of this work focuses on studying the effect of B concentration on the electronic states in polycrystalline diamond thin films grown on silicon by the hot filament chemical vapour deposition method. A combination of high-resolution LDLTS and direct-capture cross-section measurements was used to investigate whether the deep electronic states present in the layers originated from point or extended defects. There was good agreement between data on deep electronic levels obtained from DLTS and TAS experiments. A number of hole traps have been detected; the majority of these levels show an unusual dependence of the DLTS signal on the fill pulse duration which is interpreted as possibly the levels are part of extended defects within the grain boundaries. In contrast, a defect level found in a more highly doped film, with an activation energy of -0.37 eV, exhibited behaviour characteristic of an isolated point defect, which we attribute to B-related centres in the bulk diamond, away from the dislocations. The second part of this thesis presents electrical measurements carried out at temperatures up to 450 K in order to study the electronic states associated with Mg in Mg-doped GaN films grown on sapphire by metalorganic vapour phase epitaxy, and to determine how these are affected by the threading dislocation density (TDD). Two different buffer layer schemes between the film and the sapphire substrate were used, giving rise to different TDDs in the GaN. Admittance spectroscopy of the films finds a single impurity-related acceptor level. It is observed in theses experiments that admittance spectroscopy detects no traps that can be attributed to extended defects, despite the fact that the dislocations are well-known to be active recombination centres. This unexpected finding is discussed in detail.

Electrical characterisation of defects in wide bandgap semiconductors

NASA Astrophysics Data System (ADS)

Elsherif, Osama S.

Defects usually have a very large influence on the semiconductor material properties and hence on fabricated electronic devices. The nature and properties of defects in semiconducting materials can be investigated by applying electrical characterization techniques such as thermal admittance spectroscopy (TAS), deep level transient spectroscopy (DLTS) and high resolution Laplace-DLTS measurements. This dissertation presents the electrical characterisation of two different wide bandgap semiconducting materials (polycrystalline diamond and GaN) which have both recently attracted a great deal of attention because of their potential applications in the fields of power electronics and optoelectronics. Raman spectroscopy, I-V and C-V measurements were carried out as supporting experiments for the above investigations.The first part of this work focuses on studying the effect of B concentration on the electronic states in polycrystalline diamond thin films grown on silicon by the hot filament chemical vapour deposition method. A combination of high-resolution LDLTS and direct-capture cross-section measurements was used to investigate whether the deep electronic states present in the layers originated from point or extended defects. There was good agreement between data on deep electronic levels obtained from DLTS and TAS experiments. A number of hole traps have been detected; the majority of these levels show an unusual dependence of the DLTS signal on the fill pulse duration which is interpreted as possibly the levels are part of extended defects within the grain boundaries. In contrast, a defect level found in a more highly doped film, with an activation energy of -0.37 eV, exhibited behaviour characteristic of an isolated point defect, which we attribute to B-related centres in the bulk diamond, away from the dislocations.The second part of this thesis presents electrical measurements carried out at temperatures up to 450 K in order to study the electronic states associated with Mg in Mg-doped GaN films grown on sapphire by metalorganic vapour phase epitaxy, and to determine how these are affected by the threading dislocation density (TDD). Two different buffer layer schemes between the film and the sapphire substrate were used, giving rise to different TDDs in the GaN. Admittance spectroscopy of the films finds a single impurity-related acceptor level. It is observed in theses experiments that admittance spectroscopy detects no traps that can be attributed to extended defects, despite the fact that the dislocations are well-known to be active recombination centres. This unexpected finding is discussed in detail.

Deep-levels in gallium arsenide for device applications

NASA Astrophysics Data System (ADS)

McManis, Joseph Edward

Defects in semiconductors have been studied for over 40 years as a diagnostic of the quality of crystal growth. In this thesis, we investigate GaAs deep-levels specifically intended for devices. This thesis summarizes our efforts to characterize the near-infrared photoluminescence from deep-levels, study optical transitions via absorption, and fabricate and characterize deep-level light-emitting diodes (LEDs). This thesis also describes the first tunnel diodes which explicitly make use of GaAs deep-levels. Photoluminescence measurements of GaAs deep-levels showed a broad peak around a wavelength extending from 1.0--1.7 mum, which includes important wavelengths for fiber-optic communications (1.3--1.55 mum). Transmission measurements show the new result that very little of the radiative emission is self-absorbed. We measured the deep-level photoluminescence at several temperatures. We are also the first to report the internal quantum efficiency associated with the deep-level transitions. We have fabricated LEDs that, utilize the optical transitions of GaAs deep-levels. The electroluminescence spectra showed a broad peak from 1.0--1.7 mum at low currents, but the spectrum exhibited a blue-shift as the current was increased. To improve device performance, we designed an AlGaAs layer into the structure of the LEDs. The AlGaAs barrier layer acts as a resistive barrier so that the holes in the p-GaAs layer are swept away from underneath the gold p-contact. The AlGaAs layer also reduces the blue-shift by acting as a potential barrier so that only higher-energy holes are injected. We found that the LEDs with AlGaAs were brighter at long wavelengths, which was a significant improvement. Photoluminescence measurements show that the spectral blue-shift is not due to sample heating. We have developed a new physical model to explain the blue-shift: it is caused by Coloumb charging of the deep-centers. We have achieved the first tunnel diodes with which specifically utilize deep-levels in low-temperature-grown (LTG) GaAs. Our devices show the largest ever peak current density in a GaAs tunnel diode at room temperature. Our devices also show significant room-temperature peak-to-valley current ratios. The shape of the current-voltage characteristic and the properties of the optical emission enable us to determine the peak and valley transport mechanisms.

Model for determination of mid-gap states in amorphous metal oxides from thin film transistors

NASA Astrophysics Data System (ADS)

Bubel, S.; Chabinyc, M. L.

2013-06-01

The electronic density of states in metal oxide semiconductors like amorphous zinc oxide (a-ZnO) and its ternary and quaternary oxide alloys with indium, gallium, tin, or aluminum are different from amorphous silicon, or disordered materials such as pentacene, or P3HT. Many ZnO based semiconductors exhibit a steep decaying density of acceptor tail states (trap DOS) and a Fermi level (EF) close to the conduction band energy (EC). Considering thin film transistor (TFT) operation in accumulation mode, the quasi Fermi level for electrons (Eq) moves even closer to EC. Classic analytic TFT simulations use the simplification EC-EF> `several'kT and cannot reproduce exponential tail states with a characteristic energy smaller than 1/2 kT. We demonstrate an analytic model for tail and deep acceptor states, valid for all amorphous metal oxides and include the effect of trap assisted hopping instead of simpler percolation or mobility edge models, to account for the observed field dependent mobility.

Deep Change: Cases and Commentary on Schools and Programs of Successful Reform in High Stakes States. Research in Curriculum and Instruction

ERIC Educational Resources Information Center

Ponder, Gerald, Ed.; Strahan, David, Ed.

2005-01-01

This book presents cases of schools (Part One) and programs at the district level and beyond (Part Two) in which reform, while driven by high-stakes accountability, became larger and deeper through data-driven dialogue, culture change, organizational learning, and other elements of high performing cultures. Commentaries on cross-case patterns by…

Deep Space Quantum Link

NASA Astrophysics Data System (ADS)

Mohageg, M.; Strekalov, D.; Dolinar, S.; Shaw, M.; Yu, N.

2018-02-01

The Deep Space Quantum Link will test the effects of gravity on quantum systems, test the non-locality of quantum states at deep space distances, and perform long distance quantum teleportation to an Earth-based receiver.

A novel biomedical image indexing and retrieval system via deep preference learning.

PubMed

Pang, Shuchao; Orgun, Mehmet A; Yu, Zhezhou

2018-05-01

The traditional biomedical image retrieval methods as well as content-based image retrieval (CBIR) methods originally designed for non-biomedical images either only consider using pixel and low-level features to describe an image or use deep features to describe images but still leave a lot of room for improving both accuracy and efficiency. In this work, we propose a new approach, which exploits deep learning technology to extract the high-level and compact features from biomedical images. The deep feature extraction process leverages multiple hidden layers to capture substantial feature structures of high-resolution images and represent them at different levels of abstraction, leading to an improved performance for indexing and retrieval of biomedical images. We exploit the current popular and multi-layered deep neural networks, namely, stacked denoising autoencoders (SDAE) and convolutional neural networks (CNN) to represent the discriminative features of biomedical images by transferring the feature representations and parameters of pre-trained deep neural networks from another domain. Moreover, in order to index all the images for finding the similarly referenced images, we also introduce preference learning technology to train and learn a kind of a preference model for the query image, which can output the similarity ranking list of images from a biomedical image database. To the best of our knowledge, this paper introduces preference learning technology for the first time into biomedical image retrieval. We evaluate the performance of two powerful algorithms based on our proposed system and compare them with those of popular biomedical image indexing approaches and existing regular image retrieval methods with detailed experiments over several well-known public biomedical image databases. Based on different criteria for the evaluation of retrieval performance, experimental results demonstrate that our proposed algorithms outperform the state-of-the-art techniques in indexing biomedical images. We propose a novel and automated indexing system based on deep preference learning to characterize biomedical images for developing computer aided diagnosis (CAD) systems in healthcare. Our proposed system shows an outstanding indexing ability and high efficiency for biomedical image retrieval applications and it can be used to collect and annotate the high-resolution images in a biomedical database for further biomedical image research and applications. Copyright © 2018 Elsevier B.V. All rights reserved.

Scaling and intermittency of brain events as a manifestation of consciousness

NASA Astrophysics Data System (ADS)

Paradisi, P.; Allegrini, P.; Gemignani, A.; Laurino, M.; Menicucci, D.; Piarulli, A.

2013-01-01

We discuss the critical brain hypothesis and its relationship with intermittent renewal processes displaying power-law decay in the distribution of waiting times between two consecutive renewal events. In particular, studies on complex systems in a "critical" condition show that macroscopic variables, integrating the activities of many individual functional units, undergo fluctuations with an intermittent serial structure characterized by avalanches with inverse-power-law (scale-free) distribution densities of sizes and inter-event times. This condition, which is denoted as "fractal intermittency", was found in the electroencephalograms of subjects observed during a resting state wake condition. It remained unsolved whether fractal intermittency correlates with the stream of consciousness or with a non-task-driven default mode activity, also present in non-conscious states, like deep sleep. After reviewing a method of scaling analysis of intermittent systems based of eventdriven random walks, we show that during deep sleep fractal intermittency breaks down, and reestablishes during REM (Rapid Eye Movement) sleep, with essentially the same anomalous scaling of the pre-sleep wake condition. From the comparison of the pre-sleep wake, deep sleep and REM conditions we argue that the scaling features of intermittent brain events are related to the level of consciousness and, consequently, could be exploited as a possible indicator of consciousness in clinical applications.

Advanced development of double-injection, deep-impurity semiconductor switches

NASA Technical Reports Server (NTRS)

Hanes, M. H.

1987-01-01

Deep-impurity, double-injection devices, commonly refered to as (DI) squared devices, represent a class of semiconductor switches possessing a very high degree of tolerance to electron and neutron irradiation and to elevated temperature operation. These properties have caused them to be considered as attractive candidates for space power applications. The design, fabrication, and testing of several varieties of (DI) squared devices intended for power switching are described. All of these designs were based upon gold-doped silicon material. Test results, along with results of computer simulations of device operation, other calculations based upon the assumed mode of operation of (DI) squared devices, and empirical information regarding power semiconductor device operation and limitations, have led to the conculsion that these devices are not well suited to high-power applications. When operated in power circuitry configurations, they exhibit high-power losses in both the off-state and on-state modes. These losses are caused by phenomena inherent to the physics and material of the devices and cannot be much reduced by device design optimizations. The (DI) squared technology may, however, find application in low-power functions such as sensing, logic, and memory, when tolerance to radiation and temperature are desirable (especially is device performance is improved by incorporation of deep-level impurities other than gold.

A Reconstruction of Sea Surface Temperature Gradients and an Assessment of the Suspected Presence of Continental Ice During the Cold Mid-Paleocene (61-57 Ma)

NASA Astrophysics Data System (ADS)

Bijl, P.; Cramwinckel, M.; Frieling, J.; Peterse, F.

2016-12-01

The early Eocene `hothouse' climate experienced paratropical vegetation on high latitudes and high (>1100 ppmv) atmospheric CO2 concentrations. It is generally considered as analogous to the endmember climate state should we use up all available fossil fuels. However, we do not know exactly through which processes this long-term warm episode came to be nor do we understand what the initial climate state was at the onset of this long-term climate. Deep-sea warming towards early Eocene hothouse conditions started in the mid-Paleocene, ending a 2 Myr time interval of relatively cold deep ocean temperatures. Reconstructed pCO2 concentrations of the mid-Paleocene seem to have been close to those of present-day, although data is scarce. The mid-Paleocene is notoriously sparsely represented in shelf sedimentary records, as most records show a conspicuous hiatus between 58 and 60 Mys. This gives the suggestion of a major global low in sea level, which is inconsistent with estimates of global ocean spreading rates, which suggest a relatively high sea level on long time scales for the Cretaceous-early Paleogene. The cold deep-sea temperatures, the conspicuously low sea level and low atmospheric CO2 during the mid-Paleocene have stimulated suggestions of the presence of major ice sheets on the poles, yet the absence of any trace for continental ice, either direct ice-proximal evidence or from benthic foraminiferal oxygen isotope records, calls the presence of such ice sheets into question. I will present a number of high resolution sea surface temperature records (based mostly on organic geochemical biomarker proxies) which start to reveal a latitudinal temperature gradient for the mid-Paleocene. Reconstructions come from shelf sediments from Tasmania, Australia, Tanzania, Tropical Atlantic Ocean, New Jersey). With these new records, I put Paleogene climate evolution into context. I will further present a review of shelf sedimentary records across the mid-paleocene to assess the sea level variability in this time, to verifiy the suspected presence of continental ice, and speculate on possible alternative mechanisms for sea level change.

Sea-level and deep-sea-temperature variability over the past 5.3 million years.

PubMed

Rohling, E J; Foster, G L; Grant, K M; Marino, G; Roberts, A P; Tamisiea, M E; Williams, F

2014-04-24

Ice volume (and hence sea level) and deep-sea temperature are key measures of global climate change. Sea level has been documented using several independent methods over the past 0.5 million years (Myr). Older periods, however, lack such independent validation; all existing records are related to deep-sea oxygen isotope (δ(18)O) data that are influenced by processes unrelated to sea level. For deep-sea temperature, only one continuous high-resolution (Mg/Ca-based) record exists, with related sea-level estimates, spanning the past 1.5 Myr. Here we present a novel sea-level reconstruction, with associated estimates of deep-sea temperature, which independently validates the previous 0-1.5 Myr reconstruction and extends it back to 5.3 Myr ago. We find that deep-sea temperature and sea level generally decreased through time, but distinctly out of synchrony, which is remarkable given the importance of ice-albedo feedbacks on the radiative forcing of climate. In particular, we observe a large temporal offset during the onset of Plio-Pleistocene ice ages, between a marked cooling step at 2.73 Myr ago and the first major glaciation at 2.15 Myr ago. Last, we tentatively infer that ice sheets may have grown largest during glacials with more modest reductions in deep-sea temperature.

Defect states and charge transport in quantum dot solids

DOE PAGES

Brawand, Nicholas P.; Goldey, Matthew B.; Vörös, Márton; ...

2017-01-16

Defects at the surface of semiconductor quantum dots (QDs) give rise to electronic states within the gap, which are detrimental to charge transport properties of QD devices. We investigated charge transport in silicon quantum dots with deep and shallow defect levels, using ab initio calculations and constrained density functional theory. We found that shallow defects may be more detrimental to charge transport than deep ones, with associated transfer rates differing by up to 5 orders of magnitude for the small dots (1-2 nm) considered here. Hence, our results indicate that the common assumption, that the ability of defects to trapmore » charges is determined by their position in the energy gap of the QD, is too simplistic, and our findings call for a reassessment of the role played by shallow defects in QD devices. Altogether, our results highlight the key importance of taking into account the atomistic structural properties of QD surfaces when investigating transport properties.« less

Prolongation of SMAP to Spatiotemporally Seamless Coverage of Continental U.S. Using a Deep Learning Neural Network

NASA Astrophysics Data System (ADS)

Fang, Kuai; Shen, Chaopeng; Kifer, Daniel; Yang, Xiao

2017-11-01

The Soil Moisture Active Passive (SMAP) mission has delivered valuable sensing of surface soil moisture since 2015. However, it has a short time span and irregular revisit schedules. Utilizing a state-of-the-art time series deep learning neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 moisture product with atmospheric forcings, model-simulated moisture, and static physiographic attributes as inputs. The system removes most of the bias with model simulations and improves predicted moisture climatology, achieving small test root-mean-square errors (<0.035) and high-correlation coefficients >0.87 for over 75% of Continental United States, including the forested southeast. As the first application of LSTM in hydrology, we show the proposed network avoids overfitting and is robust for both temporal and spatial extrapolation tests. LSTM generalizes well across regions with distinct climates and environmental settings. With high fidelity to SMAP, LSTM shows great potential for hindcasting, data assimilation, and weather forecasting.

Multi-Atlas Based Segmentation of Brainstem Nuclei from MR Images by Deep Hyper-Graph Learning.

PubMed

Dong, Pei; Guo, Yangrong; Gao, Yue; Liang, Peipeng; Shi, Yonghong; Wang, Qian; Shen, Dinggang; Wu, Guorong

2016-10-01

Accurate segmentation of brainstem nuclei (red nucleus and substantia nigra) is very important in various neuroimaging applications such as deep brain stimulation and the investigation of imaging biomarkers for Parkinson's disease (PD). Due to iron deposition during aging, image contrast in the brainstem is very low in Magnetic Resonance (MR) images. Hence, the ambiguity of patch-wise similarity makes the recently successful multi-atlas patch-based label fusion methods have difficulty to perform as competitive as segmenting cortical and sub-cortical regions from MR images. To address this challenge, we propose a novel multi-atlas brainstem nuclei segmentation method using deep hyper-graph learning. Specifically, we achieve this goal in three-fold. First , we employ hyper-graph to combine the advantage of maintaining spatial coherence from graph-based segmentation approaches and the benefit of harnessing population priors from multi-atlas based framework. Second , besides using low-level image appearance, we also extract high-level context features to measure the complex patch-wise relationship. Since the context features are calculated on a tentatively estimated label probability map, we eventually turn our hyper-graph learning based label propagation into a deep and self-refining model. Third , since anatomical labels on some voxels (usually located in uniform regions) can be identified much more reliably than other voxels (usually located at the boundary between two regions), we allow these reliable voxels to propagate their labels to the nearby difficult-to-label voxels. Such hierarchical strategy makes our proposed label fusion method deep and dynamic. We evaluate our proposed label fusion method in segmenting substantia nigra (SN) and red nucleus (RN) from 3.0 T MR images, where our proposed method achieves significant improvement over the state-of-the-art label fusion methods.

Study of laser cooling in deep optical lattice: two-level quantum model

NASA Astrophysics Data System (ADS)

Prudnikov, O. N.; Il'enkov, R. Ya.; Taichenachev, A. V.; Yudin, V. I.; Rasel, E. M.

2018-01-01

We study a possibility of laser cooling of 24Mg atoms in deep optical lattice formed by intense off-resonant laser field in a presence of cooling field resonant to narrow (3s3s) 1 S 0 → (3s3p)3 P 1 (λ = 457 nm) optical transition. For description of laser cooling with taking into account quantum recoil effects we consider two quantum models. The first one is based on direct numerical solution of quantum kinetic equation for atom density matrix and the second one is simplified model based on decomposition of atom density matrix over vibration states in the lattice wells. We search cooling field intensity and detuning for minimum cooling energy and fast laser cooling.

Cryogenic Evaluation of an Advanced DC/DC Converter Module for Deep Space Applications

NASA Technical Reports Server (NTRS)

Elbuluk, Malik E.; Hammoud, Ahmad; Gerber, Scott S.; Patterson, Richard

2003-01-01

DC/DC converters are widely used in power management, conditioning, and control of space power systems. Deep space applications require electronics that withstand cryogenic temperature and meet a stringent radiation tolerance. In this work, the performance of an advanced, radiation-hardened (rad-hard) commercial DC/DC converter module was investigated at cryogenic temperatures. The converter was investigated in terms of its steady state and dynamic operations. The output voltage regulation, efficiency, terminal current ripple characteristics, and output voltage response to load changes were determined in the temperature range of 20 to -140 C. These parameters were obtained at various load levels and at different input voltages. The experimental procedures along with the results obtained on the investigated converter are presented and discussed.

Upper extremity deep venous thrombosis after port insertion: What are the risk factors?

PubMed

Tabatabaie, Omidreza; Kasumova, Gyulnara G; Kent, Tara S; Eskander, Mariam F; Fadayomi, Ayotunde B; Ng, Sing Chau; Critchlow, Jonathan F; Tawa, Nicholas E; Tseng, Jennifer F

2017-08-01

Totally implantable venous access devices (ports) are widely used, especially for cancer chemotherapy. Although their use has been associated with upper extremity deep venous thrombosis, the risk factors of upper extremity deep venous thrombosis in patients with a port are not studied adequately. The Healthcare Cost and Utilization Project's Florida State Ambulatory Surgery and Services Database was queried between 2007 and 2011 for patients who underwent outpatient port insertion, identified by Current Procedural Terminology code. Patients were followed in the State Ambulatory Surgery and Services Database, State Inpatient Database, and State Emergency Department Database for upper extremity deep venous thrombosis occurrence. The cohort was divided into a test cohort and a validation cohort based on the year of port placement. A multivariable logistic regression model was developed to identify risk factors for upper extremity deep venous thrombosis in patients with a port. The model then was tested on the validation cohort. Of the 51,049 patients in the derivation cohort, 926 (1.81%) developed an upper extremity deep venous thrombosis. On multivariate analysis, independently significant predictors of upper extremity deep venous thrombosis included age <65 years (odds ratio = 1.22), Elixhauser score of 1 to 2 compared with zero (odds ratio = 1.17), end-stage renal disease (versus no kidney disease; odds ratio = 2.63), history of any deep venous thrombosis (odds ratio = 1.77), all-cause 30-day revisit (odds ratio = 2.36), African American race (versus white; odds ratio = 1.86), and other nonwhite races (odds ratio = 1.35). Additionally, compared with genitourinary malignancies, patients with gastrointestinal (odds ratio = 1.55), metastatic (odds ratio = 1.76), and lung cancers (odds ratio = 1.68) had greater risks of developing an upper extremity deep venous thrombosis. This study identified major risk factors of upper extremity deep venous thrombosis. Further studies are needed to evaluate the appropriateness of thromboprophylaxis in patients at greater risk of upper extremity deep venous thrombosis. Copyright © 2017 Elsevier Inc. All rights reserved.

Deep Learning-Based Banknote Fitness Classification Using the Reflection Images by a Visible-Light One-Dimensional Line Image Sensor

PubMed Central

Pham, Tuyen Danh; Nguyen, Dat Tien; Kim, Wan; Park, Sung Ho; Park, Kang Ryoung

2018-01-01

In automatic paper currency sorting, fitness classification is a technique that assesses the quality of banknotes to determine whether a banknote is suitable for recirculation or should be replaced. Studies on using visible-light reflection images of banknotes for evaluating their usability have been reported. However, most of them were conducted under the assumption that the denomination and input direction of the banknote are predetermined. In other words, a pre-classification of the type of input banknote is required. To address this problem, we proposed a deep learning-based fitness-classification method that recognizes the fitness level of a banknote regardless of the denomination and input direction of the banknote to the system, using the reflection images of banknotes by visible-light one-dimensional line image sensor and a convolutional neural network (CNN). Experimental results on the banknote image databases of the Korean won (KRW) and the Indian rupee (INR) with three fitness levels, and the Unites States dollar (USD) with two fitness levels, showed that our method gives better classification accuracy than other methods. PMID:29415447

Convective Propagation Characteristics Using a Simple Representation of Convective Organization

NASA Astrophysics Data System (ADS)

Neale, R. B.; Mapes, B. E.

2016-12-01

Observed equatorial wave propagation is intimately linked to convective organization and it's coupling to features of the larger-scale flow. In this talk we a use simple 4 level model to accommodate vertical modes of a mass flux convection scheme (shallow, mid-level and deep). Two paradigms of convection are used to represent convective processes. One that has only both random (unorganized) diagnosed fluctuations of convective properties and one with organized fluctuations of convective properties that are amplified by previously existing convection and has an explicit moistening impact on the local convecting environment We show a series of model simulations in single-column, 2D and 3D configurations, where the role of convective organization in wave propagation is shown to be fundamental. For the optimal choice of parameters linking organization to local atmospheric state, a broad array of convective wave propagation emerges. Interestingly the key characteristics of propagating modes are the low-level moistening followed by deep convection followed by mature 'large-scale' heating. This organization structure appears to hold firm across timescales from 5-day wave disturbances to MJO-like wave propagation.

40 CFR 721.85 - Disposal.

Code of Federal Regulations, 2010 CFR

2010-07-01

..., State, or local laws and regulations. (1) Incineration. (2) Landfill. (3) Deep well injection. (d... by the following. This provision does not supercede any applicable Federal, State, or local laws and regulations. (1) Incineration. (2) Landfill. (3) Deep well injection. (b) Disposal of the process stream...

Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2018-05-02

RNA-binding proteins (RBPs) take over 5∼10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using pattern learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN run 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs. https://github.com/xypan1232/iDeepE. xypan172436@gmail.com or hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.

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

PubMed

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.

Analysis of Deep and Shallow Traps in Semi-Insulating CdZnTe

DOE PAGES

Kim, Kihyun; Yoon, Yongsu; James, Ralph B.

2018-03-13

Trap levels which are deep or shallow play an important role in the electrical and the optical properties of a semiconductor; thus, a trap level analysis is very important in most semiconductor devices. Deep-level defects in CdZnTe are essential in Fermi level pinning at the middle of the bandgap and are responsible for incomplete charge collection and polarization effects. However, a deep level analysis in semi-insulating CdZnTe (CZT) is very difficult. Theoretical capacitance calculation for a metal/insulator/CZT (MIS) device with deep-level defects exhibits inflection points when the donor/acceptor level crosses the Fermi level in the surface-charge layer (SCL). Three CZTmore » samples with different resistivities, 2 × 10 4 (n-type), 2 × 10 6 (p-type), and 2 × 10 10 (p-type) Ω·cm, were used in fabricating the MIS devices. These devices showed several peaks in their capacitance measurements due to upward/downward band bending that depend on the surface potential. In conclusion, theoretical and experimental capacitance measurements were in agreement, except in the fully compensated case.« less

Analysis of Deep and Shallow Traps in Semi-Insulating CdZnTe

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

Kim, Kihyun; Yoon, Yongsu; James, Ralph B.

Trap levels which are deep or shallow play an important role in the electrical and the optical properties of a semiconductor; thus, a trap level analysis is very important in most semiconductor devices. Deep-level defects in CdZnTe are essential in Fermi level pinning at the middle of the bandgap and are responsible for incomplete charge collection and polarization effects. However, a deep level analysis in semi-insulating CdZnTe (CZT) is very difficult. Theoretical capacitance calculation for a metal/insulator/CZT (MIS) device with deep-level defects exhibits inflection points when the donor/acceptor level crosses the Fermi level in the surface-charge layer (SCL). Three CZTmore » samples with different resistivities, 2 × 10 4 (n-type), 2 × 10 6 (p-type), and 2 × 10 10 (p-type) Ω·cm, were used in fabricating the MIS devices. These devices showed several peaks in their capacitance measurements due to upward/downward band bending that depend on the surface potential. In conclusion, theoretical and experimental capacitance measurements were in agreement, except in the fully compensated case.« less

Deep Web video

ScienceCinema

None Available

2018-02-06

To make the web work better for science, OSTI has developed state-of-the-art technologies and services including a deep web search capability. The deep web includes content in searchable databases available to web users but not accessible by popular search engines, such as Google. This video provides an introduction to the deep web search engine.

Hybrid AlGaN-SiC Avalanche Photodiode for Deep-UV Photon Detection

NASA Technical Reports Server (NTRS)

Aslam, Shahid; Herrero, Federico A.; Sigwarth, John; Goldsman, Neil; Akturk, Akin

2010-01-01

The proposed device is capable of counting ultraviolet (UV) photons, is compatible for inclusion into space instruments, and has applications as deep- UV detectors for calibration systems, curing systems, and crack detection. The device is based on a Separate Absorption and Charge Multiplication (SACM) structure. It is based on aluminum gallium nitride (AlGaN) absorber on a silicon carbide APD (avalanche photodiode). The AlGaN layer absorbs incident UV photons and injects photogenerated carriers into an underlying SiC APD that is operated in Geiger mode and provides current multiplication via avalanche breakdown. The solid-state detector is capable of sensing 100-to-365-nanometer wavelength radiation at a flux level as low as 6 photons/pixel/s. Advantages include, visible-light blindness, operation in harsh environments (e.g., high temperatures), deep-UV detection response, high gain, and Geiger mode operation at low voltage. Furthermore, the device can also be designed in array formats, e.g., linear arrays or 2D arrays (micropixels inside a superpixel).

Classification of time-series images using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Wide Bandgap Extrinsic Photoconductive Switches

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

Sullivan, James S.

2013-07-03

Semi-insulating Gallium Nitride, 4H and 6H Silicon Carbide are attractive materials for compact, high voltage, extrinsic, photoconductive switches due to their wide bandgap, high dark resistance, high critical electric field strength and high electron saturation velocity. These wide bandgap semiconductors are made semi-insulating by the addition of vanadium (4H and 6HSiC) and iron (2H-GaN) impurities that form deep acceptors. These deep acceptors trap electrons donated from shallow donor impurities. The electrons can be optically excited from these deep acceptor levels into the conduction band to transition the wide bandgap semiconductor materials from a semi-insulating to a conducting state. Extrinsic photoconductivemore » switches with opposing electrodes have been constructed using vanadium compensated 6H-SiC and iron compensated 2H-GaN. These extrinsic photoconductive switches were tested at high voltage and high power to determine if they could be successfully used as the closing switch in compact medical accelerators.« less

Theory of Semiconducting Superlattices and Microstructures

DTIC Science & Technology

1992-03-01

theory elucidated the various factors affecting deep levels, sets forth the conditions for obtaining shallow-deep transitions, and predicts that Si (a...theory elucidates the various factors affecting deep levels, sets forth the conditions for obtaining shallow-deep transitions, and predicts that Si (a...ondenotes the anion vacancy, which can be thought any quantitative theoretical factor are theof as originating from Column-O of the Period strengths of

A deep learning framework to discern and count microscopic nematode eggs.

PubMed

Akintayo, Adedotun; Tylka, Gregory L; Singh, Asheesh K; Ganapathysubramanian, Baskar; Singh, Arti; Sarkar, Soumik

2018-06-14

In order to identify and control the menace of destructive pests via microscopic image-based identification state-of-the art deep learning architecture is demonstrated on the parasitic worm, the soybean cyst nematode (SCN), Heterodera glycines. Soybean yield loss is negatively correlated with the density of SCN eggs that are present in the soil. While there has been progress in automating extraction of egg-filled cysts and eggs from soil samples counting SCN eggs obtained from soil samples using computer vision techniques has proven to be an extremely difficult challenge. Here we show that a deep learning architecture developed for rare object identification in clutter-filled images can identify and count the SCN eggs. The architecture is trained with expert-labeled data to effectively build a machine learning model for quantifying SCN eggs via microscopic image analysis. We show dramatic improvements in the quantification time of eggs while maintaining human-level accuracy and avoiding inter-rater and intra-rater variabilities. The nematode eggs are correctly identified even in complex, debris-filled images that are often difficult for experts to identify quickly. Our results illustrate the remarkable promise of applying deep learning approaches to phenotyping for pest assessment and management.

Upstream ecological risks for overweight and obesity among African American youth in a rural town in the Deep South, 2007.

PubMed

Scott, Alison J; Wilson, Rebecca F

2011-01-01

Few studies have focused on overweight and obesity among rural African American youth in the Deep South, despite disproportionately high rates in this group. In addition, few studies have been conducted to elucidate how these disparities are created and perpetuated within rural communities in this region. This descriptive study explores community-based risks for overweight and obesity among African American youth in a rural town in the Deep South. We used ecological theory in conjunction with embodiment theory to explore how upstream ecological factors may contribute to risk of overweight and obesity for African American youth in a rural town in the Deep South. We conducted and analyzed in-depth interviews with African American community members who interact with youth in varying contexts (home, school, church, community). Participants most commonly stated that race relations, poverty, and the built environment were barriers to maintaining a healthy weight. Findings suggested the need for rural, community-based interventions that target obesity at multiple ecological levels and incorporate issues related to race, poverty, and the built environment. More research is needed to determine how disparities in obesity are created and perpetuated in specific community contexts.

Earth Science Research at the Homestake Deep Underground Science and Engineering Laboratory

NASA Astrophysics Data System (ADS)

Roggenthen, W.; Wang, J.

2004-12-01

The Homestake Mine in South Dakota ceased gold production in 2002 and was sealed for entry in 2003. The announcement of mine closure triggered the revival of a national initiative to establish a deep underground facility, currently known as the Deep Underground Science and Engineering Laboratory (DUSEL). The National Science Foundation announced that solicitations were to be issued in 2004 and 2005, with the first one (known as S-1) issued in June, 2004. The focus of S-1 is on site non-specific technical requirements to define the scientific program at DUSEL. Earth scientists and physicists participated in an S-1 workshop at Berkeley in August, 2004. This abstract presents the prospects of the Homestake Mine to accommodate the earth science scientific programs defined at the S-1 workshop. The Homestake Mine has hundreds of kilometers of drifts over fifty levels accessible (upon mine reopening) for water evaluation, seepage quantification, seismic monitoring, geophysical imaging, geological mapping, mineral sampling, ecology and geo-microbiology. The extensive network of drifts, ramps, and vertical shafts allows installation of 10-kilometer-scale seismograph and electromagnetic networks. Ramps connecting different levels, typically separated by 150 ft, could be instrumented for flow and transport studies, prior to implementation of coupled thermal-hydro-chemical-mechanical-biological processes testing. Numerous large rooms are available for ecological and introduced-material evaluations. Ideas for installing instruments in cubic kilometers of rock mass can be realized over multiple levels. Environmental assessment, petroleum recovery, carbon sequestration were among the applications discussed in the S-1 workshop. If the Homestake Mine can be expediently reopened, earth scientists are ready to perform important tests with a phased approach. The drifts and ramps directly below the large open pit could be the first area for shallow testing. The 4,850 ft level is the next target area, which has a large lateral extent. Geophysical sensor stations could be installed at this level, together with stations along two main shafts accessing this level, and one winze below. After dewatering, rock mechanics and geotechnical engineering investigators could actively participate in room siting and excavation, at depths up to 8,000 ft. Geochemistry and geo-microbiology scientists would prefer additional drilling in deep zones beyond the mining and flooding perturbations. Additional earth science programs are being developed for the Homestake Mine, utilizing multiple levels and shafts. Many physics experiments require a site "as deep as possible" and special conditions to reduce background and cosmic rays. The Homestake Mine offers a very deep site and a vast amount of data and knowledge associated with its 125 years of mining operation. The cores from exploratory drilling into a mechanical strong unit, the Yates Formation, are available for scientific and engineering evaluations. A team from many institutions is being formed by Kevin Lesko, a neutrino scientist with experience in detecting neutrino oscillations with deep detectors in Canada and Japan. It is time for the United States to establish a DUSEL deep and large enough for next-generation physics and earth science long-term experiments. The Homestake Mine has these necessary attributes. The collaboration welcomes participation and contribution from scientists and engineers in the physics and earth science community for multi-disciplinary research during and after the restoration and conversion of the Homestake Mine.

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

NASA Astrophysics Data System (ADS)

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.

Quantum Simulation of the Quantum Rabi Model in a Trapped Ion

NASA Astrophysics Data System (ADS)

Lv, Dingshun; An, Shuoming; Liu, Zhenyu; Zhang, Jing-Ning; Pedernales, Julen S.; Lamata, Lucas; Solano, Enrique; Kim, Kihwan

2018-04-01

The quantum Rabi model, involving a two-level system and a bosonic field mode, is arguably the simplest and most fundamental model describing quantum light-matter interactions. Historically, due to the restricted parameter regimes of natural light-matter processes, the richness of this model has been elusive in the lab. Here, we experimentally realize a quantum simulation of the quantum Rabi model in a single trapped ion, where the coupling strength between the simulated light mode and atom can be tuned at will. The versatility of the demonstrated quantum simulator enables us to experimentally explore the quantum Rabi model in detail, including a wide range of otherwise unaccessible phenomena, as those happening in the ultrastrong and deep strong-coupling regimes. In this sense, we are able to adiabatically generate the ground state of the quantum Rabi model in the deep strong-coupling regime, where we are able to detect the nontrivial entanglement between the bosonic field mode and the two-level system. Moreover, we observe the breakdown of the rotating-wave approximation when the coupling strength is increased, and the generation of phonon wave packets that bounce back and forth when the coupling reaches the deep strong-coupling regime. Finally, we also measure the energy spectrum of the quantum Rabi model in the ultrastrong-coupling regime.

Photoinduced current transient spectroscopy of deep levels and transport mechanisms in iron-doped GaN thin films grown by low pressure-metalorganic vapor phase epitaxy

NASA Astrophysics Data System (ADS)

Muret, P.; Pernot, J.; Azize, M.; Bougrioua, Z.

2007-09-01

Electrical transport and deep levels are investigated in GaN:Fe layers epitaxially grown on sapphire by low pressure metalorganic vapor phase epitaxy. Photoinduced current transient spectroscopy and current detected deep level spectroscopy are performed between 200 and 650 K on three Fe-doped samples and an undoped sample. A detailed study of the detected deep levels assigns dominant centers to a deep donor 1.39 eV below the conduction band edge EC and to a deep acceptor 0.75 eV above the valence band edge EV at low electric field. A strong Poole-Frenkel effect is evidenced for the donor. Schottky diodes characteristics and transport properties in the bulk GaN:Fe layer containing a homogenous concentration of 1019 Fe/cm3 are typical of a compensated semiconductor. They both indicate that the bulk Fermi level is located typically 1.4 eV below EC, in agreement with the neutrality equation and dominance of the deep donor concentration. This set of results demonstrates unambiguously that electrical transport in GaN:Fe is governed by both types, either donor or acceptor, of the iron impurity, either substitutional in gallium sites or associated with other defects.

Defects in mercury-cadmium telluride heteroepitaxial structures grown by molecular-beam epitaxy on silicon substrates

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

Mynbaev, K. D., E-mail: mynkad@mail.ioffe.ru; Zablotsky, S. V.; Shilyaev, A. V.

Defects in mercury-cadmium-telluride heteroepitaxial structures (with 0.3 to 0.4 molar fraction of cadmium telluride) grown by molecular-beam epitaxy on silicon substrates are studied. The low-temperature photoluminescence method reveals that there are comparatively deep levels with energies of 50 to 60 meV and shallower levels with energies of 20 to 30 meV in the band gap. Analysis of the temperature dependence of the minority carrier lifetime demonstrates that this lifetime is controlled by energy levels with an energy of ∼30 meV. The possible relationship between energy states and crystal-structure defects is discussed.

IBS-like Symptoms in Patients with Ulcerative Colitis in Deep Remission Are Associated with Increased Levels of Serum Cytokines and Poor Psychological Well-being.

PubMed

Jonefjäll, Börje; Öhman, Lena; Simrén, Magnus; Strid, Hans

2016-11-01

Gastrointestinal symptoms (GI) compatible with irritable bowel syndrome (IBS) are common in patients with ulcerative colitis (UC) in remission. The causes of these symptoms remain to be clarified. Our aim was to investigate prevalence and factors associated with IBS-like symptoms in patients with UC in deep remission. We included 298 patients with UC and used Mayo score, sigmoidoscopy, and fecal calprotectin to define deep remission versus active disease. Presence of IBS-like symptoms according to the Rome III criteria, severity of GI, extraintestinal and psychological symptoms, stress levels, and quality of life were measured with validated questionnaires. Serum cytokines and high-sensitive C-reactive peptide were determined. The criteria for deep remission was fulfilled by 132 patients (44%) and 24 of these fulfilled the Rome III criteria for IBS (18%). Patients with UC in deep remission with IBS-like symptoms had comparable levels of GI symptoms, non-GI somatic symptoms, and quality of life as patients with active UC. The patients with UC in deep remission with IBS-like symptoms had similar levels of fecal calprotectin as patients in deep remission without IBS-like symptoms (18 versus 31 μg/g, P = 0.11), but higher levels of serum cytokines (interleukin [IL]-1β, IL-6, IL-13, IL-10 and IL-8, P < 0.05) and higher levels of anxiety (P < 0.001), depression (P = 0.02) and perceived stress (P = 0.03). IBS-like symptoms in patients with UC in deep remission are common, but not as prevalent as previously reported. Poor psychological well-being and increased serum cytokine levels, but not colonic low-grade inflammation, were associated with IBS-like symptoms.

The Deep Underground Science and Engineering Laboratory at Homestake

NASA Astrophysics Data System (ADS)

Lesko, Kevin T.

2008-11-01

The National Science Foundation and the international underground science community are well into establishing a world-class, multidisciplinary Deep Underground Science and Engineering Laboratory (DUSEL) at the former Homestake mine in Lead South Dakota. The NSF's review committee, following the first two NSF solicitations, selected the Homestake Proposal and site as the prime location to be developed into an international research facility. Homestake DUSEL will provide much needed underground research space to help relieve the worldwide shortage, particularly at great depth, and will develop research campuses at several different depths to satisfy the research requirements for the coming decades. The State of South Dakota has demonstrated remarkable support for the project and has secured the site with the transfer from the Homestake Mining Corp. The State, through its Science and Technology Authority with state funds and those of a philanthropic donor has initiated rehabilitation of the surface and underground infrastructure including the Ross and Yates hoists accessing the 4850 Level (feet below ground, 4100 to 4200 mwe). The scientific case for DUSEL and the progress in establishing the preliminary design of the facility and the associated suite of experiments to be funded along with the facility by the NSF are presented.

Levels-of-processing effects on a task of olfactory naming.

PubMed

Royet, Jean-Pierre; Koenig, Olivier; Paugam-Moisy, Helene; Puzenat, Didier; Chasse, Jean-Luc

2004-02-01

The effects of odor processing were investigated at various analytical levels, from simple sensory analysis to deep or semantic analysis, on a subsequent task of odor naming. Students (106 women, 23.6 +/- 5.5 yr. old; 65 men, 25.1 +/- 7.1 yr. old) were tested. The experimental procedure included two successive sessions, a first session to characterize a set of 30 odors with criteria that used various depths of processing and a second session to name the odors as quickly as possible. Four processing conditions rated the odors using descriptors before naming the odor. The control condition did not rate the odors before naming. The processing conditions were based on lower-level olfactory judgments (superficial processing), higher-level olfactory-gustatory-somesthetic judgments (deep processing), and higher-level nonolfactory judgments (Deep-Control processing, with subjects rating odors with auditory and visual descriptors). One experimental condition successively grouped lower- and higher-level olfactory judgments (Superficial-Deep processing). A naming index which depended on response accuracy and the subjects' response time were calculated. Odor naming was modified for 18 out of 30 odorants as a function of the level of processing required. For 94.5% of significant variations, the scores for odor naming were higher following those tasks for which it was hypothesized that the necessary olfactory processing was carried out at a deeper level. Performance in the naming task was progressively improved as follows: no rating of odors, then superficial, deep-control, deep, and superficial-deep processings. These data show that the deepest olfactory encoding was later associated with progressively higher performance in naming.

Characterisation of retention properties of charge-trapping memory cells at low temperatures

NASA Astrophysics Data System (ADS)

Yurchuk, E.; Bollmann, J.; Mikolajick, T.

2009-09-01

The density of states of deep level centers in silicon oxynitride layer of SONOS memory cells are calculated from temperature dependent retention measurement. The dominating charge loss mechanisms are direct trap-to-band tunneling (TB) and thermally stimulated emission (TE). Retention measurements at low temperatures (80 - 300K) will be dominated by TE from more "shallow" traps with energies below 1eV and by TB. Taking into account both independent and rival processes the density of states could be calculated self consisting. The results are in excellent agreement with elsewhere published data.

Deep learning for predicting the monsoon over the homogeneous regions of India

NASA Astrophysics Data System (ADS)

Saha, Moumita; Mitra, Pabitra; Nanjundiah, Ravi S.

2017-06-01

Indian monsoon varies in its nature over the geographical regions. Predicting the rainfall not just at the national level, but at the regional level is an important task. In this article, we used a deep neural network, namely, the stacked autoencoder to automatically identify climatic factors that are capable of predicting the rainfall over the homogeneous regions of India. An ensemble regression tree model is used for monsoon prediction using the identified climatic predictors. The proposed model provides forecast of the monsoon at a long lead time which supports the government to implement appropriate policies for the economic growth of the country. The monsoon of the central, north-east, north-west, and south-peninsular India regions are predicted with errors of 4.1%, 5.1%, 5.5%, and 6.4%, respectively. The identified predictors show high skill in predicting the regional monsoon having high variability. The proposed model is observed to be competitive with the state-of-the-art prediction models.

Injection deep level transient spectroscopy: An improved method for measuring capture rates of hot carriers in semiconductors

DOE PAGES

Fleming, R. M.; Seager, C. H.; Lang, D. V.; ...

2015-07-02

In this study, an improved method for measuring the cross sections for carrier trapping at defects in semiconductors is described. This method, a variation of deep level transient spectroscopy(DLTS) used with bipolar transistors, is applied to hot carrier trapping at vacancy-oxygen, carbon-oxygen, and three charge states of divacancy centers (V 2) in n- and p-type silicon. Unlike standard DLTS, we fill traps by injecting carriers into the depletion region of a bipolar transistor diode using a pulse of forward bias current applied to the adjacent diode. We show that this technique is capable of accurately measuring a wide range ofmore » capture cross sections at varying electric fields due to the control of the carrier density it provides. Because this technique can be applied to a variety of carrier energy distributions, it should be valuable in modeling the effect of radiation-induced generation-recombination currents in bipolar devices.« less

Deep convolutional networks for pancreas segmentation in CT imaging

NASA Astrophysics Data System (ADS)

Roth, Holger R.; Farag, Amal; Lu, Le; Turkbey, Evrim B.; Summers, Ronald M.

2015-03-01

Automatic organ segmentation is an important prerequisite for many computer-aided diagnosis systems. The high anatomical variability of organs in the abdomen, such as the pancreas, prevents many segmentation methods from achieving high accuracies when compared to state-of-the-art segmentation of organs like the liver, heart or kidneys. Recently, the availability of large annotated training sets and the accessibility of affordable parallel computing resources via GPUs have made it feasible for "deep learning" methods such as convolutional networks (ConvNets) to succeed in image classification tasks. These methods have the advantage that used classification features are trained directly from the imaging data. We present a fully-automated bottom-up method for pancreas segmentation in computed tomography (CT) images of the abdomen. The method is based on hierarchical coarse-to-fine classification of local image regions (superpixels). Superpixels are extracted from the abdominal region using Simple Linear Iterative Clustering (SLIC). An initial probability response map is generated, using patch-level confidences and a two-level cascade of random forest classifiers, from which superpixel regions with probabilities larger 0.5 are retained. These retained superpixels serve as a highly sensitive initial input of the pancreas and its surroundings to a ConvNet that samples a bounding box around each superpixel at different scales (and random non-rigid deformations at training time) in order to assign a more distinct probability of each superpixel region being pancreas or not. We evaluate our method on CT images of 82 patients (60 for training, 2 for validation, and 20 for testing). Using ConvNets we achieve maximum Dice scores of an average 68% +/- 10% (range, 43-80%) in testing. This shows promise for accurate pancreas segmentation, using a deep learning approach and compares favorably to state-of-the-art methods.

What's in a country average? Wealth, gender, and regional inequalities in immunization in India.

PubMed

Pande, Rohini P; Yazbeck, Abdo S

2003-12-01

Recent attention to Millennium Development Goals by the international development community has led to the formation of targets to measure country-level achievements, including achievements on health status indicators such as childhood immunization. Using the example of immunization in India, this paper demonstrates the importance of disaggregating national averages for a better understanding of social disparities in health. Specifically, the paper uses data from the India National Family Health Survey 1992-93 to analyze socioeconomic, gender, urban-rural and regional inequalities in immunization in India for each of the 17 largest states. Results show that, on average, southern states have better immunization levels and lower immunization inequalities than many northern states. Wealth and regional inequalities are correlated with overall levels of immunization in a non-linear fashion. Gender inequalities persist in most states, including in the south, and seem unrelated to overall immunization or the levels of other inequalities measured here. This suggests that the gender differentials reflect deep-seated societal factors rather than health system issues per se. The disaggregated information and analysis used in this paper allows for setting more meaningful targets than country averages. Additionally, it helps policy makers and planners to understand programmatic constraints and needs by identifying disparities between sub-groups of the population, including strong and weak performers at the state and regional levels.

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks

NASA Astrophysics Data System (ADS)

Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

2016-01-01

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named “DeepMethyl” to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

Predicting DNA Methylation State of CpG Dinucleotide Using Genome Topological Features and Deep Networks.

PubMed

Wang, Yiheng; Liu, Tong; Xu, Dong; Shi, Huidong; Zhang, Chaoyang; Mo, Yin-Yuan; Wang, Zheng

2016-01-22

The hypo- or hyper-methylation of the human genome is one of the epigenetic features of leukemia. However, experimental approaches have only determined the methylation state of a small portion of the human genome. We developed deep learning based (stacked denoising autoencoders, or SdAs) software named "DeepMethyl" to predict the methylation state of DNA CpG dinucleotides using features inferred from three-dimensional genome topology (based on Hi-C) and DNA sequence patterns. We used the experimental data from immortalised myelogenous leukemia (K562) and healthy lymphoblastoid (GM12878) cell lines to train the learning models and assess prediction performance. We have tested various SdA architectures with different configurations of hidden layer(s) and amount of pre-training data and compared the performance of deep networks relative to support vector machines (SVMs). Using the methylation states of sequentially neighboring regions as one of the learning features, an SdA achieved a blind test accuracy of 89.7% for GM12878 and 88.6% for K562. When the methylation states of sequentially neighboring regions are unknown, the accuracies are 84.82% for GM12878 and 72.01% for K562. We also analyzed the contribution of genome topological features inferred from Hi-C. DeepMethyl can be accessed at http://dna.cs.usm.edu/deepmethyl/.

Cervical flexor muscle training reduces pain, anxiety, and depression levels in patients with chronic neck pain by a clinically important amount: A prospective cohort study.

PubMed

Nazari, Goris; Bobos, Pavlos; Billis, Evdokia; MacDermid, Joy C

2018-03-14

Neck pain is the fourth leading cause of disability in the United States and exerts an important socio-economic burden around the world. The aims of this study were to determine the effectiveness of deep and superficial flexor muscle training in addition to home-based exercises in reducing chronic neck pain and anxiety/depression levels. This was a prospective cohort study. Patients between 18 and 65 years old with chronic neck pain were eligible to participate if they had disability levels at least 5 out of 50 on the Neck Disability Index. Patients were divided into three groups: Group A received deep neck flexor and home-based exercises; Group B received superficial muscle and home-based exercises; and Group C received home-based exercises only. The Numeric Pain Rating Scale (NPRS), Neck Disability Index, and Hospital Anxiety and Depression Scale were administered at baseline and 7 weeks later. The highest improvements in pain intensity levels were observed in Group A with 4.75 (1.74) NPRS points, and the lowest were in Group C with 1.00 (1.10). The highest reductions in anxiety and depression levels were noted in Group A (2.80) and Group B (1.65), respectively. The highest improvements in pain intensity levels were observed among Groups A versus C with 2.80 (0.52) NPRS. The highest reductions in anxiety and depression levels were noted among Groups A versus C with 1.75 (1.10) points and Groups B versus C with 1.60 (0.90) points, respectively. Deep and superficial flexor muscle training along with home-based exercises is likely to reduce chronic neck pain and anxiety/depression levels by a clinically relevant amount. Future larger scaled randomized controlled trials are warranted to further support these findings. Copyright © 2018 John Wiley & Sons, Ltd.

Deep Restricted Kernel Machines Using Conjugate Feature Duality.

PubMed

Suykens, Johan A K

2017-08-01

The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

Impacts of Carrier Transport and Deep Level Defects on Delayed Cathodoluminescence in Droop-Mitigating InGaN/GaN LEDs

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

Zhao, Zhibo; Singh, Akshay; Chesin, Jordan

Prevalent droop mitigation strategies in InGaN-based LEDs require structural and/or compositional changes in the active region but are accompanied by a detrimental reduction in external quantum efficiency (EQE) due to increased Shockley-Read-Hall recombination. Understanding the optoelectronic impacts of structural modifications in InGaN/GaN quantum wells (QW) remains critical for emerging high-power LEDs. In this work, we use a combination of electron microscopy tools along with standard electrical characterization to investigate a wide range of low-droop InGaN/GaN QW designs. We find that chip-scale EQE is uncorrelated with extended well-width fluctuations observed in scanning transmission electron microscopy. Further, we observe delayed cathodoluminescence (CL)more » response from designs in which calculated band profiles suggest facile carrier escape from individual QWs. Samples with the slowest CL responses also exhibit the lowest EQEs and highest QW defect densities in deep level optical spectroscopy. We propose a model in which the electron beam (i) passivates deep level defect states and (ii) drives charge carrier accumulation and subsequent reduction of the built-in field across the multi-QW active region, resulting in delayed radiative recombination. Finally, we correlate CL rise dynamics with capacitance-voltage measurements and show that certain early-time components of the CL dynamics reflect the open circuit carrier population within one or more QWs.« less

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

Matlack, K. S.; Abramowitz, H.; Miller, I. S.

About 50 million gallons of high-level mixed waste is currently stored in underground tanks at the United States Department of Energy’s (DOE’s) Hanford site in the State of Washington. The Hanford Tank Waste Treatment and Immobilization Plant (WTP) will provide DOE’s Office of River Protection (ORP) with a means of treating this waste by vitrification for subsequent disposal. The tank waste will be separated into low- and high-activity waste fractions, which will then be vitrified respectively into Immobilized Low Activity Waste (ILAW) and Immobilized High Level Waste (IHLW) products. The ILAW product will be disposed in an engineered facility onmore » the Hanford site while the IHLW product is designed for acceptance into a national deep geological disposal facility for high-level nuclear waste. The ILAW and IHLW products must meet a variety of requirements with respect to protection of the environment before they can be accepted for disposal.« less

ChemNet: A Transferable and Generalizable Deep Neural Network for Small-Molecule Property Prediction

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

Goh, Garrett B.; Siegel, Charles M.; Vishnu, Abhinav

With access to large datasets, deep neural networks through representation learning have been able to identify patterns from raw data, achieving human-level accuracy in image and speech recognition tasks. However, in chemistry, availability of large standardized and labelled datasets is scarce, and with a multitude of chemical properties of interest, chemical data is inherently small and fragmented. In this work, we explore transfer learning techniques in conjunction with the existing Chemception CNN model, to create a transferable and generalizable deep neural network for small-molecule property prediction. Our latest model, ChemNet learns in a semi-supervised manner from inexpensive labels computed frommore » the ChEMBL database. When fine-tuned to the Tox21, HIV and FreeSolv dataset, which are 3 separate chemical tasks that ChemNet was not originally trained on, we demonstrate that ChemNet exceeds the performance of existing Chemception models, contemporary MLP models that trains on molecular fingerprints, and it matches the performance of the ConvGraph algorithm, the current state-of-the-art. Furthermore, as ChemNet has been pre-trained on a large diverse chemical database, it can be used as a universal “plug-and-play” deep neural network, which accelerates the deployment of deep neural networks for the prediction of novel small-molecule chemical properties.« less

Changes in VEGF and nitric oxide after deep dermal injury in the female, red Duroc pig-further similarities between female, Duroc scar and human hypertrophic scar.

PubMed

Zhu, Kathy Q; Engrav, Loren H; Armendariz, Rebecca; Muangman, Pornprom; Klein, Matthew B; Carrougher, Gretchen J; Deubner, Heike; Gibran, Nicole S

2005-02-01

Despite decades of research, our understanding of human hypertrophic scar is limited. A reliable animal model could significantly increase our understanding. We previously confirmed similarities between scarring in the female, red, Duroc pig and human hypertrophic scarring. The purpose of this study was to: (1) measure vascular endothelial growth factor (VEGF) and nitric oxide (NO) levels in wounds on the female Duroc; and (2) to compare the NO levels to those reported for human hypertrophic scar. Shallow and deep wounds were created on four female Durocs. VEGF levels were measured using ELISA and NO levels with the Griess reagent. VEGF and NO levels were increased in deep wounds at 10 days when compared to shallow wounds (p < 0.05). At 15 weeks, VEGF and NO levels had returned to the level of shallow wounds. At 21 weeks, VEGF and NO levels had declined below baseline levels in deep wounds and the NO levels were significantly lower (p < 0.01). We found that VEGF and NO exhibit two distinctly different temporal patterns in shallow and deep wounds on the female Durocs. Furthermore, NO is decreased in female, Duroc scar as it is in human, hypertrophic scar further validating the usefulness of the model.

RaptorX-Property: a web server for protein structure property prediction.

PubMed

Wang, Sheng; Li, Wei; Liu, Shiwang; Xu, Jinbo

2016-07-08

RaptorX Property (http://raptorx2.uchicago.edu/StructurePropertyPred/predict/) is a web server predicting structure property of a protein sequence without using any templates. It outperforms other servers, especially for proteins without close homologs in PDB or with very sparse sequence profile (i.e. carries little evolutionary information). This server employs a powerful in-house deep learning model DeepCNF (Deep Convolutional Neural Fields) to predict secondary structure (SS), solvent accessibility (ACC) and disorder regions (DISO). DeepCNF not only models complex sequence-structure relationship by a deep hierarchical architecture, but also interdependency between adjacent property labels. Our experimental results show that, tested on CASP10, CASP11 and the other benchmarks, this server can obtain ∼84% Q3 accuracy for 3-state SS, ∼72% Q8 accuracy for 8-state SS, ∼66% Q3 accuracy for 3-state solvent accessibility, and ∼0.89 area under the ROC curve (AUC) for disorder prediction. © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

Electronic structure properties of deep defects in hBN

NASA Astrophysics Data System (ADS)

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).

Local discretization method for overdamped Brownian motion on a potential with multiple deep wells.

PubMed

Nguyen, P T T; Challis, K J; Jack, M W

2016-11-01

We present a general method for transforming the continuous diffusion equation describing overdamped Brownian motion on a time-independent potential with multiple deep wells to a discrete master equation. The method is based on an expansion in localized basis states of local metastable potentials that match the full potential in the region of each potential well. Unlike previous basis methods for discretizing Brownian motion on a potential, this approach is valid for periodic potentials with varying multiple deep wells per period and can also be applied to nonperiodic systems. We apply the method to a range of potentials and find that potential wells that are deep compared to five times the thermal energy can be associated with a discrete localized state while shallower wells are better incorporated into the local metastable potentials of neighboring deep potential wells.

Local discretization method for overdamped Brownian motion on a potential with multiple deep wells

NASA Astrophysics Data System (ADS)

Nguyen, P. T. T.; Challis, K. J.; Jack, M. W.

2016-11-01

We present a general method for transforming the continuous diffusion equation describing overdamped Brownian motion on a time-independent potential with multiple deep wells to a discrete master equation. The method is based on an expansion in localized basis states of local metastable potentials that match the full potential in the region of each potential well. Unlike previous basis methods for discretizing Brownian motion on a potential, this approach is valid for periodic potentials with varying multiple deep wells per period and can also be applied to nonperiodic systems. We apply the method to a range of potentials and find that potential wells that are deep compared to five times the thermal energy can be associated with a discrete localized state while shallower wells are better incorporated into the local metastable potentials of neighboring deep potential wells.

DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip.

PubMed

Zhou, Xichuan; Li, Shengli; Tang, Fang; Hu, Shengdong; Lin, Zhi; Zhang, Lei

2017-07-18

Deep neural networks (NNs) are the state-of-the-art models for understanding the content of images and videos. However, implementing deep NNs in embedded systems is a challenging task, e.g., a typical deep belief network could exhaust gigabytes of memory and result in bandwidth and computational bottlenecks. To address this challenge, this paper presents an algorithm and hardware codesign for efficient deep neural computation. A hardware-oriented deep learning algorithm, named the deep adaptive network, is proposed to explore the sparsity of neural connections. By adaptively removing the majority of neural connections and robustly representing the reserved connections using binary integers, the proposed algorithm could save up to 99.9% memory utility and computational resources without undermining classification accuracy. An efficient sparse-mapping-memory-based hardware architecture is proposed to fully take advantage of the algorithmic optimization. Different from traditional Von Neumann architecture, the deep-adaptive network on chip (DANoC) brings communication and computation in close proximity to avoid power-hungry parameter transfers between on-board memory and on-chip computational units. Experiments over different image classification benchmarks show that the DANoC system achieves competitively high accuracy and efficiency comparing with the state-of-the-art approaches.

An adaptive deep Q-learning strategy for handwritten digit recognition.

PubMed

Qiao, Junfei; Wang, Gongming; Li, Wenjing; Chen, Min

2018-02-22

Handwritten digits recognition is a challenging problem in recent years. Although many deep learning-based classification algorithms are studied for handwritten digits recognition, the recognition accuracy and running time still need to be further improved. In this paper, an adaptive deep Q-learning strategy is proposed to improve accuracy and shorten running time for handwritten digit recognition. The adaptive deep Q-learning strategy combines the feature-extracting capability of deep learning and the decision-making of reinforcement learning to form an adaptive Q-learning deep belief network (Q-ADBN). First, Q-ADBN extracts the features of original images using an adaptive deep auto-encoder (ADAE), and the extracted features are considered as the current states of Q-learning algorithm. Second, Q-ADBN receives Q-function (reward signal) during recognition of the current states, and the final handwritten digits recognition is implemented by maximizing the Q-function using Q-learning algorithm. Finally, experimental results from the well-known MNIST dataset show that the proposed Q-ADBN has a superiority to other similar methods in terms of accuracy and running time. Copyright © 2018 Elsevier Ltd. All rights reserved.

Environmental projects. Volume 3: Environmental compliance audit

NASA Technical Reports Server (NTRS)

1987-01-01

The Goldstone Deep Space Communications Complex is part of NASA's Deep Space Network, one of the world's largest and most sensitive scientific telecommunications and radio navigation networks. Activities at Goldstone are carried out in support of six large parabolic dish antennas. In support of the national goal of the preservation of the environment and the protection of human health and safety, NASA, JPL and Goldstone have adopted a position that their operating installations shall maintain a high level of compliance with Federal, state, and local laws governing the management of hazardous substances, abestos, and underground storage tanks. A JPL version of a document prepared as an environmental audit of Goldstone operations is presented. Both general and specific items of noncompliance at Goldstone are identified and recommendations are provided for corrective actions.

Vertical wind shear characteristics that promote supercell-to-MCS transitions

NASA Astrophysics Data System (ADS)

Peters, J. M.

2017-12-01

What causes supercells to transition into MCSs in some situations, but not others? To explore this question, I first examined observed environmental characteristics of supercell events when MCSs formed, and compared them to the analogous environmental characteristics of supercell events when MCSs did not form. During events when MCS growth occurred, 0-1 km (low-level) vertical wind shear was stronger and 0-10 km (deep-layer) vertical wind shear was weaker than the wind shear during events when MCS growth did not occur. Next, I used idealized simulations of supercell thunderstorms to understand the connections between low-level and deep-layer shear and MCS growth. Compared to simulations with strong deep-layer shear, the simulations with weak deep-layer shear had rain in the storm's forward-flank downdraft (FFD) that fell closer to the updraft, fell through storm-moistened air and evaporated less, and produced a more intense FFD. Compared to simulations with weak low-level shear, the simulations with stronger low-level shear showed enhanced northward low-level hydrometeor transport into the FFD. Environments with strong low-level shear and weak deep-layer shear therefore conspired to produce a storm with a more intense FFD cold pool, when compared to environments with weak low-level shear and/or strong deep-layer shear. This strong FFD periodically disrupted the supercells' mesocyclones, and favorably interacted with westerly wind shear to produce widespread linear convection initiation, which drove MCS growth. These results suggest that increasing low-level wind shear after dark - while commonly assumed to enhance tornado potential - may in fact drive MCS growth and reduce tornado potential, unless it is combined with sufficiently strong deep layer shear.

Organization of the Tropical Convective Cloud Population by Humidity and the Critical Transition to Heavy Precipitation

NASA Astrophysics Data System (ADS)

Igel, M.

2015-12-01

The tropical atmosphere exhibits an abrupt statistical switch between non-raining and heavily raining states as column moisture increases across a wide range of length scales. Deep convection occurs at values of column humidity above the transition point and induces drying of moist columns. With a 1km resolution, large domain cloud resolving model run in RCE, what will be made clear here for the first time is how the entire tropical convective cloud population is affected by and feeds back to the pickup in heavy precipitation. Shallow convection can act to dry the low levels through weak precipitation or vertical redistribution of moisture, or to moisten toward a transition to deep convection. It is shown that not only can deep convection dehydrate the entire column, it can also dry just the lower layer through intense rain. In the latter case, deep stratiform cloud then forms to dry the upper layer through rain with anomalously high rates for its value of column humidity until both the total column moisture falls below the critical transition point and the upper levels are cloud free. Thus, all major tropical cloud types are shown to respond strongly to the same critical phase-transition point. This mutual response represents a potentially strong organizational mechanism for convection, and the frequency of and logical rules determining physical evolutions between these convective regimes will be discussed. The precise value of the point in total column moisture at which the transition to heavy precipitation occurs is shown to result from two independent thresholds in lower-layer and upper-layer integrated humidity.

Collaborative Research and Development (CR&D). Task Order 0036: Physical and Chemical Processes of Operating Electronic Devices

DTIC Science & Technology

2006-09-01

actually seen. A. Hierro , … S. A. Ringel et al., Phys. Stat. Sol (b) 228, 937 (2001). Ohio State U. Use DLTS and DLOS (Deep Level Optical Spectroscopy...to threading dislocations. Also see A. Hierro et al., APL 76, 3064 (2000), where traps at EC-ET=0.58-0.62, 1.35, 2.57-2.64, 3.22eV are seen in GaN

Surface photovoltage spectroscopy applied to gallium arsenide surfaces

NASA Technical Reports Server (NTRS)

Bynik, C. E.

1975-01-01

The experimental and theoretical basis for surface photovoltage spectroscopy is outlined. Results of this technique applied to gallium arsenide surfaces, are reviewed and discussed. The results suggest that in gallium arsenide the surface voltage may be due to deep bulk impurity acceptor states that are pinned at the Fermi level at the surface. Establishment of the validity of this model will indicate the direction to proceed to increase the efficiency of gallium arsenide solar cells.

15 CFR 970.300 - Purposes and definitions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Procedures for Applications... procedures which the Administrator will apply to applications filed with NOAA covering areas of the deep... the Administrator and a reciprocating state; and (ii) In which the deep seabed areas applied for...

15 CFR 970.300 - Purposes and definitions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Procedures for Applications... procedures which the Administrator will apply to applications filed with NOAA covering areas of the deep... the Administrator and a reciprocating state; and (ii) In which the deep seabed areas applied for...

15 CFR 970.300 - Purposes and definitions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Procedures for Applications... procedures which the Administrator will apply to applications filed with NOAA covering areas of the deep... the Administrator and a reciprocating state; and (ii) In which the deep seabed areas applied for...

15 CFR 970.300 - Purposes and definitions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... the Administrator and a reciprocating state; and (ii) In which the deep seabed areas applied for... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES Procedures for Applications... procedures which the Administrator will apply to applications filed with NOAA covering areas of the deep...

The Evolution of Deep Ocean Chemistry and Respired Carbon in the Eastern Equatorial Pacific Over the Last Deglaciation

NASA Astrophysics Data System (ADS)

de la Fuente, Maria; Calvo, Eva; Skinner, Luke; Pelejero, Carles; Evans, David; Müller, Wolfgang; Povea, Patricia; Cacho, Isabel

2017-12-01

It has been shown that the deep Eastern Equatorial Pacific (EEP) region was poorly ventilated during the Last Glacial Maximum (LGM) relative to Holocene values. This finding suggests a more efficient biological pump, which indirectly supports the idea of increased carbon storage in the deep ocean contributing to lower atmospheric CO2 during the last glacial. However, proxies related to respired carbon are needed in order to directly test this proposition. Here we present Cibicides wuellerstorfi B/Ca ratios from Ocean Drilling Program Site 1240 measured by laser ablation inductively coupled plasma mass spectrometry (LA-ICPMS) as a proxy for deep water carbonate saturation state (Δ[CO32-], and therefore [CO32-]), along with δ13C measurements. In addition, the U/Ca ratio in foraminiferal coatings has been analyzed as an indicator of oxygenation changes. Our results show lower [CO32-], δ13C, and [O2] values during the LGM, which would be consistent with higher respired carbon levels in the deep EEP driven, at least in part, by reduced deep water ventilation. However, the difference between LGM and Holocene [CO32-] observed at our site is relatively small, in accordance with other records from across the Pacific, suggesting that a "counteracting" mechanism, such as seafloor carbonate dissolution, also played a role. If so, this mechanism would have increased average ocean alkalinity, allowing even more atmospheric CO2 to be "sequestered" by the ocean. Therefore, the deep Pacific Ocean very likely stored a significant amount of atmospheric CO2 during the LGM, specifically due to a more efficient biological carbon pump and also an increase in average ocean alkalinity.

Deep Levels of Processing Elicit a Distinctiveness Heuristic: Evidence from the Criterial Recollection Task

ERIC Educational Resources Information Center

Gallo, David A.; Meadow, Nathaniel G.; Johnson, Elizabeth L.; Foster, Katherine T.

2008-01-01

Thinking about the meaning of studied words (deep processing) enhances memory on typical recognition tests, relative to focusing on perceptual features (shallow processing). One explanation for this levels-of-processing effect is that deep processing leads to the encoding of more distinctive representations (i.e., more unique semantic or…

Dual-wavelength photo-Hall effect spectroscopy of deep levels in high resistive CdZnTe with negative differential photoconductivity

NASA Astrophysics Data System (ADS)

Musiienko, A.; Grill, R.; Moravec, P.; Korcsmáros, G.; Rejhon, M.; Pekárek, J.; Elhadidy, H.; Šedivý, L.; Vasylchenko, I.

2018-04-01

Photo-Hall effect spectroscopy was used in the study of deep levels in high resistive CdZnTe. The monochromator excitation in the photon energy range 0.65-1.77 eV was complemented by a laser diode high-intensity excitation at selected photon energies. A single sample characterized by multiple unusual features like negative differential photoconductivity and anomalous depression of electron mobility was chosen for the detailed study involving measurements at both the steady and dynamic regimes. We revealed that the Hall mobility and photoconductivity can be both enhanced and suppressed by an additional illumination at certain photon energies. The anomalous mobility decrease was explained by an excitation of the inhomogeneously distributed deep level at the energy Ev + 1.0 eV, thus enhancing potential non-uniformities. The appearance of negative differential photoconductivity was interpreted by an intensified electron occupancy of that level by a direct valence band-to-level excitation. Modified Shockley-Read-Hall theory was used for fitting experimental results by a model comprising five deep levels. Properties of the deep levels and their impact on the device performance were deduced.

Divacancy-hydrogen complexes in dislocation-free high-purity germanium. [Annealing, Hall effect, steady-state concentration energy dependence

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

Haller, E.E.; Hubbard, G.S.; Hansen, W.L.

1976-09-01

A defect center with a single acceptor level at E/sub v/ + 0.08 eV appears in H/sub 2/-grown dislocation-free high-purity germanium. Its concentration changes reversibly upon annealing up to 650 K. By means of Hall-effect and conductivity measurements over a large temperature range the temperature dependence of the steady-state concentration between 450 and 720 K as well as the transients following changes in temperature were determined. The observed acceptor level is attributed to the divacancy-hydrogen complex V/sub 2/H. The complex reacts with hydrogen, dissolved in the Ge lattice or stored in traps, according to V/sub 2/H + H reversible V/submore » 2/H/sub 2/. An energy level associated with the divacancy-dihydrogen complex was not observed. These results are in good agreement with the idea that hydrogen in germanium forms a ''very deep donor'' (i.e., the energy level lies inside the valence band).« less

A hybrid density functional study of silicon and phosphorus doped hexagonal boron nitride monolayer

NASA Astrophysics Data System (ADS)

Mapasha, R. E.; Igumbor, E.; Chetty, N.

2016-10-01

We present a hybrid density functional study of silicon (Si) and phosphorus (P) doped hexagonal boron nitride (h-BN). The local geometry, electronic structure and thermodynamic stability of Si B , Si N , P B and P N are examined using hybrid Heyd-Scuseria- Ernzerhof (HSE) functional. The defect induced buckling and the local bond distances around the defect are sensitive to charge state modulation q = -2, -1, 0, +1 and +2. The +1 charge state is found to be the most energetically stable state and significantly reduces the buckling. Based on the charge state thermodynamic transition levels, we noted that the Si N , Si N and P B defects are too deep to be ionized, and can alter the optical properties of h-BN material.

Electron emission from deep level defects EL2 and EL6 in semi-insulating GaAs observed by positron drift velocity transient measurements

NASA Astrophysics Data System (ADS)

Tsia, J. M.; Ling, C. C.; Beling, C. D.; Fung, S.

2002-09-01

A plus-or-minus100 V square wave applied to a Au/semi-insulating SI-GaAs interface was used to bring about electron emission from and capture into deep level defects in the region adjacent to the interface. The electric field transient resulting from deep level emission was studied by monitoring the positron drift velocity in the region. A deep level transient spectrum was obtained by computing the trap emission rate as a function of temperature and two peaks corresponding to EL2 (Ea=0.81plus-or-minus0.15 eV) and EL6 (Ea=0.30plus-or-minus0.12 eV) have been identified.

77 FR 56791 - Main Hawaiian Islands Deep 7 Bottomfish Annual Catch Limits and Accountability Measures for 2012-13

Federal Register 2010, 2011, 2012, 2013, 2014

2012-09-14

... of the State's bottomfish restricted areas. The analyses in the most recent (2010) MHI Deep 7.... 120628195-2414-02] RIN 0648-XC089 Main Hawaiian Islands Deep 7 Bottomfish Annual Catch Limits and... specifies a quota of 325,000 lb of Deep 7 bottomfish in the main Hawaiian Islands for the 2012-13 fishing...

33 CFR 148.215 - What if a port has plans for a deep draft channel and harbor?

Code of Federal Regulations, 2010 CFR

2010-07-01

... General § 148.215 What if a port has plans for a deep draft channel and harbor? (a) If a State port will be directly connected by pipeline to a proposed deepwater port, and has existing plans for a deep... deep draft channel and harbor? 148.215 Section 148.215 Navigation and Navigable Waters COAST GUARD...

Deep brain stimulation for the early treatment of the minimally conscious state and vegetative state: experience in 14 patients.

PubMed

Chudy, Darko; Deletis, Vedran; Almahariq, Fadi; Marčinković, Petar; Škrlin, Jasenka; Paradžik, Veronika

2018-04-01

OBJECTIVE An effective treatment of patients in a minimally conscious state (MCS) or vegetative state (VS) caused by hypoxic encephalopathy or traumatic brain injury (TBI) is not yet available. Deep brain stimulation (DBS) of the thalamic reticular nuclei has been attempted as a therapeutic procedure mainly in patients with TBI. The purpose of this study was to investigate the therapeutic use of DBS for patients in VS or MCS. METHODS Fourteen of 49 patients in VS or MCS qualified for inclusion in this study and underwent DBS. Of these 14 patients, 4 were in MCS and 10 were in VS. The etiology of VS or MCS was TBI in 4 cases and hypoxic encephalopathy due to cardiac arrest in 10. The selection criteria for DBS, evaluating the status of the cerebral cortex and thalamocortical reticular formation, included: neurological evaluation, electrophysiological evaluation, and the results of positron emission tomography (PET) and MRI examinations. The target for DBS was the centromedian-parafascicular (CM-pf) complex. The duration of follow-up ranged from 38 to 60 months. RESULTS Two MCS patients regained consciousness and regained their ability to walk, speak fluently, and live independently. One MCS patient reached the level of consciousness, but was still in a wheelchair at the time the article was written. One VS patient (who had suffered a cerebral ischemic lesion) improved to the level of consciousness and currently responds to simple commands. Three VS patients died of respiratory infection, sepsis, or cerebrovascular insult (1 of each). The other 7 patients remained without substantial improvement of consciousness. CONCLUSIONS Spontaneous recovery from MCS/VS to the level of consciousness with no or minimal need for assistance in everyday life is very rare. Therefore, if a patient in VS or MCS fulfills the selection criteria (presence of somatosensory evoked potentials from upper extremities, motor and brainstem auditory evoked potentials, with cerebral glucose metabolism affected not more than the level of hypometabolism, which is judged using PET), DBS could be a treatment option.

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

PubMed

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

2017-04-01

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

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms.

PubMed

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time.

Robustness of spiking Deep Belief Networks to noise and reduced bit precision of neuro-inspired hardware platforms

PubMed Central

Stromatias, Evangelos; Neil, Daniel; Pfeiffer, Michael; Galluppi, Francesco; Furber, Steve B.; Liu, Shih-Chii

2015-01-01

Increasingly large deep learning architectures, such as Deep Belief Networks (DBNs) are the focus of current machine learning research and achieve state-of-the-art results in different domains. However, both training and execution of large-scale Deep Networks require vast computing resources, leading to high power requirements and communication overheads. The on-going work on design and construction of spike-based hardware platforms offers an alternative for running deep neural networks with significantly lower power consumption, but has to overcome hardware limitations in terms of noise and limited weight precision, as well as noise inherent in the sensor signal. This article investigates how such hardware constraints impact the performance of spiking neural network implementations of DBNs. In particular, the influence of limited bit precision during execution and training, and the impact of silicon mismatch in the synaptic weight parameters of custom hybrid VLSI implementations is studied. Furthermore, the network performance of spiking DBNs is characterized with regard to noise in the spiking input signal. Our results demonstrate that spiking DBNs can tolerate very low levels of hardware bit precision down to almost two bits, and show that their performance can be improved by at least 30% through an adapted training mechanism that takes the bit precision of the target platform into account. Spiking DBNs thus present an important use-case for large-scale hybrid analog-digital or digital neuromorphic platforms such as SpiNNaker, which can execute large but precision-constrained deep networks in real time. PMID:26217169

Deep ensemble learning of sparse regression models for brain disease diagnosis

PubMed Central

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

2018-01-01

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

Trends and Future Challenges in Sampling the Deep Terrestrial Biosphere

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

Wilkins, Michael J.; Daly, Rebecca; Mouser, Paula J.

2014-09-12

Research in the deep terrestrial biosphere is driven by interest in novel biodiversity and metabolisms, biogeochemical cycling, and the impact of human activities on this ecosystem. As this interest continues to grow, it is important to ensure that when subsurface investigations are proposed, materials recovered from the subsurface are sampled and preserved in an appropriate manner to limit contamination and ensure preservation of accurate microbial, geochemical, and mineralogical signatures. On February 20th, 2014, a workshop on “Trends and Future Challenges in Sampling The Deep Subsurface” was coordinated in Columbus, Ohio by The Ohio State University and West Virginia University faculty,more » and sponsored by The Ohio State University and the Sloan Foundation’s Deep Carbon Observatory. The workshop aims were to identify and develop best practices for the collection, preservation, and analysis of terrestrial deep rock samples. This document summarizes the information shared during this workshop.« less

Trends and future challenges in sampling the deep terrestrial biosphere.

PubMed

Wilkins, Michael J; Daly, Rebecca A; Mouser, Paula J; Trexler, Ryan; Sharma, Shihka; Cole, David R; Wrighton, Kelly C; Biddle, Jennifer F; Denis, Elizabeth H; Fredrickson, Jim K; Kieft, Thomas L; Onstott, Tullis C; Peterson, Lee; Pfiffner, Susan M; Phelps, Tommy J; Schrenk, Matthew O

2014-01-01

Research in the deep terrestrial biosphere is driven by interest in novel biodiversity and metabolisms, biogeochemical cycling, and the impact of human activities on this ecosystem. As this interest continues to grow, it is important to ensure that when subsurface investigations are proposed, materials recovered from the subsurface are sampled and preserved in an appropriate manner to limit contamination and ensure preservation of accurate microbial, geochemical, and mineralogical signatures. On February 20th, 2014, a workshop on "Trends and Future Challenges in Sampling The Deep Subsurface" was coordinated in Columbus, Ohio by The Ohio State University and West Virginia University faculty, and sponsored by The Ohio State University and the Sloan Foundation's Deep Carbon Observatory. The workshop aims were to identify and develop best practices for the collection, preservation, and analysis of terrestrial deep rock samples. This document summarizes the information shared during this workshop.

Prediction of fatigue-related driver performance from EEG data by deep Riemannian model.

PubMed

Hajinoroozi, Mehdi; Jianqiu Zhang; Yufei Huang

2017-07-01

Prediction of the drivers' drowsy and alert states is important for safety purposes. The prediction of drivers' drowsy and alert states from electroencephalography (EEG) using shallow and deep Riemannian methods is presented. For shallow Riemannian methods, the minimum distance to Riemannian mean (mdm) and Log-Euclidian metric are investigated, where it is shown that Log-Euclidian metric outperforms the mdm algorithm. In addition the SPDNet, a deep Riemannian model, that takes the EEG covariance matrix as the input is investigated. It is shown that SPDNet outperforms all tested shallow and deep classification methods. Performance of SPDNet is 6.02% and 2.86% higher than the best performance by the conventional Euclidian classifiers and shallow Riemannian models, respectively.

Prolongation of SMAP to Spatio-temporally Seamless Coverage of Continental US Using a Deep Learning Neural Network

NASA Astrophysics Data System (ADS)

Fang, K.; Shen, C.; Kifer, D.; Yang, X.

2017-12-01

The Soil Moisture Active Passive (SMAP) mission has delivered high-quality and valuable sensing of surface soil moisture since 2015. However, its short time span, coarse resolution, and irregular revisit schedule have limited its use. Utilizing a state-of-the-art deep-in-time neural network, Long Short-Term Memory (LSTM), we created a system that predicts SMAP level-3 soil moisture data using climate forcing, model-simulated moisture, and static physical attributes as inputs. The system removes most of the bias with model simulations and also improves predicted moisture climatology, achieving a testing accuracy of 0.025 to 0.03 in most parts of Continental United States (CONUS). As the first application of LSTM in hydrology, we show that it is more robust than simpler methods in either temporal or spatial extrapolation tests. We also discuss roles of different predictors, the effectiveness of regularization algorithms and impacts of training strategies. With high fidelity to SMAP products, our data can aid various applications including data assimilation, weather forecasting, and soil moisture hindcasting.

The NASA/JPL 64-meter-diameter antenna at Goldstone, California: Project report, technical staff, tracking and data acquisition organization

NASA Technical Reports Server (NTRS)

1974-01-01

The significant management and technical aspects of the JPL Project to develop and implement a 64-meter-diameter antenna at the Goldstone Deep Space Communications Complex in California, which was the first of the Advanced Antenna Systems of the National Aeronautics and Space Administration/Jet Propulsion Laboratory Deep Space Network are described. The original need foreseen for a large-diameter antenna to accomplish communication and tracking support of NASA's solar system exploration program is reviewed, and the translation of those needs into the technical specification of an appropriate ground station antenna is described. The antenna project is delineated by phases to show the key technical and managerial skills and the technical facility resources involved. There is a brief engineering description of the antenna and its closely related facilities. Some difficult and interesting engineering problems, then at the state-of-the-art level, which were met in the accomplishment of the Project, are described. The key performance characteristics of the antenna, in relation to the original specifications and the methods of their determination, are stated.

N3LO corrections to jet production in deep inelastic scattering using the Projection-to-Born method

NASA Astrophysics Data System (ADS)

Currie, J.; Gehrmann, T.; Glover, E. W. N.; Huss, A.; Niehues, J.; Vogt, A.

2018-05-01

Computations of higher-order QCD corrections for processes with exclusive final states require a subtraction method for real-radiation contributions. We present the first-ever generalisation of a subtraction method for third-order (N3LO) QCD corrections. The Projection-to-Born method is used to combine inclusive N3LO coefficient functions with an exclusive second-order (NNLO) calculation for a final state with an extra jet. The input requirements, advantages, and potential applications of the method are discussed, and validations at lower orders are performed. As a test case, we compute the N3LO corrections to kinematical distributions and production rates for single-jet production in deep inelastic scattering in the laboratory frame, and compare them with data from the ZEUS experiment at HERA. The corrections are small in the central rapidity region, where they stabilize the predictions to sub per-cent level. The corrections increase substantially towards forward rapidity where large logarithmic effects are expected, thereby yielding an improved description of the data in this region.

Deep-convection events foster carbonate ion reduction in deep coral reefs

NASA Astrophysics Data System (ADS)

Perez, Fiz F.; Fontela, Marcos; Garcia-Ibañez, Maribel I.; Lherminier, Pascale; Zunino, Patricia; de la Paz, Mercedes; Padín, Xose A.; Alonso-Pérez, Fernando; Velo, Anton; Guallart, Elisa F.; Mercier, Herle

2017-04-01

Since millennial times, water mass circulation and deep-convection events have been transforming warm upper waters at high latitudes into cold and well-oxygenated deep waters. These processes have filled the deep North Atlantic Ocean with waters moderately saturated in calcium carbonate, thus promoting the growth of stony corals, which are hotspots of biodiversity. During the Anthropocene, the meridional circulation has been conveying cumulative amounts of more acidified waters with lower calcium carbonate saturation levels due to the incorporation of anthropogenic carbon dioxide, with very harsh conditions for deep cold-water corals projected by 2100. We evaluate the diminution of calcium carbonate saturation levels (aragonite form) due to the increase in anthropogenic carbon dioxide during the last two decades (2002-2016). We observe a strong decrease in the aragonite saturation levels concomitant with the reduction in the volume transport of aragonite-saturated waters. We estimate a 30-35% reduction in the transport of ion carbonate excess over the saturation levels with respect to the natural carbon cycle for the period 2002-2016. This reduction is associated with an increase in the downward transport of hydrogen ions. We also observe a heaving of the aragonite saturation horizons during the last 25 years, which is estimated at 6 m year-1 for the deep waters and 12-14 m year-1 for the intermediated waters. The harsh winters of 2015 and 2016 have fostered the fast addition of more acidified water into the lower layers of the North Atlantic through deep-convection events. In the future scenario of 2oC warming, the anthropogenic carbon dioxide in the water column would be double than today and the associated transport of hydrogen ions towards the bottom water would reduce the aragonite saturation levels to 60-80% with respect to preindustrial levels. This reduction in the aragonite saturation levels would suppose a strong diminution of the North Atlantic habitats where stony corals will be able to inhabit.

Deep learning methods for protein torsion angle prediction.

PubMed

Li, Haiou; Hou, Jie; Adhikari, Badri; Lyu, Qiang; Cheng, Jianlin

2017-09-18

Deep learning is one of the most powerful machine learning methods that has achieved the state-of-the-art performance in many domains. Since deep learning was introduced to the field of bioinformatics in 2012, it has achieved success in a number of areas such as protein residue-residue contact prediction, secondary structure prediction, and fold recognition. In this work, we developed deep learning methods to improve the prediction of torsion (dihedral) angles of proteins. We design four different deep learning architectures to predict protein torsion angles. The architectures including deep neural network (DNN) and deep restricted Boltzmann machine (DRBN), deep recurrent neural network (DRNN) and deep recurrent restricted Boltzmann machine (DReRBM) since the protein torsion angle prediction is a sequence related problem. In addition to existing protein features, two new features (predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments) are used as input to each of the four deep learning architectures to predict phi and psi angles of protein backbone. The mean absolute error (MAE) of phi and psi angles predicted by DRNN, DReRBM, DRBM and DNN is about 20-21° and 29-30° on an independent dataset. The MAE of phi angle is comparable to the existing methods, but the MAE of psi angle is 29°, 2° lower than the existing methods. On the latest CASP12 targets, our methods also achieved the performance better than or comparable to a state-of-the art method. Our experiment demonstrates that deep learning is a valuable method for predicting protein torsion angles. The deep recurrent network architecture performs slightly better than deep feed-forward architecture, and the predicted residue contact number and the error distribution of torsion angles extracted from sequence fragments are useful features for improving prediction accuracy.

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

None Available

To make the web work better for science, OSTI has developed state-of-the-art technologies and services including a deep web search capability. The deep web includes content in searchable databases available to web users but not accessible by popular search engines, such as Google. This video provides an introduction to the deep web search engine.

Actualizing culture change: The Promoting Excellent Alternatives in Kansas Nursing Homes (PEAK 2.0) program.

PubMed

Doll, Gayle A; Cornelison, Laci J; Rath, Heath; Syme, Maggie L

2017-08-01

Nursing homes have been challenged in their attempts to achieve deep, organizational change (i.e., culture change) aimed at providing quality of care and quality of life for nursing home residents through person-centered care. To attain deep change, 2 well-defined components must be in place: a shared understanding of (a) the what, or content goals, and (b) the how, or process of change. However, there are few examples of this at a macro or micro level in long-term care. In an effort to enact true culture change in nursing homes statewide, the Kansas Department for Aging and Disability Services implemented the Promoting Excellent Alternatives in Kansas Nursing Homes program. This program is a Medicaid, pay-for-performance program that formalizes the content and process of achieving culture change through person-centered care principles. This article aims to detail the content (what) and process (how) of a model macro-level program of culture change throughout the State of Kansas. Applications to the micro level (individual homes) are presented, and implications for psychologists' roles in facilitating culture change are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Electrically active induced energy levels and metastability of B and N vacancy-complexes in 4H–SiC

NASA Astrophysics Data System (ADS)

Igumbor, E.; Olaniyan, O.; Mapasha, R. E.; Danga, H. T.; Omotoso, E.; Meyer, W. E.

2018-05-01

Electrically active induced energy levels in semiconductor devices could be beneficial to the discovery of an enhanced p or n-type semiconductor. Nitrogen (N) implanted into 4H–SiC is a high energy process that produced high defect concentrations which could be removed during dopant activation annealing. On the other hand, boron (B) substituted for silicon in SiC causes a reduction in the number of defects. This scenario leads to a decrease in the dielectric properties and induced deep donor and shallow acceptor levels. Complexes formed by the N, such as the nitrogen-vacancy centre, have been reported to play a significant role in the application of quantum bits. In this paper, results of charge states thermodynamic transition level of the N and B vacancy-complexes in 4H–SiC are presented. We explore complexes where substitutional N/N or B/B sits near a Si (V) or C (V) vacancy to form vacancy-complexes (NV, NV, NV, NV, BV, BV, BV and BV). The energies of formation of the N related vacancy-complexes showed the NV to be energetically stable close to the valence band maximum in its double positive charge state. The NV is more energetically stable in the double negative charge state close to the conduction band minimum. The NV on the other hand, induced double donor level and the NV induced a double acceptor level. For B related complexes, the BV and BV were energetically stable in their single positive charge state close to the valence band maximum. As the Fermi energy is varied across the band gap, the neutral and single negative charge states of the BV become more stable at different energy levels. B and N related complexes exhibited charge state controlled metastability behaviour.

Deep Networks Can Resemble Human Feed-forward Vision in Invariant Object Recognition

PubMed Central

Kheradpisheh, Saeed Reza; Ghodrati, Masoud; Ganjtabesh, Mohammad; Masquelier, Timothée

2016-01-01

Deep convolutional neural networks (DCNNs) have attracted much attention recently, and have shown to be able to recognize thousands of object categories in natural image databases. Their architecture is somewhat similar to that of the human visual system: both use restricted receptive fields, and a hierarchy of layers which progressively extract more and more abstracted features. Yet it is unknown whether DCNNs match human performance at the task of view-invariant object recognition, whether they make similar errors and use similar representations for this task, and whether the answers depend on the magnitude of the viewpoint variations. To investigate these issues, we benchmarked eight state-of-the-art DCNNs, the HMAX model, and a baseline shallow model and compared their results to those of humans with backward masking. Unlike in all previous DCNN studies, we carefully controlled the magnitude of the viewpoint variations to demonstrate that shallow nets can outperform deep nets and humans when variations are weak. When facing larger variations, however, more layers were needed to match human performance and error distributions, and to have representations that are consistent with human behavior. A very deep net with 18 layers even outperformed humans at the highest variation level, using the most human-like representations. PMID:27601096

A physical model for the reverse leakage current in (In,Ga)N/GaN light-emitting diodes based on nanowires

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

Musolino, M.; Treeck, D. van, E-mail: treeck@pdi-berlin.de; Tahraoui, A.

2016-01-28

We investigated the origin of the high reverse leakage current in light emitting diodes (LEDs) based on (In,Ga)N/GaN nanowire (NW) ensembles grown by molecular beam epitaxy on Si substrates. To this end, capacitance deep level transient spectroscopy (DLTS) and temperature-dependent current-voltage (I-V) measurements were performed on a fully processed NW-LED. The DLTS measurements reveal the presence of two distinct electron traps with high concentrations in the depletion region of the p-i-n junction. These band gap states are located at energies of 570 ± 20 and 840 ± 30 meV below the conduction band minimum. The physical origin of these deep level states is discussed. Themore » temperature-dependent I-V characteristics, acquired between 83 and 403 K, show that different conduction mechanisms cause the observed leakage current. On the basis of all these results, we developed a quantitative physical model for charge transport in the reverse bias regime. By taking into account the mutual interaction of variable range hopping and electron emission from Coulombic trap states, with the latter being described by phonon-assisted tunnelling and the Poole-Frenkel effect, we can model the experimental I-V curves in the entire range of temperatures with a consistent set of parameters. Our model should be applicable to planar GaN-based LEDs as well. Furthermore, possible approaches to decrease the leakage current in NW-LEDs are proposed.« less

Deep geothermal processes acting on faults and solid tides in coastal Xinzhou geothermal field, Guangdong, China

NASA Astrophysics Data System (ADS)

Lu, Guoping; Wang, Xiao; Li, Fusi; Xu, Fangyiming; Wang, Yanxin; Qi, Shihua; Yuen, David

2017-03-01

This paper investigated the deep fault thermal flow processes in the Xinzhou geothermal field in the Yangjiang region of Guangdong Province. Deep faults channel geothermal energy to the shallow ground, which makes it difficult to study due to the hidden nature. We conducted numerical experiments in order to investigate the physical states of the geothermal water inside the fault zone. We view the deep fault as a fast flow path for the thermal water from the deep crust driven up by the buoyancy. Temperature measurements at the springs or wells constrain the upper boundary, and the temperature inferred from the Currie temperature interface bounds the bottom. The deepened boundary allows the thermal reservoir to revolve rather than to be at a fixed temperature. The results detail the concept of a thermal reservoir in terms of its formation and heat distribution. The concept also reconciles the discrepancy in reservoir temperatures predicted from both quartz and Na-K-Mg. The downward displacement of the crust increases the pressure at the deep ground and leads to an elevated temperature and a lighter water density. Ultimately, our results are a first step in implementing numerical studies of deep faults through geothermal water flows; future works need to extend to cases of supercritical states. This approach is applicable to general deep-fault thermal flows and dissipation paths for the seismic energy from the deep crust.

Efficacy of gradual pressure-decline compressing stockings in Asian patients with lower leg varicose veins: analysis by general measurements and magnetic resonance image.

PubMed

Leung, T K; Lin, J M; Chu, C L; Wu, Y S; Chao, Y J

2012-12-01

Most applications of gradual pressure-decline compressing stockings (GPDCS) are used in the United States and Western European countries, with over a decade of clinical experiments. Up to know, there is no standard establishment of gradual pressure-decline compressing stockings for Asian patients with venous insufficiency and varicose vein formations. We collected data on volunteer candidates of varicose vein for general measurements and assessments and magnetic resonance imaging (MRI) by non-contrast enhanced MRV techniques, and for post processing data analysis. Clinical use of GPCDS provide a mild to moderate improvement in the varicose vein conditions of patients with deep venous insufficiency by improving their deep vein circulation, by general measurements; recording major symptoms and complaint; comfort and stretching/flexibility to the candidates after using GPDCS; and area changes/flow velocity changes/available hemoglobin changes in deep veins monitored by MRI. The benefits and data collected in these results may help in developing compression stockings standards in Taiwanese and Asian countries, and to establishing criterias for product sizes, compression levels, and related parameters.

Search for sterile neutrino mixing using three years of IceCube DeepCore data

NASA Astrophysics Data System (ADS)

Aartsen, M. G.; Ackermann, M.; Adams, J.; Aguilar, J. A.; Ahlers, M.; Ahrens, M.; Al Samarai, I.; Altmann, D.; Andeen, K.; Anderson, T.; Ansseau, I.; Anton, G.; Archinger, M.; Argüelles, C.; Auffenberg, J.; Axani, S.; Bai, X.; Barwick, S. W.; Baum, V.; Bay, R.; Beatty, J. J.; Becker Tjus, J.; Becker, K.-H.; BenZvi, S.; Berley, D.; Bernardini, E.; Besson, D. Z.; Binder, G.; Bindig, D.; Blaufuss, E.; Blot, S.; Bohm, C.; Börner, M.; Bos, F.; Bose, D.; Böser, S.; Botner, O.; Braun, J.; Brayeur, L.; Bretz, H.-P.; Bron, S.; Burgman, A.; Carver, T.; Casier, M.; Cheung, E.; Chirkin, D.; Christov, A.; Clark, K.; Classen, L.; Coenders, S.; Collin, G. H.; Conrad, J. M.; Cowen, D. F.; Cross, R.; Day, M.; de André, J. P. A. M.; De Clercq, C.; del Pino Rosendo, E.; Dembinski, H.; De Ridder, S.; Desiati, P.; de Vries, K. D.; de Wasseige, G.; de With, M.; DeYoung, T.; Díaz-Vélez, J. C.; di Lorenzo, V.; Dujmovic, H.; Dumm, J. P.; Dunkman, M.; Eberhardt, B.; Ehrhardt, T.; Eichmann, B.; Eller, P.; Euler, S.; Evenson, P. A.; Fahey, S.; Fazely, A. R.; Feintzeig, J.; Felde, J.; Filimonov, K.; Finley, C.; Flis, S.; Fösig, C.-C.; Franckowiak, A.; Friedman, E.; Fuchs, T.; Gaisser, T. K.; Gallagher, J.; Gerhardt, L.; Ghorbani, K.; Giang, W.; Gladstone, L.; Glauch, T.; Glüsenkamp, T.; Goldschmidt, A.; Gonzalez, J. G.; Grant, D.; Griffith, Z.; Haack, C.; Hallgren, A.; Halzen, F.; Hansen, E.; Hansmann, T.; Hanson, K.; Hebecker, D.; Heereman, D.; Helbing, K.; Hellauer, R.; Hickford, S.; Hignight, J.; Hill, G. C.; Hoffman, K. D.; Hoffmann, R.; Hoshina, K.; Huang, F.; Huber, M.; Hultqvist, K.; In, S.; Ishihara, A.; Jacobi, E.; Japaridze, G. S.; Jeong, M.; Jero, K.; Jones, B. J. P.; Kang, W.; Kappes, A.; Karg, T.; Karle, A.; Katz, U.; Kauer, M.; Keivani, A.; Kelley, J. L.; Kheirandish, A.; Kim, J.; Kim, M.; Kintscher, T.; Kiryluk, J.; Kittler, T.; Klein, S. R.; Kohnen, G.; Koirala, R.; Kolanoski, H.; Konietz, R.; Köpke, L.; Kopper, C.; Kopper, S.; Koskinen, D. J.; Kowalski, M.; Krings, K.; Kroll, M.; Krückl, G.; Krüger, C.; Kunnen, J.; Kunwar, S.; Kurahashi, N.; Kuwabara, T.; Kyriacou, A.; Labare, M.; Lanfranchi, J. L.; Larson, M. J.; Lauber, F.; Lennarz, D.; Lesiak-Bzdak, M.; Leuermann, M.; Lu, L.; Lünemann, J.; Madsen, J.; Maggi, G.; Mahn, K. B. M.; Mancina, S.; Mandelartz, M.; Maruyama, R.; Mase, K.; Maunu, R.; McNally, F.; Meagher, K.; Medici, M.; Meier, M.; Menne, T.; Merino, G.; Meures, T.; Miarecki, S.; Micallef, J.; Momenté, G.; Montaruli, T.; Moulai, M.; Nahnhauer, R.; Naumann, U.; Neer, G.; Niederhausen, H.; Nowicki, S. C.; Nygren, D. R.; Obertacke Pollmann, A.; Olivas, A.; O'Murchadha, A.; Palczewski, T.; Pandya, H.; Pankova, D. V.; Peiffer, P.; Penek, Ö.; Pepper, J. A.; Pérez de los Heros, C.; Pieloth, D.; Pinat, E.; Price, P. B.; Przybylski, G. T.; Quinnan, M.; Raab, C.; Rädel, L.; Rameez, M.; Rawlins, K.; Reimann, R.; Relethford, B.; Relich, M.; Resconi, E.; Rhode, W.; Richman, M.; Riedel, B.; Robertson, S.; Rongen, M.; Rott, C.; Ruhe, T.; Ryckbosch, D.; Rysewyk, D.; Sabbatini, L.; Sanchez Herrera, S. E.; Sandrock, A.; Sandroos, J.; Sarkar, S.; Satalecka, K.; Schlunder, P.; Schmidt, T.; Schoenen, S.; Schöneberg, S.; Schumacher, L.; Seckel, D.; Seunarine, S.; Soldin, D.; Song, M.; Spiczak, G. M.; Spiering, C.; Stachurska, J.; Stanev, T.; Stasik, A.; Stettner, J.; Steuer, A.; Stezelberger, T.; Stokstad, R. G.; Stößl, A.; Ström, R.; Strotjohann, N. L.; Sullivan, G. W.; Sutherland, M.; Taavola, H.; Taboada, I.; Tatar, J.; Tenholt, F.; Ter-Antonyan, S.; Terliuk, A.; Tešić, G.; Tilav, S.; Toale, P. A.; Tobin, M. N.; Toscano, S.; Tosi, D.; Tselengidou, M.; Tung, C. F.; Turcati, A.; Unger, E.; Usner, M.; Vandenbroucke, J.; van Eijndhoven, N.; Vanheule, S.; van Rossem, M.; van Santen, J.; Vehring, M.; Voge, M.; Vogel, E.; Vraeghe, M.; Walck, C.; Wallace, A.; Wallraff, M.; Wandkowsky, N.; Waza, A.; Weaver, Ch.; Weiss, M. J.; Wendt, C.; Westerhoff, S.; Whelan, B. J.; Wickmann, S.; Wiebe, K.; Wiebusch, C. H.; Wille, L.; Williams, D. R.; Wills, L.; Wolf, M.; Wood, T. R.; Woolsey, E.; Woschnagg, K.; Xu, D. L.; Xu, X. W.; Xu, Y.; Yanez, J. P.; Yodh, G.; Yoshida, S.; Zoll, M.; IceCube Collaboration

2017-06-01

We present a search for a light sterile neutrino using three years of atmospheric neutrino data from the DeepCore detector in the energy range of approximately 10-60 GeV. DeepCore is the low-energy subarray of the IceCube Neutrino Observatory. The standard three-neutrino paradigm can be probed by adding an additional light (Δ m412˜1 eV2 ) sterile neutrino. Sterile neutrinos do not interact through the standard weak interaction and, therefore, cannot be directly detected. However, their mixing with the three active neutrino states leaves an imprint on the standard atmospheric neutrino oscillations for energies below 100 GeV. A search for such mixing via muon neutrino disappearance is presented here. The data are found to be consistent with the standard three-neutrino hypothesis. Therefore, we derive limits on the mixing matrix elements at the level of |Uμ 4|2<0.11 and |Uτ 4|2<0.15 (90% C.L.) for the sterile neutrino mass splitting Δ m412=1.0 eV2 .

Deep Blue Phosphorescent Organic Light-Emitting Diodes with CIEy Value of 0.11 and External Quantum Efficiency up to 22.5.

PubMed

Li, Xiaoyue; Zhang, Juanye; Zhao, Zifeng; Wang, Liding; Yang, Hannan; Chang, Qiaowen; Jiang, Nan; Liu, Zhiwei; Bian, Zuqiang; Liu, Weiping; Lu, Zhenghong; Huang, Chunhui

2018-03-01

Organic light-emitting diodes (OLEDs) based on red and green phosphorescent iridium complexes are successfully commercialized in displays and solid-state lighting. However, blue ones still remain a challenge on account of their relatively dissatisfactory Commission International de L'Eclairage (CIE) coordinates and low efficiency. After analyzing the reported blue iridium complexes in the literature, a new deep-blue-emitting iridium complex with improved photoluminescence quantum yield is designed and synthesized. By rational screening host materials showing high triplet energy level in neat film as well as the OLED architecture to balance electron and hole recombination, highly efficient deep-blue-emission OLEDs with a CIE at (0.15, 0.11) and maximum external quantum efficiency (EQE) up to 22.5% are demonstrated. Based on the transition dipole moment vector measurement with a variable-angle spectroscopic ellipsometry method, the ultrahigh EQE is assigned to a preferred horizontal dipole orientation of the iridium complex in doped film, which is beneficial for light extraction from the OLEDs. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Simulation of Variable-Density Ground-Water Flow and Saltwater Intrusion beneath Manhasset Neck, Nassau County, New York, 1905-2005

USGS Publications Warehouse

Monti, Jack; Misut, Paul E.; Busciolano, Ronald J.

2009-01-01

The coastal-aquifer system of Manhasset Neck, Nassau County, New York, has been stressed by pumping, which has led to saltwater intrusion and the abandonment of one public-supply well in 1944. Measurements of chloride concentrations and water levels in 2004 from the deep, confined aquifers indicate active saltwater intrusion in response to public-supply pumping. A numerical model capable of simulating three-dimensional variable-density ground-water flow and solute transport in heterogeneous, anisotropic aquifers was developed using the U.S. Geological Survey finite-element, variable-density, solute-transport simulator SUTRA, to investigate the extent of saltwater intrusion beneath Manhasset Neck. The model is composed of eight layers representing the hydrogeologic system beneath Manhasset Neck. Four modifications to the area?s previously described hydrogeologic framework were made in the model (1) the bedrock-surface altitude at well N12191 was corrected from a previously reported value, (2) part of the extent of the Raritan confining unit was shifted, (3) part of the extent of the North Shore confining unit was shifted, and (4) a clay layer in the upper glacial aquifer was added in the central and southern parts of the Manhasset Neck peninsula. Ground-water flow and the location of the freshwater-saltwater interface were simulated for three conditions (time periods) (1) a steady-state (predevelopment) simulation of no pumping prior to about 1905, (2) a 40-year transient simulation based on 1939 pumpage representing the 1905-1944 period of gradual saltwater intrusion, and (3) a 60-year transient simulation based on 1995 pumpage representing the 1945-2005 period of stabilized withdrawals. The 1939 pumpage rate (12.1 million gallons per day (Mgal/d)) applied to the 1905-1944 transient simulation caused modeled average water-level declines of 2 and 4 feet (ft) in the shallow and deep aquifer systems from predevelopment conditions, respectively, a net decrease of 5.2 Mgal/d in freshwater discharge to offshore areas and a net increase of 6.9 Mgal/d of freshwater entering the model from the eastern, western, and southern lateral boundaries. The 1995 pumpage rate (43.3 Mgal/d) applied to the 1945-2005 transient simulation caused modeled average water-level declines of 5 and 8 ft in the shallow and deep aquifer systems from predevelopment conditions, respectively, a net decrease of 13.2 Mgal/d in freshwater discharge to offshore areas and a net increase of 30.1 Mgal/d of freshwater entering the model from the eastern, western, and southern lateral boundaries. The simulated decrease in freshwater discharge to the offshore areas caused saltwater intrusion in two parts of the deep aquifer system under Manhasset Neck. Saline ground water simulated in a third part of the deep aquifer system under Manhasset Neck was due to the absence of the North Shore confining unit near Sands Point. Simulated chloride concentrations greater than 250 milligrams per liter (mg/L) were used to represent the freshwater-saltwater interface, and the movement of this concentration was evaluated for transient simulations. The decrease in the 1905-1944 simulated freshwater discharge to the offshore areas caused the freshwater-saltwater interface in the deep aquifer system to advance landward more than 1,700 ft from its steady-state position in the vicinity of Baxter Estates Village, Long Island, New York. The decrease in the 1945-2005 simulated freshwater discharge to the offshore areas caused a different area of the freshwater-saltwater interface in the deep aquifer system to advance more than 600 ft from its steady-state position approximately 1 mile south of the Baxter Estates Village. However, the 1945-2005 transient simulation underestimates the concentration and extent of saltwater intrusion determined from water-quality samples collected from wells N12508 and N12793, where measured chloride concentrations increased from 625 and 18 mg/L in 1997 t

Deep inelastic scattering as a probe of entanglement

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

Kharzeev, Dmitri E.; Levin, Eugene M.

Using nonlinear evolution equations of QCD, we compute the von Neumann entropy of the system of partons resolved by deep inelastic scattering at a given Bjorken x and momentum transfer q 2 = - Q 2 . We interpret the result as the entropy of entanglement between the spatial region probed by deep inelastic scattering and the rest of the proton. At small x the relation between the entanglement entropy S ( x ) and the parton distribution x G ( x ) becomes very simple: S ( x ) = ln [ x G ( x ) ] .more » In this small x , large rapidity Y regime, all partonic microstates have equal probabilities—the proton is composed by an exponentially large number exp ( Δ Y ) of microstates that occur with equal and exponentially small probabilities exp ( - Δ Y ) , where Δ is defined by x G ( x ) ~ 1 / x Δ . For this equipartitioned state, the entanglement entropy is maximal—so at small x , deep inelastic scattering probes a maximally entangled state. Here, we propose the entanglement entropy as an observable that can be studied in deep inelastic scattering. This will then require event-by-event measurements of hadronic final states, and would allow to study the transformation of entanglement entropy into the Boltzmann one. We estimate that the proton is represented by the maximally entangled state at x ≤ 10 -3 ; this kinematic region will be amenable to studies at the Electron Ion Collider.« less

Deep inelastic scattering as a probe of entanglement

DOE PAGES

Kharzeev, Dmitri E.; Levin, Eugene M.

2017-06-03

Using nonlinear evolution equations of QCD, we compute the von Neumann entropy of the system of partons resolved by deep inelastic scattering at a given Bjorken x and momentum transfer q 2 = - Q 2 . We interpret the result as the entropy of entanglement between the spatial region probed by deep inelastic scattering and the rest of the proton. At small x the relation between the entanglement entropy S ( x ) and the parton distribution x G ( x ) becomes very simple: S ( x ) = ln [ x G ( x ) ] .more » In this small x , large rapidity Y regime, all partonic microstates have equal probabilities—the proton is composed by an exponentially large number exp ( Δ Y ) of microstates that occur with equal and exponentially small probabilities exp ( - Δ Y ) , where Δ is defined by x G ( x ) ~ 1 / x Δ . For this equipartitioned state, the entanglement entropy is maximal—so at small x , deep inelastic scattering probes a maximally entangled state. Here, we propose the entanglement entropy as an observable that can be studied in deep inelastic scattering. This will then require event-by-event measurements of hadronic final states, and would allow to study the transformation of entanglement entropy into the Boltzmann one. We estimate that the proton is represented by the maximally entangled state at x ≤ 10 -3 ; this kinematic region will be amenable to studies at the Electron Ion Collider.« less

Feasibility of Lateral Emplacement in Very Deep Borehole Disposal of High Level Nuclear Waste

DTIC Science & Technology

2010-06-01

superior isolation of the waste (mitigating proliferation, terrorist and human intrusion concerns), the impermeability of available geologic formations ...Continental U.S. (Courtesy “The Future of Geothermal Energy” by MIT)7 2. Age of the granitic formation (Figure 1-4) 3. Proximity to rail, barge, and...state are of particular interest with their access to the ancient and stable Canadian granite shield, but access to suitable formations is found in

Delayed avalanche breakdown of high-voltage silicon diodes: Various structures exhibit different picosecond-range switching behavior

NASA Astrophysics Data System (ADS)

Brylevskiy, Viktor; Smirnova, Irina; Gutkin, Andrej; Brunkov, Pavel; Rodin, Pavel; Grekhov, Igor

2017-11-01

We present a comparative study of silicon high-voltage diodes exhibiting the effect of delayed superfast impact-ionization breakdown. The effect manifests itself in a sustainable picosecond-range transient from the blocking to the conducting state and occurs when a steep voltage ramp is applied to the p+-n-n+ diode in the reverse direction. Nine groups of diodes with graded and abrupt pn-junctions have been specially fabricated for this study by different techniques from different Si substrates. Additionally, in two groups of these structures, the lifetime of nonequilibrium carriers was intentionally reduced by electron irradiation. All diodes have identical geometrical parameters and similar stationary breakdown voltages. Our experimental setup allows measuring both device voltage and current during the kilovolt switching with time resolution better than 50 ps. Although all devices are capable of forming a front with kilovolt amplitude and 100 ps risetime in the in-series load, the structures with graded pn-junctions have anomalously large residual voltage. The Deep Level Transient Spectroscopy study of all diode structures has been performed in order to evaluate the effect of deep centers on device performance. It was found that the presence of deep-level electron traps negatively correlates with parameters of superfast switching, whereas a large concentration of recombination centers created by electron irradiation has virtually no influence on switching characteristics.

Effects of Pressure on Optically Active Deep Levels in Phosphorus Doped ZnSe

NASA Astrophysics Data System (ADS)

Weinstein, B. A.; Iota, V.

1998-03-01

We report high pressure photoluminescence (PL) and PL-excitation (PLE) studies at 8K of the 'midgap' emission in P-doped ZnSe using a diamond-cell with He medium. The dominant emission at low pressure is due to donor-acceptor-pair (DAP) transitions between shallow donors and deep trigonally relaxed P_Se acceptors.(J. Davies, et al., J. Luminescence 18/19, 322 (1979)) Its PL and PLE peaks shift by 8.2meV/kbar and 5.9meV/kbar, respectively -- Stokes shift decreasing with pressure. At 35kbar a new PL band, shifting to lower energy (-5.4meV/kbar), emerges from above the absorption edge, and concurrently the original DAP PL quenches. This shows that a resonant level, a deep donor or possibly a P_Se antibonding state,(R. Watts, et al., Phys. Rev. B3), 404 (1971) crosses the conduction edge into the gap. A third PL band is seen only with internse UV excitation. It occurs initially as a high energy shoulder of the original DAP peak, but shifts more rapidly upward (9.4meV/kbar) until it crosses the edge and quenches at 40kbar. We discuss candidates for this band, including donor-P_Se complexes, and we compare our results to similar work on the Zn vacancy in ZnSe. (figures)

ampliMethProfiler: a pipeline for the analysis of CpG methylation profiles of targeted deep bisulfite sequenced amplicons.

PubMed

Scala, Giovanni; Affinito, Ornella; Palumbo, Domenico; Florio, Ermanno; Monticelli, Antonella; Miele, Gennaro; Chiariotti, Lorenzo; Cocozza, Sergio

2016-11-25

CpG sites in an individual molecule may exist in a binary state (methylated or unmethylated) and each individual DNA molecule, containing a certain number of CpGs, is a combination of these states defining an epihaplotype. Classic quantification based approaches to study DNA methylation are intrinsically unable to fully represent the complexity of the underlying methylation substrate. Epihaplotype based approaches, on the other hand, allow methylation profiles of cell populations to be studied at the single molecule level. For such investigations, next-generation sequencing techniques can be used, both for quantitative and for epihaplotype analysis. Currently available tools for methylation analysis lack output formats that explicitly report CpG methylation profiles at the single molecule level and that have suited statistical tools for their interpretation. Here we present ampliMethProfiler, a python-based pipeline for the extraction and statistical epihaplotype analysis of amplicons from targeted deep bisulfite sequencing of multiple DNA regions. ampliMethProfiler tool provides an easy and user friendly way to extract and analyze the epihaplotype composition of reads from targeted bisulfite sequencing experiments. ampliMethProfiler is written in python language and requires a local installation of BLAST and (optionally) QIIME tools. It can be run on Linux and OS X platforms. The software is open source and freely available at http://amplimethprofiler.sourceforge.net .

Mechanisms of ultrafast laser-induced deep-subwavelength gratings on graphite and diamond

NASA Astrophysics Data System (ADS)

Huang, Min; Zhao, Fuli; Cheng, Ya; Xu, Ningsheg; Xu, Zhizhan

2009-03-01

Deep-subwavelength gratings with periodicities of 170, 120, and 70 nm can be observed on highly oriented pyrolytic graphite irradiated by a femtosecond (fs) laser at 800 nm. Under picosecond laser irradiation, such gratings likewise can be produced. Interestingly, the 170-nm grating is also observed on single-crystal diamond irradiated by the 800-nm fs laser. In our opinion, the optical properties of the high-excited state of material surface play a key role for the formation of the deep-subwavelength gratings. The numerical simulations of the graphite deep-subwavelength grating at normal and high-excited states confirm that in the groove the light intensity can be extraordinarily enhanced via cavity-mode excitation in the condition of transverse-magnetic wave irradiation with near-ablation-threshold fluences. This field enhancement of polarization sensitiveness in deep-subwavelength apertures acts as an important feedback mechanism for the growth and polarization dependence of the deep-subwavelength gratings. In addition, we suggest that surface plasmons are responsible for the formation of seed deep-subwavelength apertures with a particular periodicity and the initial polarization dependence. Finally, we propose that the nanoscale Coulomb explosion occurring in the groove is responsible for the ultrafast nonthermal ablation mechanism.

The effects of illumination on deep levels observed in as-grown and low-energy electron irradiated high-purity semi-insulating 4H-SiC

NASA Astrophysics Data System (ADS)

Alfieri, G.; Knoll, L.; Kranz, L.; Sundaramoorthy, V.

2018-05-01

High-purity semi-insulating 4H-SiC can find a variety of applications, ranging from power electronics to quantum computing applications. However, data on the electronic properties of deep levels in this material are scarce. For this reason, we present a deep level transient spectroscopy study on HPSI 4H-SiC substrates, both as-grown and irradiated with low-energy electrons (to displace only C-atoms). Our investigation reveals the presence of four deep levels with activation energies in the 0.4-0.9 eV range. The concentrations of three of these levels increase by at least one order of magnitude after irradiation. Furthermore, we analyzed the behavior of these traps under sub- and above-band gap illumination. The nature of the traps is discussed in the light of the present data and results reported in the literature.

Modification of electron states in CdTe absorber due to a buffer layer in CdTe/CdS solar cells

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

Fedorenko, Y. G., E-mail: y.fedorenko@liverpool.ac.uk; Major, J. D.; Pressman, A.

2015-10-28

By application of the ac admittance spectroscopy method, the defect state energy distributions were determined in CdTe incorporated in thin film solar cell structures concluded on ZnO, ZnSe, and ZnS buffer layers. Together with the Mott-Schottky analysis, the results revealed a strong modification of the defect density of states and the concentration of the uncompensated acceptors as influenced by the choice of the buffer layer. In the solar cells formed on ZnSe and ZnS, the Fermi level and the energy position of the dominant deep trap levels were observed to shift closer to the midgap of CdTe, suggesting the mid-gapmore » states may act as recombination centers and impact the open-circuit voltage and the fill factor of the solar cells. For the deeper states, the broadening parameter was observed to increase, indicating fluctuations of the charge on a microscopic scale. Such changes can be attributed to the grain-boundary strain and the modification of the charge trapped at the grain-boundary interface states in polycrystalline CdTe.« less

Two different carbon-hydrogen complexes in silicon with closely spaced energy levels

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

Stübner, R., E-mail: ronald.stuebner@physik.tu-dresden.de, E-mail: kolkov@ifpan.edu.pl; Kolkovsky, Vl., E-mail: ronald.stuebner@physik.tu-dresden.de, E-mail: kolkov@ifpan.edu.pl; Weber, J.

An acceptor and a single donor state of carbon-hydrogen defects (CH{sub A} and CH{sub B}) are observed by Laplace deep level transient spectroscopy at 90 K. CH{sub A} appears directly after hydrogenation by wet chemical etching or hydrogen plasma treatment, whereas CH{sub B} can be observed only after a successive annealing under reverse bias at about 320 K. The activation enthalpies of these states are 0.16 eV for CH{sub A} and 0.14 eV for CH{sub B}. Our results reconcile previous controversial experimental results. We attribute CH{sub A} to the configuration where substitutional carbon binds a hydrogen atom on a bond centered position between carbonmore » and the neighboring silicon and CH{sub B} to another carbon-hydrogen defect.« less

A 32-GHz solid-state power amplifier for deep space communications

NASA Technical Reports Server (NTRS)

Wamhof, P. D.; Rascoe, D. L.; Lee, K. A.; Lansing, F. S.

1994-01-01

A 1.5-W solid-state power amplifier (SSPA) has been demonstrated as part of an effort to develop and evaluate state-of-the-art transmitter and receiver components at 32 and 35 GHz for future deep space missions. Output power and efficiency measurements for a monolithic millimeter-wave integrated circuit (MMIC)-based SSPA are reported. Technical design details for the various modules and a thermal analysis are discussed, as well as future plans.

A Synopsis of Ion Propulsion Development Projects in the United States: SERT 1 to Deep Space I

NASA Technical Reports Server (NTRS)

Sovey, James S.; Rawlin, Vincent K.; Patterson, Michael J.

1999-01-01

The historical background and characteristics of the experimental flights of ion propulsion systems and the major ground-based technology demonstrations were reviewed. The results of the first successful ion engine flight in 1964, SERT I which demonstrated ion beam neutralization, are discussed along with the extended operation of SERT II starting in 1970. These results together with the technology employed on the early cesium engine flights. the Applications Technology Satellite (ATS) series, and the ground-test demonstrations, have provided the evolutionary path for the development of xenon ion thruster component technologies, control systems, and power circuit implementations. In the 1997-1999 period, the communication satellite flights using ion engine systems and the Deep Space I flight confirmed that these auxiliary and primary propulsion systems have advanced to a high-level of flight-readiness.

Deep mycoses in Amazon region.

PubMed

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.

A 400-kyr record of millennial-scale carbonate preservation events in the Southern Ocean: Implications for Atlantic Meridional Overturning Circulation and atmospheric CO2

NASA Astrophysics Data System (ADS)

Hodell, D. A.; Vautravers, M. J.; Barker, S.; Charles, C.; Crowhurst, S.

2014-12-01

Hodell et al. (2001) suggested that carbonate preservation in the deep Cape Basin represented a qualitative, high-resolution record of the temporal evolution of the carbonate saturation state of the deep sea. The carbonate signal reflects both transient events in the redistribution of alkalinity and DIC in the deep ocean and steady-state mass balance processes. Here we re-analyzed the carbonate records of Sites 1089/TN057-21 using an Avaatech XRF core scanner and measured elemental variations at 2.5-mm resolution for the past 400 kyrs. Log Ca/Ti is highly correlated to weight percent carbonate content and other dissolution proxies and resolves millennial-scale events in carbonate preservation. A high-pass filter removes the low-frequency (orbital) variability in carbonate preservation, which is attributed mainly to steady-state mass balance processes. The high-frequency (suborbital) component reflects transient responses to the redistribution of carbonate ion that is related mainly to changing deep-water circulation. During the last glacial period, distinct millennial-scale increases in carbonate preservation in piston core TN057-21 occurred during times of enhanced Atlantic Meridional Overtunring Circulation (AMOC) (Barker et al., 2010; Barker and Diz, 2014), as supported by increases in benthic δ13C and less radiogenic ɛNd values. Carbonate preservation peaked particularly during long, warm interstadials in Greenland when a deep water mass with high carbonate ion concentration was formed in the North Atlantic. Export of NADW may have been greater than the Holocene during some of these events ("overshoots") and/or preformed carbonate ion concentrations in North Atlantic source areas may have been higher owing to lower atmospheric CO2 and less carbonate production in surface water. Each South Atlantic carbonate peak is associated with the start of Antarctic cooling and declining or leveling of atmospheric CO2, reflecting the signature of a thermal bipolar seesaw. The increased flux of carbonate ion to the Southern Ocean during strong interstadials may have played a role in titrating respiratory CO2, thereby slowing CO2 degassing to the atmosphere and providing a secondary mechanism, in addition to heat transport, for interhemispheric coupling on millennial time scales.

Management and Accountability Procedures: DEEP - The New Mexico State Facilitator.

ERIC Educational Resources Information Center

New Mexico Univ., Albuquerque. Coll. of Education.

Presented is a description of how DEEP (Developmental Economic Education Program) monitors activity and achievements to ensure that objectives are met effectively and efficiently, and that evidence of achievement is available for reports. The purposes of DEEP management and accountability procedures are: (1) to maintain both long term and short…

Structural damage detection using deep learning of ultrasonic guided waves

NASA Astrophysics Data System (ADS)

Melville, Joseph; Alguri, K. Supreet; Deemer, Chris; Harley, Joel B.

2018-04-01

Structural health monitoring using ultrasonic guided waves relies on accurate interpretation of guided wave propagation to distinguish damage state indicators. However, traditional physics based models do not provide an accurate representation, and classic data driven techniques, such as a support vector machine, are too simplistic to capture the complex nature of ultrasonic guide waves. To address this challenge, this paper uses a deep learning interpretation of ultrasonic guided waves to achieve fast, accurate, and automated structural damaged detection. To achieve this, full wavefield scans of thin metal plates are used, half from the undamaged state and half from the damaged state. This data is used to train our deep network to predict the damage state of a plate with 99.98% accuracy given signals from just 10 spatial locations on the plate, as compared to that of a support vector machine (SVM), which achieved a 62% accuracy.

GaAs-oxide interface states - Gigantic photoionization via Auger-like process

NASA Technical Reports Server (NTRS)

Lagowski, J.; Kazior, T. E.; Gatos, H. C.; Walukiewicz, W.; Siejka, J.

1981-01-01

Spectral and transient responses of photostimulated current in MOS structures were employed for the study of GaAs-anodic oxide interface states. Discrete deep traps at 0.7 and 0.85 eV below the conduction band were found with concentrations of 5 x 10 to the 12th/sq cm and 7 x 10 to the 11th/sq cm, respectively. These traps coincide with interface states induced on clean GaAs surfaces by oxygen and/or metal adatoms (submonolayer coverage). In contrast to surfaces with low oxygen coverage, the GaAs-thick oxide interfaces exhibited a high density (about 10 to the 14th/sq cm) of shallow donors and acceptors. Photoexcitation of these donor-acceptor pairs led to a gigantic photoionization of deep interface states with rates 1000 times greater than direct transitions into the conduction band. The gigantic photoionization is explained on the basis of energy transfer from excited donor-acceptor pairs to deep states.

The graphical brain: Belief propagation and active inference

PubMed Central

Friston, Karl J.; Parr, Thomas; de Vries, Bert

2018-01-01

This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models. Crucially, these models can entertain both discrete and continuous states, leading to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to elucidate the requisite message passing in terms of its form and scheduling. To accommodate mixed generative models (of discrete and continuous states), one also has to consider link nodes or factors that enable discrete and continuous representations to talk to each other. When mapping the implicit computational architecture onto neuronal connectivity, several interesting features emerge. For example, Bayesian model averaging and comparison, which link discrete and continuous states, may be implemented in thalamocortical loops. These and other considerations speak to a computational connectome that is inherently state dependent and self-organizing in ways that yield to a principled (variational) account. We conclude with simulations of reading that illustrate the implicit neuronal message passing, with a special focus on how discrete (semantic) representations inform, and are informed by, continuous (visual) sampling of the sensorium. Author Summary This paper considers functional integration in the brain from a computational perspective. We ask what sort of neuronal message passing is mandated by active inference—and what implications this has for context-sensitive connectivity at microscopic and macroscopic levels. In particular, we formulate neuronal processing as belief propagation under deep generative models that can entertain both discrete and continuous states. This leads to distinct schemes for belief updating that play out on the same (neuronal) architecture. Technically, we use Forney (normal) factor graphs to characterize the requisite message passing, and link this formal characterization to canonical microcircuits and extrinsic connectivity in the brain. PMID:29417960

50 CFR 648.260 - Specifications.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., DEPARTMENT OF COMMERCE FISHERIES OF THE NORTHEASTERN UNITED STATES Management Measures for the Atlantic Deep... Atlantic Deep-Sea Red Crab FMP objectives and other FMP provisions. (b) Development of specifications. In...

Core excitations across the neutron shell gap in 207Tl

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

Wilson, E.; Podolyák, Zs.; Grawe, H.

2015-05-05

The single closed-neutron-shell, one proton–hole nucleus 207Tl was populated in deep-inelastic collisions of a 208Pb beam with a 208Pb target. The yrast and near-yrast level scheme has been established up to high excitation energy, comprising an octupole phonon state and a large number of core excited states. Based on shell-model calculations, all observed single core excitations were established to arise from the breaking of the N=126 neutron core. While the shell-model calculations correctly predict the ordering of these states, their energies are compressed at high spins. It is concluded that this compression is an intrinsic feature of shell-model calculations usingmore » two-body matrix elements developed for the description of two-body states, and that multiple core excitations need to be considered in order to accurately calculate the energy spacings of the predominantly three-quasiparticle states.« less

Comparison and Interpretation of Admittance Spectroscopy and Deep Level Transient Spectroscopy from Co-Evaporated and Solution-Deposited Cu2ZnSn(Sx, Se1-x)4 Solar Cells

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

Caruso, A. E.; Lund, E. A.; Kosyak, V.

2016-11-21

Cu2ZnSn(S, Se)4 (CZTSe) is an earth-abundant semiconductor with potential for economical thin-film photovoltaic devices. Short minority carrier lifetimes contribute to low open circuit voltage and efficiency. Deep level defects that may contribute to lower minority carrier lifetimes in kesterites have been theoretically predicted, however very little experimental characterization of these deep defects exists. In this work we use admittance spectroscopy (AS) and deep level transient spectroscopy (DLTS) to characterize devices built using CZTSSe absorber layers deposited via both coevaporation and solution processing. AS reveals a band of widely-distributed activation energies for traps or energy barriers for transport, especially in themore » solution deposited case. DLTS reveals signatures of deep majority and minority traps within both types of samples.« less

Charge carriers' trapping states in pentacene films studied by modulated photocurrent

NASA Astrophysics Data System (ADS)

Gorgolis, S.; Giannopoulou, A.; Kounavis, P.

2013-03-01

The modulated photocurrent (MPC) technique is employed to study the charge carriers' trapping states of pentacene films. The characteristics of the experimental MPC spectra were found to be compatible with trapping-detrapping process of holes in gap states in which their occupancy can be modified by the bias illumination. A demarcation energy level separating empty from partially occupied traps was deduced from the MPC spectra, which can be used to monitor bias-light induced changes in the quasi Fermi level. An exponential trap distribution from structural disorder and a deep metastable gaussian trap distribution from adsorbed environmental impurities were extracted by means of the MPC spectroscopy. An attempt to escape frequency of the order of 1010s-1 was deduced for the gap sates. The derived trap distributions agree with those found before by means of other techniques. The present results indicate that the MPC technique can be used as a valuable tool for pentacene films characterization since it can be also applied to field effect samples.

Poole-Frenkel effect in sputter-deposited CuAlO2+x nanocrystals

NASA Astrophysics Data System (ADS)

Narayan Banerjee, Arghya; Joo, Sang Woo

2013-04-01

Field-assisted thermionic emission within a sputter-deposited, nanocrystalline thin film of CuAlO2.06 is observed for the first time, and explained in terms of the Poole-Frenkel model. The presence of adsorbed oxygen ions as trap-states at the grain boundary regions of the nanostructured thin film is considered to manifest this phenomenon. Under an applied field, the barrier of the trap potential is lowered and thermal emission of charge carriers takes place at different sample temperatures to induce nonlinearity in the current (I)-voltage (V) characteristics of the nanomaterial. Fitting of the Poole-Frenkel model with the I-V data shows that the nonlinearity is effective above 50 V under the operating conditions. Calculations of the energy of the trap level, acceptor level and Fermi level reveal the existence of deep level trap-states and a shallow acceptor level with acceptor concentration considerably higher than the trap-states. Hall measurements confirm the p-type semiconductivity of the film, with a hole concentration around 1018 cm-3. Thermopower measurements give a room-temperature Seebeck coefficient around 130 μV K-1. This temperature-dependent conductivity enhancement within CuAlO2 nanomaterial may find interesting applications in transparent electronics and high-voltage applications for power supply networks.

Poole-Frenkel effect in sputter-deposited CuAlO(2+x) nanocrystals.

PubMed

Banerjee, Arghya Narayan; Joo, Sang Woo

2013-04-26

Field-assisted thermionic emission within a sputter-deposited, nanocrystalline thin film of CuAlO2.06 is observed for the first time, and explained in terms of the Poole-Frenkel model. The presence of adsorbed oxygen ions as trap-states at the grain boundary regions of the nanostructured thin film is considered to manifest this phenomenon. Under an applied field, the barrier of the trap potential is lowered and thermal emission of charge carriers takes place at different sample temperatures to induce nonlinearity in the current (I)-voltage (V) characteristics of the nanomaterial. Fitting of the Poole-Frenkel model with the I-V data shows that the nonlinearity is effective above 50 V under the operating conditions. Calculations of the energy of the trap level, acceptor level and Fermi level reveal the existence of deep level trap-states and a shallow acceptor level with acceptor concentration considerably higher than the trap-states. Hall measurements confirm the p-type semiconductivity of the film, with a hole concentration around 10(18) cm(-3). Thermopower measurements give a room-temperature Seebeck coefficient around 130 μV K(-1). This temperature-dependent conductivity enhancement within CuAlO2 nanomaterial may find interesting applications in transparent electronics and high-voltage applications for power supply networks.

Human-level control through deep reinforcement learning.

PubMed

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A; Veness, Joel; Bellemare, Marc G; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-26

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

Human-level control through deep reinforcement learning

NASA Astrophysics Data System (ADS)

Mnih, Volodymyr; Kavukcuoglu, Koray; Silver, David; Rusu, Andrei A.; Veness, Joel; Bellemare, Marc G.; Graves, Alex; Riedmiller, Martin; Fidjeland, Andreas K.; Ostrovski, Georg; Petersen, Stig; Beattie, Charles; Sadik, Amir; Antonoglou, Ioannis; King, Helen; Kumaran, Dharshan; Wierstra, Daan; Legg, Shane; Hassabis, Demis

2015-02-01

The theory of reinforcement learning provides a normative account, deeply rooted in psychological and neuroscientific perspectives on animal behaviour, of how agents may optimize their control of an environment. To use reinforcement learning successfully in situations approaching real-world complexity, however, agents are confronted with a difficult task: they must derive efficient representations of the environment from high-dimensional sensory inputs, and use these to generalize past experience to new situations. Remarkably, humans and other animals seem to solve this problem through a harmonious combination of reinforcement learning and hierarchical sensory processing systems, the former evidenced by a wealth of neural data revealing notable parallels between the phasic signals emitted by dopaminergic neurons and temporal difference reinforcement learning algorithms. While reinforcement learning agents have achieved some successes in a variety of domains, their applicability has previously been limited to domains in which useful features can be handcrafted, or to domains with fully observed, low-dimensional state spaces. Here we use recent advances in training deep neural networks to develop a novel artificial agent, termed a deep Q-network, that can learn successful policies directly from high-dimensional sensory inputs using end-to-end reinforcement learning. We tested this agent on the challenging domain of classic Atari 2600 games. We demonstrate that the deep Q-network agent, receiving only the pixels and the game score as inputs, was able to surpass the performance of all previous algorithms and achieve a level comparable to that of a professional human games tester across a set of 49 games, using the same algorithm, network architecture and hyperparameters. This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.

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

Danno, Katsunori; Kimoto, Tsunenobu

The authors have investigated deep levels in as-grown and electron-irradiated p-type 4H-SiC epilayers by deep level transient spectroscopy. In as-grown epilayers, the D center and four deep levels are observed. In p-type 4H-SiC, reactive ion etching followed by thermal treatment (at 1150 degree sign C) induces the HK0 (E{sub V}+0.79 eV) and HK2 (E{sub V}+0.84 eV) centers. By the electron irradiation, two deep levels at 0.98 eV (EP1) and 1.44 eV (EP2) are observed in all the samples irradiated at 116-400 keV, while two additional deep levels (EP3 and EP4) are observed only in the samples irradiated at 400 keV.more » After annealing at 950 degree sign C, these centers are annealed out, and the HK4 (E{sub V}+1.44 eV) concentration is increased. By the electron irradiation at more than 160 keV followed by annealing at 950 degree sign C, three deep levels are always observed at 0.30 eV (UK1), 0.58 eV (UK2), and 1.44 eV (HK4). These centers may be defect complexes including carbon displacement-related defects. All the centers except for the D center are reduced to below the detection limit (1-3x10{sup 11} cm{sup -3}) by annealing at 1550 degree sign C for 30 min.« less

Adaptive Nulling: A New Enabling Technology for Interferometric Exoplanet

NASA Technical Reports Server (NTRS)

Lay, Oliver P.; Jeganathan, Muthu; Peters, Robert

2003-01-01

Deep, stable nulling of starlight requires careful control of the amplitudes and phases of the beams that are being combined. The detection of earth-like planets using the interferometer architectures currently being considered for the Terrestrial Planet Finder mission require that the E-field amplitudes are balanced at the level of approx. 0.1%, and the phases are controlled at the level of 1 mrad (corresponding to approx.1.5 nm for a wavelength of 10 microns). These conditions must be met simultaneously at all wavelengths across the science band, and for both polarization states, imposing unrealistic tolerances on the symmetry between the optical beamtrains. We introduce the concept of a compensator that is inserted into the beamtrain, which can adaptively correct for the mismatches across the spectrum, enabling deep nulls with realistic, imperfect optics. The design presented uses a deformable mirror to adjust the amplitude and phase of each beam as an arbitrary function of wavelength and polarization. A proof-of-concept experiment will be conducted at visible/near-IR wavelengths, followed by a system operating in the Mid-IR band.

Hidden Markov models reveal complexity in the diving behaviour of short-finned pilot whales

PubMed Central

Quick, Nicola J.; Isojunno, Saana; Sadykova, Dina; Bowers, Matthew; Nowacek, Douglas P.; Read, Andrew J.

2017-01-01

Diving behaviour of short-finned pilot whales is often described by two states; deep foraging and shallow, non-foraging dives. However, this simple classification system ignores much of the variation that occurs during subsurface periods. We used multi-state hidden Markov models (HMM) to characterize states of diving behaviour and the transitions between states in short-finned pilot whales. We used three parameters (number of buzzes, maximum dive depth and duration) measured in 259 dives by digital acoustic recording tags (DTAGs) deployed on 20 individual whales off Cape Hatteras, North Carolina, USA. The HMM identified a four-state model as the best descriptor of diving behaviour. The state-dependent distributions for the diving parameters showed variation between states, indicative of different diving behaviours. Transition probabilities were considerably higher for state persistence than state switching, indicating that dive types occurred in bouts. Our results indicate that subsurface behaviour in short-finned pilot whales is more complex than a simple dichotomy of deep and shallow diving states, and labelling all subsurface behaviour as deep dives or shallow dives discounts a significant amount of important variation. We discuss potential drivers of these patterns, including variation in foraging success, prey availability and selection, bathymetry, physiological constraints and socially mediated behaviour. PMID:28361954

Deepest X-Rays Ever Reveal universe Teeming With Black Holes

NASA Astrophysics Data System (ADS)

2001-03-01

For the first time, astronomers believe they have proof black holes of all sizes once ruled the universe. NASA's Chandra X-ray Observatory provided the deepest X-ray images ever recorded, and those pictures deliver a novel look at the past 12 billion years of black holes. Two independent teams of astronomers today presented images that contain the faintest X-ray sources ever detected, which include an abundance of active super massive black holes. "The Chandra data show us that giant black holes were much more active in the past than at present," said Riccardo Giacconi, of Johns Hopkins University and Associated Universities, Inc., Washington, DC. The exposure is known as "Chandra Deep Field South" since it is located in the Southern Hemisphere constellation of Fornax. "In this million-second image, we also detect relatively faint X-ray emission from galaxies, groups, and clusters of galaxies". The images, known as Chandra Deep Fields, were obtained during many long exposures over the course of more than a year. Data from the Chandra Deep Field South will be placed in a public archive for scientists beginning today. "For the first time, we are able to use X-rays to look back to a time when normal galaxies were several billion years younger," said Ann Hornschemeier, Pennsylvania State University, University Park. The group’s 500,000-second exposure included the Hubble Deep Field North, allowing scientists the opportunity to combine the power of Chandra and the Hubble Space Telescope, two of NASA's Great Observatories. The Penn State team recently acquired an additional 500,000 seconds of data, creating another one-million-second Chandra Deep Field, located in the constellation of Ursa Major. Chandra Deep Field North/Hubble Deep Field North Press Image and Caption The images are called Chandra Deep Fields because they are comparable to the famous Hubble Deep Field in being able to see further and fainter objects than any image of the universe taken at X-ray wavelengths. Both Chandra Deep Fields are comparable in observation time to the Hubble Deep Fields, but cover a much larger area of the sky. "In essence, it is like seeing galaxies similar to our own Milky Way at much earlier times in their lives," Hornschemeier added. "These data will help scientists better understand star formation and how stellar-sized black holes evolve." Combining infrared and X-ray observations, the Penn State team also found veils of dust and gas are common around young black holes. Another discovery to emerge from the Chandra Deep Field South is the detection of an extremely distant X-ray quasar, shrouded in gas and dust. "The discovery of this object, some 12 billion light years away, is key to understanding how dense clouds of gas form galaxies, with massive black holes at their centers," said Colin Norman of Johns Hopkins University. The Chandra Deep Field South results were complemented by the extensive use of deep optical observations supplied by the Very Large Telescope of the European Southern Observatory in Garching, Germany. The Penn State team obtained optical spectroscopy and imaging using the Hobby-Eberly Telescope in Ft. Davis, TX, and the Keck Observatory atop Mauna Kea, HI. Chandra's Advanced CCD Imaging Spectrometer was developed for NASA by Penn State and Massachusetts Institute of Technology under the leadership of Penn State Professor Gordon Garmire. NASA's Marshall Space Flight Center, Huntsville, AL, manages the Chandra program for the Office of Space Science, Washington, DC. TRW, Inc., Redondo Beach, California, is the prime contractor for the spacecraft. The Smithsonian's Chandra X-ray Center controls science and flight operations from Cambridge, MA. More information is available on the Internet at: http://chandra.harvard.edu AND http://chandra.nasa.gov

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2014 CFR

2014-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2013 CFR

2013-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2010 CFR

2010-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

15 CFR 970.103 - Prohibited activities and restrictions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... THE ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR EXPLORATION LICENSES General § 970... United States or any other nation; and any other activity designed to harass deep seabed mining...

Quantum Entanglement as a Diagnostic of Phase Transitions in Disordered Fractional Quantum Hall Liquids.

PubMed

Liu, Zhao; Bhatt, R N

2016-11-11

We investigate the disorder-driven phase transition from a fractional quantum Hall state to an Anderson insulator using quantum entanglement methods. We find that the transition is signaled by a sharp increase in the sensitivity of a suitably averaged entanglement entropy with respect to disorder-the magnitude of its disorder derivative appears to diverge in the thermodynamic limit. We also study the level statistics of the entanglement spectrum as a function of disorder. However, unlike the dramatic phase-transition signal in the entanglement entropy derivative, we find a gradual reduction of level repulsion only deep in the Anderson insulating phase.

On the behaviour and origin of the major deep level (EL2) in GaAs

NASA Technical Reports Server (NTRS)

Lagowski, J.; Parsey, J. M.; Kaminska, M.; Wada, K.; Gatos, H. C.

1982-01-01

In an extensive crystal growth and characterization study of Bridgman-grown GaAs it was established that the following factors affect the concentration of the EL2 level: (1) the As pressure during growth; (2) the partial pressure of Ga2O; (3) the concentration of shallow donors and acceptors; and (4) the post-growth cooling cycle. The role of these factors is qualitatively and quantitatively explained by attributing the 0.82 eV donor state to the antisite defect As-sub-Ga formed as a result of Ga-vacancy migration during the post-growth cooling of the crystals.

Characteristics of the deep ocean carbon system during the past 150,000 years: ΣCO2 distributions, deep water flow patterns, and abrupt climate change

PubMed Central

Boyle, Edward A.

1997-01-01

Studies of carbon isotopes and cadmium in bottom-dwelling foraminifera from ocean sediment cores have advanced our knowledge of ocean chemical distributions during the late Pleistocene. Last Glacial Maximum data are consistent with a persistent high-ΣCO2 state for eastern Pacific deep water. Both tracers indicate that the mid-depth North and tropical Atlantic Ocean almost always has lower ΣCO2 levels than those in the Pacific. Upper waters of the Last Glacial Maximum Atlantic are more ΣCO2-depleted and deep waters are ΣCO2-enriched compared with the waters of the present. In the northern Indian Ocean, δ13C and Cd data are consistent with upper water ΣCO2 depletion relative to the present. There is no evident proximate source of this ΣCO2-depleted water, so I suggest that ΣCO2-depleted North Atlantic intermediate/deep water turns northward around the southern tip of Africa and moves toward the equator as a western boundary current. At long periods (>15,000 years), Milankovitch cycle variability is evident in paleochemical time series. But rapid millennial-scale variability can be seen in cores from high accumulation rate series. Atlantic deep water chemical properties are seen to change in as little as a few hundred years or less. An extraordinary new 52.7-m-long core from the Bermuda Rise contains a faithful record of climate variability with century-scale resolution. Sediment composition can be linked in detail with the isotope stage 3 interstadials recorded in Greenland ice cores. This new record shows at least 12 major climate fluctuations within marine isotope stage 5 (about 70,000–130,000 years before the present). PMID:11607737

Quantification of deep traps in nanocrystal solids, their electronic properties, and their influence on device behavior.

PubMed

Bozyigit, Deniz; Volk, Sebastian; Yarema, Olesya; Wood, Vanessa

2013-11-13

We implement three complementary techniques to quantify the number, energy, and electronic properties of trap states in nanocrystal (NC)-based devices. We demonstrate that, for a given technique, the ability to observe traps depends on the Fermi level position, highlighting the importance of a multitechnique approach that probes trap coupling to both the conduction and the valence bands. We then apply our protocol for characterizing traps to quantitatively explain the measured performances of PbS NC-based solar cells.

Southeast Asia Report, Vietnam, Tap Chi Cong San, No. 8, August 1984.

DTIC Science & Technology

1984-10-22

filled with lyricism and romance, an epic. Continuing this direction and style, recent films have taken another step forward, have reached a new level of...rich in lyricism , were compressed but moving, like poetry, and whereas the resistance against the United States for national salvation marked the...in the " Essay on the Vinh Loi Canal," Nguyen Thong displayed his scientific mind by proposing that the canal be narrow and deep so that it could

Low Cost, Low Power, Passive Muon Telescope for Interrogating Martian Sub-Surface

NASA Technical Reports Server (NTRS)

Kedar, Sharon; Tanaka, Hirukui; Naudet, Charles; Plaut, Jeffrey J.; Jones, Cathleen E.; Webb, Frank H.

2012-01-01

It has been demonstrated on Earth that a low power, passive muon detector can penetrate deep into geological structures up to several kilometers in size providing high density images of their interiors. Muon tomography is an entirely new class of planetary instrumentation that is ideally suited to address key areas in Mars Science, such as: the search for life and habitable environments, the distribution and state of water and ice and the level of geologic activity on Mars today.

Character-level neural network for biomedical named entity recognition.

PubMed

Gridach, Mourad

2017-06-01

Biomedical named entity recognition (BNER), which extracts important named entities such as genes and proteins, is a challenging task in automated systems that mine knowledge in biomedical texts. The previous state-of-the-art systems required large amounts of task-specific knowledge in the form of feature engineering, lexicons and data pre-processing to achieve high performance. In this paper, we introduce a novel neural network architecture that benefits from both word- and character-level representations automatically, by using a combination of bidirectional long short-term memory (LSTM) and conditional random field (CRF) eliminating the need for most feature engineering tasks. We evaluate our system on two datasets: JNLPBA corpus and the BioCreAtIvE II Gene Mention (GM) corpus. We obtained state-of-the-art performance by outperforming the previous systems. To the best of our knowledge, we are the first to investigate the combination of deep neural networks, CRF, word embeddings and character-level representation in recognizing biomedical named entities. Copyright © 2017 Elsevier Inc. All rights reserved.

Origin of subgap states in amorphous In-Ga-Zn-O

NASA Astrophysics Data System (ADS)

Körner, Wolfgang; Urban, Daniel F.; Elsässer, Christian

2013-10-01

We present a density functional theory analysis of stoichiometric and nonstoichiometric, crystalline and amorphous In-Ga-Zn-O (c-IGZO, a-IGZO), which connects the recently experimentally discovered electronic subgap states to structural features of a-IGZO. In particular, we show that undercoordinated oxygen atoms create electronic defect levels in the lower half of the band gap up to about 1.5 eV above the valence band edge. As a second class of fundamental defects that appear in a-IGZO, we identify mainly pairs of metal atoms which are not separated by oxygen atoms in between. These defects cause electronic defect levels in the upper part of the band gap. Furthermore, we show that hydrogen doping can suppress the deep levels due to undercoordinated oxygen atoms while those of metal defects just undergo a shift within the band gap. Altogether our results provide an explanation for the experimentally observed effect that hydrogen doping increases the transparency and improves the conductivity of a-IGZO.

Ground-water quality for Grainger County, Tennessee

USGS Publications Warehouse

Weaver, J.D.; Patel, A.R.; Hickey, A.C.

1994-01-01

The residents of Grainger County depend on ground water for many of their daily needs including personal consumption and crop irrigation. To address concerns associated with ground-water quality related to domestic use, the U.S. Geological Survey collected water samples from 35 wells throughout the county during the summer 1992. The water samples were analyzed to determine if pesticides, nutrients, bacteria, and other selected constituents were present in the ground water. Wells selected for the study were between 100 and 250 feet deep and yielded 10 to 50 gallons of water per minute. Laboratory analyses of the water found no organic pesticides at concentrations exceeding the primary maximum contaminant levels established by the State of Tennessee for wells used for public supply. However, fecal coliform bacteria were detected at concentrations exceeding the State's maximum contaminant level in water from 15 of the 35 wells sampled. Analyses also indicated several inorganic compounds were present in the water samples at concentrations exceeding the secondary maximum contaminant level.

Using regime analysis to identify the contribution of clouds to surface temperature errors in weather and climate models

DOE PAGES

Van Weverberg, Kwinten; Morcrette, Cyril J.; Ma, Hsi -Yen; ...

2015-06-17

Many global circulation models (GCMs) exhibit a persistent bias in the 2 m temperature over the midlatitude continents, present in short-range forecasts as well as long-term climate simulations. A number of hypotheses have been proposed, revolving around deficiencies in the soil–vegetation–atmosphere energy exchange, poorly resolved low-level boundary-layer clouds or misrepresentations of deep-convective storms. A common approach to evaluating model biases focuses on the model-mean state. However, this makes difficult an unambiguous interpretation of the origins of a bias, given that biases are the result of the superposition of impacts of clouds and land-surface deficiencies over multiple time steps. This articlemore » presents a new methodology to objectively detect the role of clouds in the creation of a surface warm bias. A unique feature of this study is its focus on temperature-error growth at the time-step level. It is shown that compositing the temperature-error growth by the coinciding bias in total downwelling radiation provides unambiguous evidence for the role that clouds play in the creation of the surface warm bias during certain portions of the day. Furthermore, the application of an objective cloud-regime classification allows for the detection of the specific cloud regimes that matter most for the creation of the bias. We applied this method to two state-of-the-art GCMs that exhibit a distinct warm bias over the Southern Great Plains of the USA. Our analysis highlights that, in one GCM, biases in deep-convective and low-level clouds contribute most to the temperature-error growth in the afternoon and evening respectively. In the second GCM, deep clouds persist too long in the evening, leading to a growth of the temperature bias. In conclusion, the reduction of the temperature bias in both models in the morning and the growth of the bias in the second GCM in the afternoon could not be assigned to a cloud issue, but are more likely caused by a land-surface deficiency.« less

Transition probabilities in neutron-rich Se,8280 and the role of the ν g9 /2 orbital

NASA Astrophysics Data System (ADS)

Litzinger, J.; Blazhev, A.; Dewald, A.; Didierjean, F.; Duchêne, G.; Fransen, C.; Lozeva, R.; Verney, D.; de Angelis, G.; Bazzacco, D.; Birkenbach, B.; Bottoni, S.; Bracco, A.; Braunroth, T.; Cederwall, B.; Corradi, L.; Crespi, F. C. L.; Désesquelles, P.; Eberth, J.; Ellinger, E.; Farnea, E.; Fioretto, E.; Gernhäuser, R.; Goasduff, A.; Görgen, A.; Gottardo, A.; Grebosz, J.; Hackstein, M.; Hess, H.; Ibrahim, F.; Jolie, J.; Jungclaus, A.; Kolos, K.; Korten, W.; Leoni, S.; Lunardi, S.; Maj, A.; Menegazzo, R.; Mengoni, D.; Michelagnoli, C.; Mijatovic, T.; Million, B.; Möller, O.; Modamio, V.; Montagnoli, G.; Montanari, D.; Morales, A. I.; Napoli, D. R.; Niikura, M.; Pietralla, N.; Pollarolo, G.; Pullia, A.; Quintana, B.; Recchia, F.; Reiter, P.; Rosso, D.; Sahin, E.; Salsac, M. D.; Scarlassara, F.; Söderström, P.-A.; Stefanini, A. M.; Stezowski, O.; Szilner, S.; Theisen, Ch.; Valiente-Dobón, J. J.; Vandone, V.; Vogt, A.

2018-04-01

Transition probabilities of intermediate-spin yrast and non-yrast excitations in Se,8280 were investigated in a recoil distance Doppler-shift (RDDS) experiment performed at the Istituto Nazionale di Fisica Nucleare, Laboratori Nazionali di Legnaro. The Cologne Plunger device for deep inelastic scattering was used for the RDDS technique and was combined with the AGATA Demonstrator array for the γ -ray detection and coupled to the PRISMA magnetic spectrometer for an event-by-event particle identification. In 80Se, the level lifetimes of the yrast (61+) and (81+) states and of a non-yrast band feeding the yrast 41+ state are determined. A spin and parity assignment of the head of this sideband is discussed based on the experimental results and supported by large-scale shell-model calculations. In 82Se, the level lifetimes of the yrast 61+ state and the yrare 42+ state and lifetime limits of the yrast (101+) state and of the 51- state are determined. Although the experimental results contain large uncertainties, they are interpreted with care in terms of large-scale shell-model calculations using the effective interactions JUN45 and jj44b. The excited states' wave functions are investigated and discussed with respect to the role of the neutron g9 /2 orbital.

Measurement of the hadronic final state in deep inelastic scattering at HERA

NASA Astrophysics Data System (ADS)

Ahmed, T.; Andreev, V.; Andrieu, B.; Arpagaus, M.; Babaev, A.; Bärwolff, H.; Bán, J.; Baranov, P.; Barrelet, E.; Bartel, W.; Bassler, U.; Beck, G. A.; Beck, H. P.; Behrend, H.-J.; Belousov, A.; Berger, Ch.; Bergstein, H.; Bernardi, G.; Bernet, R.; Berthon, U.; Bertrand-Coremans, G.; Besançon, M.; Biddulph, P.; Binder, E.; Bizot, J. C.; Blobel, V.; Borras, K.; Bosetti, P. C.; Boudry, V.; Bourdarios, C.; Brasse, F.; Braun, U.; Braunschweig, W.; Brisson, V.; Bruncko, D.; Bürger, J.; Büsser, F. W.; Buniatian, A.; Burke, S.; Buschhorn, G.; Campbell, A. J.; Carli, T.; Charles, F.; Clarke, D.; Clegg, A. B.; Colombo, M.; Coughlan, J. A.; Courau, A.; Coutures, Ch.; Cozzika, G.; Criegee, L.; Cvach, J.; Dainton, J. B.; Danilov, M.; Dann, A. W. E.; Dau, W. D.; David, M.; Deffur, E.; Delcourt, B.; Del Buono, L.; Devel, M.; De Roeck, A.; Dingus, P.; Dollfus, C.; Dowell, J. D.; Dreis, H. B.; Drescher, A.; Duboc, J.; Düllmann, D.; Dünger, O.; Duhm, H.; Eberle, M.; Ebert, J.; Ebert, T. R.; Eckerlin, G.; Efremenko, V.; Egli, S.; Eichenberger, S.; Eichler, R.; Eisele, F.; Eisenhandler, E.; Ellis, N. N.; Ellison, R. J.; Elsen, E.; Erdmann, M.; Evrard, E.; Favart, L.; Fedotov, A.; Feeken, D.; Felst, R.; Feltesse, J.; Feng, Y.; Fensome, I. F.; Ferencei, J.; Ferrarotto, F.; Flauger, W.; Fleischer, M.; Flower, P. S.; Flügge, G.; Fomenko, A.; Fominykh, B.; Forbush, M.; Formánek, J.; Foster, J. M.; Franke, G.; Fretwurst, E.; Fuhrmann, P.; Gabathuler, E.; Gamerdinger, K.; Garvey, J.; Gayler, J.; Gellrich, A.; Gennis, M.; Gensch, U.; Genzel, H.; Gerhards, R.; Gillespie, D.; Godfrey, L.; Goerlach, U.; Goerlich, L.; Goldberg, M.; Goodall, A. M.; Gorelov, I.; Goritchev, P.; Grab, C.; Grässler, H.; Grässler, R.; Greenshaw, T.; Greif, H.; Grindhammer, G.; Gruber, C.; Haack, J.; Haidt, D.; Hajduk, L.; Hamon, O.; Handschuh, D.; Hanlon, E. M.; Hapke, M.; Haries, J.; Hartz, P.; Haydar, R.; Haynes, W. J.; Heatherington, J.; Hedberg, V.; Hedgecock, R.; Heinzelmann, G.; Henderson, R. C. W.; Henschel, H.; Herma, R.; Herynek, I.; Hildesheim, W.; Hill, P.; Hilton, C. D.; Hladký, J.; Hoeger, K. C.; Huet, Ph.; Hufnagel, H.; Huot, N.; Ibbotson, M.; Jabiol, M. A.; Jacholkowska, A.; Jacobsson, C.; Jaffre, M.; Jöhnsson, L.; Johannsen, K.; Johnson, D. P.; Johnson, L.; Jung, H.; Kalmus, P. I. P.; Kasarian, S.; Kaschowitz, R.; Kasselmann, P.; Kathage, U.; Kaufmann, H. H.; Kenyon, I. R.; Kermiche, S.; Kiesling, C.; Klein, M.; Kleinwort, C.; Knies, G.; Köhler, T.; Kolanoski, H.; Kole, F.; Kolya, S. D.; Korbel, V.; Korn, M.; Kostka, P.; Kotelnikov, S. K.; Krasny, M. W.; Krehbiel, H.; Krücker, D.; Krüger, U.; Kubenka, J. P.; Küster, H.; Kuhlen, M.; Kurça, T.; Kurzhöfer, J.; Kuznik, B.; Lander, R.; London, M. P. J.; Langkau, R.; Lanius, P.; Laporte, J. F.; Lebedev, A.; Lebedev, A.; Leuschner, A.; Leverenz, C.; Levin, D.; Levonian, S.; Ley, Ch.; Lindner, A.; Lindström, G.; Loch, P.; Lohmander, H.; Lopez, G. C.; Lüers, D.; Magnussen, N.; Malinovski, E.; Mani, S.; Marage, P.; Marks, J.; Marshall, R.; Martens, J.; Martin, R.; Martyn, H.-U.; Martyniak, J.; Masson, S.; Mavroidis, A.; Maxfield, S. J.; McMahon, S. J.; Mehta, A.; Meier, K.; Merz, T.; Meyer, C. A.; Meyer, H.; Meyer, J.; Mikocki, S.; Milone, V.; Monnier, E.; Moreau, F.; Moreels, J.; Morris, J. V.; Morton, J. M.; Müller, K.; Murín, P.; Murray, S. A.; Nagovizin, V.; Naroska, B.; Naumann, Th.; Newton, D.; Nguyen, H. K.; Niebergall, F.; Nisius, R.; Nowak, G.; Noyes, G. W.; Nyberg, M.; Oberlack, H.; Obrock, U.; Olsson, J. E.; Orenstein, S.; Ould-Saada, F.; Pascaud, C.; Patel, G. D.; Peppel, E.; Peters, S.; Phillips, H. T.; Phillips, J. P.; Pichler, Ch.; Pilgram, W.; Pitzl, D.; Prosi, R.; Raupach, F.; Rauschnabel, K.; Reimer, P.; Ribarics, P.; Riech, V.; Riedlberger, J.; Rietz, M.; Robertson, S. M.; Robmann, P.; Roosen, R.; Rostovtsev, A.; Royon, C.; Rudowicz, M.; Ruffer, M.; Rusakov, S.; Rybicki, K.; Ryseck, E.; Sacton, J.; Sahlmann, N.; Sanchez, E.; Sankey, D. P. C.; Savitsky, M.; Schacht, P.; Schleper, P.; von Schlippe, W.; Schmidt, C.; Schmidt, D.; Schmitz, W.; Schröder, V.; Schulz, M.; Schwind, A.; Scobel, W.; Seehausen, U.; Sell, R.; Seman, M.; Semenov, A.; Shekelyan, V.; Sheviakov, I.; Shooshtari, H.; Siegmon, G.; Siewert, U.; Sirois, Y.; Skillicorn, I. O.; Smirnov, P.; Smith, J. R.; Smolik, L.; Soloviev, Y.; Spitzer, H.; Staroba, P.; Steenbock, M.; Steffen, P.; Steinberg, R.; Steiner, H.; Stella, B.; Stephens, K.; Stier, J.; Strachota, J.; Straumann, U.; Struczinski, W.; Sutton, J. P.; Taylor, R. E.; Thompson, G.; Thompson, R. J.; Tichomirov, I.; Trenkel, C.; Truöl, P.; Tchernyshov, V.; Turnau, J.; Tutas, J.; Urban, L.; Usik, A.; Valkar, S.; Valkarova, A.; Vallee, C.; Van Esch, P.; Vartapetian, A.; Vazdik, Y.; Vecko, M.; Verrecchia, P.; Vick, R.; Villet, G.; Vogel, E.; Wacker, K.; Walker, I. W.; Walther, A.; Weber, G.; Wegener, D.; Wegner, A.; Wellisch, H. P.; Willard, S.; Winde, M.; Winter, G.-G.; Wolff, Th.; Womersley, L. A.; Wright, A. E.; Wulff, N.; Yiou, T. P.; Ząçek, J.; Závada, P.; Zeitnitz, C.; Ziaeepour, H.; Zimmer, M.; Zimmermann, W.; Zomer, F.; H1 Collaboration

1993-01-01

We report on the first experimental study of the hadronic final state in deep inelastic electron-proton scattering with the H1 detector at HERA. Energy flow and transverse momentum characteristics are measured and presented both in the laboratory and in the hadronic center of mass frames. Comparison is made with QCD models distinguished by their different treatment of parton emission.

Quenched-in defects in flashlamp-annealed silicon

NASA Technical Reports Server (NTRS)

Borenstein, J. T.; Jones, J. T.; Corbett, J. W.; Oehrlein, G. S.; Kleinhenz, R. L.

1986-01-01

Deep levels introduced in boron-doped silicon by heat-pulse annealing with a tungsten-halogen flashlamp are investigated using deep-level transient spectroscopy. Two majority-carrier trapping levels in the band gap, at Ev + 0.32 eV and at Ev + 0.45 eV, are observed. These results are compared to those obtained by furnace-quenching and laser-annealing studies. Both the position in the gap and the annealing kinetics of the hole trap at Ev + 0.45 eV suggest that this center is due to an interstitial iron impurity in the lattice. The deep levels are not consistently observed in all flashlamp-annealed Si crystals utilized.

15 CFR 971.428 - Other necessary permits.

Code of Federal Regulations, 2010 CFR

2010-01-01

... ENVIRONMENTAL DATA SERVICE DEEP SEABED MINING REGULATIONS FOR COMMERCIAL RECOVERY PERMITS Issuance/Transfer.... Each permit will provide that securing the deep seabed mining permit for activities described in the... Federal, State, and local permits. ...

Alternative Cancer Treatments: 10 Options to Consider

MedlinePlus

... days of the week. Hypnosis. Hypnosis is a deep state of concentration. During a hypnotherapy session, a ... be light and gentle, or it can be deep with more pressure. Studies have found massage can ...

The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson's disease.

PubMed

Tinkhauser, Gerd; Pogosyan, Alek; Little, Simon; Beudel, Martijn; Herz, Damian M; Tan, Huiling; Brown, Peter

2017-04-01

Adaptive deep brain stimulation uses feedback about the state of neural circuits to control stimulation rather than delivering fixed stimulation all the time, as currently performed. In patients with Parkinson's disease, elevations in beta activity (13-35 Hz) in the subthalamic nucleus have been demonstrated to correlate with clinical impairment and have provided the basis for feedback control in trials of adaptive deep brain stimulation. These pilot studies have suggested that adaptive deep brain stimulation may potentially be more effective, efficient and selective than conventional deep brain stimulation, implying mechanistic differences between the two approaches. Here we test the hypothesis that such differences arise through differential effects on the temporal dynamics of beta activity. The latter is not constantly increased in Parkinson's disease, but comes in bursts of different durations and amplitudes. We demonstrate that the amplitude of beta activity in the subthalamic nucleus increases in proportion to burst duration, consistent with progressively increasing synchronization. Effective adaptive deep brain stimulation truncated long beta bursts shifting the distribution of burst duration away from long duration with large amplitude towards short duration, lower amplitude bursts. Critically, bursts with shorter duration are negatively and bursts with longer duration positively correlated with the motor impairment off stimulation. Conventional deep brain stimulation did not change the distribution of burst durations. Although both adaptive and conventional deep brain stimulation suppressed mean beta activity amplitude compared to the unstimulated state, this was achieved by a selective effect on burst duration during adaptive deep brain stimulation, whereas conventional deep brain stimulation globally suppressed beta activity. We posit that the relatively selective effect of adaptive deep brain stimulation provides a rationale for why this approach could be more efficacious than conventional continuous deep brain stimulation in the treatment of Parkinson's disease, and helps inform how adaptive deep brain stimulation might best be delivered. © The Author (2017). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved.

The modulatory effect of adaptive deep brain stimulation on beta bursts in Parkinson’s disease

PubMed Central

Tinkhauser, Gerd; Pogosyan, Alek; Little, Simon; Beudel, Martijn; Herz, Damian M.; Tan, Huiling

2017-01-01

Abstract Adaptive deep brain stimulation uses feedback about the state of neural circuits to control stimulation rather than delivering fixed stimulation all the time, as currently performed. In patients with Parkinson’s disease, elevations in beta activity (13–35 Hz) in the subthalamic nucleus have been demonstrated to correlate with clinical impairment and have provided the basis for feedback control in trials of adaptive deep brain stimulation. These pilot studies have suggested that adaptive deep brain stimulation may potentially be more effective, efficient and selective than conventional deep brain stimulation, implying mechanistic differences between the two approaches. Here we test the hypothesis that such differences arise through differential effects on the temporal dynamics of beta activity. The latter is not constantly increased in Parkinson’s disease, but comes in bursts of different durations and amplitudes. We demonstrate that the amplitude of beta activity in the subthalamic nucleus increases in proportion to burst duration, consistent with progressively increasing synchronization. Effective adaptive deep brain stimulation truncated long beta bursts shifting the distribution of burst duration away from long duration with large amplitude towards short duration, lower amplitude bursts. Critically, bursts with shorter duration are negatively and bursts with longer duration positively correlated with the motor impairment off stimulation. Conventional deep brain stimulation did not change the distribution of burst durations. Although both adaptive and conventional deep brain stimulation suppressed mean beta activity amplitude compared to the unstimulated state, this was achieved by a selective effect on burst duration during adaptive deep brain stimulation, whereas conventional deep brain stimulation globally suppressed beta activity. We posit that the relatively selective effect of adaptive deep brain stimulation provides a rationale for why this approach could be more efficacious than conventional continuous deep brain stimulation in the treatment of Parkinson’s disease, and helps inform how adaptive deep brain stimulation might best be delivered. PMID:28334851

Extraterrestrial demise of banded iron formations 1.85 billion years ago

USGS Publications Warehouse

Slack, J.F.; Cannon, W.F.

2009-01-01

In the Lake Superior region of North America, deposition of most banded iron formations (BIFs) ended abruptly 1.85 Ga ago, coincident with the oceanic impact of the giant Sudbury extraterrestrial bolide. We propose a new model in which this impact produced global mixing of shallow oxic and deep anoxic waters of the Paleoproterozoic ocean, creating a suboxic redox state for deep seawater. This suboxic state, characterized by only small concentrations of dissolved O2 (???1 ??M), prevented transport of hydrothermally derived Fe(II) from the deep ocean to continental-margin settings, ending an ???1.1 billion-year-long period of episodic BIF mineralization. The model is supported by the nature of Precambrian deep-water exhalative chemical sediments, which changed from predominantly sulfide facies prior to ca. 1.85 Ga to mainly oxide facies thereafter. ?? 2009 Geological Society of America.

Enhancement of the southward return flow of the Atlantic Meridional Overturning Circulation by data assimilation and its influence in an assimilative ocean simulation forced by CORE-II atmospheric forcing

NASA Astrophysics Data System (ADS)

Fujii, Yosuke; Tsujino, Hiroyuki; Toyoda, Takahiro; Nakano, Hideyuki

2017-08-01

This paper examines the difference in the Atlantic Meridional Overturning Circulation (AMOC) mean state between free and assimilative simulations of a common ocean model using a common interannual atmospheric forcing. In the assimilative simulation, the reproduction of cold cores in the Nordic Seas, which is absent in the free simulation, enhances the overflow to the North Atlantic and improves AMOC with enhanced transport of the deeper part of the southward return flow. This improvement also induces an enhanced supply of North Atlantic Deep Water (NADW) and causes better representation of the Atlantic deep layer despite the fact that correction by the data assimilation is applied only to temperature and salinity above a depth of 1750 m. It also affects Circumpolar Deep Water in the Southern Ocean. Although the earliest influence of the improvement propagated by coastal waves reaches the Southern Ocean in 10-15 years, substantial influence associated with the arrival of the renewed NADW propagates across the Atlantic Basin in several decades. Although the result demonstrates that data assimilation is able to improve the deep ocean state even if there is no data there, it also indicates that long-term integration is required to reproduce variability in the deep ocean originating from variations in the upper ocean. This study thus provides insights on the reliability of AMOC and the ocean state in the Atlantic deep layer reproduced by data assimilation systems.

Deep-level transient spectroscopy studies of Ni- and Zn-diffused vapor-phase-epitaxy n-GaAs

NASA Technical Reports Server (NTRS)

Partin, D. L.; Chen, J. W.; Milnes, A. G.; Vassamillet, L. F.

1979-01-01

The paper presents deep-level transient spectroscopy studies of Ni- and Zn-diffused vapor-phase epitaxy n-GaAs. Nickel diffused into VPE n-GaAs reduces the hole diffusion length L sub p from 4.3 to 1.1 microns. Deep-level transient spectroscopy was used to identify energy levels in Ni-diffused GaAs; the as-grown VPE GaAs contains traces of these levels and an electron trap. Ni diffusion reduces the concentration of this level by an amount that matches the increase in concentration of each of the two Ni-related levels. A technique for measuring minority-carrier capture cross sections was developed, which indicates that L sub p in Ni-diffused VPE n-GaAs is controlled by the E sub c - 0.39 eV defect level.

Improving protein fold recognition by extracting fold-specific features from predicted residue-residue contacts.

PubMed

Zhu, Jianwei; Zhang, Haicang; Li, Shuai Cheng; Wang, Chao; Kong, Lupeng; Sun, Shiwei; Zheng, Wei-Mou; Bu, Dongbo

2017-12-01

Accurate recognition of protein fold types is a key step for template-based prediction of protein structures. The existing approaches to fold recognition mainly exploit the features derived from alignments of query protein against templates. These approaches have been shown to be successful for fold recognition at family level, but usually failed at superfamily/fold levels. To overcome this limitation, one of the key points is to explore more structurally informative features of proteins. Although residue-residue contacts carry abundant structural information, how to thoroughly exploit these information for fold recognition still remains a challenge. In this study, we present an approach (called DeepFR) to improve fold recognition at superfamily/fold levels. The basic idea of our approach is to extract fold-specific features from predicted residue-residue contacts of proteins using deep convolutional neural network (DCNN) technique. Based on these fold-specific features, we calculated similarity between query protein and templates, and then assigned query protein with fold type of the most similar template. DCNN has showed excellent performance in image feature extraction and image recognition; the rational underlying the application of DCNN for fold recognition is that contact likelihood maps are essentially analogy to images, as they both display compositional hierarchy. Experimental results on the LINDAHL dataset suggest that even using the extracted fold-specific features alone, our approach achieved success rate comparable to the state-of-the-art approaches. When further combining these features with traditional alignment-related features, the success rate of our approach increased to 92.3%, 82.5% and 78.8% at family, superfamily and fold levels, respectively, which is about 18% higher than the state-of-the-art approach at fold level, 6% higher at superfamily level and 1% higher at family level. An independent assessment on SCOP_TEST dataset showed consistent performance improvement, indicating robustness of our approach. Furthermore, bi-clustering results of the extracted features are compatible with fold hierarchy of proteins, implying that these features are fold-specific. Together, these results suggest that the features extracted from predicted contacts are orthogonal to alignment-related features, and the combination of them could greatly facilitate fold recognition at superfamily/fold levels and template-based prediction of protein structures. Source code of DeepFR is freely available through https://github.com/zhujianwei31415/deepfr, and a web server is available through http://protein.ict.ac.cn/deepfr. zheng@itp.ac.cn or dbu@ict.ac.cn. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Eddy-resolving simulations of the Fimbul Ice Shelf cavity circulation: Basal melting and exchange with open ocean

NASA Astrophysics Data System (ADS)

Hattermann, T.; Smedsrud, L. H.; Nøst, O. A.; Lilly, J. M.; Galton-Fenzi, B. K.

2014-10-01

Melting at the base of floating ice shelves is a dominant term in the overall Antarctic mass budget. This study applies a high-resolution regional ice shelf/ocean model, constrained by observations, to (i) quantify present basal mass loss at the Fimbul Ice Shelf (FIS); and (ii) investigate the oceanic mechanisms that govern the heat supply to ice shelves in the Eastern Weddell Sea. The simulations confirm the low melt rates suggested by observations and show that melting is primarily determined by the depth of the coastal thermocline, regulating deep ocean heat fluxes towards the ice. Furthermore, the uneven distribution of ice shelf area at different depths modulates the melting response to oceanic forcing, causing the existence of two distinct states of melting at the FIS. In the simulated present-day state, only small amounts of Modified Warm Deep Water enter the continental shelf, and ocean temperatures beneath the ice are close to the surface freezing point. The basal mass loss in this so-called state of "shallow melting" is mainly controlled by the seasonal inflow of solar-heated surface water affecting large areas of shallow ice in the upper part of the cavity. This is in contrast to a state of "deep melting", in which the thermocline rises above the shelf break depth, establishing a continuous inflow of Warm Deep Water towards the deep ice. The transition between the two states is found to be determined by a complex response of the Antarctic Slope Front overturning circulation to varying climate forcings. A proper representation of these frontal dynamics in climate models will therefore be crucial when assessing the evolution of ice shelf basal melting along this sector of Antarctica.

Effective temperature in relaxation of Coulomb glasses.

PubMed

Somoza, A M; Ortuño, M; Caravaca, M; Pollak, M

2008-08-01

We study relaxation in two-dimensional Coulomb glasses up to macroscopic times. We use a kinetic Monte Carlo algorithm especially designed to escape efficiently from deep valleys around metastable states. We find that, during the relaxation process, the site occupancy follows a Fermi-Dirac distribution with an effective temperature much higher than the real temperature T. Long electron-hole excitations are characterized by T(eff), while short ones are thermalized at T. We argue that the density of states at the Fermi level is proportional to T(eff) and is a good thermometer to measure it. T(eff) decreases extremely slowly, roughly as the inverse of the logarithm of time, and it should affect hopping conductance in many experimental circumstances.

Deep X-ray lithography for the fabrication of microstructures at ELSA

NASA Astrophysics Data System (ADS)

Pantenburg, F. J.; Mohr, J.

2001-07-01

Two beamlines at the Electron Stretcher Accelerator (ELSA) of Bonn University are dedicated for the production of microstructures by deep X-ray lithography with synchrotron radiation. They are equipped with state-of-the-art X-ray scanners, maintained and used by Forschungszentrum Karlsruhe. Polymer microstructure heights between 30 and 3000 μm are manufactured regularly for research and industrial projects. This requires different characteristic energies. Therefore, ELSA operates routinely at 1.6, 2.3 and 2.7 GeV, for high-resolution X-ray mask fabrication, deep and ultra-deep X-ray lithography, respectively. The experimental setup, as well as the structure quality of deep and ultra deep X-ray lithographic microstructures are described.

Excited-state dynamics of mononucleotides and DNA strands in a deep eutectic solvent.

PubMed

Zhang, Yuyuan; de La Harpe, Kimberly; Hariharan, Mahesh; Kohler, Bern

2018-04-17

The photophysics of several mono- and oligonucleotides were investigated in a deep eutectic solvent for the first time. The solvent glyceline, prepared as a 1 : 2 mole ratio mixture of choline chloride and glycerol, was used to study excited-state deactivation in a non-aqueous solvent by the use of steady-state and time-resolved spectroscopy. DNA strands in glyceline retain the secondary structures that are present in aqueous solution to some degree, thus enabling a study of the effects of solvent properties on the excited states of stacked bases and stacked base pairs. The excited-state lifetime of the mononucleotide 5'-AMP in glyceline is 630 fs, or twice as long as in aqueous solution. Even slower relaxation is seen for 5'-TMP in glyceline, and a possible triplet state with a lifetime greater than 3 ns is observed. Circular dichroism spectra show that the single strand (dA)18 and the duplex d(AT)9·d(AT)9 adopt similar structures in glyceline and in aqueous solution. Despite having similar conformations in both solvents, femtosecond transient absorption experiments reveal striking changes in the dynamics. Excited-state decay and vibrational cooling generally take place more slowly in glyceline than in water. Additionally, the fraction of long-lived excited states in both oligonucleotide systems is lower in glyceline than in aqueous solution. For a DNA duplex, water is suggested to favor decay pathways involving intrastrand charge separation, while the deep eutectic solvent favors interstrand deactivation channels involving neutral species. Slower solvation dynamics in the viscous deep eutectic solvent may also play a role. These results demonstrate that the dynamics of excitations in stacked bases and stacked base pairs depend not only on conformation, but are also highly sensitive to the solvent.

Photoionization of radiation-induced traps in quartz and alkali feldspars.

PubMed

Hütt, G; Jaek, I; Vasilchenko, V

2001-01-01

For the optimization of luminescence dating and dosimetry techniques on the basis of the optically stimulated luminescence, the stimulation spectra of quartz and alkali feldspars were measured in the spectral region of 250-1100 nm using optically stimulated afterglow. Optically stimulated luminescence in all studied spectral regions is induced by the same kind of deep traps, that produce thermoluminescence in the regions of palaeodosimetric peaks for both minerals. The mechanism for photoionization of deep traps was proposed as being due to delocalization of the excited state of the corresponding lattice defects. The excited state overlaps the zone states; i.e. is situated in the conduction band. Because of the high quantum yield of deep electron trap ionization in the UV spectral region, the present aim was to study the possibility of using UV-stimulation for palaeodose reconstruction.

A deep learning approach for the analysis of masses in mammograms with minimal user intervention.

PubMed

Dhungel, Neeraj; Carneiro, Gustavo; Bradley, Andrew P

2017-04-01

We present an integrated methodology for detecting, segmenting and classifying breast masses from mammograms with minimal user intervention. This is a long standing problem due to low signal-to-noise ratio in the visualisation of breast masses, combined with their large variability in terms of shape, size, appearance and location. We break the problem down into three stages: mass detection, mass segmentation, and mass classification. For the detection, we propose a cascade of deep learning methods to select hypotheses that are refined based on Bayesian optimisation. For the segmentation, we propose the use of deep structured output learning that is subsequently refined by a level set method. Finally, for the classification, we propose the use of a deep learning classifier, which is pre-trained with a regression to hand-crafted feature values and fine-tuned based on the annotations of the breast mass classification dataset. We test our proposed system on the publicly available INbreast dataset and compare the results with the current state-of-the-art methodologies. This evaluation shows that our system detects 90% of masses at 1 false positive per image, has a segmentation accuracy of around 0.85 (Dice index) on the correctly detected masses, and overall classifies masses as malignant or benign with sensitivity (Se) of 0.98 and specificity (Sp) of 0.7. Copyright © 2017 Elsevier B.V. All rights reserved.

Development of a Universal Canister for Disposal of High-Level Waste in Deep Boreholes.

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

Price, Laura L.; Gomberg, Steve

2015-11-01

The mission of the United States Department of Energy’s Office of Environmental Management is to complete the safe cleanup of the environmental legacy brought about from five decades of nuclear weapons development and government-sponsored nuclear energy research. Some of the wastes that must be managed have been identified as good candidates for disposal in a deep borehole in crystalline rock. In particular, wastes that can be disposed of in a small package are good candidates for this disposal concept. A canister-based system that can be used for handling these wastes during the disposition process (i.e., storage, transfer, transportation, and disposal)more » could facilitate the eventual disposal of these wastes. Development of specifications for the universal canister system will consider the regulatory requirements that apply to storage, transportation, and disposal of the capsules, as well as operational requirements and limits that could affect the design of the canister (e.g., deep borehole diameter). In addition, there are risks and technical challenges that need to be recognized and addressed as Universal Canister system specifications are developed. This paper provides an approach to developing specifications for such a canister system that is integrated with the overall efforts of the DOE’s Used Fuel Disposition Campaign's Deep Borehole Field Test and compatible with planned storage of potential borehole-candidate wastes.« less

The Effects of Test Anxiety on Learning at Superficial and Deep Levels of Processing.

ERIC Educational Resources Information Center

Weinstein, Claire E.; And Others

1982-01-01

Using a deep-level processing strategy, low test-anxious college students performed significantly better than high test-anxious students in learning a paired-associate word list. Using a superficial-level processing strategy resulted in no significant difference in performance. A cognitive-attentional theory and test anxiety mechanisms are…

Reconciliation of late Quaternary sea levels derived from coral terraces at Huon Peninsula with deep sea oxygen isotope records

NASA Astrophysics Data System (ADS)

Chappell, John; Omura, Akio; Esat, Tezer; McCulloch, Malcolm; Pandolfi, John; Ota, Yoko; Pillans, Brad

1996-06-01

A major discrepancy between the Late Quaternary sea level changes derived from raised coral reef terraces at the Huon Peninsula in Papua New Guinea and from oxygen isotopes in deep sea cores is resolved. The two methods agree closely from 120 ka to 80 ka and from 20 ka to 0 ka (ka = 1000 yr before present), but between 70 and 30 ka the isotopic sea levels are 20-40 m lower than the Huon Peninsula sea levels derived in earlier studies. New, high precision U-series age measurements and revised stratigraphic data for Huon Peninsula terraces aged between 30 and 70 ka now give similar sea levels to those based on deep sea oxygen isotope data planktonic and benthic δ 18O data. Using the sea level and deep sea isotopic data, oxygen isotope ratios are calculated for the northern continental ice sheets through the last glacial cycle and are consistent with results from Greenland ice cores. The record of ice volume changes through the last glacial cycle now appears to be reasonably complete.

A deep learning framework for financial time series using stacked autoencoders and long-short term memory.

PubMed

Bao, Wei; Yue, Jun; Rao, Yulei

2017-01-01

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day's closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance.

Evidence for carrier localization in the pseudogap state of cuprate superconductors from coherent quench experiments

PubMed Central

Madan, I.; Kurosawa, T.; Toda, Y.; Oda, M.; Mertelj, T.; Mihailovic, D.

2015-01-01

A ‘pseudogap' was introduced by Mott to describe a state of matter that has a minimum in the density of states at the Fermi level, deep enough for states to become localized. It can arise either from Coulomb repulsion between electrons, and/or incipient charge or spin order. Here we employ ultrafast spectroscopy to study dynamical properties of the normal to pseudogap state transition in the prototype high-temperature superconductor Bi2Sr2CaCu2O8+δ. We perform a systematic temperature and doping dependence study of the pseudogap photodestruction and recovery in coherent quench experiments, revealing marked absence of critical behaviour of the elementary excitations, which implies an absence of collective electronic ordering beyond a few coherence lengths on short timescales. The data imply ultrafast carrier localization into a textured polaronic state arising from a competing Coulomb interaction and lattice strain, enhanced by a Fermi surface instability. PMID:25891310

A Deep-Structured Conditional Random Field Model for Object Silhouette Tracking

PubMed Central

Shafiee, Mohammad Javad; Azimifar, Zohreh; Wong, Alexander

2015-01-01

In this work, we introduce a deep-structured conditional random field (DS-CRF) model for the purpose of state-based object silhouette tracking. The proposed DS-CRF model consists of a series of state layers, where each state layer spatially characterizes the object silhouette at a particular point in time. The interactions between adjacent state layers are established by inter-layer connectivity dynamically determined based on inter-frame optical flow. By incorporate both spatial and temporal context in a dynamic fashion within such a deep-structured probabilistic graphical model, the proposed DS-CRF model allows us to develop a framework that can accurately and efficiently track object silhouettes that can change greatly over time, as well as under different situations such as occlusion and multiple targets within the scene. Experiment results using video surveillance datasets containing different scenarios such as occlusion and multiple targets showed that the proposed DS-CRF approach provides strong object silhouette tracking performance when compared to baseline methods such as mean-shift tracking, as well as state-of-the-art methods such as context tracking and boosted particle filtering. PMID:26313943

A 1-D model of sinking particles

NASA Astrophysics Data System (ADS)

Jokulsdottir, T.; Archer, D.

2006-12-01

Acidification of the surface ocean due to increased atmospheric CO2 levels is altering its saturation state with respect to calcium carbonate (Orr et al., 2005) and the ability of calcifying phytoplankton to calcify (Riebesell et al., 2000). Sequestration of atmospheric carbon dioxide into the deep ocean is affected by this, because calcite is the key component in ballasting sinking particles (Klaas and Archer, 2001). The settling velocity of particles is not explicitly modeled but often represented as a constant in climate models. That is clearly inaccurate as the composition of particles changes with depth as bacteria and dissolution processes act on its different components, changing their ratio with depth. An idealized, mechanistic model of particles has been developed where settling velocity is calculated from first principles. The model is forced 100m below the surface with export ratios (organic carbon/calcium carbonate) corresponding to different CO2 levels according to Riebesell et al. The resulting flux is compared to the flux generated by the same model where the settling velocity is held constant. The model produces a relatively constant rain ratio regardless of the amount of calcite available to ballast the particle, which is what data suggests (Conte et al., 2001), whereas a constant velocity model does not. Comparing the flux of particulate organic carbon to the seafloor with increasing CO2 levels, the outcome of the constant velocity model is an increase whereas when the velocity is calculated a decrease results. If so, the change in export ratio with an increase in CO2 concentrations acts as a positive feedback: as increased atmospheric CO2 levels lead to the ocean pH being lowered, reduced calcification of marine organisms results and a decrease in particulate organic carbon flux to the deep ocean, which again raises CO2 concentrations. Conte, M.,, N. Ralph, E. Ross, Seasonal and interannual variability in deep ocean particle fluxes at the Oceanic Flux Program (OFP)/Bermuda Atlantic Time Series (BATS) site in the western Sargasso Sea near Bermuda, Deep-Sea Research II 48 1471-1505, 2001 Klaas, C., and D.E. Archer, Association of sinking organic matter with various types of mineral ballast in the deep sea: Implications for the rain ratio, Global Biogeochemical Cycles, 16, 2002. Orr, J. C. and et. al. Anthropogenic ocean acidification over calcifying organisms. Nature, 437(29):681 686, 2005. U. Riebesell, I. Zondervan, B. Rost, P.D. Tortell, R.E. Zeebe, and F.M.M.Morel. Reduced calcification of marine plankton in response to increased atmospheric CO2. Nature, 407:364 368, 2000.

2-(2-Hydroxyphenyl)imidazole-based four-coordinate organoboron compounds with efficient deep blue photoluminescence and electroluminescence.

PubMed

Zhang, Zhenyu; Zhang, Zuolun; Zhang, Hongyu; Wang, Yue

2017-12-19

Two new four-coordinate organoboron compounds with 2-(2-hydroxyphenyl)imidazole derivatives as the chelating ligands have been synthesized. They possess high thermal stability and are able to form an amorphous glass state. Crystallographic analyses indicate that the differences in ligand structure cause the change of ππ stacking character. The CH 2 Cl 2 solutions and thin films of these compounds display bright blue emission, and these compounds have appropriate HOMO and LUMO energy levels for carrier injection in OLEDs. By utilizing the good thermal and luminescent properties, as well as the proper frontier orbital energy levels, bright non-doped OLEDs with a simple structure have been realized. Notably, these simple devices show deep blue electroluminescence with the Commission Internationale de l'Éclairage (CIE) coordinate of ca. (0.16, 0.08), which is close to the CIE coordinate of (0.14, 0.08) for standard blue defined by the National Television System Committee. In addition, one of the devices exhibits good performance, showing brightness, current efficiency, power efficiency and external quantum efficiency up to 2692 cd m -2 , 2.50 cd A -1 , 1.81 lm W -1 and 3.63%, respectively. This study not only provides good deep-blue emitting OLED materials that are rarely achieved by using four-coordinate organoboron compounds, but also allows a deeper understanding of the structure-property relationship of 2-(2-hydroxyphenyl)imidazole-based boron complexes, which benefits the further structural design of this type of material.

A multiple distributed representation method based on neural network for biomedical event extraction.

PubMed

Wang, Anran; Wang, Jian; Lin, Hongfei; Zhang, Jianhai; Yang, Zhihao; Xu, Kan

2017-12-20

Biomedical event extraction is one of the most frontier domains in biomedical research. The two main subtasks of biomedical event extraction are trigger identification and arguments detection which can both be considered as classification problems. However, traditional state-of-the-art methods are based on support vector machine (SVM) with massive manually designed one-hot represented features, which require enormous work but lack semantic relation among words. In this paper, we propose a multiple distributed representation method for biomedical event extraction. The method combines context consisting of dependency-based word embedding, and task-based features represented in a distributed way as the input of deep learning models to train deep learning models. Finally, we used softmax classifier to label the example candidates. The experimental results on Multi-Level Event Extraction (MLEE) corpus show higher F-scores of 77.97% in trigger identification and 58.31% in overall compared to the state-of-the-art SVM method. Our distributed representation method for biomedical event extraction avoids the problems of semantic gap and dimension disaster from traditional one-hot representation methods. The promising results demonstrate that our proposed method is effective for biomedical event extraction.

Evolution in the deep sea: biological traits, ecology and phylogenetics of pelagic copepods.

PubMed

Laakmann, Silke; Auel, Holger; Kochzius, Marc

2012-11-01

Deep-sea biodiversity has received increasing interest in the last decade, mainly focusing on benthic communities. In contrast, studies of zooplankton in the meso- to bathypelagic zones are relatively scarce. In order to explore evolutionary processes in the pelagic deep sea, the present study focuses on copepods of two clausocalanoid families, Euchaetidae and Aetideidae, which are abundant and species-rich in the deep-sea pelagic realm. Molecular phylogenies based on concatenated-portioned data on 18S, 28S and internal transcribed spacer 2 (ITS2), as well as mitochondrial cytochrome c oxidase subunit I (COI), were examined on 13 species, mainly from Arctic and Antarctic regions, together with species-specific biological traits (i.e. vertical occurrence, feeding behaviour, dietary preferences, energy storage, and reproductive strategy). Relationships were resolved on genus, species and even sub-species levels, the latter two established by COI with maximum average genetic distances ranging from ≤5.3% at the intra-specific, and 20.6% at the inter-specific level. There is no resolution at a family level, emphasising the state of Euchaetidae and Aetideidae as sister families and suggesting a fast radiation of these lineages, a hypothesis which is further supported by biological parameters. Euchaetidae were similar in lipid-specific energy storage, reproductive strategy, as well as feeding behaviour and dietary preference. In contrast, Aetideidae were more diverse, comprising a variety of characteristics ranging from similar adaptations within Paraeuchaeta, to genera consisting of species with completely different reproductive and feeding ecologies. Reproductive strategies were generally similar within each aetideid genus, but differed between genera. Closely related species (congeners), which were similar in the aforementioned biological and ecological traits, generally occurred in different depth layers, suggesting that vertical partitioning of the water column represents an important mechanism in the speciation processes for these deep-sea copepods. High COI divergence between Arctic and Antarctic specimens of the mesopelagic cosmopolitan Gaetanus tenuispinus and the bipolar Aetideopsis minor suggest different geographic forms, potentially cryptic species or sibling species. On the contrary, Arctic and Antarctic individuals of the bathypelagic cosmopolitans Gaetanus brevispinus and Paraeuchaeta barbata were very similar in COI sequence, suggesting more gene flow at depth and/or that driving forces for speciation were less pronounced in bathypelagic than at mesopelagic depths. Copyright © 2012 Elsevier Inc. All rights reserved.

Weaving a knowledge network for Deep Carbon Science

NASA Astrophysics Data System (ADS)

Ma, Xiaogang; West, Patrick; Zednik, Stephan; Erickson, John; Eleish, Ahmed; Chen, Yu; Wang, Han; Zhong, Hao; Fox, Peter

2017-05-01

Geoscience researchers are increasingly dependent on informatics and the Web to conduct their research. Geoscience is one of the first domains that take lead in initiatives such as open data, open code, open access, and open collections, which comprise key topics of Open Science in academia. The meaning of being open can be understood at two levels. The lower level is to make data, code, sample collections and publications, etc. freely accessible online and allow reuse, modification and sharing. The higher level is the annotation and connection between those resources to establish a network for collaborative scientific research. In the data science component of the Deep Carbon Observatory (DCO), we have leveraged state-of-the-art information technologies and existing online resources to deploy a web portal for the over 1000 researchers in the DCO community. An initial aim of the portal is to keep track of all research and outputs related to the DCO community. Further, we intend for the portal to establish a knowledge network, which supports various stages of an open scientific process within and beyond the DCO community. Annotation and linking are the key characteristics of the knowledge network. Not only are key assets, including DCO data and methods, published in an open and inter-linked fashion, but the people, organizations, groups, grants, projects, samples, field sites, instruments, software programs, activities, meetings, etc. are recorded and connected to each other through relationships based on well-defined, formal conceptual models. The network promotes collaboration among DCO participants, improves the openness and reproducibility of carbon-related research, facilitates accreditation to resource contributors, and eventually stimulates new ideas and findings in deep carbon-related studies.

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

PubMed

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

2017-01-01

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

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

PubMed Central

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

2017-01-01

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

The XMM deep survey in the CDF-S. X. X-ray variability of bright sources

NASA Astrophysics Data System (ADS)

Falocco, S.; Paolillo, M.; Comastri, A.; Carrera, F. J.; Ranalli, P.; Iwasawa, K.; Georgantopoulos, I.; Vignali, C.; Gilli, R.

2017-12-01

Aims: We aim to study the variability properties of bright hard X-ray selected active galactic nuclei (AGN) in the redshift range between 0.3 and 1.6 detected in the Chandra Deep Field South (XMM-CDFS) by a long ( 3 Ms) XMM observation. Methods: Taking advantage of the good count statistics in the XMM CDFS, we search for flux and spectral variability using the hardness ratio (HR) techniques. We also investigate the spectral variability of different spectral components (photon index of the power law, column density of the local absorber, and reflection intensity). The spectra were merged in six epochs (defined as adjacent observations) and in high and low flux states to understand whether the flux transitions are accompanied by spectral changes. Results: The flux variability is significant in all the sources investigated. The HRs in general are not as variable as the fluxes, in line with previous results on deep fields. Only one source displays a variable HR, anti-correlated with the flux (source 337). The spectral analysis in the available epochs confirms the steeper when brighter trend consistent with Comptonisation models only in this source at 99% confidence level. Finding this trend in one out of seven unabsorbed sources is consistent, within the statistical limits, with the 15% of unabsorbed AGN in previous deep surveys. No significant variability in the column densities, nor in the Compton reflection component, has been detected across the epochs considered. The high and low states display in general different normalisations but consistent spectral properties. Conclusions: X-ray flux fluctuations are ubiquitous in AGN, though in some cases the data quality does not allow for their detection. In general, the significant flux variations are not associated with spectral variability: photon index and column densities are not significantly variable in nine out of the ten AGN over long timescales (from three to six and a half years). Photon index variability is found only in one source (which is steeper when brighter) out of seven unabsorbed AGN. The percentage of spectrally variable objects is consistent, within the limited statistics of sources studied here, with previous deep samples.

Re-designing an Earth Sciences outreach program for Rhode Island public elementary schools to address new curricular standards and logistical realities in the community

NASA Astrophysics Data System (ADS)

Richter, N.; Vachula, R. S.; Pascuzzo, A.; Prilipko Huber, O.

2017-12-01

In contrast to middle and high school students, elementary school students in Rhode Island (RI) have no access to dedicated science teachers, resulting in uneven quality and scope of science teaching across the state. In an attempt to improve science education in local public elementary schools, the Department of Earth, Environmental, and Planetary Sciences (DEEPS) at Brown University initiated a student-driven science-teaching program that was supported by a NSF K-12 grant from 2007 to 2014. The program led to the development of an extensive in-house lesson plan database and supported student-led outreach and teaching in several elementary and middle school classrooms. After funding was terminated, the program continued on a volunteer basis, providing year-round science teaching for several second-grade classrooms. During the 2016-2017 academic year, New Generation Science Standards (NGSS) were introduced in RI public schools, and it became apparent that our outreach efforts required adaptation to be more efficient and relevant for both elementary school students and teachers. To meet these new needs, DEEPS, in collaboration with the Providence Public School District, created an intensive summer re-design program involving both graduate and undergraduate students. Three multi-lesson units were developed in collaboration with volunteer public school teachers to specifically address NGSS goals for earth science teaching in 2nd, 3rd and 4th grades. In the 2017-2018 academic year DEEPS students will co-teach the science lessons with the public school teachers in two local elementary schools. At the end of the next academic year all lesson plans and activities will be made publically available through a newly designed DEEPS outreach website. We herein detail our efforts to create and implement new educational modules with the goals of: (1) empowering teachers to instruct science, (2) engaging students and fostering lasting STEM interest and competency, (3) optimizing volunteer resources, (4) meeting new state curricular standards, (5) developing publicly available lesson plans for other teachers and outreach programs, (6) institutionalizing the outreach program within the DEEPS community, and (7) cultivating STEM retention at the grassroots level.

Deep Learning as an Individual, Conditional, and Contextual Influence on First-Year Student Outcomes

ERIC Educational Resources Information Center

Reason, Robert D.; Cox, Bradley E.; McIntosh, Kadian; Terenzini, Patrick T.

2010-01-01

For years, educators have drawn a distinction between deep cognitive processing and surface-level cognitive processing, with the former resulting in greater learning. In recent years, researchers at NSSE have created DEEP Learning scales, which consist of items related to students' experiences which are believed to encourage deep processing. In…

Deep-breathing exercises reduce atelectasis and improve pulmonary function after coronary artery bypass surgery.

PubMed

Westerdahl, Elisabeth; Lindmark, Birgitta; Eriksson, Tomas; Friberg, Orjan; Hedenstierna, Göran; Tenling, Arne

2005-11-01

To investigate the effects of deep-breathing exercises on pulmonary function, atelectasis, and arterial blood gas levels after coronary artery bypass graft (CABG) surgery. In a prospective, randomized trial, patients performing deep-breathing exercises (n = 48) were compared to a control group (n = 42) who performed no breathing exercises postoperatively. Patient management was similar in the groups in terms of assessment, positioning, and mobility. The patients in the deep-breathing group were instructed to perform breathing exercises hourly during daytime for the first 4 postoperative days. The exercises consisted of 30 slow, deep breaths performed with a positive expiratory pressure blow-bottle device (+ 10 cm H(2)O). Spirometric measurements, spiral CT (three transverse levels), arterial blood gas analysis, and scoring of subjective experience of the breathing exercises were performed on the fourth postoperative day. Atelectasis was only half the size in the deep-breathing group compared to the control group, amounting to 2.6 +/- 2.2% vs 4.7 +/- 5.7% (p = 0.045) at the basal level and 0.1 +/- 0.2% vs 0.3 +/- 0.5% (mean +/- SD) [p = 0.01] at the apical level. Compared to the control subjects, the patients in the deep-breathing group had a significantly smaller reduction in FVC (to 71 +/- 12%, vs 64 +/- 13% of the preoperative values; p = 0.01) and FEV(1) (to 71 +/- 11%, vs 65 +/- 13% of the preoperative values; p = 0.01). Arterial oxygen tension, carbon dioxide tension, fever, or length of ICU or hospital stay did not differ between the groups. In the deep-breathing group, 72% of the patients experienced a subjective benefit from the exercises. Patients performing deep-breathing exercises after CABG surgery had significantly smaller atelectatic areas and better pulmonary function on the fourth postoperative day compared to a control group performing no exercises.

Interpretable Deep Models for ICU Outcome Prediction

PubMed Central

Che, Zhengping; Purushotham, Sanjay; Khemani, Robinder; Liu, Yan

2016-01-01

Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians. PMID:28269832

The effects of deep level traps on the electrical properties of semi-insulating CdZnTe

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

Zha, Gangqiang; Yang, Jian; Xu, Lingyan

2014-01-28

Deep level traps have considerable effects on the electrical properties and radiation detection performance of high resistivity CdZnTe. A deep-trap model for high resistivity CdZnTe was proposed in this paper. The high resistivity mechanism and the electrical properties were analyzed based on this model. High resistivity CdZnTe with high trap ionization energy E{sub t} can withstand high bias voltages. The leakage current is dependent on both the deep traps and the shallow impurities. The performance of a CdZnTe radiation detector will deteriorate at low temperatures, and the way in which sub-bandgap light excitation could improve the low temperature performance canmore » be explained using the deep trap model.« less

Evidence for high salinity of Early Cretaceous sea water from the Chesapeake Bay crater

USGS Publications Warehouse

Sanford, Ward E.; Doughten, Michael W.; Coplen, Tyler B.; Hunt, Andrew G.; Bullen, Thomas D.

2013-01-01

High salinity groundwater more than 1000 metres deep in the Atlantic Coastal Plain of the United States has been documented in several locations1,2, most recently within the 35 million-year-old Chesapeake Bay impact crater3,4,5. Suggestions for the origin of increased salinity in the crater have included evaporite dissolution6, osmosis6, and evaporation from heating7 associated with the bolide impact. Here we present chemical, isotopic and physical evidence that together indicate that groundwater in the Chesapeake crater is remnant Early Cretaceous North Atlantic (ECNA) seawater. We find that the seawater is likely 100-145 million years old and that it has an average salinity of about 70 per mil, which is twice that of modern seawater and consistent with the nearly closed ECNA basin8. Previous evidence for temperature and salinity levels of ancient oceans have been estimated indirectly from geochemical, isotopic and paleontological analyses of solid materials in deep sediment cores. In contrast, our study identifies ancient seawater in situ and provides a direct estimate of its age and salinity. Moreover, we suggest that it is likely that remnants of ECNA seawater persist in deep sediments at many locations along the Atlantic margin.

Group III Acceptors with Shallow and Deep Levels in Silicon Carbide: ESR and ENDOR Studies

NASA Astrophysics Data System (ADS)

Il'in, I. V.; Uspenskaya, Yu. A.; Kramushchenko, D. D.; Muzafarova, M. V.; Soltamov, V. A.; Mokhov, E. N.; Baranov, P. G.

2018-04-01

Results of investigations of Group III acceptors (B, Al, and Ga) in crystals of silicon carbide using the most informative electron spin resonance and electron nuclear double resonance methods are presented. Structural models of the acceptors with shallow and deep levels are considered. In addition to the data obtained earlier, studies using high-frequency magnetic resonance were obtained, which allowed revealing orthorhombic deviations from the axial symmetry for the deep acceptors; theoretical analysis explains experimentally found shifts of g factors for the deep acceptors arising due to the orthorhombic deviations, which appear probably due to the Jahn-Teller effect.

Thermal stability of deep level defects induced by high energy proton irradiation in n-type GaN

NASA Astrophysics Data System (ADS)

Zhang, Z.; Farzana, E.; Sun, W. Y.; Chen, J.; Zhang, E. X.; Fleetwood, D. M.; Schrimpf, R. D.; McSkimming, B.; Kyle, E. C. H.; Speck, J. S.; Arehart, A. R.; Ringel, S. A.

2015-10-01

The impact of annealing of proton irradiation-induced defects in n-type GaN devices has been systematically investigated using deep level transient and optical spectroscopies. Moderate temperature annealing (>200-250 °C) causes significant reduction in the concentration of nearly all irradiation-induced traps. While the decreased concentration of previously identified N and Ga vacancy related levels at EC - 0.13 eV, 0.16 eV, and 2.50 eV generally followed a first-order reaction model with activation energies matching theoretical values for NI and VGa diffusion, irradiation-induced traps at EC - 0.72 eV, 1.25 eV, and 3.28 eV all decrease in concentration in a gradual manner, suggesting a more complex reduction mechanism. Slight increases in concentration are observed for the N-vacancy related levels at EC - 0.20 eV and 0.25 eV, which may be due to the reconfiguration of other N-vacancy related defects. Finally, the observed reduction in concentrations of the states at EC - 1.25 and EC - 3.28 eV as a function of annealing temperature closely tracks the detailed recovery behavior of the background carrier concentration as a function of annealing temperature. As a result, it is suggested that these two levels are likely to be responsible for the underlying carrier compensation effect that causes the observation of carrier removal in proton-irradiated n-GaN.

Policy challenges and reforms in small EU member state health systems: a narrative literature review.

PubMed

Azzopardi-Muscat, Natasha; Funk, Tjede; Buttigieg, Sandra C; Grech, Kenneth E; Brand, Helmut

2016-12-01

The EU directive on patients' rights and cross-border care is of particular interest to small states as it reinforces the concept of health system cooperation. An analysis of the challenges faced by small states, as well as a deep evaluation of their health system reform characteristics is timely and justified. This paper identifies areas in which EU level cooperation may bring added value to these countries' health systems. Literature search is based primarily on PUBMED and is limited to English-language papers published between January 2000 and September 2014. Results of 76 original research papers appearing in peer-reviewed journals are summarised in a literature map and narrative review. Primary care, health workforce and medicines emerge as the salient themes in the review. Lack of capacity and small market size are found to be the frequently encountered challenges in governance and delivery of services. These constraints appear to also impinge on the ability of small states to effectively implement health system reforms. The EU appears to play a marginal role in supporting small state health systems, albeit the stimulus for reform associated with EU accession. Small states face common health system challenges which could potentially be addressed through enhanced health system cooperation at EU level. The lessons learned from research on small states may be of relevance to health systems organized at regional level in larger European states. © The Author 2016. Published by Oxford University Press on behalf of the European Public Health Association. All rights reserved.

Management of Chronic Deep Vein Thrombosis in Women.

PubMed

Hardman, Rulon L

2018-03-01

Chronic deep vein thrombosis (DVT) affects hundreds of thousands of women in the United States. Chronic DVT can lead to pain, edema, venous ulcers, and varicosities. While there are limited data regarding the management of chronic DVT, several interventional radiology groups aggressively treat chronic DVT to aid patient symptom resolution. Recanalization of occluded veins and venous stenting re-establishes deep vein flow and decreases venous hypertension.

33 CFR 148.215 - What if a port has plans for a deep draft channel and harbor?

Code of Federal Regulations, 2013 CFR

2013-07-01

... deep draft channel and harbor? 148.215 Section 148.215 Navigation and Navigable Waters COAST GUARD... General § 148.215 What if a port has plans for a deep draft channel and harbor? (a) If a State port will... draft channel and harbor, a representative of the port may request a determination under 33 U.S.C. 1503...

Ground motion models used in the 2014 U.S. National Seismic Hazard Maps

USGS Publications Warehouse

Rezaeian, Sanaz; Petersen, Mark D.; Moschetti, Morgan P.

2015-01-01

The National Seismic Hazard Maps (NSHMs) are an important component of seismic design regulations in the United States. This paper compares hazard using the new suite of ground motion models (GMMs) relative to hazard using the suite of GMMs applied in the previous version of the maps. The new source characterization models are used for both cases. A previous paper (Rezaeian et al. 2014) discussed the five NGA-West2 GMMs used for shallow crustal earthquakes in the Western United States (WUS), which are also summarized here. Our focus in this paper is on GMMs for earthquakes in stable continental regions in the Central and Eastern United States (CEUS), as well as subduction interface and deep intraslab earthquakes. We consider building code hazard levels for peak ground acceleration (PGA), 0.2-s, and 1.0-s spectral accelerations (SAs) on uniform firm-rock site conditions. The GMM modifications in the updated version of the maps created changes in hazard within 5% to 20% in WUS; decreases within 5% to 20% in CEUS; changes within 5% to 15% for subduction interface earthquakes; and changes involving decreases of up to 50% and increases of up to 30% for deep intraslab earthquakes for most U.S. sites. These modifications were combined with changes resulting from modifications in the source characterization models to obtain the new hazard maps.

Charge carrier relaxation in InGaAs-GaAs quantum wire modulation-doped heterostructures

NASA Astrophysics Data System (ADS)

Kondratenko, S. V.; Iliash, S. A.; Mazur, Yu I.; Kunets, V. P.; Benamara, M.; Salamo, G. J.

2017-09-01

The time dependencies of the carrier relaxation in modulation-doped InGaAs-GaAs low-dimensional structures with quantum wires have been studied as functions of temperature and light excitation levels. The photoconductivity (PC) relaxation follows a stretched exponent with decay constant, which depends on the morphology of InGaAs epitaxial layers, presence of deep traps, and energy disorder due to inhomogeneous distribution of size and composition. A hopping model, where electron tunnels between bands of localized states, gives appropriate interpretation for temperature-independent PC decay across the temperature range 150-290 K. At low temperatures (T < 150 K), multiple trapping-retrapping via 1D states of InGaAs quantum wires (QWRs), sub-bands of two-dimensional electron gas of modulation-doped n-GaAs spacers, as well as defect states in the GaAs environment are the dominant relaxation mechanism. The PC and photoluminescence transients for samples with different morphologies of the InGaAs nanostructures are compared. The relaxation rates are found to be largely dependent on energy disorder due to inhomogeneous distribution of strain, nanostructure size and composition, and piezoelectric fields in and around nanostructures, which have a strong impact on efficiency of carrier exchange between bands of the InGaAs QWRs, GaAs spacers, or wetting layers; presence of local electric fields; and deep traps.

A search for sterile neutrinos with IceCube DeepCore

NASA Astrophysics Data System (ADS)

Terliuk, Andrii; IceCube Collaboration

2017-09-01

The DeepCore detector is a densely instrumented part of the IceCube Neutrino Observatory that lowers the neutrino detection threshold down to approximately 10 GeV resulting in the ability to measure atmospheric neutrino oscillations. The standard three neutrino mixing scenario can be tested by searching for an additional light sterile neutrino state, which does not interact via the standard weak interaction, but mixes with the three active neutrino states. This leads to an impact on the atmospheric neutrino oscillations below 100 GeV. We present improved limits to the sterile mixing element |U τ4|2 using three years of the DeepCore data taken during 2011-2013.

Endovascular Interventions for Acute and Chronic Lower Extremity Deep Venous Disease: State of the Art.

PubMed

Sista, Akhilesh K; Vedantham, Suresh; Kaufman, John A; Madoff, David C

2015-07-01

The societal and individual burden caused by acute and chronic lower extremity venous disease is considerable. In the past several decades, minimally invasive endovascular interventions have been developed to reduce thrombus burden in the setting of acute deep venous thrombosis to prevent both short- and long-term morbidity and to recanalize chronically occluded or stenosed postthrombotic or nonthrombotic veins in symptomatic patients. This state-of-the-art review provides an overview of the techniques and challenges, rationale, patient selection criteria, complications, postinterventional care, and outcomes data for endovascular intervention in the setting of acute and chronic lower extremity deep venous disease. Online supplemental material is available for this article.

DeepSig: deep learning improves signal peptide detection in proteins.

PubMed

Savojardo, Castrense; Martelli, Pier Luigi; Fariselli, Piero; Casadio, Rita

2018-05-15

The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. pierluigi.martelli@unibo.it. Supplementary data are available at Bioinformatics online.

Manipulating the Electronic Excited State Energies of Pyrimidine-Based Thermally Activated Delayed Fluorescence Emitters To Realize Efficient Deep-Blue Emission.

PubMed

Komatsu, Ryutaro; Ohsawa, Tatsuya; Sasabe, Hisahiro; Nakao, Kohei; Hayasaka, Yuya; Kido, Junji

2017-02-08

The development of efficient and robust deep-blue emitters is one of the key issues in organic light-emitting devices (OLEDs) for environmentally friendly, large-area displays or general lighting. As a promising technology that realizes 100% conversion from electrons to photons, thermally activated delayed fluorescence (TADF) emitters have attracted considerable attention. However, only a handful of examples of deep-blue TADF emitters have been reported to date, and the emitters generally show large efficiency roll-off at practical luminance over several hundreds to thousands of cd m -2 , most likely because of the long delayed fluorescent lifetime (τ d ). To overcome this problem, we molecularly manipulated the electronic excited state energies of pyrimidine-based TADF emitters to realize deep-blue emission and reduced τ d . We then systematically investigated the relationships among the chemical structure, properties, and device performances. The resultant novel pyrimidine emitters, called Ac-XMHPMs (X = 1, 2, and 3), contain different numbers of bulky methyl substituents at acceptor moieties, increasing the excited singlet (E S ) and triplet state (E T ) energies. Among them, Ac-3MHPM, with a high E T of 2.95 eV, exhibited a high external quantum efficiency (η ext,max ) of 18% and an η ext of 10% at 100 cd m -2 with Commission Internationale de l'Eclairage chromaticity coordinates of (0.16, 0.15). These efficiencies are among the highest values to date for deep-blue TADF OLEDs. Our molecular design strategy provides fundamental guidance to design novel deep-blue TADF emitters.

Terrestrial source to deep-sea sink sediment budgets at high and low sea levels: Insights from tectonically active Southern California

USGS Publications Warehouse

Covault, J.A.; Romans, B.W.; Graham, S.A.; Fildani, A.; Hilley, G.E.

2011-01-01

Sediment routing from terrestrial source areas to the deep sea influences landscapes and seascapes and supply and filling of sedimentary basins. However, a comprehensive assessment of land-to-deep-sea sediment budgets over millennia with significant climate change is lacking. We provide source to sink sediment budgets using cosmogenic radionuclide-derived terrestrial denudation rates and submarine-fan deposition rates through sea-level fluctuations since oxygen isotope stage 3 (younger than 40 ka) in tectonically active, spatially restricted sediment-routing systems of Southern California. We show that source-area denudation and deep-sea deposition are balanced during a period of generally falling and low sea level (40-13 ka), but that deep-sea deposition exceeds terrestrial denudation during the subsequent period of rising and high sea level (younger than 13 ka). This additional supply of sediment is likely owed to enhanced dispersal of sediment across the shelf caused by seacliff erosion during postglacial shoreline transgression and initiation of submarine mass wasting. During periods of both low and high sea level, land and deep-sea sediment fluxes do not show orders of magnitude imbalances that might be expected in the wake of major sea-level changes. Thus, sediment-routing processes in a globally significant class of small, tectonically active systems might be fundamentally different from those of larger systems that drain entire orogens, in which sediment storage in coastal plains and wide continental shelves can exceed millions of years. Furthermore, in such small systems, depositional changes offshore can reflect onshore changes when viewed over time scales of several thousand years to more than 10 k.y. ?? 2011 Geological Society of America.

Initial investigation of reinforced concrete filled tubes for use in bridge foundations.

DOT National Transportation Integrated Search

2012-06-01

The Washington State Department of Transportation (WSDOT) frequently employs deep pile or caisson bridge : foundations for its bridge structures. Deep pile and drilled shaft foundations are increasingly important for seismic : design in Washington st...

Spatial Statistics of Deep-Water Ambient Noise; Dispersion Relations for Sound Waves and Shear Waves

DTIC Science & Technology

2015-09-30

propagation in very fine-grained sediments (silt and clay ). OBJECTIVES 1) The scientific objective of the deep-water ambient noise research is to...forces in silts and clays and the role they play in controlling wave speeds and attenuations. On a 2 quantum mechanical level, these forces are... clays . APPROACH 1) Deep-water ambient noise Three deep-diving, autonomous instrument platforms, known as Deep Sound I, II, & III, have been

Levels-Of-Processing Effect on Word Recognition in Schizophrenia

PubMed Central

Ragland, J. Daniel; Moelter, Stephen T.; McGrath, Claire; Hill, S. Kristian; Gur, Raquel E.; Bilker, Warren B.; Siegel, Steven J.; Gur, Ruben C.

2015-01-01

Background Individuals with schizophrenia have difficulty organizing words semantically to facilitate encoding. This is commonly attributed to organizational rather than semantic processing limitations. By requiring participants to classify and encode words on either a shallow (e.g., uppercase/lowercase) or deep level (e.g., concrete/abstract), the levels-of-processing paradigm eliminates the need to generate organizational strategies. Methods This paradigm was administered to 30 patients with schizophrenia and 30 healthy comparison subjects to test whether providing a strategy would improve patient performance. Results Word classification during shallow and deep encoding was slower and less accurate in patients. Patients also responded slowly during recognition testing and maintained a more conservative response bias following deep encoding; however, both groups showed a robust levels-of-processing effect on recognition accuracy, with unimpaired patient performance following both shallow and deep encoding. Conclusions This normal levels-of-processing effect in the patient sample suggests that semantic processing is sufficiently intact for patients to benefit from organizational cues. Memory remediation efforts may therefore be most successful if they focus on teaching patients to form organizational strategies during initial encoding. PMID:14643082

Levels-of-processing effect on word recognition in schizophrenia.

PubMed

Ragland, J Daniel; Moelter, Stephen T; McGrath, Claire; Hill, S Kristian; Gur, Raquel E; Bilker, Warren B; Siegel, Steven J; Gur, Ruben C

2003-12-01

Individuals with schizophrenia have difficulty organizing words semantically to facilitate encoding. This is commonly attributed to organizational rather than semantic processing limitations. By requiring participants to classify and encode words on either a shallow (e.g., uppercase/lowercase) or deep level (e.g., concrete/abstract), the levels-of-processing paradigm eliminates the need to generate organizational strategies. This paradigm was administered to 30 patients with schizophrenia and 30 healthy comparison subjects to test whether providing a strategy would improve patient performance. Word classification during shallow and deep encoding was slower and less accurate in patients. Patients also responded slowly during recognition testing and maintained a more conservative response bias following deep encoding; however, both groups showed a robust levels-of-processing effect on recognition accuracy, with unimpaired patient performance following both shallow and deep encoding. This normal levels-of-processing effect in the patient sample suggests that semantic processing is sufficiently intact for patients to benefit from organizational cues. Memory remediation efforts may therefore be most successful if they focus on teaching patients to form organizational strategies during initial encoding.

Deriving dynamical models from paleoclimatic records: application to glacial millennial-scale climate variability.

PubMed

Kwasniok, Frank; Lohmann, Gerrit

2009-12-01

A method for systematically deriving simple nonlinear dynamical models from ice-core data is proposed. It offers a tool to integrate models and theories with paleoclimatic data. The method is based on the unscented Kalman filter, a nonlinear extension of the conventional Kalman filter. Here, we adopt the abstract conceptual model of stochastically driven motion in a potential that allows for two distinctly different states. The parameters of the model-the shape of the potential and the noise level-are estimated from a North Greenland ice-core record. For the glacial period from 70 to 20 ky before present, a potential is derived that is asymmetric and almost degenerate. There is a deep well corresponding to a cold stadial state and a very shallow well corresponding to a warm interstadial state.

Electronic characterization of defects in narrow gap semiconductors

NASA Technical Reports Server (NTRS)

Patterson, James D.

1993-01-01

The study of point defects in semiconductors has a long and honorable history. In particular, the detailed understanding of shallow defects in common semiconductors traces back to the classic work of Kohn and Luttinger. However, the study of defects in narrow gap semiconductors represents a much less clear story. Here, both shallow defects (caused by long range potentials) and deep defects (from short range potentials) are far from being completely understood. In this study, all results are calculational and our focus is on the chemical trend of deep levels in narrow gap semiconductors. We study substitutional (including antisite), interstitial and ideal vacancy defects. For substitutional and interstitial impurities, the efects of relaxation are included. For materials like Hg(1-x)Cd(x)Te, we study how the deep levels vary with x, of particular interest is what substitutional and interstitial atoms yield energy levels in the gap i.e. actually produce deep ionized levels. Also, since the main technique utilized is Green's functions, we include some summary of that method.

Admittance spectroscopy or deep level transient spectroscopy: A contrasting juxtaposition

NASA Astrophysics Data System (ADS)

Bollmann, Joachim; Venter, Andre

2018-04-01

A comprehensive understanding of defects in semiconductors remains of primary importance. In this paper the effectiveness of two of the most commonly used semiconductor defect spectroscopy techniques, viz. deep level transient spectroscopy (DLTS) and admittance spectroscopy (AS) are reviewed. The analysis of defects present in commercially available SiC diodes shows that admittance spectroscopy allows the identification of deep traps with reduced measurement effort compared to deep Level Transient Spectroscopy (DLTS). Besides the N-donor, well-studied intrinsic defects were detected in these diodes. Determination of their activation energy and defect density, using the two techniques, confirm that the sensitivity of AS is comparable to that of DLTS while, due to its well defined peak shape, the spectroscopic resolution is superior. Additionally, admittance spectroscopy can analyze faster emission processes which make the study of shallow defects more practical and even that of shallow dopant levels, possible. A comparative summary for the relevant spectroscopic features of the two capacitance methods are presented.

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

Putilov, L.P., E-mail: lev.putilov@gmail.com; Tsidilkovski, V.I.

The impact of deep acceptor centers on defect thermodynamics and oxidation of wide-band-gap acceptor-doped perovskites without mixed-valence cations is studied. These deep centers are formed by the acceptor-bound small hole polarons whose stabilization energy can be high enough (significantly higher than the hole-acceptor Coulomb interaction energy). It is shown that the oxidation enthalpy ΔH{sub ox} of oxide is determined by the energy ε{sub A} of acceptor-bound states along with the formation energy E{sub V} of oxygen vacancies. The oxidation reaction is demonstrated to be either endothermic or exothermic, and the regions of ε{sub A} and E{sub V} values corresponding tomore » the positive or negative ΔH{sub ox} are determined. The contribution of acceptor-bound holes to the defect thermodynamics strongly depends on the acceptor states depth ε{sub A}: it becomes negligible at ε{sub A} less than a certain value (at which the acceptor levels are still deep). With increasing ε{sub A}, the concentration of acceptor-bound small hole polarons can reach the values comparable to the dopant content. The results are illustrated with the acceptor-doped BaZrO{sub 3} as an example. It is shown that the experimental data on the bulk hole conductivity of barium zirconate can be described both in the band transport model and in the model of hopping small polarons localized on oxygen ions away from the acceptor centers. Depending on the ε{sub A} magnitude, the oxidation reaction can be either endothermic or exothermic for both mobility mechanisms.« less

State Education Governance Structures: 2017 Update. 50-State Review

ERIC Educational Resources Information Center

Railey, Hunter

2017-01-01

This 50-State Review provides an overview of governance structures in the states, as well as implications for practice, deep dives into four governance models and examples of other governance models. One appendix, State Education Governance Models by State, is included.

Characterization of Thallium Bromide (TlBr) for Room Temperature Radiation Detectors

NASA Astrophysics Data System (ADS)

Smith, Holland McTyeire

Thallium bromide (TlBr) has emerged as a remarkably well-suited material for room temperature radiation detection. The unique combination of high-Z elements, high density, suitable band gap, and excellent electrical transport properties present in TlBr have brought device performance up to par with CdZnTe (CZT), the current market-leading room temperature radiation detector material. TlBr research is at an earlier stage than that of CZT, giving hope that the material will see even further improvement in electronic properties. Improving a resistive semiconductor material requires knowledge of deep levels present in the material and the effects of these deep levels on transport properties. Very few deep level studies have been conducted on TlBr, and none with the depth required to generate useful growth suggestions. In this dissertation, deep levels in nominally undoped and doped TlBr samples are studied with electrical and optical methods. Photo-Induced Conductivity Transient Spectroscopy (PICTS) is used to discover many deep levels in TlBr electrically. These levels are compared to sub-band gap optical transitions originating from defects observed in emission spectra. The results of this research indicate that the origin of resistivity in TlBr is likely due to deep level defects pinning the Fermi level at least ˜0.7 eV from either the conduction or valence band edge. The effect of dopants and deep levels on transport in TlBr is assessed with microwave photoconductivity decay analysis. It is found that Pb-, Se-, and O-doping decreases carrier lifetime in TlBr, whereas C-doping does not. TlBr exhibits weak ionic conductivity at room temperature, which both negatively affects the leakage current of detectors and leads to device degradation over time. Researchers are actively looking for ways to reduce or eliminate the ionic conductivity, but are faced with an intriguing challenge of materials engineering: is it possible to mitigate the ionic conduction of TlBr without harming the excellent electronic transport properties? Doping TlBr in order to control the ionic conductivity has been proposed and shown to be effective in reducing dark ionic current, but the electronic effects of the dopants has not been previously studied in detail. In this dissertation, the electronic effects of dopants introduced for ionic reasons are evaluated.

The effects of deep-level defects on the electrical properties of Cd0.9Zn0.1Te crystals

NASA Astrophysics Data System (ADS)

Wang, Pengfei; Nan, Ruihua; Jian, Zengyun

2017-06-01

The deep-level defects of CdZnTe (CZT) crystals grown by the modified vertical Bridgman (MVB) method act as trapping centers or recombination centers in the band gap, which have significant effects on its electrical properties. The resistivity and electron mobility-lifetime product of high resistivity Cd0.9Zn0.1Te wafer marked CZT1 and low resistivity Cd0.9Zn0.1Te wafer marked CZT2 were tested respectively. Their deep-level defects were identified by thermally stimulated current (TSC) spectroscopy and thermoelectric effect spectroscopy (TEES) respectively. Then the trap-related parameters were characterized by the simultaneous multiple peak analysis (SIMPA) method. The deep donor level ({E}{{DD}}) dominating dark current was calculated by the relationship between dark current and temperature. The Fermi-level was characterized by current-voltage measurements of temperature dependence. The width of the band gap was characterized by ultraviolet-visible-infrared transmittance spectroscopy. The results show the traps concentration and capture cross section of CZT1 are lower than CZT2, so its electron mobility-lifetime product is greater than CZT2. The Fermi-level of CZT1 is closer to the middle gap than CZT2. The degree of Fermi-level pinned by {E}{{DD}} of CZT1 is larger than CZT2. It can be concluded that the resistivity of CZT crystals increases as the degree of Fermi-level pinned near the middle gap by the deep donor level enlarges. Project supported by the National Natural Science Foundation of China (No. 51502234) and the Scientific Research Plan Projects of Shaanxi Provincial Department of Education of China (No. 15JS040).

Antecedent reactivation by surface and deep anaphora in Norwegian

PubMed Central

HESTVIK, ARILD; NORDBY, HELGE; KARLSEN, GEIR

2005-01-01

Anaphora are expressions in language that depend on other linguistic entities for their full meaning. They can furthermore be divided into two types according to the level of representation where they find their antecedents: Surface anaphora, which resolve their reference at the sentence representation level, and deep anaphora, which resolve their reference at the non-grammatical level of discourse representation. The linguistic theory of these two anaphor types, and recent findings about processing differences at these two levels, combine to predict that surface anaphora should show fast and immediate reactivation of their antecedents, whereas deep anaphora should have a slower time course of antecedent reaccess. These predictions were confirmed with two lexical decision task experiments with Norwegian stimuli. PMID:15842413

Clinical Named Entity Recognition Using Deep Learning Models.

PubMed

Wu, Yonghui; Jiang, Min; Xu, Jun; Zhi, Degui; Xu, Hua

2017-01-01

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. This study examined two popular deep learning architectures, the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN), to extract concepts from clinical texts. We compared the two deep neural network architectures with three baseline Conditional Random Fields (CRFs) models and two state-of-the-art clinical NER systems using the i2b2 2010 clinical concept extraction corpus. The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER.

A Deep and Autoregressive Approach for Topic Modeling of Multimodal Data.

PubMed

Zheng, Yin; Zhang, Yu-Jin; Larochelle, Hugo

2016-06-01

Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as the deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.

MUFOLD-SS: New deep inception-inside-inception networks for protein secondary structure prediction.

PubMed

Fang, Chao; Shang, Yi; Xu, Dong

2018-05-01

Protein secondary structure prediction can provide important information for protein 3D structure prediction and protein functions. Deep learning offers a new opportunity to significantly improve prediction accuracy. In this article, a new deep neural network architecture, named the Deep inception-inside-inception (Deep3I) network, is proposed for protein secondary structure prediction and implemented as a software tool MUFOLD-SS. The input to MUFOLD-SS is a carefully designed feature matrix corresponding to the primary amino acid sequence of a protein, which consists of a rich set of information derived from individual amino acid, as well as the context of the protein sequence. Specifically, the feature matrix is a composition of physio-chemical properties of amino acids, PSI-BLAST profile, and HHBlits profile. MUFOLD-SS is composed of a sequence of nested inception modules and maps the input matrix to either eight states or three states of secondary structures. The architecture of MUFOLD-SS enables effective processing of local and global interactions between amino acids in making accurate prediction. In extensive experiments on multiple datasets, MUFOLD-SS outperformed the best existing methods and other deep neural networks significantly. MUFold-SS can be downloaded from http://dslsrv8.cs.missouri.edu/~cf797/MUFoldSS/download.html. © 2018 Wiley Periodicals, Inc.

Clinical Named Entity Recognition Using Deep Learning Models

PubMed Central

Wu, Yonghui; Jiang, Min; Xu, Jun; Zhi, Degui; Xu, Hua

2017-01-01

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP) task to extract important concepts (named entities) from clinical narratives. Researchers have extensively investigated machine learning models for clinical NER. Recently, there have been increasing efforts to apply deep learning models to improve the performance of current clinical NER systems. This study examined two popular deep learning architectures, the Convolutional Neural Network (CNN) and the Recurrent Neural Network (RNN), to extract concepts from clinical texts. We compared the two deep neural network architectures with three baseline Conditional Random Fields (CRFs) models and two state-of-the-art clinical NER systems using the i2b2 2010 clinical concept extraction corpus. The evaluation results showed that the RNN model trained with the word embeddings achieved a new state-of-the- art performance (a strict F1 score of 85.94%) for the defined clinical NER task, outperforming the best-reported system that used both manually defined and unsupervised learning features. This study demonstrates the advantage of using deep neural network architectures for clinical concept extraction, including distributed feature representation, automatic feature learning, and long-term dependencies capture. This is one of the first studies to compare the two widely used deep learning models and demonstrate the superior performance of the RNN model for clinical NER. PMID:29854252

Radiative Heating of the ISCCP Upper Level Cloud Regimes and its Impact on the Large-scale Tropical Circulation

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

Li, Wei; Schumacher, Courtney; McFarlane, Sally A.

2013-01-31

Radiative heating profiles of the International Satellite Cloud Climatology Project (ISCCP) cloud regimes (or weather states) were estimated by matching ISCCP observations with radiative properties derived from cloud radar and lidar measurements from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) sites at Manus, Papua New Guinea, and Darwin, Australia. Focus was placed on the ISCCP cloud regimes containing the majority of upper level clouds in the tropics, i.e., mesoscale convective systems (MCSs), deep cumulonimbus with cirrus, mixed shallow and deep convection, and thin cirrus. At upper levels, these regimes have average maximum cloud occurrences ranging from 30% tomore » 55% near 12 km with variations depending on the location and cloud regime. The resulting radiative heating profiles have maxima of approximately 1 K/day near 12 km, with equal heating contributions from the longwave and shortwave components. Upper level minima occur near 15 km, with the MCS regime showing the strongest cooling of 0.2 K/day and the thin cirrus showing no cooling. The gradient of upper level heating ranges from 0.2 to 0.4 K/(day∙km), with the most convectively active regimes (i.e., MCSs and deep cumulonimbus with cirrus) having the largest gradient. When the above heating profiles were applied to the 25-year ISCCP data set, the tropics-wide average profile has a radiative heating maximum of 0.45Kday-1 near 250 hPa. Column-integrated radiative heating of upper level cloud accounts for about 20% of the latent heating estimated by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The ISCCP radiative heating of tropical upper level cloud only slightly modifies the response of an idealized primitive equation model forced with the tropics-wide TRMM PR latent heating, which suggests that the impact of upper level cloud is more important to large-scale tropical circulation variations because of convective feedbacks rather than direct forcing by the cloud radiative heating profiles. However, the height of the radiative heating maxima and gradient of the heating profiles are important to determine the sign and patterns of the horizontal circulation anomaly driven by radiative heating at upper levels.« less

Cl-doping of Te-rich CdTe: Complex formation, self-compensation and self-purification from first principles

NASA Astrophysics Data System (ADS)

Lindström, A.; Klintenberg, M.; Sanyal, B.; Mirbt, S.

2015-08-01

The coexistence in Te-rich CdTe of substitutional Cl-dopants, ClTe, which act as donors, and Cd vacancies, VC d - 1 , which act as electron traps, was studied from first principles utilising the HSE06 hybrid functional. We find ClTe to preferably bind to VC d - 1 and to form an acceptor complex, (ClTe-VCd)-1. The complex has a (0,-1) charge transfer level close to the valence band and shows no trap state (deep level) in the band gap. During the complex formation, the defect state of VCd-1 is annihilated and leaves the Cl-doped CdTe bandgap without any trap states (self-purification). We calculate Cl-doped CdTe to be semi-insulating with a Fermi energy close to midgap. We calculate the formation energy of the complex to be sufficiently low to allow for spontanous defect formation upon Cl-doping (self-compensation). In addition, we quantitatively analyse the geometries, DOS, binding energies and formation energies of the (ClTe-VCd) complexes.

Deep learning decision fusion for the classification of urban remote sensing data

NASA Astrophysics Data System (ADS)

Abdi, Ghasem; Samadzadegan, Farhad; Reinartz, Peter

2018-01-01

Multisensor data fusion is one of the most common and popular remote sensing data classification topics by considering a robust and complete description about the objects of interest. Furthermore, deep feature extraction has recently attracted significant interest and has become a hot research topic in the geoscience and remote sensing research community. A deep learning decision fusion approach is presented to perform multisensor urban remote sensing data classification. After deep features are extracted by utilizing joint spectral-spatial information, a soft-decision made classifier is applied to train high-level feature representations and to fine-tune the deep learning framework. Next, a decision-level fusion classifies objects of interest by the joint use of sensors. Finally, a context-aware object-based postprocessing is used to enhance the classification results. A series of comparative experiments are conducted on the widely used dataset of 2014 IEEE GRSS data fusion contest. The obtained results illustrate the considerable advantages of the proposed deep learning decision fusion over the traditional classifiers.

A deep learning framework for financial time series using stacked autoencoders and long-short term memory

PubMed Central

Bao, Wei; Rao, Yulei

2017-01-01

The application of deep learning approaches to finance has received a great deal of attention from both investors and researchers. This study presents a novel deep learning framework where wavelet transforms (WT), stacked autoencoders (SAEs) and long-short term memory (LSTM) are combined for stock price forecasting. The SAEs for hierarchically extracted deep features is introduced into stock price forecasting for the first time. The deep learning framework comprises three stages. First, the stock price time series is decomposed by WT to eliminate noise. Second, SAEs is applied to generate deep high-level features for predicting the stock price. Third, high-level denoising features are fed into LSTM to forecast the next day’s closing price. Six market indices and their corresponding index futures are chosen to examine the performance of the proposed model. Results show that the proposed model outperforms other similar models in both predictive accuracy and profitability performance. PMID:28708865

Investigation of structural, morphological, luminescent and thermal properties of combusted aluminium-based iron oxide

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

Shinde, S.S.; Rajpure, K.Y., E-mail: rajpure@yahoo.co

Nanocomposites of aluminium integrated hematite {alpha}-Fe{sub 2}O{sub 3} are synthesized by combustion route using aqueous solutions of AR grade ferric trichloride and aluminium nitrate as precursors. The influence of aluminium incorporation on to the morphology, XPS, photoluminescence and thermal properties has been investigated. The FESEM and AFM micrographs depict that the samples are compact and have homogeneously distributed grains of varying sizes ({approx}20-60 nm). Chemical composition and valence states of constituent elements in hematite are analyzed by XPS. In room temperature photoluminescence (PL) study, we observed strong violet emission around 436 nm without any deep-level emission and a small PLmore » FWHM indicating that the concentrations of defects are responsible for deep-level emissions. The specific heat and thermal conductivity study shows the phonon conduction behavior is dominant. We studied interparticle interactions using complex impedance spectroscopy. We report a new potential candidate for its possible applications in optoelectronics and magnetic devices. -- Graphical abstract: Frequency and temperature dependent interparticle interactions like grains, grain boundary effects using complex impedance spectroscopy of pure and 10 at% Al:Fe{sub 2}O{sub 3} have been studied. Display Omitted« less

Microbiome analysis of a disease affecting the deep-sea sponge Geodia barretti.

PubMed

Luter, Heidi M; Bannister, Raymond J; Whalan, Steve; Kutti, Tina; Pineda, Mari-Carmen; Webster, Nicole S

2017-05-24

Reports of sponge disease are becoming increasingly frequent, although almost all instances involve shallow-water, tropical species. Here, we describe the first disease affecting the deep-water sponge, Geodia barretti. The disease is characterised by brown/black discolouration of the sponge tissue, extensive levels of tissue disintegration and increased levels of fouling. Disease prevalence was quantified using video survey transects conducted between 100 and 220 meters in Korsfjorden, Norway and the microbial communities of healthy and diseased sponges were compared using 16S rRNA gene sequencing. Highly divergent community profiles were evident between the different health states; with distinct community shifts involving higher relative abundances of Bacteroidetes, Firmicutes and Deltaproteobacteria in diseased individuals. In addition, three Operational Taxonomic Units (OTUs) were exclusively present in diseased individuals and were shared between the disease lesions and the apparently healthy tissue of diseased individuals, suggesting a non-localised infection or dysbiosis. Genomic analysis of the G. barretti microbiome combined with experimental work to assess the mechanisms of infection will further elucidate the role of microorganisms in the disease. © FEMS 2017. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Interface Si donor control to improve dynamic performance of AlGaN/GaN MIS-HEMTs

NASA Astrophysics Data System (ADS)

Song, Liang; Fu, Kai; Zhang, Zhili; Sun, Shichuang; Li, Weiyi; Yu, Guohao; Hao, Ronghui; Fan, Yaming; Shi, Wenhua; Cai, Yong; Zhang, Baoshun

2017-12-01

In this letter, we have studied the performance of AlGaN/GaN metal-insulator-semiconductor high electron mobility transistors (MIS-HEMTs) with different interface Si donor incorporation which is tuned during the deposition process of LPCVD-SiNx which is adopted as gate dielectric and passivation layer. Current collapse of the MIS-HEMTs without field plate is suppressed more effectively by increasing the SiH2Cl2/NH3 flow ratio and the normalized dynamic on-resistance (RON) is reduced two orders magnitude after off-state VDS stress of 600 V for 10 ms. Through interface characterization, we have found that the interface deep-level traps distribution with high Si donor incorporation by increasing the SiH2Cl2/NH3 flow ratio is lowered. It's indicated that the Si donors are most likely to fill and screen the deep-level traps at the interface resulting in the suppression of slow trapping process and the virtual gate effect. Although the Si donor incorporation brings about the increase of gate leakage current (IGS), no clear degradation of breakdown voltage can be seen by choosing appropriate SiH2Cl2/NH3 flow ratio.

High-pressure metamorphism and uplift of the Olympic subduction complex

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

Brandon, M.T.; Calderwood, A.R.

1990-12-01

The discovery of the critical assemblage lawsonite + quartz + calcite indicates that a significant part of the Cenozoic Olympic subduction complex of northwestern Washington State formed by underplating at a depth of about 11 km. The deep structural level exposed in this area is attributed to the presence of a 10-km-high arch in the underlying Juan de Fuca plate. The authors postulate that this arch was formed when the southern Cordilleran coastline swung westward as a result of middle Miocene to recent extension in the Basin and Range province.

A New Method for Reconstructing Sea-Level and Deep-Sea-Temperature Variability over the Past 5.3 Million Years

NASA Astrophysics Data System (ADS)

Rohling, E. J.

2014-12-01

Ice volume (and hence sea level) and deep-sea temperature are key measures of global climate change. Sea level has been documented using several independent methods over the past 0.5 million years (Myr). Older periods, however, lack such independent validation; all existing records are related to deep-sea oxygen isotope (d18O) data that are influenced by processes unrelated to sea level. For deep-sea temperature, only one continuous high-resolution (Mg/Ca-based) record exists, with related sea-level estimates, spanning the past 1.5 Myr. We have recently presented a novel sea-level reconstruction, with associated estimates of deep-sea temperature, which independently validates the previous 0-1.5 Myr reconstruction and extends it back to 5.3 Myr ago. A serious of caveats applies to this new method, especially in older times of its application, as is always the case with new methods. Independent validation exercises are needed to elucidate where consistency exists, and where solutions drift away from each other. A key observation from our new method is that a large temporal offset existed during the onset of Plio-Pleistocene ice ages, between a marked cooling step at 2.73 Myr ago and the first major glaciation at 2.15 Myr ago. This observation relies on relative changes within the dataset, which are more robust than absolute values. I will discuss our method and its main caveats and avenues for improvement.

Deep Reconditioning Testing for near Earth Orbits

NASA Technical Reports Server (NTRS)

Betz, F. E.; Barnes, W. L.

1984-01-01

The problems and benefits of deep reconditioning to near Earth orbit missions with high cycle life and shallow discharge depth requirements is discussed. A simple battery level approach to deep reconditioning of nickel cadmium batteries in near Earth orbit is considered. A test plan was developed to perform deep reconditioning in direct comparison with an alternative trickle charge approach. The results demonstrate that the deep reconditioning procedure described for near Earth orbit application is inferior to the alternative of trickle charging.

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field.

PubMed

Christiansen, Peter; Nielsen, Lars N; Steen, Kim A; Jørgensen, Rasmus N; Karstoft, Henrik

2016-11-11

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

PubMed Central

Christiansen, Peter; Nielsen, Lars N.; Steen, Kim A.; Jørgensen, Rasmus N.; Karstoft, Henrik

2016-01-01

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m) than RCNN. RCNN has a similar performance at a short range (0–30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit). PMID:27845717

Deep Learning for Computer Vision: A Brief Review

PubMed Central

Doulamis, Nikolaos; Doulamis, Anastasios; Protopapadakis, Eftychios

2018-01-01

Over the last years deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields, with computer vision being one of the most prominent cases. This review paper provides a brief overview of some of the most significant deep learning schemes used in computer vision problems, that is, Convolutional Neural Networks, Deep Boltzmann Machines and Deep Belief Networks, and Stacked Denoising Autoencoders. A brief account of their history, structure, advantages, and limitations is given, followed by a description of their applications in various computer vision tasks, such as object detection, face recognition, action and activity recognition, and human pose estimation. Finally, a brief overview is given of future directions in designing deep learning schemes for computer vision problems and the challenges involved therein. PMID:29487619

[Рroblems of ensuring the safety of deep-fried fast food products].

PubMed

Simakova, I V; Perkel, R L; Kutkina, M N; Volovey, A G

There are no doubts that fast-food restaurants, where deep-frying is actively used, are now very popular in Russia. This article focuses on the problems of deep-fried food safety. During deep-frying a considerable amount of fat penetrates the food. That is why the safety of deep-fried food depends on the fat safety and quality, on the level of fat absorption, and on the intensity of oxidative changes of fat during storage. This article contains the results of the research, which demonstrate that in order to insure the safety of fast-food products it is necessary to introduce into normative and technical documents the following standards: peroxide value, acid value, content of oxidation products insoluble in petroleum ether, and content of epoxides in fat phase and to food mass. According to the current norms on content of oxidation products in deep-frying fat and allowed level of fat absorption by a food product equal to 20%, the recommended level of oxidation products insoluble in petroleum ether for French fries is not higher than 0.2% to the food mass. As a temporary measure we can recommend the level of epoxides not higher than 5 mmol/kg to the food mass. It is important to control the content of trans-isomers in deepfrying fat, it must be not higher than 2% of fatty acid mass. In order to lower fat absorption during French fries production it is recommended to use halffinished products of high readiness, and to air fry.

Complications of Moderate Sedation Versus Deep Sedation/General Anesthesia for Adolescent Patients Undergoing Third Molar Extraction.

PubMed

Inverso, Gino; Dodson, Thomas B; Gonzalez, Martin L; Chuang, Sung-Kiang

2016-03-01

To examine the complications resulting from moderate sedation versus deep sedation/general anesthesia for adolescent patients undergoing third molar extraction and determine whether any differences in complication risks exist between the 2 levels of sedation. We performed a prospective study of the Oral and Maxillofacial Surgery Outcomes System from January 2001 to December 2010. The primary predictor variable was the level of sedation, divided into 2 groups: moderate sedation versus deep sedation/general anesthesia. The primary outcome was the incidence of adverse complications resulting from the sedation level. Differences in the cohort characteristics were analyzed using the independent samples t test, χ(2) test, and analysis of variance, as appropriate. Multivariable logistic regression was used to measure the effect the level of sedation had on the adverse complication rate. Patients in the moderate sedation group had a complication rate of 0.5%, and patients in the deep sedation/general anesthesia group had a complication rate of 0.9%. Compared with moderate sedation, deep sedation/general anesthesia did not pose a significantly increased risk of adverse anesthesia complications (adjusted odds ratio 1.63, 95% confidence interval 0.95 to 2.81; P = .077). The results of our study have shown that the risk of adverse anesthesia complications is not increased when choosing between moderate and deep sedation/general anesthesia for adolescent patients undergoing third molar extraction. Copyright © 2016 American Association of Oral and Maxillofacial Surgeons. Published by Elsevier Inc. All rights reserved.

Endovascular Interventions for Acute and Chronic Lower Extremity Deep Venous Disease: State of the Art

PubMed Central

Sista, Akhilesh K.; Vedantham, Suresh; Kaufman, John A.

2015-01-01

The societal and individual burden caused by acute and chronic lower extremity venous disease is considerable. In the past several decades, minimally invasive endovascular interventions have been developed to reduce thrombus burden in the setting of acute deep venous thrombosis to prevent both short- and long-term morbidity and to recanalize chronically occluded or stenosed postthrombotic or nonthrombotic veins in symptomatic patients. This state-of-the-art review provides an overview of the techniques and challenges, rationale, patient selection criteria, complications, postinterventional care, and outcomes data for endovascular intervention in the setting of acute and chronic lower extremity deep venous disease. Online supplemental material is available for this article. © RSNA, 2015 PMID:26101920

Structural and electronic properties of U{sub n}O{sub m} (n=1-3,m=1-3n) clusters: A theoretical study using screened hybrid density functional theory

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

Yang, Yu; Liu, Haitao; Zhang, Ping, E-mail: zhang-ping@iapcm.ac.cn

The structural and electronic properties of small uranium oxide clusters U{sub n}O{sub m} (n=1-3, m=1-3n) are systematically studied within the screened hybrid density functional theory. It is found that the formation of U–O–U bondings and isolated U–O bonds are energetically more stable than U–U bondings. As a result, no uranium cores are observed. Through fragmentation studies, we find that the U{sub n}O{sub m} clusters with the m/n ratio between 2 and 2.5 are very stable, hinting that UO{sub 2+x} hyperoxides are energetically stable. Electronically, we find that the O-2p states always distribute in the deep energy range, and the U-5fmore » states always distribute at the two sides of the Fermi level. The U-6d states mainly hybridize with the U-5f states in U-rich clusters, while hybridizing with O-2p states in O-rich clusters. Our work is the first one on the screened hybrid density functional theory level studying the atomic and electronic properties of the actinide oxide clusters.« less

Unified thalamic model generates multiple distinct oscillations with state-dependent entrainment by stimulation

PubMed Central

2017-01-01

The thalamus plays a critical role in the genesis of thalamocortical oscillations, yet the underlying mechanisms remain elusive. To understand whether the isolated thalamus can generate multiple distinct oscillations, we developed a biophysical thalamic model to test the hypothesis that generation of and transition between distinct thalamic oscillations can be explained as a function of neuromodulation by acetylcholine (ACh) and norepinephrine (NE) and afferent synaptic excitation. Indeed, the model exhibited four distinct thalamic rhythms (delta, sleep spindle, alpha and gamma oscillations) that span the physiological states corresponding to different arousal levels from deep sleep to focused attention. Our simulation results indicate that generation of these distinct thalamic oscillations is a result of both intrinsic oscillatory cellular properties and specific network connectivity patterns. We then systematically varied the ACh/NE and input levels to generate a complete map of the different oscillatory states and their transitions. Lastly, we applied periodic stimulation to the thalamic network and found that entrainment of thalamic oscillations is highly state-dependent. Our results support the hypothesis that ACh/NE modulation and afferent excitation define thalamic oscillatory states and their response to brain stimulation. Our model proposes a broader and more central role of the thalamus in the genesis of multiple distinct thalamo-cortical rhythms than previously assumed. PMID:29073146

Theoretical Explanation for Success of Deep-Level-Learning Study Tours

ERIC Educational Resources Information Center

Bergsteiner, Harald; Avery, Gayle C.

2008-01-01

Study tours can help internationalize curricula and prepare students for global workplaces. We examine benefits of tours providing deep-level learning experiences rather than industrial tourism using five main theoretical frameworks to highlight the diverse learning benefits associated with intensive study tours in particular. Relevant theoretical…

Apache Clinical Text and Knowledge Extraction System (cTAKES) | Informatics Technology for Cancer Research (ITCR)

Cancer.gov

The tool extracts deep phenotypic information from the clinical narrative at the document-, episode-, and patient-level. The final output is FHIR compliant patient-level phenotypic summary which can be consumed by research warehouses or the DeepPhe native visualization tool.

High precision in protein contact prediction using fully convolutional neural networks and minimal sequence features.

PubMed

Jones, David T; Kandathil, Shaun M

2018-04-26

In addition to substitution frequency data from protein sequence alignments, many state-of-the-art methods for contact prediction rely on additional sources of information, or features, of protein sequences in order to predict residue-residue contacts, such as solvent accessibility, predicted secondary structure, and scores from other contact prediction methods. It is unclear how much of this information is needed to achieve state-of-the-art results. Here, we show that using deep neural network models, simple alignment statistics contain sufficient information to achieve state-of-the-art precision. Our prediction method, DeepCov, uses fully convolutional neural networks operating on amino-acid pair frequency or covariance data derived directly from sequence alignments, without using global statistical methods such as sparse inverse covariance or pseudolikelihood estimation. Comparisons against CCMpred and MetaPSICOV2 show that using pairwise covariance data calculated from raw alignments as input allows us to match or exceed the performance of both of these methods. Almost all of the achieved precision is obtained when considering relatively local windows (around 15 residues) around any member of a given residue pairing; larger window sizes have comparable performance. Assessment on a set of shallow sequence alignments (fewer than 160 effective sequences) indicates that the new method is substantially more precise than CCMpred and MetaPSICOV2 in this regime, suggesting that improved precision is attainable on smaller sequence families. Overall, the performance of DeepCov is competitive with the state of the art, and our results demonstrate that global models, which employ features from all parts of the input alignment when predicting individual contacts, are not strictly needed in order to attain precise contact predictions. DeepCov is freely available at https://github.com/psipred/DeepCov. d.t.jones@ucl.ac.uk.

Lifetime degradation of n-type Czochralski silicon after hydrogenation

NASA Astrophysics Data System (ADS)

Vaqueiro-Contreras, M.; Markevich, V. P.; Mullins, J.; Halsall, M. P.; Murin, L. I.; Falster, R.; Binns, J.; Coutinho, J.; Peaker, A. R.

2018-04-01

Hydrogen plays an important role in the passivation of interface states in silicon-based metal-oxide semiconductor technologies and passivation of surface and interface states in solar silicon. We have shown recently [Vaqueiro-Contreras et al., Phys. Status Solidi RRL 11, 1700133 (2017)] that hydrogenation of n-type silicon slices containing relatively large concentrations of carbon and oxygen impurity atoms {[Cs] ≥ 1 × 1016 cm-3 and [Oi] ≥ 1017 cm-3} can produce a family of C-O-H defects, which act as powerful recombination centres reducing the minority carrier lifetime. In this work, evidence of the silicon's lifetime deterioration after hydrogen injection from SiNx coating, which is widely used in solar cell manufacturing, has been obtained from microwave photoconductance decay measurements. We have characterised the hydrogenation induced deep level defects in n-type Czochralski-grown Si samples through a series of deep level transient spectroscopy (DLTS), minority carrier transient spectroscopy (MCTS), and high-resolution Laplace DLTS/MCTS measurements. It has been found that along with the hydrogen-related hole traps, H1 and H2, in the lower half of the gap reported by us previously, hydrogenation gives rise to two electron traps, E1 and E2, in the upper half of the gap. The activation energies for electron emission from the E1 and E2 trap levels have been determined as 0.12, and 0.14 eV, respectively. We argue that the E1/H1 and E2/H2 pairs of electron/hole traps are related to two energy levels of two complexes, each incorporating carbon, oxygen, and hydrogen atoms. Our results show that the detrimental effect of the C-O-H defects on the minority carrier lifetime in n-type Si:O + C materials can be very significant, and the carbon concentration in Czochralski-grown silicon is a key parameter in the formation of the recombination centers.

Unfolding the atmospheric and deep internal flows on Jupiter and Saturn using the Juno and Cassini gravity measurements

NASA Astrophysics Data System (ADS)

Galanti, Eli; Kaspi, Yohai

2016-10-01

In light of the first orbits of Juno at Jupiter, we discuss the Juno gravity experiment and possible initial results. Relating the flow on Jupiter and Saturn to perturbations in their density field is key to the analysis of the gravity measurements expected from both the Juno (Jupiter) and Cassini (Saturn) spacecraft during 2016-17. Both missions will provide latitude-dependent gravity fields, which in principle could be inverted to calculate the vertical structure of the observed cloud-level zonal flow on these planets. Current observations for the flow on these planets exists only at the cloud-level (0.1-1 bar). The observed cloud-level wind might be confined to the upper layers, or be a manifestation of deep cylindrical flows. Moreover, it is possible that in the case where the observed wind is superficial, there exists deep interior flow that is completely decoupled from the observed atmospheric flow.In this talk, we present a new adjoint based inverse model for inversion of the gravity measurements into flow fields. The model is constructed to be as general as possible, allowing for both cloud-level wind extending inward, and a decoupled deep flow that is constructed to produce cylindrical structures with variable width and magnitude, or can even be set to be completely general. The deep flow is also set to decay when approaching the upper levels so it has no manifestation there. The two sources of flow are then combined to a total flow field that is related to the density anomalies and gravity moments via a dynamical model. Given the measured gravitational moments from Jupiter and Saturn, the dynamical model, together with the adjoint inverse model are used for optimizing the control parameters and by this unfolding the deep and surface flows. Several scenarios are examined, including cases in which the surface wind and the deep flow have comparable effects on the gravity field, cases in which the deep flow is dominating over the surface wind, and an extreme case where the deep flow can have an unconstrained pattern. The method enables also the calculation of the uncertainties associated with each solution. We discuss the physical limitations to the method in view of the measurement uncertainties.

Modelling Potential Consequences of Different Geo-Engineering Treatments for the Baltic Sea Ecosystem

NASA Astrophysics Data System (ADS)

Schrum, C.; Daewel, U.

2017-12-01

From 1950 onwards, the Baltic Sea ecosystem suffered increasingly from eutrophication. The most obvious reason for the eutrophication is the huge amount of nutrients (nitrogen and phosphorus) reaching the Baltic Sea from human activities. However, although nutrient loads have been decreasing since 1980, the hypoxic areas have not decreased accordingly. Thus, geo-engineering projects were discussed and evaluated to artificially ventilate the Baltic Sea deep water and suppress nutrient release from the sediments. Here, we aim at understanding the consequences of proposed geo-engineering projects in the Baltic Sea using long-term scenario modelling. For that purpose, we utilize a 3d coupled ecosystem model ECOSMO E2E, a novel NPZD-Fish model approach that resolves hydrodynamics, biogeochemical cycling and lower and higher trophic level dynamics. We performed scenario modelling that consider proposed geo-engineering projects such as artificial ventilation of Baltic Sea deep waters and phosphorus binding in sediments with polyaluminium chlorides. The model indicates that deep-water ventilation indeed suppresses phosphorus release in the first 1-4 years of treatment. Thereafter macrobenthos repopulates the formerly anoxic bottom regions and nutrients are increasingly recycled in the food web. Consequently, overall system productivity and fish biomass increases and toxic algae blooms decrease. However, deep-water ventilation has no long-lasting effect on the ecosystem: soon after completion of the ventilation process, the system turns back into its original state. Artificial phosphorus binding in sediments in contrast decreases overall ecosystem productivity through permanent removal of phosphorus. As expected it decreases bacterial production and toxic algae blooms, but it also decreases fish production substantially. Contrastingly to deep water ventilation, artificial phosphorus binding show a long-lasting effect over decades after termination of the treatment.

Deep anterior lamellar keratoplasty: dissection plane with viscoelastic and air can be different.

PubMed

Ross, Andrew R; Said, Dalia G; El-Amin, Abdalla; Altaan, Saif; Cabrerizo, Javier; Nubile, Mario; Hogan, Emily; Mastropasqua, Leonardo; Dua, Harminder Singh

2018-04-03

To investigate and define the nature of big bubbles (BB) formed by injection of viscoelastic in deep anterior lamellar keratoplasty. Intrastromal injections of 0.1 and 0.3 mL of sodium hyaluronate 1.2% and 0.6% were made into sclera-corneal discs (n = 32) at superficial (anterior-third), midstromal (middle-third) and deep (posterior-third) levels to simulate deep anterior lamellar keratoplasty. Postinjection optical coherence tomograms (OCT) were obtained with the needle in situ. The samples were sectioned and examined histologically. Twelve control samples were injected with air. With superficial injections (n=8) only intrastromal accumulation of viscoelastic was noted. With midstromal injections (n=10) intrastromal accumulation of viscoelastic (n=6) and intrastromal big bubbles (IBB) (n=4) with substantial and variable stromal tissue in the walls were noted. No type 1, type 2 or mixed BB were noted. With deep injections (n=14), type 1 BB (n=4), IBB (n=4) and mixed BB (n=6) were obtained.There was no difference in the results with the two different concentrations of viscoelastic used. With air injection (n=12), 10 type 1 and 1 type 2 BB and 1 mixed BB were obtained. No IBB was noted. BB obtained by injection of viscoelastic and air can be different. The former tends to occur at the site of injection, especially with midstromal injections, takes the form of tissue separation by stretch and tearing and does not cleave in a consistent plane like air. Surgeons should be aware of IBB created by viscodissection and not confuse it for a type1 BB. Intraoperative OCT should help identify IBB. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Holocene history of deep-seated landsliding in the North Fork Stillaguamish River valley from surface roughness analysis, radiocarbon dating, and numerical landscape evolution modeling

NASA Astrophysics Data System (ADS)

Booth, Adam M.; LaHusen, Sean R.; Duvall, Alison R.; Montgomery, David R.

2017-02-01

Documenting spatial and temporal patterns of past landsliding is a challenging step in quantifying the effect of landslides on landscape evolution. While landslide inventories can map spatial distributions, lack of dateable material, landslide reactivations, or time, access, and cost constraints generally limit dating large numbers of landslides to analyze temporal patterns. Here we quantify the record of the Holocene history of deep-seated landsliding along a 25 km stretch of the North Fork Stillaguamish River valley, Washington State, USA, including the 2014 Oso landslide, which killed 43 people. We estimate the ages of more than 200 deep-seated landslides in glacial sediment by defining an empirical relationship between landslide deposit age from radiocarbon dating and landslide deposit surface roughness. We show that roughness systematically decreases with age as a function of topographic wavelength, consistent with models of disturbance-driven soil transport. The age-roughness model predicts a peak in landslide frequency at 1000 calibrated (cal) years B.P., with very few landslide deposits older than 7000 cal years B.P. or younger than 100 cal years B.P., likely reflecting a combination of preservation bias and a complex history of changing climate, base level, and seismic shaking in the study area. Most recent landslides have occurred where channels actively interact with the toes of hillslopes composed of glacial sediments, suggesting that lateral channel migration is a primary control on the location of large deep-seated landslides in the valley.

Conjunction of 2D and 3D modified flow solvers for simulating spatio-temporal wind induced hydrodynamics in the Caspian Sea

NASA Astrophysics Data System (ADS)

Zounemat-Kermani, Mohammad; Sabbagh-Yazdi, Saeed-Reza

2010-06-01

The main objective of this study is the simulation of flow dynamics in the deep parts of the Caspian Sea, in which the southern and middle deep regions are surrounded by considerable areas of shallow zones. To simulate spatio-temporal wind induced hydrodynamics in deep waters, a conjunctive numerical model consisting of a 2D depth average model and a 3D pseudo compressible model is proposed. The 2D model is applied to determine time dependent free surface oscillations as well as the surface velocity patterns and is conjunct to the 3D flow solver for computing three-dimensional velocity and pressure fields which coverage to steady state for the top boundary condition. The modified 2D and 3D sets of equations are conjunct considering interface shear stresses. Both sets of 2D and 3D equations are solved on unstructured triangular and tetrahedral meshes using the Galerkin Finite Volume Method. The conjunctive model is utilized to investigate the deep currents affected by wind, Coriolis forces and the river inflow conditions of the Caspian Sea. In this study, the simulation of flow field due to major winds as well as transient winds in the Caspian Sea during a period of 6 hours in the winter season has been conducted and the numerical results for water surface level are then compared to the 2D numerical results.

Theory of nitrogen doping of carbon nanoribbons: Edge effects

DOE PAGES

Jiang, Jie; Turnbull, Joseph; Lu, Wenchang; ...

2012-01-01

Nitrogen doping of a carbon nanoribbon is profoundly affected by its one-dimensional character, symmetry, and interaction with edge states. Using state-of-the-art ab initio calculations, including hybrid exact-exchange density functional theory, we find that, for N-doped zigzag ribbons, the electronic properties are strongly dependent upon sublattice effects due to the non-equivalence of the two sublattices. For armchair ribbons, N-doping effects are different depending upon the ribbon family: for families 2 and 0, the N-induced levels are in the conduction band, while for family 1 the N levels are in the gap. In zigzag nanoribbons, nitrogen close to the edge is amore » deep center, while in armchair nanoribbons its behavior is close to an effective-mass-like donor with the ionization energy dependent on the value of the band gap. In chiral nanoribbons, we find strong dependence of the impurity level and formation energy upon the edge position of the dopant, while such site-specificity is not manifested in the magnitude of the magnetization.« less

Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.

PubMed

Kim, Seong Gon; Theera-Ampornpunt, Nawanol; Fang, Chih-Hao; Harwani, Mrudul; Grama, Ananth; Chaterji, Somali

2016-08-01

Gene expression is mediated by specialized cis-regulatory modules (CRMs), the most prominent of which are called enhancers. Early experiments indicated that enhancers located far from the gene promoters are often responsible for mediating gene transcription. Knowing their properties, regulatory activity, and genomic targets is crucial to the functional understanding of cellular events, ranging from cellular homeostasis to differentiation. Recent genome-wide investigation of epigenomic marks has indicated that enhancer elements could be enriched for certain epigenomic marks, such as, combinatorial patterns of histone modifications. Our efforts in this paper are motivated by these recent advances in epigenomic profiling methods, which have uncovered enhancer-associated chromatin features in different cell types and organisms. Specifically, in this paper, we use recent state-of-the-art Deep Learning methods and develop a deep neural network (DNN)-based architecture, called EP-DNN, to predict the presence and types of enhancers in the human genome. It uses as features, the expression levels of the histone modifications at the peaks of the functional sites as well as in its adjacent regions. We apply EP-DNN to four different cell types: H1, IMR90, HepG2, and HeLa S3. We train EP-DNN using p300 binding sites as enhancers, and TSS and random non-DHS sites as non-enhancers. We perform EP-DNN predictions to quantify the validation rate for different levels of confidence in the predictions and also perform comparisons against two state-of-the-art computational models for enhancer predictions, DEEP-ENCODE and RFECS. We find that EP-DNN has superior accuracy and takes less time to make predictions. Next, we develop methods to make EP-DNN interpretable by computing the importance of each input feature in the classification task. This analysis indicates that the important histone modifications were distinct for different cell types, with some overlaps, e.g., H3K27ac was important in cell type H1 but less so in HeLa S3, while H3K4me1 was relatively important in all four cell types. We finally use the feature importance analysis to reduce the number of input features needed to train the DNN, thus reducing training time, which is often the computational bottleneck in the use of a DNN. In this paper, we developed EP-DNN, which has high accuracy of prediction, with validation rates above 90 % for the operational region of enhancer prediction for all four cell lines that we studied, outperforming DEEP-ENCODE and RFECS. Then, we developed a method to analyze a trained DNN and determine which histone modifications are important, and within that, which features proximal or distal to the enhancer site, are important.

Deep Water Ocean Acoustics (DWOA): The Philippine Sea, OBSANP, and THAAW Experiments

DTIC Science & Technology

2015-09-30

the travel times. 4 The ocean state estimates were then re-computed to fit the acoustic travel times as integrals of the sound speed, and...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Deep Water Ocean Acoustics (DWOA): The Philippine Sea...deep-water acoustic propagation and ambient noise has been collected in a wide variety of environments over the last few years with ONR support

Deep Learning and Its Applications in Biomedicine.

PubMed

Cao, Chensi; Liu, Feng; Tan, Hai; Song, Deshou; Shu, Wenjie; Li, Weizhong; Zhou, Yiming; Bo, Xiaochen; Xie, Zhi

2018-02-01

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitates the understanding of human health and disease. Developed from artificial neural networks, deep learning-based algorithms show great promise in extracting features and learning patterns from complex data. The aim of this paper is to provide an overview of deep learning techniques and some of the state-of-the-art applications in the biomedical field. We first introduce the development of artificial neural network and deep learning. We then describe two main components of deep learning, i.e., deep learning architectures and model optimization. Subsequently, some examples are demonstrated for deep learning applications, including medical image classification, genomic sequence analysis, as well as protein structure classification and prediction. Finally, we offer our perspectives for the future directions in the field of deep learning. Copyright © 2018. Production and hosting by Elsevier B.V.

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks.

PubMed

Movahedi, Faezeh; Coyle, James L; Sejdic, Ervin

2018-05-01

Deep learning, a relatively new branch of machine learning, has been investigated for use in a variety of biomedical applications. Deep learning algorithms have been used to analyze different physiological signals and gain a better understanding of human physiology for automated diagnosis of abnormal conditions. In this paper, we provide an overview of deep learning approaches with a focus on deep belief networks in electroencephalography applications. We investigate the state-of-the-art algorithms for deep belief networks and then cover the application of these algorithms and their performances in electroencephalographic applications. We covered various applications of electroencephalography in medicine, including emotion recognition, sleep stage classification, and seizure detection, in order to understand how deep learning algorithms could be modified to better suit the tasks desired. This review is intended to provide researchers with a broad overview of the currently existing deep belief network methodology for electroencephalography signals, as well as to highlight potential challenges for future research.

Identifying and forecasting deep stratospheric ozone intrusions over the western United States from space

NASA Astrophysics Data System (ADS)

Lin, M.; Fiore, A. M.; Horowitz, L. W.; Cooper, O. R.; Langford, A. O.; Pan, L.; Liu, X.; Reddy, P. J.

2012-12-01

Recent studies have shown that deep stratospheric ozone intrusions can episodically enhance ground-level ozone above the health-based standard over the western U.S. in spring. Advanced warning of incoming intrusions could be used by state agencies to inform the public about poor air quality days. Here we explore the potential for using total ozone retrievals (version 5.2, level 3) at twice daily near global coverage from the AIRS instrument aboard the NASA Aqua satellite to identify stratospheric intrusions and forecast the eventual surface destination of transported stratospheric ozone. The method involves the correlation of AIRS daily total ozone columns at each 1ox1o grid box ~1-3 days prior to stratospheric enhancements to daily maximum 8-hour average ozone at a selected surface site using datasets from April to June in 2003-2011. The surface stratospheric enhancements are estimated by the GFDL AM3 chemistry-climate model which includes full stratospheric and tropospheric chemistry and is nudged to reanalysis winds. Our earlier work shows that the model presents deep stratospheric intrusions over the Western U.S. consistently with observations from AIRS, surface networks, daily ozone sondes, and aircraft lidar available in spring of 2010 during the NOAA CalNex field campaign. For the 15 surface sites in the U.S. Mountain West considered, a correlation coefficient of 0.4-0.7 emerges with AIRS ozone columns over 30o-50oN latitudes and 125o-105oW longitudes - variability in the AIRS column within this spatial domain indicates incoming intrusions. For each "surface receptor site", the spatial domain can narrow to an area ~5ox5o northwest of the individual site, with the strong correlation (0.5-0.7) occurring when the AIRS data is lagged by 1 day from the AM3 stratospheric enhancements in surface air. The spatial pattern of correlations is consistent with our process-oriented understanding developed from case studies of extreme intrusions. Surface observations during these events show that the sites experiencing elevated ozone levels are typically located over the southeastern side of the enhanced ozone columns captured by AIRS ~12 hours to 1 day prior. This first scoping study suggests there is potential to use near-daily global coverage of ozone in total column or in UT/LS levels from the space-based instruments (e.g. AIRS, OMI, MLS) to serve as a qualitative early-warning indicator of incoming stratospheric intrusions with a lead time of ~1-3 days. There is more skill in ~12 hours to 1 day as to where the intrusion will reach the surface, particularly during the ENSO years (i.e. 2003, 2008, 2010, 2011) when deep intrusions are more likely to occur as compared to other years. These space-based ozone products can also provide some indication of whether a historic exceedance was caused by an intrusion.

HD-MTL: Hierarchical Deep Multi-Task Learning for Large-Scale Visual Recognition.

PubMed

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.

Correlation Between Body Movements and Salivary Secretion During Sedation.

PubMed

Sasaki, Yoko; Kato, Seiichi; Miura, Masaaki; Fukayama, Haruhisa

2016-01-01

During dental sedation, control of the cough reflex is crucial for a safe and smooth procedure. Accumulated saliva is one of the predisposing factors for coughing. Body movements during dental sedation appear to enhance salivation. Therefore, the aim of this study was to investigate the difference in salivary secretion between the with-movements state and the without-movements state during sedation. Salivary weight for 1 min was measured 3 times in 27 patients with intellectual disability during dental treatment under deep sedation with midazolam and propofol. The observed variables were body movements, bispectral index (BIS), and predicted propofol effect-site concentration. A total of 81 measurements were classified into the with-movements state (n = 39; ie, measurements during which body movements were observed) or the without-movements state (n = 42; ie, measurements during which no body movements were observed). The median salivary weight was significantly smaller in the without-movements state compared with the with-movements state (0.03 vs 0.11 g, P < .0001). The BIS was significantly lower in the without-movements state. There was no significant difference in the predicted propofol effect-site concentration between the 2 states. Significant correlation was observed between salivary weight and BIS in the with-movements state (r = 0.44, P = .004). The findings indicate that salivary secretion decreased according to deep sedation. Furthermore, immobility also reduced salivary secretion. We concluded that one reason that immobility is beneficial is because of the resulting decreased salivary secretion during dental treatment under deep sedation.

Model United Nations and Deep Learning: Theoretical and Professional Learning

ERIC Educational Resources Information Center

Engel, Susan; Pallas, Josh; Lambert, Sarah

2017-01-01

This article demonstrates that the purposeful subject design, incorporating a Model United Nations (MUN), facilitated deep learning and professional skills attainment in the field of International Relations. Deep learning was promoted in subject design by linking learning objectives to Anderson and Krathwohl's (2001) four levels of knowledge or…

The relationship between the deep-level structure in crust and brewing of strong earthquakes in Xingtai area

NASA Astrophysics Data System (ADS)

Xiao, Lan-Xi; Zhu, Yuan-Qing; Zhang, Shao-Quan; Liu, Xu; Guo, Yu

1999-11-01

In this paper, crust medium is treated as Maxwell medium, and crust model includes hard inclusion, soft inclusion, deep-level fault. The stress concentration and its evolution with time are obtained by using three-dimensional finite element method and differential method. The conclusions are draw as follows: (1) The average stress concentration and maximum shear stress concentration caused by non-heterogeneous of crust are very high in hard inclusion and around the deep fault. With the time passing by, the concentration of average stress in the model gradually trends to uniform. At the same time, the concentration of maximum shear stress in hard inclusion increases gradually. This character is favorable to transfer shear strain energy from soft inclusion to hard inclusion. (2) When the upper mantle beneath the inclusion upheave at a certain velocity of 1 cm/a, the changes of average stress concentration with time become complex, and the boundary of the hard and soft inclusion become unconspicuous, but the maximum shear stress concentration increases much more in the hard inclusion with time at a higher velocity. This feature make for transformation of energy from the soft inclusion to the hard inclusion. (3) The changes of average stress concentration and maximum shear stress concentration with time around the deep-level fault result in further accumulation of maximum shear stress concentration and finally cause the deep-level fault instable and accelerated creep along fault direction. (4) The changes of vertical displacement on the surface of the model, which is caused by the accelerated creep of the deep-level fault, is similar to that of the observation data before Xingtai strong earthquake.

Effects of cooking on radiocesium in fish from the Savannah River: exposure differences for the public.

PubMed

Burger, Joanna; Gaines, Karen F; Boring, C Shane; Snodgrass, J; Stephens, W L; Gochfeld, M

2004-02-01

Understanding the factors that contribute to the risk from fish consumption is an important public health concern because of potential adverse effects of radionuclides, organochlorines, other pesticides, and mercury. Risk from consumption is normally computed on the basis of contaminant levels in fish, meal frequency, and meal size, yet cooking practices may also affect risk. This study examines the effect of deep-frying on radiocesium (137Cs) levels and risk to people fishing along the Savannah River. South Carolina and Georgia have issued consumption advisories for the Savannah River, based partly on 137Cs. 137Cs levels were significantly higher in the cooked fish compared to the raw fish on a wet weight basis. Mean 137Cs levels were 0.61 pCi/g (wet weight basis) in raw fish, 0.81 pCi/g in cooked-breaded, and 0.99 pCi/g in cooked-unbreaded fish. Deep-frying with and without breading resulted in a weight loss of 25 and 39%, while 137Cs levels increased by 32 and 62%, respectively. Therefore, the differences were due mainly to weight loss during cooking. However, the data suggest that risk assessments should be based on cooked portion size for contaminant analysis, or the risk from 137Cs in fish will be underestimated. People are likely to estimate the amounts of fish they eat based on a meal size of the cooked portion, while risk assessors determine 137Cs levels in raw fish. A conversion factor of at least two for 137Cs increase during cooking is reasonable and conservative, given the variability in 137Cs levels. The data also suggest that surveys determining consumption should specifically ask about portion size before or after cooking and state which was used in their methods.

Are memberships in race, ethnicity, and gender categories merely surface characteristics?

PubMed

Eagly, Alice H; Chin, Jean Lau

2010-12-01

Comments on Deep-level diversity and leadership (see record 2010-24768-017) by Kristen M. Klein and Mo Wang. In the special issue on Diversity and Leadership (April 2010), the authors made a strong case for the importance of diversity in workplace leadership, rejected premature declarations that workplace discrimination is obsolete, and called for leadership theories that acknowledge and promote the value of diversity. We suggest that researchers could better predict and increase leader effectiveness by explicitly addressing deep-level characteristics in theory and practice. By promoting surface-level diversity in leadership opportunities and deep-level similarities in leadership training, it is conceivable that organizations could counter adverse impact in leader selection while also improving organizational outcomes. PsycINFO Database Record (c) 2010 APA, all rights reserved.

Defect physics in intermediate-band materials: Insights from an optimized hybrid functional

NASA Astrophysics Data System (ADS)

Han, Miaomiao; Zeng, Zhi; Frauenheim, Thomas; Deák, Peter

2017-10-01

Despite the efforts to implement the idea of a deep level impurity intermediate band (IB) into bulk solar cell materials, a breakthrough in efficiency increase has not yet been achieved. Taking Sn-doped CuGaS2 as an example, we investigate the problem here from the perspective of defect physics, considering all possible charge states of the dopant and its interaction with native defects. Using an optimized hybrid functional, we find that SnGa has not only a donor-type (+/0), but also an acceptor-type (0 /- ) charge transition level. We estimate the probability of the optical transition of an electron from/to the neutral defect to/from the conduction-band edge to be about equal, therefore, the lifetimes of the excited carriers are probably quite short, limiting the enhancement of the photocurrent. In addition, we find that doping with SnGa leads to the spontaneous formation of the intrinsic acceptor CuGa defects which passivate the donor SnGa and pin the Fermi level to a position (1.4 eV above the valence-band edge) where both defects are ionized. As a result, the possibility of absorption in the middle of the visible range gets lost. These two recombination and passivation mechanisms appear to be quite likely the case for other donors and other similar host materials as well, explaining some of the experimental bottlenecks with IB solar cells based on deep level impurities.

Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval.

PubMed

Zhang, Haofeng; Liu, Li; Long, Yang; Shao, Ling

2018-04-01

In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.

Deep Space Positioning System

NASA Technical Reports Server (NTRS)

Vaughan, Andrew T. (Inventor); Riedel, Joseph E. (Inventor)

2016-01-01

A single, compact, lower power deep space positioning system (DPS) configured to determine a location of a spacecraft anywhere in the solar system, and provide state information relative to Earth, Sun, or any remote object. For example, the DPS includes a first camera and, possibly, a second camera configured to capture a plurality of navigation images to determine a state of a spacecraft in a solar system. The second camera is located behind, or adjacent to, a secondary reflector of a first camera in a body of a telescope.

Deep vs shallow nature of oxygen vacancies and consequent n -type carrier concentrations in transparent conducting oxides

NASA Astrophysics Data System (ADS)

Buckeridge, J.; Catlow, C. R. A.; Farrow, M. R.; Logsdail, A. J.; Scanlon, D. O.; Keal, T. W.; Sherwood, P.; Woodley, S. M.; Sokol, A. A.; Walsh, A.

2018-05-01

The source of n -type conductivity in undoped transparent conducting oxides has been a topic of debate for several decades. The point defect of most interest in this respect is the oxygen vacancy, but there are many conflicting reports on the shallow versus deep nature of its related electronic states. Here, using a hybrid quantum mechanical/molecular mechanical embedded cluster approach, we have computed formation and ionization energies of oxygen vacancies in three representative transparent conducting oxides: In2O3 ,SnO2, and ZnO. We find that, in all three systems, oxygen vacancies form well-localized, compact donors. We demonstrate, however, that such compactness does not preclude the possibility of these states being shallow in nature, by considering the energetic balance between the vacancy binding electrons that are in localized orbitals or in effective-mass-like diffuse orbitals. Our results show that, thermodynamically, oxygen vacancies in bulk In2O3 introduce states above the conduction band minimum that contribute significantly to the observed conductivity properties of undoped samples. For ZnO and SnO2, the states are deep, and our calculated ionization energies agree well with thermochemical and optical experiments. Our computed equilibrium defect and carrier concentrations, however, demonstrate that these deep states may nevertheless lead to significant intrinsic n -type conductivity under reducing conditions at elevated temperatures. Our study indicates the importance of oxygen vacancies in relation to intrinsic carrier concentrations not only in In2O3 , but also in SnO2 and ZnO.

THE MINIMUM OF SOLAR CYCLE 23: AS DEEP AS IT COULD BE?

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

Muñoz-Jaramillo, Andrés; Longcope, Dana W.; Senkpeil, Ryan R.

2015-05-01

In this work we introduce a new way of binning sunspot group data with the purpose of better understanding the impact of the solar cycle on sunspot properties and how this defined the characteristics of the extended minimum of cycle 23. Our approach assumes that the statistical properties of sunspots are completely determined by the strength of the underlying large-scale field and have no additional time dependencies. We use the amplitude of the cycle at any given moment (something we refer to as activity level) as a proxy for the strength of this deep-seated magnetic field. We find that themore » sunspot size distribution is composed of two populations: one population of groups and active regions and a second population of pores and ephemeral regions. When fits are performed at periods of different activity level, only the statistical properties of the former population, the active regions, are found to vary. Finally, we study the relative contribution of each component (small-scale versus large-scale) to solar magnetism. We find that when hemispheres are treated separately, almost every one of the past 12 solar minima reaches a point where the main contribution to magnetism comes from the small-scale component. However, due to asymmetries in cycle phase, this state is very rarely reached by both hemispheres at the same time. From this we infer that even though each hemisphere did reach the magnetic baseline, from a heliospheric point of view the minimum of cycle 23 was not as deep as it could have been.« less

Peripheral Nerve Block as a Supplement to Light or Deep General Anesthesia in Elderly Patients Receiving Total Hip Arthroplasty: A Prospective Randomized Study.

PubMed

Mei, Bin; Zha, Hanning; Lu, Xiaolong; Cheng, Xinqi; Chen, Shishou; Liu, Xuesheng; Li, Yuanhai; Gu, Erwei

2017-12-01

Peripheral nerve block combined with general anesthesia is a preferable anesthesia method for elderly patients receiving hip arthroplasty. The depth of sedation may influence patient recovery. Therefore, we investigated the influence of peripheral nerve blockade and different intraoperative sedation levels on the short-term recovery of elderly patients receiving total hip arthroplasty. Patients aged 65 years and older undergoing total hip arthroplasty were randomized into 3 groups: a general anesthesia without lumbosacral plexus block group, and 2 general anesthesia plus lumbosacral plexus block groups, each with a different level of sedation (light or deep). The extubation time and intraoperative consumption of propofol, sufentanil, and vasoactive agent were recorded. Postoperative delirium and early postoperative cognitive dysfunction were assessed using the Confusion Assessment Method and Mini-Mental State Examination, respectively. Postoperative analgesia was assessed by the consumption of patient-controlled analgesics and visual analog scale scores. Discharge time and complications over a 30-day period were also recorded. Lumbosacral plexus block reduced opioid intake. With lumbosacral plexus block, intraoperative deep sedation was associated with greater intake of propofol and vasoactive agent. In contrast, patients with lumbosacral plexus block and intraoperative light sedation had lower incidences of postoperative delirium and postoperative cognitive decline, and earlier discharge readiness times. The 3 groups showed no difference in complications within 30 days of surgery. Lumbosacral plexus block reduced the need for opioids and offered satisfactory postoperative analgesia. It led to better postoperative outcomes in combination with intraoperative light sedation (high bispectral index).

Capacitance spectroscopy on n-type GaNAs/GaAs embedded quantum structure solar cells

NASA Astrophysics Data System (ADS)

Venter, Danielle; Bollmann, Joachim; Elborg, Martin; Botha, J. R.; Venter, André

2018-04-01

In this study, both deep level transient spectroscopy (DLTS) and admittance spectroscopy (AS) have been used to study the properties of electrically active deep level centers present in GaNAs/GaAs quantum wells (QWs) embedded in p-i-n solar cells. The structures were grown by molecular beam epitaxy (MBE). In particular, the electrical properties of samples with Si (n-type) doping of the QWs were investigated. DLTS revealed four deep level centers in the material, whereas only three were detected by AS. NextNano++ simulation software was used to model the sample band-diagrams to provide reasoning for the origin of the signals produced by both techniques.

Cancer Precision Medicine: Why More Is More and DNA Is Not Enough.

PubMed

Schütte, Moritz; Ogilvie, Lesley A; Rieke, Damian T; Lange, Bodo M H; Yaspo, Marie-Laure; Lehrach, Hans

2017-01-01

Every tumour is different. They arise in patients with different genomes, from cells with different epigenetic modifications, and by random processes affecting the genome and/or epigenome of a somatic cell, allowing it to escape the usual controls on its growth. Tumours and patients therefore often respond very differently to the drugs they receive. Cancer precision medicine aims to characterise the tumour (and often also the patient) to be able to predict, with high accuracy, its response to different treatments, with options ranging from the selective characterisation of a few genomic variants considered particularly important to predict the response of the tumour to specific drugs, to deep genome analysis of both tumour and patient, combined with deep transcriptome analysis of the tumour. Here, we compare the expected results of carrying out such analyses at different levels, from different size panels to a comprehensive analysis incorporating both patient and tumour at the DNA and RNA levels. In doing so, we illustrate the additional power gained by this unusually deep analysis strategy, a potential basis for a future precision medicine first strategy in cancer drug therapy. However, this is only a step along the way of increasingly detailed molecular characterisation, which in our view will, in the future, introduce additional molecular characterisation techniques, including systematic analysis of proteins and protein modification states and different types of metabolites in the tumour, systematic analysis of circulating tumour cells and nucleic acids, the use of spatially resolved analysis techniques to address the problem of tumour heterogeneity as well as the deep analyses of the immune system of the patient to, e.g., predict the response of the patient to different types of immunotherapy. Such analyses will generate data sets of even greater complexity, requiring mechanistic modelling approaches to capture enough of the complex situation in the real patient to be able to accurately predict his/her responses to all available therapies. © 2017 S. Karger AG, Basel.

Combination of rs-fMRI and sMRI Data to Discriminate Autism Spectrum Disorders in Young Children Using Deep Belief Network.

PubMed

Akhavan Aghdam, Maryam; Sharifi, Arash; Pedram, Mir Mohsen

2018-05-07

In recent years, the use of advanced magnetic resonance (MR) imaging methods such as functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) has recorded a great increase in neuropsychiatric disorders. Deep learning is a branch of machine learning that is increasingly being used for applications of medical image analysis such as computer-aided diagnosis. In a bid to classify and represent learning tasks, this study utilized one of the most powerful deep learning algorithms (deep belief network (DBN)) for the combination of data from Autism Brain Imaging Data Exchange I and II (ABIDE I and ABIDE II) datasets. The DBN was employed so as to focus on the combination of resting-state fMRI (rs-fMRI), gray matter (GM), and white matter (WM) data. This was done based on the brain regions that were defined using the automated anatomical labeling (AAL), in order to classify autism spectrum disorders (ASDs) from typical controls (TCs). Since the diagnosis of ASD is much more effective at an early age, only 185 individuals (116 ASD and 69 TC) ranging in age from 5 to 10 years were included in this analysis. In contrast, the proposed method is used to exploit the latent or abstract high-level features inside rs-fMRI and sMRI data while the old methods consider only the simple low-level features extracted from neuroimages. Moreover, combining multiple data types and increasing the depth of DBN can improve classification accuracy. In this study, the best combination comprised rs-fMRI, GM, and WM for DBN of depth 3 with 65.56% accuracy (sensitivity = 84%, specificity = 32.96%, F1 score = 74.76%) obtained via 10-fold cross-validation. This result outperforms previously presented methods on ABIDE I dataset.

Parkinsonian gait improves with bilateral subthalamic nucleus deep brain stimulation during cognitive multi-tasking.

PubMed

Chenji, Gaurav; Wright, Melissa L; Chou, Kelvin L; Seidler, Rachael D; Patil, Parag G

2017-05-01

Gait impairment in Parkinson's disease reduces mobility and increases fall risk, particularly during cognitive multi-tasking. Studies suggest that bilateral subthalamic deep brain stimulation, a common surgical therapy, degrades motor performance under cognitive dual-task conditions, compared to unilateral stimulation. To measure the impact of bilateral versus unilateral subthalamic deep brain stimulation on walking kinematics with and without cognitive dual-tasking. Gait kinematics of seventeen patients with advanced Parkinson's disease who had undergone bilateral subthalamic deep brain stimulation were examined off medication under three stimulation states (bilateral, unilateral left, unilateral right) with and without a cognitive challenge, using an instrumented walkway system. Consistent with earlier studies, gait performance declined for all six measured parameters under cognitive dual-task conditions, independent of stimulation state. However, bilateral stimulation produced greater improvements in step length and double-limb support time than unilateral stimulation, and achieved similar performance for other gait parameters. Contrary to expectations from earlier studies of dual-task motor performance, bilateral subthalamic deep brain stimulation may assist in maintaining temporal and spatial gait performance under cognitive dual-task conditions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Stable architectures for deep neural networks

NASA Astrophysics Data System (ADS)

Haber, Eldad; Ruthotto, Lars

2018-01-01

Deep neural networks have become invaluable tools for supervised machine learning, e.g. classification of text or images. While often offering superior results over traditional techniques and successfully expressing complicated patterns in data, deep architectures are known to be challenging to design and train such that they generalize well to new data. Critical issues with deep architectures are numerical instabilities in derivative-based learning algorithms commonly called exploding or vanishing gradients. In this paper, we propose new forward propagation techniques inspired by systems of ordinary differential equations (ODE) that overcome this challenge and lead to well-posed learning problems for arbitrarily deep networks. The backbone of our approach is our interpretation of deep learning as a parameter estimation problem of nonlinear dynamical systems. Given this formulation, we analyze stability and well-posedness of deep learning and use this new understanding to develop new network architectures. We relate the exploding and vanishing gradient phenomenon to the stability of the discrete ODE and present several strategies for stabilizing deep learning for very deep networks. While our new architectures restrict the solution space, several numerical experiments show their competitiveness with state-of-the-art networks.

Effortful semantic decision-making boosts memory performance in older adults.

PubMed

Fu, Li; Maes, Joseph H R; Varma, Samarth; Kessels, Roy P C; Daselaar, Sander M

2017-04-01

A major concern in age-related cognitive decline is episodic memory (EM). Previous studies indicate that both resource and binding deficits contribute to EM decline. Environmental support by task manipulations encouraging stronger cognitive effort and deeper levels of processing may facilitate compensation for these two deficits. To clarify factors that can counteract age-related EM decline, we assessed effects of cognitive effort (four levels) and level of processing (LoP, shallow/deep) during encoding on subsequent retrieval. Young (YAs, N = 23) and older (OAs, N = 23) adults performed two incidental encoding tasks, deep/semantic and shallow/perceptual. Cognitive effort was manipulated by varying decision-making demands. EM performance, indexed by d-prime, was later tested using a recognition task. Results showed that regardless of LoP, increased cognitive effort caused higher d-primes in both age groups. Compared to YAs, OAs showed a lower d-prime after shallow encoding across all cognitive effort levels, and after deep encoding with low cognitive effort. Deep encoding with higher levels of cognitive effort completely eliminated these age differences. Our findings support an environmental-compensatory account of cognitive ageing and can have important therapeutic implications.

Subgap time of flight: A spectroscopic study of deep levels in semi-insulating CdTe:Cl

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

Pousset, J.; Farella, I.; Cola, A., E-mail: adriano.cola@le.imm.cnr.it

2016-03-14

We report on a study of deep levels in semi-insulating CdTe:Cl by means of a time-of-flight spectral approach. By varying the wavelength of a pulsed optical source within the CdTe energy gap, transitions to/from localized levels generate free carriers which are analysed through the induced photocurrent transients. Both acceptor-like centers, related to the A-center, and a midgap level, 0.725 eV from the valence band, have been detected. The midgap level is close to the Fermi level and is possibly a recombination center responsible for the compensation mechanism. When the irradiance is varied, either linear or quadratic dependence of the electron andmore » hole collected charge are observed, depending on the dominant optical transitions. The analysis discloses the potentiality of such a novel approach exploitable in the field of photorefractive materials as well as for deep levels spectroscopy.« less

Concurrent deep-superficial dyspareunia: prevalence, associations, and outcomes in a multidisciplinary vulvodynia program.

PubMed

Yong, Paul J; Sadownik, Leslie; Brotto, Lori A

2015-01-01

Little is known about women with concurrent diagnoses of deep dyspareunia and superficial dyspareunia. The aim of this study was to determine the prevalence, associations, and outcome of women with concurrent deep-superficial dyspareunia. This is a prospective study of a multidisciplinary vulvodynia program (n = 150; mean age 28.7 ± 6.4 years). Women with superficial dyspareunia due to provoked vestibulodynia were divided into two groups: those also having deep dyspareunia (i.e., concurrent deep-superficial dyspareunia) and those with only superficial dyspareunia due to provoked vestibulodynia. Demographics, dyspareunia-related factors, other pain conditions, and psychological variables at pretreatment were tested for an association with concurrent deep-superficial dyspareunia. Outcome in both groups was assessed to 6 months posttreatment. Level of dyspareunia pain (0-10) and Female Sexual Distress Scale were the main outcome measures. The prevalence of concurrent deep-superficial dyspareunia was 44% (66/150) among women with superficial dyspareunia due to provoked vestibulodynia. At pretreatment, on multiple logistic regression, concurrent deep-superficial dyspareunia was independently associated with a higher level of dyspareunia pain (odds ratio [OR] = 1.19 [1.01-1.39], P = 0.030), diagnosis of endometriosis (OR = 4.30 [1.16-15.90], P = 0.022), history of bladder problems (OR = 3.84 [1.37-10.76], P = 0.008), and more depression symptoms (OR = 1.07 [1.02-1.12], P = 0.007), with no difference in the Female Sexual Distress Scale. At 6 months posttreatment, women with concurrent deep-superficial dyspareunia improved in the level of dyspareunia pain and in the Female Sexual Distress Scale to the same degree as women with only superficial dyspareunia due to provoked vestibulodynia. Concurrent deep-superficial dyspareunia is reported by almost half of women in a multidisciplinary vulvodynia program. In women with provoked vestibulodynia, concurrent deep-superficial dyspareunia may be related to endometriosis or interstitial cystitis, and is associated with depression and more severe dyspareunia symptoms. Standardized multidisciplinary care is effective for women with concurrent dyspareunia. © 2014 International Society for Sexual Medicine.

Less is More: Membrane Protein Digestion Beyond Urea–Trypsin Solution for Next-level Proteomics*

PubMed Central

Zhang, Xi

2015-01-01

The goal of next-level bottom-up membrane proteomics is protein function investigation, via high-coverage high-throughput peptide-centric quantitation of expression, modifications and dynamic structures at systems scale. Yet efficient digestion of mammalian membrane proteins presents a daunting barrier, and prevalent day-long urea–trypsin in-solution digestion proved insufficient to reach this goal. Many efforts contributed incremental advances over past years, but involved protein denaturation that disconnected measurement from functional states. Beyond denaturation, the recent discovery of structure/proteomics omni-compatible detergent n-dodecyl-β-d-maltopyranoside, combined with pepsin and PNGase F columns, enabled breakthroughs in membrane protein digestion: a 2010 DDM-low-TCEP (DLT) method for H/D-exchange (HDX) using human G protein-coupled receptor, and a 2015 flow/detergent-facilitated protease and de-PTM digestions (FDD) for integrative deep sequencing and quantitation using full-length human ion channel complex. Distinguishing protein solubilization from denaturation, protease digestion reliability from theoretical specificity, and reduction from alkylation, these methods shifted day(s)-long paradigms into minutes, and afforded fully automatable (HDX)-protein-peptide-(tandem mass tag)-HPLC pipelines to instantly measure functional proteins at deep coverage, high peptide reproducibility, low artifacts and minimal leakage. Promoting—not destroying—structures and activities harnessed membrane proteins for the next-level streamlined functional proteomics. This review analyzes recent advances in membrane protein digestion methods and highlights critical discoveries for future proteomics. PMID:26081834

Perceived Deep-Level Dissimilarity: Personality Antecedents and Impact on Overall Job Attitude, Helping, Work Withdrawal, and Turnover

ERIC Educational Resources Information Center

Liao, Hui; Chuang, Aichia; Joshi, Aparna

2008-01-01

The current research extends three research areas in relational demography: considering deep-level dissimilarity in theory building, assessing dissimilarity perceptions directly in theory testing, and examining the antecedents of dissimilarity perceptions. The results, based on two field studies using diverse samples, demonstrate the effects of…

Detection of deep-level defects and reduced carrier concentration in Mg-ion-implanted GaN before high-temperature annealing

NASA Astrophysics Data System (ADS)

Akazawa, Masamichi; Yokota, Naoshige; Uetake, Kei

2018-02-01

We report experimental results for the detection of deep-level defects in GaN after Mg ion implantation before high-temperature annealing. The n-type GaN samples were grown on GaN free-standing substrates by metalorganic vapor phase epitaxy. Mg ions were implanted at 50 keV with a small dosage of 1.5×1011 cm-2, which did not change the conduction type of the n-GaN. By depositing Al2O3 and a Ni/Au electrode onto the implanted n-GaN, metal-oxide-semiconductor (MOS) diodes were fabricated and tested. The measured capacitance-voltage (C-V) characteristics showed a particular behavior with a plateau region and a region with an anomalously steep slope. Fitting to the experimental C-V curves by simulation showed the existence of deep-level defects and a reduction of the carrier concentration near the GaN surface. By annealing at 800oC, the density of the deep-level defects was reduced and the carrier concentration partially recovered.

The effect of aerosol-derived changes in the warm phase on the properties of deep convective clouds

NASA Astrophysics Data System (ADS)

Chen, Qian; Koren, Ilan; Altaratz, Orit; Heiblum, Reuven; Dagan, Guy

2017-04-01

The aerosol impact on deep convective clouds starts in an increased number of cloud droplets in higher aerosol loading environment. This change drives many others, like enhanced condensational growth, delay in collision-coalescence and others. Since the warm processes serve as the initial and boundary conditions for the mixed and cold-phase processes in deep clouds, it is highly important to understand the aerosol effect on them. The weather research and forecasting model (WRF) with spectral bin microphysics was used to study a deep convective system over the Marshall Islands, during the Kwajalein Experiment (KWAJEX). Three simulations were conducted with aerosol concentrations of 100, 500 and 2000 cm-3, to reflect clean, semipolluted, and polluted conditions. The results of the clean run agreed well with the radar profiles and rain rate observations. The more polluted simulations resulted in larger total cloud mass, larger upper level cloud fraction and rain rates. There was an increased mass both below and above the zero temperature level. It indicates of more efficient growth processes both below and above the zero level. In addition the polluted runs showed an increased upward transport (across the zero level) of liquid water due to both stronger updrafts and larger droplet mobility. In this work we discuss the transport of cloud mass crossing the zero temperature level (in both directions) in order to gain a process level understanding of how aerosol effects on the warm processes affect the macro- and micro-properties of deep convective clouds.

Lack of mutagens in deep-fat-fried foods obtained at the retail level.

PubMed

Taylor, S L; Berg, C M; Shoptaugh, N H; Scott, V N

1982-04-01

The basic methylene chloride extract from 20 of 30 samples of foods fried in deep fat failed to elicit any mutagenic response that could be detected in the Salmonella typhimurium/mammalian microsome assay. The basic extracts of the remaining ten samples (all three chicken samples studied, two of the four potato-chip samples, one of four corn-chip samples, the sample of onion rings, two of six doughnuts, and one of three samples of french-fried potato) showed evidence of weak mutagenic activity. In these samples, amounts of the basic extract equivalent to 28.5-57 g of the original food sample were required to produce revertants at levels of 2.6-4.8 times the background level. Only two of the acidic methylene chloride extracts from the 30 samples exhibited mutagenic activity greater than 2.5 times the background reversion level, and in both cases (one corn-chip and one shrimp sample) the mutagenic response was quite weak. The basic extract of hamburgers fried in deep fat in a home-style fryer possessed higher levels of mutagenic activity (13 times the background reversion level). However, the mutagenic activity of deep-fried hamburgers is some four times lower than that of pan-fried hamburgers.

Deep level defects in dilute GaAsBi alloys grown under intense UV illumination

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

Mooney, P. M.; Tarun, Marianne; Beaton, D. A.

2016-07-21

Dilute GaAs1-xBix alloys exhibiting narrow band edge photoluminescence (PL) were recently grown by molecular beam epitaxy (MBE) with the growth surface illuminated by intense UV radiation. To investigate whether the improved optical quality of these films results from a reduction in the concentration of deep level defects, p+/n and n+/p junction diodes were fabricated on both the illuminated and dark areas of several samples. Deep Level Transient Spectroscopy (DLTS) measurements show that the illuminated and dark areas of both the n- and p-type GaAs1-xBix epi-layers have similar concentrations of near mid-gap electron and hole traps, in the 1015 cm-3 range.more » Thus the improved PL spectra cannot be explained by a reduction in non-radiative recombination at deep level defects. We note that carrier freeze-out above 35 K is significantly reduced in the illuminated areas of the p-type GaAs1-xBix layers compared to the dark areas, allowing the first DLTS measurements of defect energy levels close to the valence band edge. These defect levels may account for differences in the PL spectra from the illuminated and dark areas of un-doped layers with a similar Bi fraction.« less

Revealing the fast atomic motion of network glasses.

PubMed

Ruta, B; Baldi, G; Chushkin, Y; Rufflé, B; Cristofolini, L; Fontana, A; Zanatta, M; Nazzani, F

2014-05-19

Still very little is known on the relaxation dynamics of glasses at the microscopic level due to the lack of experiments and theories. It is commonly believed that glasses are in a dynamical arrested state, with relaxation times too large to be observed on human time scales. Here we provide the experimental evidence that glasses display fast atomic rearrangements within a few minutes, even in the deep glassy state. Following the evolution of the structural relaxation in a sodium silicate glass, we find that this fast dynamics is accompanied by the absence of any detectable aging, suggesting a decoupling of the relaxation time and the viscosity in the glass. The relaxation time is strongly affected by the network structure with a marked increase at the mesoscopic scale associated with the ion-conducting pathways. Our results modify the conception of the glassy state and asks for a new microscopic theory.

Modulation of the electrical properties in amorphous indium-gallium zinc-oxide semiconductor films using hydrogen incorporation

NASA Astrophysics Data System (ADS)

Song, Aeran; Park, Hyun-Woo; Chung, Kwun-Bum; Rim, You Seung; Son, Kyoung Seok; Lim, Jun Hyung; Chu, Hye Yong

2017-12-01

The electrical properties of amorphous-indium-gallium-zinc-oxide (a-IGZO) thin films were investigated after thermal annealing and plasma treatment under different gas conditions. The electrical resistivity of a-IGZO thin films post-treated in a hydrogen ambient were lower than those without treatment and those annealed in air, regardless of the methods used for both thermal annealing and plasma treatment. The electrical properties can be explained by the quantity of hydrogen incorporated into the samples and the changes in the electronic structure in terms of the chemical bonding states, the distribution of the near-conduction-band unoccupied states, and the band alignment. As a result, the carrier concentrations of the hydrogen treated a-IGZO thin films increased, while the mobility decreased, due to the increase in the oxygen vacancies from the occurrence of unoccupied states in both shallow and deep levels.

The Meyer-Neldel rule and the statistical shift of the Fermi level in amorphous semiconductors

NASA Astrophysics Data System (ADS)

Kikuchi, Minoru

1988-11-01

The statistical model is used to study the origin of the Meyer-Neldel (MN) rule [σ0∝exp(AEσ)] in a tetrahedral amorphous system. It is shown that a deep minimum in the gap density of states spectrum can lead to the linearity of the Fermi energy F(T) to the derivative (dF/dkT), as required from the rule. An expression is derived which relates the constant A in the rule to the gap density of states spectrum. The dispersion ranges of σ0 and Eσ are found to be related with the constant A. Model calculations show a magnitude of A and a wide dispersion of σ0 and Eσ in fair agreement with the experimental observations. A discussion is given to what extent the MN rule is dependent on the gap density of states spectrum.

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

Nesaraja, C.D.; McCutchan, E.A.

Available information pertaining to the nuclear structure of all nuclei with mass numbers A=41 ranging from Al (Z=13) to Ti (Z=22) are presented. The experimental reaction and decay data are evaluated and any inconsistencies or discrepancies are noted. The adopted values for various level properties (such as the spin, parity and and halflife) and gamma properties (energy, intensity and multipole character) are given. Since the prior evaluation several new measurements have expanded our knowledge of A=41 nuclides. The half-life of the ground state of {sup 41}Si has been determined and a single excited state identified. Excited levels in {sup 41}Pmore » have been observed for the first time. In {sup 41}Cl, seven new excited states have been identified in deep inelastic and heavy ion transfer reactions. Half-lifes for four states in {sup 41}Ar have been updated and additional levels with gammas have been included from a new measurement using the multiple ion transfer reaction. In {sup 41}Ca via charge-exchange reaction measurements, several new excited states were observed. A number of new resonances in {sup 41}K have been identified via the (p,γ) reaction. There remains a significant discrepancy in the half-life of the first excited state (980 keV) in {sup 41}K, with measurements differing by more than an order of magnitude. Transfer reactions suggest that this M1 transition should be l-forbidden, however, several measurements yield a lifetime which suggests a sizable M1 strength. Further measurements to resolve the current conflicts would be beneficial.« less

Nuclear Data Sheets for A = 41

DOE PAGES

Nesaraja, C. D.; McCutchan, E. A.

2016-03-01

Available information pertaining to the nuclear structure of all nuclei with mass numbers A=41 ranging from Al (Z=13) to Ti (Z=22) are presented. The experimental reaction and decay data are evaluated and any inconsistencies or discrepancies are noted. The adopted values for various level properties (such as the spin, parity and and halflife) and gamma properties (energy, intensity and multipole character) are given. Since the prior evaluation several new measurements have expanded our knowledge of A=41 nuclides. The half–life of the ground state of 41Si has been determined and a single excited state identified. Excited levels in 41P have beenmore » observed for the first time. In 41Cl, seven new excited states have been identified in deep inelastic and heavy ion transfer reactions. Half–lifes for four states in 41Ar have been updated and additional levels with gammas have been included from a new measurement using the multiple ion transfer reaction. In 41Ca via charge–exchange reaction measurements, several new excited states were observed. A number of new resonances in 41K have been identified via the (p, γ ) reaction. There remains a significant discrepancy in the half–life of the first excited state (980 keV) in 41K, with measurements differing by more than an order of magnitude. Transfer reactions suggest that this M1 transition should be l–forbidden, however, several measurements yield a lifetime which suggests a sizable M1 strength. Further measurements to resolve the current conflicts would be beneficial.« less

Ultrafast exciton dynamics in cadmium selenide nanocrystals determined by femtosecond fluorescence upconversion spectroscopy

NASA Astrophysics Data System (ADS)

Underwood, David Frederick

Femtosecond fluorescence upconversion spectroscopy is a technique that allows the unambiguous determination of the excited state dynamics of an analyte. Combining this method with the use of tunable laser excitation, the exciton dynamics in semiconducting nanocrystals (NC's) of cadmium selenide (CdSe) have been determined, devoid of the complications arising from more common spectroscopic methods such as pump-probe. The results of this investigation were used to construct a model to fully describe the three-level system comprising of the valence and conduction bands and surface states, which have been calculated by others to lie mid-gap in energy. Smaller NC's showed faster decay components due to increased interaction between the exciton and surface states. The deep trap emission, which has never before been measured by ultrafast fluorescence techniques, shows a rapid rise time (˜2 ps), which is attributed to surface selenium dangling bonds relaxing to the valence band and radiatively combining with the photo-generated hole. The band edge fluorescence decays as the deep trap emission grows in, inherently coupling the two processes. An experiment which measured the dependence of the excitation energy showed that increased energy imparted to the NC's resulted in increased rise times, yielding the timescales for exciton relaxation through the valence and conduction band states to the lowest emitting state. Surface-oxidized and normally-passivated NC's display the same decay dynamics in time but differ in relative amplitude; the latter point agrees with steady-state measurements. The rotational anisotrophy of the NC's was measured and agrees with previous pump-probe data. Upconversion on the red and blue sides of the static fluorescence spectrum showed no discernable differences, which is either and inherent limitation of the experimental apparatus, or the possibility that lower-lying triplet states are populated on a timescale below the instrument resolution.

Ka-band (32 GHz) allocations for deep space

NASA Technical Reports Server (NTRS)

Degroot, N. F.

1987-01-01

At the 1979 World Administrative Conference, two new bands were allocated for deep space telecommunications: 31.8 to 32.3 GHz, space-to-Earth, and 34.2 to 34.7 GHz, Earth-to-space. These bands provide opportunity for further development of the Deep Space Network and its support of deep space research. The history of the process by which JPL/NASA developed the rationale, technical background, and statement of requirement for the bands are discussed. Based on this work, United States proposals to the conference included the bands, and subsequent U.S. and NASA participation in the conference led to successful allocations for deep space telecommunications in the 30 GHz region of the spectrum. A detailed description of the allocations is included.

The Arm is Not the Leg: Pathophysiology, Diagnosis, and Management of Upper Extremity Deep Vein Thrombosis.

PubMed

Noyes, Adam M; Dickey, John

2017-05-01

Upper extremity deep venous thrombosis (UEDVT) involves thrombosis of the deep veins of the arm as they enter the thorax. They are increasing in frequency, largely due to the rising use of central venous catheters and implantable cardiac devices, and represent more than 10% of all DVT cases, Upper extremity deep venous thrombosis has been historically misunderstood when compared to lower extremity deep vein thrombosis (LEDVT). Their associated disease states may carry devastating complications, with mortality rates often higher than that of LEDVT. Thus, education on recognition, classification and management is critical to avoid long-term sequelae and mortality from UEDVT. [Full article available at http://rimed.org/rimedicaljournal-2017-05.asp].

The Effects of Test Trial and Processing Level on Immediate and Delayed Retention.

PubMed

Chang, Sau Hou

2017-03-01

The purpose of the present study was to investigate the effects of test trial and processing level on immediate and delayed retention. A 2 × 2 × 2 mixed ANOVAs was used with two between-subject factors of test trial (single test, repeated test) and processing level (shallow, deep), and one within-subject factor of final recall (immediate, delayed). Seventy-six college students were randomly assigned first to the single test (studied the stimulus words three times and took one free-recall test) and the repeated test trials (studied the stimulus words once and took three consecutive free-recall tests), and then to the shallow processing level (asked whether each stimulus word was presented in capital letter or in small letter) and the deep processing level (whether each stimulus word belonged to a particular category) to study forty stimulus words. The immediate test was administered five minutes after the trials, whereas the delayed test was administered one week later. Results showed that single test trial recalled more words than repeated test trial in immediate final free-recall test, participants in deep processing performed better than those in shallow processing in both immediate and delayed retention. However, the dominance of single test trial and deep processing did not happen in delayed retention. Additional study trials did not further enhance the delayed retention of words encoded in deep processing, but did enhance the delayed retention of words encoded in shallow processing.

The Effects of Test Trial and Processing Level on Immediate and Delayed Retention

PubMed Central

Chang, Sau Hou

2017-01-01

The purpose of the present study was to investigate the effects of test trial and processing level on immediate and delayed retention. A 2 × 2 × 2 mixed ANOVAs was used with two between-subject factors of test trial (single test, repeated test) and processing level (shallow, deep), and one within-subject factor of final recall (immediate, delayed). Seventy-six college students were randomly assigned first to the single test (studied the stimulus words three times and took one free-recall test) and the repeated test trials (studied the stimulus words once and took three consecutive free-recall tests), and then to the shallow processing level (asked whether each stimulus word was presented in capital letter or in small letter) and the deep processing level (whether each stimulus word belonged to a particular category) to study forty stimulus words. The immediate test was administered five minutes after the trials, whereas the delayed test was administered one week later. Results showed that single test trial recalled more words than repeated test trial in immediate final free-recall test, participants in deep processing performed better than those in shallow processing in both immediate and delayed retention. However, the dominance of single test trial and deep processing did not happen in delayed retention. Additional study trials did not further enhance the delayed retention of words encoded in deep processing, but did enhance the delayed retention of words encoded in shallow processing. PMID:28344679

76 FR 13604 - Western Pacific Fishery Management Council; Public Meeting

Federal Register 2010, 2011, 2012, 2013, 2014

2011-03-14

... information on the Essential Fish Habitat (EFH) and Habitat of Particular Concern (HAPC) for deep slope... commissions, Federal agencies, state agencies, and other interested parties. The National Marine Fisheries Service has completed this process for deep slope bottomfish in the Main Hawaiian Islands, and the...

A search for higher twist effects in the hadronic distributions in deep inelastic muon proton scattering

NASA Astrophysics Data System (ADS)

Aubert, J. J.; Bassompierre, G.; Becks, K. H.; Benchouk, C.; Best, C.; Böhm, E.; de Bouard, X.; Brasse, F. W.; Broll, C.; Brown, S.; Carr, J.; Clifft, R.; Cobb, J. H.; Coignet, G.; Combley, F.; Court, G. R.; D'Agostini, G.; Dau, W. D.; Davies, J. K.; Déclais, Y.; Dosselli, U.; Drees, J.; Edwards, A.; Edwards, M.; Favier, J.; Ferrero, M. I.; Flauger, W.; Forsbach, H.; Gabathuler, E.; Gamet, R.; Gayler, J.; Gerhardt, V.; Gössling, C.; Haas, J.; Hamacher, K.; Hayman, P.; Henckes, M.; Korbel, V.; Korzen, B.; Landgraf, U.; Leenen, M.; Maire, M.; Mohr, W.; Montgomery, H. E.; Moser, K.; Mount, R. P.; Nagy, E.; Nassalski, J.; Norton, P. R.; McNicholas, J.; Osborne, A. M.; Payre, P.; Peroni, C.; Peschel, H.; Pessard, H.; Pietrzyk, U.; Rith, K.; Schneegans, M.; Schneider, A.; Sloan, T.; Stier, H. E.; Stockhausen, W.; Thénard, J. M.; Thompson, J. C.; Urban, L.; Villers, M.; Wahlen, H.; Whalley, M.; Williams, D.; Williams, W. S. C.; Williamson, J.; Wimpenny, S. J.

1986-03-01

The hadronic distributions in Q 2, y, z, p T and ϕ in deep inelastic muon proton scattering have been studied to search for higher twist effects in the hadronic final state. The expected effects are not observed.

Deep two-photon microscopic imaging through brain tissue using the second singlet state from fluorescent agent chlorophyll α in spinach leaf

NASA Astrophysics Data System (ADS)

Shi, Lingyan; Rodríguez-Contreras, Adrián; Budansky, Yury; Pu, Yang; An Nguyen, Thien; Alfano, Robert R.

2014-06-01

Two-photon (2P) excitation of the second singlet (S) state was studied to achieve deep optical microscopic imaging in brain tissue when both the excitation (800 nm) and emission (685 nm) wavelengths lie in the "tissue optical window" (650 to 950 nm). S2 state technique was used to investigate chlorophyll α (Chl α) fluorescence inside a spinach leaf under a thick layer of freshly sliced rat brain tissue in combination with 2P microscopic imaging. Strong emission at the peak wavelength of 685 nm under the 2P S state of Chl α enabled the imaging depth up to 450 μm through rat brain tissue.

Deep two-photon microscopic imaging through brain tissue using the second singlet state from fluorescent agent chlorophyll α in spinach leaf.

PubMed

Shi, Lingyan; Rodríguez-Contreras, Adrián; Budansky, Yury; Pu, Yang; Nguyen, Thien An; Alfano, Robert R

2014-06-01

Two-photon (2P) excitation of the second singlet (S₂) state was studied to achieve deep optical microscopic imaging in brain tissue when both the excitation (800 nm) and emission (685 nm) wavelengths lie in the "tissue optical window" (650 to 950 nm). S₂ state technique was used to investigate chlorophyll α (Chl α) fluorescence inside a spinach leaf under a thick layer of freshly sliced rat brain tissue in combination with 2P microscopic imaging. Strong emission at the peak wavelength of 685 nm under the 2P S₂ state of Chl α enabled the imaging depth up to 450 μm through rat brain tissue.

Growth control and design principles of self-assembled quantum dot multiple layer structures for photodetector applications

NASA Astrophysics Data System (ADS)

Asano, Tetsuya

Self-assembled quantum dots (SAQDs) formed by lattice-mismatch strain-driven epitaxy are currently the most advanced nanostructure-based platform for high performance optoelectronic applications such as lasers and photodetectors. While the QD lasers have realized the best performance in terms of threshold current and temperature stability, the performance of QD photodetectors (QDIPs) has not surpassed that of quantum well (QW) photodetectors. This is because the requirement of maximal photon absorption for photodetectors poses the challenge of forming an appropriately-doped large number of uniform multiple SAQD (MQD) layers with acceptable structural defect (dislocation etc.) density. This dissertation addresses this challenge and, through a combination of innovative approach to control of defects in MQD growth and judicious placement of SAQDs in a resonant cavity, shows that SAQD based quantum dot infrared photodetectors (QDIPs) can be made competitive with their quantum well counterparts. Specifically, the following major elements were accomplished: (i) the molecular beam epitaxy (MBE) growth of dislocation-free and uniform InAs/InAlGaAs/GaAs MQD strained structures up to 20-period, (ii) temperature-dependent photo- and dark-current based analysis of the electron density distribution inside the MQD structures for various doping schemes, (iii) deep level transient spectroscopy based identification of growth procedure dependent deleterious deep traps in SAQD structures and their reduction, and (iv) the use of an appropriately designed resonant cavity (RC) and judicious placement of the SAQD layers for maximal enhancement of photon absorption to realize over an order of magnitude enhancement in QDIP detectivity. The lattermost demonstration indicates that implementation of the growth approach and resonant cavity strategy developed here while utilizing the currently demonstrated MIR and LWIR QDIPs with detectivities > 10 10 cmHz1/2/W at ˜ 77 K will enable RC-QDIP with detectivites > 1011 cmHz1/2/W that become competitive with other photodetector technologies in the mid IR (3 -- 5 mum) and long wavelength IR (8 -- 12 mum) ranges with the added advantage of materials stability and normal incidence sensitivity. Extended defect-free and size-uniform MQD structures of shallow InAs on GaAs (001) SAQDs capped with In0.15Ga0.85As strain relief layers and separated by GaAs spacer layer were grown up to 20 periods employing a judicious combination of MBE and migration enhanced epitaxy (MEE) techniques and examined by detailed transmission electron microscopy studies to reveal the absence of detectable extended defects (dislocation density < ˜ 107 /cm2). Photoluminescence studies revealed high optical quality. As our focus was on mid-infrared detectors, the MQD structures were grown in n (GaAs) -- i (MQD) -- n (GaAs) structures providing electron occupancy in at least the quantum confined ground energy states of the SAQDs and thus photodetection based upon transitions to electron excited states. Bias and temperature-dependent dark and photocurrent measurements were carried out for a variety of doping profiles and the electron density spatial distribution was determined from the resulting band bending profiles. It is revealed that almost no free electrons are present in the middle SAQD layers in the 10-period and 20-period n--i--n QDIP structures, indicating the existence of a high density (˜1015/cm3) of negative charges which can be attributed to electrons trapped in deep levels. To examine the nature of these deep traps, samples suitable for deep level transient spectroscopy measurement were synthesized and examined. These studies, carried out for the first time for SAQDs, revealed that the deep traps are dominantly present in the GaAs overgrowth layers grown at 500°C by MBE. For structures involving GaAs overgrowths using MEE at temperatures as low as 350°C, the deep trap density in the GaAs overgrowth layer was found to be significantly reduced by factor of ˜ 20. Thus, employing MEE growth for GaAs spacer layers in n--i(20-period MQD)-- n QDIP structures, electrons could be provided to all the SAQDs owing to the significantly reduced deep trap density. Finally, for enhancement of the incident photon absorption, we designed and fabricated asymmetric Fabry-Perot resonant cavity-enhanced QDIPs. For effective enhancement, SAQDs with a narrow photoresponse in the 3 -- 5 mum infrared regime were realized utilizing [(AlAs)1(GaAs)4]4 short-period superlattices as the confining barrier layers. Incorporating such SAQDs in RC-QDIPs, we successfully demonstrated ˜ 10 times enhancement of the QDIP detectivity. As stated above, this makes RC-QDIPs containing QDIPs with the currently demonstrated detectivities of ˜ 1010 cmHz 1/2/W at ˜ 77 K competitive with other IR photodetector technologies.

Nondestructive Evaluation Methods for Characterization of Corrosion: State of the Art Review

DTIC Science & Technology

1988-12-01

form molecules of hydrogen gas damage is characterized by surface discolora- and leave the surface. Under some circum- tion and deep gouges or pits...large electromagnet and low operating granular corrosion without stress-related crack- frequencies resulted in deep penetration of ing can produce a...focus, and then the spray al. (11) showed that thermography was able to and the focus were moved together down the detect 3-mm deep , 50-mm diameter

Visualization of Whole-Night Sleep EEG From 2-Channel Mobile Recording Device Reveals Distinct Deep Sleep Stages with Differential Electrodermal Activity.

PubMed

Onton, Julie A; Kang, Dae Y; Coleman, Todd P

2016-01-01

Brain activity during sleep is a powerful marker of overall health, but sleep lab testing is prohibitively expensive and only indicated for major sleep disorders. This report demonstrates that mobile 2-channel in-home electroencephalogram (EEG) recording devices provided sufficient information to detect and visualize sleep EEG. Displaying whole-night sleep EEG in a spectral display allowed for quick assessment of general sleep stability, cycle lengths, stage lengths, dominant frequencies and other indices of sleep quality. By visualizing spectral data down to 0.1 Hz, a differentiation emerged between slow-wave sleep with dominant frequency between 0.1-1 Hz or 1-3 Hz, but rarely both. Thus, we present here the new designations, Hi and Lo Deep sleep, according to the frequency range with dominant power. Simultaneously recorded electrodermal activity (EDA) was primarily associated with Lo Deep and very rarely with Hi Deep or any other stage. Therefore, Hi and Lo Deep sleep appear to be physiologically distinct states that may serve unique functions during sleep. We developed an algorithm to classify five stages (Awake, Light, Hi Deep, Lo Deep and rapid eye movement (REM)) using a Hidden Markov Model (HMM), model fitting with the expectation-maximization (EM) algorithm, and estimation of the most likely sleep state sequence by the Viterbi algorithm. The resulting automatically generated sleep hypnogram can help clinicians interpret the spectral display and help researchers computationally quantify sleep stages across participants. In conclusion, this study demonstrates the feasibility of in-home sleep EEG collection, a rapid and informative sleep report format, and novel deep sleep designations accounting for spectral and physiological differences.

Negative thermal quenching of photoluminescence in zinc oxide nanowire-core/graphene-shell complexes.

PubMed

Lin, S S; Chen, B G; Xiong, W; Yang, Y; He, H P; Luo, J

2012-09-10

Graphene is an atomic thin two-dimensional semimetal whereas ZnO is a direct wide band gap semiconductor with a strong light-emitting ability. In this paper, we report on photoluminescence (PL) of ZnO-nanowires (NWs)-core/Graphene-shell heterostructures, which shows a negative thermal quenching (NTQ) behavior both for the near band-edge and deep level emission. The abnormal PL behavior was understood through the charging and discharging processes between ZnO NWs and graphene. The NTQ properties are most possibly induced by the unique rapidly increasing density of states of graphene as a function of Fermi level, which promises a higher quantum tunneling probability between graphene and ZnO at a raised temperature.

The assessment of waters ecological state of the Crimea coastal near high-rise construction zones

NASA Astrophysics Data System (ADS)

Vetrova, Natalya; Ivanenko, Tatyana; Mannanov, Emran

2018-03-01

The relevance of our study is determined by the significant level of coastal sea waters pollution by sewage near high-rise construction zones, which determines the violation of the sanitary and hygienic of sea waters `characteristics and limits the possibilities for organizing recreational activities. The purpose of this study is to identify the ecological state of the marine aquatic area by the example of the Western Crimea near high-rise construction zones. The studies confirmed that the recreational and coastal area wastewater is intensely mixed with seawater, as a result, the pollution in the coastal strip of the sea in the area of deep water discharges sharply decrease. This happens because of water rapid rise to the surface and under the influence of the continuous movement of sea water huge masses with deep-water discharge, fresh wastewater is actively mixed with sea water. However, with no doubt, it is inadmissible to discharge sewage into the sea directly from the shore, but only at the estimated distance from the coast. The materials of the article can be useful for the management bodies and organizations involved in monitoring the quality of the coastal zone of the sea, teachers and students of higher educational institutions when assessing the ecological situation of the territories.

Evolution of AlGaN deep level defects as a function of alloying and compositional grading and resultant impact on electrical conductivity

DOE PAGES

Armstrong, Andrew M.; Allerman, Andrew A.

2017-07-24

AlGaN:Si epilayers with uniform Al compositions of 60%, 70%, 80%, and 90% were grown by metal-organic vapor phase epitaxy along with a compositionally graded, unintentionally doped (UID) AlGaN epilayer with the Al composition varying linearly between 80% and 100%. The resistivity of AlGaN:Si with a uniform composition increased significantly for the Al content of 80% and greater, whereas the graded UID-AlGaN film exhibited resistivity equivalent to 60% and 70% AlGaN:Si owing to polarization-induced doping. Deep level defect studies of both types of AlGaN epilayers were performed to determine why the electronic properties of uniform-composition AlGaN:Si degraded with increased Al content,more » while the electronic properties of graded UID-AlGaN did not. The deep level density of uniform-composition AlGaN:Si increased monotonically and significantly with the Al mole fraction. Conversely, graded-UID AlGaN had the lowest deep level density of all the epilayers despite containing the highest Al composition. These findings indicate that Si doping is an impetus for point defect incorporation in AlGaN that becomes stronger with the increasing Al content. However, the increase in deep level density with the Al content in uniform-composition AlGaN:Si was small compared to the increase in resistivity. This implies that the primary cause for increasing resistivity in AlGaN:Si with the increasing Al mole fraction is not compensation by deep levels but rather increasing activation energy for the Si dopant. As a result, the graded UID-AlGaN films maintained low resistivity because they do not rely on thermal ionization of Si dopants.« less

Evolution of AlGaN deep level defects as a function of alloying and compositional grading and resultant impact on electrical conductivity

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

Armstrong, Andrew M.; Allerman, Andrew A.

AlGaN:Si epilayers with uniform Al compositions of 60%, 70%, 80%, and 90% were grown by metal-organic vapor phase epitaxy along with a compositionally graded, unintentionally doped (UID) AlGaN epilayer with the Al composition varying linearly between 80% and 100%. The resistivity of AlGaN:Si with a uniform composition increased significantly for the Al content of 80% and greater, whereas the graded UID-AlGaN film exhibited resistivity equivalent to 60% and 70% AlGaN:Si owing to polarization-induced doping. Deep level defect studies of both types of AlGaN epilayers were performed to determine why the electronic properties of uniform-composition AlGaN:Si degraded with increased Al content,more » while the electronic properties of graded UID-AlGaN did not. The deep level density of uniform-composition AlGaN:Si increased monotonically and significantly with the Al mole fraction. Conversely, graded-UID AlGaN had the lowest deep level density of all the epilayers despite containing the highest Al composition. These findings indicate that Si doping is an impetus for point defect incorporation in AlGaN that becomes stronger with the increasing Al content. However, the increase in deep level density with the Al content in uniform-composition AlGaN:Si was small compared to the increase in resistivity. This implies that the primary cause for increasing resistivity in AlGaN:Si with the increasing Al mole fraction is not compensation by deep levels but rather increasing activation energy for the Si dopant. As a result, the graded UID-AlGaN films maintained low resistivity because they do not rely on thermal ionization of Si dopants.« less

Photoluminescence transient study of surface defects in ZnO nanorods grown by chemical bath deposition

NASA Astrophysics Data System (ADS)

Barbagiovanni, E. G.; Strano, V.; Franzò, G.; Crupi, I.; Mirabella, S.

2015-03-01

Two deep level defects (2.25 and 2.03 eV) associated with oxygen vacancies (Vo) were identified in ZnO nanorods (NRs) grown by low cost chemical bath deposition. A transient behaviour in the photoluminescence (PL) intensity of the two Vo states was found to be sensitive to the ambient environment and to NR post-growth treatment. The largest transient was found in samples dried on a hot plate with a PL intensity decay time, in air only, of 23 and 80 s for the 2.25 and 2.03 eV peaks, respectively. Resistance measurements under UV exposure exhibited a transient behaviour in full agreement with the PL transient, indicating a clear role of atmospheric O2 on the surface defect states. A model for surface defect transient behaviour due to band bending with respect to the Fermi level is proposed. The results have implications for a variety of sensing and photovoltaic applications of ZnO NRs.

A Strategy for Maintenance of the Long-Term Performance Assessment of Immobilized Low-Activity Waste Glass

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

Ryan, Joseph V.; Freedman, Vicky L.

2016-09-28

Approximately 50 million gallons of high-level radioactive mixed waste has accumulated in 177 buried single- and double-shell tanks at the Hanford Site in southeastern Washington State as a result of the past production of nuclear materials, primarily for defense uses. The United States Department of Energy (DOE) is proceeding with plans to permanently dispose of this waste. Plans call for separating the tank waste into high-level waste (HLW) and low-activity waste (LAW) fractions, which will be vitrified at the Hanford Waste Treatment and Immobilization Plant (WTP). Principal radionuclides of concern in LAW are 99Tc, 129I, and U, while non-radioactive contaminantsmore » of concern are Cr and nitrate/nitrite. HLW glass will be sent off-site to an undetermined federal site for deep geological disposal while the much larger volume of immobilized low-activity waste will be placed in the on-site, near-surface Integrated Disposal Facility (IDF).« less

Gallium interstitial in irradiated germanium: Deep level transient spectroscopy

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

Kolkovsky, Vl.; Petersen, M. Christian; Larsen, A. Nylandsted

Two electronic levels at 0.34 eV above the valence band and 0.32 eV below the conduction band, in gallium doped, p-type Ge irradiated with 2 MeV electrons have been studied by deep level transient spectroscopy (DLTS) with both majority- and minority-carrier injections, and Laplace DLTS spectroscopy. It is concluded that these levels, having donor and acceptor characters, respectively, are correlated with interstitial Ga atoms, formed by the Watkins-replacement mechanism via self-interstitials.

Gallium interstitial in irradiated germanium: Deep level transient spectroscopy

NASA Astrophysics Data System (ADS)

Kolkovsky, Vl.; Petersen, M. Christian; Mesli, A.; van Gheluwe, J.; Clauws, P.; Larsen, A. Nylandsted

2008-12-01

Two electronic levels at 0.34 eV above the valence band and 0.32 eV below the conduction band, in gallium doped, p -type Ge irradiated with 2 MeV electrons have been studied by deep level transient spectroscopy (DLTS) with both majority- and minority-carrier injections, and Laplace DLTS spectroscopy. It is concluded that these levels, having donor and acceptor characters, respectively, are correlated with interstitial Ga atoms, formed by the Watkins-replacement mechanism via self-interstitials.

Eustatic control of turbidites and winnowed turbidites

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

Shanmugam, G.; Moiola, R.J.

1982-05-01

Global changes in sea level, primarily the results of tectonism and glaciation, control deep-sea sedimentation. During periods of low sea level the frequency of turbidity currents is greatly increased. Episodes of low sea level also cause vigorous contour currents, which winnow away the fines of turbidites. In the rock record, the occurrence of most turbidites and winnowed turbidities closely corresponds to global lowstands of paleo-sea level. This observation may be useful in predicting the occurrence of deep-sea reservoir facies in the geologic record.

Factors Contributing to Changes in a Deep Approach to Learning in Different Learning Environments

ERIC Educational Resources Information Center

Postareff, Liisa; Parpala, Anna; Lindblom-Ylänne, Sari

2015-01-01

The study explored factors explaining changes in a deep approach to learning. The data consisted of interviews with 12 students from four Bachelor-level courses representing different disciplines. We analysed and compared descriptions of students whose deep approach either increased, decreased or remained relatively unchanged during their courses.…

Dust transport model validation using satellite- and ground-based methods in the southwestern United States

NASA Astrophysics Data System (ADS)

Mahler, Anna-Britt; Thome, Kurt; Yin, Dazhong; Sprigg, William A.

2006-08-01

Dust is known to aggravate respiratory diseases. This is an issue in the desert southwestern United States, where windblown dust events are common. The Public Health Applications in Remote Sensing (PHAiRS) project aims to address this problem by using remote-sensing products to assist in public health decision support. As part of PHAiRS, a model for simulating desert dust cycles, the Dust Regional Atmospheric Modeling (DREAM) system is employed to forecast dust events in the southwestern US. Thus far, DREAM has been validated in the southwestern US only in the lower part of the atmosphere by comparison with measurement and analysis products from surface synoptic, surface Meteorological Aerodrome Report (METAR), and upper-air radiosonde. This study examines the validity of the DREAM algorithm dust load prediction in the desert southwestern United States by comparison with satellite-based MODIS level 2 and MODIS Deep Blue aerosol products, and ground-based observations from the AERONET network of sunphotometers. Results indicate that there are difficulties obtaining MODIS L2 aerosol optical thickness (AOT) data in the desert southwest due to low AOT algorithm performance over areas with high surface reflectances. MODIS Deep Blue aerosol products show improvement, but the temporal and vertical resolution of MODIS data limit its utility for DREAM evaluation. AERONET AOT data show low correlation to DREAM dust load predictions. The potential contribution of space- or ground-based lidar to the PHAiRS project is also examined.

Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks

PubMed Central

2017-01-01

Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. This study explores using recorded deep brain local field potentials (LFPs) for robust movement decoding of Parkinson's disease (PD) and Dystonia patients. The LFP data from voluntary movement activities such as left and right hand index finger clicking were recorded from patients who underwent surgeries for implantation of deep brain stimulation electrodes. Movement-related LFP signal features were extracted by computing instantaneous power related to motor response in different neural frequency bands. An innovative neural network ensemble classifier has been proposed and developed for accurate prediction of finger movement and its forthcoming laterality. The ensemble classifier contains three base neural network classifiers, namely, feedforward, radial basis, and probabilistic neural networks. The majority voting rule is used to fuse the decisions of the three base classifiers to generate the final decision of the ensemble classifier. The overall decoding performance reaches a level of agreement (kappa value) at about 0.729 ± 0.16 for decoding movement from the resting state and about 0.671 ± 0.14 for decoding left and right visually cued movements. PMID:29201041

Representation learning: a unified deep learning framework for automatic prostate MR segmentation.

PubMed

Liao, Shu; Gao, Yaozong; Oto, Aytekin; Shen, Dinggang

2013-01-01

Image representation plays an important role in medical image analysis. The key to the success of different medical image analysis algorithms is heavily dependent on how we represent the input data, namely features used to characterize the input image. In the literature, feature engineering remains as an active research topic, and many novel hand-crafted features are designed such as Haar wavelet, histogram of oriented gradient, and local binary patterns. However, such features are not designed with the guidance of the underlying dataset at hand. To this end, we argue that the most effective features should be designed in a learning based manner, namely representation learning, which can be adapted to different patient datasets at hand. In this paper, we introduce a deep learning framework to achieve this goal. Specifically, a stacked independent subspace analysis (ISA) network is adopted to learn the most effective features in a hierarchical and unsupervised manner. The learnt features are adapted to the dataset at hand and encode high level semantic anatomical information. The proposed method is evaluated on the application of automatic prostate MR segmentation. Experimental results show that significant segmentation accuracy improvement can be achieved by the proposed deep learning method compared to other state-of-the-art segmentation approaches.

FAR-TECH's Nanoparticle Plasma Jet System and its Application to Disruptions, Deep Fueling, and Diagnostics

NASA Astrophysics Data System (ADS)

Thompson, J. R.; Bogatu, I. N.; Galkin, S. A.; Kim, J. S.

2012-10-01

Hyper-velocity plasma jets have potential applications in tokamaks for disruption mitigation, deep fueling and diagnostics. Pulsed power based solid-state sources and plasma accelerators offer advantages of rapid response and mass delivery at high velocities. Fast response is critical for some disruption mitigation scenario needs, while high velocity is especially important for penetration into tokamak plasma and its confining magnetic field, as in the case of deep fueling. FAR-TECH is developing the capability of producing large-mass hyper-velocity plasma jets. The prototype solid-state source has produced: 1) >8.4 mg of H2 gas only, and 2) >25 mg of H2 and >180 mg of C60 in a H2/C60 gas mixture. Using a coaxial plasma gun coupled to the source, we have successfully demonstrated the acceleration of composite H/C60 plasma jets, with momentum as high as 0.6 g.km/s, and containing an estimated C60 mass of ˜75 mg. We present the status of FAR-TECH's nanoparticle plasma jet system and discuss its application to disruptions, deep fueling, and diagnostics. A new TiH2/C60 solid-state source capable of generating significantly higher quantities of H2 and C60 in <0.5 ms will be discussed.

Process-based approach for the detection of deep gas invading the surface

DOEpatents

Romanak, Katherine; Bennett, Philip C.

2017-05-09

The present invention includes a method for determining the level of deep gas in a near surface formation that includes: measuring CO.sub.2, O.sub.2, CH.sub.4, and N.sub.2 levels in percent by volume from one or more surface or near surface geological samples; adding the water vapor content to the measured CO.sub.2, O.sub.2, CH.sub.4, and N.sub.2 levels in percent by volume; normalizing the gas mixture to 100% by volume or 1 atmospheric total pressure; and determining the ratios of: O.sub.2 versus CO.sub.2 to distinguish in-situ vadose zone CO.sub.2 from exogenous deep leakage CO.sub.2; CO.sub.2 versus N.sub.2 to distinguish whether CO.sub.2 is being removed from the near surface formation or CO.sub.2 is added from an exogenous deep leakage input; or CO.sub.2 versus N.sub.2/O.sub.2 to determine the degree of oxygen influx, consumption, or both; wherein the ratios are indicative of natural in situ CO.sub.2 or CO.sub.2 from the exogenous deep leakage input.

Broadband calibration of the R/V Marcus G. Langseth four-string seismic sources

NASA Astrophysics Data System (ADS)

Tolstoy, M.; Diebold, J.; Doermann, L.; Nooner, S.; Webb, S. C.; Bohnenstiehl, D. R.; Crone, T. J.; Holmes, R. C.

2009-08-01

The R/V Marcus G. Langseth is the first 3-D seismic vessel operated by the U.S. academic community. With up to a four-string, 36-element source and four 6-km-long solid state hydrophone arrays, this vessel promises significant new insights into Earth science processes. The potential impact of anthropogenic sound sources on marine life is an important topic to the marine seismic community. To ensure that operations fully comply with existing and future marine mammal permitting requirements, a calibration experiment was conducted in the Gulf of Mexico in 2007-2008. Results are presented from deep (˜1.6 km) and shallow (˜50 m) water sites, obtained using the full 36-element (6600 cubic inches) seismic source. This array configuration will require the largest safety radii, and the deep and shallow sites provide two contrasting operational environments. Results show that safety radii and the offset between root-mean-square and sound exposure level measurements were highly dependent on water depth.

A top-down manner-based DCNN architecture for semantic image segmentation.

PubMed

Qiao, Kai; Chen, Jian; Wang, Linyuan; Zeng, Lei; Yan, Bin

2017-01-01

Given their powerful feature representation for recognition, deep convolutional neural networks (DCNNs) have been driving rapid advances in high-level computer vision tasks. However, their performance in semantic image segmentation is still not satisfactory. Based on the analysis of visual mechanism, we conclude that DCNNs in a bottom-up manner are not enough, because semantic image segmentation task requires not only recognition but also visual attention capability. In the study, superpixels containing visual attention information are introduced in a top-down manner, and an extensible architecture is proposed to improve the segmentation results of current DCNN-based methods. We employ the current state-of-the-art fully convolutional network (FCN) and FCN with conditional random field (DeepLab-CRF) as baselines to validate our architecture. Experimental results of the PASCAL VOC segmentation task qualitatively show that coarse edges and error segmentation results are well improved. We also quantitatively obtain about 2%-3% intersection over union (IOU) accuracy improvement on the PASCAL VOC 2011 and 2012 test sets.

Metastable Bound States of Two-Dimensional Magnetoexcitons in the Lowest Landau Levels Approximation

NASA Astrophysics Data System (ADS)

Moskalenko, S. A.; Khadzhi, P. I.; Podlesny, I. V.; Dumanov, E. V.; Liberman, M. A.; Zubac, I. A.

2017-12-01

The possible existence of the two-dimensional bimagnetoexcitons and metastable bound states formed by two magnetoexcitons with opposite in-plane wave vectors k and -k has been studied. Magnetoexcitons taking part in the formation of molecules look as two electric dipoles with the arms oriented in-plane perpendicular to the respective wave vectors and with the length of the arms d=k(l_0)^2, where l_0 is the magnetic length. Two antiparallel dipoles moving with equal, yet antiparallel, wave vectors have the possibility of moving with equal probability in any direction of the plane, which is determined by the trial wave function of relative motion φ_n(|k|), depending on modulus k. The magnetoexcitons are composed of electrons and holes situated on the lowest Landau levels with the cyclotron energies greater than the binding energy of the 2D Wannier-Mott exciton. The description has been made in Landau gauge. The spin states of two electrons have been chosen in the form of antisymmetric or symmetric combinations with parameter η=+/-1. The effective spins of two heavy holes have been combined in the same resultant spinor states as the spin of the electrons. Because the projections of the both spinor states with η=+/-1 are equal to zero, the influence of the Zeeman splitting effect vanishes. In the case of trial wave function, the maximal density of the magnetoexcitons in the momentum space is concentrated on the in-plane ring. In the approximation of the lowest Landau levels, when the influence of the excited Landau levels is neglected, stable bound states of bimagnetoexcitons do not exist for both spin orientations. Instead, in the case of α=0.5 and η=1, a deep metastable bound state with the activation barrier comparable with two magnetoexciton ionization potentials 2I_l has been revealed. In the case of η=-1 and α=3.4, only a shallow metastable bound state can appear.